ADHD, a neurodevelopmental condition, is distinguished by a triad of symptoms including inattention, hyperactivity, and impulsivity. It is frequently accompanied by comorbidities such as anxiety, depression, and learning disabilities. As a result, clinicians often face challenges in accurately diagnosing ADHD and differentiating it from other conditions. As a prospective remedy for ADHD, scientists have investigated Transcranial Direct Current Stimulation (tDCS), a non-invasive technique of stimulating the brain. It involves applying low-intensity electrical currents to specific regions of the brain to modulate neural activity. This review paper aims to provide an overview of the comorbidities associated with ADHD and the differential diagnosis of the condition. It also explores the potential of tDCS as a treatment option for ADHD, including its mechanisms of action and efficacy in improving ADHD symptoms. The comorbidities discussed in this review include anxiety disorders, mood disorders, and substance use disorders. These comorbidities are frequently seen in individuals with ADHD and can complicate the diagnosis and treatment of the condition. The paper also highlights the importance of considering comorbidities when assessing ADHD, as well as the potential impact of these conditions on treatment outcomes. The differential diagnosis section of the paper explores conditions that can present with symptoms similar to ADHD, such as anxiety disorders, mood disorders, and learning disabilities. It emphasizes the need for a thorough assessment and differential diagnosis to identify ADHD and differentiate it from other conditions accurately. The final section of the paper discusses the potential of tDCS as a treatment option for ADHD. It examines the mechanisms of action of tDCS and its efficacy in improving ADHD symptoms, including attention, hyperactivity, and impulsivity. It also explores the potential for tDCS to improve comorbid conditions associated with ADHD. Overall, this review provides a comprehensive overview of comorbidities and differential diagnoses in ADHD and the potential of tDCS as a treatment option. The paper highlights the importance of a thorough assessment and personalized treatment plan for individuals with ADHD, particularly those with comorbidities.

Borderline Personality Disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study we applied for the first time an unsupervised machine learning approach known as mCCA+jICA, in combination with a supervised machine learning approach known as Random Forest, to possibly find covarying GM-WM circuits that separate BPD from controls and that are also predictive of this diagnosis. To this aim, we analyzed the structural images of patients with BPD and matched HCs. Results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in GM and WM circuits related to early traumatic experiences and specific symptoms.

Here we investigated the longer-term effect of Ncald-ASOs by additional i.c.v. bolus injection at PND28. Two weeks after injection of 500 µg Ncald-ASO in wild-type mice, NCALD was significantly reduced in brain and spinal cord and well tolerated. Next, we performed a double-blinded preclinical study combining low-dose SMN-ASO (PND1) with 2x i.c.v. Ncald-ASO or CTRL-ASO (100 µg at PND2, 500 µg at PND28). Ncald-ASO re-injection significantly ameliorated electrophysiological defects and NMJ denervation at 2 months. Moreover, we developed and identified a nontoxic and highly efficient human NCALD-ASO that significantly reduced NCALD in hiPSCs-derived MNs. This improved both neuronal activity and growth cone maturation of SMA MNs, emphasizing the additional protective effect of NCALD-ASO treatment.

We show that the van der Waals gas does not exhibit just the celebrated liquid-gas 1 transition but also a quantum-classical one. We study the issue with reference to the LMC statistical 2 complexity and in relation to the order-disorder contrast.

Context: Mental and menstrual health, two emerging aspects of health critical to the female population, are bidirectionally linked. But there is limited empirical evidence that establishes the association between these entities. This essay aims to draw the attention of researchers to this healthcare niche. Evidence Acquisition: A systematic review was carried out on literatures sourced from medical databases (e.g. PubMed, Google Scholar, Scopus, Science Direct, and Web of Science), and gray sources—popular and reputable institutional and journalistic websites that publishes mental or menstrual health research. The key words used for the search are “Menstruation, menstrual hygiene, menstrual disorders, premenstrual syndrome, period poverty, menarche, menopause, mental health, mental disorder, mental illness, depression, anxiety, phobia, mania, mood, and affect”. The initial search generated 368 results. But after the duplicates were removed, the exclusion criteria (publication before 2000) was applied, and manual review of abstract (for relevance) was done, 21 publications from the databases and 5 from gray sources were included in this essay. Results: Associations were found between menstrual cycle irregularities and disorders; and mental disorders such as premenstrual syndrome, premenstrual dysphoric disorder, major depressive disorder, major anxiety disorder, psychotic disorder, bipolar disorder, borderline personality disorder, sleep disorder, substance use/abuse, and suicidal ideation and attempts.Few positive associations between mental and menstrual health were recorded, and several research and treatment gaps were identified. Conclusion: Research into the links between mental and menstrual health should not remain a fringe area of scientific curiosity, as it shows tremendous promise in improving healthcare offered to women/girls globally.

Apathy is a fundamental neuropsychiatric symptom of Korsakoff’s syndrome (KS) and has also been reported in patients with alcohol use disorder with no (AUD) or less severe cognitive disorders (ALC). However, research on the nature of apathy is limited in these groups. Aim of this study was to examine the multidimensional nature of apathy in patients with KS, ALC and AUD. Moreover, we examined differences between apathy ratings by patients and their professional caregivers, and related apathy to everyday functioning and overall cognition. Twenty-four patients with KS, 24 patients with ALC and 21 patients with AUD participated in this study. Apathy was measured using the Apathy Motivation Index (AMI), which distinguishes behavioral, emotional and social apathy. Both patients and professional caregivers reported social apathy as most prominent symptom, compared to behavioral and emotional apathy. Apathy ratings did not differ across the three patient groups. Discrepancies between patient and caregiver ratings were observed in patients with KS and ALC, with more severe apathy reported by caregivers. Caregiver-reported behavioral and social, but not emotional, apathy was related to everyday functioning. These results show that apathy is present in a substantial proportion of patients with alcohol addiction with or without cognitive impairments.

Corona Virus Infectious Disease (COVID-19) is newly emerging infectious disease. This disease is known to the globe in early 2019. Poor status of mental health is often caused by unemployment, ongoing socio-economic condition. Poor mental health may even accelerate the process of panic attack. It has been happening rapidly during COVID-19. It has a great effect on human health. This paper utilizes multiple related factors those have impact on causing panic attack. Recurrent Neural Network (RNN) based framework is utilized in this paper that assembles multiple RNN layers along with other parameters into a single platform. This method is implemented by capturing interfering factors and predicts panic attack tendency of people during COVID-19. Early prediction of panic attacks may assist in saving life from unwanted circumstances.

Since January, the disease caused by the Sars Cov-2 virus has spread and following pandemic. In a few months, the virus is seriously affecting the health systems of the various countries of the world and placing people in difficult psychological conditions. Few scientific resources on mental health have been published but still no one has proposed an action plan to cope the future psychological problems.This manuscript provides a plan to easily frame the priorities of mental health area related to COVID-19 to be taken into consideration which should be disseminated and that should be known by all health professionals and also by the major administrators of public health.

The incidence of cancer in organ transplant patients is higher than in the non-transplanted population. The incidence increases with increasing age. The use of Fludeoxyglucose positron emission tomography/CT (PET/CT) is sensitive and specific to detect PTLD LD compared with conventional CT imaging. We analyzed the medical data of 127 patients aged over 60 years, who underwent heart transplantation in the above period, who have been practicing early CT-PET for diagnostic purposes between February 2007 and October 2018. Of 127 consecutive patients who underwent CT-PET, SUVs up than 4, were found in 84 patients of which 20 were affected by PTLDs, seven patients were affected by chronic non neoplastic inflammatory diseases, the remainder were affected by other neoplasms. The favorable effect of physical activity programs on cardiorespiratory and psychomotor function occur in all patients. In conclusion, CT-PET at the first doubts about the possibility of the development of neoplasms has allowed a diagnosis and then a treatment more quickly. Education in behavioral norms that improve the patient's quality of life is necessary.

Objective: Major depression (MDD) and a lifetime history of MDD are characterized by increased nitrosylation, while bipolar disorder type 1 (BP1), but not BP2, is accompanied by highly increased levels of oxidative stress and nitric oxide (NO) production. Nevertheless, it is unknown whether nitrosylation is involved in BP and whether there are differences in nitrosylation between BP1 and BP2.Methods: Serum IgM antibodies directed against nitroso (NO)-adducts were examined in MDD, BP1, BP2 and healthy controls, namely IgM responses to NO-cysteine, NO-tryptophan (NOW), NO-arginine and NO-albumin (SBA) in association with IgA/IgM responses to Gram-negative bacteria, IgG responses to oxidized low-density lipoprotein (oxLDL) and serum peroxides.Results: Serum IgM levels against NO adducts were significantly higher in BP1 and MDD as compared with healthy controls, whereas BP2 patients occupied an intermediate position. IgM responses to NO-albumin were significantly higher in BP1 and MDD than in BP2 patients. There were highly significant associations between the IgM responses to NO-adducts and IgG responses to oxLDL and IgA/IgM responses to Gram-negative bacteria.Conclusions: BP1 and MDD are characterized by an upregulation of the nitrosylome (the proteome of nitrosylated proteins), and increased IgM responses to nitrosylated conjugates. Increased nitrosylation may be driven by increased bacterial translocation and is associated with lipid peroxidation processes. Innate like (B1 and marginal zone) B cells and increased nitrosylation may play a key role in the major affective disorders through activation of immune-inflammatory and oxidative pathways, cardiovascular comorbidity and impairments in antioxidant defenses, neuro-glial interactions, synaptic plasticity, neuroprotection, neurogenesis, etc.

We aimed to evaluate the feasibility and preliminary efficacy of trauma-focused group psychotherapy in adolescents who experienced traumatic events in Korea. Participants were assigned and recruited from two sites in Korea. Children in Disaster: Evaluation and Recovery (CIDER) V1.0 is a trauma-focused group psychotherapy approach consisting of psychoeducation, normalization, stabilization, and techniques of managing the traumatic memory. The CIDER intervention consists of eight 50-minute-long sessions. The effectiveness of the intervention was evaluated using the Korean version of the Children’s Response to Traumatic Events Scale-Revised (K-CRTES-R), the Beck Depression Inventory (BDI), the State Anxiety Inventory for Children (SAIC), and the Pediatric Quality of Life Inventory (PedQL). Data were analyzed by Wilcoxon signed-rank test. We recruited 22 traumatized adolescents (mean age 16 years; SD 1.43; range 13–18 years old; 71.4% boys) in this pilot study. The K-CRTES-R scores were significantly improved (Z = −2.85, p < 0.01). The BDI demonstrated the effectiveness of the therapy (Z = −2.35, p < 0.05). The assessment of the PedQL supported the effect of CIDER (Z = −3.08, p < 0.01). However, there was no statistically significant differences in the SAIC scores (Z = −1.90, p > 0.05). The results show that there is preliminary evidence that CIDER intervention reduces post-traumatic stress and depressive symptoms and improves quality of life. Our findings indicate that CIDER is feasible for treating adolescents exposed to traumatic events. Larger controlled trials are needed to establish the efficacy of this trauma-focused group psychotherapy and examine its impact on post-traumatic stress disorder.

In 2011, it was reviewed that there is a strong co-occurrence between major depression and chronic fatigue syndrome (CFS), with fatigue and physio-somatic symptoms being key symptoms of depression, and depressive symptoms appearing during the course of CFS. Moreover, the comorbidity between both conditions may in part be explained by activated immune-inflammatory pathways, including increased translocation of Gram-negative bacteria and increased levels of pro-inflammatory cytokines, such as interleukin (IL)-1. Nevertheless, the possible involvement of activated microglia in this comorbidity has remained unclear. This paper aims to review microglial disturbances in major depression, CFS and their comorbidity. A comprehensive literature search was conducted using the PubMed / MEDLINE database to identify studies that are relevant to this current review. Depressed patients present neuroinflammatory alterations, probably related to microglial activation, while animal models show that a microglial response to immune challenges including lipopolysaccharides is accompanied by depressive-like behaviors. Recent evidence from preclinical studies indicate that activated microglia have a key role in the onset of fatigue. In chronic inflammatory conditions, such as infections and senescence, microglia orchestrate an inflammatory microenvironment thereby causing fatigue. In conclusion, based on our review we may posit that shared immune-inflammatory pathways and activated microglia underpin comorbid depression and CFS and that activated microglia are the main orchestrators of this comorbidity. As such, microglial activation and neuro-inflammation may be promising targets to treat the overlapping manifestations of both depression and CFS.

The ongoing growth of international occurrence of depression and its ability to co-occur with other serious medical disorders such as heart disease, cancer, diabetes, and Parkinson’s disease is a current public health problem. Inhibitor of DNA-Binding/Differentiation (ID) proteins are part of a group of transcriptional factors that have been seen to be involved in neurocognitive disorders and therefore, may have influence on depressive disorders. Previously, it has been established that environmental estrogenic endocrine disruptors (EEDs) such as polychlorinated biphenyls (PCBs) & bisphenol A (BPA) have played an important role in the impact of depressive disorders. Hence, based on many studies, we consider the impact of these environmental pollutants on the group of ID proteins. Improved understanding of how the interaction of ID proteins by EED exposure can influence depressive disorders will contribute essential evidence that can further benefit our public health community with innovative knowledge to prevent these types of mental illnesses.

Abstract: This study evaluates the effect of paravertebral spinal injection (PSI), utilizing both subjective and objective assessments in chronic low back pain (LBP) associated with facet joint arthrosis over a one-month duration. Subjective questionnaires included the Visual Analogue Scale (VAS) for pain, Oswestry Disability Index, Health Survey SF-12, and the short Falls Efficacy Scale International (FES-I). Objective assessments included in-clinic gait and timed-up-and-go (TUG) tests using wearable sensors, as well as 48-hour daily physical activity (DPA) monitored using a chest-worn tri-axial accelerometer. Subjective and objective measures were performed prior to treatment, immediately after the treatment, and one-month afterthe treatment. Eight LBP patients were recruited for this study (mean age = 54±13 years, body mass index = 31.41±6.52 kg/m2, 50% males). Results show significant decrease in pain (~55%, p<0.05) and disability (Oswestry scores, ~21%, p<0.05). In-clinic gait and TUG were also significantly improved (~16% and ~18% faster walking and shorter TUG, p<0.05); however, DPA (including percentage of physical activities (walking and standing) and the number of steps) showed no significant change after PSI (p>0.25; effect size≤0.44). We hypothesize that DPA may continue to be truncated by conditioned fear-avoidance, a psychological state that may prevent increase in daily physical activity to avoid pain.

Background: Post-traumatic stress disorder (PTSD) is a common debilitating psychiatric condition for which pharmacological therapy is not always solvable. Various treatments have been suggested for these patients. Deep brain stimulation (DBS) is currently under investigation for patients affected by PTSD. 2) Methods: We review the neurocircuitry and up to date clinical concepts that may be of relevance for the implementation of DBS in posttraumatic stress disorder (PTSD). 3) Results: The role of DBS in treatment-refractory PTSD patients has been investigated relying on both preclinical and clinical studies. 4) Conclusions: DBS for PTSD is in its preliminary phases and likely to provide hope to patients with medical refractory PTSD following the results of randomized controlled studies.

1) Background: Bipolar or manic-depressive disorder is a malign mental disease that frequently faces social stigma. Educational and thinking models are needed to increase people’s awareness and understanding of the disorder. The arts have potential to achieve this goal. 2) Methods: This paper builds on the recent use of heavy metal music as a thinking and education model. It emphasizes the artistic component of heavy metal and its potential to characterize the symptomatology during the episodes of (hypo)mania and depression and the recurrence of these episodes. Heavy metal music has diversified into subgenres that become allegorical to both the symptoms of episodes and the recurrence of bipolar cycles. 3) Results: Examples of songs are given that mirror distinct facets of the disorder. 4) Conclusion: Although the links drawn between art (music) and science (psychiatry) are inherently subjective, such connections might be used to trigger a learning process, facilitate judgment and decision-making, and induce affective reactions and memory formation in the listener. The approach may facilitate collaborative efforts and serve healthcare professionals and educators as a communication tool to aid the public’s comprehension of the disease and an associated social paradox: On one hand, bipolar disorder incurs substantial costs to society. On the other hand, it benefits from the creative artistic and scientific endeavors of bipolar individuals from which cultural and political gains may ensue.

In Brain-Computer Interface Motor Imagery (BCI-MI) systems, Convolutional Neural Networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces the EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. The EEG-TCNTransformer achieves an accuracy of 82.97% without the application of bandpass filtering. The source code of EEG-TCNTransformer is available on GitHub.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth application of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, are progressively integrated into NIRS. In this paper, two discriminant analysis problems including wheat kernels and Yali pears were used as examples, and several representative calibration models to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which is based on gramian angular difference field and coordinate attention convolutional neural networks (G-CACNN). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results of using the original spectrum. The accuracy of G-CACNN in two discrimination tasks is 98.48% and 99.39%, respectively. Finally, This research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

The commercialization of historic streets constitutes a pivotal aspect of urban cultural heritage, and the comprehension of their visual perception serves as a valuable asset to urban planning and cultural conservation efforts. However, current research concerning the disparities in visual perception among diverse demographics within historic streets, as well as their spatiotemporal dynamics, remains insufficient. This study aims to utilize mobile eye-tracking technology to delve deeply into the visual perception characteristics of diverse demographics within commercialized historic streets, while also analyzing their spatiotemporal dynamics. We explore the disparities in visual perception of historic streets among groups with varying degrees of architectural knowledge and professional literacy. Through meticulous observation using mobile eye-tracking technology, we elucidate the visual perception patterns of different demographic characteristics through statistical analysis and spatiotemporal modeling. The findings reveal significant discrepancies in visual attention and perception among various groups. Participants with a higher understanding of architecture tend to focus more on architectural details and structures within the streetscape compared to those with a lower understanding, who exhibit greater interest in elements such as shops and signage. Moreover, during the process of perceiving the streetscape, participants’ eye movements transition from surrounding landscapes and historical edifices upon initial entry to the district, gradually shifting towards the commercial activities and distinctive architecture within the district. This study delves deeply into the visual perception characteristics and spatiotemporal dynamics of commercialized historic streets, providing invaluable insights for urban planning and cultural preservation endeavors. By revealing the impact of different demographic characteristics on the visual perception of historic streets, it offers new insights for urban planning and cultural heritage conservation.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% [email protected], which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.

(1) Background: Mindfulness-based interventions (MBIs) have shown interesting preliminary ef-fects over self-regulation processes in early adolescence. However, interventions usually integrate various types of practices with no clarity about the specific effect of each of them over attentional, emotional, and behavioral regulation. The objective of this research was to evaluate the effect of two MBIs, one focused on classic attentional practices and another one focused on the recognition and expression of emotions, over attentional, emotional, and behavioral self-regulation in early adolescents. (2) Method: An experimental method was carried out. A sample of 70 children be-tween 8 and 12 years old were randomized and assigned to three experimental conditions: 1) MBI with a focus on attentional practices, 2) MBI with a focus on recognition and expression of emotions, 3) control group, waiting list. The programs lasted 8 weeks, with a weekly 1-hour online synchronic session, plus home practices. Children were evaluated before starting the in-tervention and at the end of the 8-week program. The assessed variables were: 1) Mindfulness; 2) Emotional Regulation; 3) Attentional Regulation and 4) Behavioral regulation. (3) Results: Chil-dren that participated in both programs, increased their mindfulness and emotional and be-havioral regulation scores. Only children who participated in the program with a focus on at-tention showed significant changes in their ability to self-regulate attention. (4) Conclusions: online MBIs, with attention to external and internal stimuli practices, can be a good strategy to strengthen self-regulation skills of attention, emotions, and behavior in early adolescence.

Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid Autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial, and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) Simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions such as fluctuating water demands, data noise, and the presence of leaks are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to a random forest model baseline based on supervised learning with a false positive rate of 15% due to unbalanced data. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in operational conditions of water supply networks using inline sensor data.

The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in the study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we analyzed the performance of eight deep learning models: LSTM (4 layers), CNN, BiLSTM (4 layers), CNN Attention, BiGRU CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market.

In recent years, intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example, (1) very few trainable image samples containing mineral point labels; (2) The geological image features are small and irregular, and the image similarity is high; (3) It is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model Multiscale Feature Attention Framework (MFAF) based on geo-image data. The results show that MFCA-Net module in MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference of influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of Qin-hang metallogenic Belt. In the forecast map, many regions have high metallogenic potential. Through observation, it is found that the above predicted area covers 100% of the known ore deposits at present, which has a good prediction effect. Later, we can find out the specific reasons through field verification. The multi-scale feature fusion and attention mechanism MFA Framework in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantee and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network LBFNet is proposed in this paper.First, a lightweight convolutional neural network LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning makes the model accuracy reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers.

To address the problems of poor recognition, low detection accuracy, a large number of model parameters and computation, complex network structure, and unfavorable portability to embedded devices in traditional tennis ball detection algorithms, this study proposes a lightweight tennis ball detection algorithm YOLOv5s-Z based on Robomater EP. The main work is as follows: Firstly, constructing lightweight G-Backbone and G-Neck network layers to reduce the number of parameters and computation of the network structure. Secondly, the convolutional coordinate attention is incorporated in G-Backbnone to embed the location information into the channel attention, which makes the network obtain the location information in a larger area through multiple convolutions and enhances the expression ability of the mobile network learning features. In addition, the Concat module in the original feature fusion is modified into a weighted bi-directional feature pyramid W-BiFPN with settable learning weights to improve the feature fusion capability and achieve efficient weighted feature fusion and bi-directional cross-scale connectivity. The EIOU loss is introduced to split the influence factor of aspect ratio and calculate the length and width of the target frame and anchor frame respectively, combined with Focal-EIOU Loss to solve the problem of imbalance between difficult and easy samples. The activation function Meta-ACON is introduced to achieve an adaptive selection of whether to activate the neurons and improve the detection accuracy. Finally, the experimental results show that compared with the original algorithm, the YOLOv5s-Z algorithm reduces the number of parameters and computation by 42$\%$ and 44$\%$, the model size by 39$\%$, and 2$\%$ improvement in average accuracy mean value, which verifies the effectiveness of the improved algorithm and the light weight of the model to meet the deployment requirements of embedded devices, and adapts Robomaster EP for accurate detection and real-time recognition of tennis balls.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying breast cancer from unregistered multi-view mammograms. This motivates us to leverage the architecture of Vision Transformers to capture long-range relationships of multiple mammograms from the same patient. For this purpose, we employ local Transformer blocks to separately learn patch relationships within a specific view (CC/MLO) of one side (right/left) of mammogram. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of CC and MLO view mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our two-view Transformer-based model achieves lesion classification accuracy of 77.0% and area under ROC curve (AUC = 0.814), outperforming state-of-the-art multi-view CNNs by 3.1% and 3%, respectively. Meanwhile, the new two-view model improves mammographic case classification accuracies of two single-view models by 7.4% (CC) and 4.5% (MLO), respectively. The promising results unveil the great potential of using Transformers to develop high-performing computer-aided diagnosis (CADx) schemes of mammography.

Triple X syndrome (TXS, also known as trisomy X or 47,XXX) has been associated with impaired overall neurocognitive functioning in children and relatively young adults. However, neurocognitive functioning in adults with TXS is poorly understood. The aim of this study was, therefore, to examine cognitive functioning in adults with TXS. Methods: In this cross-sectional study, data were collected from 34 adult women with TXS (mean age = 32.9; SD = 13.1) and 31 controls (mean age = 34.9; SD = 13.7). General intellectual functioning, semantic/verbal memory, visual/episodic memory, psychomotor speed, and attention and executive functioning were then compared between these two groups. Results: We found that general intellectual functioning was significantly lower in the TXS group compared to the control group. In addition, women with TXS had more attention problems and lower psychomotor speed, particularly motor processing speed. When the analyses were adjusted for IQ, the strength of these associations decreased. The women in the TXS group also scored significantly lower at free recall in the verbal memory test, but not in immediate or delayed recognition. Finally, visual/episodic memory and executive functioning did not differ significantly between groups. Conclusions: Our analysis revealed that women with TXS score lower in general intellectual functioning and have impairments in motor processing speed and attention compared to controls, but do not differ with respect to executive functioning. These results offer new insights for improving the support of adults with TXS both at school and in the workplace.

A hemispheric asymmetry is known for the processing of global vs. local visual information. In this study, we investigated the existence of a hemispheric asymmetry for visual processing of low vs. high spatial frequency gratings. Event-related potentials were recorded in a group of healthy right-handed volunteers from 30 scalp sites. Six types of stimuli (1.5, 3 and 6 c/deg gratings) were randomly flashed 180 times in the left and right upper hemi-fields. Stimulus duration was 80 ms and ISI ranged between 850-1000 ms. Participants had to pay attention and respond to targets based on their spatial frequency and location, or to passively look at the stimuli. C1 and P1 visual responses, as well as a later Selection negativity and a P300 components of ERPs were quantified and subjected to repeated-measure ANOVAs. Overall, performance was faster for the RVF, thus suggesting a left hemispheric advantage for attentional selection of local elements. Similarly, the analysis of mean area amplitude of C1 (60-110 ms) sensory response showed a stronger attentional effect (F+L+ vs. F-L+) at left occipital areas, thus suggesting the sensory nature of this hemispheric asymmetry.

A 47-year-old physician suddenly noticed a persistent difficulty maintaining attention. He was awake, alert, and oriented. After two hours he developed fever, ageusia, and anosmia. He denied any previous history of psychiatric illness and was hydrated at the time of the subjective attention impairment. On admission, the patient remained oriented. He reported the persistence of attention problems, anosmia, and mild fatigue. The oxygen saturation 99% while he was breathing ambient air. Laboratory tests were unremarkable. A high-resolution computed tomography of the chest was normal. Nasopharyngeal and throat swabs specimens on reverse transcription-polymerase chain reaction analysis tested positive for SARS-CoV2. On illness day 3, the examination was unchanged, but he continued to complain of difficulties to stay focused. Then, he performed an objective attention test. The test demonstrated a moderate attentional impairment. On day 6, the patient reported a subjective worse in his concentration and performed a second test. Although his physical examination remained normal, the attention performance was worse as compared to day 3. Eight hours after worsening of attention impairment, the patient’s oxygen saturation dropped to 94%. From illness days 9 to 14, the patient evolved with clinical improvement. On day 10, a third objective attention test indicated a mild deficit. On day 16, he did not report any other symptom and the attention test was completely normal. Then, the patient returned to work. Neurological symptoms had been previously described in COVID- 19 patients. However, no previous research had investigated early cognitive deficits preceding the traditional symptoms.

Humans are incapable of judging the temporal order of visual events at brief temporal separations with perfect accuracy. Their performance---which is of much interest in visual cognition and attention research---can be measured with the temporal-order judgment task, which typically produces S-shaped psychometric functions. Occasionally, researchers reported plateaus within these functions, and some theories predict such deviation from the basic S shape. However, the centers of the psychometric functions result from the weakest performance at the most difficult presentations and therefore fluctuate strongly, leaving existence and exact shapes of plateaus unclear. This study set out to investigate whether plateaus disappear if the data accuracy is enhanced, or if we are ``stuck on a plateau'', or rather with it. For this purpose, highly accurate data were assessed by model-based analysis. The existence of plateaus is confidently confirmed and two plausible mechanisms derived from very different models are presented. Neither model, however, performs well in the presence of a strong attention manipulation, and model comparison remains unclear on the question which of the models describes the data best. Nevertheless, the present study includes the highest accuracy in visual TOJ data and the most explicit models of plateaus in TOJ studied so far.

As on 31.03.2020, 55.4 % (205135 MW) of total installed capacity (370106 MW) in India is through coal and lignite based power plants. These plants, set up by central, state and private utilities with substantial capital investment are facing consistently reducing Plant Utilization Factor (known as Plant Load Factor, PLF, in India). In the year 2019-20 the national average thermal power PLF stood at 55.4%, down from 78.6 % in 2007-08. On the other hand, the electricity demand is consistently rising in the country and there exists a peak and energy shortage at national level. In 2019-20 energy shortage was 0.7 % and peak shortage was 0.5 %. A disturbing paradox therefore exists here. On one hand, the country is power deficit, and on the other hand, a large amount of coal based affordable power, ready to be generated by thermal power generators, remains grossly unused. Looking into the fact that considerable investment has gone into developing these thermal power generation assets in the country, the falling PLF is a matter of concern for all the key stakeholders including the power producers, lenders, regulators and consumers. This paper identifies seven major factors that are affecting PLF of thermal power plants and then makes an attempt to project future scenario of PLF so that critical stakeholders can intervene through appropriate actions. Primary research with responses from power professionals has been used to find out the major factors. Future projection of PLF has been done using Partial Least Square (PLS) regression. Projection shows that in the Business As Usual case (Factors increasing at the current CAGR rate), the thermal power plants will face very low level of PLF (14.76 %) by 2024-25. This will mean that many plants will be shut down and many will run for only few hours in a day that too at very low loads. If the future generation mix is kept as indicated by Central Electricity Authority (CEA), a Govt. of India in its report (Draft report on optimal generation capacity mix for 2029-30- CEA- Govt of India) then the thermal power plant average PLF can sustain above 68 % until 2024-25. If followed, this path can be a breather for the thermal power plants.

Autism spectrum disorder (ASD) involves social communication difficulties and repetitive behaviors, with a growing prevalence worldwide. Symptoms include cognitive impairments, gastrointestinal (GI) issues, feeding difficulties, and psychological problems. A significant concern in ASD is food selectivity, leading to nutrient deficiencies. Common GI issues in ASD, such as constipation and irritable bowel syndrome, stem from abnormal gut flora and immune system dysregulation. Sensory sensitivities and behavioral challenges exacerbate these problems, correlating with neurological symptom severity. Children with ASD also exhibit higher oxidative stress due to low antioxidant levels like glutathione. Therapeutic diets, including ketogenic, high-antioxidant, gluten-free, and casein-free, show potential in managing ASD symptoms like behavior, communication, GI issues, and oxidative stress, though the evidence is limited. Various studies have focused on different populations, but there is increasing concern about the impact on children. This review examines the effect of food selectivity, GI problems, and antioxidant deficiencies in ASD and revises the effectiveness of various therapeutic diets, highlighting the need for further research to confirm their long-term benefits.

Infantile Neuroaxonal Dystrophy (INAD) is a rare neurodegenerative disorder, affecting 1:1,000,000 population. It results from pathogenic variants in the PLA2G6 gene located on chromosome (22q13.1). The onset of symptoms usually occurs between 6 to 18 months, causing developmental regression leading to debilitating symptoms such as muscle weakness, dementia, and loss of basic skills. Eventually, it progresses to life-threatening symptoms including breathing difficulties, which limits the life expectancy to 5-10 years. While potential genetic therapy for treatment is are being developed, they are yet to be approved for use, and management remains essentially supportive. This case report is of a 9-year-old Pakistani girl with INAD. She presented with recurrent chest infections, developmental regression, loss of speech, paralysis, hypertension, and eventually breathing difficulties. Brain magnetic resonance imaging and genetic testing con-firmed the diagnosis. This case posed diagnostic challenges in view of its overlapping clinical presentation. Through this report, we aim to raise awareness about the condition among practitioners, outline the importance of genetic counseling in susceptible couples and suggest potential areas of further research.

Obsessive-Compulsive Disorder (OCD) is a mental health condition marked by recurrent intrusive thoughts or sensations that compel individuals to perform repetitive behaviors or mental acts. Obsessions and compulsions significantly disrupt daily life and cause considerable distress. Early identification and intervention improve long-term outcomes. This study aimed to evaluate the ability of four advanced artificial intelligence models (ChatGPT-3.5, ChatGPT-4, Claude, and Bard) to accurately recognize OCD compared to human professionals and to assess recommended therapies and stigma attributions. This study was conducted during March 2024 utilizing 12 vi-gnettes. Each vignette depicted a client, either a young adult or a middle-aged male or female, attending an initial therapy session. Each vignette was evaluated ten times, resulting in 480 evaluations. The results were compared with those of a human sample of 514 psychotherapists, as reported by Canavan. Significant differences were found. AI models demonstrated higher OCD recognition rates and confidence levels than human professionals and showed 100% confidence in recognition, compared to 87% among psychotherapists. AI models also recommended evi-dence-based interventions more frequently, with ChatGPT-3.5 and Claude at 100%, ChatGPT-4 at 90%, and Bard at 60%, compared to 61.9% among psychotherapists. Additionally, AI models ex-hibited significantly lower stigma and danger estimations, though both AI and psychotherapists demonstrated high willingness to treat the described cases. The findings suggest that AI models surpass human professionals in recognizing OCD and recommending evidence-based treatments while also demonstrating lower stigma. These results highlight the potential of AI tools to enhance OCD diagnosis and treatment in clinical settings.

Background: Older adults with bipolar disorder (OABD) are individuals aged 50 years and older with bipolar disorder (BD). People with BD may have fewer coping strategies or resilience. A long course of the disease, such as in OABD, could impact the development of resilience strategies, but this remains under-researched in OABD. Therefore, this study aims to assess resilience levels within the OABD and explore associated factors, hypothesizing that resilience could improve psychosocial functioning, wellbeing and quality of life of OABD patients. Methods: This study sampled 33 OABD patients from the OABD cohort at the Bipolar and Depressive Disorders Unit of Hospital Clinic of Barcelona. This was an observational, descriptive and cross-sectional study. Demographic and clinical variables as well as psychosocial functioning, resilience and cognitive reserve were analyzed. Resilience was measured using the CD-RISC-10. Non-parametric tests were used for statistical analysis. Results: The average CD-RISC-10 score was 25.67 points (SD 7.87). Resilience negatively correlated with the total number of episodes (p = 0.034), depressive episodes (p = 0.001), and the FAST (p < 0.001). Participants with normal resilience had a lower FAST (p = 0.046), a higher CRASH (p = 0.026), and more EOBD (p = 0.037) compared to those with low resilience. Conclusions: OABD individuals may exhibit lower resilience levels which correlate with more psychiatric episodes, particularly the number of depressions and worse psychosocial functioning and cognitive reserve. Better understanding and characterization of resilience could aid in early identification of patients requiring additional support to foster resilience and enhance OABD management.

Although the dopamine hypothesis of schizophrenia explains the effects of all the available antipsychotics in clinical use, there is an increasing need for developing new drugs for the treatment of positive, negative, and cognitive symptoms of chronic psychoses. Xanomeline-trospium (KarXT) is a drug combination that is based on the essential role played by acetylcholine in the regulation of cognitive processes and the interactions between this neurotransmitter and other signaling pathways in the central nervous system, with a potential role in the onset of schizophrenia, Alzheimer’s disease, and substance use disorders. A systematic literature review that included four electronic databases (PubMed, Cochrane, Clarivate/Web of Science, and Google Scholar) and the US National Library of Medicine database for clinical trials detected 21 sources referring to 14 studies focused on KarXT, out of which only four have published results. Based on the results of these trials, the short-term efficacy and tolerability of xanomeline-trospium are good, but more data is needed before this drug combination may be recommended for clinical use. However, on a theoretical level, the exploration of KarXT is useful for increasing the interest of researchers in finding new, non-dopaminergic, antipsychotics that could be used either as monotherapy or as add-on drugs.

Type 2 diabetes mellitus (T2DM) is a common metabolic disease characterised by insulin resistance and elevated blood glucose levels, affecting millions of people worldwide. T2DM patients with dyslipidaemia have an increased risk of cardiovascular disease (CVD). A complex interplay of risk factors such as hyperglycaemia, dyslipidaemia, hypertension, obesity, inflammation and oxidative stress favour the development of atherosclerosis, a central mechanism in the pathogenesis of cardiovascular disease. Dyslipidaemia, a hallmark of T2DM, is characterised by elevated triglycerides, decreased high-density lipoprotein (HDL) cholesterol and the presence of small, dense low-density lipoprotein (LDL) particles, all of which promote atherosclerosis. In this article, we have attempted to present various treatment strategies that include pharmacological interventions such as statins, ezetimibe, PCSK9 inhibitors, fibrates and omega-3 fatty acids. We have also tried to highlight the pivotal role of lifestyle modifications, including physical activity and dietary changes, in improving lipid profiles and overall cardiovascular health in T2DM patients. We have also tried to present the latest clinical guidelines for the management of dyslipidaemia in T2DM patients. In conclusion, the treatment of dyslipidaemia in T2DM patients is of great importance as it lowers lipid particle levels, slows the progression of atherosclerosis and ultimately reduces susceptibility to cardiovascular disease.

The present review aimed to identify through what means neurologic injury can predispose individuals to Post-Traumatic Stress Disorder (PTSD). In recent years, comprehensive studies have helped to clarify which structures in the central nervous system (CNS) that, when damaged, lead to distinct PTSD symptoms–namely dissociative reactions or flashbacks. Our review narrowed its focus to three common neurologic injuries including traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), and stroke. Beyond discussing the potential mechanisms by which neurotrauma may lead to PTSD, we summarized our current understanding of the pathophysiology of the disorder and discussed predicted associations between the limbic system and PTSD. In particular, the role of noradrenergic neuromodulatory signaling on the HPA axis as it pertains to fear memory recall needs to be further explored to better understand its effects on limbic structures in PTSD patients. This paper also described how damage to the CNS can alter the function of the limbic system and result in characteristic symptoms of PTSD like intrusive memories and acute psychological distress. Further, our review intended to draw attention to a lack of PTSD prognosis in TBI, SAH, and stroke patients who could benefit from early treatment. PTSD symptoms often compound with pre-existing issues further deteriorating health outcomes for these patients. It is ultimately our goal to clarify the relationship between neurotrauma and PTSD such that earlier diagnoses and appropriate treatment are observed in clinic.

Autism Spectrum Disorder (ASD) is characterized by varying degrees of difficulty in social interaction and communication. These deficits are often associated with gastrointestinal symptoms, indicating alterations in both intestinal microbiota composition and metabolic activities. The intestinal microbiota influences the function and development of the nervous system. In individuals with ASD, there is an increase in bacterial genera such as Clostridium, as well as species involved in the synthesis of branched-chain amino acids (BCAA) like Prevotella copri. Conversely, decreased amounts of Akkermansia muciniphila, and Bifidobacterium spp. are observed. Epigallocatechin gallate (EGCG) is one of the polyphenols with the greatest beneficial activity on microbial growth and its consumption is associated with reduced psychological distress. Therefore, the objective of this review is to analyze how EGCG and its metabolites can improve the microbial dysbiosis present in ASD and its impact on the pathology. The analysis reveals that EGCG inhibits the growth of pathogenic bacteria like Clostridium perfringens and Clostridium difficile. Moreover, it increases the abundance of Bifidobacterium spp. and Akkermansia spp. As a result, EGCG demonstrates efficacy in increasing the production of metabolites involved in maintaining epithelial integrity and improving brain function. This identifies EGCG as highly promising for complementary treatment in ASD.

For a subject suffering from complex post-traumatic stress disorder, a psychological intervention program was designed, monitored in real-time during psychotherapy, and evaluated quantitatively and qualitatively after psychotherapy. One male subject participated in this program under consent. The proposed intervention program was designed using cognitive behavioral therapy and stabilization treatments for body-based sensory processes in four sessions of 90 min each. During psychotherapy, a wearable heart sensor and a communication application were utilized to determine the subject’s current psychological state. After the intervention, the effect of the proposed program was analyzed qualitatively and quantitatively using the Impact of Event Scale–Revised (IES-R-K), Athens insomnia scale (AIS), and heart rate variability (HRV). After the intervention program was conducted, the subject reconstructed his traumatic events and trained himself with certain psychological techniques to decrease his negative thoughts and emotions induced by his previous traumatic events. The IES-R-K and AIS ten months after the last session were changed positively by approximately 25% compared with the subject’s state before the first session. During psychotherapy, the HRV exhibited a significant correlation with the subject’s emotional state. The proposed intervention program induced a positive change in the subject. Although the HRV was well utilized in this investigation, more sophisticated statistical analysis will be required for clinical trials.

Neuropsychiatric disorders are globally public-health concern, in which diagnosis might be based on symptoms that often vary across individuals. Schizophrenia is one of the major neuropsychiatric disorders, which may affect millions worldwide. Detection of predictive biomarkers is the most intensively developing approach for the disease. However, the biochemical alterations have not been comprehensively distinguished up to the present time. In addition, there is less confidence of finding a specific biomarker for neuropsychiatric disorders including schizophrenia, but rather a specific characteristic behavioral pattern. Maternal immune activation has been considered to be one of the important factors for the development of neuropsychiatric disorders. Here, the mouse model of neuropsychiatric disorders has been built, in which poly-I:C, sodium dextran sulfate (DSS) and κ-carrageenan (CGN) were used for the maternal immune activation during the pregnancy of individuals. Subsequently, we challenged to link some of biochemical changes of p62 and GLAST in the offspring mice brain to the alteration of several pathological behaviors. Consequently, it has been shown that autophagy might be involved in the development of neuropsychiatric disorders including schizophrenia. To our knowledge, this is the first report of the significant correlation between pathological behavior and the biochemical alterations in neuropsychiatric disorder model animals. The relative ease of conducting these evaluation-tasks would make them useful for testing novel therapeutics designed to ameliorate the symptoms of several psychiatric disorders.

Although early work on parental burnout in the 1980s did not link parental burnout to specific parenting situations, the idea that parents affected by specific vulnerability factors were particularly at risk of burnout quickly emerged. Based on the etiological model of parental burnout (i.e., the balance between risks and resources), the objective of this study was to determine if there were specific vulnerability factors that significantly increase the risk of parental burnout. 2,563 parents participated in the study. We compared parents with a child with special needs (n = 25), parents who had adopted a child (n = 130), and single parents (n = 109), to strictly matched control groups of parents. Parents with a child with special needs displayed higher burnout and lower balance between risk and resources than the control group; parents with an adopted child had similar levels of both parental burnout and balance; and single parents had higher parental burnout but similar balance. Parents who combined specific several vulnerability factors were at greater risk of burnout. Single parenthood and adoption do not in themselves trigger a process leading de facto to other risk factors, but having a child with special needs does.

The cytosolic PNGase (peptide:N-glycanase; Png1 in yeast; NGLY1/Ngly1 in human/mice), also known as peptide-N4-(N-acetyl-beta-glucosaminyl)-asparagine ami-dase, is a well-conserved deglycosylation enzyme (EC 3.5.1.52) which catalyzes the non-lysosomal hydrolysis of an N(4)-(acetyl-β-D-glucosaminyl) asparagine residue into N-acetyl-β-D-glucosaminylamine and a peptide containing an aspartate residue. This enzyme (NGLY1) plays essential roles in clearance of misfolded or unassembled gly-coproteins through a process named ER-associated degradation (ERAD). Accumulating evidence also points out that NGLY1 deficiency can cause an autosomal recessive hu-man genetic disorder associated with abnormal development and congenital disorder of deglycosylation. In addition, the loss of NGLY1 can affect multiple cellular pathways, including but not limited to NFE2L1 pathway, Creb1/Atf1-AQP pathway, BMP path-way, AMPK pathway, and SLC12A2 ion transporter, which might be the underlying reasons for a constellation of clinical phenotypes of NGLY1 deficiency. The current comprehensive review indeed uncovers the detailed NGLY1’s structure and its im-portant roles for participation in ERAD, involvement in CDDG and potential treatment for NGLY1 deficiency.

Eating disorders (ED) are characterized by alterations in eating behavior. The genetic factors shared between ED diagnoses have been underexplored. The present study aimed to perform a genome-wide association study on individuals with disordered eating behaviors in the Mexican population, blood methylation quantitative trait loci (blood-meQTL) analysis, and in silico function prediction by different algorithms. The analysis included a total of 1803 individuals. Genome-wide association study and blood-meQTL analysis were performed by logistic and linear regression. In silico functional variant prediction, phenome-wide, and transcriptome-wide association studies by different algorithms were analyzed. In the genome-wide association study, we identified 44 single-nucleotide polymorphisms (SNP) associated at a nominal value and 7 blood-meQTL at a genome-wide umbral. The SNPs were enriched in genome-wide associations of the metabolic and immunologic domains. In the in silico analysis, the SNP rs10419198 located on an enhancer mark could change the expression of PRR12 on blood, adipocytes, and brain areas that regulate food intake. The present study supports the previous associations of genetic variation in the metabolic domain with ED.

The devastating impact of the ongoing coronavirus disease 2019 (COVID-19) on public health, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has made fighting of the COVID-19 pandemic is a top priority in medical research and pharmaceutical development. Surveillance of SARS-CoV-2 mutations is essential for the comprehension of SARS-CoV-2 variant diversity and their impact on virulence and pathogenicity. The SARS-CoV-2 open reading frame 10 (ORF10) protein interacts with multiple human proteins CUL2, ELOB, ELOC, MAP7D1, PPT1, RBX1, THTPA, TIMM8B, and ZYG11B expressed in the lung tissues. Mutations and co-mutations in the emerging SARS-CoV-2 ORF10 variants are expected to impact the severity of the virus and its associated consequences. In this article, We highlight 128 single mutations and 35 co-mutations in the unique SARS-CoV-2 ORF10 variants in this article. The possible predicted effects of these mutations and co-mutations on the secondary structure of ORF10 variants and host protein interactomes are presented. The findings highlight the possible effects of mutations and co-mutations on the emerging 140 ORF10 unique variants from secondary structure and intrinsic protein disorder perspectives.

Gender and identity issues permeate society as a whole. Therefore, the matters involving transgender individuals should be analised in order to understand the difficulties experienced by this population and the social practices implemented. In this sense, the objective of this study was to investigate the strategic themes and their evolution in relation to the theme. For this, a bibliometric performance and network analysis (BPNA) was carried out with the existing data in the Web of Science database between 1954 and march 2021. Twenty-three thousand and four hundred and seventy-one (23,471) articles were identified, which were included in the SciMAT software to perform a bibliometric analysis, resulting in the graph of the thematic evolution structure and the strategic diagram, in which 8 motor themes and a cross-cutting theme of great magnitude are highlighted, which are discussed in depth. The results show the relation between the transgender theme and gender, identity, sexual orientation, hormone therapy and gender-affirming surgery. It is concluded that, despite the large number of associated researches, some areas of study are still incipient, such as the inclusion of transgender people in the formal labor market and in the prison context, thus opening field for further studies.

Background: There is a strong comorbidity between mood disorders and metabolic syndrome (MetS). Increased levels of reactive oxygen and nitrogen species (RONS) and nitro-oxidative stress toxicity (NOSTOX) partially underpin this comorbidity.Aims: To examine the associations of RONS/NOSTOX biomarkers with MetS after adjusting for the significant effects of mood disorders (major depression, and bipolar type 1 and 2), generalized anxiety disorder (GAD), tobacco use disorder (TUD), and male sex.Methods: The study included subjects with (n=65) and without (n=107) MetS and measured levels of superoxide dismutase 1 (SOD1), lipid hydroperoxides (LOOH), nitric oxide metabolites (NOx), malondialdehyde (MDA), and advanced oxidation protein products (AOPP) and computed z unit-weighted composite scores which reflect RONS/NOSTOX. The study included 105 patients with mood disorders, 46 with GAD, and 95 with TUD.Results: MetS was associated with increased levels of MDA and AOPP, independently from mood disorders, TUD, sex and GAD. Atherogenicity and insulin resistance (IR) were significantly associated with a NOSTOX composite score. Mood disorders, TUD, GAD, male sex and MetS independently contribute to increased RONS/NOSTOX. The RONS/NOSTOX profile of MetS was different from that of GAD, which showed increased SOD1 and NOx levels. TUD was accompanied by increased SOD1, LOOH and MDA, and male sex by increased LOOH and AOPP.Conclusions: MetS is characterized by increased lipid peroxidation with aldehyde formation and chlorinative stress, and atherogenicity and IR are strongly mediated by RONS/NOSTOX. Partially shared RONS/NOSTOX pathways underpin the comorbidity of MetS with mood disorders, GAD, and TUD.

Background: Clozapine (CLZ) use is precarious due to its neurological, cardiovascular, and hematological side effects; however, it is the gold standard in therapy-resistant schizophrenia (TRS) in adults and is underused. Objective: to examine the most recent CLZ data on (a) side effects concerning (b) recent pharmacological mechanisms, (c) therapy benefits, and (d) the particularities of the COVID-19 pandemic. Data sources: a search was performed in two databases (PubMed and Web of Science) using the specific keywords "clozapine" and "schizophrenia," "side effects," "agranulocytosis," "TRS," or "bipolar affective disorder (BAF)" for the last ten years. Study eligibility criteria: clinical trials on adults with acute symptoms of schizophrenia or related disorders. Results: We selected 37 studies, randomized controlled trials (RCTs), and clinical case series (CCS), centered on six main topics in the search area: (a) CLZ in schizophrenia, (b) CLZ in bipolar disorder, (c) side effects during the clozapine therapy, (d) CLZ in pregnancy, (e) CLZ in early-onset schizophrenia, and (f) CLZ therapy and COVID-19 infection. Limitations: We considered RCTs and CCS from two databases, limited to the search topics. Conclusions and implications of key findings: (a) Clozapine doses should be personalized for each patient based on pharmacogenetics testing when available; the genetic vulnerability postulates predictors of adverse reactions' severity; patients with a lower genetic risk could have less frequent hematological monitoring; (b) CLZ-associated risk of pulmonary embolism imposes prophylactic measures for venous thromboembolism; (c) convulsive episodes are not an indication for stopping treatment; the plasma concentration of clozapine is a better side effect predictor than the dosage; (d) COVID-19 infection may enhance clozapine toxicity, generating an increased risk of pneumonia. Therapy must be continued with proper monitoring of the white blood count, and the clozapine dose decreased by half until three days after the fever breaks; psychiatrists and healthcare providers must act together. Background: Clozapine (CLZ) use is precarious due to its neurological, cardiovascular, and hematological side effects; however, it is the gold standard in therapy-resistant schizophrenia (TRS) in adults and is underused. Objective: to examine the most recent CLZ data on (a) side effects concerning (b) recent pharmacological mechanisms, (c) therapy benefits, and (d) the particularities of the COVID-19 pandemic. Data sources: a search was performed in two databases (PubMed and Web of Science) using the specific keywords "clozapine" and "schizophrenia," "side effects," "agranulocytosis," "TRS," or "bipolar affective disorder (BAF)" for the last ten years. Study eligibility criteria: clinical trials on adults with acute symptoms of schizophrenia or related disorders. Results: We selected 37 studies, randomized controlled trials (RCTs), and clinical case series (CCS), centered on six main topics in the search area: (a) CLZ in schizophrenia, (b) CLZ in bipolar disorder, (c) side effects during the clozapine therapy, (d) CLZ in pregnancy, (e) CLZ in early-onset schizophrenia, and (f) CLZ therapy and COVID-19 infection. Limitations: We considered RCTs and CCS from two databases, limited to the search topics. Conclusions and implications of key findings: (a) Clozapine doses should be personalized for each patient based on pharmacogenetics testing when available; the genetic vulnerability postulates predictors of adverse reactions' severity; patients with a lower genetic risk could have less frequent hematological monitoring; (b) CLZ-associated risk of pulmonary embolism imposes prophylactic measures for venous thromboembolism; (c) convulsive episodes are not an indication for stopping treatment; the plasma concentration of clozapine is a better side effect predictor than the dosage; (d) COVID-19 infection may enhance clozapine toxicity, generating an increased risk of pneumonia. Therapy must be continued with proper monitoring of the white blood count, and the clozapine dose decreased by half until three days after the fever breaks; psychiatrists and healthcare providers must act together.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric problem with a few core symptoms: weaknesses in social behavior, verbal impairments, repetitive behavior and restricted interests. Beyond the core symptoms, autism has strong association with other disorders such as intellectual disability, epilepsy, schizophrenia among many others. This paper outlines a theory of ASD with capacity to connect heterogeneous ‘core’ symptoms, medical and psychiatric comorbidities as well as other etiological theories of autism in a unifying cognitive framework rooted in neuroscience and genetics. Cognition is embedded into an ever-developing structure modified by experiences, including the outcomes of environment influencing behaviors. We introduce the hypothesis that autism is caused by deficits in component-based cognition and the internal learning reinforcing machinery. Specifically, we outline our Cartesian Factor forming autoencoder like model that supports cognition by breaking combinatorial explosion and discuss the cognitive and neural processes behind our model. The high dimensionality of sensory information poses serious problems, since the brain can handle only 7±2 relevant variables at a time making processes, such as the extraction and encoding of the relevant variables and their efficient manipulation critical. These processes are influenced by previous experiences and the internal reward system. In addition, large delays of distributed information processing should be counteracted by learned predictive models to synchronize sensory, proprioceptive, and cognitive signals and have timely and accurate model-based actions. Impairments in any of these aspects may disrupt learning and execution. Combinations of small impairments may allow the solving of low complexity tasks but may become visible if learned variables and the related metric are improper and imprecise, respectively, especially if their number is large. We claim that social interactions are amongst the most challenging cognitive tasks in terms of the number of variables involved. In turn, they are highly susceptible to combinations of small impairments. We consider impairments as the basic colors of autism, whereas the combinations of diverse impairments make the palette of autism. In turn, social processes can be spoiled in many ways and can lead to diverse comorbidities.

Major Depression Disorder (MDD) is accompanied by an immune response characterized by increased levels of pro-inflammatory and immune-regulatory cytokines and cytokine-induced stimulation of indoleamine-2,3-dioxygenase (IDO). There is also some evidence that anti-inflammatory drugs may have a clinical efficacy in MDD.The aim of this study is to examine the clinical effects of an eight-week combinatorial treatment of ketoprofen (a nonsteroidal anti-inflammatory drug) combined or not with sertraline, on serum levels of IDO, interferon (IFN)-γ, interleukin (IL)-4 and transforming growth factor (TGF)-β1 in association with changes in the Beck-Depression Inventory-II (BDI-II). The study included 140 MDD patients and 40 normal controls. The pre-treatment serum levels of IDO, IFN-γ, TGF-β1 and IL-4 were significantly higher in MDD patients compared with the control group. Treatment with sertraline with or without ketoprofen significantly reduced the increased baseline production of all 4 biomarkers to levels which were similar as those of normal controls. Ketoprofen add-on had a significantly greater effect on IDO and BDI-II as compared with placebo. The reductions in IDO, IL-4 and TGF-β1 during treatment were significantly associated with those in the BDI-II.In conclusion, the clinical efficacy of both sertraline + ketoprofen may be ascribed at least in part to attenuated IDO levels and immune-inflammatory responses in MDD. Moreover, add-on treatment with ketoprofen may augment the efficacy of sertraline by attenuating IDO. However, these treatments may also significantly reduce the more beneficial properties of T helper-2 and T regulatory (Treg) immune subsets. Future research should develop immune treatments that target the immune-inflammatory response in MDD, while enhancing the compensatory immune-regulatory system (CIRS).

Sleep disorders (SD) have a complex aetiology, and socioeconomic status (SES) as determined by social class, household income, ethnicity and education plays an important role in their development. As SD are associated with cognitive impairment and mood disorders, they in turn impact SES. Socioeconomic status also influences allostatic load caused by chronic accumulation of stress throughout life. Environmental and psychological stressors have a direct effect on SD, and they are modulated by SES, in combination with comorbidities like obesity and cardiovascular disease. This review explores the recent theories about the influence of SES on the development of SD in the general population, whether or not occurring with comorbidities, and also focusses on the interplay between socioeconomic status, circadian rhythms, aging and clinical outcomes like metabolic diseases and cancer.

Urea cycle disorders (UCDs) are common inborn errors of metabolism, with an incidence of one in 30,000 births. They are caused by deficiencies in any of six enzymes and two carrier proteins, the most common being Ornithine Transcarbamylase Deficiency (OTCD). OTCD results in impairment to excrete nitrogen, causing toxic buildup of ammonia with resultant encephalopathy. Hyperammonemia (HA) induces the conversion of glutamate to glutamine in the brain. Excess glutamine in the brain causes osmotic changes cerebral edema, changes in astrocyte morphology, and cell death. Acute symptoms of HA include vomiting, hyperventilation, seizures, and irritability. Long-term neurological changes include deficits in working memory and executive function. To date, there are no predictors of prognosis of infants with neonatal onset OTCD outside of plasma ammonia level at presentation and duration of hyperammonemic coma. We provide a comprehensive analysis of a 16-year-old male with neonatal onset of OTCD as an example of how brain biomarkers may be useful to monitor disease course and outcome. This male presented at 8 days post natal with plasma ammonia and glutamine of 677 and 4024 micromol/L and had a missense mutation in Exon 4 (p.R129H). Treatment included protein restriction, sodium benzoate, and citrulline, arginine, and iron. He suffered recurrent acute hyperammonemic episodes despite compliance, triggered by infections or catabolic stressors. We discuss the long-term effects of the hyperammonemic episodes by following MRI based disease biomarkers.

Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) is one of the most controversial diseases in pediatric rheumatology. Despite its first description being published more than 25 years ago as the sudden and rapid onset of obsessive-compulsive disorder (OCD) and/or tic disorder symptoms as complications of a Group A beta-hemolytic Streptococcus (GAS) infection, precise epidemiological data are still lacking, and there are no strong recommendations for its treatment. Recent advances in the comprehension of PANDAS pathophysiology are largely attributable to animal model studies and the understanding of the roles of Ca++/calmodulin-dependent protein kinase (CaM kinase) II, disrupted dopamine re-lease in the basal ganglia, and striatal cholinergic interneurons. The diagnosis of PANDAS should be made after an exclusion process and should include prepubescent children with a sudden onset of OCD and/or a tic disorder, with a relapsing/remitting disease course, a clear temporal association between GAS infection and onset or exacerbation of symptoms, and the association with other neurological abnormalities such as motoric hyperactivity and choreiform movements. Antibiotic medications are the primary therapeutic modality. Nonetheless, there is a paucity of randomized studies and validated data, resulting in a scarcity of solid recommendations.

The traditional sequent peak algorithm (SPA) was used to assess the reservoir volume (VR) for comparison with deficit volume, DT, (subscript T representing the return period) obtained from the drought magnitude (DM) based method with draft level set at the mean annual flow on 15 rivers across Canada. At an annual scale, the SPA based estimates were found to be larger with an average of nearly 70% compared to DM based estimates. To ramp up DM based estimates to be in parity with SPA based values, the analysis was carried out through the counting and the analytical procedures involving only the annual SHI (standardized hydrological index, i.e. standardized values of annual flows) sequences. It was found that MA2 or MA3 (moving average of 2 or 3 consecutive values) of SHI sequences were required to match the counted values of DT to VR. Further, the inclusion of mean, as well as the variance of the drought intensity in the analytical procedure, with aforesaid smoothing led DT comparable to VR. The distinctive point in the DM based method is that no assumption is necessary such as the reservoir being full at the beginning of the analysis - as is the case with SPA.

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, \eg{} low accuracy, slow speed, \etc The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Luoyang Pedestrian Dataset \footnote{The dataset can be downloaded through this anonymous link: \url{https://drive.google.com/drive/folders/13po1FX7Qk5RDgb60-dzOi74cq_kqDJtM?usp=sharing} }, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Luoyang Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperform the most commonly used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

Drugs-drugs interactions(DDI) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is considered the center of a substructure, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differ- ences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. To address these issues, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

AVs suffer reduced maneuverability and performance due to the degradation in sensor performances in fog. Such degradation causes significant object detection errors essential for AVs' safety-critical conditions. For instance, YOLOv5 performs significantly well under favorable weather but suffers miss detections and false positives due to atmospheric scattering caused by fog particles. Existing deep object detection techniques often exhibit a high degree of accuracy. The drawback is being sluggish at object detection in fog. Object detection methods with fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persist. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar's object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image on the camera image. Using the attention mechanism, we emphasized and improved important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our result shows that the proposed method significantly enhances the detection of distant and small objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed with an accuracy of 0.849 at 69 fps.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

Substance use disorder (SUD) is a global health crisis that is becoming increasingly prevalent among younger generations and impacting the overall health of millions of people worldwide. Traditional treatment methods, including cognitive-behavioral therapies and medicinal-assisted treatments, often face limitations such as inconsistent efficacies and varying degrees of accessibility. Thus, there is a clear need for a new and innovative treatment strategy. Music therapy has recently gained attention through research as a promising non-pharmacological supplement to strengthen conventional SUD treatment methods. This review explores the potential benefits of music therapy in reducing substance cravings, alleviating depressive symptoms, and improving emotional expression among patients in the rehabilitation phase of SUD recovery. We review evidence from various studies and clinical trials to evaluate the efficacy of music therapy in improving standard treatment outcomes, with the results indicating that music therapy could be an effective complement to traditional treatment methods while noting that its efficacy could vary depending on the individual and type of intervention provided. The benefits of music therapy include accessibility of treatment for diverse patient groups and the potential for highly individualized care, although more research is needed to compare the efficacies of specific music treatment types for certain conditions and patient populations. Further research is also needed to validate the neurobiological mechanisms of music therapy, as well as increased communication between music therapists worldwide to enforce consistency in treatment methods and share learnings in this growing field.

Introduction. Autism Spectrum Disorder (ASD) includes different manifestations of an individual’s level of functioning. It is characterised by qualitative alterations in reciprocal social interaction and social communication, and the presence of repetitive behaviours and restricted interests. Based on two dimensions, three levels are differentiated: social communication, restricted interests, and repetitive behaviour. Aim. To gain knowledge of the application and usefulness of BRIEF‐P in the evaluation of Executive Functions (EFs) in people with ASD. Method. Out of a total of 161,773 potentially eligible published articles from different databases. 13 appropriate articles were revised and 4 articles were selected. Studies that were included evaluated samples involving individuals with ASD aged 2 to 8 years, and were published in English or Spanish during the period of 2012‐2022. Results. The executive profile obtained from the application of BRIEF‐P in individuals with ASD was analysed. Conclusion. Results confirm deficits in flexibility, although they are not conclusive. This may be due to aspects related to methodology, whereby the studies (i) include very large and heterogeneous age groups, (ii) do not discriminate based on the level of competence, and (iii) use instruments for evaluating Executive Functions that are not validated or adapted to people with ASD. Another reason is due to the lack of consensus in the very operational definition of the Executive Functions construct, with the studies focusing mainly on the cold dimension while ignoring the hot dimension.

Obsessive–compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing behaviors, while new-onset symptoms were predominantly of the hoarding type. In the present study, we followed the evolution of OCD symptoms, anxiety, depression, and insight of illness in forty-six OCD patients throughout the pandemic. Clinical measures were collected at four different time points before and during the COVID-19 pandemic in Italy. Within-subject comparisons were used to compare clinical scale scores across time, and correlations were examined between patients' baseline characteristics and changes in clinical scores. We found that all clinical measures increased during the first Italian lockdown, with respect to the pre-pandemic values. Anxiety decreased during the temporary elimination of restriction provisions, whereas severity of OCD symptoms and insight returned to pre-pandemic values during the second mandatory lockdown. These results were observed only in two sub-groups of patients: those taking benzodiazepines and those with shorter illness duration. Our findings suggest the need of additional clinical attention to these specific sub-groups of OCD patients in case of particularly distressing circumstances, while pointing to a possible adaptive role of their OCD symptoms when the environment requires a higher care of hygiene and extraordinary supply of essential resources.

N-methyl-D-aspartate receptors (NMDARs) are the main class of ionotropic receptor for the ex-citatory neurotransmitter glutamate. It plays a crucial role in the permeability of Ca2+ ions and excitatory neurotransmission in the brain. Being heteromeric receptors, they are composed of sev-eral subunits, including two obligatory GluN1 subunits (eight splice variants) and regulatory GluN2 (GluN2A~D) and/or GluN3 (GluN3A~B) subunits. Widely distributed in the brain, they regulate other neurotransmission systems and are therefore involved in essential functions such as synaptic transmission, learning and memory, plasticity, excitotoxicity. The present review will discuss the physiopathological impacts of NMDAR and particularly GluN2A and GluN2B subu-nits in cognitive processes and neurodegenerative diseases (Alzheimer's disease, Huntington's disease, Parkinson's disease). The pharmacology of different NMDAR antagonists and their po-tentialities will be presented. In particular, a focus will be given on fluoroethylnormemantine (FENM), an investigational drug with very promising developments as a tomography radiotracer for NMDARs, an anxiolytic in post-traumatic stress disorder and a neuroprotective agent in Alzheimer's disease.

Individuals with BPD grapple with unstable relationships, marked by extreme emotional swings and interpersonal conflicts. Emotional instability, a distorted self-image, and impulsivity further complicate matters, often leading to self-harm and suicide attempts as desperate coping mechanisms. Genetics and adverse childhood experiences contribute to BPD's development, shaping brain function and emotional regulation. Diagnosis involves meticulous assessments, paving the way for personalized treatments like dialectical behavior therapy and, in severe cases, hospitalization. Early intervention, professional support, and societal understanding are pivotal for positive outcomes, and challenging mental health stigma.

Background: Dual disorders (DD) involve the coexistence of substance use disorder (SUD) with another mental illness, often from psychotic and affective categories. This study explores the ef-fectiveness of brexpiprazole, a third-generation antipsychotic, in individuals diagnosed with schizophrenia spectrum disorder (DSM-V) and comorbid AUD/SUD (DSM-V). Methods: Eighteen enrolled patients underwent psychometric assessments at baseline and one month into brexpiprazole treatment administered at a mean dosage of 2 mg/day. Results: Brexpiprazole demonstrated significant reductions in psychopathological burden (PANSS total score, p=0.004), improved CGI total score (p=0.042), and decreased MOAS total score (p=0.012). Quality of life improved in SF-36 subscales (p<0.05). Substance craving reduction, though observed, did not reach statistical significance. Conclusions: This study provides initial evidence supporting brexpiprazole's efficacy and safety with positive effects on psychopathology and quality of life, emphasizing the need for tailored, comprehensive treatment strategies in this complex patient population.

Background: Patients with bipolar disorder (BD) receive traditional Chinese Medicine (TCM) for unmet clinical needs with psychotropic medications. However, the clinical characteristics of practices and outcomes of TCM in BD are not fully understood. This cohort study investigated the clinical characteristics, principal diagnoses, TCM interventions, and TCM prescriptions in patients with BD. Methods: 12,113 patients with BD were withdrawn from Taiwan's longitudinal health insurance database 2000 (LHID 2000) between 1996 and 2013. Chi-squared test for categorical variables and independent t-test for continuous variables. A p-value less than 0.05 indicated significance. Results: One-thousand and three hundred nineteen patients who visited TCM clinics after the diagnosis of BD were in the TCM group, while those who never visited TCM were in the non-TCM group (N= 1,053). Compared to the non-TCM group, patients in the TCM group had younger average age, a higher percentage of female individuals, more comorbidities of anxiety and alcohol use disorders, and higher mood stabilizer usage rates. The TCM group exhibited pain-related indications, including joint pain, myalgia, myositis, headache, and sleep disturbances. Corydalis yanhusuo and Shu-Jing-Huo-Xue-Tang were the most useful single herb and herbal formulas. Conclusions: Physicians need to be aware of the use of TCM in Patients with BD.

Neuromyelitis Optica Spectrum Disorder (NMOSD) is a rare immune-mediated relapsing-remitting disease of the central nervous system. The usage of rituximab, to prevent NMOSD relapses, is common in Sweden, but the knowledge on long-term efficacy and risk for severe infections are limited. We performed a retrospective cohort study with medical chart review of rituximab-treated NMOSD patients at the Karolinska University Hospital, Sweden, to assess the occurrence of relapses and severe infectious events (SIE). Furthermore, we collected data on CD19+ lymphocyte, and IgG levels during therapy. A total of 42 NMOSD patients; 24 with aquaporin -4 antibodies (AQP4+), eight with myelin oligodendrocyte glycoprotein antibodies (MOG+) and 10 with absence of antibodies (double-seronegative) were included. During a mean treatment time of 4.4 years, 37% AQP4+, 75% MOG+, and 60% double-seronegative patients experienced 1 relapse. SIEs were common with 46% of AQP4+, 25% of MOG+, and 40% of double-seronegative patients affected. Incomplete CD19+ lymphocyte suppression did not predict a relapse and there was no correlation between IgG levels and SIEs. In conclusion, our data demonstrated a high risk for both relapses and SIEs in rituximab-treated NMOSD patients highlighting the need for close clinical vigilance of disease activity and infections during treatment.

Brain arteriovenous malformations (BAVMs) are a critical concern in hereditary hemorrhagic telangiectasia (HHT) patients, carrying the risk of life-threatening intracranial hemorrhage. While traditionally seen as congenital, the debate continues due to documented de novo cases. Our primary goal was to identify the precise postnatal window in which deletion of the HHT gene Endoglin (Eng) triggers BAVM development. We employed SclCreER(+);Eng2f/2f mice, enabling timed Eng gene deletion in endothelial cells via tamoxifen. Tamoxifen was given at four postnatal periods: P1-3, P8-10, P15-17, and P22-24. BAVM development was assessed at 2-3 months using latex dye perfusion. We examined angiogenic activity by assessing vascular endothelial growth factor receptor 2 (VEGFR2) expression via Western blotting and Flk1-LacZ reporter mice. Longitudinal magnetic resonance angiography (MRA) was conducted up to 9 months. BAVMs emerged in 88% (P1-3), 86% (P8-10), and 55% (P15-17) of cases, with varying localization. Notably, the P22-24 group did not develop BAVMs but exhibited skin AVMs. VEGFR2 expression peaked in the initial 2 postnatal weeks, coinciding with BAVM onset. These findings support the “second hit” theory, highlighting the role of early postnatal angiogenesis, in initiating BAVM development in HHT type I mice.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition, characterized by persistent deficits in communication/social interaction and restricted and repetitive patterns of behavior, interests or activities. It is usually identified in childhood, despite late diagnoses in cases of greater functionality. Havighurst proposes a set of fundamental tasks throughout the life cycle, whose achievement is probably compromised in the ASD, even though it lacks scientific evidence. This study innovates, as it is based on Havighurst's theoretical model for understanding the development process of three adults and one young person, diagnosed with ASD level I, at different life stages. Through the methodology of life histories, interviews were subject to content analysis. Results pointed out the early detection and interventions importance, minimizing adversities, above all by those who had late diagnoses. Self-perceived success, not expected, was found in some development tasks (e.g. conscience and morality development). Also were noticed expected limitations related to ASD (e.g. difficulties in relationships with peers and in the labor market inclusion). Probably, in cases of greater severity and less functionality/autonomy, there would be greater failure in development tasks. It is crucial to improve research on this developmental model and on the factors that delay the diagnosis, including cases of different levels of severity.

This paper addresses the grammatical challenges associated with the development of clause complexity, focusing on the performance of a group of monolingual Spanish-speaking schoolchildren with Specific Language Impairment/Developmental Language Disorder (SLI/DLD) in a longitudinal corpus of oral narrative samples. The study examines the presence of interclause relations of subordination and equivalence (hypotaxis and parataxis) in language samples of two groups: an experimental group made up of 24 schoolchildren with SLI/DLD and a control group made up of 24 schoolchildren with typical development (TD). The results show that while both groups use parataxis as the most common relation between clauses in all school grades, there is a significant decrease in paratactic relations and a significant increase in hypotactic relations from first to fourth grade of primary education. Although the development patterns are highly similar, the SLI/DLD group shows greater difficulties in mastering more complex (hypotactic) relations in fourth grade compared to the control group, indicating that they are less sophisticated in the use of these types of complex relations. These findings suggest that focused support on the most complex structures is needed towards the fourth grade of primary education, given the demands of the school academic register from 6 and 7 years of age, and the potential problems that the development of clause complexity can cause in school-age children.

Equine Assisted Activities (EAA) are considered a suitable innovative rehabilitative practice for children with neurodevelopmental disorders, including Autism Spectrum Disorder (ASD). While standardized scales have been previously used as a tool to evaluate the effects of EAA on different domains of functioning in ASD children, few studies have considered an ethological approach as a means to describe human-horse interactions in the context of ASD. In this study, we aimed to evaluate the behaviour of 19 children with ASD - in comparison with 19 typically developing children (TD) - during EAA sessions. We developed an ethogram from the video-recordings, to assess spatial relationships, social interactions and communicative behaviours displayed by the child towards the horse, as well as the occurrence of problem behaviours. Results indicate that children’s behaviours during EAA sessions are modulated by sex and age, while previous children’s experience with EAA appeared to improve interpersonal distance and horse handling. Results from this study highlight the importance of exploring children's behavioural response during animal-assisted activities through direct measurements. This may allow linking the quality/strength of the child-horse relationship to the benefits obtained by the child, particularly in the social/communicative domain, a core symptom of ASD.

Rabies is a neurological disease that causes between 40,000 and 70,000 deaths every year. Once a rabies patient has become symptomatic, there is no effective treatment for the illness, and in unvaccinated individuals, the case-fatality rate of rabies is close to 100%. French scientists Louis Pasteur and Émile Roux developed the first vaccine for rabies in 1885. If administered before the virus reaches the brain, the modern rabies vaccine imparts long-lasting immunity to the virus and saves more than 250,000 people every year. However, the rabies virus can suppress the host’s immune response once it has entered the cells of the brain, making death likely. This study aims to make use of disorder-based proteomics and bioinformatics to determine the impact that intrinsically disordered protein regions (IDPRs) in the proteome of the rabies virus have on the infectivity and lethality of the disease. This study uses the proteome of Rabies Lyssavirus (RABV) strain Pasteur Vaccins (PV), one of the best understood strains due to its use in the first rabies vaccine, as a model. The study suggests that the high levels of intrinsic disorder in the phosphoprotein (P-protein) and nucleoprotein (N-protein) allow them to participate in creation of the Negri bodies and help this virus suppress the antiviral immune response in the host cells. Additionally, the study suggests that there is a link between disorder in the matrix (M) protein and the modulation of viral transcription. The disordered regions in the M protein have a possible role in initiating viral budding within the cell. Furthermore, we checked the prevalence of functional disorder in a set of 37 host proteins directly involved in the interaction with the RABV proteins. The hope is that these new insights will aid in the development of treatments for rabies that are effective after infection.

Background and Aims. Most research on Parkinson's disease (PD) focuses on describing symp-toms and movement characteristics. Studies rarely focus on the early detection of PD and the search for suitable markers of a prodromal stage. Early detection is important, so treatments that may potentially change the course of the disease can be attempted early on. While gait disturb-ances are less pronounced in the early stages of the disease, the prevalence, and severity increase with disease progression. Therefore, postural instability and gait difficulties could be identified as sensitive biomarkers. The aim was to evaluate the discriminatory power of the Trail-Walking Test (Schott, 2015) as a potential diagnostic instrument to improve the predictive power of the clinical evaluation concerning the severity of the disease and record the different aspects of walking. Methods. 20 older healthy (M = 72.4 years, SD = 5.53) adults and 46 older adults with PD and the motor phenotypes postural instability/gait difficulty (PIGD; M = 69.7 years, SD = 8.68) and tremor dominant (TD; M = 68.2 years, SD = 8.94) participated in the study. The participants performed a motor-cognitive dual task (DT) of increasing cognitive difficulty in which they had to walk a given path (condition 1), walk to numbers in ascending order (condition 2), and walk to numbers and letters alternately and in ascending order (condition 3). Results. With an increase in the cognitive load, the time to complete the tasks (seconds) become longer in all groups, F(1.23, 73.5) = 121, p < .001, ɳ2p = .670. PD-PIGD shows the longest times in all conditions of the TWT, F(2, 60) = 8.15, p < .001, ɳ2p = .214. Mutual interferences in the cognitive and motor domain can be observed. How-ever, clear group-specific patterns cannot be identified. A differentiation between the motor phenotypes of PD is especially feasible with the purely motor condition (TWT-M; AUC = . 685, p = 0.44). Conclusions. PD patients with PIGD must be identified by valid, well-evaluated clinical tests that allow a precise assessment of the disease's individual fall risk, the severity of the dis-ease, and the prognosis of progression. The TWT covers various aspects of mobility, examines the relationship between cognitive functions and walking, and enables differentiation of the motor phenotypes of PD.

Long-acting injectable antipsychotics (LAIs) offer many benefits to patients with schizophrenia spectrum disorder (SSD). They are used with very different frequencies due to questions of eligibility or patients’ and prescribers’ attitudes towards LAI use. We assessed the prescribing rates of LAIs in a large academic psychiatric hospital with public service mandate in Switzerland and compared them with other countries and health care systems. To our knowledge this study is the first to investigate the inpatient LAI-use in Europe. Medical records of all patients diagnosed with SSD discharged from the Clinic of Adult Psychiatry of the University Hospital of Psychiatry Zurich over a 12-month period from January to December 2019 were evaluated regarding the prescribed antipsychotics at the time of discharge. The rates of use of LAIs among all patients and among patients receiving LAI eligible antipsychotic substances were assessed retrospectively. We assessed records of 885 patients with SSD. Among all cases 13.9% received an LAI. Among patients who received antipsychotic medication that was eligible for LAI use 28.3% (n=434) received an agent as LAI. These included paliperidone palmitate (69.9%), aripiprazole monohydrate (14.6%), risperidone (4.9%) and first-generation LAIs (9.8%). Compared to international frequencies of LAI administration, the prescription rate of LAIs in SSD patients was low. Further studies will evaluate patient- and prescriber-related reasons for this low rate.

In this paper, the deformation and phase transformation of disorder α phase at (α + γ) two phase region in as-forged Ti-44Al-8Nb-(W, B, Y) alloy are investigated by hot compression and hot packed rolling. Detailed microstructural evolution demonstrates that the as-deformed microstructure is significantly affected by deformation conditions. The mircrostructure differences are mainly due to temperature drop and strain rate. The evolution of α lamelae into α grains is detailed descripted. Moreover, the disorder α lamellae can also be decomposed into some new α grains by the assisted decomposition mechanism of γ grains. Microstructure evolution model of current TiAl alloy at 1250 °C during hot rolling is built.

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

On March 2020 strict measures took place in Finland to limit the COVID -19 pandemic. A majority of the Finnish COVID -19 –patients have been located in the southern Finland and consequently cared for in the HUS Helsinki University Hospital. During the ongoing pandemic, HUS personnel’s psychological symptoms are followed via an electronic survey, which also delivers information on psychosocial support services. The baseline survey in June 2020 was sent to 25494 HUS employees out of whom 4804 (19%) answered; altogether 62.4% of the respondents were nursing staff and 8.9% medical doctors. While the follow-up continues for a year and a half, this report shares the sociodemographic characteristics of the respondents and the first results of psychological symptoms from the baseline survey. Out of those who were directly involved in pandemic patients` care, 43.4% reported potentially traumatic COVID-19 pandemic-related experiences vs. 21.8% among the other (p &lt; 0.001). While over a half of the personnel was symptomless, a group of respondents reported pandemic work –related traumatic events and concurrent depressive, insomnia and anxiety symptoms. This highlights the need to ensure appropriate psychosocial support services to all traumatized personnel and PTEs were present especially among nursing staff.

Background: Social Anxiety Disorder (SAD) is among the most common anxiety disorders worldwide with data largely emerging from the Euro-American and Pacific Rim populations. In contrast, there is a dearth of studies among the populations of Arabian Gulf countries including Oman. This study has two interrelated aims: (i) to explore the prevalence of SAD among Omani adults, and (ii) to tease out the links between sociodemographic factors and SAD in Oman. Methods: A cross-sectional study via an online survey was conducted among 1019 adult Omani nationals residing in Oman. The presence of SAD was assessed using the Arabic version of the Liebowitz Social Anxiety Scale (LSAS). Result: Nearly half the participants (45.9%, n=468) endorsed themselves as having features of SAD as defined by LSAS. In the multivariate logistic analysis, participants below 40 years of age were 1.6 times (OR=1.568, p=0.026) more likely to have SAD than those who were 40 and older. Women were 1.3 times (OR=1.348, p=0.038) more likely to endorse SAD than men. Participants who had secondary or undergraduate education were respectively 1.5 times (OR=1.45, p=0.014) and 2.5 times (OR=2.509, p<.001) to have SAD than who were postgraduates. Conclusion: The present data suggest that 45.9% of the participants reached the cut-off for case-ness in LSAS, which is high compared to reports from other populations. As online survey respondents tend to belong to similar demographics, the current results need not be representative of the Omani adult population, which calls for studies that adopt more inclusive survey methods.

The etiopathogenesis of autism spectrum disorder (ASD) remains largely unclear. Among other biological hypotheses, researchers have evidenced an imbalance in the endocannabinoid (eCB) system, which regulates some functions typically impaired in ASD, such as emotional responses and social interaction. Also, cannabidiol (CBD), the non-intoxicating component of Cannabis sativa, has been recently approved for treatment-resistant epilepsy. Seizures represent frequent medical comorbidities of ASD and could be responsible for the onset or worsening of behavioral problems. Thus, it has been hypothesized that cannabinoids could be useful in improving some ASD symptoms. Our systematic review was conducted according to the PRISMA guidelines and aimed to summarize the literature regarding the use of cannabinoids in ASD. After searching in Web of Knowledge^TM, PsycINFO, and Embase, we included ten studies (eight papers and two abstracts). Four ongoing trials were retrieved in ClinicalTrials.gov. Findings are promising, as cannabinoids appeared to improve problem behaviors, sleep, hyperactivity, and communication deficits, with limited cardiac and metabolic side effects. Interestingly, they generally allowed to reduce the number of prescribed medications and decreased the frequency of seizures in epileptic patients. Mechanisms of action could be linked to the excitatory/inhibitory imbalance found in people with ASD. However, further trials need to be implemented with better characterization and homogenization of samples, and well-defined outcomes.

Major depression (MDD) is accompanied by higher serum IgM/IgA responses to LPS of Gram-negative bacteria, suggesting increased bacterial translocation and gut dysbiosis. Gut dysbiosis may occur in bipolar disorder (BD) and there are differences between MDD and BD type 1 (BP1) and -2 (BP2) in nitro-oxidative stress biomarkers associated with leaky gut. This study examines serum IgM/IgA responses directed to LPS of 6 Gram-negative bacteria in 29 BP1, 37 BP2, 44 MDD and 30 healthy individuals. MDD plus BD was best discriminated from controls by increased IgM/IgA responses to Pseudomonas aeruginosa. BP1 patients showed higher IgM responses to Morganella morganii as compared with MDD and BP2 patients. Patients with melancholia showed higher IgA responses to Citrobacter koseri as compared to controls and non-melancholic depression. The total score on the Hamilton Depression Rating Scale was significantly associated with IgA responses, especially C. koseri. IgG responses to oxidized low-density lipoprotein were significantly associated with signs of increased bacterial translocation. In conclusion, not only MDD but also BP1 and BP2 are accompanied by an immune response due to the increased load of plasma LPS of gut commensal bacteria while these aberrations in the gut-brain axis are most pronounced in BP1 and patients with melancholic features. Activated oxidative stress pathways and autoimmune responses to oxidative specific epitopes in mood disorders may be driven by a breakdown in gut paracellular, transcellular and/or vascular pathways. If replicated, drugs that protect the integrity of the gut barrier may offer novel therapeutic opportunities for BP1 and MDD.

Research on image-inpainting tasks has mainly focused on enhancing performance by augmenting various stages and modules. However, this trend does not consider the increase in the number of model parameters and operational memory, which increases the burden on computational resources. To solve this problem, we propose a Parametric Efficient Image InPainting Network (PEIPNet) for efficient and effective image-inpainting. Unlike other state-of-the-art methods, the proposed model has a one-stage inpainting framework in which depthwise and pointwise convolutions are adopted to reduce the number of parameters and computational costs. To generate semantically appealing results, we selected three unique components: spatially-adaptive denormalization (SPADE), dense dilated convolution module (DDCM), and efficient self-attention (ESA). The SPADE was adopted to conditionally normalize activations according to the mask to distinguish between damaged and undamaged regions. The DDCM was employed at every scale to overcome the gradient-vanishing obstacle and gradually fill-in pixels by capturing global information along the feature maps. The ESA was utilized to obtain clues from unmasked areas by extracting long-range information. In terms of efficiency, our model has the lowest operational memory compared with other state-of-the-art methods. Both qualitative and quantitative experiments demonstrate the generalized inpainting of our method on three public datasets: Paris StreetView, CelebA, and Places2.

Contact with nature is suggested to be as an important resource for human health and well-being. Stress alleviation and reduction of mental fatigue are main instances of improvement of human health and well-being in contact with nature. Although the term restoration experience in contact with nature has been used repeatedly for concepts of stress alleviation and reduction of mental fatigue, these refer to different sets of theories and research studies. In this paper, by reviewing the nature-health related theories of Biophilia, Stress Recovery Theory, Attention Restoration Theory and Supportive Environment Theory (SET), the similarities and differences between these two concepts have been discussed. Moreover, the measurement approach instruments based on different study methodologies and aims are provided. This paper could provide information for future studies with focus on factors to be of benefit for restoration experience in design and planning of natural landscape environments with the aim of health and well-being development.

Betel quid (BQ) use disorder (BUD) is prevalent in many Asian countries, impacting approximately 600 million people. We conducted a randomized clinical trial to analyze the impact of MAOA genetic variations on the severity of BQ craving. This was measured using DSM-5 criteria and Yale-Brown Obsessive Compulsive Scale modified for betel-quid (Y-BOCS-BQ). Participants were grouped according to the severity of BUD and MAOA gene single nucleotide polymorphism (SNP) rs5953210 genotypes. Y-BOCS-BQ were assessed at baseline (week 0) and during follow-up at weeks 2, 4, 6, and 8. The AA genotype group showed significantly greater reductions in Y-BOCS-BQ at weeks 2 (p=0.0194), 4 (p=0.0078), 6 (p=0.0277), and 8 (p=0.0376) compared to the GG genotype group. Additionally, within the antidepressant group, the AA genotype showed significant reductions in Y-BOCS-BQ at weeks 2 (p=0.0313), 4 (p=0.0134), 6 (p=0.0061), and 8 (p=0.0241) compared to the GG genotype. Statistical analysis revealed a significant interaction between the treatment and placebo groups based on MAOA genotypes, with the AA genotype in the treatment group exhibiting a more pronounced decrease in Y-BOCS-BQ (p interaction <0.05) at week 6. These findings highlight the importance of considering genetic factors when developing personalized treatment plans for BUD.

Edvard Munch, a founder of Expressionism was pre-occupied with depicting his subjective experience and the most prolific painter of self-portraits since Rembrandt. Based on evidence that faces and self-related images are more salient than other objects it is plausible that self-portraits, in which the subject and object are the same, reflect the artist’s state of mind. Although he suffered from a variety of physical and psychological illness Munch was not diagnosed with any specificity in his lifetime. Posthumous diagnoses include schizophrenia, anxiety, bipolar and other disorders. Recent research has revealed altered visual perception in such patients. The present study empirically analysed three stylistic elements of Munch’s painted self-portraits and portraits: contrast, colour and fractal dimension and his painted productivity, to determine if variations correlate with critical life events and if so whether they indicate states of mind. It found significant increases in contrast and colour brightness and persistent high complexity during critical periods supporting the conclusion that Munch is diagnostically best described as suffering from early onset schizophrenia and comorbid social anxiety disorder.

Selective mutism (SM) is an “Anxiety Disorder” characterized by a child’s “persistent inability” to communicate verbally in some (or all) contexts of social life that is associated with other cognitive-affective disorders. Cognitive-behavioural assessment and psychological intervention may be complex to be performed due to both the difficulty to administered standardized neuropsychological batteries (e.g., language-mediated tests) and the difficulty to involve family and teachers in the intervention program, respectively. Here, a single-case study is described in which a female Filipina child with SM underwent a comprehensive neuropsychological assessment and multimodal therapeutical intervention between the ages of 7 to 11 years. More specifically, the psychological intervention included cognitive-behavioral psychotherapy to both improve social-cognitive skills and learning abilities and to reduce anxiety, and speech therapy. Moreover, the parents and teachers were actively involved in the therapeutic process and they were also administered a psycho-education program. After this specific treatment, at the age of 11 years the girl began to verbalize in both therapy and school contexts, although she still was used to adopt non-verbal strategies. A parallel and gradual improvement in communicative-linguistic skills and school learning was also observed. In conclusion, the present report highlights the importance to apply an "integrated and multimodal intervention" to treat SM in children that includes also psycho-education intervention for both parents and teachers.

Limited knowledge is available about the association between autistic spectrum disorder (ASD) and precocious puberty. Our study examined the association between the two medical conditions, and the effect modification by sex and neuropsychiatric comorbidities in a nationwide population. To compare the risk of precocious puberty between ASD and non-ASD cases, we conducted Cox regression analysis, using ASD as exposure and time-to-precocious puberty as outcome. We adjusted for sex, attention-deficit/hyperactivity disorder (ADHD), tics disorder, obsessive–compulsive disorder (OCD), anxiety disorder, intellectual disability, and epilepsy. We performed a moderation analysis to examine the potential moderating effects by sex or comorbidities. Patients with ASD were prone to have precocious puberty, with an adjusted hazard ratio (aHR) of 1.80 (95% CI: 1.61-2.01). For the effect modification, sex, specifically females, moderated the association between ASD and precocious puberty, with relative excess risk due to interaction (RERI) 7.35 (95% CI 4.90-9.80). No significant effect modification was found for any of the comorbidities within the scope of additive effect modification. We found that patients with ASD were prone to precocious puberty, regardless of sex or comorbid neuropsychiatric disorders. Girls with ASD are at particularly higher risk to develop precocious puberty.

The Sars-Cov-2 pandemic led to several needed containing measures that conditioned the onset of depressive, anxiety and post-traumatic stress symptoms in population. These symptoms, espe-cially if not diagnosed and treated, can also occur in patients undergoing surgery with high im-pact on people’s lives, like hysterectomy. To evaluate the post-surgical distress and anx-ious-depressive symptoms following hysterectomy for benign disease focusing on the impact of COVID-19 pandemic. The prospective observational cohort study included patients undergoing hysterectomy for benign disease. Psychologic evaluation through social-demographic question-naires was obtained before surgery (T1), postoperatively (T2), and 3 months after surgery (T3). The HADS (Hospital Anxiety and Depression Scale) was used to evaluate anxious-depressive symptoms and the PCL-5 (Post-traumatic Stress Disorder Checklist for DSM-5) compared the on-set of post-surgical distress and anxiety and depressive symptoms. The pre-COVID-19 pan-demic period was compared to the post-COVID-19 pandemic phase. Patients treated after COVID-19 pandemic showed higher depressive symptoms rate compared to those treated before (p-value=0.02); conversely, pre-COVID-19 patients were more prone to develop a PTSD (p-value=0.04). A significative association between the occurrence PTSD and anxiety-depressive symptoms registered at T2 a (p-value=0.007) and T3 (p-value

This review aims to explore the intricate relationship between epigenetic mechanisms, stress, and affective disorders, focusing on how early life experiences and coping mechanisms contribute to susceptibility to mood disorders. Epigenetic factors play a crucial role in regulating gene expression without altering the DNA (deoxyribonucleic acid) sequence, and recent research has revealed associations between epigenetic changes and maladaptive responses to stress or psychiatric disorders. A scoping review of 33 studies employing the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Statement) guidelines investigates the role of stress-induced epigenetic mechanisms and coping strategies in affective disorders' occurrence, development, and progression. The analysis encompasses various stress factors, including childhood trauma, work-related stress, and dietary deficiencies, alongside epigenetic changes such as DNA methylation and altered gene expression. Findings indicate that specific stress-related genes frequently exhibit epigenetic changes associated with affective disorders. Moreover, the review examines coping mechanisms in patients with bipolar disorder and major depressive disorder, revealing mixed associations between coping strategies and symptom severity. While active coping is correlated with better outcomes, emotion-focused coping may exacerbate depressive or manic episodes. Overall, this review underscores the complex interplay between genetic predisposition, environmental stressors, coping mechanisms, and affective disorders. Understanding these interactions is essential for developing targeted interventions and personalized treatment strategies for individuals with mood disorders. However, further research is needed to elucidate specific genomic loci involved in affective disorders and the clinical implications of coping strategies in therapeutic settings.

Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by persistent deficits in social interaction and communication, repetitive movements, abnormal focusing on objects, or activity that can significantly affect the quality of life of the afflicted. Brain neuronal and glial cells have been implicated. It has a genetic component but can also be triggered by environmental factors or drugs. For example, prenatal exposure to valproic acid (VPA) or acetaminophen (APAP) or ingestion of propionic acid (PPA) can increase the risk of ASD. Recently, epigenetic influences on ASD have been brought to the forefront of investigations on the etiology, prevention, and treatment of this disorder. Epigenetics refers to DNA modifications that alter gene expression without making any changes to the DNA sequence. Although an increasing number of pharmaceuticals and environmental chemicals are being implicated in the etiology of ASD, here we specifically focus on the molecular influence of the above-mentioned drugs on epigenetic alterations in neuronal and glial cells and their potential connection to ASD. It is concluded that a better understanding of these phenomena can lead to more effective interventions in ASD.

A great deal of studies have assessed the expression of noncoding RNAs (ncRNAs) as well as their relevant molecular functions and biological mechanisms in the individuals of alcohol dependence. Alcohol dependence is one of the most prevalent neuropsychological disorders worldwide, and its pathogenesis is intricate and inadequately understood. There is considerable evidence demonstrating substantial links between multiple genetic factors and the development of alcohol dependence. Critical roles of ncRNAs have been emphasized in the pathophysiology of mental diseases including alcohol dependence. In the comprehension of ncRNAs action and their mechanisms of modification, they have emerged as therapeutic targets for a variety of psychiatric illness including alcohol dependence. Worth mentioning, dysregulated expression of ncRNAs has been regularly detected in individuals of alcohol dependence. An in-depth knowledge of roles of ncRNAs and m6A modification may be valuable for the development of a novel treatment against alcohol dependence. In general, a deeper comprehension of functional roles ncRNAs may make significant contributions to the precise diagnosis and/or actual treatment of alcohol dependence. Here in this review, we mostly focused on the up-to-date knowledge regarding alterations and/or modifications in the expression of ncRNAs in alcohol dependence, and then, presented their prospects for future research and therapeutic application with a novel concept of engram-system.

The clinical practice implies a research translation that helps the use of scientific data and thera-peutic evidence for patient benefits. This review critically summarizes the potential impact of cannabinoids in concomitance with other drugs in the following chronic diseases as Epilepsy, Au-tism Spectrum Disorders (ASD), Oncology, Multiple Sclerosis, and Chronic Pain. The potential in-teractions can change the predicted clinical outcomes of therapeutic protocols and need to be evaluated. Some of the effects would be additive, synergistic or antagonistic, but can also enroll changes in absorption, distribution, metabolism, particularly via cytochrome P450 (CYP) isoen-zymes (e.g CYP2C9 and CYP3A4) and excretion. For instance, the combination of canna-bis-derived compounds and the antifungal drug ketoconazole, a CYP3A4 inhibitor, increases plasma concentration of Δ-9-tetrahydrocannabinol (THC) and CBD (CBD). Opposable, rifampic-in, a CYP3A4 inducer, stands out for the approximately 20-40% reduction in plasma THC levels and 50% to 60% for CBD. Other CYP3A4 inhibitors and inducers are likely to have a similar effect on plasma concentrations if co-administered. Indeed, pharmacokinetic interactions have been also reported with antiseizure drugs. Moreover, pharmacodynamic interactions between cannabinoids and drugs with sympathomimetic effects (eg tachycardia, hypertension), central nervous system depressants (eg drowsiness, ataxia), and anticholinergics (eg tachycardia and drowsiness) are also expected. Even though pending further studies, there is currently clinical evidence supporting drug interaction with cannabinoids, demanding doctors evaluate the risk of drug combinations with cannabinoids. The tables herein provided were designed to facilitate the identification of bi-orelevant interactions that may compromise therapeutic efficacy and toxicity.

Research conducted on individuals with depression reveals that major depressive disorders (MDDs) coincide with diminished levels of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) in the brain, as well as modifications in the subunit composition of the primary receptors (GABAA receptors) responsible for mediating GABAergic inhibition. Furthermore, there is substantial evidence supporting the significant role of GABA in regulating stress within the brain, which is a pivotal vulnerability factor in mood disorders. Although there is substantial evidence indicating that GABA may be affecting GABA receptors in peripheral tissues, there is comparatively less evidence supporting its direct action within the brain. Emerging evidence highlights that GABA may exert beneficial effects on the brain and psyche through the gut-brain axis. GABA is readily available and approved as a food supplement in many countries. While GABA enjoys wide consumer acceptance in Eastern Asian markets, with many consumers reporting favorable effects on stress regulation, mood, and sleep, rigorous independent research is still largely lacking. Basic research, coupled with initial clinical findings, makes GABA an intriguing neuro-nutritional compound deserving of clinical studies in individuals with depression and other psychological problems.

Background: Migraine is one of the most common health conditions in the reproductive male and female population. Premenstrual syndrome (PMS) is a common condition that affects many women during their menstrual cycle. around 50 to 60% have menstrual headaches among the migraineur women. The symptoms in the majority were mild, but 5–8% may suffer moderate to severe symptoms, accompanied by considerable distress or functional dysfunction. Premenstrual migraine (PMM) refers to migraines that occur with the menstrual cycle. It affects approximately 50% of women with migraine and can be debilitating in terms of both frequency and severity of symptoms. This information is important for medical professionals in Saudi Arabia to be aware of so, as to provide better medical care and support which can significantly improve the person's quality of life of women who suffer from PMS and menstrual migraines. Objectives: To estimate the prevalence of menstrual migraines in women, to evaluate the severity and frequency of menstrual migraines in women with PMS, and to identify potential risk factors aggravating menstrual migraines in women with PMS. Methodology: A cross-sectional community-based study was conducted on reproductive age (18-50Years) women who have regular menstrual cycles with experience migraine headaches and are diagnosed with PMS and menstrual migraine, using a self-administered questionnaire between December 2022 to May 2023 in Saudi Arabia. Results: Out of 2130 women participants 397 were suffering from migraine which is 18.6%. Of the total 397(18.6%) Migraineurs individuals 230 (57.9%) were suffering from menstrual migraine and 167 (42.1%) were non-menstrual migraineurs, in general, reproductive women menstrual migraine is 10.7% and non-menstrual migraine is 7.8%. The BMI is increasing the incidence of menstrual migraines is increasing. Migraine Disability Around one-third of the participants were suffering from III–moderate disability 134 (33.8) Most of the menstrual migraine victims at least 3 days in the last three months were absent from their duties due to their menstrual migraines. Conclusions: Migraine attacks during the menstrual cycle cause the impairment of their social and physical ability to perform their routine household, and academic activities and unable to fulfill their professional commitments. To better understand the Menstrual Migraine and Non-menstrual migraine large prospective studies are required to plan the best management methods.

The Coronavirus Disease 2019 (COVID-19) pandemic has had a major impact on the mental and physical health of hospitalized patients. In our study we focused on the onset of symptoms correlated with Post-traumatic stress disorder (PTSD), depression and physical disabilities in patients admitted to the Intensive Care Unit (ICU) because of a severe respiratory distress related to COVID-19 (COVID Group) compared with patients admitted to the same ICU for trauma and other medical conditions than COVID-19 (No-COVID Group). The physical symptoms and the level of disability were evaluated with the Glasgow Outcome Scale-Extended (GOS-E), the Quality of Life after Brain Injury (QOLIBRI) and the 3 levels version of EQ-5D (EQ-5D-3L) questionnaire; psychiatric symptoms were investigated using the Impact of Event Scale-Revised 22-item (IES-R), the Patient Health Questionnaire, 9-Item Version (PHQ-9) and the Generalized Anxiety Disorder Assessment, 7-items version (GAD-7). These questionnaires were administered 6 months after discharge. Patients in the No-COVID Group showed statistically significant more severe scores in all the physical assessments while similar relevant PTSD and depressive symptoms were reported in both groups. The results of the present study underline the psychopathological impact of being hospitalized in ICU because of COVID-19 even after 6 months from discharge ,suggesting the importance of assessing the psychiatric effects of COVID-19 in the long term in order to create supportive measures.

This review examines the evidence supporting the role of dysbiosis in the development of obses-sive-compulsive disorders (OCD). We review the molecular mechanisms and role of the microbiota in the microbiota-gut-brain axis, focusing on the endocrine, nervous, and immune pathways. We then propose a model that positions dysbiosis as the central unifying element in the neurochemi-cal, immunological, genetic, and environmental factors leading to OCD. Based on this, we review the animal and human clinical evidence for the use of microbial reprogramming strategies such as probiotic or fecal microbiota transplants to treat OCD. Finally, we discuss the unique challenges that must be addressed in future clinical interventions.

Background: According to metacognitive theory, Cognitive–Attentional Syndrome (CAS) is a transdiagnostic factor and main mechanism of psychopathology maintenance. The main goal of this study was to examine whether CAS predicted symptoms of stress and trauma-related symptomatology in the first months of the COVID-19 pandemic and three months later. Methods: Initially 1,792 participants were recruited online via social media; data was collected at two time points. Measures included the Cognitive–Attentional Syndrome Questionnaire, Adjustment Disorder – New Module 20, the International Trauma Questionnaire, and additional measures. Results: Structural equation modeling was conducted in order to determine relations between reported stressors, CAS, and symptomatology. At both time points, CAS was a significant mediator between stressors and symptoms of adjustment disorder. Despite the decrease in the intensity of adjustment disorder symptoms between waves, it was a significant predictor of other psychopathology at both time points, except for traumatic stress. Conclusions: The findings confirm the assumption that CAS is a transdiagnostic factor of psychopathology, and has a mediating role in the relationship between stressors and adjustment disorder and co-occurring symptomatology. The effect was particularly significant in the initial phase of the pandemic, which was highly stressful for many people.