Background: The burden of resistant fungal infection is rising in patients with pulmonary disease. Options for antifungal therapy are limited, and the only orally-available antifungals, the triazoles, demonstrate inter and intra-patient variability, non-linear kinetics, toxicity, drug interactions and increasing antifungal resistance. Therapeutic drug monitoring (TDM) of itraconazole, voriconazole and posaconazole has been necessary to ensure their safety and efficacy, but is considered unnecessary for the newest triazole isavuconazole, use of which is increasing. Aims: To characterise isavuconazole susceptibility of Aspergillus fumigatus isolates in a tertiary respiratory referral centre to understand prevalence of isavuconazole antimicrobial resistance. To retrospectively review experience of isavuconazole use in this setting, assessing tolerability and therapeutic drug monitoring. Methods: A retrospective observational analysis of adult patients with respiratory disease in a tertiary hospital setting between Sept 2016 and Aug 2021. Clinical cultures were collected and triazole Minimum inhibitory concentration (MIC) were recorded (based on Clinical & Laboratory Standards Institute (CLSI method)). Isavuconazole trough drug levels were carried out as part of the standard of care. Clinical outcomes of treatment were evaluated, along with drug tolerance and TDM. Results: During the study period, isavuconazole susceptibility testing was performed on 26 Aspergillus spp isolates. 80.8% of Aspergillus fumigatus isolates were non-wild type and had isavuconazole MIC > 1mg/L, and 73.0% had MIC above the EUCAST (European Committee on Antimicrobial Susceptibility Testing) epidemiological cut-off (ECOFF) of 2mg/L. There was good correlation between isavuconazole MIC and voriconazole MIC (r =0.7, p=0.0002). 54 patients had isavuconazole therapy over the study period with a median duration of 7.7 months (IQR 0.79 - 16.42). 67% of patients were able to tolerate isavuconazole, despite toxicity with prior azole treatment being the primary indication for use (in 61.8%). Increased age (r=0.29; p=0.03 (95%CI 0.02,0.52)) and gender (r for female sex=-0.31; p=0.027 (95%CI -0.52,0.036) were associated risk factors for development of adverse events (AEs). 127 Isavuconazole TDM levels were performed over the study period with 90% >1mg/L and 72% >2mg/L. Dose change from manufacturer’s dose recommendation, however, was required in 15% of patients to achieve a serum drug concentration above the EUCAST ECOFF or Area of technical uncertainty (ATU) value of 2mg/L. Conclusion: In our study, we show use of Isavuconazole as salvage therapy in chronic pulmonary fungal disease setting with high prevalence of azole resistance. Isavuconazole MICs demonstrated good correlation with voriconazole MICs suggesting the latter could be a useful surrogate marker for isavuconazole susceptibility. Although Isavuconazole achieved excellent serum drug concentrations at standard dose compared to other azole drugs, we highlight the importance of antifungal stewardship and TDM monitoring to optimise therapy in this setting.

Aedes aegypti transmits major arboviruses of public health importance, including dengue, chikungunya, Zika, and yellow fever. The use of insecticides represents the cornerstone of vector control, however insecticide resistance in Ae. aegypti has become widespread. Understanding the molecular basis of insecticide resistance in this species is crucial to design effective resistance management strategies. Here, we applied Illumina RNA-Seq to study the gene expression patterns associated with resistance to three widely used insecticides (malathion, alpha-cypermethrin and lambda-cyhalothrin) in Ae. aegypti populations from 2 sites (Manatí and Isabela) in Puerto Rico (PR). Cytochrome P450s were the most over-expressed detoxification genes across all resistant phenotypes. Some detoxification genes (CYP6Z7, CYP28A5, CYP9J2, CYP6Z6, CYP6BB2, CYP6M9, and two CYP9F2 orthologs) were commonly over-expressed in mosquitoes that survived exposure to all three insecticides (independent of geographical origin) while others including CYP6BY1 (malathion), GSTD1 (alpha-cypermethrin), CYP4H29 and GSTE6 (lambda-cyhalothrin) were uniquely overexpressed in mosquitoes that survived exposure to specific insecticides. The gene ontology (GO) terms associated with monooxygenase, iron binding, and passive transmembrane transporter activities were significantly enriched in 4 out of 6 resistant vs susceptible comparisons while serine protease activity was elevated in all insecticide resistant groups relative to the susceptible strain. Interestingly, cuticular-related protein genes (chinase and chitin) were predominantly downregulated, which was also confirmed in the functional enrichment analysis. This RNA-Seq analysis presents a detailed picture of the candidate detoxification genes and other pathways that are potentially associated with pyrethroid and organophosphate resistance in Ae. aegypti populations from PR. These results could inform development of novel molecular tools for detection of resistance-associated gene expression in this important arbovirus vector and guide the design and implementation of resistance management strategies.

Pyrethroid resistance is widespread in malaria vectors. However, differential mortality in discriminating dose assays to different pyrethroids is often observed in wild populations. When this occurs, it is unclear if this differential mortality should be interpreted as an indication of differential levels of susceptibility within the pyrethroid class, and if so, if countries should consider selecting one specific pyrethroid for programmatic use over another. A review of evidence from molecular studies, resistance testing with laboratory colonies and wild populations, and mosquito behavioural assays was conducted to answer these questions. Evidence suggests that in areas where pyrethroid resistance exists, different results in insecticide susceptibility assays with specific pyrethroids currently in common use (deltamethrin, permethrin, α-cypermethrin and λ-cyhalothrin) are not necessarily indicative of an operationally relevant difference in potential performance. Consequently, it is not advisable to use rotation between these pyrethroids as an insecticide resistance management strategy. Less commonly used pyrethroids (bifenthrin and etofenprox) may have sufficiently different modes of action, though further work would be needed to examine how this may apply to insecticide resistance management.

The discovery of antibiotics was one of the greatest achievements in human history, however, antibiotic resistance evolved no later than the introduction of antibiotics. The rapid evolution of antibiotic-resistant pathogens soon became a nightmare and remained a global healthcare threat. There is an urgent need to have new alternatives or new strategies to combat the multi-drug resistant superbugs such as methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), carbapenem-resistant Pseudomonas aeruginosa (CR-PA), extended-spectrum β-lactamases (ESBL) bearing multidrug-resistant Acinetobacter baumannii (MDR-AB), Escherichia coli, and Klebsiella pneumonia. Antimicrobial peptides (AMPs) have been considered promising agents equipped with unique mechanisms of action along with several other benefits to fight the battle against drug-resistant superbugs. Overall, the current review summarizes the mechanisms of drug-resistant development, the mechanism of action adopted by AMPs to combat drug-resistant pathogens, and the immunomodulatory properties of AMPs. Additionally, we have also reviewed the synergistic potential of AMPs with conventional antibiotics and the associated challenges and limitations of AMPs in the way of pharmacological development for therapeutic applications in clinical settings.

Nickel electrodeposition is a widely utilized method for creating thin films on various substrates with various desirable attributes. Recently, there has been a growing interest in developing nickel composite coatings that incorporate additional elements or particles into the nickel matrix to enhance their properties. These composite coatings offer superior wear resistance, corrosion resistance, hardness, and other functional benefits compared to pure nickel coatings. Some of the recent advancements in electrodeposited nickel composite coatings include improved wear resistance, enhanced mechanical properties and better corrosion resistance. Researchers have discovered that adding Al, Si, Zr, C, W, and Ti particles to nickel coatings can significantly enhance wear resistance. The uniform distribution of these particles within the nickel matrix acts as a barrier to wear and tear. Studies have also shown that nickel composite coatings with Al, Si, Zr, C, W, and Ti particles exhibit superior mechanical properties, including increased hardness. These particles refine the grain size of the nickel matrix and hinder defect movement, leading to greater mechanical strength. Moreover, nickel composite coatings offer improved protection against corrosion compared to pure nickel coatings. This review provides a detail discussion of nickel composite coatings with regard to their comparative advantaged compared to pure nickel coatings on different substrates.

Enterococci are gastrointestinal commensals whose hardiness allowed them to colonize very diverse environments, including soils, water, food and feed. This ability to overcome adverse conditions makes enterococci problematic once they colonize hospital niches. Together with the malleability of their genomes, the capacity to acquire and disseminate determinants of antibiotic resistance have contributed to convert what was once just another opportunistic pathogen into a first-class clinical problem. This review discusses the dimension of the emergence of enterococcal resistance to key antimicrobial agents, the dissemination of this resistance and its significance in terms of public health, with the aim of raising the awareness to the need to devise and implement monitoring programmes and effective antibiotic usage guidelines.

Background: Inappropriate use of antibiotics has been reported to contribute to the emergence and increase of antimicrobial resistance (AMR) in the world. Antibiotics are prescription-only medicines to be dispensed to a person with a legal prescription inscribed by a qualified medical practitioner. Enforcing the dispensing of antibiotics with prescription is a way to promote the rational use of antibiotics and preventing the development and spread of AMR. It is, therefore, the responsibility of a pharmacist to dispense or supervise the dispensing of antibiotics in pharmacies and ensure its rational use. This study aimed to assess pharmacists’ knowledge, attitude and practice regarding the dispensing of antibiotics without prescription in Tanzania. Methods: An online semi-structured questionnaire was designed, tested and shared with licensed pharmacists in Tanzania through an invitation link sent in their official WhatsApp groups. A list of names, contacts and emails of licensed pharmacists obtained from the Pharmacy Council was used to directly contact and request pharmacist to fill the questionnaire, in case the pharmacist contact was not on WhatsApp, a text SMS invitation was sent. Study data were collected and managed using REDCap electronic data capture tools hosted at Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania. Data were then downloaded and exported into Statistical Package for Social Sciences (SPSS) version 20 for data analysis; Chi-square test was used to test association for categorical data, where a p-value of less than 0.05 was considered statistically significant. Results: More than 75% of pharmacists had excellent knowledge about the legal requirements for dispensing antibiotics and of the AMR challenge. Of the interviewed pharmacists, seventy-four percent admitted to dispensing antibiotics without prescription in their daily practice. The main reasons for administering antibiotics without prescription were the profitability nature of pharmacy business, a failure of the patient to get a prescription and lack of stringent regulatory authorities. Penicillins, macrolides and floroquinolones were the classes of antibiotics mostly dispensed without a prescription. Conclusion: The study shows that the dispensing of antibiotics without prescription is a common practice in Tanzania. The regulatory authorities should make regular inspections in pharmacies to detect this malpractice. The community should be trained on the importance of taking laboratory tests before getting medications for their sickness by a qualified medical practitioner.

The antibiotic or antimicrobial resistance is rapidly spreading in microbes relevant to human health. Two visible major contributory factors have been the indiscriminate overuse of antimicrobials for preventing diseases in human and to enhance the productivity in agriculture sector. To mitigate the potential threat posed by post-antibiotic era, the global health stakeholders have been making extra efforts at a war footing to formulate and implement global and national plans of action. In the current article, an endeavour is made to provide a perspective to look beyond the current focus on just use of the antimicrobials. Attention has been drawn towards various obvious and not-so-obvious self-preservation infection-prevention practices in vogue from the pre-antibiotic era whose usage has been on decline in the antibiotic era for various reasons. Particularly, the practices with a clear potential to effectively decrease the spread of pathogens through contact, curtail the evolution and dissemination of the antimicrobial resistance in local environment and its introduction into the global community, should be Identified and strengthened to make them part of comprehensive hygiene and quarantine practices. Broadly, the suggestions pertaining to the personal and community hygiene including bereavement practices, isolation and quarantine of suspected pathogen carriers, and water and environment security have been made to invoke a constructive debate and discussion among various stakeholders for their evaluation and implementation to effectively delay the development of antimicrobial resistance wherever possible and disrupt its spread to pathogens.

Background: Research has consistently shown increased drug use among lesbian, gay, bisexual, transgender, intersex, queer/questioning, and asexual (LGBTQIA+) individuals. This is particularly the case among LGBTQIA+ adolescents. Substance use within this vulnerable community can propagate mental health issues leading to psychiatric disorders, self-harm and even suicide. Therefore, the objective of this scoping review was to evaluate the determinants of drug use among LGBTQIA+ adolescents. Methods: A comprehensive search of mainly primary research was conducted using several databases. Peer-reviewed articles published between 2018 and 2023 were included. The scoping review was conducted using the framework outlined by Joanna Briggs Institute (JBI) and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses- Extension for Scoping Reviews (PRISMA-ScR) statement. Results: A total of 29 articles (including 400194 participants) were included in the analysis. The articles reported that the main determinants of drug use among LGBTQIA+ adolescents include homelessness, peer-peer interactions, mental health, and protective factors. The articles reported that mental health issues, which are mainly triggered by rejection, were the main determinants of drug use among LGBTOIA+ adolescents. Conclusion: Findings from this scoping review provide relatively reliable evidence that homelessness, mental health, peer-peer interactions, and protective factors are the main determinants of illicit drug use among LGBTQ+ adolescents. Rigorous studies that include large sample sizes, and systematic reviews are needed to further confirm these findings and assist in developing interventions to combat the unusually high level of drug use among this group.

Accurately predicting drug-drug interactions (DDIs) is crucial for preventing adverse drug events in clinical settings. However, existing methodologies often rely on complex models built from diverse data sources, posing challenges in computational drug discovery. To address the need for precise computational methods in predicting unknown DDIs, this study introduces a novel Deep Neural Network (DNN)-based approach. By leveraging a wide range of drug-related information, including substructure, targets, side effects, pathways, and indications, our method calculates multiple drug similarities. These similarities are then synthesized using a nonlinear fusion method to extract high-level features. Subsequently, a tailored neural network is deployed for interaction prediction. Comparative evaluation against three prominent machine learning classifiers—Extreme Gradient Boosting (XGBoost), Adaptive Gradient Boosting (AdaBoost), and Light Gradient-Boosting Machine (LGBM)—using three benchmark datasets demonstrates the superior performance of DNN. It achieves outstanding accuracy, precision, recall, and F1-score metrics, all reaching 94.6% in cross-validation. Additionally, case studies involving numerous drug pairs confirm the reliability of DNN in accurately predicting unknown DDIs. These findings underscore DNN as a potent and reliable method for DDI prediction, with promising implications for drug discovery and healthcare applications.

Background & Aim: Drug sustainability (DS) is a surrogate marker for treatment efficacy. We aimed to compare the DS of two main biologics used to treat moderate-to-severe ulcerative colitis (UC), infliximab (IFX) and vedolizumab (VDZ), in a real-world setting. Methods: We conducted a retrospective cohort study at a tertiary medical center in Israel. We included patients treated between Dec 1st, 2017, and May 1st, 2021, who were followed for up to 300 weeks. DS was defined as corticosteroid, surgical, and hospitalization-free treatment. Results: 217 patients with UC were included. VDZ had a significantly longer median DS of 265.6 weeks compared to IFX's 106.5 weeks (p=0.001) in treatment-naïve patients, even when adjusting for disease severity (HR 0.55 95 CI 0.3-0.98, p=0.042). In treatment-experienced patients, DS was comparable between IFX and VDZ (p=0.593). Conclusion: VDZ showed significantly longer DS in treatment-naïve patients with UC compared to IFX, also when adjusted for disease severity. There was no difference in DS between VDZ and IFX in treatment-experienced patients and patients switching from one drug to another. VDZ may be a suitable first-line treatment for biologic-naïve patients with moderate-to-severe UC.

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numer- ous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

Although marketing authorization holders (MAHs) are involved in monitoring medication safety, it was unclear how they experience their role and current monitoring activities in pregnancy. Therefore, a qualitative study using online focus groups with MAHs and the Belgian umbrella organisation of MAHs was conducted in June-July 2021. In total, 38 representatives of nine organisations participated. Overall, participants reported multiple difficulties with data collection, including underreporting, collection of incomplete information and loss to follow-up. The limited number of high-quality data collected, the unknown denominator and the lack of comparator data complicate MAHs’ data processing activities, preventing them to timely provide evidence in the pregnancy label. Three ‘conflicts’ inherent to the specific position of MAHs were identified explaining the difficulties they experience, i.e., 1) mistrust from patients and healthcare professionals (HCPs); 2) MAHs’ legal obligations and regulatory framework; 3) MAHs’ position outside the healthcare context. To overcome these barriers, MAHs suggested that data registration should occur in close collaboration with patients and HCPs, organized within the healthcare context and performed by using a user-friendly system. In conclusion, the reported difficulties and underlying conflicts of MAHs highlight the need for more effective, collaborative data collection strategies to generate new evidence on this topic.

Drought events are predicted to become more prevalent in the future. Evaluating the performance of herbicide-resistant and susceptible weed ecotypes to progressive drought can provide insights into whether resistance trait(s) increased or reduced the fitness of a resistant population. Two experiments were conducted in the greenhouse between January and May 2021 to evaluate drought tolerance differences between Palmer amaranth accessions resistant to S-metolachlor or glyphosate and their susceptible counterparts. The accessions used were: S-metolachlor-resistant (17TUN-A), a susceptible standard (09CRW-A), and glyphosate-resistant (22 to 165 EPSPS copies) and glyphosate-susceptible (3 to 10 EPSPS copies) plants from accession 16CRW-D. Daily transpiration of each plant was measured. The daily transpiration rate was converted to normalized transpiration ratio (NTR) using a double-normalization procedure. The daily soil water content was expressed as a fraction of transpirable soil water (FTSW). The threshold FTSW (FTSWcr), after which NTR decreases linearly, was estimated using a two-segment linear regression analysis. The data showed differences between S-metolachlor- resistant and -susceptible accessions (P ≤ 0.05). The FTSW remaining in the soil at the breakpoint for the S-metolachlor-susceptible accession (09CRW-A) was 0.17±0.007. The FTSW remaining in the soil at the breakpoint for the S-metolachlor-resistant accession (17TUN-A) was 0.23±0.004. Although the mechanism endowing resistance to S-metolachlor might have contributed to increased drought tolerance, follow-up experiments are needed to verify this finding. Increased EPSPS copy number did not improve drought tolerance of Palmer amaranth.

Movement disorders can be associated with various drugs and systemic diseases, posing diagnostic challenges for clinicians. This review explores the spectrum of movement disorders associated with pharmacological agents and medical conditions. Drug-induced movement disorders encompass a broad range of conditions resulting from exposure to medications that affect several neurotransmitters. Neuroleptic medications can lead to tardive dyskinesia, characterized by involuntary, repetitive movements. Antipsychotics and antiemetics, including metoclopramide, may induce acute dystonic reactions. Other drugs, such as dopamine receptor-blocking agents, can contribute to drug-induced parkinsonism. Beyond medications, movement disorders can also arise secondary to systemic diseases. Metabolic disorders like Wilson's disease, characterized by copper accumulation, may manifest as chorea or dystonia. Inflammatory conditions such as rheumatoid arthritis and systemic lupus erythematosus can result in secondary movement disorders due to autoimmune-mediated processes affecting the central nervous system. Movement disorders linked to drugs or systemic diseases often necessitate a thorough clinical evaluation, including detailed medication histories and comprehensive systemic assessments. Neuroimaging, laboratory investigations, and, in some cases, cerebrospinal fluid analyses and electrodiagnostic studies help in establishing accurate diagnoses. Management involves addressing the underlying cause, modifying medications, and employing symptomatic treatments.

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the MAMBA structure. The MAMBA structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the Multi-Head Attention for Multi-Scale Feature Fusion (MHAMFF) module to capture multi-scale contextual information effectively. Moreover, we incorporate the Bidirectional Aggregation Mechanism (BAM) to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Furthermore, extensive ablation studies validate the effectiveness of each component in the MAMBA structure. Overall, the proposed MAMBA-based vehicle logo detection approach shows promising potential for real-world applications in intelligent transportation systems.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. In this study, we assessed the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open-pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha^-1, compared to 5.4 tha^-1 in FMF. Without accounting for between- and within-field variations, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r<0.02, P>0.1), but significant correlations were observed at 8WAS (r≤0.3; p<0.001). Ht was positively correlated with grain yield at 4WAS (r=0.5, R²=0.25, p<0.001), and more strongly at 8WAS (r=0.7, R²=0.55, p<0.001), while relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R²≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R² ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), than in actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal within the vegetation canopy.

In this work, we show that a late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a CNN and optimised MLP and the Leap Motion model is implemented by an evolutionary optimised deep MLP topology search. Next, the two best networks are fused for synchronised processing which results in a better overall result (94.44%) since complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multi-modality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via BSL, all three models outperform standard random weight distribution when classifying ASL, and the best model overall for ASL classification was the transfer learning multi-modality approach which scored 82.55% accuracy.

Immunotherapy with programmed Cell Death Protein/Programmed Cell Death Ligand 1 (PD-1/PD-L1) targeting antibodies has transformed clinical outcomes for patients with non-small cell lung cancer (NSCLC) and has become an integral part of standard treatment regimens. However, the response to PD-1/PD-L1 inhibitors varies, and many tumors present with either primary or acquired resistance to immunotherapy. There are emerging data on potential mechanisms underlying resistance to immunotherapy which is derived mainly from either preclinical studies or secondary correlative analyses from clinical trials. Resistance to immunotherapy is complex and can present immediately after treatment initiation (primary resistance) or after initial clinical benefit (secondary or acquired resistance). The tumor microenvironment, particularly the presence, and activity of T cells, also significantly influences resistance with both tumor-extrinsic as well as tumor-intrinsic mechanisms such as lack of T-cell infiltration, insufficient neoantigens, or absence of an interferon signature identified as being associated with ICI resistance. Improved understanding of the molecular and immunologic mechanisms underlying immunotherapy resistance is essential for the future development of effective therapies targeting immunotherapy resistance. In this review, we discuss the emerging data identifying the molecular mechanisms of primary resistance to immunotherapy and explore potential therapeutic strategies to target these.

The release of drugs from core/shell nanoparticles (NPs) is a crucial factor in ensuring high re-producibility, stability, and quality control. It serves as the scientific basis for the development of nanocarriers. Several factors, such as composition, composition ratio, ingredient interactions, and preparation methods, influence the drug release from these carrier systems. The objective of our study was to investigate and discuss the relationship between modifications of core/shell NPs as multifunctional drug delivery systems and the properties and kinetics of drug release using an in vitro drug release model. In this paper, we prepared four core/shell NPs consisting of a super-paramagnetic iron oxide NPs (Fe3-δO4) core encapsulated by a biocompatible thermo-responsive copolymer, poly(2-(2-methoxy) ethyl methacrylate-oligo (ethylene glycol) methacrylate) or P(MEO2MAx-OEGMA100-x) (where x and 100-x represent the molar fractions of MEO2MA and OEGMA, respectively), and loaded with doxorubicin (DOX). Colloidal behavior measurements in water and PBS as a function of temperature showed an optimization of the lower critical solu-tion temperature (LCST) depending on the molar fractions of MEO2MA and OEGMA used to form each NPs. In vitro studies of doxorubicin release as a function of temperature demonstrated a high control of release based on the LCST. A temperature of approximately 45°C for 60 h was sufficient to release 100 % of the DOX loaded in the NPs for each sample. In conclusion, external stimuli can be used to modulate the drug release behavior. Core/shell NPs hold great promise as a technique for multifunctional drug delivery systems.

The multifactorial nature of some diseases, and in particular neurodegenerative diseases such as Alzheimer’s disease, frequently requires the use of several drugs. These drug cocktails are not without drawbacks in terms of increased adverse effects, drug-drug interactions or adherence to treatment. The concept of pleiotropic drugs, which combine, within a single molecule, several activities directed against distinct therapeutic targets, makes it possible to overcome some of these problems. In addition, these pleiotropic drugs, generally, lead to the expression of a synergy of effects, sometimes greater than that observed with a combination of drugs. This article will review, through recent examples, the different kinds of pleiotropic drugs being studied or already present on the market of medicines.

Background: The increasing prevalence of Mycoplasma genitalium infections with macrolide resistance causing high azithromycin failure rates is a major concern internationally. In response to this challenge, diagnostics that simultaneously detect M. genitalium and genetic markers for macrolide resistance enable the therapy to be individually tailored, i.e. to implement resistance-guided therapy (RGT). Objectives: This study aimed to evaluate patient treatment outcomes of M. genitalium therapy guided by a macrolide-resistance assay in Bulgaria; Methods: Consecutively referred M. genitalium infection cases (n=17) were analyzed for macrolide-resistance mutations (MRMs) and specific antimicrobial treatment was recommended accordingly (MRMs-negative infections received azithromycin and MRMs-positive received moxifloxacin). Treatment outcome based on test-of-cure was recorded and treatment failure rates and time to microbiological cure were compared with treatment outcomes in patients treated before the implementation of RGT; Results: Among patients given RGT (n=17), the overall treatment failure rate was 1/17 (5.9%). This was significantly lower than the rate (47.6%) observed in patients treated pre-RGT (p=0.002). The time to microbiological cure was 29.4 days (CI 24.5 – 34.3) compared to 45.2 days (CI 36.5 – 53.7) pre-RGT (p=0.001); Conclusions: Implementation of M. genitalium diagnostics with macrolide-resistance detection improved treatment outcomes in Bulgaria with significantly lower treatment failure rates and reduced time to microbiological cure. In light of limited treatment options and concerns about their decreasing efficacy in response to misuse and overuse, diagnostic macrolide-resistance assay is critical to direct appropriate first-line treatment, to maintain the efficacy of antimicrobial treatment (antibiotic stewardship) and to minimize the spread of antimicrobial resistance.

This study investigated the toxigenic genes and antimicrobial resistance profiles Staphylococcus aureus strains isolated from 260 raw milk samples collected from dairy farms in Türkiye. Results indicated that 60.7% of staphylococcal enterotoxin genes (sea, seb, sed, seg, sei, sej, sek, seq, sem, seo, and seu) and 21.4% of the tst and eta genes were positive, with most enterotoxin-positive samples carrying more than one gene. The genes sec, see, seh, sel, sen, sep, and etb were not identified in any samples. The prevalence of antibiotic resistance genes (mecA, blaR, blaI, blaZ, vanA, ermT, tetK, aac/aph, ant, dfrA, tcaR, IS256, and IS257) was high at 89.2%, with bla being the most frequently detected gene (75%). The mecA gene was present in 14.2% of samples, while tcaR was detected in 78.5%. Nevertheless, the mecC was not identified. Disinfectant resistance genes (qacA/B, qacC, qacJ, smr) were detected in 21.4% of the samples. The results of the disc diffusion test showed that 64% of strains were resistant to penicillin G and ampicillin, with additional resistance found for cefoxitin, teicoplanin, levofloxacin, norfloxacin, and other antibiotics. These findings highlight a significant public health and food safety risk associated with raw milk due to the presence of S. aureus strains with toxigenic genes and high antimicrobial resistance.

Chili peppers (Capsicum annuum L.) are economically important crop worldwide. Pepper yellow leaf cure Thailand virus (PepYLCTHV), a Begomovirus causing yellow leaf mosaic disease of chili pepper has been reported to damage 95% economic loss under favorable epidemic conditions. Thirty-one chili genotypes were screened for resistance to PepYLCTHV disease through challenge inoculation using 10-15 viruliferous whiteflies per plant. We purified two resistant lines (PEP6 and PEP12) through four generations of selfing and selection through challenged inoculations. At the 28 days after inoculations two chili genotypes showed symptoms less based on disease severity and percentage of disease incidence of 30 plants PEP6 showed a disease severity 3.64 with 69.33 % DI and PEP12 showed a disease severity 3.83 with 77.67 % DI. PEP6 and PEP12 were selected for pure-line development for four cycles.Both varieties showed an increase in percentage of the resistant plant 12.78 % and 16.77 %, respectively. These lines have potential for release as resistant lines for improvement chili pepper resistance to PepYLCTHV and for developing maker associated with the resistant trait.

Global crop yield and food security are being threatened by phytophagous insects. Innovative methods are required to increase agricultural output while reducing reliance on hazardous synthetic insecticides. It appears to be quite effective at reducing production costs and boosting farm profitability to use the ground-breaking CRISPR-Cas technology to create plants that are insect resistant. In contrast, this new technique can modify an insect's genome to either produce gene drive or get beyond an insect's tolerance to various insecticides. This paper reviews and critically discusses the use of CRISPR-Cas genome editing technology in long-term insect pest management. The emphasis of this review is on the prospective uses of the CRISPR-Cas system for insect stress management in crop production by creating genome-edited crops and insects. The potential and difficulties of using CRISPR-Cas technology to reduce pest stress in crop plants are critically examined and discussed.

: As antibiotic resistance undermines efforts to treat bacterial infections, phage therapy is being increasingly considered as an alternative in clinical settings and agriculture. However, a major concern in using phages is that pathogens will develop resistance to the phage. Due to the constant evolutionary pressure by phages, bacteria have evolved numerous mechanisms to block infection. If we determine the most common among them, we could use this knowledge to guide phage therapeutics. Here we compile data from 88 peer-reviewed studies where phage resistance was experimentally observed and linked to a bacterial gene, then assessed these data for patterns. In total, 141 host genes were identified to block infection against one or more of 80 phages (representing five families of the Caudovirales) across 16 microbial host genera. These data suggest that bacterial phage resistance is diverse, but even well-studied systems are understudied, and there are gaping holes in our knowledge of phage resistance across lesser-studied regions of microbial and viral sequence space. Fortunately, scalable approaches are newly available that, if broadly adopted, can provide data to power ecosystem-aware models that will guide harvesting natural variation towards designing effective, broadly applicable phage therapy cocktails as an alternative to antibiotics.

We propose the traditional goal of cancer therapists to develop a single drug or drug combination that can, by itself, eliminate all cancer cells within a host has neglected potential treatments that may achieve curative outcomes by strategically combining agents that are individually effective but non-curative. We derive basic principles for such an approach from the eco-evolutionary dynamics of background extinctions in which a “first strike” reduces the size and heterogeneity of the initial population and is followed immediately by demographic and ecological “second strikes” that push the population below an extinction threshold. This proposed strategy appears identical to the empirically-derived curative therapy in childhood Acute Lymphocytic Leukemia.

Skin and soft-tissue infections (SSTI) are common cases of hospital-acquired infections with aetiologic agents exhibiting antimicrobial resistance (AMR). We determined the prevalence, proportion of laboratory-investigated cases, AMR-profiles, and factors associated with SSTI and multi-drug resistance (MDR). This study was based on archived data of patients suspected of SSTI from 2019-2021 at Jinja Regional Referral Hospital. The analysis involved 268 randomly selected patient reports. Prevalence of SSTI was 66.4%. Laboratory-investigated cases were 14.11%. Staphylococcus aureus (n=51) was the most isolated organism. MDR pathogens explained 47% of infections. Methicillin-resistant S. aureus was up to 44%. In addition, 61% of Gram-negatives had the potential to produce extended-spectrum beta-lactamases, while 27% were non-susceptible to carbapenems. Ward of admission was significantly associated with infection (aPR=1.78, 95% CI: 1.003-3.18, p-value=0.04). Age category (19-35) was an independent predictor for MDR infections (aPR=2.30, 95%CI:1.02-5.23, p-value=0.04). The prevalence is relatively high with MDR pathogens responsible for almost half of the infections. Routine use of culture and sensitivity testing should be done for proper infection management. Gentamicin and ciprofloxacin can be considered for empirical management of emergency SSTI suspected of S. aureus. Recognizing SSTI under the Global Antimicrobial resistance Surveillance System would lead to improved preparedness and response to AMR.

SPH5030 is developed to improve the selectivity of irreversible HER2 inhibitors, which shows excellent activity and better HER2 selectivity against four common HER2 mutants than neratinib and pyrotinib, and consumed to attenuate the off-target adverse events in patients. To comprehensively evaluate SPH5030, the preclinical pharmacokinetic studies of SPH5030 were systematically completed, including in vivo pharmacokinetic, tissue distribution and excretion in Sprague Dawley rats and Cynomolgus monkeys, as well as in vitro drug-drug interaction assessment and metabolite identification in different species. The results showed low clearance, reasonable bioavailability and widely tissues distributed properties, and mainly excreted in the feces in rats. In vitro studies SPH5030 showed stable metabolism and metabolized mainly by CYP3A4/5 and CYP2C8, with no or weak inhibitory potential on CYP450 enzymes and efflux transporters. In Phase I clinical trials, SPH5030 also showed well tolerated at doses ranging from 50 to 600mg and is expected to have an ideal clinical application prospect in the future.

With opioid overdose rates on the rise, we aim to develop a county-level risk stratification, specifically focusing on access to Medications for Opioid Use Disorder (MOUD) and high overdose rates. We examined over 15 million records from the South Carolina Prescription Tracking System (SCRIPTS) across 46 counties. Additionally, we incorporated data from opioid treatment programs, healthcare professionals prescribing naltrexone, clinicians with buprenorphine waivers, and county-level overdose fatality statistics. To assess the risk of opioid misuse, we classified counties into high-risk and low-risk categories based on prescription rates, overdose fatalities, and treatment service availability. Utilizing statistical methods, we compared prescription patterns and outcomes between these groups. Our study shows that between 2017 and 2021, opioid prescriptions decreased from 64,223 to 41,214 per 100,000 residents, while fentanyl-related overdose deaths increased by 312%. High-risk counties have significantly higher rates of fentanyl prescriptions and rely more on out-of-state doctors. They also exhibit higher instances of doctor shopping and had fewer medical doctors per capita, with limited access to MOUD. To effectively combat the opioid crisis, we advocate for improved local healthcare infrastructure, broader treatment access, stricter management of out-of-state prescriptions, and vigilant tracking of prescription patterns. Tailored local strategies are essential for mitigating the opioid epidemic in these communities.

This study proposes an innovative pharmacovigilance approach, VIGI-4, leveraging ChatGPT Teams Pro version. Our method involves aggregating data from all phase 4 studies published in February 2024 to identify adverse reactions. These results are categorized by disease state alphabetically, demonstrating the method's capacity to generate structured and easily navigable outputs. The results section is tailored to exemplify the valuable, actionable insights attainable through our workflow. This research emphasizes the game-changing potential of integrating advanced technologies such as ChatGPT into pharmacovigilance, signaling a new era in safeguarding drug safety.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic presented a new challenge in modern medicine. The development of vaccines was followed by massive population vaccinations. A few reports on post-vaccination allergic reactions have made patients and medical personnel uneasy as to anti-COVID-19 vaccines’ allergising potential. Most of the studies in this area to date have been small, and some that were based on global databases skipped most of the allergic diseases and concentrated only on anaphylaxis. We aimed to analyse the incidence of serious allergic reactions based on the EudraVigilance (EV) database, regardless of the reported symptoms and allergy mechanism. Methods: The total number of administrated vaccine doses was extracted on the 5th October 2023 from Vaccine Tracker and included all administrations since vaccinations began in the European Economic Area (EEA). Data on serious allergic reactions to COVID-19 vaccines was extracted from the EudraVigilance database with the same time point. The code names of 147 allergic symptoms or diseases were used. Results: The frequency of serious allergic reactions per 100,000 administered vaccine doses was 1.53 for Comirnaty, 2.16 for Spikevax, 88.6 for Vaxzevria, 2.11 for Janssen, 7.9 for Novavax, 13.3 for VidPrevtyn Beta and 3.1 for Valneva. The most prevalent reported reactions were oedema (0.46) and anaphylaxis (0.40). Only 6% of these reactions were delayed hypersensitivity-oriented. Conclusions: The overall frequency of potential serious allergic reactions to COVID-19 is very rare. Therefore, COVID-19 vaccines seem to be safe for human use. The lowest frequency of allergic reaction was observed for Comirnaty and the highest for Vaxzevria.

This report provides a comprehensive overview of occupational asthma and respiratory hypersensitivity, focusing on some chemicals used in the workplace Includes numerous case studies and research papers related to occupational exposure methacrylates, such as HEMA-HPMA, and respiratory hypersensitivity are discussed And the relationship is explored The book also examines asthma pathophysiology, emerging drug delivery systems used to treat asthma, and the potential of nanoparticles , dendrimers and micellar systems have to improve pulmonary drug delivery in detail The worldwide prevalence of asthma is highlighted, with higher rates in high-GDP countries. The book highlights the need to improve asthma diagnosis, treatment and biomarkers, particularly in rural and low-income countries where the disease is often underdiagnosed and inadequately treated, and discusses the role of inflammation in asthma, in particular secondary inflammation characterized by specific cytokines and inflammation cells In addition, the paper explores various drug delivery methods, such as parenteral and parenteral drugs, pressurized inhalers (pMDIs), dry powder inhalers ( DPIs), nebulizers, nanoparticles, liposomes, micellar systems, microparticles , dendrimers, and gene therapy . These systems aim to increase pulmonary delivery of drugs and improve clinical outcomes. The potential of dendrimer bilirubin-based nanoparticles and micellar systems in the treatment of asthma and lung inflammatory diseases, including asthma, has been discussed in summary, this paper provides an overview of occupational asthma and respiratory hypersensitivity, and this discussion highlights the association between methacrylate exposure and respiratory hypersensitivity.

Intermolecular interactions are the fabrics underlying almost all processes in living organisms, where two cornerstone concepts, intermolecular binding affinity (K d ) and binding energy (ΔG), have long been established to physically describe the strengths of biomolecular interactions, e.g., drug-target K d and ΔG to describe the strength of drug-target interaction. The past two-three years saw a big step forward in the use of artificial intelligence (AI) in structural biology (e.g., AlphaFold for protein structure prediction) and drug discovery & design. In light of the roles of K d and ΔG in drug discovery & design, the speed of this AI progress raises a question of what’s next for its practical application in the pharmaceutical industry, in addition to a system-wide account of biomolecular structures and motions. Last August, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined and proposed in an MDPI-published preprint. Here, this article puts forward an updated conceptual and practical framework of GIBAC, including its inception, definition, construction, practical applications, technical challenges and limitations, and future directions. Moreover, this article argues that the time is now ripe for the construction of such an accurate, precise and efficient GIBAC to be on the agenda of the entire drug discovery & design community, to ensure its applicability & reliability, and to enhance its value in drug R&D in future.

Background: Chronic Hepatitis B Virus (HBV) infection is a global health concern, associated with severe liver diseases, necessitating ongoing research on novel drug candidates. This study aims to identify potential drug candidates targeting HBV core protein (HBcAg) and disrupting capsid assembly, a critical step in the virus's life cycle. Methods: HBcAg in complex with HBV inhibitors were obtained from the Protein Data Bank (PDB). CavityPlus server was used for analysis of druggable cavity. Structure-based pharmacophores were extracted from identified cavities, and potential allosteric ligand binding sites were assessed using CavPharmer, CorrSite, and CovCys. LigandScout was employed for ligand-based pharmacophore screening against an FDA-approved library. The ZINC database was screened with features extracted from CavPharmer. Molecular docking studies were conducted using Autodock Vina. Lead compounds were selected based on docking scores, binding modes, and interactions within the druggable cavity. Results: Strong druggable pockets were found for Ciclopirox, while Compound 24, NVR10-001E2, and others showed medium to weak pockets. Ligand-based pharmacophores varied in size and complexity. Screening revealed potential hits matching these pharmacophores, including Ciclopirox olamine, Voriconazole, Enasidenib, and Statins. A large compound database search yielded additional hits like ZINC86859997 and ZINC63280172. Docking analyses confirmed these hits' potential, highlighting their interactions with critical HBc protein residues, offering promising leads for hepatitis B drug development. Conclusions: Voriconazole, Enasidenib, and Lovastatin have shown promises. These hits displayed favorable interactions with crucial HBc protein residues, indicating their potential as lead compounds The mechanism of action of statins with anti-HBV activities also highlighted. This comprehensive approach offers valuable insights into targeting HBc protein for antiviral drug discovery.

Artificial intelligence AI or machine learning has proven to be a potential activity in the health and biomedical sciences. Previous research it has found that AI can learn new data and transform it into the useful knowledge. In the field of pharmacology, the aim is to design more efficient and novel vaccines using this method which are also cost effective. The underlying fact is to predict the molecular mechanism and structure for increased likelihood of developing new drugs. Clinical, electronic and high resolution imaging datasets can be used as inputs to aid the drug development niche. Moreover, the use of comprehensive target activity has been performed for repurposing a drug molecule by extending target profiles of drugs which also include off targets with therapeutic potential providing a new indication.

Introduction: Valproic acid (VPA) is an antiepileptic drug extensively used for treating partial and generalised seizures, acute mania and as prophylaxis for bipolar disorder. Drug-induced liver injury (DILI) persists as a significant issue related to fatal outcomes by VPA. The aim of this study was to increase our knowledge about this condition and to better identify patients affected. Methods: We conducted an observational retrospective case-control study that identified cases of DILI by VPA from the Pharmacovigilance Programme from our Laboratory Signals at La Paz University Hospital from January 2007 to December 2019. From the Therapeutic VPA Monitoring Programme, two control groups were assigned, VPA-tolerant patients and the other with patients who developed mild VPA-related hepatitis but who did not meet the DILI criteria, matched for date, age and sex. Results: A total of 60 patients were included in the study: 15 cases of DILI, 30 VPA-tolerant controls and 15 controls with mild hepatitis. Mean age for the cases was 45.7 years, 4(26.7%) were women and 5(33.34%) were children under 18 years, of them 3(20%) were fatal. Polytherapy with other antiepileptic drugs (p=0.047) and alcohol consumption (p<0.001) were associated with a greater risk of developing DILI by VPA. A diagnosis of epileptic seizure was more frequently related to DILI when compared with the VPA-tolerant controls (p<0.001). The cases developed hepatocellular hepatitis (p<0.001), while the mild hepatitis controls had a higher rate of cholestatic hepatitis (p<0.001). The laboratory lactate dehydrogenase values were statistically higher (even at baseline) in patients with DILI than in both control groups (p= 0.033 and p=0.039). Conclusions: VPA hepatotoxicity remains a considerable problem. This study offers interesting findings for characterising VPA-induced liver injury and at-risk patients.

The Novel Coronavirus (COVID-19) is a positive-sense single-stranded RNA ((+)ssRNA) virus. The COVID-19 Main Proteases play very important role in the propagation of the Novel Coronavirus (COVID-19). It has already killed more than 8000 people around the world and thousands of people are getting infected every day. Therefore, it is very important to identify a potential inhibitor against COVID-19 Main Proteases to inhibit the propagation of the Novel Coronavirus (COVID-19). We have applied a drug repurposing approach of computational methodology, depending on the synergy of molecular docking and virtual screening techniques, aimed to identify possible potent inhibitors against Novel Coronavirus (COVID-19) from FDA approved antiviral compounds and from the library of active phytochemicals. On the basis of recently resolved COVID-19 Main Protease crystal structure (PDB:6LU7), the library of 100 FDA approved antiviral compounds and 1000 active components of Indian Medicinal Plants extracted for screening against COVID-19 Main Protease. The compounds were further screened using Pyrex virtual screening tool and then best inhibitors, top 19 compounds optimally docked to the COVID-19 Main Protease structure to understand the participation of specific amino acids with inhibitors at active sites. Total 19 best compounds were identified after screening based on their highest binding affinity with respect to the other screened compounds. Out of 19, 6 best compounds were further screened based on their binding affinity and best ADME properties. Nelfinavir exhibited highest binding energy -8.4 kcal/mol and strong stability with the TRP207, ILE281, LEU282, PHE3, PHE291, GLN127, ARG4, GLY283, GLU288, LYS5, LYS137, TYR126, GLY138, TYR126, SER139 and VAL135 amino acid residues of COVID-19 Main Protease participating in the interaction at the binding pocket. In addition to Nelfinavir (-8.4), Rhein (-8.1), Withanolide D (-7.8), Withaferin A (-7.7), Enoxacin (-7.4), and Aloe-emodin (-7.4) also showed good binding affinity and best ADME properties. Our findings suggest that these compounds can be used as potential inhibitors against COVID-19 Main Protease, which could be helpful in inhibiting the propagation of the Novel Coronavirus (COVID-19). Moreover, further in vitro and in vivo validation of these findings would be very helpful to bring these inhibitors to next level study.

The similarities between exosomes and liposomes, together with the high organotropism of several types of exosomes, have recently prompted the development of engineered-exosomes or exosome-mimetics, which may be artificial (liposomal) or cell-derived vesicles, as advanced platforms for targeted drug delivery. Here we provide the current state-of-the-art of using exosome or exosome-inspired systems for drug delivery. We review the various approaches investigated and the shortcomings of each approach. Finally the challenges identified up-to-date in this field are summarized.

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

This project investigates the behavior of multi-head attention in Transformer models, specifically focusing on the differences between benign and trojan models in the context of sentiment analysis. Trojan attacks cause models to perform normally on clean inputs but exhibit misclassifications when presented with inputs containing predefined triggers. We characterize attention head functions in trojan and benign models, identifying specific 'trojan' heads and analyzing their behavior.

Urgent environmental challenges and emerging additive manufacturing (AM) technologies push research towards more performant and new materials. In the field of metallurgy, high entropy alloys (HEAs) have recently represented a topic of intense research because of their promising properties, such as high temperature strength and stability. Moreover, this class of multi-principal element alloys (MPEAs) have opened up researcher community to unexplored compositional spaces, making prosper literature of high-throughput methodologies and tools for rapidly screening large number of alloys. However, none of the methods has been aimed to design new MPEAs for AM process known as selective laser melting (SLM) so far. Here we conducted nanoindentation testing on single scan tracks of elemental powder blends and pre-alloyed powders after ball milling of AlTiCuNb and AlTiVNb. Results show that nanoindentation can represent an effective technique to gain information about phase evolution during laser scanning, contributing to accelerate the development of new MPEAs.

COP21 represented a starting point for several nations to develop and implement energy transition strategies to face and mitigate climate change, making the electrical power sector crucial in achieving the established goals and commitments. This research presents a 7-step methodology to improve decision-making in energy transition strategies for the power sector by integrating an economic dispatch optimization model based on linear programming to determine vulnerable aspects of power generation and transmission in strategic planning scenarios that could jeopardize the country’s energy transition. The application of the methodology is implemented under the open-source platform Python Optimization Modelling (PYOMO) and illustrated through a case study of the Mexican Electrical Power System (SEN) during the year 2025. The case study shows that the reserve margin fluctuated due to the variable renewable energy installed despite having a vast installed capacity to supply the country’s total demand. In addition, the results showed that most of the transmission lines had a congestion frequency higher than 90% of their capacity during most of the year. Two regions were identified as the best options for reducing greenhouse gas emissions by installing new power plants. Finally, most technologies reflected an under-generation, suggesting a high dependence on some fuels to supply the Mexican demand. The model’s programming is freely available in GitHub.

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

Abstract: Thermal desalination is yet a reliable technology in the treatment of brackish water and seawater; however, its demanding high energy requirements have lagged it compared to other non-thermal technologies such as reverse osmosis. This review provides an outline of the development and trends of the three most commercially used thermal or phase change technologies worldwide: Multi Effect Distillation (MED), Multi Stage Flash (MSF), and Vapor Compression Distillation (VCD). First, state of water stress suffered by regions with little fresh water availability and existing desalination technologies that could become an alternative solution are shown. The most recent studies published for each commercial thermal technology are presented, focusing on optimizing the desalination process, improving efficiencies, and reducing energy demands. Then, an overview of the use of renewable energy and its potential for integration into both commercial and non-commercial desalination systems is shown. Finally, research trends and their orientation towards hybridization of technologies and use of renewable energies as a relevant alternative to the current problems of brackish water desalination are discussed. This reflective and updated review will help researchers to have a detailed state of the art of the subject and to have a starting point for their research, since current advances and trends on thermal desalination are shown.

The aim of this investigation is to offer a data-based scheme for predicting electrode wear in resistance spot welding. Electrode wear and alloying is one of the most significant factors that affect the mechanical properties of spot welds and the variation in weld quality. In this study, Rogowski coils and twisted pairs attached to the top and bottom electrodes were used to obtain the welding current and the inter-electrode voltage in the welding process, thereby calculating the dynamic resistance value during the welding process. The electrode diameter was obtained from the pressure exerted by the upper and lower electrodes on the carbon paper when the current was cut off and was regarded as an indicator of electrode wear. By continuously welding 0.5mm thick BH 340 steel plates until the electrode failed, the dynamic resistance signal was recorded in real time. Simultaneously, the electrode diameter after every 4 welds was also recorded. On this basis, the correlation between electrode diameter and dynamic resistance is studied. In order to quantitatively study the mapping relationship between dynamic resistance and electrode wear, 11 characteristic values were extracted from the dynamic resistance, and the stepwise regression method was used to obtain the regression formula between the characteristic values and the electrode diameter. Using new data to verify the effectiveness of the regression model, the acquired results display that the maximum error between the predicted value of the electrode diameter and the measured value obtained by the regression equation with the interactive quadratic term is 0.3 mm, and the corresponding relative error is 7.69 %. This result demonstrates that the method proposed in this paper can effectively monitor the electrode wear and failure process.

The proliferation of SARS-COV-2 through enhanced viral replication and its subsequent triggering of cytokine storm, are some of the hallmark phenotypes in severe COVID-19 patient cases. Cyclosporine, an immunosuppressant drug and Selinexor, an inhibitor of nuclear transporter protein, both have been successfully demonstrated to be effective against SARS-COV-2 infection by targeting those functions individually. However, the highly multifactorial pathology of SARS-COV-2 infection hinders any mono-therapeutic strategy to become an optimal option. In this study, we assess the potential efficacy of the Cyclosporine-Selinexor combination on an integrated interactome by adopting a network-medicine-based repositioning technique, where disease proximity, functional proximity and their topological separation are evaluated, followed by a robust statistical significance test. Results have shown that both drug target modules are highly proximal to the SARS-COV-2 disease modules in terms of network topology and functional associations, in a statistically significant manner, individually. Functional enrichment of both drug modules and SARS-COV-2 infected modules has shown that two drugs target the functions related to viral replication and cytokine storm during infection. Moreover, a high degree of network separation been those two drug target modules has been observed, revealing ``complementary exposure`` patterns, rendering this drug combination as an effective one against SARS-COV-2 infection. We hope that our results will encourage researchers to further investigate the potency of Cyclosporine and Selinexor combination in vivo or in vitro, and ultimately lead that up to clinical trials to treat SARS-COV-2 patients.

Hypertension (HTN) is the foremost contributor to cardiovascular diseases (CVD), which remain a leading cause of death. However, adherence to the properly prescribed dosage form is critical and challenging for patients with chronic diseases such as HTN to prevent illness progression and death. The study’s objective was to develop AMB-loaded subcutaneous implants to reduce the frequency of administration, thus improving patient compliance during HTN management. AMB subcutaneous implants were prepared using continuous hot melt extrusion technology using PCL and PLGA with dimensions of 7.5 cm (length) by 2 mm (diameter). The implants were characterized for thermal characteristics, drug-excipient incompatibilities, surface morphology, fracturability, in vitro release, and stability studies. DSC showed that the drug was in the crystalline state in the fabricated implants. Scanning Electron Microscopy (SEM) revealed a smooth surface. The lead formulation presented good drug content, showed an extended drug release profile over 30 days, and followed zero-order release kinetics with a mean dissolution time of 15 days. The formulation was stable over 30 days in accelerated stability conditions (the last time point was tested, 40℃/75% RH), and the release profile was similar to the initial drug release profile. The lead formulation showed good fracturability during textural analysis. Therefore, the hot melt extruded implants could be an alternative management strategy for conventional amlodipine besylate (AMB) formulation.

The primary purpose of this study was to determine the prevalence of drug-drug interaction (DDI) and duplicate therapy in chronic patients in a completely random study population engaged in digital health apps. In this cross-sectional study, polypharmacy checks for 100 completely anonymous patients were analyzed for the occurrence of DDIs and duplicate therapy. Logistic regression models were used to identify factors associated with DDIs and duplicate therapy. DDIs and duplicate therapy prevalence were 34% and 33%, respectively. Chi-Square test discovered a significant association between the DDIs and duplicate therapy variables. Logistic regression models showed a strong association between the number of medications taken and higher odds of DDIs occurring in our population only. In conclusion, our study shows that polypharmacy is a determining factor for the occurrence of unwanted DDIs, and the prevalence of duplicate therapy and DDIs is around 33%, increasing an issue regarding patient safety and its burden to the healthcare system.

Overt hyperthyroidism during pregnancy is associated with risk of maternal-fetal complications. The antithyroid drugs (ATD) have a potential risk for teratogenic effects and fetal–neonatal hy-pothyroidism. This study evaluated ATD treatment and thyroid function control during preg-nancy, and pregnancy outcome in women with hyperthyroidism. Patients and methods: retro-spective analysis of 36 single fetus pregnancies in 29 consecutive women (median age 30.3 ± 4.7 years) with hyperthyroidism diagnosed before or during pregnancy; a control group of 39 healthy euthyroid pregnant women was used. Results: 26 women had Graves’ disease (GD, 33 pregnan-cies), 1 had a hyperfunctioning autonomous nodule, 2 had gestational transient thyrotoxicosis (GTT). Methimazole (MMI) was administered in 22 pregnancies (78.5%), Propylthiouracil (PTU) in 2 (7.1%), switch from MMI to PTU in 4 (14.2%), no treatment in 8 pregnancies (3 with subclinical hyperthyroidism, 5 euthyroid with previous GD remission before conception). One spontaneous abortion at 5 weeks (3.4% of pregnancies) and 1 premature delivery at 32 weeks with perinatal death in 24h (3.4%) were recorded in 2 of the 8 pregnancies of GD patients diagnosed shortly before (< 6 weeks) or during gestation. In women treated more than 6 months until conception (20 pregnancies): a) median ATD doses were lower than those in women diagnosed shortly before or during pregnancy; b) ATD was withdrawn in 40% of pregnancies in trimester (T) I, all on MMI < 10 mg/day (relapse in 14.2%), and in up to 55% in TIII; c) TSH level was below normal in 37%, 35% and 22% of pregnancies in T I, II and III respectively; FT4 was increased in 5.8% (T I) and sub-normal in 11.75% in TII and III; d) one fetal death due to a true umbilical cord knot was recorded. Hyperthyroidism relapsed postpartum in 83% of GD patients (at median 3 ± 2.6 months). One child had neonatal hyperthyroidism (3.3% of live children in GD women) and a small atrial sept defect (4% of live children in ATD treated women). Mean birth weight did not differ from that of the control group. Conclusion. In hyperthyroid women with long-term ATD control before con-ception, drugs could be withdrawn in TI in a third of them, and fetal complications were rare. Frequent serum TSH and FT4 monitoring is needed in order to maintain optimal thyroid function during pregnancy.

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search, due to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape, complicating UAVs' search efforts and hindering the convergence of the MARL algorithm training. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of Decentralized Partially Observable Markov Decision Process. Based on this model, a Spatio-Temporal Efficient Exploration network and a Global Convolutional Local Ascent mechanism are proposed. Subsequently,we introduce a Multi-UAV Escape Target Search Algorithm Based on MAPPO(ETS-MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS-MAPPO algorithm outperforms five classical MARL algorithms in terms of the number of target searches,the area coverage rate, and other metrics.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset enhances the water information and reduces the data dimension. In particular, the Cloud-Free label provided in the database effectively mitigates the issue of scarcity of labeled samples Since a single form or uniform kernel size cannot handle the variety in size and shape of water bodies, we proposed the gated multi-filter inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize the attention mechanism by introducing the Gated Channel Transform (GCT) skip connection and integrating GCT into GMF- Inception to further improve model performance. Extensive experiments on three benchmarks, including WIPI, Chengdu and GF2020 datasets demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 of 0.9191, 0.7410, and 0.8421, respectively, which average F1 on three datasets is 0.0045 higher than the U-Net model and reduces 62% average GFLOPs. New WIPI dataset, code and trained models have been released on GitHub

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

The framework of product life cycle (PLC) cost analysis is one of the most important evaluation tools for a contemporary high-tech company in an increasingly competitive market environment. The PLC-purchasing strategy provides the framework for a procurement plan and examines the sourcing strategy of a firm. The marketing literature emphasizes that ongoing technological change and shortened life cycles are important elements in commercial organizations. From a strategic viewpoint, the vendor has an important position between supplier, buyer and manufacturer. The buyer seeks to procure the products from a set of vendors to take advantage of economies of scale and to exploit opportunities for strategic relationships. However, previous studies have seldom considered vendor selection (VS) based on PLC cost (VSPLCC) analysis. The purpose of this paper is to solve the VSPLCC problems considering the situation of a single-buyer-multiple-supplier. For this issue, a new VSPLCC procurement model and solution procedure are derived by this paper to minimize net cost, rejection rate, late delivery and PLC cost subject to vendor capacities and budget constraints. Moreover, a real case in Taiwan is provided to show how to solve the VSPLCC procurement problem.

In this paper, a new approach to the sliding-mode control of single-phase inverters under linear and non-linear loads is introduced. The main idea behind this approach is to utilize a non-linear, flexible and multi-slope function in controller structure. This non-linear function makes the controller possible to control the inverter by a non-linear multi-slope sliding surface. In general, this sliding surface has two parts with different slopes in each part and the flexibility of the sliding surface makes the multi-slope sliding-mode controller (MSSMC) possible to reduce the total harmonic distortion, to improve the tracking accuracy, and to prevent overshoots leading to undesirable transient-states in output voltage which are occurred when the load current sharply rises. In order to improve the tracking accuracy and to reduce the steady-state error, an integral term of the multi-slope function is also added to the sliding surface. The improved performance of the proposed controller is confirmed by simulations and finally, the results of the proposed approach are compared with a conventional SMC and a SRFPI controller.

The emergence of antimicrobial resistance have represented a serious threats for public health and infections due to multidrug-resistant (MDR) microorganisms represent one of the most important causes of death worldwide. The renew of old antimicrobials, such as colistin, have been proposed as valuable therapeutic alternative to the emergence of the MDR microorganisms. Although colistin is well known to presents several adverse toxic effect, its usage in clinical practice have been reconsidered due to the broad spectrum of activity to gram-negative (GN) bacteria and to the important role of “last resort” agent against MDR-GN. Despite the revolutionary prospective of treatment of this old antimicrobial molecule, many questions remain open regarding the emergence of novel phenotypic traits of resistance and the optimal usage of the colistin in clinical practice. In the last years, several forward steps have been done in the understanding of resistance determinants, clinical usage and pharmacological dosage of this molecule, however, different points regarding the role of colistin in clinical practice and the optimal pharmacokinetic/pharmacodynamic targets are not well defined yet. In this review, we summarize the mode of action, the emerging resistance determinants and its optimal administration in the treatment of difficult-to-treat infections due to MDR Gram-negative bacteria.

In the current study we test the hypothesis that the consumption of macrolides in food-animals is positively associated with the prevalence of macrolide resistance in M. pneumoniae at a country-level. The prevalence of M. pneumoniae macrolide resistance was positively associated with the consumption of macrolides for both food animals (Rho = 0.66; P=0.007) and humans (Rho = 0.54; P=0.040). Linear regression analysis revealed that macrolide consumption in food animals but not humans was borderline significantly associated with macrolide resistance (coef. 5547 [95% CI -596-11691] and coef. 0.006 [95% CI -0.008-0.020], respectively).

In the absence of information on Borrelia species causing Lyme borreliosis (LB) in Serbia, we were interested in the pathogenic potential of local strains of Borrelia burgdorferi sensu lato isolated from ticks. The susceptibility of B. burgdorferi sensu lato isolates to human complement was tested in vitro. A total of 31 strains were analysed by serum susceptibility testing - 12 Borrelia afzelii, 12 Borrelia lusitaniae, three Borrelia bavariensis, two Borrelia garinii, and two Borrelia valaisiana. The serum-resistant strains (B. afzelii, B. bavariensis and B. valaisiana) expressing OspA and OspB or OspA and OspC proteins might be related to the different pathogenic potential of the specific Borrelia strains and their ability to cause different clinical manifestations of LB in humans. Among the B. lusitaniae strains from Serbia, two were significantly more motile than others (all p < 0.05) and equally motile as human isolate (both p > 0.05), and since they express OspC, their pathogenic potential remains unclear. For further studies, it is of particular interest to explore the mechanism of resistance of B. valaisiana and to explain the fair motility of B. lusitaniae, a predominant Borrelia species in Serbia, in the presence of human complement.

The emergence of carbapenemase-producing Enterobacteriaceae has limited therapeutic options and presents a major public health problem. Resistances against carbapenems in this bacterial family are mostly conveyed by metallo-beta-lactamases (MBL) including VIM, which are often encoded on resistance plasmids. We characterized four VIM-positive isolates that were obtained as part of a routine diagnostic screening from two laboratories in north-eastern Germany be-tween June and August 2020. Whole-genome sequences were generated to address a) their phylo-genetic properties, b) their plasmid content, and c) the underlying mechanisms of resistance and virulence. In addition, we performed phenotypic antibiotic and mercury resistance analyses. The genomic analysis revealed three different bacterial species including C. freundii, E. coli and K. oxytoca with four different sequence types. All isolates were geno- and phenotypically multi-drug-resistant (MDR). Three isolates of four carried identical VIM-1-resistance plasmids, which – in addition – encoded for a mercury resistance operon and showed some similarity to two ex-ternal plasmid sequences. The distribution of virulence features among the genomes was limited. Our results highlight the circulation of an identical VIM-1-resistance plasmid in three of four bacterial isolates of different Enterobacteriaceae in north-eastern Germany.

Resistance training increases myofiber hypertrophy, but the morphological adaptations that occur within myofibers remain largely unresolved. Fifteen males with minimal training experience (24±4 years, 17.9±1.4 kg/m2 lean body mass index) performed 10 weeks of conventional, full-body resistance training (2x weekly). Body composition, the radiological density of the vastus lateralis muscle using peripheral quantitative computed tomography (pQCT), and vastus lateralis muscle biopsies were obtained one week prior to and 72 hours following the last training bout. Fiber typing and the quantification of myofibril and mitochondrial areas per fiber were performed using histology/immunohistochemistry (IHC) techniques. Relative myosin heavy chain and actin protein abundances per wet muscle weight as well as citrate synthase (CS) activity assays were also obtained on tissue lysates. Training increased whole-body lean mass, mid-thigh muscle cross-sectional area, various strength metrics, and mean and type II fiber cross sectional areas (fCSA) (p<0.05). Myofibril areas in type I or II fibers were not altered with training, suggesting a proportional expansion with fCSA increases. Relative myosin heavy chain and actin protein abundances also did not change with training. IHC indicated training increased mitochondrial areas in both fiber types (p=0.018). However, CS activity levels remained unaltered with training. Interestingly, although pQCT-derived muscle density increased with training (p=0.036), suggestive of myofibril packing, a positive association existed between training-induced changes in this metric and changes in type I+II myofibril areas (r=0.600, p=0.018). Shorter-term resistance training seemingly involves a proportional expansion of myofibrils and an accelerated expansion of mitochondria in type I and II fibers. Additionally, histological and biochemical techniques should be viewed independently from one another given the lack of agreement between the variables assessed herein. Finally, the pQCT may be a viable tool to non-invasively track morphological changes in muscle tissue.

Klebsiella pneumoniae, a major cause of both hospital and community-acquired infections, is listed by the World Health Organization as a critical priority antibiotic- resistant bacterial pathogen. With the appearance of sequencing techniques such as Next-generation Sequencing (NGS), there is the possibility to obtain the whole genome of the bacteria, getting to know all antimicrobial resistance determinants. The purpose of this study has been to apply this new technology to clinical microbiology, in order to characterize the resistome present in carbapenem-resistant K.pneumoniae strains isolated in a tertiary hospital in Valencia, Spain. A total of 234 isolates were prepared for whole-genome sequencing with Ilumina MiSeq, and sequences were later studied for antimicrobial resistance genes, sequence-typing and plasmids. Sequence-typing showed four major circulating clones in our hospital settings: ST11, ST307, ST101 and ST147, carrying different plasmids and different resistance determinants such as OXA-48 and NDM-1 carbapenemase. Application of new technologies such as whole-genome sequencing in clinical microbiology gives advantages when it comes to rapid therapy adjustment, consequently improving the patient’s clinical outcomes.

Rosette caused by rose rosette virus (RRV) is the most devastating malady of rose in the United States. Because of the recent discovery of the virus and the completion of Koch’s postulates all assumptions about the disease are based on visual observations of material that may or may have not been infected by the virus. This study addresses several aspects of virus and disease epidemiology. Twenty rose genotypes were screened for mite and/or virus resistance. Phyllocoptes fructiphilus the only known vector of RRV, was able to establish, lay eggs and develop to nymphs and adults in all genotypes. ‘Stormy Weather’ shows resistance to the virus as assessed by both mite and cleft-grafting transmission experiments. The acquisition/latent and inoculation access periods were studied revealing long acquisition/latent periods but rapid inoculation time frames. The outputs of this study will assist in the better management of the vector and the disease. The resistant genotype identified could be used in areas with high disease pressure to minimize spread and for identification of the mechanisms behind resistance or as breeding material to incorporate virus resistance to new cultivars. The short inoculation access period indicates that chemical control for the vector may be a challenging undertaking.

In vitro tests of cellular activity form part of the diagnostic algorithm of drug hypersensitivity reactions. Because of the wide range of pharmacological mechanisms, clinical symptoms, genetic components and laboratory tests, it is important to know how a particular test performs in the diagnostic procedure. We carried out the detailed retrospective analysis of more than 6,000 measurements of numerous drug compounds tested in the past 6 years. Our cell viability-based lymphocyte transformation had a coefficient of variation of 10% and showed similar performance over the whole range of tested ages. With an adequate number of parallel measurements, the test can identify modest increases in stimulation indices with high confidence. Similar percentages of analytically positive responses were observed for the three most frequently tested drug groups, namely antibiotics, non-steroid anti-inflammatory agents and anesthetics. These results confirm that cell viability tests are suitable alternatives of proliferation assays in drug allergy testing.

Lignin is a complex organic polymer. It has a crucial role in providing structural support, rigidity, and defense against disintegration. Following cellulose, it ranks as the second most abundant natural polymer discovered on Earth. Lignin is composed of several phenolic compounds that form a complex, three-dimensional framework. Lignin, unlike cellulose, is a non-carbohydrate polymer rather than a polysaccharide. Lignin's characteristics pose both difficulties and prospects in several sectors, spanning from agriculture to renewable energy. The investigation of the composition of Lignin and its manipulation through engineering are closely intertwined. Disruptions of genes implicated in the lignin biosynthesis pathway can result in substantial alterations in the composition and architecture of Lignin. These modifications can offer novel approaches for altering Lignin and deliberately engineering the polymer to enhance its worth or simplify its extraction from cell wall polysaccharides. As a result, the definition of Lignin is constantly expanding and being adjusted to accommodate its diversity and adaptability. This review examines explicitly the versatility of the lignin molecule.

Functional genomics, a multidisciplinary subject, investigates the functions of genes and their products in biological systems to better understand diseases and find new drugs. Drug repurposing is an economically efficient approach that entails discovering novel therapeutic applications for already-available medications. Genomics enables the identification of illness and therapeutic molecular characteristics and interactions, which in turn facilitates the process of drug repurposing. Techniques like gene expression profiling and Mendelian randomization are helpful in identifying possible medication candidates. Progress in computer science allows for the investigation and modelling of gene expression networks that involve large amounts of data. The integration of data from DNA, RNA, and protein activities has resemblance to pharmacogenomics, which is of utmost importance in the development of cancer drugs. Functional genomics in drug discovery, particularly for cancer, is still not thoroughly investigated, despite the existence of a significant amount of literature on the subject. Next-generation sequencing and proteomics present highly intriguing opportunities. Publicly available databases and mining techniques facilitate the development of cancer treatments based on functional genomics. Expanding the study and application of functional genomics has great promise to advance the process of discovering and repurposing drugs, especially in the field of oncology.

Melatonin is a ubiquitous molecule found in living organisms, ranging from bacteria to plants and mammals. It possesses various properties, partly due to its robust antioxidant nature and partly owed to its specific interaction with melatonin receptors present in almost all tissues. Melatonin regulates different physiological functions and contributes to the homeostasis of the entire organism. In the human eye, a small amount of melatonin is also present, produced by cells in the anterior segment and the posterior pole, including the retina. In the eye, melatonin may provide antioxidant protection along with regulating physiological functions of ocular tissues, including intraocular pressure (IOP). Therefore, it is conceivable that the exogenous administration of sufficiently high amounts of melatonin to the eye could be beneficial in several instances: for the treatment of eye pathologies like glaucoma, due to the IOP-lowering and neuroprotection effects of melatonin; for the prevention of other dysfunctions, such as dry eye and refractive defects (cataract and myopia) mainly due to its antioxidant properties; for diabetic retinopathy due to its metabolic influence and neuroprotective effects; for macular degeneration due to the antioxidant and neuroprotective properties; and for uveitis, mostly owing to anti-inflammatory and immunomodulatory properties. This paper reviews the scientific evidence supporting the use of melatonin in different ocular districts and provides data suggesting that the topical administration of melatonin as eye drops is a real possibility. This way, its distribution and concentration in different ocular tissues may support its pleiotropic therapeutic effects.

The paradigm of molecular communications is applied to the concrete case for delivering electrically charged nanoparticles to tumor. Once them have been injecetd in blood it is expecetd an optimal outcome as to reduce toxicity and minimal dispersion of drugs in the blood stream. With a fraction of nanoparticles arrives to surface of tumor, the scattered part of injected ones can minimize the success of scheme of drug delivery. In this paper is presented a theory based at the sequence electrodynamics-diffusion-Bayes theorem. The resulting probability of Bayes at the end of sequence, might be telling us that dynamical processes based in the injection of electrically charged nanoparticles might be dictated by stochastic formalism more that biochemical approaches by which makes impossible to know the success or fail of drug delivery dynamics. Illustrations demonstrating the transition of a linear to nonlinear scenario are presented

Nanosystems-based antifungal agents have emerged as an effective strategy to address issues related to drug resistance, drug release, and toxicity. Among the different materials used for drug delivery, multifunctional polymers have proven to be ideal due to their versatility. This review provides an overview of the various types of nanoparticles used in antifungal drug delivery systems, with a particular emphasis on the types of polymers used. The review focuses on the application of drug delivery systems and the release behavior of these systems. Furthermore, the review summa-rizes the critical physical properties and relevant information utilized in antifungal polymer nanomedicine delivery systems, and briefly discusses the application prospects of these systems.

Background: Although Tuberculosis (TB) is one of the oldest diseases known to mankind, many practitioners are still unaware of various aspects of management of TB including drug resistant TB. To be able to make an impact on the disease burden we need to understand the knowledge, attitude and practices (KAP) of practitioners, both in government and private sector, towards TB case management. Methodology: A random cross-sectional survey of 99 respondents to assess the KAP towards TB case management using a digital semi-structured questionnaire which was pre-tested and administered to allopathic practitioners from across States in North and Central India. Results: 77% responders were government doctors and 23% were private practitioners. Chest physicians accounted for 61% of the responders. 26% of government and 78% of private doctors relied on sources which are difficult to validate for their knowledge on drug resistant TB management. There were large variations seen in knowledge on modes of transmission, standard precautions, extent of drug resistant TB, diagnostics as well as treatment regimen for drug resistant TB. Conclusion: The knowledge of both sector doctors especially amongst private practitioners with respect to the awareness of extent, diagnosis, and treatment and infection control measures for drug resistant TB is suboptimal which translates to poor diagnostic, therapeutic and infection control choices amongst private practitioners. If India has to achieve the targets for TB Elimination by 2025, serious work needs to be done to upgrade the knowledge of the private sector doctors on drug resistant tuberculosis. The Ministry of Health and Family Welfare, GoI needs to have regular educational programs for the private practitioners coupled with awareness campaigns and frequent surveys to assess the knowledge, attitude and practices being followed in the private sector for TB management including drug resistant TB.

Both Stevens Johnson Syndrome (SJS) and toxic epidermal necrolysis (TEN) are main injurious cutaneous medication reactions that mostly affect the epidermis and mucus membranes. TEN and SJS affecting nearly 1 or 2/1,000,000 people per year, and can recognized as medical crises since they may be deadly. Mucocutaneous discomfort, hemorrhagic erosions, erythema, and more or less severe epidermal separation that appear as ulcer and patches of dermic loss are their defining characteristics. The sole difference between TEN and SJS at this time is the degree of skin detachment, making them two extremes of a spectrum of severe cutaneous adverse drug reactions (cADRs). In the majority of cases, drugs are considered as the principal reason of SJS/TEN, but herpes simplex virus and Mycoplasma pneumoniae infections are also recognized causes, along with lesser number of cases in which the cause is still unknown. Among the drugs with a "high" likelihood of producing TEN/SJS are carbamazepine (CBZ), trimethoprim-sulfamethoxazole, phenytoin, aminopenicillins, allopurinol, cephalosporins, other sulfonamide antibiotics, quinolones, phenobarbital, and NSAIDs of the oxicam variety. There is strong genetic evidence for SJS and TEN in Han Chinese due to the substantial association between the human leukocyte antigen (HLA-B*1502) and SJS brought on by CBZ. The diagnosis is made mostly based on clinical symptoms and the histological study of a dermal biopsy. Pemphigus vulgaris, bullous pemphigoid, linear IgA dermatosis, paraneoplastic pemphigus, disseminated fixed bullous drug eruption, acute generalized exanthematous pustulosis (AGEP), and staphylococcal scalded skin syndrome (SSSS) are among the differential diagnoses. The management of patients with SJS/TEN is complicated by the high risk of mortality, necessitating early diagnosis, estimation of the SCORTEN prognosis, identification and discontinuation of the causative drug, specialized supportive care, and high-dose injectable Ig therapeutic interventions. The reported fatality rates for SJS are 1-5% on average and 25-35% for TEN; it can be even higher in patients who are elderly or who have a significant amount of epidermal detachment on their skin. More than 50% of TEN patients who survive the disease experience long-term consequences.

Nanotechnology is making significant transformation to our world, especially in healthcare and the treatment of diseases. It is widely used in different medical applications, such as in treatment and detection. Targeting diseased cell with nanomedicines is one of the numerous applications of nanotechnology. Targeted drug delivery systems for delivering various types of drugs to specific sites are such a dynamic area in pharmaceutical biotechnology and nanotechnology. Compared to conventional drugs, nanomedicines have a higher absorption and bioavailability rate, improving efficacy and minimizing side effects. There are several drug delivery systems including metallic nanoparticles, polymers, liposomes, and microspheres, but one of the most important is the niosomes, which are produced by nonionic surfactants. Because of the amphiphilic nature and structure, hydrophilic or hydrophobic drugs can be loaded into niosome structures. Other compounds, including cholesterol, can also be applied to the niosomes' backbone to rigidize the structure. Several variables such as the type of surfactant in niosome production, the preparation method, and the hydration temperature can affect the structure of the niosomes. Nevertheless, in-silico design of drug delivery formulations requires molecular dynamic simulation tools, molecular docking, and ADME (absorption; distribution; excretion; metabolism) properties, which evaluate physicochemical features of formulation and ADME attitudes before synthesis, investigating the interaction between nano-carriers and specific targets. Hence, experimenting in-vitro and in-vivo is essential. In this review, the basic aspects of niosomes are described including their structure, characterization, preparation methods, optimization with in-silico tools, factors affecting their formation, and limitations.

Drug discovery has been initially attributed to coincidence or trial and error where the traditional approach was complex, lengthy, and expensive. Conventional drug discovery methods require the costly random screening of synthesized compounds or natural products. Another downside for this approach is the wide dependency on the experimental use of animals for in vi-vo testing. Currently, in silico modeling has become a vital tool for drug discovery and repurposing, and molecular docking is being used to find the best matching between a ligand and a molecule. Practical application of in silico docking will predict the biomolecular interactions between the drug and the target host. Beauvericin (BEA) is an emerging mycotoxin produced by the entomopathogenic fungus Beauveria bassiana. Originally investigated for its pesticide capability, BEA is now considered as a molecule of interest for its potentially diverse biotechnological applications in the pharmacological industry and the field of medicine. In this manuscript, we will provide an overview of the repurposing of BEA into a potentially superior therapeutic molecule in a broad range of diseases. Furthermore, considerable attention has been given to the fundamental role of in silico techniques to i) further investigate the spectrum of this secondary metabolite and ii) elucidate the pathways of BEA for its promising therapeutic action

With increasing awareness amongst physicians and improved radiological imaging techniques, the peritoneal cavity is increasingly recognized as an important metastatic site in various malignancies. Prognosis of these patients is usually poor as traditional treatment including surgical resection or systemic treatment is relatively ineffective. Intraperitoneal delivery of chemotherapeutic agents is thought to be an attractive alternative as this results in high tumor tissue concentrations with limited systemic exposure. The addition of hyperthermia aims to potentiate the anti-tumor effects of chemotherapy, resulting in the concept of heated intraperitoneal chemotherapy (HIPEC) for the treatment of peritoneal metastases as it was developed about 3 decades ago. With increasing experience, HIPEC has become a safe and accepted treatment offered in many centers around the world. However, standardization of the technique has been poor and results from clinical trials have been equivocal. As a result, the true value of HIPEC in the treatment of peritoneal metastases remains a matter of debate. The current review aims to provide a critical overview of the theoretical concept and preclinical and clinical study results, to outline areas of persisting uncertainty, and to propose a framework to better define the role of HIPEC in the treatment of peritoneal malignancies.

The influence of lignin modification on drug release and pH-dependent releasing behaviour of oral solid dosage form was investigated using three different formulations. The first formulation contains microcrystalline cellulose (MCC101) as excipient and paracetamol as active pharmaceutical ingredient (API). The second formulation includes Alcell lignin and MCC 101 as excipient and paracetamol, and the third formulation consists of carboxylated Alcell lignin, MCC 101 and paracetamol. Direct compaction was carried out in order to prepare the tablets. Lignin can be readily chemically modified due to the existence of different functional groups in its structure. The focus of this investigation is on lignin carboxylation and its influence on paracetamol control release behaviour at varying pH. Results suggest that carboxylated lignin tablets had the highest drug release, which is linked to their faster disintegration and lower tablet hardness.

Objective: To evaluate the status of receiving education on rational drug use, the criteria in medical drug selection, and level of knowledge of dentists working in a dentistry faculty in Turkey. Material and Methods: This was a descriptive study based on a questionnaire. One hundred seventeen (74%) dentists volunteered to participate in the study. The questionnaire consisted of 20 questions investigating sociodemographic features and rational drug use. Results: The mean age of the dentists was 30.8 ± 7.2 years, and 62.4% were men. The mean period of professional experience was 8.9±7.1 years. The most frequently used resources of references while prescribing medicine were Vademecum (medical drug guide) (61.5%), the internet (59.0%), and colleagues (49.6%). The most frequently reported condition described as ‘good’ was drug indications (43.6%). The dentists had a moderate level of information about posology, and administration route (48.7%), pharmacologic features (48.7%), and contraindications (46.2%). The number of dentists who stated that they considered cost while prescribing was low [always (6%), and frequently (15.4%)]. Rational drug use education had been received by 23.9% of the dentists. Conclusions: The dentists were found to have a lack of adequate and effective education on rational use of drugs. Regular and continuous education before and after graduation is a necessity for dentists and for their patients.

Salmonella enterica serovar Typhi, is one of the major infection spreading from consuming contaminated food and water. The poor sanitation is one the major cause for successful infection. S. typhi is responsible for causing typhoid fever, which is a growing public health concern in developing countries. This bacterium has acquired antimicrobial resistance to many promising which is a growing health concern for community. This study is aim to elucidate the molecular complexity of flouroquinolone resistance in S. Typhi isolates (n=286). Genomic data was downloaded from NCBI and analysis was done by Resfinder, MLST, and Pointfinder. Genomic analysis unveils a complex resistance profile, featuring antimicrobial resistance genes (ARG) including blaCTXM-15, blaTEM-1D, catA1, dfrA7, qnrS1, sul1, sul2, and tetA(B). The investigation focuses on mutations in DNA gyrA, parC, and qnrS1 genes which plays an important role in quinolone resistance. Mutation analysis reveals mutations such as DNA gyrA_S83F, DNA gyrA_D87N, DNA gyrA_S83Y, DNA gyrB_S464F, parC_S80I, and parE_L416F with the presence of qnrS1 gene produces resistance against quinolones. The Docking studies was conducted by “Autodock vina” and visualize by “Discovery studio” which demonstrate that these mutations leads to ciprofloxacin resistance, indicating reduced binding affinity, and altered solvent accessibility which indicates the structural changes at mutation sites. This study provides crucial insights into the molecular mechanisms driving flouroquinolone resistance in S. typhi, guiding future strategies to combat antibiotic resistance. Further validation through experimental mutagenesis is recommended, for targeted therapeutic interventions against the mounting threat of antibiotic-resistant S. typhi.

Technical sectors are an inseparable and elementary part of a critical infrastructure (CI) complex system. The services they provide are essential to the functioning of all the dependent sectors of CI on whose services society depends, especially in areas with high levels of urbanisation. The starting point for effective CI elements protection of is permanent assessing and strengthening their resilience to the negative effects of internal and external threats. Current perceptions of resilience focus primarily on repressive components responsive to incident (i.e., robustness, recoverability, and adaptability), while little attention is paid to preventative components. Therefore, the benefit of the article is to define resistance which could be seen as the CI element ability or characteristic to prevent the occurrence of incidents. Based on that, the article defines 1) the individual factors (variables and parameters) determining the CI resistance and 2) the methodological procedure for infrastructure elements resistance assessment in order to identify weak points and subsequently strengthen them. The essence of the article is defining the starting points for extending the CIERA method by a component strengthening the critical infrastructure resilience in the prevention phase. A practical example of resistance assessment for a selected critical energy infrastructure element is presented at the end of the article.

The main aim of this study was to determine the prevalence of plasmid-mediated colistin resistance mcr-1 to mcr-5 genes among colistin and multi-drug resistant Enterobacterales, Pseudomonas aeruginosa and Acinetobacter sp strains isolated from patients in a tertiary hospital in the city of Toluca, Mexico. 241 strains were included in the study. The presence of mcr genes among these strains was assessed by PCR and sequencing. In the case of mcr-carrying E. coli, further PCR tests were performed to determine the presence of blaCTX-M and whether the strains belonged to the O25b-ST131 clone. Conjugation experiments were carried to assess plasmid-mediated colistin resistance horizontal transmission. 12 strains (5.0%), of which four were E. coli; four, P. aeruginosa; three, K. pneumoniae and one, E. cloacae, were found to be resistant to colistin. Of these strains, two E. coli isolates were found to carry mcr-1. Both mcr-1-carrying E. coli strains were found to co-express blaCTX-M, belong to the O25b-ST131 clone and horizontally transmit their colistin resistance. The results of this study confirm the presence of plasmid-mediated colistin resistance in hospitalized patients in Mexico and demonstrated that the multidrug-resistant O25b-ST131 E. coli clone can acquire mcr genes and transmit such resistance trait to other bacteria.

Same-day desensitization (SDD) is a procedure in which desensitization to the drug suspected of causing the hypersensitivity reaction (HR) is performed on the same day as the patient's reaction. In rapid drug desensitization (RDD), desensitization to the suspected drug is performed after an allergological study. The aim of the study was to analyze the diagnostic capacity of the allergist in Oncology Day Hospital (ODH) to establish the phenotype of the HR, in the context of SDD, based exclusively on clinical biomarkers. This is a study of patients treated with desensitization on the same day they had a HR. Later an allergological study (clinical history, skin test, risk assessment, serological biomarkers, drug provocation test if applicable) was performed prior to the following administrations. A total of 35 successful SDDs were performed. A high concordance (91.5% of patients) was found between the phenotype suspected by the allergist after the initial assessment of the patient prior to SDD and the final phenotype after subsequent allergological study performed in the Allergy Department. The presence of the allergist at the ODH allows, in addition to initial treatment according to phenotype, desensitization on the day of the reaction. This will lead to personalized and precision allergology.

In this study, a simple machine learning-based quantitative structure-activity relationship (QSAR) model was developed to predict the inhibitory potency (pIC50 values) of FLT3 tyrosine kinase inhibitors, pivotal in treating Acute Myeloid Leukemia (AML). Distinctively, our model leverages an extensive and diverse dataset, 14 times larger than those employed in prior studies within this field, enabling an unparalleled scope of compound analysis. This vast dataset, combined with further exploration of molecular descriptors, enabled predictions of extraordinary precision, covering a broader spectrum of FLT3 inhibitors than was previously possible. The Random Forest Regressor (RFR) algorithm, selected for its superior predictive performance, was trained with 1080 inputs and validated through comprehensive external and internal methods. It achieved an remarkable coefficient of determination (R^2) of 0.941 and a standard deviation of 0.235 on a test set of 270 compounds, highlighting the efficacy of model in predicting FLT3 inhibitory activity. Key molecular descriptors were identified, enhancing our understanding of structural requirements for inhibitor potency. Additionally, we developed a user-friendly computational tool that enables the rapid prediction of pIC50 values. Utilizing this tool, potential FLT3 inhibitors were identified through ligand-based virtual screening. This study represents a major advancement in FLT3 inhibitor discovery by utilizing a simple QSAR-machine learning model. It enables more efficient and precise identification of potential drug candidates at an early stage, promising a faster development of targeted therapies and streamlining the ligand-based drug design process.

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.

Gastro-retentive floating microspheres were developed as a result of the recent advancements in floating delivery systems for drugs (FDDS), which included the uniform dispersion of multiparticulate dosage forms along the GIT. This could lead to more consistent drug absorption and a lower risk of local irritation. Microballoons (MB), a multi-unit extended release with a sphere-shaped cavity encased in a tough polymer shell, have been developed as a dosage form with exceptional buoyancy in the stomach. This preparation for constrained intestinal absorption is made to float on top of gastric acid, that has a relative density lower than 1.By using enteric acrylic polymers and the emulsion solvent diffusion method, microballoons are prepared and filled to drug in one‘s outer polymer casings. Enteric acrylic plastics are used to generate microballoons that are drug-loaded in one‘s external polymer casings and dissipate in a solution of dichloromethane and ethanol. Cavity development in microparticles seems to be particularly correlated with dichloromethane evaporation. Microballoons with a drug distributed or dispersed all through the particle-matrix have the potential for a controlled drug release and float continuously for more than 12 hours in vitro out over the surface of an acidified dissolution medium with surfactant. The drug is released slowly and at the desired rate as the microballoons glide over the components of the stomach, increasing gastro-retention time and lowering fluctuations in plasma concentration.

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration (1). The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health’s (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER)) policies to motivate researchers to consider sex differences (2). However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses (1,3–5). Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information (3,6,7). They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex (8). Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods (3). However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods (9,10). Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

NF-κB is a central mediator of inflammation, response to DNA damage and oxidative stress. As a result of its central role in so many important cellular processes, NF-κB dysregulation has been implicated in the pathology of important human diseases. NF-κB activation causes inappropriate inflammatory responses in diseases including rheumatoid arthritis (RA) and multiple sclerosis (MS). Thus, modulation of NF-κB signaling is being widely investigated as an approach to treat chronic inflammatory diseases, autoimmunity and cancer. The emergence of COVID-19 in late 2019, the subsequent pandemic and the huge clinical burden of patients with life-threatening SARS-CoV-2 pneumonia led to a massive scramble to repurpose existing medicines to treat lung inflammation in a wide range of healthcare systems. These efforts continue and these efforts continue to be con-troversial. Drug repurposing strategies are a promising alternative to de-novo drug development, as they minimize drug development timelines and reduce the risk of failure due to unexpected side effects. Different experimental approaches have been applied to identify existing medicines which inhibit NF-κB that could be repurposed as anti-inflammatory drugs.

Elevated levels of oxidative stress in the corneal epithelium contribute to the progression of dry eye disease pathology. Previous studies have shown that antioxidant therapeutic intervention is a promising avenue to reduce disease burden and slow disease progression. In this study, we evaluated the pharmacological efficacy of Xanthohumol in preclinical models for dry eye disease. Xanthohumol is a naturally occurring prenylated chalconoid that promotes the transcription of phase II antioxidant enzymes. Xanthohumol exerted a dose-response in preventing tert-butylhydroxide-induced loss of cell viability in human corneal epithelial (HCE-T) cells and resulted in a significant increase in expression of nuclear factor erythroid 2-related factor 2 (Nrf2), the master regulator of the endogenous antioxidant system. Xanthohumol-encapsulating poly(lactic-co-glycolic acid) nanoparticles (PLGA NP) were cytoprotective against oxidative stress in vitro, and significantly reduced corneal fluorescein staining in the mouse desiccating stress/ scopolamine model for dry eye disease in vivo by reducing oxidative stress-associated DNA damage in corneal epithelial cells. PLGA NP represent a safe and efficacious drug delivery vehicle for hydrophobic small molecules to the ocular surface. Optimization of NP-based antioxidant formulations with the goal to minimize instillation frequency may represent future therapeutic options for dry eye disease and related ocular surface disease.

We use state-of-the-art computer-aided drug design (CADD) techniques to identify prospective inhibitors of the main protease enzyme, 3CLpro of the SARS-CoV-2 virus causing COVID-19. From our screening of over one million compounds including approved drugs, investigational drugs, natural products, and organic compounds, and a rescreening protocol incorporating enzyme dynamics via ensemble docking, we have been able to identify a range of prospective 3CLpro inhibitors. Importantly, some of the identified compounds had previously been reported to exhibit inhibitory activities against the 3CLpro enzyme of the closely related SARS-CoV virus. The top- ranking compounds are characterized by the presence of multiple bi- and monocyclic rings, many of them being heterocycles and aromatic, which are flexibly linked allowing the ligands to adapt to the geometry of the 3CLpro substrate site and involve a high amount of functional groups enabling hydrogen bond formation with surrounding amino acid residues, including the catalytic dyad residues H41 and C145. Among the top binding compounds we identified several tyrosine kinase inhibitors, which include a bioflavonoid, the group of natural products that binds best to 3CLpro. Another class of compounds that decently binds to the SARS-CoV-2 main protease are steroid hormones, which thus may be endogenous inhibitors and might provide an explanation for the age-dependent severity of COVID-19. Many of the compounds identified by our work show a considerably stronger binding than found for reference compounds with in vitro demonstrated 3CLpro inhibition and anticoronavirus activity. The compounds determined in this work thus represent a good starting point for the design of inhibitors of SARS-CoV-2 replication.

Endocannbinoids system (ECS) engrossed a considerable interest as potential therapeutic targets in various carcinomas and cancer related conditions alongside with neurodegenerative diseases. Cannabinoids are implemented in several physiological processes such as appetite stimulation, energy balance, pain modulation and the control of chemotherapy induced nausea and vomiting (CINV). However, pharmacokinetics and pharmacodynamics interactions could be perceived in drug combinations, so in this short review we tried to shed the light over the potential drug interactions of medicinal cannabis. Hitherto, few data have been provided to the healthcare practitioners about the drug-drug interactions of cannabinoids with other prescription medications. In general, cannabinoids are usually well tolerated, but the bidirectional effects may be expected with concomitant administered agents via affected membrane transporters (glycoprotein p, breast cancer resistance proteins) and metabolizing enzymes (Cytochrome P450 and UDP- glucuronosyltransferases). The caveats should be undertaken to closely monitor the responses of cannabis users with certain drugs to guard their safety, especially for the elderly and people with chronic diseases or kidney and liver conditions.

There are dozens of approved, investigational and experimental antiviral agents. Many of these agents cause serious side effects, which can be revealed only after drug administration. Identification of the side effects prior to drug administration is challenging. Here we describe an ex vivo approach for studying immuno- and neuro-modulatory properties of antiviral agents, which could be associated with potential side effects of these therapeutics. The approach combines drug toxicity/efficacy tests and transcriptomics, which is followed by cytokine and metabolite profiling. We demonstrated the utility of this approach with several examples of antiviral agents. We also showed that the approach can utilize different immune stimuli and cell types. It can also include other omics techniques, such as genomics and epigenomics, to allow identification of individual markers associated with adverse reactions to antivirals with immuno- and neuro-modulatory properties.

Fragmentation processes of multiply charged ions of C10H8 isomers produced in fast proton collision (velocity between 1.41 and 2.4 a.u.) is discussed in view of their fundamental molecular dynamics. Especially so in the mechanisms to produce a variety of carbon clusters from such ions. This aspect is assessed with the help of a multihit analysis of daughter ions in coincidence with elimination of H+ and CHn+ (n = 0 to 3). The elimination of H+/C+ was found to be very different from CH3+ loss. The loss of CH3+ was found to be accompanied with the formation of heavy ions like C9H5+, C9H5++ and C7H3+ by a cascade of momentum correlated dissociations. The most probable structures of large fragment ions in the CH3+ loss cascade are predicted with the help of computed electronic energy and the multihit ToF correlations of second and third hit. In addition, we report observation of super-dehydrogenation of naphthalene and azulene targets, with evidence of complete dehydrogenation in a single collision.

This paper proposes a multi-terminal adaptive collaborative operation method to solve the problem of unstable internal force tracking in clamping and handling unknown objects by multi-terminal robots. In the proposed method, the internal command force changes the complex internal force control problem into an internal force tracking problem from multi-slave to master. Moreover, we develop an algorithm for multi-slave setups to estimate object stiffness and motion uncertainty in the direction of the internal command force according to Lyapunov theory. Finally, the impedance control generates a reference trajectory for the multi-slave to maintain the desired internal force and track the master’s motion. Several experiments are conducted on a self-made robot equipped. The experimental results show that the oscillation amplitude of each slave end is less than 1 mm, the directional oscillation amplitude is less than 1 degree during the tracking of the desired commanded internal force. For objects with low stiffness, the error of the commanded internal force is less than 1 N (6%) per slave. The error in tracking the commanded internal force for objects with high stiffness is less than 2 N (8%). The results prove the feasibility and effectiveness of the proposed method.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

Dracunculiasis (also known as Guinea worm disease) is caused by Dracunculus medinensis parasite and it spreads by drinking water containing Larvae of Guinea worm. The lack of safe water facilities, preventions and treatments resulted in highly dangerous consequences in its endemic regions. The economy of the affected regions totally falls down due to less production which is the result of agricultural field worker’s bad health. In this study, a multi epitope vaccine was designed against Dracunculus medinensis by using immune-informatics. The vaccine was designed by using T-Cell and B-Cell epitopes derived from Dracunculus medinensis proteins (Lactamase-B domain-containing protein, G-Domain containing protein and Ferrochelatase) in addition to Adjuvants and Linkers. The tertiary structure, physiochemical properties and immunogenic elements of vaccine were achieved. The validation of tertiary structure was accessed, and quality was achieved. In addition, the world coverage of parasite’s CTL and HTL epitopes is 95.61%. The stability of the chimeric vaccine was achieved through disulfide engineering. The molecular docking with Toll Like Receptor 4 (TLR-4) of vaccine showed its binding efficiency followed by Molecular Dynamic Simulation. The immune simulation suggested the mediated cell immunity and repeated antigen clearance. At the end, the optimized codon was used in in silico cloning to ensure vaccine’s higher exposure in bacterium E. coli strain K12. With further assessments, it is believed that the proposed multi epitope vaccine has strong immunogen to control Dracunculus medinensis which may result in better social and economic conditions of endemic regions.