Abstract: We have filled a 24 mm diameter glass sphere with a transparent polymer-gel that is radio-fluorogenic, i.e. it becomes (permanently) fluorescent when irradiated, with an intensity proportional to the local dose deposited. The gel consists of >99.9% tertiary-butyl acrylate (TBA) pre-polymerized to ~15% conversion, and ~100 ppm maleimido-pyrene (MPy). Its dimensions and physical properties are close to those of the vitreous body of the human eye. We have irradiated the gel with a 3 mm diameter, 200 kVp X-ray beam with a dose rate of ~1 Gy/min. A 3D (video) view of the beam within the gel has been constructed from tomographic images obtained by scanning the sample through a thin sheet of UV light. To minimize optical artifacts, the cell was immersed in a square tank containing a refractive-index-matching medium. The 20%-80% penumbra of the beam was determined to be ~0.4 mm. The research was a preparatory investigation of the possibility of using the method to monitor the millimetre diameter proton pencil beams used in ocular radiotherapy.

In this paper, we aim to present real implementation cases where non-visible elements of environmental impact assessments (EIA), wind flow, are visualized using a 3D digital twin technology. The conventional EIA process serves as a crucial tool in the evaluation of potential environmental impacts prior to the initiation of development projects. This evaluation helps to establish a legal and institutional framework that seeks to minimize the adverse effects on the environment. However, traditional EIA methods present challenges, particularly for non-experts, in comprehending the results of assessments involving non-visible environmental factors such as noise, air quality, and wind flow. These elements are often represented in static images or numerical tables, limiting accurate understanding and engagement. Our proposed method, termed Digital Twin based EIA, addresses these limitations by leveraging digital twin technology. This advanced methodology allows for the visualization of non-visible environmental elements in a spatio-temporal dimension, providing a more interactive and comprehensive user interface/experience for both citizens and stakeholders. In this context, we present a unique case study that focuses on visualizing wind flow using this technology. This paper serves as an example of how digital twin platforms can enhance traditional EIA processes, offering a more immersive and informative experience. The evolution of EIA into the digital realm signifies an important step forward in fostering public understanding and engagement in environmental protection efforts.

Background and Objectives To compare navigator-gated three-dimensional T1-weighted gradient-echo sequences (NG-3D T1W-GRE) with standard breath-hold T1W-GRE (BH T1W-GRE) for the detection of HCC nodules in patients with chronic viral hepatitis which undergo at magnetic resonance imaging (MRI) using gadoxetic acid. Methods and materials We evaluated 58 patients with hepatic nodules at least 1 cm in diameter detected by ultrasound who underwent liver MRI including NG-3D T1W-GRE sequences and breath hold T1-weighted gradient-echo sequences with different flip angles(FA) at 10-25-40° after contrast administration of gadoxetic acid at magnetic scanner operating at1.5 Tesla. Image quality and relative hepatic and nodular enhancement were evaluated by three blinded reviewers. Results In these patients, image quality of NG-3D T1W-GRE was assessed significantly better than quality of BHT1W- GRE (P < 0.005) and relative enhancement was significantly higher than that of BH- T1W-GRE ( P

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that provides information on the microstructural organization of biological tissue. dMRI allows for non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, at 7T wavelength effects cause lower transmit magnetic field efficiency in the human brain, mainly in the posterior fossa region, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. Here we demonstrate the feasibility and effectiveness of using this RF enhancer for in-vivo dMRI at 7T. The use of the RF enhancer can effectively recover signal dropouts in the regions with intrinsically lower SNR such as cerebellum, leading to a better depiction of principal fiber orientations and enhanced visualization of extended tracts to the brainstem and pons enabling more comprehensive delineation of the full white matter architecture.

Extremely low-frequency electromagnetic fields (ELF-EMF) are ubiquitous in industrial-11 ized environments due to the continuous use of electrical devices. Our previous studies demon-12 strated ELF-EMF affects muscle cells by modulating oxidative stress and enhancing myo-genesis. 13 This pilot study investigated these effects on skeletal muscles of sedentary adult mice, assessing 14 physiological responses to ELF-EMF exposure and potential modulation by antioxidant supplemen-15 tation. Male C57BL/6 mice were exposed to ELF-EMF (0.1 or 1.0 mT) 1 hour/day up to 5 weeks, and 16 fed a standard diet without or with N-acetyl-cysteine (NAC). Results showed transient increases in 17 muscle strength (after 2-weeks’ exposure at 1.0 mT), potentially linked to muscle fiber recruitment 18 and activation, revealed by higher PAX7 and MyH expression levels. After ELF-EMF exposure, ox-19 idative status assessment revealed transient increases in expression levels of SOD1 and catalase en-20 zymes, in total antioxidant capacity and in protein carbonyl levels, marker of oxidative damage. 21 These effects were partially reduced by NAC. In conclusion, ELF-EMF exposure affects skeletal 22 muscle physiology and NAC supplementation partially mitigates these effects, highlighting the 23 complex interactions between ELF-EMF and antioxidant pathways in vivo. Further investigations 24 on ELF-EMF as a therapeutic modality for muscle health are necessary.

Diseases in articular cartilages have affected millions of people globally. Although the biochemical and cellular composition of articular cartilages is relatively simple, there is the limitation in self-repair ability of cartilage. Therefore, developing the strategies for cartilage repair is very important. Here, we reported a new manufacturing process of water-based polyurethane based photosensitive materials with hyaluronic acid and applied the materials for 3D printed customized cartilage scaffolds. The scaffold has high cytocompatibility and is one that closely mimics the mechanical properties of articular cartilages. It is suitable for culturing human Wharton's jelly mesenchymal stem cells (hWJMSCs) and the cells showed an excellent chondrogenic differentiation capacity. We consider that the 3D printing hybrid scaffolds may have potential in customized tissue engineering and facilitate the development of cartilage tissue engineering.

Three-dimensional (3D) imaging has attracted more and more interests because of its widespread applications, especially in information and life science. These techniques can be broadly divided into two types: ray-based and wavefront-based 3D imaging. Issues such as imaging quality and system complexity of these techniques limit the applications significantly, and therefore many investigations have focused on 3D imaging from depth measurements. This paper presents an overview of 3D imaging from depth measurements, and provides a summary of the connection between these the ray-based and wavefront-based 3D imaging techniques.

Simple summary This reassessment of the findings from our Phase I/II study seeks to evaluate the effectiveness of SBRT for treating local intermediate-high risk prostate cancer in the absence of androgen-deprivation therapy due to patient’s refusal, with a median follow-up of 6.5 years. Our primary focus is on analysing the treated areas compared to recurrence patterns in the seven patients who experienced local relapse. Additionally, we provide updated data on late toxicities of grade 2 or higher, both genitourinary and gastrointestinal. Abstract Objectives: We investigated spatial patterns between primary and recurrent tumor sites and assessed long-term toxicity after dose escalation stereotactic body radiation therapy (SBRT) to the dominant intraprostatic nodule (DIN). Materials and methods: In 33 patients with intermediate-high risk prostate cancer (PCa), doses up to 50 Gy were administered to the DIN. Recurrence sites were determined and compared to the original tumor development sites through multiparametric MRI and 68Ga-labelled prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (68Ga-PSMA-PET/CT) images. Overlap rates, categorized as 75% or higher for full overlap, and 25-74% for partial overlap, were assessed. Long-term toxicity is reported. Results: All patients completed treatment, with only one receiving concomitant androgen deprivation therapy (ADT). Recurrences were diagnosed after a median of 33 months (range: 17-76 months), affecting 13 out of 33 patients (39.4%). Intraprostatic recurrences occurred in 7 patients (21%), with ≥ 75% overlap in two, a partial overlap in another two, and no overlap in the remaining three patient. Notably, 5 patients with intra-prostatic recurrences had synchronous bone and/or lymph node metastases, while six patients had isolated bone or lymph node metastasis without intraprostatic recurrences. Extended follow-up revealed grade 2 or higher late GU and GI toxicity in 18% (n=6) and 6% (n=2) of the patients. Conclusions: Among patients with intermediate-high risk PCa undergoing focal dose-escalated SBRT without ADT, DIN recurrences were infrequent. When present, these recurrences were typically located at the original site or adjacent to the initial tumor. Conversely, relapses beyond the DIN and in extra-prostatic (metastatic) sites were prevalent, underscoring the significance of systemic ADT in managing this patient population. Advances in knowledge: focal dose escalated prostate SBRT prevented recurrences in the dominant nodule, however extra-prostatic recurrence sites were frequent.

In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, a high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, compressed sensing approach is further investigated. The simulation and experimental result further illustrated the design. Because a single transceiver circuit is needed, a light, affordable and high resolution 3D mmWave imaging radar is illustrated in the paper.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

Optimum design and accurate control of the internal structure of the laser target materials remain an important goal for various laser physics experiments, especially for generating high flux photon and neutron beams. The low-density material is considered to be one of the most difficult for X-ray study due to its high transparency and imperceptible contrast. To produce clear visualization of foam containing sparse structures we used a high-quality monochromatic X-ray beam of synchrotron radiation source PETRA-III at DESY. The X-ray beam parameters allow tomographic scanning with application of phase contrast retrieval algorithms. A series of 3D images of foam-suspended glass microsphere inside the plastic cylinder were obtained with the quality high enough to observe the internal structure and to visualize uniformity, displacement and surface roughness on both sides of the microsphere. The main object under investigation was a CH-plastic capillary including 10 mg/cc CHO-foam with the centered glass microsphere. The results of this work demonstrate that tomographic visualization based on a high quality X-ray radiation and phase contrast analysis is an effective and useful technique for development of new laser targets containing structured low density materials.

This study aimed to demonstrate the potential role of Dual Energy CT in assessing Bone Mineral Density (BMD) in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using an HAP(fat) map. Statistical analysis was performed using the Bland-Altman method to assess agreement between DXA and DECT HAP(fat) measurements. Mean BMD, vertebral and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP(fat) values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) it was found for measurements at the femoral neck. In conclusion, we demonstrated the enhanced accuracy of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Total-body PET/CT scanners provide increased sensitivity, enabling the adjustment of imaging parameters by reducing injected activity or shortening acquisition time. This study aimed to evaluate the limitations of reduced [18F]FDG activity doses on image quality, lesion detectabil-ity, and quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [18F]FDG-PET/CT (3.0 MBq/kg, 5 min. scan time) were included in this analysis. PET raw data was rebinned for shorter frame durations to simulate 5 min. scans with lower activities in high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n=82) were assessed using a 5-point Likert scale. The co-efficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUVmean, SUVmax, and SUVpeak were evaluated. Sub-jective image ratings were generally superior in UHS compared to HS mode. At 0.5 MBq/kg, le-sion detectability decreased to 95% (HS) and 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7±0.6) and 0.5 MBq/kg in UHS (5.5±0.5). With lower doses, there were negligible reductions in SUVmean and SUVpeak, whereas SUVmax increased steadily. Reducing [18F]FDG activity to 1.0 MBq/kg (HS/UHS) in a total-body PET/CT provides diagnostic image quality without statistical-ly significant changes in uptake parameters. UHS mode improves image quality, noise, and le-sion detectability compared to HS mode.

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Our simulation framework could contribute to better understand performances for species discrimination and relationship between spectral variations and taxonomic and functional dimensions of biodiversity.

Morphological dissimilarity and its evolution over time are one of the most unexpected variations found when comparing cell cultures in 2D and 3D. Monolayer cells appear to flatten in the lower part of the plate, adhering to and spreading in the horizontal plane while not extending vertically. Consequently, cells developed in two dimensions have a forced apex-basal polarity. Co-cultivation and crosstalking between multiple cell types, which control development and formation in the in vivo counterpart, are possible in 3D cultures. With or without a scaffold matrix, 3D model culture may exhibit more in vivo-like morphology and physiology. 3D cultures mimic relevant physiological cellular processes, transforming them into one-of-a-kind drug screening platforms. The structures and dynamics of regulatory networks, which are increasingly studied with live-imaging microscopy, must be considered to help and guarantee the functional maintenance of a 3D structure. However, commercially available technologies that can be used for current laboratory needs are minimal, despite the need to make it easier to acquire cellular kinetics with high spatial and temporal resolution, in order to improve visual efficiency and, as a result, experimentation performance. The CELLviewer is a newly developed multi-technology instrument that integrates and synchronizes the work of various scientific disciplines. The aim of this study is to test the device using two different models: a single Jurkat cell and an MCF-7 spheroid. The two models are loaded into the microfluidic cartridge for each experiment after they have been grown and captured in time-lapse for a total of 4 hours. The samples used are tracked under the operation of the optics after adaptive autofocus, while slipping inside the cartridge chamber, and the 3D rotation was successfully obtained experimentally. The MitoGreen dye, a fluorescence marker selectively permeable to live cells, was then used to determine cell viability. To measure the model diameter, construct fluorescence intensity graphs along a straight line passing through the cell, and visualize the spatial fluorescence intensity distribution in 3D, ImageJ software was used.

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with ClariCT. AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p<0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p<0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusion: AI-based noise reduction using ClariCT. AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

The morphological and compositional complexities of keratinized components make feathers ingenious skin appendages adapted to diverse ecological needs. Frizzling feathers, characterized by their distinct curling phenotypes, offer a unique model to explore the intricate morphogenesis in developing a keratin-based bioarchitecture over a wide range of morphospace. Here, we investigated the heterogeneous allocation of α- and β-keratins in flight feather shafts of homozygous and heterozygous frizzle chickens by analyzing the medulla-cortex integrations using quantitative morphology characterizations across scales. Our results reveal intriguing construction of frizzling feather shaft through modified medulla development, leading to a perturbed balance of the internal biomechanics and, therefore, introducing the inherent natural frizzling compared to those from wild-type chickens. We elucidate how the localized developmental suppression of the α-keratin in the medulla interferes with the growth of the hierarchical keratin organization by changing the internal stress in the frizzling feather shaft. This research not only offers insights into the morphogenetic origin of the inherent bending of frizzling feathers but also facilitates our in-depth understanding of the developmental strategies toward the diverse integuments adapted for ecological needs.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

The nuclear magnetic resonance (NMR) metabolomic analysis was applied to investigate the differences within nineteen Sicilian Nocellara del Belice mono varietal Extra Virgin Olive Oils (EVOO), grown in two zones different for altitude and soil composition. Several classes of endogenous olive oil metabolites were quantified through a nuclear magnetic resonance (NMR) three-experiment protocol coupled with a yet developed data-processing called MARA-NMR. This method, taking around one-hour experimental time per sample, faces the possible quantification of different class of compounds at different concentration’s range which would require at least three alternative traditional methods. NMR results were compared with the data of traditional analytical methods to quantify free fatty acidity (FFA), fatty acid methyl esters (FAMEs), and total phenol content. The presented NMR methodology is compared with traditional analytical practices and its consistency is also tested through slightly different data treatment. Despite the rich literature about the NMR of EVOOs, the paper points out that there are still several advances potentially improving this general analysis and overcoming the other cumbersome and multi-devices analytical strategies. Monovarietal EVOO’s composition is mainly affected by pedoclimatic conditions in turn relaying upon the nutritional properties, quality, and authenticity. Data collection, analysis and statistical processing are discussed touching the important issues related to the climate changes in Sicily and to the specific influence of pedoclimatic conditions.

Mesenteric ischemia diagnosis is challenging with an overall mortality in up to 50% of cases despite advances in treatment. The main problem that affects the outcome is the delayed diagnosis because of non-specific clinical presentation. Multi Detector CT Angiography (MDCTA) is the first line investigation for the suspected diagnosis of vascular abdominal pathologies and is the diagnostic test of choice in suspected mesenteric bowel ischemia. MDCTA can accurately detect the presence of arterial and venous thrombosis, determine the extension and the gastrointestinal tract involved, and provide detailed information determining the subtype and the stage progression of the diseases helping clinicians and surgeons for the proper management. CT can differentiate forms that are still susceptible to pharmacological or interventional treatment (NOM= non-operative management) from advanced disease with transmural necrosis in which a surgical approach is required. Knowledge of CT imaging patterns and corresponding vascular pathways is mandatory in Emergency settings to reach a prompt and accurate diagnosis. The aims of this paper are 1. to provide technical information about the optimal CTA protocol; 2. To empathize the CTA arterial and venous supply to gastrointestinal tract and relative ischemic pattern; 3. To describe vascular, bowel and extraintestinal CT findings for the diagnosis of acute mesenteric ischemia.

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images, and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges to achieve accurate stereo SAR image matching. To address the aforementioned challenges, this paper proposes a radargrammetric method based on composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged by the proposed radargrammetric 3D imaging method, resulting in 3D imaging of target scenes based on multi-aspect SAR images. For validation purpose, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) by using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency while enhances the imaging accuracy of crucial areas with limited data.

Background and Objectives: High job strain is worsening the outcome in coronary artery disease, CAD. Severe dissatisfaction on workplace, imbalance between high demands , and low rewards, are affecting the quality of life, and diminishing the duration of life, especially in CAD patients. Our study included two samples : CAD patients (experimental group), and without CAD patients, but with traditional risk factors (control group). The main objective of the study was the role played by high sensitivity C reactive protein, hs CRP, and microalbuminuria, as mediators of job strain and CAD relationship. Secondary objectives were the following: identification of the most vulnerable patients( with significant job strain) and intervention towards this category, for cardiovascular events prevention (primary or secondary). Materials and Methods: The utilized methods were the following : clinical exam; electrocardiogram; echocardiogram; non contrast enhanced computed chest tomography; laboratory findings ( lipid profile, glycemia, hs CRP, microalbuminuria); type D scale, DS 14; depression questionnaire, PHQ-9; and satisfaction with work scale, SWWS. Results: The highest values for hs CRP and microalbuminuria were noticed in patients with the lowest Likert score, for both samples CAD, and without CAD patients. The highest job strain was noticed in females. More than half of CAD patients ,with low Likert scores, revealed myocardial infarction. Conclusions: Microalbuminuria and hs CRP were statistical significant in the study as intercessors, in job strain and CAD relationship. The most vulnerable patients for vascular events were females, in both groups : CAD and “CAD waiting desk”. In CAD sample, the vascular vulnerability was also increased by high job strain.

Abstract: Lipodystrophies (LDs) are rare, complex disorders of the adipose tissue characterized by selective fat loss, altered adipokines profile and metabolic impairment. Sirtuins (SIRTs) are class III NAD+-dependent histone-deacetylases linked to fat metabolism. SIRT1 plays a critical role in metabolic health by deacetylating target proteins in tissues including liver, muscle and adipose tissue. Circulating SIRT1 levels have been found to be reduced in obesity while increased in anorexia nervosa and in patients experiencing weight loss. We evaluated circulating SIRT1 levels in relation to fat amount in 32 lipodystrophic patients affected by congenital or acquired LDs compared to non-LD subjects (24 with anorexia nervosa, 22 normal weight and 24 with obe-sity). SIRT1 serum levels were higher in LDs as compared with normal weight subjects (mean ± SEM: 4.17 ± 0.48 vs. 2.59 ± 0.20 ng/mL) or subjects with obesity (1.7 ± 0.39 ng/mL), whereas they were close to those measured in anorexia nervosa (3.44 ± 0.46 ng/mL). Our findings show that within the LD group there was no relationship between SIRT1 levels and the amount of body fat. The mechanisms responsible for secretion and regulation of SIRT1 in LD deserve further investi-gation.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Abstract: The promise of regenerative medicine and tissue engineering is founded on the ability to regenerate diseased or damaged tissues and organs into functional tissues and organs or the creation of new tissues and organs altogether. In theory, all damaged and diseased tissues and organs can be regenerated or created using different configurations and combinations of extracellular matrix, cells and inductive biomolecules. Currently, regenerative medicine and tissue engineering can allow the improvement of patients’ quality of life through availing novel treatment options. Tissues and organs have a specific ECM, with specific proteins and factors released by cells residing within the local microenvironment. The coupling of regenerative medicine and tissue engineering field with 3D printing is revolutionizing the treatment of patients in a huge way. 3D bioprinting allows the proper placement of cells and ECMs, allowing the recapitulation of native microenvironments of tissues and organs. 3D bioprinting utilizes different bioinks made up of different formulations of ECM/biomaterials, biomolecules and even cells. The choice of the bioink used during 3D bioprinting is very important as properties such as printability, compatibility and physical strength influence the final construct printed. The extracellular matrix (ECM) provides both physical and mechanical microenvironment needed by cells to survive and proliferate. Decellularized ECM bioink contains biochemical cues from the original native ECM and also the right proportions of ECM proteins. Different techniques and characterization methods are used to derive bioinks from several tissues and organs and to evaluate their quality. This review discusses the uses of decellularized ECM bioinks and argues that they represent the most biomimetic bioinks available. In addition, we briefly discuss some polymer-based bioinks utilized in 3D bioprinting.

The MAPK signaling pathway with BRAF mutations has been shown to drive the pathogenesis of 40-60% of melanomas. Inhibitors of this pathway's BRAF and MEK components are currently used to treat these malignancies. However, responses to these treatments are not always successful. Therefore, identifying noninvasive biomarkers to predict treatment responses is essential for personalized medicine of melanoma patients. Using noninvasive 1H magnetic resonance spectroscopy (1H MRS), we previously showed that BRAF inhibition reduces lactate and alanine tumor levels at early stages of effective therapy and could be construed as metabolic imaging biomarkers for drug response. The present work aimed to demonstrate if these metabolic changes and those assessed by 31P MRS are also found in preclinical human melanoma models treated with MEK inhibitors. Apart from 1H and 31P MRS, additional supporting in vitro biochemical analyses are described. Our results indicate significant early metabolic correlations with response levels to MEK inhibition in the melanoma models and are consistent with our previous study of BRAF inhibition. Given these results, our study supports the potential clinical utility of noninvasive MRS to objectively image metabolic biomarkers for early predicting melanoma response to MEK inhibition.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

Abstract: The pursuit of youth and attractiveness is a fundamental desire in contemporary society. For many individuals, the appearance of visible signs of aging, such as skin laxity, gravitational changes in facial and neck soft tissues, and loss of harmonious proportions, can be psychologically distressing. The choice of effective methods for addressing these changes depends on their specific characteristics, the patient's anatomical features, and the type of facial aging. The introduction of 3D imaging to determine the hyoid bone level, the volume of soft tissue structures in the cervico-mental area, ptosis of subplatysmal structures, and the volume of submandibular glands, in addition to ultrasound study of the submental projection at the preoperative stage, are key for taking the best decision regarding preoperative planning. This case report aims to illustrate how modern imaging techniques are a fundamental part of the preoperative assessment of the mid and lower third of the face to ensure the most favorable aesthetic outcomes for each patient. Key message: To attain harmonious results in surgical correction of age-related facial and neck changes, it is crucial to consider the patient's anatomical features and the extent of age-related alterations. A comprehensive facial analysis, taking into account the type of facial aging and employing a CT scan and ultrasound examination of the soft tissues in the chin region, facilitates the development of an individualized plan tailored to achieve the patient's desired outcome.

Coded aperture 3D imaging techniques have been rapidly evolving in the recent years. The two main directions of evolution are in aperture engineering to generate the optimal optical field and in development of computational reconstruction to reconstruct the object’s image from the intensity distribution with a minimal noise. The goal is to find the ideal aperture-reconstruction method pair and if not, to optimize one to match the other for designing an imaging system with required 3D imaging characteristics. Lucy-Richardson-Rosen algorithm (LR²A), a recently developed computational reconstruction method was found to perform better than its predecessors such as matched filter, Weiner filter, phase-only filter, Lucy-Richardson algorithm and non-linear reconstruction (NLR) for certain apertures when the point spread function (PSF) is a real and symmetric function. For other cases of PSF, NLR performed better than the rest of the methods. In this tutorial, LR²A has been presented as a generalized approach for any optical field along with MATLAB codes for reconstruction of any image when the PSF is known. The common problems and pitfalls in using LR²A has been discussed. Simulation and experimental studies for common optical fields such as spherical, Bessel, vortex beams and exotic optical fields such as Airy, scattered and self-rotating beams have been presented. From this study, it can be seen that it is possible to transfer the 3D imaging characteristics from non-imaging type exotic fields to indirect imaging systems faithfully using LR²A. The application of LR²A to medical images such as colonoscopy images and cone beam computed tomography images with synthetic PSF has been demonstrated. We believe that the tutorial will provide a deeper understanding of computational reconstruction using LR²A.

Quantum correlations, especially time correlations, are crucial in ghost imaging in order to significantly reduce the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy-state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon-number regimes for producing high-quality, noise-reduced images. The versatility of decoy-state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum communication.

Laser scanning 3D imaging technology, because it can get accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater rotating laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object's surface. To solve the problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed-light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging and the dynamic refraction mathematical model is established by the motion of the imaging device. During the imaging process, the incident angle between the laser and the light window varies due to the scanning mode of the system. The experimental results show that the reconstruction radius error is reduced by 60% (from 2.5 mm to about 1 mm) when the measurement data for a standard sphere with a radius of 20 mm are compensated. Moreover, the compensated point cloud data exhibits a higher degree of correspondence with the model of the standard spherical point cloud. This study has a specific reference value for the refractive error analysis of an underwater laser scanning imaging system, and it provides certain research ideas for the subsequent refractive error analysis of various scanning imaging modalities.

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

Defectively manufactured and deliberately damaged composite laminates fabricated with different continuous reinforcing fibres (respectively, carbon and glass) and polymer matrices (respectively, thermoset and thermoplastic) were inspected in magnetic resonance imaging equipment. Two pulse sequences were evaluated during non-destructive examination conducted in saline solution-immersed samples to simulate load-bearing orthopaedic implants permanently in contact with biofluids. The orientation, positioning, shape, and especially the size of translaminar and delamination fractures were determined according to stringent structural assessment criteria. The spatial distribution, shape, and contours of water-filled voids were sufficiently delineated to infer the amount of absorbed water if thinner image slices than this study were used. The surface texture of composite specimens featuring roughness, waviness, indentation, crushing, and scratches was outlined, with fortuitous artefacts not impairing the image quality and interpretation. Low electromagnetic shielding glass fibres delivered the highest, while electrically conductive carbon fibres produced the poorest quality images, particularly when blended with thermoplastic polymer, though reliable image interpretation was still attainable.

Hypoxia is a hallmark of solid tumors, including hepatocellular carcinoma (HCC). Hypoxia has proved to be involved in multiple tumor biological processes and associated with malignant progression and resistance to therapy. Transarterial chemoembolization (TACE) is a well-established locoregional therapy for patients with unresectable HCC. However, TACE-induced hypoxia regulates tumor angiogenesis, energy metabolism, epithelial-mesenchymal transition (EMT), and immune processes through hypoxia-inducible factor 1 (HIF-1), which may have adverse effects on the therapeutic efficacy of TACE. Hypoxia has emerged as a promising target for combination with TACE in treatment of HCC. This review summarizes impact of hypoxia on HCC tumor biology, adverse effects of TACE-induced hypoxia on its therapeutic efficacy, highlighting the therapeutic potential of hypoxia-targeted therapy in combination with TACE for HCC.

Introduction Atherosclerotic disease is a major contributor to heart failure, stroke, and myocardial infarction, significantly lowering the quality of life and life expectancy, with a significant burden on healthcare. Not all lesions deemed non-significant are benign, and conversely, not all significant lesions are causative of ischemia. Fractional flow reserve (FFR) provides a functional assessment of coronary lesions, while optical coherence tomography (OCT) offers detailed imaging of plaque morphology, aiding in therapeutic decision-making. The objective of this study was to evaluate the utility of OCT and FFR as adjunctive tools in the catheterization laboratory for guiding therapeutic decisions in patients with multivessel disease for non-culprit vessels. Specifically, we aimed to assess how OCT and FFR influence therapeutic decision-making in patients with multi-vessel coronary artery disease. Methods 36 patients with acute coronary syndrome (ACS) and multivessel disease were randomized 1:1 into two groups: one guided by FFR alone, and the other by a combination of FFR and OCT. For the FFR group, revascularization decisions for non culprit lesions were based solely on FFR measurements. If the FFR was >0.8, the procedure concluded, and the patient received maximal medical treatment. If the FFR was ≤0.8, a stent was placed. For the FFR + OCT group, if the FFR was >0.8, the revascularization decision was based on OCT findings. If there were no vulnerable plaques (VP), the procedure concluded, and the patient received maximal medical treatment. If OCT imaging indicated VP, then the patient underwent revascularization. If the FFR was ≤0.8, the patient underwent revascularization regardless of OCT findings. Results OCT imaging altered the therapeutic decision in 11 cases where FFR measurements were above 0.8, but the lesions were characterized as VP. Analyzing the total change in the decision to stent, 4 cases in the FFR group and 15 cases in the FFR and OCT group (4 based on FFR and 11 on OCT) revealed a statistically significant difference (p = 0.0006; Relative Risk = 0.2556; 95% CI: 0.1013 to 0.5603). When analyzing the change in the total decision both to stent and not to stent, we observed a statistically significant difference, with Group 1 having 7 cases and Group 2 having 15 cases (p = 0.0153; Relative Risk = 0.4050; 95% CI: 0.2004 to 0.7698) Conclusion Based on the findings of this study, OCT significantly increases the percentage of stenting procedures by identifying vulnerable lesions. The use of intracoronary imaging facilitates the timely identification and treatment of these vulnerable lesions. This underscores the crucial role of OCT in enhancing the precision of coronary interventions by ensuring timely intervention for vulnerable lesions, thereby potentially improving patient outcomes Acknowledgment funding „This work was supported by the University of Medicine, Pharmacy, Science and Technology ,, George Emil Palade’’ of Targu Mures Research Grant number 510/14/17.01.2022”

In the contemporary world of science, continuous observation of specific fixed regions is required, awaiting the emergence of targets or phenomena within those areas to locate and identify them. Astronomy requires continuous monitoring of stars, swiftly pinpointing occurrences, such as flares, and acquiring their spectral data to aid identification. In military contexts, continuous observation of specific areas is essential for promptly locating enemy movements and obtaining target spectral data for deciphering enemy disguises. Additionally, continuous observation of experimental samples in biochemistry is mandatory to pinpoint localized changes and acquire spectral data from those areas. Hence, this paper develops a novel imaging spectrometer solution to monitor localized regions continuously. Specifically, this paper first elaborates on the developed instrument’s characteristics and optical structure, followed by optical design software simulations. The instrument has an F-number of 5, a focal length of 100mm, and a field of view covering the wavelength range from 400nm to 600nm. The optical path diagram indicates that the system’s dispersion and imaging images can be separated, meeting the system’s requirements. Besides, the Modulation Transfer Function (MTF) graph confirms that the image quality meets the requirements. Furthermore, a region-monitoring type imaging spectrometer was constructed to assess the instrument’s capability for spectral observation of fixed regions. The instrument can detect a fixed area, with the target placed within the detection area. Spectral data were successfully obtained by conducting image processing on the acquired images. The correlation coefficient between this data and reference data was as high as 0.9226, indicating the instrument’s successful measurement of the target’s spectrum. Thus, the developed instrument demonstrated that it can image the observed areas and acquire spectral data from targets within those regions.

Although ovarian cystic teratoma is the most common ovarian tumor, complications are quite rare. However, it is important to be recognized by the radiologist in order to avoid inaccurately diagnosing them as malignant lesions. This case report describes a 61-year-old postmenopausal woman, who presented to the emergency room with abdominal pain following a minor blunt abdominal trauma. In this context, a CT scan was performed, which showed the presence of round, hypodense masses randomly distributed in the peritoneum, with coexisting ascites in moderate amount; ovarian carcinoma with peritoneal carcinomatosis was suspected. The patient was hospitalized and an MRI of the abdomen and pelvis was recommended for a more detailed lesion characterization. Following this examination, the patient was diagnosed with mature cystic ovarian teratoma complicated by rupture. Surgery was performed, and the outcome was favorable. The cases of ruptured cystic teratomas are rare, and to our knowledge, this is the first occurrence described in literature. Special attention must be paid when confronting with such a case in medical practice, since it can easily misdiagnosed as peritoneal carcinomatosis

Objective: We report a case of acute pancytopenia and liver injury after concomitant administration of low-dose methotrexate (MTX), and high-dose esomeprazole and metamizole. Case summary: A 71-year-old patient with chronic systemic idiopathic erosive arthritis was admitted after a pelvic ring fracture. After hospital admission, she received MTX in addition to esomeprazole and metamizole. Subsequent laboratory tests revealed pancytopenia and elevated liver enzymes. The follow-up clinical examinations were unremarkable, with the exception of sub-febrile temperatures. Further investigations did not detect a definitive etiology. Due to the suspicion of methotrexate-induced hematotoxicity and hepatotoxicity, antagonizing therapy with calcium folinate was administered, after which the blood counts and liver parameters normalized. Discussion: Due to the administration of high-dose metamizole immediately before the administration of low-dose MTX, the pre-existing hematotoxic pharmacodynamic effect of MTX was acutely enhanced by that of metamizole, although folic acid was administered preemptively. In addition, the concomitant administration of high-dose esomeprazole and normal dose torasemide resulted in a pharmacokinetic interaction with MTX by decreasing its renal secretion and elimination, further enhancing the concentration-dependent hematotoxic and hepatotoxic side effects of MTX. Conclusions: The relatively high demand for analgesics in patients with chronic rheumatic diseases already being treated with MTX and proton pump inhibitors necessitates that clinicians consider drug-drug interactions as potential causes of adverse drug reactions after the administration of metamizole or nonsteroidal anti-inflammatory drugs. In this category of patients, it is strongly recommended to switch to analgesics of other classes.

The paper presents the findings of research on companies in the early stage of development based on the concept of ensuring their sustainability. The starting point is attributes shaping a sustainable model of start-ups. A solution has been proposed and examined that identifies the factors dynamising a business model, which give the company the ability to make fast changes in the configuration of the business model, and that applies stabilizing factors based on the concept of the Triple Bottom Line. The research problem is a response to the phenomena of the bankruptcy of many start-ups that cannot find a way to survive in the long term. The business models of companies in the early stage of development should be designed taking into account the possibility of their dynamic and iterative changes when the earlier assumptions do not work when implemented in practice. A cognitive gap has been observed as the relevant literature does not propose a systemic solution to the problem of instability of the startups viewed from the perspective of the business model concept. The research problem, therefore, is to identify potential new methods to ensure the sustainability of young companies trying to combine aspects dynamising changes in business models, taking into account economic, environmental and social aspects. The findings of research on companies in the early stage of development show that when the methods of increasing the flexibility of the business model are applied and attention is drawn to environmental and social activities and a strong emphasis is put on financial performance, reflecting the expectations of shareholders, the concept of ensuring the sustainability of young companies from the point of view of sustainable business model attributes emerges.

MAD is the gold standard therapy for mild/moderate OSA and is a second-choice treatment in patients with severe OSA not responding to CPAP. The aim of this study was to analyze the upper airway characteristics of OSA patients and the morphological changes in the upper airway, inducted by four different types of mandibular advancement devices, using CBCT 3D imaging. Twenty-two patients were recruited after OSA diagnosis with polysomnography. Four different customized and titratable MAD were used and an initial CBCT scan was obtained for each patient. After 6 months to the end of the MAD titration phase, all the subjects showed an AHI of &lt; 5 or a decrease of 50% in AHI with MAD when compared with initial AHI and a second CBCT scan with MAD in situ was performed. An important improvement of the airway was observed (+33.76%) and a significant difference in the enlargement ratio between the posterior soft palate (+32.41%) and the posterior tongue (+36.96%) region was also found. The greatest increase in airway volume was achieved in patients treated with the MAD Forward and TAP (respectively +42.77% and + 41.63%). MAD therapy is effective to treat mild/severe OSA with increased upper airway volume. The design of the MAD can influence the effectiveness of the treatment.

Graph visualization has evolved significantly over time, offering various methods to effectively represent complex data structures. This study introduces a novel force-directed algorithm for 3D data visualization, specifically tailored for commonly used datasets in linked data visualization. Leveraging the power of graph theory and methods, the algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations, which often necessitate advanced user interaction. By utilizing 3D space and the Unity Engine, the research successfully visualizes data and develops interactive visualizations, overcoming limitations of basic force-directed implementations. The main contribution of presented research is in the force-directed algorithm with springs and controlled placement as a visualization technique that combines the use of springs and attractive forces to stabilize a graph in a 3D environment. The combination of these two techniques also helps prevent the formation of a single large cluster within the graph, enhancing the overall visualization quality.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

This study investigated a novel radioimmunotherapy strategy for targeting tumor angiogenesis. We developed a radiopharmaceutical complex by labeling an anti-adenosine triphosphate syn-thase (ATPS) monoclonal antibody (mAb) with the radioisotope 177Lu using DOTA as a chelating agent. 177Lu-DOTA-ATPS mAb demonstrated high labeling efficiency (99.0%) and stability in se-rum. MKN-45 cancer cells exhibited the highest cellular uptake, which could be specifically blocked by unlabeled ATPS mAb. In mice, 177Lu-DOTA-ATPS mAb accumulated significantly in tumors, with a tumor uptake of 16.0 ± 1.5%ID/g on day 7. 177Lu-DOTA-ATPS mAb treatment sig-nificantly reduced the viability of MKN-45 cells in a dose-dependent manner. In a xenograft tumor model, this radioimmunotherapy strategy led to substantial tumor growth inhibition (82.8%). Furthermore, combining 177Lu-DOTA-ATPS mAb with sunitinib, an anti-angiogenic drug, en-hanced the therapeutic efficacy of sunitinib in the mouse model. Our study successfully developed 177Lu-DOTA-ATPS mAb, a radioimmunotherapy agent targeting tumor blood vessels. This ap-proach demonstrates significant promise for inhibiting tumor growth, both as a single therapy and in combination with other anti-cancer drugs.

This review is focused on the current status of quantitative MR (qMR) of skeletal muscle. The first section covers the techniques of qMR in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/ pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1 weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, diffusion tensor imaging to emerging qMR features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMR of muscle; the intent is to demonstrate the utility qMR in different disease states of the muscle rather than a complete comprehensive survey.

Computation of Common Middle Point seismic sections and their subsequent time migration and diffraction imaging provides very important knowledge about the internal structure of 3D heterogeneous geological media and are key elements for successive geological interpretation. Full-scale numerical simulation, that computes all single shot seismograms, provides a full understanding of how the features of the image reflect the properties of the subsurface prototype. Unfortunately, this kind of simulations of 3D seismic surveys for realistic geological media needs huge computer resources, especially for simulation of seismic waves’ propagation through multiscale media like cavernous fractured reservoirs. In order to significantly reduce the query of computer resources we propose to model these 3D seismic cubes directly rather than the shot-by-shot simulation with subsequent CMP stacking. To do that we modify the well known "exploding reflectors principle" for 3D heterogeneous multiscale media by use of the finite-difference technique on the base of grids locally refined in time and space. To be able to simulate realistic models and acquisition we develop scalable parallel software, which needs reasonable computational costs. Numerical results for simulation of Common Middle Points sections and their time migration are presented and discussed.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which provides powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

Ultrasound (US) scans of the inferior vena cava (IVC) provide useful information on the volume status of a patient. However, their investigation is user-dependent and prone to measurement errors. An important technical problem is the objective difficulty in studying a very compliant blood vessel like IVC, which makes large respirophasic movements and shows a complicated three dimensional geometry. Using bi-dimensional (2D) B-mode views either in long or short axis has improved the characterization of IVC dynamics compared to a measurement along a single direction (M-mode). However, specific movements of the IVC can challenge also the information provided by these 2D sections. Thus, these two orthogonal views, provided by an US system in X-plane, are here integrated by an innovative method. It is tested on simulated videos of the IVC performing complicated movements, that are compensated by the new method, overcoming the biased measurements provided by 2D scans. The method is then applied on example experimental data.

D-dimer testing in combination with clinical assessment is commonly used to diagnose suspected DVT and pulmonary embolism. However, as D-dimer levels are physiologically higher in elderly patients, this test loses clinical specificity in the setting of geriatric patients. The aim of our study was to investigate the changes in D-dimer levels with age and the specificity of the D-dimer test in the diagnosis of pulmonary embolism. 1617 elderly patients consecutively admitted to the Geriatric Emergency Care Department of the INRCA-IRCCS hospital between January 2013 and February 2014 and for whom D-dimer results were available were included. The D-dimer immunoturbidimetric assay was performed on a cs-2100i Sysmex Siemens using a conventional cut-off level of 500 µg/L. The median D-dimer level was 1200 [550-2990] µg/L FEU, and the percentage of patients with D-dimer levels below the conventional cut-off was only 22.1%. As expected, there was a significant age-related increase in D-dimer, even when sex was considered, and a significant decrease in the specificity of D-dimer assay with age. The most established use of D-dimer testing is in the diagnosis of deep vein thrombosis or pulmonary embolism. Our study confirms the need for an age-adjusted cut-off for D-dimer testing in geriatric patients.

Diabetes is a major health concern that the next-generation of on-demand insulin releasing implants may overcome for personalized therapy. Therein, 3D-printed phenylboronic acid-containing subcutaneous implants with on-demand glucose-triggered drug release abilities are produced using high resolution stereolithography technology. To that end, the methacrylation of phenylboronic acid is targeted following a two-step reaction. The resulting photocurable phenylboronic acid derivative is accordingly incorporated within bioinert polyhydroxyethyl methacrylate-based hydrogels at varying loadings. The end result are sub-centimeter scaled 3D-printed bionert implants that can be remotely activated with 1,2-diols and 1,3-diols such as glucose for on-demand drug administration such as insulin. As a proof of concept, varying glucose concentration from hypoglycemia to hyperglycemia levels readily allow the release of pinacol, i.e. a model molecule with similar release properties than insulin, at respectively low and high rates. In addition, the results demonstrated that adjusting the geometry and size of the 3D-printed part is a simple and suitable method for tailoring the release behavior and dosage.

The quest for youthful, healthy skin and full, vibrant hair has long been a driving force in the dermocosmetics field. However, traditional approaches often struggle to address the underlying causes of aging, damage, and hair loss. Regenerative cosmetics, powered by skin tissue engineering, offer a transformative alternative. This review explores the emerging field of using engineered skin tissues for cosmetic purposes, focusing specifically on their potential for anti-aging, repair, and hair restoration applications. We discuss how these technologies aim to rejuvenate aging skin by promoting collagen production, reducing wrinkles, and improving overall skin function. Additionally, the use of engineered skin for wound healing and scar reduction is examined, highlighting their potential to improve the appearance and functionality of damaged skin. Finally, we advance the exciting prospects of utilizing skin tissue engineering techniques to regenerate hair follicles, potentially offering solutions for hair loss and promoting denser hair growth.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Abstract: NFinFET transistors with fin channel length 90nm and a planar MOSFET transistor with channel length 180nm and 90nm are presented with characteristic curves at various gate biases. A finalized algorithm with kink effects is effectively responsible for addressing the field effect transistors. The algorithm includes the modified conventional current-voltage formula and a nonlinear heat-associated kink solution which is simplified as a Gaussian form. Three parameters in modified model includes kN (related with channel width, channel length, and gate oxide capacitor, and proportional to the mobility of carriers)Vth(Threshold Voltage), and (the inverse of Early Voltage). Those parameters are determined to minimize the discrepancies between the measured data and the fitting values, but leave kinks located at around (VGS－Vth), which are deliberately eliminated by the Gaussian form because of the agitation of thermal kink effects. The whole fitting is made to be as close as the as-measured IDS-VDS. In the mean time, those determined parameters are physically meaningful after the analysis has been done.

Traditional photodynamic therapy (PDT) is often limited in its efficacy by insufficient light penetration, inefficient photosensitizer energy transfer, and inadequate target tissue oxygenation. X-ray activated PDT (XPDT), using nanoparticle (NPs) photosensitizers comprised of europium-doped yttrium oxide cores encased within silica shells (Y₂O₃:Eu@SiO₂), offers the potential of overcoming most of these limitations. Aimed at maximizing the generation of reactive oxygen species (ROS), this study reports methodologies for investigation of the effects of synthesis conditions on the characteristics of Y₂O₃:Eu@SiO₂ NP. Transmission electronic microscope (TEM) revealed that the NP core diameter was linearly, positively correlated with condensation time (R2 = 0.7357, p < 0.05) and linearly, negatively correlated with urea concentration (R2 = 0.958, p < 0.05). The addition of cetyltrimethylammonium bromide (CTAB) during core synthesis demonstrated enhanced particle dispersion. Silica coating via APTES-1 and TEOS-3 conditions resulted in Y₂O₃:Eu@SiO₂ NPs that generated the highest ROS, with 8.7-fold and 8.9-fold increase respectively in relative fluorescence intensity at 8 Gy irradiation compared to a 5.2-fold increase in PBS under the same condition. In vivo evaluation using 18F-fluorothymidine positron emission tomography (18F-FLT PET) of mouse xenografts of a human prototypical cancer (ovarian) revealed a 55% reduction in tumor proliferation of radiation-sensitive CAOV3 tumors treated with Y₂O₃:Eu@SiO₂ NPs compared to a 19% reduction in tumor proliferation of radiation-resistant SKOV3 tumors on the fourth day post-treatment. Taken together, these findings suggest Y₂O₃:Eu@SiO₂ NPs can serve as effective photosensitizers for XPDT and their performance characteristics can be optimized by selecting synthesis conditions and parameters to deliver the desirable therapeutic effects, offering novel approaches to circumventing many of the limitations of conventional PDT in the treatment of cancer.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

The growing expansion of the economy and population has resulted in an increased inclination towards the utilization of groundwater resources. Conducting a geophysical survey is a widely employed method for subsurface mapping and the detection of groundwater. A geophysical study was conducted in Nanshan township, Huarong County, located in the Hunan province of the South-Central region of China. The investigation involved the utilization of a 2D Electrical Resistivity Imaging technique employing forward and inverse pole-dipole electrode array. A total of six survey lines were established, each with an electrode distance from the nearest measuring point to exceeding 800 m. The maximum current electrode separation was utilized in this setup. The spacing between the electrical resistivity sounding points was established at regular intervals. The findings from the exploration indicate the existence of multiple faults within the surveyed region. The study additionally identified two regions of structural failure, which occurred due to the convergence of faults oriented in different directions. This convergence led to the fracturing of rocks, an increase in water content, and a decrease in resistivity. The findings from the exploration were utilized in the formulation of five verification target boreholes. The results of this study offer significant insights that can inform future investigations into groundwater exploration endeavors within the region.

Traumatic vascular injuries consist in direct or indirect damage to arteries and/or veins and accounts for 3% of all traumatic injuries. Typical consequences are hemorrhage and ischemia. Extremities vascular injuries can occur isolated or in association in major trauma with other organ injuries. They account for 1-2% of patients admitted to Emergency Department, for approximately 50% of all arterial injuries. Lower extremities are more frequently injured than the upper ones in adult population. The outcome of vascular injuries is strictly correlated to the environment and the time background. Treatment can be challenging notably in polytrauma because of the dilemma of which injury should be prioritized and treatment delay can cause disability or even death especially for limb vascular injury. Our purposes are to discuss the role of Computed Tomography Angiography (CTA) in the diagnosis of vascular trauma and its optimized protocol to achieve the definitive diagnosis, and to assess the radiological signs of vascular injuries and the possible pitfalls.

We are currently investigating the ultrasound imaging of a sensor consisting of a randomized encoding mask attached to a single lead zirconate titanate (PZT) oscillator for use in a puncture microscope. The proposed model was performed using a finite element method (FEM) simulator. To increase the number of measurements required by a single element system, the transducer was rotated at different angles. The image was constructed by solving a linear equation of the image model. In a previous study, the image resolution was improved by extracting phase information. In this study, we propose a strategy by integrating the weighted frequency subbands compound and a super resolution method (SCM) to improve the resolution in range and lateral resolution. We also evaluated the image performance by applying different methods to improve the image quality. The results show that better image resolution and speckle suppression were obtained by applying the proposed method.

The production of recombinant proteins in Escherichia coli is often challenged by cytoplasmic expression due to proteolytic degradation and inclusion body formation. Extracellular expression can overcome these problems by simplifying downstream processing and improving protein yields. This study aims to compare the efficiency of two Bacillus subtilis chitosanase signal peptides in mediating extracellular secretion in E. coli. We identified a naturally occurring mutant signal peptide (mCsn2-SP) from B. subtilis CH2 chitosanase (CH2CSN), which is characterized by a deletion of six amino acids in the N-region relative to the signal peptide (Csn1-SP) from B. subtilis CH1 chitosanase (CH1CSN). The CH1CSN and CH2CSN genes were cloned into the pET-11a vector and protein secretion was evaluated in E. coli BL21(DE3) host cells. Expression was induced with 0.1 mM and 1 mM IPTG at 30°C for one and three days. CH2CSN showed higher secretion levels compared to CH1CSN under all experimental conditions, especially with 0.1 mM IPTG induction for 3 days, which resulted in a 2.37-fold increase in secretion. These results suggest that signal peptide deletion variants may have an effect on protein secretion efficiency and help to further understand the function of signal peptides.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

Localized protein translation occurs through trafficking of mRNAs and protein translation machineries to different compartments of the cell, leading to rapid on-site synthesis of proteins in response to signaling cues. The spatiotemporally precise nature of the local translation process necessitates continual developments of technologies reviewed herein to visualize and map biomolecular components and the translation process with better spatial and temporal resolution, and with fewer artifacts. We also discuss approaches to control local translation, which can serve as a design paradigm for subcellular genetic devices for eukaryotic synthetic biology.

Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it was unclear whether intentions relating to mental imagery of movement could be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

Background Endometrial cancer is the fourth most common type of cancer in the world. Due to the prevalence and high morbidity, it is of key importance to make a quick and accurate diagnosis and effective therapy. The use of photodynamic therapy (PDT) in the treatment of endometrial cancer is a significant challenge in conducting clinical trials. PDT is non-invasive, with few side effects, damaging only neoplastic tissue, leaving healthy adjacent structures intact. Thanks to numerous experiments (also in vitro), PDT is gaining more and more recognition as a potential tool in endometrium cancer treatment. Objective The aim of the study was to analyze the effectiveness of photodynamic therapy on endometrial cancer tissue samples Methods In the in vitro experiment of PDT, sections of endometrial cancer tissue taken from female patients were subjected to. Rose Bengal was used as a photosensitizer in order to assess the usefulness of the applied PDT and to introduce these solutions into the in vivo test procedure. Results Changes on the cellular substrate, such as: chromatin condensation, disturbed structure and shape of cell nuclei were observed in all tissues subjected to PDT. Conclusions The PDT experiment in vitro offers opportunities and hopes for using the chosen procedure also in vivo. Keywords endometrial cancer; photodynamic therapy; in vitro; treatment; rose bengal

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.

The combination of immunoPET—where an antibody (Ab) is labeled with an isotope for PET imaging—and radioimmunotherapy (RIT), using the same antibody with a therapeutic isotope, offers significant advantages in cancer management. ImmunoPET allows non-invasive imaging of antigen expression, which aids in patient selection for a subsequent radioimmunotherapy. It also facilitates the assessment of tumor response to therapy, allowing for treatment adjustments if necessary. In addition, immunoPET provides critical pharmacokinetic data, including antibody biodistribution and clearance rates, which are essential for dosimetry calculations and treatment protocol optimization. There are still challenges to overcome. Identifying appropriate target antigens that are selectively expressed on cancer cells while minimally expressed on normal tissues remains a major hurdle to reduce off-target toxicity. In addition, it is critical to optimize the pharmacokinetics of radio-labeled antibodies to maximize tumor uptake and minimize normal tissue uptake, particularly in vital organs such as the liver and kidney. This approach offers the potential for targeted and personalized cancer therapy with reduced systemic toxicity by exploiting the specificity of monoclonal antibodies and the cytotoxic effects of radiation. However, further research is needed to address remaining challenges and to optimize these technologies for clinical use.

The work aims to develop an electromagnetic shielding measurement methodology and design an electromagnetic shielding effectiveness test setup for thin sheet material testing. In brief, multiple shielding effectiveness standards are described and analyzed in the thin sheet material testing framework. Two types of the developed shielding effectiveness test probe in high and low-frequency ranges are validated comprehensively. Extensive descriptions and data of developed testing methodology are also reported.

With the development of organizational biology and forced from the EU Cosmetic Regulation (EU/1223/2009) with the complete ban of animal testing for cosmetic purposes, 3D skin models have been widely developed due to their similar physiological structure and metabolic function to native human tissue. 3D skin models for in vitro cosmetic evaluation are still promising alternative methods for evaluating phototoxicity, corrosivity and irritation. These models have been used for early screening and later validation of toxicity and efficacy evaluation by many well-known cosmetic companies. 3D skin models have become an important tool in cosmetic evaluation research. This article reviews the development of 3D skin models and their application in cosmetic evaluation. An outlook on the current application and future technological development directions in the cosmetic field is also presented.

X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing molecular-level data, gaining attention for high-resolution imaging. However, increased sensitivity with benchtop X-ray sources raises radiation exposure. Artificial Intelligence (AI), particularly deep learning (DL), has revolutionized medical imaging by delivering high-quality images in the presence of noise. In XFCT, traditional methods rely on complex algorithms for noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized SCUNet model for noise reduction in low-concentration XRF images. Compared to higher-dose images, our method’s effectiveness is evaluated. While various denoising techniques exist for X-ray and CT, few address to XFCT. The DL model is trained and assessed using the augmented data, focusing on background noise reduction. Image quality is measured using PSNR and SSIM, comparing outcomes with 100% X-ray dose images. Results show the proposed algorithm achieves high-quality images from low-dose and low-contrast agents, with a maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms BM3D, NLM, DnCNN, and SCUNet in both visual inspection and quantitative analysis, particularly in high-noise scenarios. This indicates the potential of AI, specifically the SCUNet model, in significantly improving XFCT imaging by mitigating the trade-off between sensitivity and radiation exposure.

Camouflage is a natural or artificially process to prevent an object from being detected while camouflage breaking is a countering process for the identification of the concealed object. We report that a perfectly camouflaged object in a two-dimensional scene can be retrieved and detected with stereo-vision assisted three-dimensional (3D) imaging perceived with stereopsis. The analysis is based on binocular energy model applied to general 3D settings. We show that a perfectly concealed object with random noise background can be retrieved with vision’s stereoacuity to resolve the hidden structures. The theoretical analysis is further tested and demonstrated with distant natural images taken by a drone-camera, processed with a computer and displayed in an autostereoscopy. The recovered imaging is presented with removal of the background interference to demonstrate the general applicability for camouflage breaking with stereo imaging and sensing.

This paper presents the design and analysis of a novel 3-degree-of-freedom (3-DOF) parallel ma-nipulator equipped with self-sensing Ni-Ti (Nitinol) actuators. The manipulator's architecture and mechanical design are elucidated, emphasizing the integration of Nitinol actuators. The self-sensing technique implemented in a previous work was extended to a 20 mm actuator length, and the actuator was used to design the 3 DOF manipulator. Kinematic analyses were conducted to eval-uate the manipulator's performance under various operating conditions. A dynamic model was implemented for the dynamic dimensioning of the actuators, which work synergistically with a bias spring. The manipulator was realized, and a control strategy was implemented. Experimental tests, although documenting some positioning accuracy issues, show the efficacy and potential applica-tions of the proposed manipulator in robotics and automation systems, highlighting the advantages of self-sensing Nitinol actuators in small parallel manipulator designs.

Our hypothesis that controlled ozone applications interfere into the redox balance of the biological organism (first published in 1998 with a preclinical trial on protecting the liver from CCl4 intoxication), has been verified over the last 2 decades in reactive oxygen species (ROS)-induced mitochondrial pathologies, such as rheumatoid arthritis, osteoarthritis, aging processes, diabetes-2 and in prevention of intoxications. Low-dose ozone acts as a redox bioregulator: The restoration of the disturbed redox balance is comprehensible in a number of preclinical and clinical studies by a remarkable increase of the antioxidant repair markers, here mainly shown as glutathione increase and a reduction in oxidative stress markers, mainly malondialdehyde. The mechanism of action is shown, relevant data are displayed, evaluated and comprehensively discussed: The repair side of the equilibrium increases by 21% up to 140% compared to the non-ozone treated groups and depending on the indication; the stress markers are simultaneously reduced, the redox system regains its balance.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

Observe the synergistic effect and dose-effect relationship of Trimethoprim (TMP) on bactericidal activity with Flos Lonicerae in vitro. Microamount chessboard dilution method was conducted to determine the minimal inhibitory concentration (MIC) of Trimethoprim, Flos Lonicerae, as well as the combination of Trimethoprim and Flos Lonicerae separately against Staphylococcus aureus, Escherichia coli in vitro and Salmonella. The pour plate count method was used to determine the combined bactericidal activity of Flos Lonicerae combined with Different concentrations TMP. The results showed that the MIC values of the combination of Flos Lonicerae with TMP was much less than the MIC values of the independent use of Flos Lonicerae or TMP, The FIC values of the combination of Flos Lonicerae with TMP were between 0.5 and 1, there was additive effect between them. The bactericidal rates were fitted with least square method, the 95% confidence intervals of the optimal blending quantity about the combination of Flos Lonicerae with TMP on the test organisms were 231μg·mL^-1-249μg·mL^-1, 237μg·mL^-1-259μg·mL^-1, and 235 -259μg·mL^-1

An analysis of the physical and chemical phenomena accompanying the electrical discharges was carried out, principal factors inactivating microorganisms is studied. Characteristic factors of an electric discharge in cavitation mode providing a synergistic effect when disinfecting water with a discharge and chemical reagents are identified in order of importance. As a result of the integral action of cavitation, the inactivation of bacteria is intensified both due to the emergence of chem-ically active particles and due to local shock waves, acoustic currents and ultraviolet radiation in the area of cavitating bubbles. The similarity of cavitation processes in water systems implemented by underwater electric discharges and with the ultrasound has been experimentally proved. The main advantage of electric discharge cavitation in comparison with ultrasonic one is the wide band of ultrasonic frequencies of radiation inherent in electric discharge, high intensity and power of cav-itation processes, and, accordingly, the possibility of a significant increase in the volume of the disinfected liquid. The study enable better understanding and prediction of the effects that occur during a high-voltage underwater electrical discharge and have a bactericidal effect.

Cystic artery bleeding (CAB), a relatively uncommon entity, can arise from both traumatic and non-traumatic etiologies. This pictorial essay aims to elucidate the computed tomography (CT) findings associated with CAB in these distinct contexts. In traumatic CAB, particularly blunt or penetrating trauma, a cystic artery lesion is the culprit behind the hemoperitoneum characteristically located within the gallbladder fossa. Prompt diagnosis is crucial. Fortunately, trans-arterial embolization offers a successful treatment option for traumatic cystic artery lesions, especially in settings where non-operative management is preferred. Non-traumatic CAB, on the other hand, often stems from inflammation in the hepatobiliary and pancreatic system, gallbladder tumours, or iatrogenic injuries. The underlying mechanism typically involves cystic artery wall damage, either due to inflammation or procedural complications, leading to the formation of a cystic artery pseudoaneurysm (CAP). Notably, CAP bleeding can manifest long after the causative event, posing a diagnostic challenge with a variable clinical presentation. This pictorial essay serves to illustrate the CT signs and features of CAB in both traumatic and non-traumatic scenarios. Additionally, it will delve into the characteristic CT appearance of CAP.

Computed tomography (CT) is a widely employed imaging modality with diverse clinical applications in pediatric and adult populations, offering numerous advantages. Despite several alternatives to imaging modalities with ionising radiation, CT scans have demonstrated persistent global popularity, contributing to a substantial amount of effective radiation doses for patients. This exposure results in hazards, including stochastic effects that amplify the radiation-induced cancer risk proportionally to the radiation dose received. CT-related radiation comprises a substantial portion of cumulative effective doses within diagnostic radiology, specifically impacting younger patient cohorts undergoing repeated investigations for chronic diseases over time. Scientific literature over recent decades has explored various methods to mitigate radiation doses during medical imaging. One notable strategy involves implementing low-dose and ultralow-dose CT protocols, maintaining diagnostic efficacy in acute and non-acute medical and surgical conditions. Utilising innovative image reconstruction algorithms, such as iterative reconstruction and emerging deep-learning algorithms based on artificial intelligence, plays a pivotal role in achieving an approximately 80% reduction in patient radiation compared to standard doses. This review article discusses the significance of low-dose protocols across a spectrum of acute and chronic medical conditions affecting different body systems.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. Early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings pose challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we developed a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieved the highest performance with accuracy, sensitivity, specificity, and combined AUC of 0.93±0.04, 0.94±0.04, 0.93±0.08 and 0.97 respectively in a 5-fold cross-validation study with pulmonary sarcoidosis (n=126) and LCa (n=93) cases. The model offers promising potential for improving the diagnosis of pulmonary sarcoidosis. A detailed ablation study showing the impact of CNN+ViT compared to CNN or ViT alone, and CT+radiomics input, compared to CT or radiomics alone, is also presented in this work.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

Backyard chickens often suffer from foot pad dermatitis (FPD), a condition exacerbated by poor husbandry, nutritional deficiencies, and obesity. Pressure-relieving bandages, commonly used in treatment of FPD, are impractical for outdoor chickens as they quickly become wet and dirty, necessitating daily changes which are often unfeasible. This retrospective study explores the use of custom-fit silicone shoes, created via 3D-printed molds, as an alternative to traditional bandages. CT scans were used to design shoes, adapted from a design used for birds of prey. Over three years, 16 chickens with varying degrees of FPD were treated. The results demonstrated that sili-cone shoes were an effective treatment for FPD, allowing outdoor activity, and facilitated daily lesion care without frequent bandage changes. The median shoe-wearing period was 14 days, and most owners provided positive feedback. Despite some attachment issues, the occurrence of sec-ondary pressure marks and feather regrowth challenges, the silicone shoes offered better ventila-tion, reusability, and consistent pressure relief compared to traditional bandages. This study con-cludes that silicone shoes are a viable solution for managing FPD in backyard chickens, promoting faster healing and improved owner compliance.

Elbow dislocation and instability present significant clinical challenges, necessitating a thorough understanding of the underlying bio-mechanical mechanisms. In this study, a quasi-static three-dimensional finite element model of the human elbow joint is developed to investigate stress distribution and stages of dislocation in the human elbow under various loading conditions. The model simulates the elbow joint in different degrees of flexion (30°, 45°, 60°, and 90°) and forearm positions (pronation and supination), providing detailed insights into the progression of dislocation. Significant findings include the identification of high stress concentrations on the humerus at 90° flexion and on the radial and coronoid processes at 30°, 45°, and 60° flexion. Three reproducible stages of dislocation were observed, particularly in flexed positions with forearm pronation or supination. These stages align with experimental observations and highlight the initial occurrence of bony failures, such as radial head and ulnar coronoid fractures, preceding soft tissue tears. Clinically, the study underscores that early-stage low-impact posterior elbow dislocations retain enough stability to be managed with closed reduction and early mobilization. However, as dislocations progress, significant damage to the medial and lateral collateral ligaments is expected, necessitating more invasive treatments. This research provides valuable bio-mechanical insights into elbow dislocation, aiding in the development of improved treatment strategies and enhancing patient outcomes through precise and timely clinical interventions. The validated FEM serves as a powerful tool for pre-surgical planning offering a comprehensive understanding of elbow joint mechanics.

The objective of this study is to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the equivalence ratio distributions and the injected fuel droplet behavior in the cylinder of the RCCI engine. The spray simulation was conducted by dividing two-part. First, the spray validation simulations progressed to find the spray-influenced factor of the test injector. Next, the engine simulations were performed with the spray-influenced factor obtained from spray validation simulations to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the injected fuel atomization and the equivalence ratio distributions. The introduced total heating value was fixed at 595J based on the lower heating value of diesel 14mg. The heating value of the premixed ethanol ratio varied from 0% to 40% based on the same total heating value in-cylinder in steps of 10%. It was revealed that when the premixed ethanol ratio based on the same total heating value in-cylinder was increased, the spray tip penetration value was reduced from the time after 4deg of diesel injection start because of the short injection duration by the small amount of diesel fuel. The SMD value was also more enormous up to 32.58% with increasing the premixed ethanol ratio because of the low kinetic energy of the injected fuel by the short injection duration and the slow evaporation of the injected fuel by the low cylinder temperature.

Background: External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of the planning procedure, machine learning predictions of the dose distributions have been introduced to speed up the planning procedure and to serve as quality assurance. The most recent dose prediction models are based on deep learning U-Nets that give good approximations of the dose in 3D almost instantly. It is our purpose to train a 3D dose prediction for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. Methods: From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a concatenated 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes according to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases.Results: The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy.Conclusions: We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness, by targeted updating the training set, making it a useful technique for dose awareness in the contouring evaluation and quality assurance.

Neuroinflammation, defined as inflammatory reactions mediated by cytokines, chemokines, reactive oxygen species, and secondary messengers in the central nervous system (CNS) including the brain and spinal cord is the basis of many neurological disorders. Recently, erythropoietin (EPO) has been considered and studied as a modulator of neuroinflammation. On this article minireview of pathophysiology of neuroinflammation and the neuroprotective effects of EPO is discussed and a case of subacute huge subdural hematoma with double mydriasis operated urgently, treated with low daily dose (vs high dose once or twice a month in the literature) of EPO and recovered fully and discharged home with good consciousness is reported. In addition, the probable unfavorable outcome of erythropoietin administration in patients with neuroinflammation in COVID-19 is considered.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

Cannabidiol (CBD), a non-psychotropic main component of the Cannabis plant, is approved as a drug in the European Union (EU) under the name “Epidyolex”. However, the approval process as a food ingredient under the Novel Food Regulation was paused by the European Food Safety Authority (EFSA) due to a lack of safety data. Nevertheless, there is a growing, unregulated market in which CBD is advertised with various health claims and different dosage instructions. Of particular concern is its toxic effect on the liver and possible reproductive toxicity in humans. Studies suitable for calculating a benchmark dose were identified from the available data. Animal studies yielded a Benchmark Dose Lower Confidence Limit (BMDL) of 43 mg/kg bw/day, which translates into a safe human dose of approximately 15 mg/day. Only a Lowest Observed Adverse Effect Level (LOAEL) of 4.3 mg/kg bw/day could be identified from the human data. This updated risk assessment confirmed the Health-based Guidance Value (HBGV) of 10 mg/day based on human LOAEL. Despite existing data gaps, preliminary regulation appears advisable as the current form of the gray CBD market is unacceptable from the standpoint of consumer safety and protection.

Abstract: Background/Objectives: There are indications that the microbial composition of the maternal mucosal surfaces is associated with adverse events during pregnancy. The aim of this review is to investigate the link between vaginal microbiome alterations and gestational com-plications risk. Methods: This comprehensive literature review was performed using Medline and Scopus databases. The following search algorithm was used "Pregnancy Complications"[Mesh] AND (Vagin*) and after literature screening 44 studies were included in the final review. Results: The studies that were included investigated the association between vaginal microbial composi-tion and preterm birth, miscarriage, preeclampsia, ectopic pregnancy, gestational diabetes mellitus, chorioamnionitis and preterm premature rupture of membranes. In most of the studies, it was well established that increased microbial diversity is associated with these conditions. Also, the depletion of Lactobacillus species is linked to most of the gestational complication, while the increased relative abundance and especially L. crispatus may exerts a protective effect in favor of the pregnant woman. Several pathogenic taxa including Gardnerella, Prevotella, Sneathia, Bac-terial Vaginosis-Associated Bacteria-2, Atopobium and Megasphera seem to be correlated to higher maternal morbidity. Conclusions: Vaginal microbiome aberrations seem to have an as-sociation with pregnancy related adverse events, but more high-quality homogenous studies are necessary to reliably verify this link.

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. It posits two identical deterministic worlds -- one comprising a human agent and the other a machine agent. These agents exhibit different information processing mechanisms despite their apparent sameness in a causal sense. Providing a conceptual modeling of their difference, this paper resolves what it calls “the vantage point problem” – namely, how to justify an omniscient perspective through which a determinist asserts determinism from within the supposedly deterministic universe.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

Background/Objectives: Encouraging results reported of Platelet-Rich Plasma (PRP) treatment for knee osteoarthritis (KOA). The present retrospective study reports the efficacy and safety of a high dose of neutrophile and red blood cell-depleted PRP to treat 212 patients with KOA. Methods: 212 consecutive patients diagnosed with Kellgren-Lawrence (KL) grading 1-3 KOA chronic knee pain for at least 1 year were treated with 3 injections at 15-day intervals with a high dose of neutrophil-depleted PRP (4 billion platelets). Clinical outcomes were retrospectively recorded as a percentage of responders at 3,6, and 12-month follow-up, following the OMERACT-OARSI criteria. Pain through VAS score and WOMAC score have also been recorded. Results: 4ml of PRP containing 4X109 platelets were obtained by single spin centrifugation and injected intra-articular into each patient with no pre-activation. The overall responder rate of patients responding to OMERACT-OARSI criteria at 3,6,12 months was 68.9%, 72,7% and 70,6%. There was a significant improvement in VAS and WOMAC scores at 3-, 6- and 12-month follow-up compared to the pre-treatment value (p&lt; 0.01). The lowest VAS score was observed at 6 months overall and in all three-grade KL groups. KL2 groups showed the best results regarding pain reduction and WOMAC score at 6 months (p&lt; 0.01). Conclusions: a high dose of neutrophil-depleted PRP is an effective treatment for KL1-3 KOA. It provides symptomatic relief, can potentially reduce disease progression, and has sustained effects for up to 12 months.

Metasurface constitutes one of the most promising technologies for holographic imaging; however, the work for terahertz (THz) metasurface holographic imaging is relatively limited. Here, we propose a THz dielectric geometric-propagation phase metasurface, which can operate dual-mode (reflection and transmission) and reconfigurable multifunctional holographic imaging. The dual-mode is realized by controlling the Fermi energy level (Ef) of the graphene integrated into the metasurface unit, and the reconfigurable three-channel holographic imaging in reflection or transmission mode are achieved by switching the feed polarization among left-handed circular polarization (LCP), right-handed circular polarization (RCP) and linear polarization (LP). The metasurface is designed based on the transmission mode, and a physical model for switching to the reflection mode is established. For the first time to the best of our knowledge, a reflection-transmission dynamic modulation THz holographic imaging metasurface has been developed. The holographic metasurface operates in transmission mode at Ef = 0.1 eV, and in reflection mode at Ef = 0.9 eV. Compared with recently published holographic imaging metasurfaces, the proposed metasurface offers the following advantages: high holographic efficiencies (42.5% to 49%), more holographic imaging channels, dynamic modulation dual-mode operations, and reconfigurability.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for real-time, non-invasive monitoring of pro-inflammatory biomarkers in human sweat. The patch-like device, designed using COMSOL Multiphysics, integrates magnetofluidic manipulation with direct-field capacitive sensing. The sensor comprises two distinct units: an immunocomplex enhancement unit, employing a series of microcoils to optimize the binding efficiency, reaction kinetics, and homogeneity of biomarker-magnetic nanoparticle (MNP) interactions, thereby enhancing the sensor's specificity; and a layered capacitive sensing unit, designed to concentrate and detect biomarker-tagged MNPs, thus amplifying sensitivity. Simulations of the capacitive sensing unit revealed a substantial sensitivity increase of up to 42.48\% at an 85\% MNP concentration within the detection zone. These findings highlight the potential of the proposed immunosensor for efficient and precise real-time biomarker monitoring, which may facilitate early disease diagnosis and enable personalized healthcare interventions.

Background and Objectives: The complications seen after lower impacted third molar surgery affect the quality of life of the patients. The aim of the study was to investigate the efficacy of a single preoperative dose of deflazacort on pain, swelling and trismus following impacted lower third molar surgery. Materials and Methods: This randomised, prospective, double-blind, split-mouth clinical study included 30 healthy individuals with bilaterally impacted lower third molars that was removed for orthodontic purposes between September 2017 and September 2018. Group 1 was given a placebo (single dose vitamin C tablet) and group 2 was given a single 30 mg dose of deflazacort 1 hour prior to the surgery. Pain was evaluated using the visual analog scale for 1 week postoperatively. Edema (in mm) and trismus (in mm) were evaluated preoperatively and on postoperative days 2 and 7. The Mann-Whitney U test was applied for group analysis. p values < 0.05 were considered statistically significant. Results: When postoperative pain scores were evaluated, a significant difference was found between the groups only at the 6th and 12th hours (p0.05). Even high trismus values were recorded on the 2nd day, it was observed that the trismus resolved on the 7th day. There were no significant differences in trismus between the groups at any time point (p>0.05). Evaluation of swelling showed that there was no statistically significant difference between the study and control groups at any time point (p>0.05). No side effect was detected in both the study and the control groups. Conclusions: This was the first split-mouth study evaluating the anti-inflammatory effect of deflazacort after lower impacted third molar surgery. A single dose of 30 mg deflazacort was shown to be effective in reducing pain at 6 and 12 hours postoperatively.

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

Short video addiction refers to a psychological dependency on short video applications, characterized by an intense attachment and an inability to control usage, leading to typical addiction symptoms. While its definition bears similarities to substance addiction, significant differences also exist. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), published in 2013, short video addiction has not yet been officially classified as an addiction disorder. To facilitate more in-depth and standardized research on short video addiction, it is essential to synthesize and organize existing studies on its theoretical foundations and mechanisms.

A population dynamics epidemic model is developed that incorporates age of infection and age of vaccination. The model analyzes pre-symptomatic and symptomatic periods of an infected individual in terms of infection age. The model analyzes the efficacy of vaccination in terms of vaccination age. The model is applied to the 2003 SARS epidemic in Taiwan and the current COVID-19 epidemic in New York State.

Objective: The aim of this in vitro study was to evaluate the accuracy of resin-based fixed dental restorations, namely veneers, single crowns, and four-unit fixed partial dental prosthesis (FPDs) using two different 3D printers and polymer-based materials. Materials and Methods: A standard maxillary polyurethane jaw model containing prepared teeth was scanned using an intraoral scanner. The generated STL data were used to design the restorations virtually using CAD software. Two 3D printers were utilized for the provisional digital light processing and stereolithography for the castable resin patterns. Each printer produced 10 specimens of each type of restoration, for a total of 80 restorations. The 3d-printed restorations were then 3D scanned using the same intraoral scanner and evaluated for external and internal dimensional accuracy in terms of trueness and precision. A one-way ANOVA and Two-sample T-Test were implemented to compute the precision (variability between groups) and trueness (with the designed CAD model). A level of statistical significance of p-Value