How to overcome the difficulty in assaying Ni in biodiesel samples? Extraction induced by microemulsion breaking and square wave adsorptive stripping voltammetry could be the answer.

Herein, a simple, green, and relatively inexpensive approach to determine nickel (Ni) in biodiesel samples by square wave adsorptive cathodic stripping voltammetry (SWAdCSV) is presented. A method based on the accumulation of Ni as Ni(II)-dimethylglyoxime (Ni(II)(HDMG)2) on the glassy carbon electrode was carried out in a solution containing the aqueous phase extract (APhEx) obtained from an extraction induced by microemulsion breaking (EIMB), which was achieved by adding a few microliters of ultrapure water to a microemulsion composed of biodiesel, n-propanol and a diluted HNO3 solution. The LOD and LOQ were 0.2 µg L-1 and 0.8 µg L-1, respectively, and the accuracy was evaluated by recovery assays of spiked samples and by analyzing a standard reference material. Results obtained from a comparative method (HR-CS GF AAS) were also used for this evaluation. The method was applied to biodiesel samples produced from different feedstocks. To the best of the authors knowledge, it is the first time that: 1) Ni in biodiesel is determined by a voltammetric method; 2) EIMB is applied to extract Ni from this matrix and 3) this type of sample preparation method is used with adsorptive stripping voltammetry.

New thymol-derived triazole exhibits promising activity against Trichophyton rubrum.

Fungal infections have emerged worldwide, and azole antifungals are widely used to control these infections. However, the emergence of antifungal resistance has been compromising the effectiveness of these drugs. Therefore, the objective of this study was to evaluate the antifungal and cytotoxic activities of the nine new 1,2,3 triazole compounds derived from thymol that were synthesized through Click chemistry. The binding mode prediction was carried out by docking studies using the crystallographic structure of Lanosterol 14α-demethylase G73E mutant from Saccharomyces cerevisiae. The new compounds showed potent antifungal activity against Trichophyton rubrum but did not show relevant action against Aspergillus fumigatus and Candida albicans. For T. rubrum, molecules nº 5 and 8 showed promising results, emphasizing nº 8, whose fungicidal and fungistatic effects were similar to fluconazole. In addition, molecule nº 8 showed low toxicity for keratinocytes and fibroblasts, concluding that this compound demonstrates promising characteristics for developing a new drug for dermatophytosis caused by T. rubrum, or serves as a structural basis for further research.

Mode I Fatigue and Fracture Assessment of Polyimide-Epoxy and Silicon-Epoxy Interfaces in Chip-Package Components.

Semiconductor advancements demand greater integrated circuit density, structural miniaturization, and complex material combinations, resulting in stress concentrations from property mismatches. This study investigates the failure in two types of interfaces found in chip packages: silicon-epoxy mold compound (EMC) and polyimide-EMC. These interfaces were subjected to quasi-static and fatigue loading conditions. Employing a compliance-based beam method, the tests determined interfacial critical fracture energy values, (GIC), of 0.051 N/mm and 0.037 N/mm for the silicon-EMC and polyimide-EMC interfaces, respectively. Fatigue testing on the polyimide-epoxy interface revealed a fatigue threshold strain energy, (Gth), of 0.042 N/mm. We also observed diverse failure modes and discuss potential mechanical failures in multi-layer chip packages. The findings of this study can contribute to the prediction and mitigation of failure modes in the analyzed chip packaging. The obtained threshold energy and crack growth rate provide insights for designing safe lives for bi-material interfaces in chip packaging under cyclic loads. These insights can guide future research directions, emphasizing the improvement of material properties and exploration of the influence of manufacturing parameters on delamination in multilayer semiconductors.

Pseudouridylation-mediated gene expression modulation.

RNA-guided pseudouridylation, a widespread post-transcriptional RNA modification, has recently gained recognition for its role in cellular processes such as pre-mRNA splicing and the modulation of premature termination codon (PTC) readthrough. This review provides insights into its mechanisms, functions, and potential therapeutic applications. It examines the mechanisms governing RNA-guided pseudouridylation, emphasizing the roles of guide RNAs and pseudouridine synthases in catalyzing uridine-to-pseudouridine conversion. A key focus is the impact of RNA-guided pseudouridylation of U2 small nuclear RNA on pre-mRNA splicing, encompassing its influence on branch site recognition and spliceosome assembly. Additionally, the review discusses the emerging role of RNA-guided pseudouridylation in regulating PTC readthrough, impacting translation termination and genetic disorders. Finally, it explores the therapeutic potential of pseudouridine modifications, offering insights into potential treatments for genetic diseases and cancer and the development of mRNA vaccine.

Cohesive Properties of Bimaterial Interfaces in Semiconductors: Experimental Study and Numerical Simulation Using an Inverse Cohesive Contact Approach.

Examining crack propagation at the interface of bimaterial components under various conditions is essential for improving the reliability of semiconductor designs. However, the fracture behavior of bimaterial interfaces has been relatively underexplored in the literature, particularly in terms of numerical predictions. Numerical simulations offer vital insights into the evolution of interfacial damage and stress distribution in wafers, showcasing their dependence on material properties. The lack of knowledge about specific interfaces poses a significant obstacle to the development of new products and necessitates active remediation for further progress. The objective of this paper is twofold: firstly, to experimentally investigate the behavior of bimaterial interfaces commonly found in semiconductors under quasi-static loading conditions, and secondly, to determine their respective interfacial cohesive properties using an inverse cohesive zone modeling approach. For this purpose, double cantilever beam specimens were manufactured that allow Mode I static fracture analysis of the interfaces. A compliance-based method was used to obtain the crack size during the tests and the Mode I energy release rate (GIc). Experimental results were utilized to simulate the behavior of different interfaces under specific test conditions in Abaqus. The simulation aimed to extract the interfacial cohesive contact properties of the studied bimaterial interfaces. These properties enable designers to predict the strength of the interfaces, particularly under Mode I loading conditions. To this extent, the cohesive zone modeling (CZM) assisted in defining the behavior of the damage propagation through the bimaterial interfaces. As a result, for the silicon-epoxy molding compound (EMC) interface, the results for maximum strength and GIc are, respectively, 26 MPa and 0.05 N/mm. The second interface tested consisted of polyimide and silicon oxide between the silicon and EMC layers, and the results obtained are 21.5 MPa for the maximum tensile strength and 0.02 N/mm for GIc. This study's findings aid in predicting and mitigating failure modes in the studied chip packaging. The insights offer directions for future research, focusing on enhancing material properties and exploring the impact of manufacturing parameters and temperature conditions on delamination in multilayer semiconductors.

Depression among Brazilian medical students exposed to remote learning and the role of scopophobia.

Medical education was greatly affected by the COVID-19 pandemic, and remote teaching through lectures and classes through videoconferencing was heavily used. However, the need to use cameras led to scopophobia, which is the fear of being watched, which can lead to psychological symptoms. Despite the relevance and prevalence of depression and the increase in the use of cameras for learning, research evaluating the impact of scopophobia on students' mental health is surprisingly scarce. Hence, to fill up this gap, a cross-sectional study was carried out in medical schools in Brazil. To assess the presence of depressed mood, the Patient Health Questionnaire-9 (PHQ-9) was applied. We used logistic regression models to verify the associations. The overall prevalence of positive PHQ9 found in our study was 62%. By studying the factors associated with a high risk of scopophobia, we could identify that the PHQ was statistically associated with scopophobia (odds ratio 2.43 (confidence interval 1.11-5.26), adjusted p value = .0269). Also, a lower family income, a higher number of household inhabitants, and female gender were associated. These results suggest that scopophobia is associated with depression, leading us to believe that interventions to mitigate this risk in students are opportune, especially if targeted at lower-income students.

Classification of Continuous ECG Segments - Performance Analysis of a Deep Learning Model.

Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. It is a complex and non-linear signal, which is the first option to preliminary identify specific pathologies/conditions (e.g., arrhythmias). Currently, the scientific community has proposed a multitude of intelligent systems to automatically process the ECG signal, through deep learning techniques, as well as machine learning, where this present high performance, showing state-of-the-art results. However, most of these models are designed to analyze the ECG signal individually, i.e., segment by segment. The scientific community states that to diagnose a pathology in the ECG signal, it is not enough to analyze a signal segment corresponding to the cardiac cycle, but rather an analysis of successive segments of cardiac cycles, to identify a pathological pattern.In this paper, an intelligent method based on a Convolutional Neural Network 1D paired with a Multilayer Perceptron (CNN 1D+MLP) was evaluated to automatically diagnose a set of pathological conditions, from the analysis of the individual segment of the cardiac cycle. In particular, we intend to study the robustness of the referred method in the analysis of several simultaneous ECG signal segments. Two ECG signal databases were selected, namely: MIT-BIH Arrhythmia Database (D1) and European ST-T Database (D2). The data was processed to create datasets with two, three and five segments in a row, to train and test the performance of the method. The method was evaluated in terms of classification metrics, such as: precision, recall, f1-score, and accuracy, as well as through the calculation of confusion matrices.Overall, the method demonstrated high robustness in the analysis of successive ECG signal segments, which we can conclude that it has the potential to detect anomalous patterns in the ECG signal. In the future, we will use this method to analyze the ECG signal coming in real-time, acquired by a wearable device, through a cloud system.Clinical Relevance-This study evaluates the potential of a deep learning method to classify one or several segments of the cardiac cycle and diagnose pathologies in ECG signals.

Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study.

Accurate lesion classification as benign or malignant in breast ultrasound (BUS) images is a critical task that requires experienced radiologists and has many challenges, such as poor image quality, artifacts, and high lesion variability. Thus, automatic lesion classification may aid professionals in breast cancer diagnosis. In this scope, computer-aided diagnosis systems have been proposed to assist in medical image interpretation, outperforming the intra and inter-observer variability. Recently, such systems using convolutional neural networks have demonstrated impressive results in medical image classification tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the performance comparison of networks. This work is a benchmark for lesion classification in BUS images comparing six state-of-the-art networks: GoogLeNet, InceptionV3, ResNet, DenseNet, MobileNetV2, and EfficientNet. For each network, five input data variations that include segmentation information were tested to compare their impact on the final performance. The methods were trained on a multi-center BUS dataset (BUSI and UDIAT) and evaluated using the following metrics: precision, sensitivity, F1-score, accuracy, and area under the curve (AUC). Overall, the lesion with a thin border of background provides the best performance. For this input data, EfficientNet obtained the best results: an accuracy of 97.65% and an AUC of 96.30%.Clinical Relevance- This study showed the potential of deep neural networks to be used in clinical practice for breast lesion classification, also suggesting the best model choices.

Deep learning networks in the segmentation of the left atrial appendage in 2D ultrasound: A comparative analysis.

Left atrial appendage (LAA) is the major source of thromboembolism in patients with non-valvular atrial fibrillation. Currently, LAA occlusion can be offered as a treatment for these patients, obstructing the LAA through a percutaneously delivered device. Nevertheless, correct device sizing is a complex task, requiring manual analysis of medical images. This approach is sub-optimal, time-demanding, and highly variable between experts. Different solutions were proposed to improve intervention planning, but, no efficient solution is available to 2D ultrasound, which is the most used imaging modality for intervention planning and guidance. In this work, we studied the performance of recently proposed deep learning methods when applied for the LAA segmentation in 2D ultrasound. For that, it was created a 2D ultrasound database. Then, the performance of different deep learning methods, namely Unet, UnetR, AttUnet, TransAttUnet was assessed. All networks were compared using seven metrics: i) Dice coefficient; ii) Accuracy iii) Recall; iv) Specificity; v) Precision; vi) Hausdorff distance and vii) Average distance error. Overall, the results demonstrate the efficiency of AttUnet and TransAttUnet with dice scores of 88.62% and 89.28%, and accuracy of 88.25% and 86.30%, respectively. The current results demonstrate the feasibility of deep learning methods for LAA segmentation in 2D ultrasound.Clinical relevance- Our results proved the clinical potential of deep neural networks for the LAA anatomical analysis.

Comparative analysis of deep learning methods for lesion detection on full screening mammography.

Breast cancer is the most prevalent type of cancer in women. Although mammography is used as the main imaging modality for the diagnosis, robust lesion detection in mammography images is a challenging task, due to the poor contrast of the lesion boundaries and the widely diverse sizes and shapes of the lesions. Deep Learning techniques have been explored to facilitate automatic diagnosis and have produced outstanding outcomes when used for different medical challenges. This study provides a benchmark for breast lesion detection in mammography images. Five state-of-art methods were evaluated on 1592 mammograms from a publicly available dataset (CBIS-DDSM) and compared considering the following seven metrics: i) mean Average Precision (mAP); ii) intersection over union; iii) precision; iv) recall; v) True Positive Rate (TPR); and vi) false positive per image. The CenterNet, YOLOv5, Faster-R-CNN, EfficientDet, and RetinaNet architectures were trained with a combination of the L1 localization loss and L2 localization loss. Despite all evaluated networks having mAP ratings greater than 60%, two managed to stand out among the evaluated networks. In general, the results demonstrate the efficiency of the model CenterNet with Hourglass-104 as its backbone and the model YOLOv5, achieving mAP scores of 70.71% and 69.36%, and TPR scores of 96.10% and 92.19%, respectively, outperforming the state-of-the-art models.Clinical Relevance - This study demonstrates the effectiveness of deep learning algorithms for breast lesion detection in mammography, potentially improving the accuracy and efficiency of breast cancer diagnosis.

Dual consistency loss for contour-aware segmentation in medical images.

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object's boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance- The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.

A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images.

Ultrasound (US) imaging is a widely used medical imaging modality for the diagnosis, monitoring, and surgical planning for kidney conditions. Thus, accurate segmentation of the kidney and internal structures in US images is essential for the assessment of kidney function and the detection of pathological conditions, such as cysts, tumors, and kidney stones. Therefore, there is a need for automated methods that can accurately segment the kidney and internal structures in US images. Over the years, automatic strategies were proposed for such purpose, with deep learning methods achieving the current state-of-the-art results. However, these strategies typically ignore the segmentation of the internal structures of the kidney. Moreover, they were evaluated in different private datasets, hampering the direct comparison of results, and making it difficult to determination the optimal strategy for this task. In this study, we perform a comparative analysis of 7 deep learning networks for the segmentation of the kidney and internal structures (Capsule, Central Echogenic Complex (CEC), Cortex and Medulla) in 2D US images in an open access multi-class kidney US dataset. The dataset includes 514 images, acquired in multiple clinical centers using different US machines and protocols. The dataset contains the annotation of two experts, but 321 images with complete segmentation of all 4 classes were used. Overall, the results demonstrate that the DeepLabV3+ network outperformed the inter-rater variability with a Dice score of 78.0% compared to 75.6% for inter-rater variability. Specifically, DeepLabV3Plus achieved mean Dice scores of 94.2% for the Capsule, 85.8% for the CEC, 62.4% for the Cortex, and 69.6% for the Medulla. These findings suggest the potential of deep learning-based methods in improving the accuracy of kidney segmentation in US images.Clinical Relevance- This study shows the potential of DL for improving accuracy of kidney segmentation in US, leading to increased diagnostic efficiency, and enabling new applications such as computer-aided diagnosis and treatment, ultimately resulting in improved patient outcomes and reduced healthcare costs.1.

Robust 3D breast reconstruction based on monocular images and artificial intelligence for robotic guided oncological interventions.

Breast cancer is a global public health concern. For women with suspicious breast lesions, the current diagnosis requires a biopsy, which is usually guided by ultrasound (US). However, this process is challenging due to the low quality of the US image and the complexity of dealing with the US probe and the surgical needle simultaneously, making it largely reliant on the surgeon's expertise. Some previous works employing collaborative robots emerged to improve the precision of biopsy interventions, providing an easier, safer, and more ergonomic procedure. However, for these equipment to be able to navigate around the breast autonomously, 3D breast reconstruction needs to be available. The accuracy of these systems still needs to improve, with the 3D reconstruction of the breast being one of the biggest focuses of errors. The main objective of this work is to develop a method to obtain a robust 3D reconstruction of the patient's breast, based on RGB monocular images, which later can be used to compute the robot's trajectories for the biopsy. To this end, depth estimation techniques will be developed, based on a deep learning architecture constituted by a CNN, LSTM, and MLP, to generate depth maps capable of being converted into point clouds. After merging several from multiple points of view, it is possible to generate a real-time reconstruction of the breast as a mesh. The development and validation of our method was performed using a previously described synthetic dataset. Hence, this procedure takes RGB images and the cameras' position and outputs the breasts' meshes. It has a mean error of 3.9 mm and a standard deviation of 1.2 mm. The final results attest to the ability of this methodology to predict the breast's shape and size using monocular images.Clinical Relevance- This work proposes a method based on artificial intelligence and monocular RGB images to obtain the breast's volume during robotic guided breast biopsies, improving their execution and safety.

The benthic food web connects the estuarine habitat mosaic to adjacent ecosystems.

Energy flows from land to sea and between pelagic and benthic environments have the potential to increase the connectivity between estuaries and adjacent ecosystems as well as among estuarine habitats. To identify such energy flows and the main trophic pathways of energy transfer in the Minho River estuary, we investigated the spatial and temporal fluctuations of carbon and nitrogen stable isotope ratios in benthic (and their potential food sources) and epibenthic consumers. Sampling was conducted along the estuarine salinity gradient from winter to summer of 2011. We found that the carbon (δ13C = 13C/12C) and nitrogen (δ15N = 15N/14N) stable isotope ratios of the most abundant benthic and epibenthic consumers varied along the salinity gradient. The δ13C values increased seaward, whereas the opposite pattern was found for the δ15N, especially during the summer. The stable isotope ratios revealed two trophic pathways in the Minho estuary food web. The first pathway is supported by phytoplankton and represented by filter feeders such as zooplankton and some deposit feeders, particularly amphipods and polychaetes. The second pathway is supported by detritus and composed essentially of deposit feeders, which by being consumed, allow detritus to be incorporated into higher trophic levels. Spatial and temporal feeding variations in the estuarine benthic food web are driven by hydrology and proximity to adjacent ecosystems (terrestrial, marine). During high river discharge periods, the δ13CPOC (ca. -28‰) and C: NPOM (>10) values suggested an increase of terrestrial-derived OM to the particulate OM pool, which was then used by suspension feeders. During low river discharge periods, marine intrusion increased upriver, which was reflected in benthic consumers' 13C-enriched stable isotope values. No relationship was found between food quality (phytoplankton vs. detritus) and food chain length because the lowest and highest values were associated with freshwater and saltmarsh areas, respectively, both dominated by the detrital pathway. This study demonstrates that benthic consumers enhance the connectivity between estuaries and its adjacent ecosystems by utilizing subsidies of terrestrial and marine origin and that benthic-pelagic coupling is an important energy transfer mechanism to the benthic food web.

Infective endocarditis risk in patients with bicuspid aortic valve: Systematic review and meta-analysis.

Background: Antibiotic prophylaxis in bicuspid aortic valve patients is currently a matter of debate. Although it is no longer recommended by international guidelines, some studies indicate a high risk of infective endocarditis. We aim to evaluate the risk of native valve infective endocarditis in bicuspid aortic valve patients and compare to individuals with tricuspid aortic valve. Methods: Study search of longitudinal studies regarding infective endocarditis incidence in bicuspid aortic valve patients (compared with tricuspid aortic valve/overall population) was conducted through OVID in the following electronic databases: MEDLINE, CENTRAL, EMBASE; from inception until October 2020. The outcomes of interest were the incidence rate and relative risk of infective endocarditis. The relative risk and incidence rate (number of cases for each 10 000 persons-year) with their 95 % confidence intervals (95 %CI) were estimated using a random effects model meta-analysis. The study protocol was registered at PROSPERO CRD42020218639. Results: Eight cohort studies were selected, with a total of 5351 bicuspid aortic valve patients. During follow up, 184 bicuspid aortic valve patients presented infective endocarditis, with an incidence rate of 48.13 per 10,000 patients-year (95 %CI 22.24-74.02), and a 12-fold (RR: 12.03, 95 %CI 5.45-26.54) increased risk compared with general population, after adjusted estimates. Conclusions: This systematic review and meta-analysis suggests that bicuspid aortic valve patients have a significant high risk of native valve infective endocarditis. Large prospective high-quality studies are required to estimate more accurately the incidence of infective endocarditis, the relative risk and the potential benefit of antibiotic prophylaxis.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

In Vitro Reconstitution of Pseudouridylation Catalyzed by Human Box H/ACA Ribonucleoprotein Particles.

Pseudouridine (Ψ) is the most common chemical modification in RNA. In eukaryotes and archaea, pseudouridine synthases, mainly guided by box H/ACA snoRNAs, convert uridine to Ψ. Ψ stabilizes RNA structure and alters RNA-RNA and RNA-protein interactions, conferring important roles in gene expression. Notably, several Ψ-linked human diseases have been identified over the years. In addition, Ψ has also been extensively used in developing mRNA vaccines. Furthermore, it has been shown that pseudouridylation can be site-specifically directed to modify specific nonsense codons, leading to nonsense suppression. All of these, together with a need to better understand the specific functions of Ψs, have motivated the development of in vitro pseudouridylation assays using purified and reconstituted box H/ACA RNPs. Here, we describe an in vitro system for box H/ACA RNA-guided RNA pseudouridylation using human cell extracts. We show that a half guide RNA (only one hairpin) is just as functionally competent as the full-length guide RNA (two hairpins) in guiding site-specific pseudouridylation in the human cell extracts. This discovery offers the opportunity for direct delivery of a short guide RNA to human cells to promote site-specific nonsense suppression and therefore has potential clinical applications.

Remote Monitoring System of Dynamic Compression Bracing to Correct Pectus Carinatum.

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of an orthosis. While this treatment is widely adopted, the correct magnitude of applied compressive forces remains unknown, leading to suboptimal results. Moreover, the current orthoses are not suitable to monitor the treatment. The purpose of this study is to design a force measuring system that could be directly embedded into an existing PC orthosis without relevant modifications in its construction. For that, inspired by the currently commercially available products where a solid silicone pad is used, three concepts for silicone-based sensors, two capacitive and one magnetic type, are presented and compared. Additionally, a concept of a full pipeline to capture and store the sensor data was researched. Compression tests were conducted on a calibration machine, with forces ranging from 0 N to 300 N. Local evaluation of sensors' response in different regions was also performed. The three sensors were tested and then compared with the results of a solid silicon pad. One of the capacitive sensors presented an identical response to the solid silicon while the other two either presented poor repeatability or were too stiff, raising concerns for patient comfort. Overall, the proposed system demonstrated its potential to measure and monitor orthosis's applied forces, corroborating its potential for clinical practice.

Synthesis and Fungicide Activity on Asperisporium caricae of Glycerol Derivatives Bearing 1,2,3-Triazole Fragments.

In agriculture, the control of fungal infections is essential to improve crop quality and productivity. This study describes the preparation and fungicidal activity evaluation of 12 glycerol derivatives bearing 1,2,3-triazole fragments. The derivatives were prepared from glycerol in four steps. The key step corresponded to the Cu(I)-catalyzed alkyne-azide cycloaddition (CuAAC) click reaction between the azide 4-(azidomethyl)-2,2-dimethyl-1,3-dioxolane (3) and different terminal alkynes (57-91% yield). The compounds were characterized by infrared spectroscopy, nuclear magnetic resonance (1H and 13C), and high-resolution mass spectrometry. The in vitro assessment of the compounds on Asperisporium caricae, that is, the etiological agent of papaya black spot, at 750 mg L-1 showed that the glycerol derivatives significantly inhibited conidial germination with different degrees of efficacy. The most active compound 4-(3-chlorophenyl)-1-((2,2-dimethyl-1,3-dioxolan-4-yl) methyl)-1H-1,2,3-triazole (4c) presented a 91.92% inhibition. In vivo assays revealed that 4c reduced the final severity (70.7%) and area under the disease severity progress curve of black spots on papaya fruits 10 days after inoculation. The glycerol-bearing 1,2,3-triazole derivatives also present agrochemical-likeness properties. Our in silico study using molecular docking calculations show that all triazole derivatives bind favorably to the sterol 14α-demethylase (CYP51) active site at the same region of the substrate lanosterol (LAN) and fungicide propiconazole (PRO). Thus, the mechanism of action of the compounds 4a-4l may be the same as the fungicide PRO, blocking the entrance/approximation of the LAN into the CYP51 active site by steric effects. The reported results point to the fact that the glycerol derivatives may represent a scaffold to be explored for the development of new chemical agents to control papaya black spot.

Association of scopophobia with online learning fatigue among medical students in Brazil.

BACKGROUND: Scopophobia can be described in the medical field as the fear of being watched or stared at. Despite the relevance of scopophobia in remote learning scenarios, which have always existed and have been largely expanded during the pandemic in medical education, studies on this topic are exceedingly rare worldwide. Hence, to fill up this gap, a cross-sectional study of medical students was developed to assess the association of scopophobia with the prevalence of online learning fatigue. METHODS: A cross-sectional, quantitative, analytical study was carried out in Medical Schools of Brazil. To assess the risk of scopophobia, questions were developed, based on the literature on the topic. The Zoom Exhaustion & Fatigue Scale (ZEF) was used, and the questions have currently been validated for Brazilian Portuguese. Logistic regression models were also used to assess the relationship of scopophobia risk and ZEF scores. RESULTS: A total of 283 students from Brazil participated in the study. The median age was 23 years, and 64% of the participants were female. In total, 14.5% were considered to be at high risk for scopophobia. It was found that after adjusting for sex, income and number of residents in the household, scopophobia and the total zoom fatigue score remained associated. For the total score, each additional point on the scale increased the chance of scopophobia by 3%, and for the overall domain, 19% (p-values < 0.05). CONCLUSIONS: In conclusion, this study shows a relevant prevalence of students with scopophobia, which requires a differentiated approach on the part of teachers. The causes of scopophobia are often specific and have a psychological origin that goes beyond the usual pedagogical management. Therefore, motivation strategies are necessary in a general, as well as an individualized manner, aiming to favor the improvement of the online teaching and learning process.