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Combined method of identification of free oscillations of perforated riffled plates
Abstract Presented experimental–numerical method of prediction of modal characteristics of rectangular perforated plates with riffles. The technique allows to analyze the oscillation frequency of plates with holes and stamped riffles. The methodology is based on numerical modeling by FE-methods and experiments to obtain the regularities of changes in the natural oscillation frequency of plate. The methodology involves numerical modelling in Abaqus, the adequacy of which is experimentally verified on a vibrometer PSV-500. Experimental studies are conducted on basic plates with elongated holes measuring 3.2 × 20 mm and 5 × 20 mm, as well as riffled plates with similar holes and different riffles. As a result of the research, dependences of the natural oscillation frequency of riffled perforated plates on their parameters are obtained in comparison with unriffled surfaces. The analysis of the obtained models is carried out and the levels of influence of significant parameters of riffled plates on their natural frequency are determined. The dependences of the natural oscillation frequency of plates on the plate thickness, height, pitch and riffles arrangement, as well as the equations describing them are obtained. The greatest influence on the natural frequencies of riffled perforated plates is exerted by: the plate thickness, the pitch arrangement and the riffle’s height. The obtained regularities of the natural oscillation frequencies of riffled perforated plates may allow for predicting the appearance of deformations in the form of cracks between the holes and determining their durability
Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles
Abstract Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity of the tumor microenvironment (TME). Here, we integrate nine deconvolution tools to assess 79 TME cell types in 10,592 tumors across 33 different cancer types, creating the most comprehensive analysis of the TME. In total, we found 41 patterns of immune infiltration and stroma profiles, identifying heterogeneous yet unique TME portraits for each cancer and several new findings. Our findings indicate that leukocytes play a major role in distinguishing various tumor types, and that a shared immune-rich TME cluster predicts better survival in bladder cancer for luminal and basal squamous subtypes, as well as in melanoma for RAS-hotspot subtypes. Our detailed deconvolution and mutational correlation analyses uncover 35 therapeutic target and candidate response biomarkers hypotheses (including CASP8 and RAS pathway genes).
Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2
Abstract Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DINOv2 are encouraging for 2D image analysis, these methods have not been applied to 3D medical images. Furthermore, deep learning models often lack explainability due to their “black-box” nature. This study aims to extend 2D self-supervised models, specifically DINOv2, to 3D medical imaging while evaluating their potential for explainable outcomes. We introduce the Medical Slice Transformer (MST) framework to adapt 2D self-supervised models for 3D medical image analysis. MST combines a Transformer architecture with a 2D feature extractor, i.e., DINOv2. We evaluate its diagnostic performance against a 3D convolutional neural network (3D ResNet) across three clinical datasets: breast MRI (651 patients), chest CT (722 patients), and knee MRI (1199 patients). Both methods were tested for diagnosing breast cancer, predicting lung nodule dignity, and detecting meniscus tears. Diagnostic performance was assessed by calculating the Area Under the Receiver Operating Characteristic Curve (AUC). Explainability was evaluated through a radiologist’s qualitative comparison of saliency maps based on slice and lesion correctness. P-values were calculated using Delong’s test. MST achieved higher AUC values compared to ResNet across all three datasets: breast (0.94 ± 0.01 vs. 0.91 ± 0.02, P = 0.02), chest (0.95 ± 0.01 vs. 0.92 ± 0.02, P = 0.13), and knee (0.85 ± 0.04 vs. 0.69 ± 0.05, P = 0.001). Saliency maps were consistently more precise and anatomically correct for MST than for ResNet. Self-supervised 2D models like DINOv2 can be effectively adapted for 3D medical imaging using MST, offering enhanced diagnostic accuracy and explainability compared to convolutional neural networks.
The prediction of pre-eclampsia by using high resolution flow ultrasonography in determination vascularization of placental villi in late pregnancy
Conceptualizing phytoplankton communities in the absence of resource based competitive exclusion
Comparative mitochondrial genome and phylogenetic analysis of malaria mosquitoes Anopheles hyrcanus and Anopheles messeae
Changes in the comprehensive unassisted pregnancy rate as a possible marker of declining human fecundity
Prior knowledge of anatomical relationships supports automatic delineation of clinical target volume for cervical cancer
Morphologic and functional alterations in the parasagittal dural space in mild cognitive impairment
Unravelling the temporal dynamics of community functions in protists induced by treated wastewater exposure using metatranscriptomics
Abstract The discharge of treated wastewater (TWW) into freshwater ecosystems poses a significant impact on microbial communities, particularly protists, which play a crucial role in nutrient cycling and ecosystem stability. While the ecological effects of TWW on microbial diversity have been studied, understanding the functional responses of protist communities remains limited. This study employs metatranscriptomics to unravel the temporal dynamics of protist community functions in response to TWW exposure. Using mesocosm experiment, water samples were analyzed over a ten-day period to monitor shifts in metabolic pathways and community interactions. Our results indicate that processed metatranscriptomic data, focusing on treatment-significant pathways, is more sensitive than traditional methods, such as meta-barcoding, and non-target screening, in detecting wastewater-induced perturbations. Early exposure to TWW significantly altered expression of pathways associated with signal transduction and environmental interaction, while general metabolic pathways showed resilience. Over time, the protist community showed signs of adaptation with expression levels stabilizing towards the end of the experiment. This study underscores the importance of focussing on functional shifts rather than just taxonomic changes for assessing wastewater impacts on freshwater ecosystems. Our findings advocate for the use of metatranscriptomics as a robust indicator for TWW detection, aiding in development of targeted environmental management strategies.
Pharmacovigilance analysis of drug-induced hypertrophic rhinitis using FAERS data
Ring-finger protein RNF126 promotes prostate cancer progression via regulation of MBNL1
5-Hydroxymethylcytosine signatures as diagnostic biomarkers for septic cardiomyopathy
Causal association between cathepsins and asthma: a Mendelian randomization study
A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images
Bidirectional Mendelian randomization analysis of the causal associations between serum vitamin D levels and multiple kidney diseases
Abstract The relationship between vitamin D levels and the risk of kidney diseases, such as IgA nephropathy (IgAN), membranous nephropathy (MN), and diabetic nephropathy (DN), is still debated in observational studies. This research aims to evaluate the causal relationships between vitamin D and these kidney diseases using a bidirectional Mendelian randomization (MR) approach. We obtained summary-level data from genome-wide association studies (GWAS) on serum 25(OH)D levels, IgAN, MN, and DN to assess the causal impact of vitamin D on these kidney diseases. The primary method used for MR analysis was the inverse variance weighted (IVW) approach. To further ascertain the stability and reliability of our results, we performed sensitivity analyses including Cochran’s Q test, MR-Egger intercept test, and leave-one-out analysis, which helped identify potential pleiotropy and outlier single nucleotide polymorphisms (SNPs) influencing the associations. Our analysis revealed no causal relationships between serum 25(OH)D levels and the risks of IgAN, MN, and DN. Sensitivity analyses confirmed the robustness of the MR findings. This MR analysis robustly refutes causal associations between genetically determined 25(OH)D levels and IgAN, MN, and DN. These null findings challenge the paradigm of vitamin D supplementation as a preventive strategy for these nephropathies, urging clinicians to prioritize interventions targeting modifiable risk factors over vitamin D optimization in kidney disease management.
The impact of changes in breast density over time on breast cancer risk
Investigation of traumatic ulcer locations and healing progression during denture adjustment periods in Syrian complete denture patients
Predictive analysis of pediatric gastroenteritis risk factors and seasonal variations using VGG Dense HybridNetClassifier a novel deep learning approach
Abstract Pediatric gastroenteritis is a major reason for sickness and death among children worldwide, especially in places where healthcare and clean sanitation are scarce. Conventional methods of diagnosis overlook possible risks and seasonal trends, which results in patients receiving treatment too late and more of them being hospitalized. The study sets out to create a new deep learning method that boosts the initial prediction, proper classification, and seasonal trends of pediatric gastroenteritis through the use of hybrid convolutions. The VDHNC model was formed by merging the strong feature learning of VGG16 with the efficient information sharing feature of DenseNet. To create the model, data about clinical, demographic, and environmental aspects of pediatric patients were used. The dataset was preprocessed by using imputation, normalization, managing outliers, and using SMOTE to balance classes. Further validation was performed by analyzing the model performance using one-way ANOVA and pairwise t-tests with several baselines such as SVM, Random Forest, and XGBoost. The VDHNC model was able to achieve a high accuracy of 97%, and was more precise, recalled more information, and reported a higher AUC-ROC score than any other model. The model was able to discover signs of seasonal gastroenteritis, which assisted in predicting future outbreaks. A statistical test proved that VDHNC was better than the other approaches with a p-value of less than 0.05. VDHNC proves reliable when it comes to early detection and assessment of risk in pediatric gastroenteritis cases. The solidness and ease of understanding in this model suggest it can be helpful for making real-time public health decisions and planning hospital resources.