Role of induced-strain and interlayer coupling in contact resistance of VS-BGaX (X = S, Se) van der Waals heterostructures.

Using Density Functional Theory (DFT) calculations, we explored the electronic band structure and contact type (Schottky and Ohmic) at the interface of VS-BGaX (X = S, Se) metal-semiconductor (MS) van der Waals heterostructures (vdWHs). The thermal and dynamical stabilities of the investigated systems were systematically validated using energy-strain analysis, molecular dynamics (AIMD) simulations, as well as binding energy and phonon spectrum calculations. After analyzing the band structure, VS-BGaX (X = S, Se) MS vdWHs metallic behavior with type-III band alignment is revealed.

Diagnostic Reversion in Dementia Care: A Real-World Analysis of Mild Cognitive Impairment Diagnoses Following Dementia in a Large Electronic Medical Record System.

Introduction: If mild cognitive impairment (MCI) is diagnosed after dementia, it suggests either the dementia diagnosis was premature, or the MCI diagnosis is incorrect. We investigated the prevalence and predictors of such "diagnostic reversion"-MCI diagnosis following dementia diagnosis-in a large academic health system.

Methods: Among 5,965 patients aged 50+ with incident dementia in UCSF Health electronic health records, we identified "reverters" with a subsequent MCI diagnosis.

Single-Cell Profiling Reveals Altered Endometrial Cellular Features Across the Menstrual Cycle in Endometriosis Patients.

Endometriosis is a chronic, estrogen-dependent condition affecting over 190 million women globally, characterized by the ectopic presence of endometrial-like tissue that leads to inflammation, pain, and infertility. Despite its prevalence, the pathogenesis of endometriosis remains poorly understood. Here, we present a comprehensive single-cell transcriptomic atlas comprising 228,000 cells derived from 43 eutopic endometrial biopsies from patients with endometriosis, fibroid controls and healthy controls, sampled across the menstrual cycle.

Comorbidity analysis and clustering of endometriosis patients using electronic health records.

Endometriosis is a prevalent, complex, inflammatory condition associated with a diverse range of symptoms and comorbidities. Despite its substantial burden on patients, population-level studies that explore its comorbid patterns and heterogeneity are limited. In this retrospective case-control study, we analyze comorbidities from over forty thousand endometriosis patients across six University of California medical centers using de-identified electronic health record (EHR) data.

The unfolded protein response influences therapy outcome and disease progression in chronic lymphocytic leukaemia.

Since genomics, epigenomics and transcriptomics have provided only a partial explanation of chronic lymphocytic leukaemia (CLL) heterogeneity, and since concordance between mRNA and protein expression is incomplete, we related the CLL proteome to clinical outcome. CLL samples from patients who received fludarabine-containing chemoimmunotherapy were analysed by mass spectrometry (SWATH-MS). One dataset compared pre-treatment samples associated with an optimal versus suboptimal response, while another compared paired samples collected before treatment and at disease progression.

Decoding subphenotypes in electronic medical records within late-onset Alzheimer's disease reveals heterogeneity and sex-specific differences.

Background: Alzheimer's disease is a progressive neurodegenerative disorder with no curative treatment. Identifying distinct subphenotypes and understanding potential personalized modifications remain critical unmet needs.

Methods: We applied unsupervised learning techniques to electronic medical records from UCSF to identify distinct Alzheimer's disease subphenotypes based on comorbidity profiles.

Numerical analysis of electrochemically radiative and higher thermally conductive nanomaterials spinning motion due to rotating disk.

Optimization of heat and mass transfer via higher thermally conductive generalized nonlinear materials namely, the Cross fluid is one of the major contributions of this work. This particular work is further analyzed effectively in the presence of linear reactions as well as solar radiation. The flow configuration is assumed with anticlockwise rotation which guarantees more heat transfer as compared to the linear or translator motion of such materials.

Computational electrostatic engineering of nanobodies for enhanced SARS-CoV-2 receptor binding domain recognition.

This study presents a novel computational approach for engineering nanobodies (Nbs) for improved interaction with receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Using Protein Structure Reliability reports, RBD (7VYR_R) was selected and refined for subsequent Nb-RBD interactions. By leveraging electrostatic complementarity (EC) analysis, we engineered and characterized five Electrostatically Complementary Nbs (ECSb1-ECSb5) based on the CeVICA library's SR6c3 Nb.

Comorbidity analysis and clustering of endometriosis patients using electronic health records.

Endometriosis is a prevalent, complex, inflammatory condition associated with a diverse range of symptoms and comorbidities. Despite its substantial burden on patients, population-level studies that explore its comorbid patterns and heterogeneity are limited. In this retrospective case-control study, we analyzed comorbidities from over forty thousand endometriosis patients across six University of California medical centers using de-identified electronic health record (EHR) data.

Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis.

The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs). This causes degraded AI performance and poses a challenge for widespread clinical application, as fine-tuning algorithms for each site is impractical. Changes in the imaging workflow can also compromise diagnostic accuracy and patient safety.

The association of cholesterol levels with memory and memory change over a 14-year period in a US national cohort.

Introduction: The impact of cholesterol on late-life cognition remains controversial. We investigated the association of high-density lipoprotein cholesterol (HDL-C) and non-HDL-C with memory in a nationally representative cohort.

Methods: Health and Retirement Study (HRS) participants ( = 13,258) aged 50+ (mean age: 67.

The Role of Group and Save Testing Prior to Emergency Laparoscopic Appendicectomy.

The routine use of preoperative group and save (G&S) blood tests in emergency laparoscopic appendicectomies has been a standard yet often unquestioned practice. However, with the advancements in laparoscopic techniques and the low risk of intraoperative bleeding, is this precaution necessary? Analysing 276 emergency appendicectomy cases over a year, our study revealed that no transfusions were required due to surgical complications. Nevertheless, routine G&S testing causes considerable financial and resource strains, consuming valuable time and delaying treatment.

Th17/Treg cell balance in patients with papillary thyroid carcinoma: a new potential biomarker and therapeutic target.

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid carcinoma. The most effective treatment for PTC is surgical resection, and patients who undergo surgery have good survival outcomes, but some patients have distant metastasis or even multiorgan metastases at the time of initial diagnosis. Distant metastasis is associated with poorer prognosis and a higher mortality rate.

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory conditions in neonates. This evaluation is mainly based on the interpretation of visual patterns (horizontal artifacts, vertical artifacts, and consolidations).

Implications of Convolutional Neural Network for Brain MRI Image Classification to Identify Alzheimer's Disease.

Alzheimer's disease is a chronic clinical condition that is predominantly seen in age groups above 60 years. The early detection of the disease through image classification aids in effective diagnosis and suitable treatment. The magnetic resonance imaging (MRI) data on Alzheimer's disease have been collected from Kaggle which is a freely available data source.

FLUEnT: Transformer for detecting lung consolidations in videos using fused lung ultrasound encodings.

Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings.

Data-driven insights can transform women's reproductive health.

This perspective explores the transformative potential of data-driven insights to understand and address women's reproductive health conditions. Historically, clinical studies often excluded women, hindering comprehensive research into conditions such as adverse pregnancy outcomes and endometriosis. Recent advances in technology (e.

Perceptions and Information-Seeking Behavior Regarding COVID-19 Vaccination Among Patients With Chronic Kidney Disease in 2023: A Cross-Sectional Survey.

Background: People living with chronic kidney disease (CKD) face an increased risk of severe outcomes such as hospitalization or death from COVID-19. COVID-19 vaccination is a vital approach to mitigate the risk and severity of infection in patients with CKD. Limited information exists regarding the factors that shape COVID-19 vaccine uptake, including health information-seeking behavior and perceptions, within the CKD population.

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method.

Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation.

Virtual staining for histology by deep learning.

In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure.

Curated and Annotated Dataset of Lung US Images in Zambian Children with Clinical Pneumonia.

See also the commentary by Sitek in this issue.

Synergistic effect of silicon and arbuscular mycorrhizal fungi reduces cadmium accumulation by regulating hormonal transduction and lignin accumulation in maize.

Cadmium (Cd) stress causes serious damage to plants, inducing various physiological and biochemical disruptions that lead to reduced plant biomass and compromised growth. The study investigated the combined effects of silicon (Si) and arbuscular mycorrhizal fungi (AMF) on mitigating Cd stress in plants, revealing promising results in enhancing plant tolerance to Cd toxicity. Under Cd stress, plant biomass was significantly reduced (-33% and -30% shoot and root dry weights) as compared to control.