Prevalence of High-Risk CTA-Based Carotid Plaque-RADS Subtypes in Patients With Embolic Stroke of Undetermined Source.

Background: A modified computed tomography angiography (CTA)-based Carotid Plaque Reporting and Data System (Plaque-RADS) classification was applied to a cohort of patients with embolic stroke of undetermined source to test whether high-risk Plaque-RADS subtypes are more prevalent on the ipsilateral side of stroke. With the widespread use of CTA for stroke evaluation, a CTA-based Plaque-RADS would be valuable for generalizability.

Methods: A retrospective observational cross-sectional study was conducted at a single integrated health system comprised of 3 hospitals with a comprehensive stroke center between October 1, 2015, and April 1, 2017.

The association between total social exposure and incident multimorbidity: A population-based cohort study.

Background: Multimorbidity, the co-occurrence of two or more chronic conditions, is associated with the social determinants of health. Using comprehensive linked population-representative data, we sought to understand the combined effect of multiple social determinants on multimorbidity incidence in Ontario, Canada.

Methods: Ontario respondents aged 20-55 in 2001-2011 cycles of the Canadian Community Health Survey were linked to administrative health data ascertain multimorbidity status until 2022.

Detecting anomalies in smart wearables for hypertension: a deep learning mechanism.

Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).

Impact of crop commercialization on multidimensional poverty in rural Ethiopia: propensity score approach.

Introduction: Reducing poverty through crop commercialization is one of the antipoverty efforts that helps promote health. This study explored the prevalence and the causal relationship between crop commercialization and rural Ethiopian households' multidimensional poverty using multilevel data.

Methods: The study uses data from the most recent nationally representative Ethiopian socioeconomic survey 2018/19 to calculate the rural multidimensional poverty index using the Alkire and Foster technique.

Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education.

Background: Large language models (LLMs) have demonstrated impressive performance on medical licensing and diagnosis-related exams. However, comparative evaluations to optimize LLM performance and ability in the domain of comprehensive medication management (CMM) are lacking. The purpose of this evaluation was to test various LLMs performance optimization strategies and performance on critical care pharmacotherapy questions used in the assessment of Doctor of Pharmacy students.

Contrast-enhanced ultrasound of collecting duct carcinoma in the renal: a case report misdiagnosed as renal abscess on computed tomography scan.

Collecting duct carcinoma (CDC), also known as Bellini duct carcinoma, is a rare malignancy with significant challenges in early diagnosis. This paper presents a case report of CDC that was misdiagnosed as renal abscess on computed tomography (CT). A 49-year-old male patient was admitted to the hospital with bilateral lumbar pain, exacerbated on the left side, accompanied by hematuria.

Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.

Introduction: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure.

Climate friendly MOFs synthesis for drug delivery systems by integrating AI, intelligent manufacturing, and quantum solutions in industry 6.0 sustainable approach.

Since the Industrial Revolution, ecological damage, ecosystem disruption, and climate change acceleration have frequently resulted from human advancement at the price of the environment. Due to the rise in illnesses, Industry 6.0 calls for a renewed dedication to sustainability with latest technologies.

Bioethics Principles in Machine Learning-Healthcare Application Design: Achieving Health Justice and Health Equity.

Health technologies featuring artificial intelligence (AI) are becoming more common. Some healthcare AIs are exhibiting bias towards underrepresented persons and populations. Although many computer scientists and healthcare professionals agree that eliminating or mitigating bias in healthcare AIs is needed, little information exists regarding how to operationalize bioethics principles like autonomy in product design and implementation.

Evaluation of reporting trends in the MAUDE Database: 1991 to 2022.

Unlabelled: Adverse event reporting for medical devices is critical for risk mitigation. The Food and Drug Administration's (FDA) Manufacturer and User Facility Device Experience (MAUDE) database serves as a key tool for post-market surveillance, receiving reports from various sources. Ensuring information integrity, especially across diverse reporting sources, is paramount.

Computer-assisted discovery of natural inhibitors for platelet-derived growth factor alpha as novel therapeutics for thyroid cancer.

Platelet-derived growth factor alpha (PDGFRA) plays a significant role in various malignant tumors. PDGFRA expression boosts thyroid cancer cell proliferation and metastasis. Radiorefractory thyroid cancer is poorly differentiated, very aggressive, and resistant to radioiodine therapy.

Synthetic data in generalizable, learning-based neuroimaging.

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.

Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks.

Objectives: This study aims to explore the capabilities of dendritic learning within feedforward tree networks (FFTN) in comparison to traditional synaptic plasticity models, particularly in the context of digit recognition tasks using the MNIST dataset.

Methods: We employed FFTNs with nonlinear dendritic segment amplification and Hebbian learning rules to enhance computational efficiency. The MNIST dataset, consisting of 70,000 images of handwritten digits, was used for training and testing.

Discordant Responses Between Imaging Examination and Surgical Pathology of Head and Heck Squamous Cell Carcinoma After Neoadjuvant Immunotherapy Combined With Chemotherapy.

Background: We here investigated the value of imaging examination in evaluating tumor remission-based surgery in patients with head and neck squamous cell carcinoma (HNSCC), who had undergone neoadjuvant immunotherapy combined with chemotherapy (NICC).

Methods: HNSCC patients who underwent NICC and surgery from May 2021 to September 2023 were retrospectively analyzed. All patients had to undergo imaging examination evaluation, including enhanced computed tomography (CT) and enhanced magnetic resonance (MR) imaging before and after NICC.

Investigating Risk Factors for Racial Disparity in E-Cigarette Use with PATH Study.

Background: Previous research has identified differences in e-cigarette use and socioeconomic factors between different racial groups However, there is little research examining specific risk factors contributing to the racial differences.

Objective: This study sought to identify racial disparities in e-cigarette use and to determine risk factors that help explain these differences.

Methods: We used Wave 5 (2018-2019) of the Adult Population Assessment of Tobacco and Health (PATH) Study.

Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism.

The prediction of energy consumption in households is essential due to the reliance on electrical appliances for daily activities. Accurate assessment of energy demand is crucial for effective energy generation, preventing overloads and optimizing energy storage. Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques.

Ghadeer-speech-crowd-corpus: Speech dataset.

The availability of raw data is a considerable challenge across most branches of science. In the absence of data, neither experiments can be conducted nor development can be undertaken. Despite their importance, raw data are still lacking across many scientific fields.

Proton beam therapy image-guided radiotherapy training for RTTs - Experience from a single institution.

This study investigates whether an Image-Guided Radiation Therapy (IGRT) workbook and Cone Beam Computed Tomography (CBCT) case studies enhances Radiation Therapists' (RTTs) confidence analysing Proton Beam Therapy (PBT) CBCTs. An 11-participant questionnaire-based study was conducted to assess pre- and post-training confidence. Prior to training, RTTs exhibited higher confidence in photon CBCT decision-making over proton CBCT, highlighting the need for PBT-specific IGRT training, irrespective of prior photon experience.

Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring.

Introduction: Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification.

Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery.

Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets.

Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic.

Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data.

First-principles predictions of two-dimensional Ce-based ferromagnetic semiconductors: CeF and CeFCl monolayers.

Two-dimensional (2D) ferromagnetic (FM) semiconductors hold great promise for the next generation spintronics devices. By performing density functional theory first-principles calculations, both CeF and CeFCl monolayers are studied, our calculation results show that CeF is a FM semiconductor with sizable magneto-crystalline anisotropy energy (MAE) and high Curie temperature (290 K), but a smaller band gap and thermal instability indicate that it is not applicable at higher temperature. Its isoelectronic analogue, the CeFCl monolayer, is a bipolar FM semiconductor, its dynamics, elastic, and thermal stability are confirmed, our results demonstrate promising applications of the CeFCl monolayer for next-generation spintronic devices owing to its high Curie temperature (200 K), stable semiconducting features, and stability.

Deciphering allosteric mechanisms in KRAS activation: insights from GTP-induced conformational dynamics and interaction network reorganization.

The conformational dynamics and activation mechanisms of KRAS proteins are of great importance for targeted cancer therapy. However, the detailed molecular mechanics of KRAS activation induced by GTP binding remains unclear. In this study, we systematically investigated how GTP/GDP exchange affects the thermodynamic and kinetic properties of KRAS and explored the activation mechanism using molecular dynamics (MD) simulations, Markov state models (MSMs), and neural relational inference (NRI) models.

Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.

Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.

Ethnic and racial differences in children and young people with respiratory and neurological post-acute sequelae of SARS-CoV-2: an electronic health record-based cohort study from the RECOVER Initiative.

Background: Children from racial and ethnic minority groups are at greater risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they have increased risk for post-acute sequelae of SARS-CoV-2 (PASC). Our objectives were to assess whether the risk of respiratory and neurologic PASC differs by race/ethnicity and social drivers of health.

Methods: We conducted a retrospective cohort study of individuals <21 years seeking care at 24 health systems across the U.