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Daily briefing: Bird flu is ‘everywhere’ on dairy farms

Instructor-Led VR training in medical emergency rescue education for road traffic accidents: a questionnaire-based study

Dichloroacetate enhances Chemo-sensitivity in wild-type P53 breast cancer cells by modulating ABCG2 and NKG2DL

Focused ultrasound suppresses pentylenetetrazol-induced epileptiform activity in rats and alters connectivity measured by functional MRI

How Paris dealt with lightning in the Age of Enlightenment

Distinct metabolic syndrome profiles across Asian American subpopulations

Abstract Metabolic Syndrome (MetS) is a cluster of conditions increasing risks for cardiovascular diseases. Existing research on Asian Americans has been limited by aggregate data reporting, which masks critical intra-group health variations. In this study, we conducted a comprehensive disaggregated analysis of MetS prevalence across diverse Asian American ethnic groups, revealing nuanced health disparities that challenge monolithic health narratives. We analyzed National Health and Nutrition Examination Survey data from 2011 to 2016. The Analysis sample consisted of five Asian American ethnic groups: Chinese (n = 652), Asian Indian (n = 409), Filipino (n = 262), Vietnamese (n = 243), and Korean (n = 215). Logistic regression assessed associations between MetS and demographic factors. Significant variations in MetS prevalence emerged across subpopulations and sexes. For example, among women with BMI < 23, Filipino women showed significantly higher MetS prevalence (31.70%) compared to Chinese women (14.45%). Among men with BMI 23-27.4, Asian Indian men exhibited higher prevalence (50.80%) than Vietnamese men (22.66%). Our findings also shed light on the nuanced associations between modifiable lifestyle factors and MetS risks in these Asian subpopulations. This study underscores the critical importance of data disaggregation in understanding health disparities. The findings support developing targeted interventions that address the unique metabolic health profiles within Asian American communities.

Impact of sacubitril/valsartan on chronic heart failure patients with sleep-disordered breathing: a systematic review and meta-analysis

Postdoc depression and anxiety rates are rising, finds survey of 872 researchers

CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance

Technoeconomic analysis of hydrogen versus natural gas considering safety hazards and energy efficiency indicators

Correction: Embedded solution to detect and classify head level objects using stereo vision for visually impaired people with audio feedback

High-efficiency dual-band switched beam antenna with back lobe suppression using parasitic elements and patch etching for 5G

Trends and anomalies in the coherence of the Southern Polar Vortex: A 26 year meta-study

Andrographolide promotes the ex vivo expansion of CD34+ hematopoietic stem cells derived from human umbilical cord blood

Enhanced residual-attention deep neural network for disease classification in maize leaf images

Abstract Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstanding potential for image classification. This study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves. Maize Net uses an attention mechanism to increase the model’s efficiency by focusing on the relevant features and residual learning to improve the gradient flow. This also addresses the vanishing gradient problem while training deeper neural networks. A five-fold cross-validation test is conducted for generalization across the dataset, generating five models based on distinct training and testing sets. The macro-average of all evaluation metrics is considered to address the dataset’s class imbalance problem. Maize Net achieved an average F1-score of 0.9509, recall of 0.9497, precision of 0.9525, and classification accuracy of 0.9595. These outcomes demonstrate MaizeNet’s robustness and reliability in automated plant disease classification.

Systematic review and meta-analysis of the impact of loss of consciousness on clinical outcomes in mild traumatic brain injury

Abstract While loss of consciousness (LOC) is a key factor in assessing head injuries, its impact on clinical outcomes, including persistent post-concussive symptoms, mental health disorders, quality of life, and neurodegeneration, remains unclear. This systematic review explores the association of LOC in Mild Traumatic Brain Injury (mTBI) with clinical outcomes such as mental health, quality of life, and risk of neurodegenerative diseases. Comprehensive systematic review methodology; two electronic databases (PubMed, Embase) were systematically searched from January 1990 to December 2024. Pooled odds ratios (OR) were obtained using a random effects model. A total of 595 studies were assessed with 30 trials meeting inclusion criteria. The presence of LOC is associated with worsened clinical outcomes including persistent post-concussive symptoms (OR 1.89, 95% CI: 1.59–2.25), post-traumatic stress disorder (OR 1.81, 95% CI: 1.54–2.12), depression (OR 2.69, 95% CI: 2.10–3.43), and overall health-related quality of life (OR 1.84, 95% CI: 1.49–2.26). These findings suggest that the role of LOC in the outcomes of mTBI supports a higher risk of poorer short and long-term outcomes. Future studies may investigate variation in post-mTBI sequelae among those with similar LOC timelines.

A novel 3D hemispherical reconfigurable antenna with a switchable radiation pattern using Butler matrix

Combined preoperative platelet-albumin ratio and cancer inflammation prognostic index predicts prognosis in colorectal cancer: a retrospective study

Amino acids and BCAA composition of Mungbean (Vigna radiata L.) seeds and sprouts for plant-based protein applications

Development of in-house software to process real-time cine magnetic resonance images acquired during 1.5 T MR-guided radiation therapy

Abstract This study aimed to develop and publicly release an in-house software package that converts the binary file format of 2D cine magnetic resonance (MR) images acquired through the Treatment Session Manager (TSM) of MOSAIQ on the Elekta Unity (Elekta AB, Stockholm, Sweden) into standard readable data formats. The software was developed using MATLAB (MathWorks, Natick, MA, USA) and includes an automatic image-sorting algorithm to classify the images into coronal, sagittal, and axial planes. To verify the geometric accuracy of the converted images, they were acquired from an MRgRT motion management QA phantom, both with and without motion. For the converted images without motion, the geometric size of the phantom was measured and compared with the known values. For the images acquired with motion, the magnitudes measured using the converted cine MR images were compared with the artificially introduced motions. The results showed that the 2D cine MR images in the binary file format were successfully converted into the metadata/DICOM format and accurately classified into different planes. The accuracies of the geometric size and motion in the converted 2D cine MR images were greater than 99.6% and 94.2%, respectively. This software is expected to be useful for 1.5 T MR-linac users in analyzing internal organ motion during radiation treatment.