Browse Articles

Association between optical coherence tomography–quantified retinal features and cardiovascular risk in cardiovascular–kidney–metabolic syndrome stages 0–3: An analysis of a prospective UK biobank cohort

Background Cardiovascular–kidney–metabolic (CKM) syndrome is strongly associated with cardiovascular disease (CVD) and mortality. Stages 0–3 represent a key preclinical window for early intervention. Retinal optical coherence tomography (OCT) enables non-invasive evaluation of systemic microcirculation, but its prognostic value for CVD in CKM 0–3 remains undetermined. Methods We conducted a prospective study of 40,516 UK Biobank participants with CKM syndrome stages 0–3 who had retinal OCT. Cox regression, trend tests, and predictive metrics (NRI, IDI, AUC) were used to evaluate associations and incremental prognostic value of retinal structural markers. Results Baseline thinner retinal nerve fiber layer (RNFL) and macular thickness were independently associated with higher risks of incident CVD, coronary heart disease, all-cause mortality, and cardiovascular mortality over a subsequent median 14.6-year follow-up (all P < 0.001). Significant linear dose–response relationships were observed. Adding these markers to traditional risk models improved risk classification and discrimination (all P < 0.001). Conclusion Thinner RNFL and macular thickness are independent predictors of adverse cardiovascular outcomes in CKM 0–3. Integrating these OCT-derived biomarkers improves risk stratification, supporting a non-invasive approach for early cardiovascular risk assessment in this high-risk population.

Advancing Quality in the Evaluation, Surveillance, and Management of Aortic Stenosis: A Report From the AHA Target: AS Registry

BACKGROUND: Undertreatment and delayed treatment of aortic stenosis (AS) are common, both of which are associated with increased mortality. Historically, assessment of quality for AS care has focused on peri- and postprocedural outcomes. The American Heart Association Target: AS registry is the first national registry to provide data and evaluate quality for upstream processes of care for patients with AS. METHODS: Randomly selected patients from 2023 and 2024 with moderate or severe AS from 58 sites in the Target: AS registry were included. The 2 primary quality measures were (1) timely diagnosis (percentage of patients with an echocardiogram consistent with possible severe AS who had all assessments to clarify AS severity and symptoms within 30 days) and (2) timely treatment (percentage of patients with a Class I indication for aortic valve replacement treated within 90 days). Secondary measures included documentation of key echocardiographic parameters in the report, clinical recommendations in the echocardiogram report summary, multidisciplinary heart valve team evaluation, guideline-based performance of multimodality testing, and timely surveillance echocardiograms. RESULTS: Among 8097 patients, 47% were women, 7% Black, 6% Hispanic, and 3% Asian. Timely diagnosis occurred in 54% in 2023, improving to 61% in 2024 ( P =0.027) with gaps caused by lack of timely symptom assessment (23% [2023]; 16% [2024]), lack of stroke volume index (35% [2023]; 18% [2024]), or lack of timely multimodality testing (87% [2023]; 75% [2024]). Among those with a Class I indication for aortic valve replacement, timely treatment occurred in 82% (2023) versus 85% (2024) ( P =NS). Multidisciplinary heart valve team evaluation occurred in 78% (2023) versus 84% (2024) ( P =0.008). Key findings in the echocardiography report were documented in 83% (2023) and 85% (2024) ( P =NS), but a clinical recommendation was included in <10% of the time. Timely surveillance echocardiograms for moderate (2 years) and severe AS (1 year) were not performed in ~40% of patients. CONCLUSIONS: The national American Heart Association Target: AS registry demonstrates opportunities for improvement in timely diagnosis, surveillance, and treatment for patients with AS at participating centers. By providing an infrastructure to measure performance and identify best practices, the Target: AS registry has the potential to elevate and optimize care and patient outcomes.

Inside the new political screening that’s stalling NIH grants

Comparison of the effects of four atlantoaxial fusions on adjacent segments

Bio-derived bran particle inclusion for tailoring the thermo-mechanical behaviour of Kevlar fiber-reinforced sustainable composites

Abstract This research presents a comprehensive evaluation of the thermo-mechanical behavior of Kevlar fiber-reinforced epoxy composites modified with bio-derived bran particle fillers. Composite laminates were fabricated using a hand lay-up technique with a constant Kevlar fiber content (200 g) and bran filler incorporated at varying loadings from 0 to 4 wt% (samples S0–S4). The 3 wt% bran-filled composite (S3) exhibited the most balanced and enhanced performance across all evaluated properties. Tensile strength, flexural strength, and impact energy reached peak values of 161.39 MPa, 172.43 MPa, and 33.94 kJ/m², respectively, while Shore D surface hardness improved to 63. Fatigue testing demonstrated superior cyclic durability for S3, maintaining a stress resistance of 104 MPa after 30,000 cycles. Thermal analysis revealed a significant reduction in thermal conductivity from 0.98 W/mK (S0) to 0.74 W/mK (S3), alongside a decrease in the coefficient of linear thermal expansion from 1.29 × 10⁻⁵ /°C to 1.12 × 10⁻⁵ /°C. The heat deflection temperature increased notably from 91 °C (S0) to 116 °C (S3), indicating improved dimensional stability under thermal stress. Thermogravimetric analysis confirmed enhanced thermal resistance, with S3 showing a delayed onset of decomposition temperature at ~ 357 °C, a DTG peak at 482 °C, and a maximum residual char yield of 23.5% at 700 °C. Scanning electron microscopy of fractured specimens revealed uniform bran dispersion and strong interfacial adhesion in S3, contributing to reduced void formation and improved stress transfer. While water absorption increased with filler content due to the hydrophilic nature of bran, the uptake remained moderate, rising from 0.67% (S0) to 3.84% (S3). Overall, the strategic inclusion of 3 wt% bran filler effectively enhanced the thermal and mechanical performance of Kevlar/epoxy composites, offering a sustainable and high-performance material solution for structural applications operating under thermal and cyclic mechanical loads.

Interference resistant contactless heart rate variability monitoring via millimeter wave radar

Daily briefing: Ovaries start a second job after menopause

A multiscale interpretability framework for identifying actionable road network features to mitigate congestion in highly congested cities

Abstract Transferring congestion mitigation strategies across cities remains challenging due to two compounding issues: road network features affect congestion differently in different cities, and their influence may vary across spatial scales. We propose a multiscale framework to identify “actionable” road network features, defined as those maintaining a consistent directional association with traffic congestion across varying spatial resolutions. Through systematic investigation of seven highly congested cities worldwide, our findings reveal strong city-specific signatures in feature importance, indicating supply-side planning must be tailored individually. However, the most consistently exacerbating features for demand-side consideration show notable similarities across cities. We emphasise that the directional associations identified reflect patterns in the training data and should not be interpreted as causal effects; the identified features are policy-relevant candidates subject to validation through counterfactual modelling. Feature attribution is model-dependent: results are derived from RF-based TreeSHAP and should be interpreted accordingly. In the short term, our results directly inform ongoing measures like congestion pricing and personalised route choice applications, making them more robust and publicly acceptable. Long-term, our insights encourage re-evaluating scepticism toward infrastructure planning and highlight opportunities for designing congestion-resistant future cities. Given the diverse city selection, common observations identified can likely be extrapolated to new cities when detailed analysis is constrained by data limitations. We provide key recommendations for researchers. First, data-driven congestion studies must explicitly interrogate microscopic spatial scales, because coarse-scale models can conceal scale-dependent reversals in feature influence that directly undermine policy robustness. Second, simulation-based counterfactual approaches should test road network features beyond known value ranges.

Briefing Chat: What tickling a chimpanzee can tell us about the evolution of speech

TLR 9 (rs352140) gene polymorphism in Helicobacter pylori infection in children

Abstract Most of the global populace is susceptible to Helicobacter pylori ( H. pylori ) infection, which typically manifests in childhood. This study aimed to identify the role of TLR9 (rs352140) gene in suppressing or promoting the inflammation related to H. pylori infection in children. This cross-sectional study enrolled 100 children with dyspeptic symptoms undergoing upper endoscopy, including 50 with confirmed H. pylori infection and 50 age- and sex-matched H. pylori-negative controls. All children undertook full history, complete clinical investigation, laboratory testing, upper digestive endoscopies and genotyping of TLR9 (rs352140). A statistically significant difference presented among H. pylori positive and H. pylori negative children as regards TLR rs352140 gene polymorphism, as patients have statistically higher frequencies of homozygous TT genotype, CT genotype and of variant T allele in contrast to H. pylori negative group. Hazard of H. pylori is 7.9 times higher in children with TT genotype and 3.6 times higher in children with CT genotype in contrast to children carrying CC genotype. Also, children carrying the altered T allele had a statistically higher risk of H. pylori , 2.3 times in contrast to those carrying C allele. TLR9 rs352140 gene polymorphism was correlated with of H. pylori infection and incidence of gastritis in children.

Quad-port mm-wave MIMO antenna with a unified ground plane for 5G and beyond applications

Pressure-conditioned temporal graph-attention network for robotic tactile perception on uneven surfaces and varying velocities

A secure and lightweight cryptographic-machine learning framework for IoT-based cyber defense in resource-constrained environments

Abstract The rapid expansion of Internet of Things (IoT) systems has introduced significant security challenges, particularly in resource-constrained environments where traditional security mechanisms are often impractical. This paper presents a secure and lightweight hybrid framework that integrates cryptographic techniques with machine learning–based anomaly detection for IoT-based cyber defense. The proposed framework employs Elliptic Curve Cryptography (ECC) for key exchange, SPECK for lightweight encryption, and SHA-3 for data integrity, combined with a Random Forest classifier for anomaly detection. The framework is implemented and evaluated on a Raspberry Pi–based edge environment using the CIC-BCCC-NRC-IoT-2023 dataset. Experimental results demonstrate an accuracy of 89.5% and an F1-score of 90%, with an average end-to-end latency of 1.08 ms and energy consumption of approximately 4.5 mJ per inference. These results indicate that the proposed approach achieves a practical balance between security, computational efficiency, and detection performance under constrained conditions. While the framework shows promising results, its evaluation is limited to a controlled setup and a single primary dataset. Future work will focus on cross-dataset validation, adversarial robustness, and large-scale deployment analysis.

Differential functional and lipidomic impact of tofacitinib and other disease-modifying antirheumatic drugs on HDL in rheumatoid arthritis

Household insecticide uses and flea infestation in plague-endemic regions of Madagascar

Activation of the FAK-MAPK/ERK1 pathway promotes intestinal epithelial cell sheet migration during fistula tract repair

Abstract Postoperative fistula tracts remain a significant clinical challenge, yet the molecular mechanisms governing their healing are poorly understood. This study aimed to investigate the regulatory role of focal adhesion kinase (FAK) in intestinal mucosal epithelial cell migration during drainage tube-induced fistula tract healing and to elucidate its underlying mechanisms through the MAPK/ERK1 signaling pathway. A rabbit small intestinal fistula tract model was established and tissues were collected at 0, 7, 14, and 28 days post-surgery. Tissue characterization included histopathological evaluation (H&E and Masson trichrome staining) and molecular assessment of FAK phosphorylation, collagen deposition, and epithelial–mesenchymal transition (EMT) markers via immunohistochemistry, immunofluorescence, and Western blot analysis. In vitro, primary intestinal epithelial cells were isolated and FAK activity was modulated using pharmacological activation (ZINC40099027) or shRNA knockdown. Cell proliferation and migration were assessed via CCK-8, EdU, wound healing, and Transwell assays. Mechanistic involvement of the MAPK/ERK1 signaling pathway was validated using inhibitor (MAPK-IN-1) and agonist (C16-PAF) rescue experiments. In vivo, local injection of sh-FAK adenovirus evaluated FAK’s role in fistula tract healing. FAK phosphorylation (p-FAK) and Collagen I expression increased progressively during fistula tract maturation, exhibiting clear spatial co-localization in tissue sections. Pharmacological FAK activation enhanced epithelial cell proliferation, migration, MMP2/9 and Vimentin expression, and reduced E-cadherin levels, while shRNA-mediated FAK knockdown produced the opposite effects. Mechanistically, FAK activity positively correlated with MEK/ERK phosphorylation. Bidirectional rescue experiments using pathway inhibitors and agonists demonstrated that MAPK/ERK1 signaling is essential for FAK-induced cell migration. In vivo, local sh-FAK adenovirus injection suppressed MAPK/ERK1 activity, reduced granulation tissue and collagen deposition, and delayed fistula tract healing. FAK facilitates fistula tract repair by activating the MAPK/ERK1 pathway, driving epithelial cell proliferation, migration, and EMT-like transformation during the healing process. Targeting the FAK–MAPK/ERK1 axis represents a promising therapeutic strategy for managing refractory postoperative fistulas.

Innovative air assisted electrostatic sprayer for managing jassids (amrasca biguttula biguttula Ishida) and aphids (aphis gossypii Glover) in cotton

Adaptive burst routing in optical burst switching networks via graph-derived structural features and reinforcement learning

Abstract Optical Burst Switching (OBS) networks require adaptive and interpretable routing mechanisms to handle dynamic traffic variation and structural complexity. In this work, they aim to design a benchmark-oriented framework, called OBS-GraphSyn-2025, that combines graph-derived structural modeling, traffic temporal representation, and reinforcement learning based on PPO to evaluate the adaptivity of burst routing. It converts 3.57 M real traffic flows into 199,870 routing flows (with topology information), 79,133 burst-level routing states, and 15,000 PPO-compatible samples. A Traffic Burst Complexity Index–Graph (TBCI-G) is proposed, which combines the routing state complexity metrics of hop count, path complexity, edge load, and route uniqueness to quantify routing complexity. The PPO policy is based on fused states across time and space, and is able to adaptively route under controlled benchmark conditions using reward signals. The results of experimental evaluation over 250 episodes show stable learning performance, with a mean reward of 0.5610 ± 0.0068, throughput of 0.6717 ± 0.0709, and stability score of 0.5856 ± 0.0627. The scalability analysis showed that the runtime increases by a controlled amount (0.32–1.56 s) and the reward retention remains fairly unchanged (0.97–1.00). The consistency of the policy behavior was assessed by reproducibility, and resulted in a coefficient of variation of less than 1.20% in 30 independent runs. The findings show that OBS-GraphSyn-2025 offers a scalable, interpretable, and reproducible complexity-aware metric for evaluating routing-state in OBS-inspired networks while abstracting from the constraints of the optical-layer.

Biological buffering maintains stable pH from acidic to alkaline conditions in single nutrient source hydroponics

A lightweight hybrid ViT-GNN framework for data-centric land cover mapping in the amazon biome using graph structural priors

Abstract Continuous monitoring of the Amazon biome demands land cover classification models that are both highly sensitive and computationally feasible. To resolve the inherent trade-off between architectural complexity and predictive performance in spatial deep learning, this study introduces the Vision Transformer–Graph Neural Network with Feature Adaptation (ViT-GNN RFFA). In contrast to conventional end-to-end pixel models, this hybrid architecture operates exclusively on an 11-dimensional vector of extracted color-based vegetation indices (e.g., GRVI) and textural statistics. The dual-branch design isolates global sequence context via the ViT module while leveraging the GNN branch as a structural prior to learn non-linear covariance between specific features. Evaluated against a suite of benchmarks including MiniViT, Baseline CNN, Random Forest, XGBoost, and LightGBM, the proposed algorithm attained the highest Overall Accuracy of 0.930 utilizing merely 16,323 trainable parameters—a nearly 75% reduction in footprint versus pixel-based models. Crucially, a McNemar’s statistical test confirmed that the accuracy gain over the strongest classical baseline (XGBoost, OA: 0.927) is statistically significant ( p  < 0.05). By pairing rigorous spatial cross-validation with an interpretable feature space, this work establishes that intelligent data pre-processing combined with graph-based relational learning offers a robust framework for high-precision environmental mapping under severe resource limitations.