Browse Articles

Optimization of the extraction process of Sanhuang Qingre Formula by integrating response surface methodology, grey correlation analysis, and machine learning

A comparative evaluation of time-series models for forecasting inpatient deaths and discharges against medical advice

Evaluation and correlation of heart rate variability and ventricular repolarization parameters in an Indian pediatric clinical hypothyroid population: a prospective cohort study

Abstract To compare ventricular repolarization and heart rate variability in hypothyroid and healthy children and to assess the effects of levothyroxine therapy on cardiac electrophysiological and autonomic parameters. This study investigated ventricular repolarization parameters and heart rate variability (HRV) in hypothyroid children (5–12 years) compared with age-matched healthy controls and patients before and after thyroxine treatment. This study included 64 participants divided into hypothyroid and healthy groups (32 each). Parameters such as the Tpe interval, QT interval, and HRV indices were measured via standard electrocardiographic and HRV analysis techniques. Thyroid hormone levels were measured. Post treatment measurements were also performed on hypothyroid children following 3 months of levothyroxine therapy. Hypothyroid children presented significantly prolonged Tpe intervals (58.07 ± 9.21 ms vs. 53.80 ± 8.89 ms, p = 0.039), reflecting increased heterogeneity in ventricular repolarization. HRV analysis revealed decreased parasympathetic modulation (e.g., lower RMSSD and pNN50 values) in hypothyroid participants. Posttreatment improvements in HRV indices and thyroid hormone levels correlated with normalization of cardiac electrophysiological parameters. This study highlights that even mild thyroid dysfunction in children can subtly disrupt cardiac electrical stability and autonomic balance which may increase the risk of arrhythmia in this population. Timely thyroxine therapy not only restores thyroid levels but also powerfully rebalances the electrical and autonomic balance of the heart. These findings reaffirm the critical need for early detection and proactive treatment of pediatric hypothyroidism to safeguard lifelong cardiovascular health.

Ultra-high resolution OCT imaging reveals retinal layer degeneration in RD10 mice with comparison to commercial standard resolution OCT

Content style decoupling for multi style image generation using latent diffusion architecture

Mixing regimes shape microbial community composition, nutrient regimes, and plant growth attributes in Jeevamrit: metagenomics and culturomics-based insights

Commonalities and distinctions of static and dynamic functional connectivity density between left and right temporal lobe epilepsy

Real-world performance of the AI diagnostic system IDx-DR in the diagnosis of diabetic retinopathy and its main confounders

Abstract The escalating prevalence of diabetes mellitus (DM) emphasizes the critical need for early detection of diabetic retinopathy (DR). This study assesses the performance of the autonomous AI-based diagnostic system IDx-DR in detecting DR and its associated confounders in a real-world clinical setting. This prospective cross-sectional study involved 875 diabetic patients with a mean age of 52 years (range: 8–92). Retinal images were captured by trained assistants. IDx-DR results were compared with mydriatic fundus examination (gold standard) and Ophthalmologists’ image analysis. Factors impacting image acquisition or analyzability were examined. Among all patients, 10.5% yielded no image in miosis, and 26.1% were unanalyzable by IDx-DR. Confounders affecting image acquisition were examiner, pupil size, patient age and patients’ visual acuity. When good quality images were achieved, IDx-DR performed well, particularly in detection of severe DR (sensitivity 94.4%; specificity 90.5%). IDx-DR results exactly matched Ophthalmologists’ mydriatic fundoscopy gradings in 54.2% if images of sufficient quality were obtainable. Undergrading of DR severity by IDx-DR was rare (4.8%). IDx-DR shows promise in detecting DR, especially in resource-limited settings and in detecting severe DR. One remaining challenge is good image acquisition in miotic patients.

Characterization and dynamics of lignocellulosic components, enzyme activities and microbial populations in diverse crop residues during decomposition

Association between multimorbidity and childhood socioeconomic status with depressive symptoms among middle-aged and older adults in rural western China

SHI: a framework for spatial harmonic imaging

Abstract Despite the growing interest in multicontrast X-ray imaging, spatial harmonic imaging remains limited by a lack of specialized computational resources. In this paper, we present SHI , a high-performance software framework that covers the range from data acquisition to processing in spatial harmonic imaging experiments. SHI is an open-source software package that facilitates the acquisition of precise measurement data and streamlines the workflow, ensuring that data can be efficiently organized, processed, and visualized, leading to high quality results. In addition, SHI includes higher-order harmonic extraction. Preliminary results show that spatial harmonic imaging improves experimental robustness and retrieves refraction and scattering information, albeit with reduced resolution. However, using these lower-resolution images enables faster CT reconstruction with fewer projections, while preserving essential sample features and allowing a substantial reduction in exposure levels. This research focuses on the acquisition methodology and the subsequent data processing for contrast retrieval and multicontrast computed tomography.

Refractory eosinophilic duodenal bulb ulcer associated with Helicobacter pylori eradication in children: a multicenter study

Quantum spin resonance in engineered proteins for multimodal sensing

Abstract Sensing technologies that exploit quantum phenomena for measurement are finding increasing applications across materials, physical and biological sciences 1–7 . Until recently, biological candidates for quantum sensors were limited to in vitro systems, had poor sensitivity and were prone to light-induced degradation. These limitations impeded practical biotechnological applications, and high-throughput study that would facilitate their engineering and optimization. We recently developed a class of magneto-sensitive fluorescent proteins including MagLOV, which overcomes many of these challenges 8 . Here we show that through directed evolution, it is possible to engineer these proteins to alter the properties of their response to magnetic fields and radio frequencies. We find that MagLOV exhibits optically detected magnetic resonance in living bacterial cells at room temperature, at sufficiently high signal-to-noise for single-cell detection. These effects are explained through the radical-pair mechanism, which involves the protein backbone and a bound flavin cofactor. Using optically detected magnetic resonance and fluorescence magnetic-field effects, we explore a range of applications, including spatial localization of fluorescence signals using gradient fields (that is, magnetic resonance imaging using a genetically encoded probe), sensing of the molecular microenvironment, multiplexing of bio-imaging and lock-in detection, mitigating typical biological imaging challenges such as light scattering and autofluorescence. Taken together, our results represent a suite of sensing modalities for engineered biological systems, based on and designed around understanding the quantum-mechanical properties of magneto-sensitive fluorescent proteins.

Orientation driven design and mechanical optimization of gyroid TPMS lattice structures

Abstract Triply Periodic Minimal Surface (TPMS) lattice structures, particularly Gyroid morphologies, are gaining attention for their high specific strength, energy absorption, and geometric adaptability. However, the large body of prior studies has focused on comparing different TPMS topologies or materials, with limited attention to how the build orientation of a single Gyroid structure influences its mechanical behavior. Addressing this gap, this study presents a novel design strategy by systematically varying the build orientation of Gyroid structures fabricated via Fused Filament Fabrication (FFF) using PLA-metal composite. Six models, termed G0 - G5, were created by altering orientation angles relative to the z-axis. Experimental and finite element analyses showed strong agreement and revealed that axially aligned models (G1, G3, G5) achieved significantly higher stiffness, strength, and energy absorption. Homogenization confirmed orientation-dependent anisotropy, and true stress analysis based on CAD-derived cross-sections improved stress accuracy. Increasing wall thickness induced a shift from bending- to stretch- and shear-dominated deformation. Power-law fitting of mechanical properties versus relative density achieved R² > 0.95, validating Gibson-Ashby scaling. These insights support tailored Gyroid designs for crashworthiness, aerospace, automotive and biomedical applications.

Designing an eco-friendly graphene oxide-based titanium tetra-acetoximate modified epoxy coating for anti-corrosion properties of aluminium

Thalamocortical transcriptional gates coordinate memory stabilization

Evidence of a genomic basis for growth rate variation in a natural kelp population

Abstract Understanding the genetic architecture of functional traits can provide key insights into the ecological dynamics and adaptive potential of species. We investigated whether genetic data can predict growth rate variation in a natural population of the widespread kelp, Ecklonia radiata . We tagged kelps and tracked their growth in situ over spring when growth is maximal. Individual kelps were then genotyped using reduced representation sequencing (ddRAD) and we employed multiple approaches to assess whether genetic variation corresponded with growth rate variation. Despite a limited sample size, we found evidence that growth rate can be strongly predicted from genetic variation, with approximately half of the variation in growth rate predicted by only 18 loci (R 2  = 0.499). Leveraging published transcriptomic data, we confirm that most of these loci are expressed or are linked to expressed putative genes. However, many of these genes are of unknown function and do not match well-known gene families. These findings have important implications for understanding natural kelp forest dynamics and for applied approaches such as selective breeding and aquaculture. While our study offers an important first assessment of the possible genomic architecture underlying growth rate in E. radiata , future work is needed to confirm this apparent link between genetic and functional variation.

An intelligent single valued neutrosophic MCDM framework for Business English language analysis curriculum planning and pedagogical support under uncertainty

A multi-source physiological data-driven method for assessing the hazard perception level of operators

Mapping at-risk transportation infrastructure assets using statistical and machine learning methods

Abstract Geotechnical assets such as highway embankments and slopes (HWS) are critical to the integrity of transportation infrastructure. However, they are largely overlooked by Transportation Asset Management programs in the United States. The HWS are vulnerable to landslides induced by several factors including frequent occurrences of extreme rainfall events. Therefore, mapping vulnerable HWS and developing an inventory will significantly help with infrastructure asset management. To this end, this research adopted proven geographical information systems based on landslide susceptibility mapping methods typically applied to hillside slopes, and a method for mapping at-risk HWS assets was developed. Several supervised machine learning (ML) classification models were developed and evaluated to accurately map at-risk HWS in the study area of central Mississippi. Digital Elevation Models (DEMs) created from remote sensing data obtained from satellites, drone sensors, and terrestrial LiDAR were utilized to develop rasterized causative factors. The causative factors used included: Geotechnical and geomorphological attributes, such as slope, aspect, curvature, elevation, normalized difference vegetation index (NDVI), soil composition, and terrain from DEM; and hydrological factors, including precipitation, distance from the stream, groundwater depth, and topographic wetness index. Known locations of failed and not-failed HWS were selected and rasterized, and the pixels were extracted as ground truth data. The rasterized causative factors were utilized as independent features to train the classification ML models for predicting HWS failure susceptibility. Models were evaluated by developing confusion matrices and using probabilistic metrics such as area under curve (AUC) score, F-1score, and Accuracy scores. Random forest outperformed the other models (AUC, F1, and Accuracy scores of 1.0). Probability threshold tuning was performed on the random forest model, and susceptibility maps with different thresholds were evaluated. An optimal threshold of 0.75 was used to balance false negatives and false positives in the predicted results, ensuring more reliable identification of hazard-prone slopes. The trained RF model revealed that the elevation, distance from streams, the NDVI, and precipitation were the top four factors influencing HWS failures in this study. The method allows for easy identification of vulnerable HWS across vast geographic areas. This method helps in effective fund utilization by doing targeted interventions and preventative maintenance efforts. Transportation agencies can implement this methodology on HWS at any location to strategize geotechnical asset management efforts.