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Metabolomics analysis identifies differential metabolites and potential diagnostic biomarkers among pediatric sepsis subtypes
Background Sepsis in children can be caused by a variety of pathogens, with bacteria and viruses being the most common. This study used metabolomics to identify differences in metabolic profiles and potential biomarkers among pathogens causing pediatric sepsis. Methods Serum metabolomic profiles of pediatric bacterial and viral sepsis were obtained from the MetaboLights database (MTBLS563). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA were employed to explore metabolic distinctions. Differential expression metabolites (DEMs) were identified using the Wilcoxon rank-sum test and variable importance in projection (VIP) scores. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, receiver operating characteristic (ROC) analysis, Extreme Gradient Boosting (XGBoost) modeling, and Shapley Additive exPlanations (SHAP) analysis were conducted to determine diagnostic metabolites and evaluate model performance. Results PCA and PLS-DA revealed distinct metabolic profiles among bacterial pediatric sepsis (PBID_PS), viral pediatric sepsis (VID_PS), and healthy controls. Fourteen differential metabolites were identified, primarily enriched in nitrogen metabolism, arginine biosynthesis, and the metabolism of alanine, aspartate, and glutamate. Among them, choline, glutamate, and glutamine exhibited strong discriminatory ability between PBID_PS and VID_PS. XGBoost and SHAP analyses confirmed these metabolites as key diagnostic indicators, achieving excellent predictive performance and revealing distinct metabolic reprogramming underlying different etiologies of pediatric sepsis. Conclusion Metabolomic profiling revealed distinct metabolic signatures between bacterial and viral pediatric sepsis, with glutamate, glutamine, and choline serving as potential biomarkers.
Nests in an egg cell: structures of protein-storage units in oocytes
Revenue-sensitive evaluation of AI-assisted ICD-10-CM coding and human-AI collaboration under dual DRG payment systems
Abstract Automated ICD-10-CM coding is critical for hospital reimbursement under Diagnosis-Related Group (DRG) payment systems, yet standard metrics weight all errors equally. This study evaluated 11 models on MIMIC-IV under heterogeneous conditions (the full 7942-code space, top-50 self-trained baselines, and 200-admission zero-shot LLM samples) and proposed two revenue-sensitive metrics: the Revenue Sensitivity Index (RSI) and Coding Reimbursement Score (CRS). Performance was compared across US Medicare Severity DRG (MS-DRG) and Taiwan DRG (Tw-DRG) systems, with five human-AI review strategies simulated. PLM-ICD achieved the highest micro-averaged F1 (0.5934), while open-source zero-shot LLMs performed markedly worse in this exploratory comparison. A 26.5% CRS gap separated the best and worst fine-tuned models. Rankings were identical under both DRG schemes (Spearman ρ = 1.00), indicating stability under a tiered Tw-DRG approximation (93.9% coverage), not the official grouper. At a 20% review rate, revenue-targeted prioritization achieved 43.2% CRS reduction versus 20.0% for random sampling, reaching 91% of the oracle bound. Revenue-aware evaluation captures financially meaningful differences missed by standard metrics, and revenue-guided human-AI collaboration emerges as a candidate deployment framework requiring prospective validation.
Evaluation of the implementation process of a new emergency triage system: West coast system for triage (WEST)
Background Emergency Department (ED) crowding has escalated globally and has been associated with delayed treatment and increased short-term mortality. In western Sweden, several EDs transitioned from the Rapid Emergency Triage and Treatment System (RETTS©) to the West coast system for triage (WEST), based on principles of the South African Triage Scale. While WEST was introduced and spread rapidly and may have potential benefits, reports suggested challenges during its implementation. Objective This study examined the implementation of WEST at the first two EDs (ED1 and ED2) and identified barriers and facilitators. The study focused on the transition from RETTS© to WEST and explored staff experiences across implementation stages. Methods The study used a qualitative descriptive design with a mixed-methods approach and included semi-structured individual interviews with 36 triage nurses from two hospitals (ED1 n = 17; ED2 n = 19). Interviews explored experiences with WEST, implementation challenges, and comparisons with RETTS©, using Rogers’ “Diffusion of Innovations” as a sensitizing framework and treating each ED as a distinct social system. A deductive qualitative content analysis guided by Rogers’ Diffusion of Innovations was conducted, with inductive coding of unexpected themes from the final open question. Descriptive analyses of Likert-scale items complemented the qualitative analysis. The study received ethical approval, and written informed consent was obtained from all participants before interviews. Results ED1 participants expressed great enthusiasm and perceived WEST as a notable improvement. ED2 responses were more varied, with reservations commonly targeting the implementation process and concurrent organizational changes. Across both sites, most nurses (30/36) perceived WEST as more accurate than RETTS© in identifying critically ill patients. Conclusions Implementing WEST was feasible but context dependent. Information, motivation, and consensus-building, education, and organizational readiness emerged as important factors in the implementation process. Viewing each ED as a distinct social system, with its own culture and readiness for change, helped explain why adoption of WEST varied between sites.
Corvis ST parameters in primary open angle and angle closure glaucoma after propensity score matching
Abstract This retrospective study evaluated differences in Corvis ST tonometry (CST) parameters between eyes with primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG) using propensity score matching. We analyzed 48 eyes with POAG and 48 eyes with PACG. Twelve CST parameters were compared using linear mixed-effects models, with group as a fixed effect and subject ID as a random intercept to account for inter-eye correlation, adjusting for age, sex, axial length, intraocular pressure, and central corneal thickness. Multiple comparisons were controlled using the Benjamini–Hochberg procedure. Compared with POAG eyes, PACG eyes showed longer A2 time, greater A2 deformation amplitude, and greater whole-eye movement; however, these differences were no longer statistically significant after correction for multiple testing. After adjustment for relevant covariates and correction for multiple comparisons, this study did not demonstrate statistically significant differences in CST parameters between POAG and PACG eyes. The uncorrected trends toward greater indentation-related responses in PACG eyes should be regarded as exploratory.
Production of anti-inflammatory and antidiabetic oligosaccharides from okra mucilage through one-step microbial fermentation
Plant-based natural products are increasingly being explored as safer and more sustainable alternatives than synthetic drugs for controlling globally rising chronic health conditions, including inflammation and diabetes. Microbial fermentation has long been used to enhance the value and bioactivity of natural products. In this study, we produced bioactive oligosaccharides from okra ( Abelmoschus esculentus ) mucilage polysaccharides using a sustainable one-step Pichia kudriavzevii -mediated fermentation approach. The oligosaccharide fractions separated by size-exclusion chromatography primarily contained uronic acids, pentoses, and hexoses. In vitro assays showed that these oligosaccharides possess notable anti-inflammatory and antidiabetic potential. Among the fractions, ODF91 exhibited the strongest membrane-stabilizing activity, providing 92.86% protection of the erythrocyte membrane, comparable to that of the standard anti-inflammatory drug. ODF91, at 200 mg/kg, markedly reduced paw edema by 94.69% in the carrageenan-induced mouse model, supporting preliminary in vivo anti-inflammatory activity. ODF91, at 200 mg/kg, also lowered blood glucose levels in streptozotocin-induced diabetic mice by 86.91%, followed by ODF83 and ODF84, suggesting promising preliminary antihyperglycemic activity. Furthermore, lipid profile parameters significantly improved in diabetic mice after treatment with these oligosaccharides. Collectively, these results suggest that the fermented okra-derived oligosaccharides possess promising anti-inflammatory and antidiabetic properties and highlight microbial fermentation as a scalable strategy for converting underutilized plant polysaccharides into value-added bioactive products.
Cavity-driven attractive interactions in quantum materials
Analytical comparisons for solving the modified fractional Kawahara equation: application on numerical simulation of chemical signaling processes
Abstract Fractional models are essential for describing nonlinear, memory dependent wave phenomena in complex media, yet solving high order fractional nonlinear PDEs such as the Modified Caputo Fractional Kawahara Equation (MCFKE) remains challenging. This work introduces the Yang Residual Power Series Method (Yang RPSM), which integrates the Yang transform with a residual-based power series expansion to generate efficient semi-analytical solutions. Stability and convergence of the iterative scheme are established. Numerical comparisons show that the Yang RPSM outperforms natural transform decomposition technique (NTDT) and homotopy analysis technique (HAT) in accuracy and computational behavior. Applications to intracellular Ca $$^{2+}$$ propagation further demonstrate that the MCFKE effectively captures memory effects, nonlinear wave steepening, and dispersion-driven attenuation. The Yang RPSM provides a reliable computational tool for high order fractional PDEs and highlights the MCFKE as a biologically meaningful model for anomalous, memory driven wave processes.
A simulation-based hybrid causal predictive framework for stockout risk analysis in supply chain
Stockout risk is a persistent challenge in supply chain management, undermining both operational efficiency and customer satisfaction. This study adopts a multi-method approach to investigate the causal effect of lead time on stockout risk by integrating causal inference techniques with predictive analytics. The proposed framework combines Propensity Score Matching (PSM), Instrumental Variables (IV-2SLS), Inverse Probability Weighting (IPW), and Doubly Robust Estimation (DRE) alongside machine learning (ML) algorithms and time series forecasting. Using a dataset of 20,000 supply chain incidents, the study estimates the Average Treatment Effect (ATE) and evaluates predictive model performance. PSM generated the most credible ATE (0.882), confirming a strong causal link between lead time and stockout risk. IV analysis using supplier distance as an instrument yielded a reduced and statistically insignificant ATE (0.5535, p = 0.3148), suggesting instrument weakness. Among ML models, Random Forest and LightGBM achieved superior predictive accuracy (R 2 = 0.25; MSE = 0.736), while Moving Average forecasting effectively captured stockout patterns over time (R 2 = 0.883). The findings identify PSM as the most robust technique for causal inference. This study advances the literature by integrating causal inference, ML, and time series methods, offering practical, data-driven insights to strengthen operational resilience and guide proactive inventory management.
Gut microbiome profiles in Chilean participants with colorectal adenomas: an exploratory 16S rRNA sequencing study
Convergence of alimentary air inflation and adult non-feeding in insects, and possible adaptive functions
Among the many diverse traits of insects, the most speciose and successful terrestrial animals, is an incredible range of lifespans. While some are quite long-lived, such as cicadas (years as larvae) or termite queens (decades as adults), many insects, such as mayflies (Insecta: Ephemeroptera), have exceedingly short adult lifespans that serve essentially just for mating and selecting oviposition sites, foregoing feeding to reproduce as quickly as possible. This behavior is correlated with mouthparts that are highly reduced or even absent, and the alimentary system is converted into an air-filled space, possibly non-functional for digestion. The order-wide phenomenon of the co-opted gut is found in only one other group, the twisted-wing parasites (Insecta: Strepsiptera). Here, we present micro-CT scans and volume measurements (body and alimentary air) that reveal a previously undocumented inflated alimentary system in non-feeding species from five additional insect orders: Plecoptera, Embioptera, Megaloptera, Lepidoptera, and Diptera. The association between reduction or complete loss of adult feeding and a large volume of air in the gut is statistically highly significant. The reduction of mouthparts in these taxa reflects non-feeding in adults, indicating that convergence of this trait with alimentary inflation probably has adaptive functions. We discuss several, nonexclusive ways in which an inflated alimentary system can be adaptive for adult insects.
Detecting application layer DDoS attack using an advanced signature detection algorithm
Abstract Application-layer Distributed Denial of Service (App-DDoS) attacks are an ongoing issue in the cyber security world. The attack constructs request headers and uses a large number of channels to disrupt targeted services, such as an automated attack tool. A variety of approaches have been attempted, but the detection of attacks through the identification of forged request headers is a significant gap in the research. Signature detection, which shows a strong ability to accurately identify attacks with low false positive and false negative rates, can be used to address this challenge. The paper introduces a new detection method to categorize traffic as malicious or legitimate by analyzing the request headers of the traffic. To address the concerns of various researchers regarding outdated attack patterns and the lack of datasets available for public research, the dataset used in this research is recent and representative of real-world App DDoS attack patterns. The signature detection demonstrates promising performance in detecting the attack with the latest data set, which has strong implications for adaptation to real-world applications. The key contributions of this study are the proposed detection algorithms that can detect forged request headers at the initial stage, prior to their processing by the web server, and the practical analysis, which showed that the attack strategy approach relies on the manipulation of request headers. The hybrid feature selection method employed in this research was proven to be workable and successfully identified the features which contribute significantly to the detection performance with an accuracy of 96.93%, a precision of 99.11%, a recall of 97.55%, and an F1-score of 98.32%. These results demonstrate that the signature-based detection is effective and appropriate for detecting the attack. Although Machine Learning (ML) is gaining traction, signature-based detection can still be effective for checking signatories generated by the attack in the request headers.
Economic evaluation of atezolizumab in combination with bevacizumab and chemotherapy for metastatic, persistent, or recurrent cervical cancer in China: A cost-effectiveness analysis
Background From China’s healthcare perspective, this analysis compared the cost-effectiveness of Atezolizumab in combination with Bevacizumab and chemotherapy for Metastatic, Persistent, or Recurrent Cervical Cancer. Methods A Markov model was developed to track patients’ transitions over 3-week cycles and evaluate the health and economic outcomes over a 10-year horizon for the two competing treatments. The survival data were gathered from the BEATcc trial, and cost and utility values were obtained from the published studies. Total costs, life-years, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER) were the model outcomes. Sensitivity analyzes were performed to examine the robustness of the model results. Results In the base case, atezolizumab plus bevacizumab and chemotherapy yielded a marginal cost of $233,602.51 and an additional 0.54 QALYs, resulting in an ICER of $432,597.24 per additional QALY gained, which exceeded the willingness-to-pay (WTP) threshold of $36,859 in China. Sensitivity analyzes confirmed the robustness of the model outcomes. Conclusions Atezolizumab plus bevacizumab and chemotherapy was not a cost-effective treatment for patients with metastatic, persistent, or recurrent cervical cancer compared with bevacizumab plus chemotherapy from the perspective of the Chinese health-care system.
Macroevolutionary trends of avian ichnodisparity in Gondwana
Delocalized Electronic States: The High-Shell Nitrogen Effects on Metal–Nitrogen–Carbon Catalysts
Effect of a freeze–thaw cycle on amniotic fluid interleukin-6 concentrations in pregnancies with preterm prelabor rupture of membranes: Implications for the diagnosis of intra-amniotic inflammation
Objective Preterm prelabor rupture of membranes (PPROM) is frequently complicated by intra-amniotic inflammation. Interleukin-6 (IL-6) measured in amniotic fluid is considered the gold-standard biomarker for the diagnosis of this condition; however, diagnostic thresholds have been derived primarily from biobanked samples. It remains unclear whether IL-6 concentrations measured in fresh amniotic fluid are directly comparable with those obtained from previously processed samples. The primary aim of this study was to compare IL-6 concentrations in fresh amniotic fluid samples with those in biobanked samples that had undergone a single freeze–thaw cycle. Method This retrospective study included consecutive singleton pregnancies with PPROM between 24 + 0 and 36 + 6 weeks of gestation. Amniotic fluid samples were collected on admission, prior to the administration of antibiotics and corticosteroids. IL-6 concentrations were measured in both fresh and biobanked (processed) samples using an automated electrochemiluminescence immunoassay. Results The study population comprised 152 women. IL-6 concentrations in fresh and processed samples were strongly correlated (rho = 0.97; p < 0.0001). Bland–Altman analysis demonstrated a systematic bias toward higher concentrations in processed samples, with a mean ratio of 1.2, indicating that processed samples yielded on average approximately 20% higher values than fresh samples. When applying the validated diagnostic threshold of 3,000 pg/mL (established in processed samples) to fresh samples, three false-negative cases were observed. In these cases, intra-amniotic inflammation was present, but IL-6 concentrations in fresh samples were below the threshold despite corresponding processed values ≥ 3,000 pg/mL. Using intra-amniotic inflammation defined by processed samples as the reference, the diagnostic performance of the 3,000 pg/mL threshold applied to fresh samples was as follows: sensitivity 92%, specificity 100%, positive predictive value 100%, and negative predictive value 97%. Conclusions IL-6 concentrations in fresh and processed amniotic fluid samples are highly correlated, but processed samples consistently yield higher values.
Author Correction: Satellite megaconstellations will threaten space-based astronomy
Integration of geophysical and remote sensing data for structural analysis and delineation of gold, fluorite, and barite mineralization in the Dawi shear belt, Egypt
Abstract This study integrates multispectral satellite imagery, aperture radar (SAR) data, aeromagnetic data, and field observations to identify and delineate hydrothermal alteration zones associated with gold, fluorite, and barite deposits. This integration complements lithologic and structural mapping of the Dawi shear belt within the East African Orogenic Belt. The Enhanced Horizontal Gradient Amplitude (EHGA) filter was applied to RTP aeromagnetic and upward-continued (UWC) data at altitudes of 0.5, 1, and 2 km. The data delineated shallow and deep structural trends that may control mineralized zones of gold, fluorite, and barite. Euler deconvolution and source-parameter imaging were used together to reveal changes in the basement surface. The Dawi shear belt has experienced multiple deformation phases, beginning with NNW-SW shortening, followed by ENE-WSW compressional and sinistral transpression. This deformation is driven by NW-SE-oriented Najd shearing, which creates N-S-trending folds and dextral shearing, culminating in NE-trending folds. Sentinel-1 A backscatter images were used to enhance structural mapping and lineament detection, which are important for hydrothermal ore deposits and gold mineralization. PCA-based lineament density mapping from S1-A data revealed medium to moderately high concentrations in gneissic, ophiolitic, sedimentary, and volcanic units. The area is divided into three prospective zones for ore exploration, where minerals such as gold and fluorite are likely to be found. Fluorite deposits, classified as vein-type mineralization resulting from hydrothermal fluids, are located within pegmatitic veins aligned NE-SW and NW-SE, crossing schists and metavolcanics. Several zones of gold mineralization occur in the Dawi shear belt, with quartz and quartz-carbonate veins aligned with the Najd Fault System, and gold grades ranging from 0.003 to 0.6 g/t.