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Environmental exposure to perfluorooctane sulfonate and its role in esophageal cancer progression: a comprehensive bioinformatics and experimental study

Evaluating the associations and predictive performance of triglyceride-glucose index and related indicators for chronic diseases in a Chinese cohort

Background Triglyceride and glucose (TyG) indices have been used as predictors of several chronic diseases. However, there is currently a lack of research that can comprehensively reflect the impact of TyG-related indicators on chronic diseases in middle-aged and elderly populations. The aim of this study was to investigate the relationship of TyG and its related indicators with chronic diseases and their time-dependent predictive ability in the elderly. Study design Retrospective observational cohort study using China Health and Retirement Longitudinal Study (CHARLS) 2011–2020 data. Methods Based on longitudinal data obtained from the CHARLS from 2011 to 2020, a total of 12,966 participants were included in the study. Participants were stratified into three groups according to their TyG index. Pearson correlation coefficient and Cox model are used to assess the relationship between the TyG index, its parameters, and common chronic diseases, while Harrell’s C-index is used to evaluate their risk prediction capability. Results The TyG index and its related indicators exhibit a positive dose-response relationship with the risk of diabetes, heart disease, dyslipidemia, hypertension, and stroke, while demonstrating a negative dose-response relationship with digestive system diseases. Harrell’s C-index results indicated that TyG-WC demonstrates superior predictive performance overall. Conclusion The TyG index and its related indicators are significantly correlated with newly onset emerging chronic diseases, with TyG-WC exhibits superior risk prediction performance.

RyR1-mediated Ca ²⁺ -induced Ca ²⁺ release plays a negligible role in excitation–contraction coupling of normal skeletal muscle

Type 1 ryanodine receptor (RyR1) is a Ca 2+ release channel in the sarcoplasmic reticulum in skeletal muscle. In excitation–contraction (E-C) coupling, RyR1 opens by depolarization of transverse tubule membrane via physical interaction with dihydropyridine receptor, which is referred to as depolarization-induced Ca 2+ release (DICR). RyR1 can also be gated via Ca 2+ -induced Ca 2+ release (CICR), in which binding of Ca 2+ directly opens the channel. Thus, RyR1 has two Ca 2+ release modes; DICR and CICR, but the physiological role of CICR has been a matter of debate: whether CICR can amplify Ca 2+ signals in E-C coupling. To address this issue, we created a mouse model carrying a mutation in the Ca 2+ -binding site in RyR1 (RyR1-E3896A), which selectively inhibits CICR. Surprisingly, the homozygous RyR1-E3896A mice show no appreciable changes in E-C coupling, ex vivo muscle contraction, in vivo muscle performance, or muscle fiber type. Gain-of-function mutations in RyR1 cause malignant hyperthermia (MH), which is a lethal disease triggered by inhalational anesthetics. The E3896A mutation conferred resistance to isoflurane-induced MH episodes and severe heat stroke triggered by environmental heat stress. Our data suggest that RyR1-mediated CICR plays a negligible role in E-C coupling of normal skeletal muscle but may increase the risk for muscle diseases when excessively activated.

PKM2 regulates angiogenic activation via ANGPT2 in endothelial cells

Modular and cloud-based bioinformatics pipelines for high-confidence biomarker detection in cancer immunotherapy clinical trials

Background The Cancer Immune Monitoring and Analysis Centers – Cancer Immunologic Data Center (CIMAC-CIDC) network aims to improve cancer immunotherapy by providing harmonized molecular assays and standardized bioinformatics analysis. Results In response to evolving bioinformatics standards and the migration of the CIDC to the National Cancer Institute (NCI), we undertook the enhancement of the CIDC’s extant whole exome sequencing (WES) and RNA sequencing (RNA-Seq) pipelines. Leveraging open-source tools and cloud-based technologies, we implemented modular workflows using Snakemake and Docker for efficient deployment on the Google Cloud Platform (GCP). Benchmarking analyses demonstrate improved reproducibility, precision, and recall across validated truth sets for variant calling, transcript quantification, and fusion detection. Conclusion This work establishes a scalable framework for harmonized multi-omic analyses, ensuring the continuity and reliability of bioinformatics workflows in multi-site clinical research aimed at advancing cancer biomarker discovery and personalized medicine.

The functional dynamics of FicD’s TPR domain are modulated by the interaction with ATP and BiP

The human Fic enzyme FicD plays an important role in regulating the Hsp70 homolog BiP in the endoplasmic reticulum: FicD reversibly modulates BiP’s activity through attaching an adenosine monophosphate to the substrate binding domain. This reduces BiP’s chaperone activity by shifting it into a conformation with reduced substrate affinity. Crystal structures of FicD in the apo, adenosine triphosphate (ATP)-bound, and BiP-bound states suggested significant conformational variability in the tetratricopeptide repeat (TPR) motifs. However, nothing is known about the underlying dynamics. In this study, we investigate the conformational dynamics of FicD’s TPR motifs using two-color, single-molecule Förster resonance energy transfer (smFRET) experiments. We demonstrate that the TPR motifs exhibit conformational dynamics between a TPR-out and a TPR-in conformation on timescales ranging from microseconds to milliseconds. In addition, we extend our investigation on multiple labeling positions within FicD, revealing how conformational dynamics vary depending on the location within the TPR motif. We quantify the motions with dynamic photon distribution analysis for the FRET constructs and generate an ensemble of structures for the different states consistent with the smFRET data using molecular dynamic simulations. We propose a conformational landscape model for FicD where the TPR-in/out states exist in equilibrium and the fraction of dynamic population is altered due to the presence of ATP and BiP. These results indicate that not only is FicD dynamic, but the dynamics are linked to the functionality and interactions of FicD with BiP.

Design and optimization of a metamaterial absorber for enhanced solar cell efficiency and wide band microwave cross polarization conversion

Abstract In this work, the design and construction of a metamaterial (MTM) absorber to increase solar cell efficiency is proposed. MTM is use as frequency selective surface (FSS) in the infrared band. The design is made up of a split ring resonator (SRR) imprinted on the substrate’s top surface, with a copper layer serving as a ground on the back layer of the substrate material. The structure works at tera frequencies to take in all the sun’s infrared wavelengths. Furthermore, a MTM array absorption is formed by using the newly generated MTM unit cell, which improves harvesting energy from the sun spectrum. Both designs demonstrated absorption rates of roughly 99% at resonance frequency 13.29 THz. The parameters of the proposed unit cell are also edited and optimized to operate in the microwave frequency ranges where the wide-band microwave cross polarization conversion (CPC) metasurface (MTS) is simulated, fabricated and validated. The measured data much agrees with the simulation one. The suggested CPC MTS achieves efficient cross-conversion over a large frequency range (19.6–25.9 GHz), with a polarization efficiency of 90% and two bands operation. It has a fractional bandwidth (FBW) of 27.7%. The polarization interaction is stable up to 45° oblique incident angle.

Overlapping community detection based on bridging structural features and fuzzy C-means

In recent years, research on community structure for complex networks has received increasing greater attention, and the overlapping community structure is more closely related to the actual social structure than the non-overlapping community structure, so it is necessary to identify and detect the overlapping communities of social networks. In this paper, we propose an overlapping community optimization method (OSFCM) based on network structure characteristics and fuzzy C-means clustering. We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. Experimental results show that the algorithm exhibits higher delineation accuracy and better algorithmic performance on seven real network datasets and four synthetic networks.

Correction for Rege et al., Evolution of insulin at the edge of foldability and its medical implications

Dual graph attention network for robust fault diagnosis in photovoltaic inverters

Clinical learning environments and experiences of nursing students in West Bank Universities: A mixed-methods study

Background Clinical learning environments (CLEs) play a vital role in shaping nursing students’ competencies, yet their dynamics in conflict-affected settings remain underexplored. This study investigated how CLE factors, measured via the Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) scale, relate to students’ clinical learning (CL) experiences across West Bank universities in Palestine. The findings are specific to the Palestinian context but may offer insights for other conflict-affected educational settings. Methods A convergent mixed-methods design integrated quantitative data from 306 nursing students across governmental, public, and private universities with qualitative insights from 14 in-depth interviews. The validated Arabic CLES+T scale, culturally adapted for the Palestinian context, assessed five CLE dimensions. Quantitative analysis included ANOVA, correlation, and hierarchical multiple regression. Assumptions for parametric tests (normality, multicollinearity, and homoscedasticity) were verified before conducting ANOVA and regression analyses. Qualitative data underwent inductive content analysis with dual coding by two independent researchers to ensure reliability. Results CLE dimensions were strongly correlated with students’ clinical learning experiences (r = 0.758, p < 0.001). Pedagogical atmosphere (β = 0.365, p < 0.001) and supervisory relationships (β = 0.264, p = 0.001) were significant predictors, jointly explaining 59% of the variance (R² = 0.59). Students reported more positive CL experiences in governmental hospitals (M = 3.92 ± 0.91) compared to private facilities (M = 3.59 ± 1.06, p = 0.032). Significant differences emerged across clinical wards (F[6,299]=2.56, p = 0.019), with orthopedic wards receiving the highest scores and pediatric wards the lowest. Qualitative findings highlighted four themes: students’ perceptions of clinical experiences, key facilitators (e.g., instructor expertise), barriers (e.g., movement restrictions, limited resources), and strategies for improvement (e.g., expanding clinical exposure and diversifying placement sites). Conclusion In the conflict-affected Palestinian context, positive pedagogical atmospheres and supportive supervisory relationships substantially enhance nursing students’ clinical learning. These human factors may help offset systemic and geopolitical barriers. Findings support targeted efforts in faculty development, clinical teaching strategies, and institutional coordination to build educational resilience.

Correction for Bastani et al., Generative AI without guardrails can harm learning: Evidence from high school mathematics

Thermodynamic mechanism in colored glass substrates of interference filters under continuous-wave laser irradiation

Abstract The ablation perforation damage of double-sided coated narrow-band filters based on RG-850 colored glass under out-of-band laser irradiation is investigated. A temperature-triggered nonlinear absorption mechanism is identified where substrate absorption sharply increases beyond a critical temperature. To quantify the resulting energy deposition dynamics, the multiple reflection model is employed, revealing the absorption enhancement by partial-transmission/high-reflection coatings. Building on this foundation, a parameter inversion method derives the equivalent average absorption coefficient from dual-transmittance laser-induced damage threshold (LIDT) ratios, thereby establishing a LIDT predictive framework for arbitrary transmittance. Finally, finite element analyses (FEA) provide validation for the multiple reflection model and inversion method, demonstrating the coating structure’s role in absorption enhancement and successfully predicting damage thresholds across three transmittance configurations.

Application of three-dimensional fluorescence spectral characterization and chemometrics in the analysis of traceability of Paeoniae Radix Rubra

Natural products are treasure troves of resources that the environment has given upon humans and are directly linked to human health and well-being. Extracting natural products from medicinal plants is the material basis for treating various diseases but the natural product content of the same medicinal plant can vary due to environmental conditions, which may exert an influence on the therapeutic outcome. Since the existing identification methods for the origin of medicinal plants are cumbersome, it is necessary to find a easy, quick, and accurate way to trace the origins of medicinal plants and ensures the quality of natural products. This experiment uses chemometric techniques in conjunction with three-dimensional fluorescence technology to classify Paeoniae Radix Rubra (PRR) from various geographical sources, taking the natural products of PRR as the research object. Three-dimensional fluorescence technology can be used to identify the origin of PRR based on the presence of different endogenous luminous chemicals. In this experiment, the principal component analysis (PCA) algorithm was used to examine the overall distribution and grouping of the samples after initial characterizing the 3D fluorescence spectrum of PRR using the alternating trilinear decomposition (ATLD) algorithm. In order to predict the origin traceability of PRR samples, we combined the 3D fluorescence spectral features with four pattern recognition techniques: random forest (RF), partial least squares-discriminant analysis (PLS-DA), and k-nearest neighbor (kNN) method. The findings demonstrated that, following ATLD factorization, the sample data could successfully identify, using various models, the PRR’s production areas (Heilongjiang, Greater Khingan Mountains, Inner Mongolia, Liaoning, Hebei, Gansu, Sichuan), with 100% correct recognition rates for both the cross-validation and external validation sets. This technique offers a fresh and quick fix for PRR grading and origin tracing. Besides, this method also provides a new research idea for the origin traceability and quality evaluation of other Medicinal Plants.

Identification of broadly inhibitory anti-PfEMP1 antibodies by mass spectrometry sequencing of plasma IgG from a malaria-exposed child

Plasmodium falciparum pathology is driven by the accumulation of parasite-infected erythrocytes in blood capillaries. This sequestration process is mediated by the parasite’s P. falciparum erythrocyte membrane protein 1 (PfEMP1) adhesins, which bind select endothelial cell receptors. A subset of PfEMP1 binding human endothelial protein C receptor (EPCR) through their cysteine-rich interdomain region alpha 1 (CIDRα1) domains drives the pathogenesis to severe malaria. Despite high sequence diversity among CIDRα1 domains, individuals living in malaria-endemic regions become immune to severe disease in part through acquisition of antibodies inhibiting the PfEMP1–EPCR interaction. Here, we demonstrate an approach to identify pathogen-specific human monoclonal antibodies from plasma, combining mass spectrometry analysis of antigen-purified polyclonal plasma IgG and Ig transcript sequencing. We identified a clonal family of broadly reactive and EPCR binding-inhibitory human monoclonal antibodies against CIDRα1. The antibodies, isolated from a 9-y-old child, exhibited potent inhibition of EPCR binding broadly across CIDRα1 domains as well as binding of infected erythrocytes to EPCR. Structural analysis of one antibody variant complexed with CIDRα1 revealed a shared epitope of the clonal antibody family overlapping the EPCR binding site and the epitopes of two previously identified monoclonal antibodies, C7 and C74, with similar functional patterns. However, although C7, C74, and 110-3 antibodies all depend on the same few residues conserved in CIDRα1 to retain EPCR binding, the 110-3 antibodies contact additional variable residues, reducing their breadth of reactivity across the CIDRα1 family. These data bolster the hypothesis that broadly inhibitory antibodies against severe malaria-associated PfEMP1 target similar epitopes and are commonly developed in malaria-exposed individuals.

Degradation of cationic dyes and alkali lignin using DyP-producing Bacillus cereus SDP6 isolated from similipal biosphere reserve soil

Revealing potential interfering genes between abdominal aortic aneurysm and periodontitis through machine learning and bioinformatics analysis

This study aimed to identify potential interacting genes between abdominal aortic aneurysm (AAA) and periodontitis. To achieve this, we obtained datasets of AAA and periodontitis from the GEO database, conducted differential analysis on the AAA dataset, and performed weighted gene co-expression network analysis (WGCNA) on the periodontitis dataset to preliminarily identify interacting genes via intersection. Subsequently, we refined key candidate genes by constructing a PPI network and applying three machine learning algorithms. These candidate genes were further validated through external independent datasets, receiver operating characteristic (ROC) curves, and Nomograms. Finally, single-gene Gene Set Enrichment Analysis (GSEA), immune landscape analysis, and targeted drug prediction were performed on the identified key genes. In our study, a total of 323 differentially expressed genes (DEGs) related to AAA and 4,412 periodontitis-related module genes were identified, producing 90 interacting genes through intersection initially. Through PPI network analysis and machine learning, we prioritized 7 key interacting genes. Validation confirmed that IL1B, PTGS2, and SELL were robustly associated with both diseases. Immune landscape assessment demonstrated that these three genes exhibited significant negative correlations with regulatory T cells (Tregs) and positive correlations with neutrophil infiltration. Additionally, ten drugs with the highest predicted target specificity were identified. In conclusion, we utilized various machine learning and bioinformatics approaches to preliminarily elucidate potential comorbid mechanisms between AAA and periodontitis from a multidisciplinary perspective.

Correction for Kuroda et al., Bim and Bad mediate imatinib-induced killing of Bcr/Abl ⁺ leukemic cells, and resistance due to their loss is overcome by a BH3 mimetic

A multilingual BERT-based classification of reviews for enhanced visitors’ experience analysis

The influence of stress hyperglycemia on consciousness disturbance and short- and long-term outcomes in stroke patients without documented diabetes: Differences between ischemic and hemorrhagic stroke

Background Although hyperglycemia is a well-known prognostic factor in diabetic stroke patients, its impact on outcomes in individuals without documented diabetes remains insufficiently explored. This study aimed to investigate the relationship between stress-induced hyperglycemia and both short- and long-term mortality, as well as impaired consciousness, in patients without documented diabetes with ischemic or hemorrhagic stroke. Methods A retrospective cohort study was conducted using the MIMIC-IV (v3.1) database, including ICU-admitted patients without documented diabetes (ischemic or hemorrhagic stroke identified via ICD-9/10 codes). Exclusion criteria were: age < 18 years, missing glucose values, and severe impairment of consciousness upon ICU admission, defined as a Glasgow Coma Scale (GCS) score < 8. After applying these criteria, 4,151 patients were enrolled. Propensity score matching (PSM) was performed to balance baseline covariates between the hyperglycemic (HG) and non-hyperglycemic (Non-HG) groups. Cox proportional hazards models and Kaplan-Meier survival curves were used to assess the impact of hyperglycemia on mortality and neurological outcomes. Then subgroup analysis was conducted. Results Following PSM, 1,170 matched pairs were analyzed. In patients with hemorrhagic stroke (HS), stress hyperglycemia was associated with an increased 1-year mortality risk (HR = 0.71, p = 0.029). Among ischemic stroke (IS) patients, hyperglycemia independently predicted both in-hospital and 1-year mortality (HR range: 0.65–0.79, p < 0.001). Kaplan-Meier analyses showed significantly poorer survival in hyperglycemic patients. Subgroup analysis demonstrated that stress hyperglycemia was particularly detrimental in HS patients with heart failure, pneumonia, vasopressin use, or severe neurological impairment (GCS < 8 with heart failure). In IS patients, hyperglycemia was significantly associated with worse outcomes in those with advanced age, coronary artery disease, COPD, pneumonia, renal failure, or norepinephrine treatment. Conclusion Stress hyperglycemia independently predicts poor outcomes in stroke without documented diabetes, especially in ischemic subtypes. These findings highlight the importance of early glycemic assessment and tailored management strategies. Further research is warranted to validate causal mechanisms. In hemorrhagic stroke, its impact appears more delayed, affecting long-term prognosis. These findings support the need for early recognition and tailored glycemic control strategies based on stroke subtype.