Browse Articles

Gut hormones in POTS and their relation to hemodynamic parameters and gastrointestinal symptoms

Abstract Postural orthostatic tachycardia syndrome (POTS) is associated with multiple autonomic symptoms, including gastrointestinal (GI) complaints, and has been linked to insulin resistance. We aimed to explore HbA1c, circulating metabolic hormones (insulin, C-peptide, GIP, GLP-1, glucagon, leptin, and peptide YY) and cortisol, and their associations with hemodynamic parameters and GI symptoms in POTS. Two POTS cohorts were studied and compared with matched controls. In the fasting cohort, blood samples were drawn in 42 patients and 41 controls, followed by active standing tests with measurement of pulse and blood pressure (BP) in supine and standing positions. In the non-fasting cohort, questionnaires assessed GI symptoms and HbA1c was measured in 43 patients and 52 controls. Fasting C-peptide and insulin levels correlated with BP in POTS (q = 0.002) but not in controls. Fasting insulin tended to be higher in POTS but was not statistically significant after adjustment for BMI (β = 6.85; 95% CI: −1.04–14.74; p  = 0.085). Morning cortisol was comparable between groups. In the non-fasting cohort, HbA1c and metabolic hormones were comparable between groups, with no associations with GI symptoms. Together, these findings suggest a potential link between insulin-related pathways and BP regulation in POTS. Future studies are warranted to further investigate insulin dynamics in POTS.

Integration of resting-state and stimulus-fMRI uncovers reduced network flexibility in post-surgical pain

Abstract Functional MRI (fMRI) provides complementary insights into brain network organization during rest (rs-fMRI) and external stimulation (t-fMRI). While rs-fMRI reveals intrinsic connectivity patterns, t-fMRI reflects stimulus-dependent network responses. However, how these states relate to each other in animal disease models remains incompletely understood. We performed sequential acquisition of rs-fMRI and t-fMRI during low- and high-intensity mechanical paw stimulation in a rat model of post-surgical pain (PSP) and SHAM controls. Functional connectivity and network organization were analyzed using network-based statistics and graph-theoretical metrics. In addition, supervised classification of node-level network parameters was performed using linear discriminant analysis (LDA), and regional contributions to network separation were quantified using Mahalanobis distance metrics. Global connectivity strength and small-world organization were preserved across different imaging conditions in both groups, indicating stable network topology. However, multivariate classification revealed clear modality-dependent network signatures in SHAM animals that were markedly reduced in PSP. Region-wise Mahalanobis analyses showed that stimulus-related network shifts were more heterogeneous in SHAM animals, with higher dispersion and recurrent regional “hotspots”, whereas PSP animals exhibited more uniform and spatially diffuse responses. These findings indicate that post-surgical pain primarily affects the flexibility of regional network reconfiguration rather than global topology. Combining rs- and stimulus-based fMRI thus provides complementary insight into disease-related network alterations that are not detectable from resting-state data alone.

Critical thermal thresholds for survival and feeding in the invasive bark beetle Pagiocerus frontalis reveal mechanisms of stored-maize vulnerability

Investigating magnetic and optical properties and morphology of a nanocomposite of intercalated layered material and polymer compounds

Abstract The study focuses on doping polyvinyl pyrrolidone (PVP), polyacrylic (PAC), and nanosilica (SiO 2 ) into the layered material FePS 3 intercalated with 1,3-bis(amino-5-phenyl-1,2,4-triazol-3-ylsulfanyl) propane (FePS 3 -BAPT). Different ratios of FePS 3 -BAPT were incorporated into the polymers (PVP, PAC, and SiO 2 ) to form a nanocomposite. This nanocomposite was characterized by a number of tools: X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), Scanning Electron Microscopy supported by Energy Dispersive X-ray analysis (SEM-EDX), and Transmission Electron Microscope (TEM). Magnetic and optical properties of the prepared nanocomposite were evaluated. XRD analysis indicates that the ratio of doped materials has affected the detection of FePS 3 -BAPT. The XRD patterns also reveal the amorphous nature of the polymers. The nanocomposite crystallinity is about 1.5%, while the amorphous content percentage is about 98.5%. TEM results confirm the amorphous structure of the polymers within the nanocomposite. The magnetic properties of FePS 3 -BAPT have been affected after doping with the PVP/PAC/SiO 2 ; this shift is due to the lack of charged ions, the cluster size, and the amorphous nature of the polymers. According to the magnetic hysteresis loop, the magnetic behavior shifted from paramagnetic to diamagnetic. This shift is because of the revealing of the amorphous nature of polymers and the absence of unpaired electrons. The nanocomposites of FePS 3 -BAPT/PVP/PAC/SiO 2 (H11, H13, and H19) show an increase in band gap energy; this increase is due to strong interfacial interactions between the layered FePS₃ structure and polymer matrix and also the effect of insulation in the presence of SiO₂ nanoparticles.

A gated task-attentive multi-task network for unified retinal image analysis

Robust transcriptomic signatures of Alzheimer’s disease progression: validated explainable AI approach

Abstract The lack of validated stage-specific biomarkers hampers the understanding of Alzheimer’s disease (AD) progression and clinical translation. Current transcriptomic methods often produce unstable results with limited stage discrimination. We aimed to develop an explainable machine learning pipeline to identify robust, interpretable gene signatures linked to distinct AD neuropathological stages. We analyzed multi-region transcriptomic data from the MSBB cohort using a multi-class XGBoost-SHAP-driven framework. Class imbalance was addressed using SMOTE, and model robustness was assessed through cross-validation and permutation-based validation. Our pipeline accurately classified Early, Mid, and Late Braak stages and achieved accurate Braak stage classification (regional ROC AUCs up to 0.76). The method identified a concise set of high-confidence, stage-specific genes, showing minimal signature overlap (~ 1.7%). Key validated novel candidate biomarkers included ARX (Early), MKNK2 (Mid), and SLC25A16/NEURL1B (Late), linked to GABAergic, inflammatory, mitochondrial, and synaptic pathways. This explainable framework overcomes key limitations of conventional analyses, providing a stable, interpretable gene signature for AD staging. It establishes a robust method for transcriptional biomarker discovery and offers new biological insight into AD progression, highlighting potential stage-specific therapeutic targets.

Fecal microbiome of patients with ulcerative colitis reflects their phenotype and inflammatory level

Abstract Inflammatory bowel diseases affect ever-increasing numbers of individuals worldwide. Alterations of the intestinal microbiome were reported for Crohn’s disease and at relapse in Ulcerative Colitis (UC); they were not clearly detected in UC at remission. Here we report the characterization of the microbiome by quantitative metagenomics in a cohort of 121 individuals, composed of 65 UC adult patients in remission and 56 healthy controls. A cross-sectional comparison revealed substantial microbiome differences, patients in remission having lower microbiome richness and paucity of the Ruminococcus species driven enterotype. The observed microbiome alterations allowed robust classification of patients by intestinal species abundance, yielding an area under the curve (AUC) of 0.87 in a Receiver-Operator Characteristic (ROC) analysis. Loss of richness was linked to an aggressive UC phenotype and to the importance of past relapses; it was associated with a worse IBD quality of life score (IBDQ-36). Unexpectedly, onset of inflammatory bouts, as assessed by white blood cell count and fecal calprotectin levels, was associated with higher richness; in a longitudinal study of patients at high risk of disease flare, we observed a link between increasing gut microbiome richness over time and calprotectin level, in turn related to clinical inflammatory response and relapse.

Divergent trajectories of psychological distress during the prolonged COVID-19 pandemic in Japan

Abstract Psychological responses to the COVID-19 pandemic showed heterogeneous trajectories rather than uniform recovery. We analyzed seven waves of nationwide web-based surveys conducted in Japan between June 2020 and December 2022 (approximately 1,000 participants per wave) to examine long-term trends in depression, anxiety, and stress and their associations with self-evaluative traits. DASS-21 scores were significantly elevated during the initial emergency phase (June 2020) and declined substantially thereafter (Kruskal–Wallis tests, all p < .001). However, distributional analyses indicated that a subgroup of individuals remained persistently distressed despite overall population-level improvement. Decision-tree–based variable-importance analyses identified the impostor phenomenon as the strongest contributor to depression, anxiety, and stress, followed by self-esteem. For anxiety, survey timing showed the strongest linear association, whereas non-linear models consistently highlighted self-evaluative variables as dominant predictors. These findings indicate that prolonged societal crises are associated with divergent mental-health adaptation patterns. Psychological self-evaluation factors, particularly impostor feelings and low self-esteem, may play a central role in persistent distress beyond demographic and social-network factors.

Convergent validation of the Involuntary Autobiographical Memory Inventory across levels of analysis in a Polish sample

Abstract We adapted the Involuntary Autobiographical Memory Inventory (IAMI) into Polish and examined its psychometric properties and convergent validity using a multi-level validation approach. The IAMI assesses individual differences in the frequency of involuntary autobiographical memories and future-oriented thoughts. In Study 1, we conducted confirmatory factor analysis and tested convergent validity using a laboratory vigilance task that objectively captured involuntary past and future thoughts under controlled conditions. In Study 2, we replicated the factorial structure and further examined convergent validity through associations with mind-wandering, emotional distress, and interoceptive sensitivity. Across both studies, confirmatory factor analyses supported a two-factor structure distinguishing involuntary past and future thoughts, with strong internal consistency. Importantly, IAMI scores predicted both laboratory-elicited involuntary thoughts and theoretically related self-report constructs, providing convergent validity across self-report, behavioural, and bodily-level measures. Together, these findings demonstrate that the Polish adaptation of the IAMI is not only a reliable and valid instrument but also offers novel insights into the cognitive and embodied mechanisms underlying involuntary mental time travel. We discuss the implications of these findings for future research on spontaneous thought processes and their links to cognition and emotion.

Deepfake face detection using hybrid bag-of-visual-words and multi-CNN feature fusion

Abstract Recent advances in generative modeling have enabled the creation of highly realistic deepfake facial images, posing significant risks to digital security, media integrity, and public trust. Although deep learning–based detection methods have achieved strong performance, they often suffer from limited cross-dataset generalization, sensitivity to manipulation-specific artifacts, and reduced interpretability. To address these limitations, this paper proposes a forensic-first hybrid deepfake face detection framework that integrates handcrafted local forensic descriptors with multi-CNN deep semantic representations. Specifically, manipulation-sensitive regions are captured using a Bag-of-Visual-Words (BoVW) model constructed from Histogram of Oriented Gradients (HOG) features extracted at salient keypoints detected via SURF, FAST, and BRISK. In parallel, high-level features are obtained from fine-tuned ResNet-50, MobileNet, and ShuffleNet models and fused at the feature level to capture complementary semantic information. The combined feature representation is classified using a Support Vector Machine (SVM), enabling stable decision boundaries and improved generalization. Extensive experiments on six benchmark datasets of varying scale and complexity demonstrate that the proposed approach consistently outperforms state-of-the-art methods, achieving up to 97.55% accuracy while maintaining robustness under cross-dataset and challenging forensic conditions. The results highlight the effectiveness of integrating explicit forensic features with deep representations to achieve a robust, interpretable, and generalizable solution for deepfake face detection.

From EEG signals to quantitative assessment: predicting depression severity using a novel deep learning framework

Regulatory T cell attracting therapy accelerates skeletal muscle functional recovery following injury

Valorization of fruit pomaces for glycosidic enzymes production via solid state fermentation

Abstract Agro-industrial fruit pomaces represent complex, nutrient-rich substrates that can support microbial enzymes production within circular bioeconomy frameworks. This study systematically compared grape, mango, orange, and pomegranate pomaces as solid substrates for glycosidic enzymes production (amylase, xylanase, pectinase) using 14 microbial strains under solid-state fermentation conditions with the aim of identifying an efficient microorganism–substrate system that produces the highest glycosidic enzyme activity. For the 14 strains studied, Candida guilliermondii NRRL Y-2075 yielded the highest reported amylase activity (4344.67 U/gds) when cultivated on pomegranate pomace with no detectable activity in the unfermented pomace. Response surface methodology (RSM) based on a central composite design was subsequently applied to identify the optimal operational region for amylase production by evaluating pH, inoculum size, incubation temperature and time. Maximum amylase activity (4839.05 U/gds) was obtained at pH 5.6, 12.2% inoculum size, 30.7 °C incubation temperature, and 24 h of incubation. Experimental validation closely matched model predictions. Additional one-factor-at-a-time experiments demonstrated that supplementation with external carbon, nitrogen, amino acids, or metal ions did not enhance enzyme production, indicating that pomegranate pomace alone provides sufficient nutrients for efficient amylase synthesis. Collectively, the results suggest that pomegranate pomace can function as a nutritionally sufficient SSF substrate, reducing process complexity and supplementation requirements for sustainable amylase production.

Coherent cavity coupling in O-band silicon photonic sensors for water environment detection

The impact and application exercises on vocal fatigue

Abstract To investigate the therapeutic efficacy and application of different straw phonation exercise regimens as interventions for vocal fatigue in vocally healthy adults under simulated occupational vocal loading conditions. One hundred and fourteen vocally healthy adults aged 20–40 years with no organic lesions confirmed by fibrolaryngoscope examination were randomly assigned into the following groups: (1) control group, (2) Experimental Group A, and (3) Experimental Group B. Immediately following a one-hour vocal loading task (VLT), intervention was provided to all three groups. The control group underwent a 10-minute vocal rest, Experimental Group A performed a 10-minute straw phonation exercise, and Experimental Group B performed a 5-minute straw phonation exercise after a 5-minute vocal rest. Straw phonation consisted of sustained vowel /u:/ phonation through a straw (inner diameter: 5 mm; length: 19.5 cm), performed continuously for the assigned duration. Acoustic parameters including maximum phonation time (MPT), Fundamental frequency (F0), jitter, shimmer, harmonic-to-noise ratio (HNR) and cepstral peak prominence for speech (CPP-s) and perceptual voice assessment including Vocal Tract Discomfort (VTD) and Perceived Phonatory Effort (PPE) were performed before VLT (T0), immediately after VLT (T1), and post-intervention (T2). Data were analyzed using generalized estimating equations (GEE) to evaluate time, group, and time × group interaction effects. For acoustic voice measures, no significant changes were observed at T2 compared with T1 in the control group. Experimental Group A exhibited significant changes in MPT, F0, jitter and CPP-s at T2 compared with T1, whereas Experimental Group B demonstrated significant changes in MPT, jitter, shimmer and HNR at T2 compared with T1 ( p  < 0.05). No differences were observed among the three groups at T0. At T2, Experimental Group B showed superior jitter, shimmer, HNR, and CPP-s values compared with the control group, while Experimental Group A had higher CPP-s values than the control group. Additionally, Experimental Group B had higher HNR compared with Experimental Group A . For subjective voice measures, both Experimental Groups A and B outperformed the control group in the VTD (lump in the throat) and PPE scores at T2, and Experimental Group A had higher scores of VTD (Total) than Experimental Group B. Straw phonation produced greater short-term improvements than vocal rest alone in acoustic and subjective voice measures. After prolonged vocal use, a 5-minute vocal rest followed by 5-minute straw phonation yielded greater immediate voice quality improvements and reduced vocal tract discomfort compared with 10-minute straw phonation training, illustrating the importance of timing in the clinical application of straw phonation after prolonged phonatory loading tasks as indicated by objective and subjective voice measures. Trial registration: This study was registered in the Ethics Committee of the Third Xiangya Hospital of Central South University (Protocol no. 24508) on 2024-06-14, and approved by the Chinese Clinical Trial Registry (ChiCTR2400087198) on 2024-07-22. This trial record can be accessed on the official ChiCTR website https://www.chictr.org.cn/bin/project/edit?pid=232,332.

Choice of cardioplegia influences metabolomics of human cardiac tissue

Cardioplegia is often used prior to acquisition of human cardiac tissue to minimize warm ischemia time, which can severely confound studies of cardiac metabolism. However, there are several choices of cardioplegia solutions, and whether these solutions differentially impact tissue metabolism or metabolomic studies is not known. Here, we perform untargeted metabolomics, using both liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry, on a large cohort of hearts transplanted for cardiomyopathy or from gift-of-life donors, and who have received different cardioplegia solutions. We show that different cardioplegia solutions distinctly impact cardiac metabolism and tissue metabolomic studies. Notably, these differences are mild relative to those seen comparing failing to nonfailing hearts, and identification of cardioplegia components in mass spectra should enable rigorous interpretation of changes between conditions. These data demonstrate how cardioplegia solutions may influence cardiac metabolism in human heart samples and underscore the need to report specific details of cardioplegia solution use in studies of human cardiac metabolism.

Impact of treated wastewater on the mechanical properties and durability of concrete

Abstract Recently, the world has been facing a crisis in water demand. Therefore, countries started to focus on wastewater treatment to address the shortage of freshwater. Since the concrete industry consumes vast amounts of water, researchers started investigating the incorporation of treated wastewater (TWW) in concrete instead of potable water. However, the impact of variations in the properties of TWW on the durability of concrete is still underexplored. This study investigates the influence of TWW on the mechanical properties and durability of concrete. Two secondary treated TWW samples were collected from different sources. The effect of using TWW for mixing and curing of concrete was evaluated through slump, compressive strength, modulus of elasticity, split tensile strength, water absorption, water penetration resistance, rapid chloride penetration, sorptivity, accelerated corrosion, and SEM analysis. Results showed that using TWW caused a reduction in compressive strength at early ages, while the reduction became negligible at later ages. Moreover, the impact of TWW on the durability was not significant, except for accelerated corrosion results as the mass loss was significantly higher compared to OPC. Moreover, variations in TWW properties affected its performance, as TWW with higher TDS showed better performance, due to the pore filling effect.

Mix proportion optimization of red mud manufactured sand foam concrete and its sulfate resistance

A lightweight perceptual-guided VQVAE for high-fidelity image compression

Abstract Low-bit-rate image compression faces a persistent quality-efficiency dilemma: lightweight models such as VQ-VAE produce perceptually degraded reconstructions, while high-quality alternatives like VQGAN and diffusion models incur prohibitive computational costs. To bridge this gap, we propose HiRes-VQ, a lightweight perceptual-guided VQ-VAE that achieves high-fidelity reconstruction without sacrificing efficiency. Built upon VQ-VAE-2’s hierarchical quantization, HiRes-VQ introduces two key innovations: (1) an asymmetric encoder-decoder architecture, where the encoder hierarchically extracts semantic features at multiple spatial scales and the decoder reconstructs low-frequency structures and high-frequency textures through separate frequency-domain pathways, together ensuring pixel-level fidelity; and (2) a multi-scale perceptual alignment loss that jointly optimizes pixel accuracy, semantic feature consistency, and style statistics, enabling perceptual-quality gains without compromising structural metrics. With only 3.21M parameters, HiRes-VQ achieves 18%–40% fidelity gains over similar-sized baselines on FFHQ-256 and ImageNet-256 across both pixel-level and semantic-level metrics, while surpassing high-complexity models such as VQGAN and OptVQ in quality-efficiency trade-off. Ablation experiments confirm that the dual-path decoder and the perceptual loss serve complementary roles, together enabling significant improvements in both pixel-level fidelity and semantic perceptual quality. These results demonstrate that HiRes-VQ effectively resolves the quality-efficiency dilemma, offering a practical solution for resource-constrained deployment.

Multi-scale fusion convolution network with progressive dilation for real-time salient object detection of surface defects on strip steel

Abstract Accurate and efficient salient object detection (SOD) of strip-steel surface defects plays a critical role in maintaining product quality in modern industrial manufacturing. However, existing SOD methods often struggle to balance detection accuracy with inference efficiency, especially when handling complex defect patterns in real-time production environments. To address this challenge, we propose a novel framework named Multi-Scale Fusion Convolution Network with Progressive Dilation (MSFNet-PD), which is specifically designed for real-time salient defect detection. The proposed MSFNet-PD introduces a multi-scale feature fusion architecture that aggregates contextual information from different receptive fields, enabling the model to capture both fine-grained local textures and broader semantic structures of surface defects. In addition, we incorporate a progressive dilation strategy, where dilation rates are gradually increased across convolutional layers. This design enhances the model’s ability to perceive defects of varying sizes without significantly increasing computational cost or degrading feature resolution. Furthermore, MSFNet-PD employs a lightweight backbone and an efficient fusion mechanism, which collectively contribute to faster inference speed, making the network well-suited for deployment in real-world, high-speed strip steel inspection lines. Extensive experiments conducted on the SD-Saliency-900 dataset demonstrate that our method achieves competitive performance in both detection accuracy and processing speed compared with several recent baselines. The promising results affirm the effectiveness of our approach in practical industrial defect inspection scenarios.