Search Results (6,123)

Malaria pathogenesis involves complex immunopathological mechanisms that hinder early diagnosis and effective treatment. This study integrates multi-omics data and experimental models to identify host-derived biomarkers and elucidate their functional roles. By combining human transcriptomic datasets, weighted gene co-expression network analysis (WGCNA), and machine learning (LASSO, SVM, RF), we identified C1QB as a key hub gene. In human data, C1QB was significantly upregulated in both training and validation cohorts (AUC 0.983 and 0.970). Single-gene GSEA and immune infiltration analyses linked C1QB to apoptosis, inflammation, and altered immune cell composition, including increased activated dendritic cells and neutrophils, and decreased naïve B cells and CD8⁺ T cells. In a murine malaria model (Plasmodium berghei ANKA), C1QB expression rose as early as day one post-infection, preceding detectable parasitemia. Immunohistochemistry revealed C1QB accumulation in the liver and spleen. Single-cell RNA sequencing in the murine model confirmed monocyte-predominant expression, and scTenifoldKnk analysis suggested its role in immune regulation. Crucially, inhibiting C1q in mice via antibody intervention alleviated malaria-induced inflammation, tissue damage, and apoptosis, indicating that C1QB/C1q actively contributes to immunopathology. AI-based drug prediction and molecular docking further supported its therapeutic potential. Collectively, our findings establish C1QB as a dual biomarker and pathogenic driver in malaria, with diagnostic and therapeutic implications. Further studies are required to validate direct target engagement and clarify upstream regulatory mechanisms. Full article

Petrochemical plants are complex facilities composed of interconnected equipment to produce essential products for daily human activities. In view of the adoption of the Industrial Internet of Things (IIoT), these facilities use various sensors, including those for level, flow, temperature, and pressure, to monitor and control operations. Several process faults can be detected and classified using data from these sensors. Detecting and classifying these faults helps mitigate production losses, improve product quality, decrease environmental concerns, and improve human safety. Conventional static learning, which learns from historical data, and continual learning, which learns incremental tasks with known preset boundaries, fall short on asynchronous online unknown streaming boundary tasks such as the OpenWorld Problem, which is the true representation of real-world dynamic plant scenarios. To tackle these challenges, we propose a novel online asynchronous framework termed OD-ViTFScL. Our framework utilizes a dual-attention backbone, which concatenates orthogonal information from attention on the vertical time-series data and horizontal inter-sensor relationships. The fused MLP from the temporal and sensor streams learns independent weights for each orthogonal axis. Our framework also uses ODIN, an out-of-distribution technique, to trigger the arrival of a new fault class in the streaming data. Extensive experiments on the Tennessee Eastman Process (TEP) and Case Western Reserve University (CWRU) bearing datasets validate that our proposed OD-ViTFScL outperforms other state-of-the-art fault detection and diagnosis (FDD) approaches. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article

Japanese encephalitis virus (JEV) is a zoonotic pathogen transmitted primarily by Culex mosquitoes and causes severe neurological diseases in humans and animals. The main endemic areas are the Western Pacific and Southeast Asia, and its geographical distribution has expanded in recent years. The development of a diagnosis for orthoflavivirus infections is hampered by two main problems: the short duration of viremia, resulting in a narrow detection window, and severe cross-reactivity. NS1, a secreted nonstructural protein of orthoflavivirus, holds promise as a new target for overcoming these limitations. In this study, we established a highly specific and sensitive capture ELISA for the JEV NS1 protein. First, the JEV NS1 protein was successfully expressed in mammalian cells and purified using affinity chromatography. Seven mAbs recognizing JEV NS1 were generated, and the mAb 20B6 exhibited the strongest binding affinity. Based on 20B6, a capture ELISA was developed with an optimal coating concentration of 3 μg/mL and an optimal detection antibody working concentration of 0.432 μg/mL. No cross-reactivity was observed with other orthoflaviviruses (including WNV, KUNV, USUV, MVEV, SLEV, and ZIKV) or common porcine viruses. The method could effectively detect NS1 protein in cell culture medium, cell lysates, mouse tissues, and porcine serum samples from JEV-infected subjects. This study provides a solid foundation that may be further developed into an efficient and specific tool for epidemiological surveillance of Japanese encephalitis. Full article

Sexually transmitted infections (STIs) are common and probably underreported causes of proctitis and colitis. Bacterial (chlamydia, gonorrhoea, syphilis, Mycoplasma genitalium), viral (herpes simplex virus, mpox), and amoebic (Entamoeba histolytica) pathogens can cause inflammatory proctitis or colitis, which, depending on the pathogen, can mimic inflammatory bowel disease both on endoscopy and histopathology. Rectal and colonic masses are uncommon, but important manifestations of these infections, especially with chlamydia, syphilis, and E. histolytica. Testing for HIV is important in this context, since it allows for the inclusion of opportunistic pathogens into the differential diagnosis. Chronic diarrhoea can be a feature of chronic HIV infection. Enteric pathogens, such as Salmonella spp., Shigella spp., or Campylobacter spp., can be transmitted during sex, especially during oral–anal contact (“rimming”). The most common STI, human papillomavirus, is not associated with colitis, but is important because of its causal association with genital warts and anal cancer. Full article

Depression is defined by clinical symptoms, but its biology varies considerably among patients. In this review, dietary tryptophan allocation describes the routes taken by tryptophan after intestinal absorption. Some is incorporated into proteins. Some enters serotonin and melatonin synthesis or the kynurenine pathway, while gut bacteria convert another fraction into indole compounds. Brain availability also depends on the circulating free pool and competition with other large neutral amino acids. This term does not imply a new biochemical pathway. It allows these known processes to be considered in relation to inflammation, metabolism, the gut microbiota, medication use, and current disease state. Recent meta-analyses indicate that peripheral tryptophan is lower in depression. They do not show a consistent increase in the kynurenine-to-tryptophan ratio, and findings from cerebrospinal fluid vary between studies. Human multiomics studies have associated microbial and metabolite profiles with cognition and response to treatment, although the evidence remains largely correlational. Changes in kynurenine, 3-hydroxykynurenine, and quinolinic acid are more apparent in inflammatory subgroups than in unselected samples. Modern evidence for L-tryptophan monotherapy is sparse. Trials of 5-hydroxytryptophan and interventions directed at the gut microbiota have also produced mixed results. Studies should characterize participants and sampling conditions more carefully. Diet and competition among amino acids need to be recorded. Albumin concentration, medication exposure, and disease state also affect interpretation. Considering these variables together may improve biomarker analyses and support trials in more biologically homogeneous groups. Dietary tryptophan allocation is proposed for these research purposes, not for clinical diagnosis or routine supplementation. Full article

Operator diagnosis level in a main control room (MCR) of Nuclear Power Plants (NPPs) is a core factor in preventing human errors and ensuring the safe operation of NPPs. Due to the high uncertainty of human behaviors and the scarcity of relevant data, traditional analysis methods mainly rely on empirical judgment, which suffer from insufficient dynamics and poor engineering adaptability. To address the issues, this paper conducts a study on an AI prediction model for operator diagnosis level in a MCR of NPPs based on the SACADA database and machine learning technology. The model adopts a probabilistic neural network (PNN) as the main architecture, and proposes a hybrid method of network search considering density distribution combined with K-fold cross-validation, which breaks the traditional mode of a single smoothing factor adapting to an entire dataset. The analysis results show that the performance of the hybrid method proposed in this paper outperforms network search + K-fold cross-validation and particle swarm optimization + K-fold cross-validation methods in terms of accuracy, precision, recall, and F1-score. The five-fold cross-validation verifies that the model has good stability and good generalization ability. Further, the model is compared with common AI models such as BP neural network and RBF neural network. The results demonstrate that the proposed model has advantages in core indicators including overall accuracy (0.9444), macro-precision (0.9783), macro-recall (0.9063), and macro-F1-score (0.9362), and can effectively solve the problems of insufficient recognition of minority-class samples, overfitting, and underfitting. This research achieves professional and in-depth application of the SACADA database for diagnosis level prediction, extends existing research on prediction tasks, and delivers valuable theoretical insights and practical application significance. Full article

Integrated qiaopi office–residence heritage preserves the material setting of remittance-letter operations together with domestic, educational, and ritual activities. In Lingnan’s hot–humid and disaster-prone environment, condition records need to be repeatable, spatially traceable, and continuously updatable. Taking Jingzu Jiashu and Mingde Jiashu, two former qiaopi office sites in Chaoshan, as case studies, this research develops an evidence-traceable digital conservation workflow integrating multi-source documentation; an adopted YOLOv8 surface-deterioration baseline; qualitative Grad-CAM visualization; structured deterioration records; and semi-automatic, human-confirmed Revit/HBIM association. UAV and terrestrial photography, mobile LiDAR/scanning, handheld measurement, measured drawings, point-cloud and reality-based products, and geometric models were organized into case-specific HBIM environments. The adopted deterioration dataset comprised 362 original images at 512 × 512 pixels and 2024 bounding-box annotations for five visually identifiable categories: spalling, staining, plants, saltpetering, and crack. The original images were divided into 253 training, 72 validation, and 37 independent-test images, while augmentation was restricted to the training subset, increasing the training pool to 1600 images. The previously established YOLOv8 baseline achieved a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58. Grad-CAM heatmaps were used as qualitative aids to examine model-emphasized image regions. Retained detections associated with Jingzu Jiashu and Mingde Jiashu were converted into versioned records containing source-image identifiers, deterioration classes, detector confidence, survey information, spatial references, verification states, and revision histories. Candidate spatial associations were generated through case identifiers, façade or space zones, element identifiers, and available spatial evidence, while final M1–M3 associations required human confirmation. By preserving source provenance, spatial uncertainty, and record histories, the workflow provides an auditable information basis for routine inspection, post-event review, maintenance prioritization, repair interpretation, and resilience-oriented preventive conservation. The workflow supports screening-level deterioration recognition and information management but does not provide causal diagnosis, structural assessment, exact affected-area measurement, building-independent generalization, or automatic repair recommendations. Full article

Background: Elevated plasma homocysteine (HCY) is a risk factor for Alzheimer’s disease (AD), but its relationship with structural brain changes across the AD continuum remains unclear. The choroid plexus (CP) regulates cerebrospinal fluid homeostasis and may interface with peripheral metabolic signals. Whether HCY relates to CP structural alterations and disease progression remains unknown. Methods: We analyzed 819 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (229 cognitively normal (CN), 397 with mild cognitive impairment (MCI), and 193 with AD dementia). Multinomial logistic regression assessed associations between HCY and diagnosis under stepwise covariate adjustment. Phenotype-wide structural magnetic resonance imaging (MRI) mapping identified HCY-associated signals. Cox models evaluated associations of CP volume (CPV) with CN-to-MCI and MCI-to-AD dementia conversion and whether CPV added prognostic discrimination beyond baseline disease-severity markers. Independent human CP single-nucleus and spatial transcriptomic datasets were reanalyzed to characterize epithelial expression states and their spatial organization in a hypothesis-generating analysis. Results: Higher HCY was associated with MCI and AD dementia; however, the AD association attenuated after adjustment for renal function, vitamin B12, and medications, whereas the MCI association remained stable. CPV was among the HCY-associated MRI signals that persisted after progressive covariate adjustment. Right and bilateral CPV showed model-dependent associations with MCI-to-AD dementia conversion. In the disease-severity sensitivity analysis, larger right and bilateral CPV remained associated with a higher risk of progression from MCI to AD dementia. Single-nucleus analysis identified two CP epithelial states with relatively high expression of one-carbon metabolism-related genes, termed one-carbon metabolism-enriched epithelial state A (OCM-Epi-A) and state B (OCM-Epi-B). Donor-level pseudobulk analysis did not identify pathway enrichment after false discovery rate correction, whereas OCM-Epi-A–like spots were located near endothelial spots more often than expected by chance in three of the four spatial samples. Conclusions: Circulating HCY was associated with larger CPV, and larger CPV showed model-dependent associations with MCI-to-AD dementia progression. Independent transcriptomic reanalysis identified one-carbon metabolism-enriched epithelial states and their spatial organization in postmortem CP tissue, providing hypothesis-generating tissue-level context for the ADNI associations. Full article

Animal micro-expressions constitute transient behavioral windows that link internal states to externally observable signals, while artificial intelligence (AI) serves as the critical bridge that transforms these windows into measurable, interpretable, and applicable scientific tools. Rather than imposing a human-centered lexicon of expressions, AI-driven decoding aims to develop biologically grounded and increasingly comparable behavioral biomarkers that link computable facial dynamics to internal states; however, a validated universal cross-species framework has not yet been established. This review summarizes the common behavioral characteristics of animal micro-expressions, their cross-species expressive forms, and functional differences; systematically outlines the methodological spectrum through which AI captures, encodes, recognizes, and interprets these brief yet complex signals; and finally discusses the expanded applications of AI plus micro-expression analysis in basic research, clinical diagnosis, and animal welfare governance, thereby promoting a paradigm shift in the decoding of animal micro-expressions. Full article

RASopathies are a group of genetic disorders caused by germline variants affecting the RAS/MAPK pathway. Their shared phenotypic features—craniofacial anomalies, cardiac defects, cutaneous findings, neurodevelopmental issues, and cancer predisposition—make diagnosis challenging, especially since most lack standardized clinical criteria. This study aimed to develop a practical diagnostic algorithm based on high-frequency Human Phenotype Ontology (HPO) features. Key clinical variables for each RASopathy were identified through HPO, PubMed, and GeneReviews. Only findings present in 80–99% of cases or supported by expert consensus were included. A decision-tree algorithm was constructed and preliminarily evaluated using a blinded cohort of 50 individuals with confirmed molecular diagnoses. Patients were eligible for inclusion if they met the following criteria: (1) molecularly confirmed diagnosis of a RASopathy by next-generation sequencing identifying a pathogenic or likely pathogenic variant; (2) availability of complete phenotypic records in the institutional clinical database; and (3) age at evaluation between 0 and 18 years. Patients were excluded if phenotypic data were incomplete or if molecular confirmation was absent. The algorithm integrates phenotypic patterns and genotype–phenotype correlations. Validation showed 78% accuracy (95% CI: 64.0–88.4%) for clinical diagnosis and 66% accuracy (95% CI: 51.2–78.8%) for molecular prediction. To our knowledge, this is the first HPO-based diagnostic algorithm for the clinical and molecular approach to RASopathies. It provides a structured, accessible tool to improve early recognition and guide molecular testing, particularly for the RASopathy subtypes represented in the validation cohort. Further external validation including underrepresented subtypes is required. Full article

Non-tuberculous mycobacteria are recognized as pathogens affecting aquatic animals and humans. Although Mycobacterium marinum is traditionally considered the principal etiological agent of fish mycobacteriosis and fish-tank granuloma in humans, the contribution of Mycobacterium chelonae in fish as well as in humans remains underestimated, particularly in Ziehl–Neelsen (ZN)-negative lesions. In this study, granulomatous lesions of marine fish species and human skin biopsies were investigated using histopathology and molecular diagnostics. Following histological evaluation, all samples were analysed by PCR amplification and sequencing of the hsp65 gene to identify the causative agent. Fish and human tissues showed similar chronic granulomatous lesions, with all cases negative for acid-fast bacilli by ZN staining. Molecular analysis consistently identified M. chelonae in both marine fish organs and human skin lesions exhibiting clinicopathological features traditionally attributed to M. marinum. Phylogenetic analysis revealed high genetic similarity between fish- and human-derived sequences, suggesting shared environmental reservoirs rather than host-specific lineages. These findings identify M. chelonae as an underrecognized cause of granulomatous disease in fish and humans and demonstrate the limitations of histochemical methods in these infections. Incorporating molecular diagnostics into routine investigations of granulomatous disease supports more accurate diagnosis across veterinary and human medicine and strengthens One Health surveillance of aquatic non-tuberculous mycobacterial infections. Full article

Uterine fibroids, the most common tumors in reproductive-age women, remain without a defined precursor tissue state. Unlike cervical dysplasia preceding cervical cancer, or colonic polyps preceding colorectal malignancy, no equivalent “at-risk” tissue marker exists for fibroids, and diagnosis relies on radiological imaging only after tumors are already well-established and often symptomatic including excessive menstrual bleeding, pelvic pain, infertility and obstetric complications. In this narrative review, we propose that a subset of women with unexplained AUB may harbor a chronic, non-infectious inflammatory condition of the myometrium, which we term Chronic Aseptic Myometritis (CAM). We synthesize mechanistic and human tissue evidence suggesting that sterile inflammation driven by damage-associated molecular patterns, NLRP3 inflammasome activation, oxidative DNA damage, and TGF-β–mediated extracellular-matrix remodeling may underlie the transition from normal myometrium (MyoN) to a pre-fibroid, inflamed and stiffened state (MyoF), and may contribute both to abnormal uterine bleeding (AUB) and to fibroid initiation. We propose a preliminary framework for future CAM research, including the identification of candidate biomarker categories and imaging correlates. We also discuss whether early mechanism-based interventions, such as vitamin D and epigallocatechin gallate (EGCG), may offer a potential pathway toward primary prevention. Because the components of this model derive largely from experimental and cross-sectional human studies, CAM is presented as a hypothesis-generating, myometrium-centered framework rather than a validated clinical entity, and prospective validation is required. Full article

Determining progesterone concentration is currently considered the most reliable hormonal test for pregnancy detection in belugas. In other cetaceans, additional steroid hormones also serve as pregnancy biomarkers. Whether these hormones are useful for beluga pregnancy diagnosis is unknown. The objective of this study was to conduct circulating steroid hormone analysis across normal pregnancy and different reproductive states in the beluga. Serum samples (n = 240) collected from nine females representative of 20 pregnancies (1991–2017) were analyzed using immunoassays for progesterone, progestagens, estradiol, testosterone, androstenedione, and cortisol. Across pregnancy trimesters (early, mid, late), progesterone was highest during early and mid, and decreased slightly during late, but still maintained concentrations above pre- and post-pregnancy levels. Progestagens increased above luteal phase concentrations during early and continued to increase and remained elevated during mid and late. Estradiol and androstenedione increased during mid and late trimesters. Testosterone was higher at all stages of pregnancy (highest at mid and late) compared to other reproductive states. Across gestation, cortisol was only elevated during late pregnancy. The present study established pregnancy profiles for various steroid hormones during normal beluga pregnancy for animals under human care and identified other non-progestagen hormones that may possibly serve as gestational biomarkers in this species. Full article

Insulin resistance (IR) represents one of the most pressing problems in contemporary public health. Its estimated global prevalence ranges from approximately 15.5% to over 61%, depending on the population studied, the diagnostic criteria applied, and the method used for its assessment. Despite the scale of the problem, IR remains underrecognized and lacks formal definition as a distinct disease entity, even as a growing number of clinicians and researchers worldwide describe it as such. Its asymptomatic or mildly symptomatic course allows it to remain undetected for years, during which it makes a significant contribution to the development of type 2 diabetes, cardiovascular disease (CVD) and metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD), and has been increasingly linked to cellular senescence, certain cancers, neuropsychiatric disorders, and other metabolic conditions. The aim of this review was to summarize current knowledge on the pathophysiology, diagnosis, and clinical implications of insulin resistance, to discuss current challenges in its diagnosis, and to evaluate whether available scientific evidence supports its recognition as a distinct disease entity. This narrative review is based on clinical, epidemiological, and mechanistic data retrieved from PubMed and Google Scholar. Meta-analyses, systematic reviews, clinical and observational studies, clinical guidelines, and expert position statements were analyzed. Animal studies were excluded to maintain a focus on human public health implications. The diagnostic gold standard—the hyperinsulinemic-euglycemic clamp—was discussed, along with surrogate methods used in clinical practice (HOMA-IR, OGTT with insulin measurements, the TyG index, and the TG/HDL-C ratio). Factors potentially contributing to the pathogenesis of IR were examined, including hyperinsulinemia (HI), high-carbohydrate diets, inflammation, stress, and sleep disturbances, as well as conditions in which IR occurs physiologically. The findings indicate that current evidence supports the need for a clearer clinical and diagnostic framework for insulin resistance and suggest that its recognition as a distinct disease entity could facilitate earlier diagnosis, improve the standardization of clinical management, and enable earlier metabolic intervention. Given the steadily rising prevalence of metabolic disease, systemic efforts directed at the early identification and treatment of IR may be a key component of strategies aimed at reducing the population-level burden of metabolic disease and its negative consequences. Full article