Search Results (1,948)

Chronic obstructive pulmonary disease (COPD) and insomnia are highly comorbid, yet their shared pathogenesis and therapeutic targets remain unclear. This study employed multidimensional approaches—including bidirectional Mendelian randomization (MR), transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and computational drug repositioning—to investigate causal relationships, shared pathways, and therapeutic strategies for COPD–insomnia comorbidity. MR analysis indicated that insomnia is a causal risk factor for COPD (OR = 2.04, 95% CI: 1.18–3.51; p = 0.011), with no reverse causality. Integrated transcriptomics of COPD (GSE148004) and insomnia (GSE208668) identified 230 co-dysregulated genes enriched in immune-inflammatory pathways (e.g., NF-κB signaling and cytokine response) and oxidative stress. Protein–protein interaction networks highlighted TNFAIP3 as a hub gene, confirmed by LASSO regression as a shared diagnostic biomarker. A co-expression network of 190 overlapping genes linked circadian disruption and airway inflammation. Drug repositioning nominated TNFAIP3-targeting agents, and molecular docking revealed high-affinity binding between berbamine and the TNFAIP3 OTU domain (ΔG = −9.25 kcal/mol). TNFAIP3 emerges as a dual regulator of inflammatory signaling and redox homeostasis. Our systems pharmacology approach bridges epidemiological causality and molecular mechanisms, supporting single-agent polypharmacology for COPD–insomnia comorbidity. Full article

Assisted reproductive technologies (ARTs) have revolutionized infertility treatment, leading to the birth of over 10 million children worldwide. Despite their success, increasing concerns have been expressed regarding the potential long-term outcomes of ART-conceived individuals, particularly in relation to imprinting disorders (IDs). IDs result from the abnormal expression of imprinted genes, which are expressed in a parent-of-origin-specific manner and regulated by epigenetic mechanisms (e.g., DNA methylation). Disruption of these processes, through environmental, genetic, or procedural factors, can lead to disorders such as Beckwith–Wiedemann syndrome (BWS), Silver–Russell syndrome (SRS), Angelman syndrome (AS), and Prader–Willi syndrome (PWS). These syndromes are characterized by distinct clinical features, including growth abnormalities, neurodevelopmental delay, endocrine dysfunction, and cancer predisposition. ART procedures, especially ovarian hyperstimulation, in vitro fertilization (IVF), and embryo culture, coincide with critical periods of epigenetic reprogramming and may contribute to epimutations in imprinting control regions. In this review, we explored epidemiology, molecular mechanisms, and prenatal diagnostic strategies related to these four IDs in the context of ART. The findings suggest a higher prevalence of BWS and SRS in ART-conceived children. The data regarding AS and PWS are more controversial, with conflicting results across populations and methodologies. Although a causal link between ART and IDs remains debated, evidence suggests the potential contribution of ART procedures to epigenetic dysregulation in susceptible individuals. Further large-scale, methodologically rigorous studies will be essential to clarify this association and inform safer ART practices. Full article

Background: Chronic diseases have proliferated worldwide and become one of the foremost public health challenges. The provincial pooling policy of Chinese Basic Medical Insurance Program (BMIP) (hereinafter the Policy) is conducive to coordinating healthcare resources more broadly and containing medical costs more effectively, which creates opportunities to improve chronic disease patients’ health outcomes. Against this backdrop, this study aims to identify how the Policy affects chronic disease patients’ health outcomes. Methodology: Utilizing data from the China Family Panel Studies (CFPS) across 31 provinces (except Hong Kong, Macao, and Taiwan) from 2010 to 2022, we constructed a panel of 26,585 observations on chronic disease patients enrolled in the BMIP. We employed a difference-in-differences (DID) design to identify the causal effects of the Policy on self-rated health (SRH) supplemented by a series of robustness checks, including event-study analysis, placebo tests, and propensity score matching DID (PSM-DID). Results: The results show that the Policy enhances Chinese chronic disease patients’ health outcomes across various robustness assessments. However, the effects exhibit heterogeneity in that the Policy can more effectively improve the health outcomes of urban patients, low-income patients, and highly educated patients. The mechanism analysis indicates that the Policy can enhance chronic disease patients’ health outcomes by reducing the out-of-pocket ratio, increasing household income, and stimulating consumer expenditure. Furthermore, digital technology can amplify the effectiveness of the Policy in Chinese chronic disease patients’ health outcomes. Conclusions: These findings provide valuable insights into the potential of provincial pooling and digital technology to optimize Chinese chronic disease management. Full article

Pavement performance prediction serves as a core basis for maintenance decision-making. Although numerous studies have been conducted, most focus on road segments and aggregate indicators such as IRI and PCI, with limited attention to the daily deterioration of individual distresses. Subject to the combined influence of multiple factors, pavement distress deterioration exhibits pronounced nonlinear and time-lag characteristics, making distress-level predictions prone to disturbances and highly uncertain. To address this challenge, this study investigates the distress-level deterioration of three representative distresses—transverse cracks, alligator cracks, and potholes—with causal analysis and uncertainty quantification. Based on two years of high-frequency road inspection data, a continuous tracking dataset comprising 164 distress sites and 9038 records was established using a three-step matching algorithm. Convergent cross mapping was applied to quantify the causal strength and lag days of environmental factors, which were subsequently embedded into an encoder–decoder framework to construct a BayesLSTM model. Monte Carlo Dropout was employed to approximate Bayesian inference, enabling probabilistic characterization of predictive uncertainty and the construction of prediction intervals. Results indicate that integrating causal and time-lag characteristics improves the model’s capacity to identify key drivers and anticipate deterioration inflection points. The proposed BayesLSTM achieved high predictive accuracy across all three distress types, with a prediction interval coverage of 100%, thereby enhancing the reliability of prediction by providing both deterministic results and interval estimates. These findings facilitate the identification of high-risk distresses and their underlying mechanisms, offering support for rational allocation of maintenance resources. Full article

Long COVID, also referred to as post-acute sequelae of SARS-CoV-2 infection (PASC), is characterized by persistent multi-systemic symptoms such as fatigue, cognitive impairment, and respiratory dysfunction. Accumulating evidence indicates that gut and oral microbiota play an important role in its pathogenesis. Patients with long COVID consistently exhibit reduced microbial diversity, depletion of beneficial short-chain fatty acid (SCFA)-producing species such as Faecalibacterium prausnitzii and Bifidobacterium spp. and enrichment of proinflammatory taxa including Ruminococcus gnavus, Bacteroides vulgatus, and Veillonella. These alterations may disrupt intestinal barrier integrity, sustain low-grade systemic inflammation, and influence host immune and neuroendocrine pathways through the gut–brain and gut–lung axes. Distinct microbial signatures have also been associated with symptom clusters, including neuropsychiatric, respiratory, and gastrointestinal manifestations. Proposed mechanisms linking dysbiosis to long COVID include impaired SCFA metabolism, tryptophan depletion, microbial translocation, and interactions with host immune and inflammatory responses, including autoantibody formation and viral antigen persistence. Preliminary interventional studies using probiotics, synbiotics, and fecal microbiota transplantation suggest that microbiome-targeted therapies may alleviate symptoms, although evidence remains limited and heterogeneous. This review synthesizes current literature on the role of gut and oral microbiota in long COVID, highlights emerging microbial biomarkers, and discusses therapeutic implications. While causality remains to be firmly established, restoring microbial balance represents a promising avenue for diagnosis, prevention, and management of long COVID. Full article

Background: Insomnia is markedly more prevalent in rheumatoid arthritis (RA) patients than in the general population and is closely linked to pain, fatigue, psychological comorbidities, and systemic inflammation. Evidence suggests a bidirectional relationship, where active disease worsens sleep quality, while poor sleep amplifies inflammatory activity and symptom severity. Methods: A narrative review was conducted using PubMed, Scopus, Web of Science, and Embase to identify studies from the last 15 years involving adult RA patients. Inclusion criteria required assessment of insomnia or sleep quality in relation to disease activity, treatment outcomes, or inflammatory markers. Data from clinical trials, cohort studies, and reviews were synthesized to examine prevalence, mechanisms, and therapeutic implications. Results: Insomnia affects up to 45% of RA patients and correlates with higher DAS28 scores, elevated CRP/ESR, increased pain sensitivity, and reduced quality of life. Contributing factors include chronic pain, stiffness, elevated IL-6 and TNF-α, depression, anxiety, and medication side effects. Conventional DMARDs, corticosteroids, and biologics indirectly improve sleep via inflammation control, with IL-6 inhibition showing potential sleep-specific benefits. Psychotropic agents may help in comorbid depression/anxiety but are best combined with cognitive behavioral therapy for insomnia (CBT-I). Conclusions: Insomnia is a prevalent, multifactorial problem in RA that adversely affects disease activity, symptom burden, and functional outcomes. Integrating sleep evaluation into routine RA management and adopting interdisciplinary strategies that address both inflammation and sleep disturbance may enhance patient outcomes. High-quality longitudinal studies using objective sleep measures are needed to clarify causal relationships and optimize therapy. Full article

Background: Immune dysregulation has emerged as a central mechanism in atrial fibrillation (AF), with accumulating evidence implicating T-cell subsets, cellular senescence, checkpoint dysfunction, and inflammatory signaling. Although individual studies have provided important insights, a comprehensive synthesis across histological, mechanistic, prognostic, and genetic domains has been lacking. Methods: We systematically reviewed 16 studies published between 2009 and 2025, encompassing histological investigations, translational and mechanistic analyses, interventional cohorts, prognostic studies, and Mendelian randomization. Data on immune cell subsets, cytokines, signaling pathways, and clinical outcomes were extracted. Risk of bias was assessed using ROBINS-I and RoB 2, while certainty of evidence was graded using the GRADE framework. Results: Histological studies consistently demonstrated infiltration of atrial tissue by T lymphocytes and macrophages, with greater intensity in persistent and permanent AF, causally linked to atrial dilatation and fibrosis. Epicardial adipose tissue emerged as a key reservoir of tissue-resident memory T cells that promote IL-17- and IFN-γ-mediated fibroinflammatory remodeling. Mechanistic analyses highlighted CD8⁺PAR1⁺ cytotoxic T cells, PD-1/PD-L1 checkpoint disruption, and adipose–myocardial crosstalk as pivotal drivers of AF. Prognostic studies indicated that immune biomarkers provide incremental predictive value beyond conventional risk scores, while genetic evidence supported a causal role for immune dysregulation in AF susceptibility and progression. Conclusions: Across multiple levels of evidence, immune dysregulation is a primary determinant of AF development, progression, and outcomes. Integration of immune biomarkers into clinical practice may enhance risk stratification and inform the design of immune-targeted therapies for atrial fibrillation. Full article

The swine industry is vital to economic stability and household welfare in China and worldwide but remains highly vulnerable to price volatility driven by multiple factors. Capturing the underlying mechanisms of hog price formation is particularly challenging, as conventional models often fail to represent its nonlinear structures and complex multivariate causal dependencies. This study proposes a Deep Granger Causality Inference (DGCI) model that integrates deep learning with causal inference to identify the key driving factors of hog price dynamics. The DGCI model contains a Feature Reconstruction Module (FRM) and a Granger Causality Module (GCM). The FRM integrates a Variational Autoencoder (VAE) with a Transformer to capture latent temporal representations of multivariate variables. Meanwhile, the GCM quantifies nonlinear Granger causality strength by systematically excluding features to measure their causal impact on hog price. Furthermore, this study proposes the Causal Feature Importance (CFI) metric, which jointly evaluates reconstruction fidelity and causal strength to identify key determinants. To evaluate the model performance, this study utilizes a real-world hog dataset from China. The results demonstrate considerable gains, with DGCI decreasing MSE by 17.59% to 39.22% and MSPE by 32.35% to 54.90% relative to baseline models. The DGCI model highlights pork price, piglet cost, and slaughter volume as the primary determinants of hog price, with CFI values of 1.5216, 1.4451, and 1.4266, respectively. By advancing understanding of the causal drivers of price volatility, this study contributes to informed decision-making, enhanced food security, and the sustainable development of the swine industry. Moreover, as a generalizable methodology, the proposed framework can be broadly applied to analyze the influencing factors of other agricultural and livestock products. Full article

Offline reinforcement learning suffers from critical vulnerability to data corruption from sensor noise or adversarial attacks. Recent research has achieved a lot by downweighting corrupted samples and fixing the corrupted data, while data corruption induces feature entanglement that undermines policy robustness. Existing methods fail to identify causal features behind performance degradation caused by corruption. To analyze causal relationships in corrupted data, we propose a method, Robust Causal Feature Disentanglement(RCFD). Our method introduces a learnable causal feature disentanglement mechanism specifically designed for reinforcement learning scenarios, integrating the CausalVAE framework to disentangle causal features governing environmental dynamics from corruption-sensitive non-causal features. Theoretically, this disentanglement confers a robustness advantage under data corruption conditions. Concurrently, causality-preserving perturbation training injects Gaussian noise solely into non-causal features to generate counterfactual samples and is enhanced by dual-path feature alignment and contrastive learning for representation invariance. A dynamic graph diagnostic module further employs graph convolutional attention networks to model spatiotemporal relationships and identify corrupted edges through structural consistency analysis, enabling precise data repair. The results exhibit highly robust performance across D4rl benchmarks under diverse data corruption conditions. This confirms that causal feature invariance helps bridge distributional gaps, promoting reliable deployment in complex real-world settings. Full article

Background: Perfectionism is a multidimensional personality trait encompassing both adaptive and maladaptive aspects that strongly influence students’ psychological health. Spiritual well-being, defined by existential and religious components, has been suggested as a protective factor, yet its relationship with perfectionism remains underexplored in university populations. This study aimed to investigate these associations in a large nationwide sample of Italian undergraduates. Methods: A total of 2103 students from public and private universities across Northern, Central, and Southern Italy participated in an online cross-sectional survey. Validated instruments were used to assess multidimensional perfectionism and spiritual well-being. Results: Self-oriented perfectionism emerged as the most prevalent dimension, followed by other-oriented and socially prescribed perfectionism. Scores for existential well-being were higher than those for religious well-being. Existential well-being was negatively associated with socially prescribed perfectionism, suggesting a buffering role against maladaptive forms of striving. Religious well-being showed only a small positive association with perfectionism. Gender and age differences were also observed, with women and younger students reporting higher levels of perfectionism. Conclusions: Findings highlight existential well-being as a potential protective factor in academic contexts, supporting meaning-centered strategies to mitigate maladaptive perfectionism. Longitudinal and cross-cultural studies are warranted to clarify causal mechanisms and inform culturally sensitive educational and clinical practices. Full article

Background: The second-to-fourth digit ratio (2D:4D) serves as a non-invasive proxy for prenatal androgen exposure. While its relationship with adult athletic ability is well documented, evidence for its association with childhood physical fitness remains inconsistent, and links between 2D:4D and objective fitness measures in prepubertal children are unclear. Methods: In this cross-sectional study, 338 prepubertal children (181 girls, 157 boys; aged 5–12 years) underwent precise measurement of right- and left-hand 2D:4D ratios (intra-class correlation coefficient = 0.94). Physical fitness was evaluated using standardized tests: the Illinois agility run, bent-arm hang, and standing long jump. Results: Among boys, higher 2D:4D ratios were modestly associated with prolonged bent-arm hang performance (β = 0.19, q = 0.04) and shorter Illinois agility times (β = −0.19, q = 0.04). No significant associations were observed in girls. All effect sizes were small, suggesting subtle, sex-dependent influences rather than robust predictors of performance. Conclusions: These findings indicate that prenatal hormonal environment may exert a limited, sex-specific influence on early physical fitness characteristics. Although biologically informative, the observed associations are insufficient for direct application in talent identification in sports. Longitudinal research incorporating direct hormonal measurements and broader populations is recommended to clarify developmental mechanisms and causal pathways. Full article

Climate change is a critical constraint on the development of new-quality productive forces (NQPFs), making it essential to clarify its relationship with urban development strategies to enhance productivity. Using panel data from 284 Chinese cities during 2010–2022, this study leverages the climate-resilient city pilot policy as a quasi-natural experiment and applies a double machine learning approach to estimate both the causal impact and underlying mechanisms of this policy on NQPFs. We further examine heterogeneous effects across geographic regions and city types. Our findings show that first, climate-resilient urban development significantly boosts NQPFs, with results remaining robust across multiple sensitivity tests. Second, this effect operates through three key channels—talent agglomeration, data flow enhancement, and infrastructure-related industrial upgrading. Third, the policy’s impact is stronger in western and coastal cities; resource-based cities and non-environmentally protected cities exhibit greater responsiveness, amplifying the positive outcomes. This study provides systematic empirical evidence on the nexus between climate resilience and high-quality development, offering actionable insights for designing localized strategies to advance climate-resilient urbanization and foster high-quality productive forces. Full article

Novel coronavirus (SARS-CoV-2) is highly infectious and pathogenic. Novel coronavirus infection can not only cause respiratory diseases but also lead to multiple organ damage through direct or indirect mechanisms, in which the liver is one of the most frequently affected organs. It has been reported that 15–65% of coronavirus disease 2019 (COVID-19) patients experience liver dysfunction, mainly manifested as mild to moderate elevation of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Severe patients may progress to liver failure, develop hepatic encephalopathy, or have poor coagulation function. The mechanisms underlying this type of liver injury are complex. Pathways—including direct viral infection (via ACE2 receptors), immune-mediated responses (e.g., cytokine storm), ischemic/hypoxic liver damage, thrombosis, oxidative stress, neutrophil extracellular trap formation (NETosis), and the gut–liver axis—remain largely speculative and lack robust clinical causal evidence. In contrast, drug-induced liver injury (DILI) has been established as a well-defined causative factor using the Roussel Uclaf Causality Assessment Method (RUCAM). Treatment should simultaneously consider antiviral therapy and liver protection therapy. This article systematically reviewed the mechanism, clinical diagnosis, treatment, and management strategies of COVID-19-related liver injury and discussed the limitations of current research and the future directions, hoping to provide help for the diagnosis and treatment of such patients. Full article

Background: The tumor microenvironment offers a new perspective in gynecologic oncology. In ovarian cancer, numerous preclinical studies, especially organoid models, have highlighted cellular, immune, and biochemical mechanisms. Beyond these sophisticated findings, more practical aspects require attention, such as the role of vaginal microbiota, which represents an interplay between external agents and internal genitalia, and its potential profiling role in early detection beyond the promise of microbiota-targeted therapies. Objectives: This review aims to assess whether such a correlation is speculative or scientifically grounded. Methods: A focused literature search was conducted on vaginal microbiota and its correlation with ovarian cancer to define the current state of knowledge. Results: Mixed outcomes have been reported, yet there is a rational and scientific basis supporting further investigation. Clinical approaches increasingly consider vaginal microbiota as relevant. However, we have to say that most available evidence is still preliminary and largely preclinical to set realistic expectations for readers. Although additional studies are needed, emerging insights highlight its importance and practical implications. We present a diagnostic–therapeutic management flowchart summarizing current evidence). Discussion: Most links between the vaginal microbiota and ovarian cancer are correlational rather than causal. The idea that microbes ascend from the vagina to the ovaries is proposed but still definitely not demonstrated. Confounding factors like age, hormones, and BRCA status complicate interpretation, and ovarian cancer itself could secondarily alter the microbiota. Mechanistic studies and longitudinal data are still needed to clarify whether dysbiosis contributes to carcinogenesis or is merely a consequence. As gynecologists, we summarize key aspects and emphasize to colleagues the importance of incorporating these findings into daily clinical practice. Vaginal dysbiosis should be considered not only a local imbalance but also a potential strategy for primary cancer prevention. Conclusions: Future research on the tumor microenvironment and vaginal microbiota will expand scientific knowledge and guide innovative preventive and therapeutic strategies. Full article

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous syndrome with a continuing high mortality rate. Despite intensive research, established therapies consist mainly of supportive measures, while pharmacological approaches have not yet shown any consistent survival benefits. In recent years, it has become clear that the great clinical and biological diversity of ARDS contributes significantly to the difficulty of demonstrating therapeutic effects. The phenotyping of ARDS has therefore become a central field of research. Different approaches—from clinical parameters and imaging to inflammatory and cardiovascular profiles and multi-omics analyses—have repeatedly identified reproducible subphenotypes that differ in prognosis and, in some cases, in response to therapies. Hypo- and hyperinflammatory subphenotypes have been described as particularly consistent. These are prognostically relevant and, in retrospective analyses, have also shown a differentiated response to glucocorticoids, statins, or fluid strategies. However, endotypes based on causal pathophysiological mechanisms are still largely theoretical. The concept of treatable traits illustrates the potential of personalized therapy but is currently based predominantly on retrospective findings. Future studies should use standardized terminology and multimodal approaches, take longitudinal data into account, and aim for prospective validation to define robust subphenotypes and causal endotypes. This could lay the foundation for true precision medicine in ARDS. Full article