Search Results (60,626)

Background: Osteoarthritis (OA) is a prevalent degenerative joint disease lacking effective disease-modifying therapies, which necessitates the discovery of key genes for mechanistic exploration and therapeutic development. Methods: We integrated three large-scale cis-protein quantitative trait locus datasets and two OA genome-wide association study summary statistics to screen candidate proteins by two-stage proteome-wide Mendelian randomization (MR). Causal association reliability was validated via summary-data-based Mendelian randomization (SMR) and Bayesian colocalization analyses. A phenome-wide association study (PheWAS) was performed to evaluate potential pleiotropic effects of the candidates. Subsequently, transcriptomic and single-cell RNA sequencing datasets were employed to evaluate the candidate genes’ expression stability, classification efficacy in the in vitro models of OA, cell-specific enrichment, and pseudotime expression dynamics in cartilage. Finally, drug repurposing potential was explored by integrating drug–gene interaction database searches and molecular docking. Results: Two-stage cis-pQTL MR combined with cis-eQTL-based SMR analysis identified 14 plasma proteins with consistent effects at the protein and transcript levels. RNA-seq revealed that adrenomedullin (ADM) and complement factor H (CFH) were upregulated in two in vitro models of OA, and both genes exhibited favorable classification efficacy in these models. Bayesian colocalization analysis provided evidence of shared causal variants for ADM, and PheWAS did not detect significant pleiotropic associations for ADM or CFH across the tested phenotypes. Single-cell analysis indicated that ADM was enriched in pre-fibrocartilage chondrocytes with biphasic pseudotime expression, whereas CFH was widely expressed across chondrocyte subsets. Database screening identified 15 potential drugs for ADM and 6 for CFH. Conclusions: Combining MR, multi-omics and pharmacological evidence, we prioritized ADM and CFH as OA candidate genes. Full article

1-deoxy-D-xylulose-5-phosphate synthase (DXS) is the first key enzyme in the methylerythritol phosphate (MEP) pathway of plant terpenoid biosynthesis, and it plays a vital role in Eucommia ulmoides terpenoid biosynthesis. In this study, bioinformatics methods were used to comprehensively identify and analyze the expression pattern of the EuDXS gene family, aiming to provide a basis for further functional study of EuDXS genes. A total of four EuDXS gene family members were identified and named EuDXS1 to EuDXS4. The encoded proteins contained 625 to 713 amino acid residues, with molecular weight ranging from 67.98 kDa to 76.61 kDa. The theoretical isoelectric points varied from 6.79 to 8.69, aliphatic index was between 85.29 and 91.29. In silico subcellular localization prediction revealed that all EuDXS proteins were localized in chloroplasts. EuDXS gene members were categorized into three subfamilies, which were unevenly distributed on three chromosomes. The promoters of EuDXS genes contained various cis-acting elements related to stress response, phytohormone signaling, light response and growth regulation. Expression pattern analysis showed that EuDXS genes exhibited tissue-specific expression: EuDXS1 was highly expressed in stem, leaf and fruit, EuDXS2 was predominantly expressed in fruit. EuDXS1 and EuDXS2 exhibited high expression levels at the early developmental stage of fruits and leaves. In addition, EuDXS genes responded to salt and drought stress in varying degrees. Transient expression in tobacco revealed that EuDXS1 and EuDXS2 could increase carotenoid and total chlorophyll content. This study will provide important genetic resources for further exploration of EuDXS gene function and germplasm innovation in E. ulmoides. Full article

Risk assessment of engineered nanomaterials (ENMs) is essential for protecting human health and the environment. Traditional hazard assessments rely primarily on in vivo testing, which faces technical challenges in extrapolation validity, ethical dilemmas, and high costs. Machine learning (ML) models offer alternative approaches that are aligned with the 3R principles (Replacement, Reduction, and Refinement) for reducing animal use. ML methods help address the economic, ethical, and temporal limitations of traditional nanotoxicology while advancing mechanistic understanding. This review presents a cross-scale framework integrating nano–bio/nano–environmental interfaces, organ-specific toxicity, in vitro-to-in vivo extrapolation (IVIVE), interpretable ML, and regulatory translation. Future directions include building comprehensive databases to replace sparse literature data, developing ML models that bridge in vitro and in vivo nanotoxicity, incorporating co-exposure scenarios of nanomaterials and chemicals, and further exploring protein/lipid corona formation and structures. Full article

Background/Objectives: Drug resistance in tuberculosis remains one of the major challenges to global disease control. Traditionally, its emergence has been attributed to factors such as treatment interruption, poor adherence, and the selective pressure exerted by antimicrobial agents. However, recent advances in bacterial genetics, within-host evolution, cellular persistence, and heteroresistance suggest that relevant biological processes may develop before treatment initiation. The aim of this critical integrative reflection was to explore the biological plausibility that diagnostic delay may act as an amplifying factor for adaptive and evolutionary mechanisms potentially associated with drug resistance in Mycobacterium tuberculosis. Methods: A structured documentary search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Of the 86 documents initially identified, 29 studies were selected using predefined inclusion and exclusion criteria. The evidence was analysed through a conceptual convergence matrix, which enabled the organisation of findings into five analytical categories: diagnostic delay and programmatic determinants; mechanisms of resistance, bacterial adaptation, and clinical implications; persistence and drug tolerance; within-host evolution and genetic diversity; and heteroresistance and resistant subpopulations. In addition, four institutional reports were incorporated to contextualise the issue from a global perspective. Results: The reviewed evidence suggests that extended persistence of infection before diagnosis may favour biological opportunities for bacterial adaptation, the persistence of tolerant subpopulations, the accumulation of genetic diversity, and the emergence of variants with distinct drug susceptibility profiles. Although the available studies do not demonstrate a direct causal relationship, they support the plausibility of an evolutionary trajectory capable of influencing the dynamics of drug resistance. Conclusions: An expanded model of drug resistance in tuberculosis is proposed, in which diagnostic delay does not constitute a direct cause of resistance but rather a potential amplifying factor for biological adaptation and evolutionary change occurring before treatment initiation. This hypothesis generates new avenues for research on the interaction between timely diagnosis, bacterial evolution, and drug resistance. Full article

Global water scarcity necessitates precision irrigation management to optimize the balance between yield, water productivity (WP), and fruit quality. This study employed structural equation modeling (SEM) to explore the relationships between physiological responses, growth indicators, and yield of eight-year-old kiwifruit (Actinidia chinensis, cv. Jin Yan) under drip irrigation. A two-year field experiment (2018–2019) was conducted in a seasonally dry region of Southwest China, including 17 treatments across four growth stages: bud burst to leafing (I), flowering to fruit set (II), fruit expansion (III), and fruit maturation (IV). The treatments included a control (CK) and four irrigation lower limits at different growth stages: low (LL), mild (L), moderate (S), and severe (SS). Compared with CK, I-SS, II-S, III-LL, and IV-LL treatments significantly increased leaf instantaneous water use efficiency (WUEi) by 9.05%, 4.52%, 7.89%, and 9.55% (p < 0.05) respectively. After re-watering, II-L treatment significantly boosted Pn by 13.56% and Tr by 10.12%, leading to a 3.33% improvement in WUEi. Compared with CK, I-SS, and I-S treatments reduced the length of new shoots by 4.02% and 1.18% and the diameter of new shoots by 4.84% and 1.11%, while they increased fruit volume by 5.03% and 8.01%, respectively. I-SS and I-S treatments greatly increased the yield by 0.86–1.68% (p < 0.05), and increased water productivity (WP) by 3.90–4.31% (p < 0.05), respectively. Based on correlation analysis and SEM, kiwifruit yield was directly influenced by gs, leaf relative chlorophyll content, fruit volume, and shoot growth with standardized path coefficients of 0.377, 0.367, 0.546 and −0.04, respectively. For WP, gs, leaf relative chlorophyll content, fruit volume, and shoot growth exhibited direct path coefficients of −0.501, 0.342, 0.315, and 0.508, respectively. The IV-S, IV-SS, and I-SS treatments were consistently identified as the top three performers in both years using TOPSIS method of combining weights based on game theory. The suitable irrigation pattern was irrigation thresholds of 55%FC, 80%FC, 80%FC, and 60% FC for stages I, II, III, and IV, which enhanced the yield and WP of kiwifruit in Southwest China. This study could provide a scientific basis for precise soil moisture regulation of kiwifruit for similar production conditions. Full article

After a cancer diagnosis, patients experience intense uncertainty and increasingly resort to artificial intelligence chatbots in this process; however, how this use is experienced by patients has not been adequately studied. This study aimed to explore the experiences of adult patients diagnosed with cancer in the last six months using artificial intelligence (AI) chatbots. Using a descriptive phenomenological approach, face-to-face semi-structured interviews were carried out with 20 adults sampled by a criterion-based purposive sampling method in an oncology clinic in Türkiye. The data were analyzed according to Colaizzi’s method, and the following four main themes were revealed: artificial intelligence usage purposes, the experience of interacting with AI, the effect of the patient’s reflection on the AI experience on the relationship with the treatment team, and evaluation of the use of AI. The participants used chatbots as an intermediate resource during the period of uncertainty between the examination result and clinical explanation; some learned their diagnosis for the first time in this way. However, the relief provided by chatbots was temporary, and trust in chatbot results was constantly tested by the statements of the healthcare professional. As a result, some participants hid their use of chatbots for fear of being judged. AI chatbots served as a complementary source of information, but they did not replace the relationship with the healthcare professional. These findings suggest that healthcare professionals should ask patients about chatbot use without judgment, and that support provided during the post-examination waiting process could be strengthened. Full article

In distribution network and microgrids, energy storage (ES) systems possess four-quadrant operational capabilities, making them inherently high-quality resources for reactive power (RP) regulation. However, existing research has primarily focused on optimizing the active power of ES to achieve economic objectives, while the potential for RP and voltage control has not been fully explored. Meanwhile, traditional RP optimization methods have inherent limitations in terms of real-time performance, addressing uncertainty, and handling nonlinear problems. To address these challenges, this paper proposes a data-driven adaptive RP and voltage control method for distribution network with ES using deep reinforcement learning (DRL)–Soft Actor–Critic (SAC) algorithm. First of all, this method models the grid’s RP and voltage control problem as a sequential decision-making process, with the core being the construction of a control agent that integrates grid operational states with a deep neural network. Through continuous interaction with the environment, this agent autonomously learns and dynamically adapts to the random fluctuations in photovoltaic (PV) output and load without relying on precise physical models. Secondly, this paper sets minimizing network losses, voltage deviations, and the operational costs of RP equipment in ES as comprehensive optimization objectives, translating them into a reward function within the DRL-SAC framework. Leveraging the powerful nonlinear mapping capabilities and extremely fast forward computation speed of deep neural networks, the strategy achieves a data-driven approximation of the optimal RP control strategy in complex grid environments. Finally, the superiority of the strategy is comprehensively verified on the modified IEEE 33-bus system under three typical operating conditions (daytime fluctuation, extreme weather, sudden load change). The results show that the voltage qualification rate is increased to 99.1% and the network loss is reduced by 33.7%, providing an engineering-feasible solution for ES systems to participate in distribution network RP and voltage regulation. Full article

Background/Objectives: Inadequate gestational weight gain (GWG), encompassing both low and excessive, is a significant concern because of its association with adverse maternal and neonatal health outcomes. The relationships between maternal lifestyle factors, such as diet, physical activity, prepregnancy body mass index (BMI), and GWG, are complex. This study aims to explore these associations via correspondence analysis (CA), a method that visualizes the relationships between categorical variables. Methods: A CA was conducted using baseline data from the ongoing LIMIT (Lifestyle and Microbiome Interaction Early Adiposity Rebound in Children) prospective cohort study. The analysis included 162 subjects whose complete maternal characteristic data were available to explore associations between maternal features and GWG. Results: The results were visualized through a two-dimensional graphical representation, with the related statistical measures. The horizontal axis of the CA map captures 63.4% of the total inertia of the data, and the vertical axis captures 36.6%. Maternal profiles with lower education, smoking, high meat consumption, and prepregnancy overweight or obesity showed a higher prevalence of excess GWG than did the average profile. Compared with the average profile, medium Mediterranean diet adherence was associated with a greater prevalence of low GWG and underweight and obese mothers with adequate GWG. Conclusions: This study identifies maternal lifestyle factors, such as diet and physical activity, as key determinants of GWG, with excess GWG linked to unhealthy behaviors. Tailored interventions focusing on prepregnancy BMI and dietary habits could optimize pregnancy outcomes. Personalized prenatal care is essential for managing GWG and improving maternal health. Full article

Background/Objectives: In this exploratory longitudinal study, we examined dietary intake and gut microbiota across preparation, competition, and transition phases in competitive long-distance runners and explored microbial patterns associated with phase-specific dietary intake. Methods: Seven competitive long-distance runners were assessed across three training phases. At each phase, dietary intake and body composition were assessed, and fecal samples were collected. Gut microbiota composition was analyzed using 16S rRNA gene sequencing. Phase-related taxonomic differences were evaluated using repeated-measures approaches with multiple-testing correction, and differential abundance analysis was performed using DESeq2 with participant identity included as a blocking factor. Additionally, exploratory k-means clustering based on energy, carbohydrate, and protein intake was used to characterize dietary intake patterns. Results: Energy, protein, and fat intake differed significantly across training phases, whereas carbohydrate intake did not reach statistical significance. Exploratory alpha- and beta-diversity analyses yielded no statistically detectable differences across phases. At the phylum level, Synergistetes showed an overall phase effect after FDR correction, although no pairwise comparison remained significant after Bonferroni adjustment. In the DESeq2 analysis, Haemophilus was more abundant during the transition than preparation phase, Enterococcus was more abundant during preparation than transition, and Cronobacter was more abundant during preparation than competition (all adjusted p < 0.05). Exploratory dietary clustering identified additional cluster-associated microbial differences, but cluster membership substantially overlapped with the training phase. Conclusions: Dietary intake varied across the competitive training cycle. Community-level diversity results were presented for exploratory purposes only in this small cohort. A limited number of taxa showed phase-related differences after multiple-testing correction. Because dietary intake and training phase changed concurrently, the cluster-associated microbial findings should be interpreted as exploratory patterns within the combined nutritional and training context rather than as independent dietary effects. Full article

Background/Objectives: Pregnancy may involve psychological and existential concerns, particularly for women experiencing pregnancy for the first time. This study examined the relationships among spiritual well-being, perceived social support, and death anxiety in primigravid women and explored the cross-sectional indirect association through perceived social support. Methods: This cross-sectional study included 390 primigravid women attending an antenatal outpatient clinic in northern Türkiye. Data were collected using the Three-Factor Spiritual Well-Being Scale, Multidimensional Scale of Perceived Social Support, and Death Anxiety Scale. Pearson correlation, hierarchical multiple regression, and an exploratory cross-sectional indirect association analysis using PROCESS Model 4 with 5000 bootstrap samples were performed. Results: Spiritual well-being was positively correlated with perceived social support (r = 0.495, p < 0.001) and negatively correlated with death anxiety (r = −0.554, p < 0.001). Perceived social support was also negatively correlated with death anxiety (r = −0.522, p < 0.001). After adjustment for the prespecified covariates, spiritual well-being (β = −0.152, p < 0.001) and perceived social support (β = −0.145, p < 0.001) remained negatively associated with death anxiety. A statistically significant adjusted indirect association was observed in the specified cross-sectional model (B = −0.91, 95% percentile bootstrap CI [−1.44, −0.44]). Conclusions: Higher spiritual well-being and perceived social support were associated with lower death anxiety among primigravid women. A statistically significant adjusted cross-sectional indirect association between spiritual well-being and death anxiety through perceived social support was also observed; however, this finding does not establish mediation, temporal ordering, or causality. Full article

Rituximab induces prolonged B-cell depletion and may alter protective antibody levels, but longitudinal antibody to hepatitis B surface antigen (anti-HBs) patterns in pemphigus vulgaris remain poorly characterized. This study evaluated anti-HBs levels and seroprotection after rituximab and explored associated factors. Methods: This retrospective cohort included 84 patients with pemphigus vulgaris treated with rituximab between 2010 and 2026 who had baseline and post-treatment anti-HBs measurements. Longitudinal changes in log-transformed anti-HBs levels were analyzed using a linear mixed-effects model with categorical time and a patient-specific random intercept. The probability of anti-HBs < 10 IU/L was assessed using a binomial generalized estimating equation model. Paired Wilcoxon signed-rank tests were performed as sensitivity analyses. Results: The median baseline anti-HBs level was 49.7 IU/L, and 34 patients (40.5%) had levels < 10 IU/L. Time was associated with longitudinal anti-HBs levels overall (p = 0.002). Compared with baseline, model-estimated levels were lower at month 6 (geometric mean ratio [GMR] 0.61, 95% CI 0.42–0.88), month 9 (GMR 0.64, 95% CI 0.45–0.89), and month 18 (GMR 0.50, 95% CI 0.32–0.78). Time was not significantly associated with the odds of anti-HBs < 10 IU/L (global p = 0.490). Positivity for antibody to hepatitis B core antigen (anti-HBc) IgG was associated with higher longitudinal anti-HBs levels (adjusted GMR 13.24, 95% CI 5.32–32.95; p < 0.001). No clinically or biochemically evident hepatitis B virus (HBV) reactivation was documented, although HBV DNA was not routinely monitored. Conclusions: Quantitative anti-HBs levels varied significantly during follow-up; whereas, the probability of crossing below the conventional seroprotection threshold did not increase significantly. These findings support HBV serological assessment and cautious interpretation of serial anti-HBs measurements in rituximab-treated patients with pemphigus vulgaris. Full article

Background: Digitalization in medicine is becoming increasingly important to ensure low-threshold, comprehensive, flexible, multimodal, and multiprofessional treatment. This qualitative interview study explores the nuanced perspectives of mental health professionals regarding a digital inpatient-like psychotherapy concept. Methods: This study employed semi-structured qualitative interviews to assess attitudes and expectations toward a digital inpatient-like psychotherapy concept among mental health professionals in Germany. The resulting data were analyzed using a hybrid deductive–inductive codebook thematic analysis involving a consensus-based coding process by three independent researchers. Results: A total of n = 25 interviews with physicians (assistant physicians, specialists), psychotherapists, nurses, psychologists, and individuals from other professions were included in the data analysis. The interviewees held various roles and brought diverse levels of experience to the study, in the context of inpatient mental healthcare. The general attitude toward digital inpatient-like psychotherapy was positive, particularly when viewed as a supplement to traditional inpatient mental healthcare and implemented in a hybrid model. However, the technical infrastructure and the suitability of patient cohorts warrant further examination. Conclusions: Digital inpatient-like psychotherapy offers a promising approach to enhancing mental health treatment. Mental health professionals generally expressed a positive attitude toward such concepts, though certain structural and contextual conditions—such as establishing adequate technical support structures and improving the digital literacy of mental health professionals—must be addressed prior to implementation. Full article

Ancestral halls represent vital vernacular cultural heritage, attracting growing academic interest in their spatial order, social functions, conservation and adaptive reuse. This paper takes Ninghai ancestral hall architectural heritage as the research object and applies Space Syntax theory. It selects four nationally protected historical and cultural sites to carry out convex space, axial and visual field analyses, exploring their spatial form, patriarchal ritual order and spatial logic. The findings indicate that Ninghai ancestral halls serve dual functions of opera performance and ancestor worship. Adopting a central axis layout, they separate the ritual center from the public activity center. In the selected cases, Temple-type ancestral halls indicate a tendency towards higher spatial integration and openness than clan-type ones, reflecting evident functional and spatial differentiation. This study combines Space Syntax with traditional ancestral hall research, shifting from qualitative description to quantitative analysis. It supplies empirical support for ancestral hall spatial structure correlation, and provides new technical methods and perspectives for the scientific conservation and sustainable utilization of such architectural heritage. Full article

Background: Accurately assessing perioperative risk after elective on-pump coronary artery bypass grafting (CABG) is challenging, especially when it comes to identifying patients at high risk for early mortality. We explored whether including perioperative biochemical and hemodynamic variables could improve established preoperative risk evaluation. Methods: We analyzed 451 consecutive patients who underwent elective on-pump isolated CABG over two years, focusing on in-hospital mortality as the primary endpoint. The predictors were selected based on univariable analysis, clinical relevance, and multicollinearity assessment. To address the low number of mortality events, we used Firth-penalized logistic regression. The final model was assessed using discrimination, calibration, and internal validity using receiver operating characteristic analysis, calibration measures, and 1000-bootstrap resampling. A dataset-derived risk threshold was explored for risk stratification. Results: Eleven patients (2.44%) died during hospitalization. The final multivariable model included three variables independently associated with in-hospital mortalityEuroSCORE II (OR1.57, CI95%:1.13–2.14, p = 0.011), early postoperative vasoactive-inotropic score (VIS) (OR1.017, CI95%:1.008–1.033, p < 0.001) and preoperative CK-MB/CK ratio (OR21.06, CI95%:1.06–355, p = 0.046). The final model demonstrated good discrimination with an apparent AUC of 0.908 and an optimism-corrected AUC of 0.892. A dataset-derived risk-stratification threshold of predicted mortality ≥2.35% identified the highest-risk quintile, which contained 10 of the 11 observed deaths. Observed mortality was 11.1% in this quintile versus 0.28% among the remaining patients. Inflammatory indices showed limited predictive performance. Additionally, the use of an intra-aortic balloon pump was not independently associated with in-hospital mortality after adjusting for baseline risks. Conclusions: A dynamic model integrating preoperative risk assessment (EuroSCORE II and preoperative CK-MB/CK ratio) with early postoperative hemodynamic data (VIS) showed encouraging internal discrimination and calibration for mortality prediction after isolated on-pump CABG. Given the limited number of events and substantial uncertainty surrounding the CK-MB/CK ratio estimate, these findings should be considered exploratory and hypothesis-generating until externally validated. Full article

Aims: Morin is a flavonoid with potential neuroprotective and anti-inflammatory properties. This study evaluated the anticonvulsant and anxiolytic effects of morin-loaded PLGA-chitosan nanoparticles (Morin-PLGA-CS NPs) in male Wistar rats. Methods: Morin-PLGA-CS NPs were synthesized using a modified single emulsion-solvent evaporation method followed by CS coating. NPs were characterized by dynamic light scattering (DLS), scanning electron microscopy (SEM), and in vitro drug release analysis. Adult male Wistar rats received intraperitoneal injections of free morin (25 mg/kg), Morin-PLGA-CS NPs, diazepam (1 mg/kg), blank NPs, or vehicle. Behavioral assessments included the open-field test (OFT), elevated-plus maze (EPM), novel object recognition (NOR) test, and pentobarbital-induced sleep test. Anticonvulsant activity was evaluated using PTZ-induced seizure latency. Cytokine concentrations in cortical and hippocampal tissue lysates were quantified by ELISA at 12 h post-PTZ. Hippocampal relative mRNA expression of Nrf2, HO-1, GFAP, and Iba1 was quantified by quantitative real-time PCR (qRT-PCR). Results: Morin-PLGA-CS NPs demonstrated a hydrodynamic diameter of 221.6 nm, a zeta potential of +23.3 mV, and an encapsulation efficiency of 81%. FTIR spectroscopy showed spectral changes compatible with morin incorporation and possible hydrogen-bonding interactions. The NPs exhibited approximately 81.4% morin release over 72 h in vitro. Compared to free morin, Morin-PLGA-CS NPs increased center-zone exploration in the OFT and open-arm behavior in the EPM, but also reduced total distance traveled in the OFT, indicating that motor suppression or sedation may have contributed to the behavioral profile (p < 0.001). It also improved the discrimination index (DI) in the NOR test and elevated sleep duration in the pentobarbital test (p < 0.001). The NPs also prolonged seizure latency (137.2 s vs. 114.5 s for free morin; p < 0.001) and markedly reduced IL-1β, IL-6, and TNF-α levels in both the cortex and hippocampus. At the molecular level, Morin-PLGA-CS NPs were associated with significantly increased hippocampal Nrf2 and HO-1 mRNA transcript levels and decreased GFAP and Iba1 transcript levels relative to free morin and the PTZ-challenged vehicle control group. Conclusions: Morin-PLGA-CS NPs produced greater behavioral, anticonvulsant, inflammatory, and redox effects than free morin in male rats. Molecular data revealed changes in hippocampal mRNA expression, including increased Nrf2 and HO-1 transcripts and decreased GFAP and Iba1 transcripts. However, these transcript-level findings are preliminary and require protein-level validation. Full article