PLoS ONE最新文献

The present study investigated associations between Big Five personality traits, their facets, and the frequency and variety of sexual fantasies in a sample of 5,225 adults (M = 58.30 years old; 56.5% men). People high in conscientiousness and agreeableness report fewer sexual fantasies across exploratory, intimate, impersonal, and sadomasochistic domains; these effects were primarily driven by respectfulness and responsibility facets. Negative emotionality, particularly the depression facet, was associated with more frequent sexual fantasizing. Extraversion and open-mindedness showed minimal relationships with sexual fantasy frequency. These results underscore the importance of personality dimensions in understanding sexual thoughts, emphasizing the heterogeneity of sexual fantasies across individuals. Having a broader appreciation of the heterogeneity of sexual experiences can foster more inclusive approaches to sexual well-being in psychological research and clinical practice.

Psychosocial stress involving multiple life changes has well-documented effects on health, yet the physiological mechanisms linking stress exposure to emotional recovery remain incompletely understood. This simulation study examines how clustered life stress, quantified through the Holmes and Rahe Social Readjustment Rating Scale (SRRS) and Life Change Units (LCUs), influences synchrony between cortisol and C-reactive protein (CRP), and how this coupling predicts mood recovery in emerging adults. Using fully synthetic longitudinal data parameterized from published empirical ranges and established biometric patterns, we modeled cortisol-CRP coordination across varying LCU loads and buffering capacities. Results indicated that higher cumulative LCUs, particularly when stressors were temporally clustered, were associated with disrupted cortisol-CRP synchrony and delayed mood rebound. Conversely, stronger physiological coupling between endocrine and immune responses predicted more rapid emotional recovery, suggesting a potential biomarker of stress resilience. These findings identify a bi-axial pathway through which life stress may influence psychological outcomes and underscore the importance of multisystem coordination during vulnerable developmental periods. By integrating a validated stress inventory with biologically grounded simulation, this study contributes novel insights into stress responsivity and affective adaptation.

Mesoporous silica (MS) is widely recognized as a local drug delivery system in bone-related diseases. Although MS enables controlled or sustained release and improved bioavailability of therapeutic agents, its limited native osseointegration capacity remains a critical barrier to effective bone regeneration. Numerous engineering strategies have therefore been proposed to enhance its biological performance. This scoping review protocol aims to collect studies, published from January 2015 onwards, that report evidence on the osseointegration potential (i.e., osteoconductive, osteoinductive, or proangiogenic properties) of MS bone drug delivery systems. Studies indexed in PubMed, Scopus, and Embase will be screened to identify the strategies used to improve MS-mediated bone regeneration, including structural modifications, formulation into composites, and incorporation of bioactive molecules. A structured analytical framework will be applied to explore how specific design approaches relate to biological outcomes across experimental models in vitro, in vivo, or ex vivo. The resulting scoping review will identify trends and knowledge gaps in strategies intended to enhance the osseointegration of MS bone drug delivery systems, supporting their future development and rational optimization for bone repair.

This study proposes a novel, domain-specific optimization framework for the Stable Diffusion XL (SDXL) model, addressing the critical challenges of structural consistency and aesthetic fidelity in AI-assisted interior design. Unlike generic applications of diffusion models, this research introduces a systematic pipeline integrating automated semantic cleaning with a rigorous hyperparameter optimization strategy. A high-quality, annotated dataset was constructed using a semi-automated YOLO-based filtering process to minimize noise. Furthermore, we established an empirically validated training protocol-combining optimal Dropout rates, L1/L2 regularization, and dynamic learning rates-specifically tuned to preserve the geometric constraints of interior spaces. Experimental results demonstrate that this optimized framework significantly outperforms baseline models, achieving superior Fréchet Inception Distance (FID), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) scores, alongside robust CLIP Semantic Alignment. Furthermore, a systematic ablation study confirms that while domain-specific data provides the foundation, our semantic cleaning pipeline and structural regularization are critical for achieving high geometric fidelity, reducing FID by 51.1% compared to the baseline. The study contributes a technically robust methodology for adapting large-scale diffusion models to the specialized requirements of spatial design.

Background: Guideline-Directed Medical Therapy (GDMT) is central to managing ischemic heart disease (IHD), yet its implementation remains suboptimal in low- and middle-income countries (LMICs), including Pakistan.

Aim: This study assessed the knowledge, attitudes, and practices (KAP) of healthcare professionals (HCPs) toward GDMT and identified key barriers to its application.

Methods: A cross-sectional survey was conducted among HCPs including cardiologists and clinical pharmacists using a validated questionnaire. Data was collected from Punjab Institute of Cardiology, Lahore, Pakistan using convenience sampling. Descriptive statistics, t-tests, ANOVA, Mann-Whitney U, Kruskal-Wallis, and multiple linear regression analyses were used to evaluate KAP scores and their association with demographic and professional role. Statistical adjustment for multiple comparisons was done by Bonferroni correction.

Results: A total of 76 HCPs participated in the survey, comprising 42 cardiologists (55.3%) and 34 clinical pharmacists (44.7%). he overall mean knowledge score was 18.64 ± 2.02 out of 22 (84.7%). However, cardiologists (M = 19.54) scored significantly higher than clinical pharmacists (M = 17.52, p < 0.001); thus rejecting the null hypothesis. Knowledge scores were significantly higher among older professionals, those with postgraduate education, and clinic-based practitioners (p < 0.05). The average attitude score was 10.42 ± 2.06 out of 14 (74.4%), with younger professionals (aged 28-33), cardiologists, and postgraduates showing more favorable attitudes (p < 0.05). The mean practice score was 9.51 ± 2.55 (67.9%), with no significant differences by gender, role, or setting. Regression models showed age and profession significantly predicted knowledge, while attitude was influenced by education, experience, gender, and profession. Practice behaviors were not predicted by any demographic variables. Key barriers to GDMT implementation included limited consultation time (47.4%) and poor patient adherence (25%).

Conclusion: Although GDMT knowledge and attitudes were generally high among Pakistani cardiologists and clinical pharmacists, reported practice remained moderate. The results underscore the need for targeted educational interventions and system-level strategies to support consistent GDMT implementation.

Non-alcoholic fatty liver disease (NAFLD) is a type of metabolic stress-induced liver injury that is closely related to type 2 diabetes mellitus (T2DM). Ursolic acid (UA), a natural pentacyclic triterpenoid compound, has anti-inflammatory, hypoglycemic and liver-protective effects. However, its role in regulating liver injury through the NLRP3 inflammasome pathway in a T2DM combined with NAFLD model has not been systematically elucidated. This study systematically evaluated the protective effect of UA on NAFLD and its molecular mechanism through in vivo (STZ + high-fat diet-induced NAFLD mouse model) and in vitro (high glucose + palmitic acid-induced LO2 cell oxidative stress model) experiments. The results showed that UA significantly improved hepatic lipid deposition, reduced serum ALT/AST levels, and effectively alleviated oxidative stress, as indicated by decreased malondialdehyde (MDA) content and increased activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) in liver tissues. Further mechanism studies revealed that UA could significantly down-regulate the expression levels of pro-inflammatory factor IL-1β and pro-fibrotic factor TGF-β1 by inhibiting NLRP3 inflammasome activation, and simultaneously reduce the deposition of type IV collagen. This study demonstrated that ursolic acid (UA) has a protective effect on T2DM combined with NAFLD, and its mechanism of action may be related to the regulation of the NLRP3 signaling pathway by UA, which inhibits oxidative stress, inflammation and fibrosis.

As intelligent manufacturing advances, computer vision-based defect detection systems have become essential components of industrial automation. However, this progress has also revealed new security vulnerabilities. In this work, we identify and examine a stealthy adversarial vector-the Alpha Channel Attack-which exploits the often-ignored transparency layer in RGBA images to inject imperceptible perturbations, thereby evading both human perception and conventional preprocessing defenses.We evaluate this threat across diverse model architectures, including YOLOv5, FastGAN, and state-of-the-art vision-language models such as DeepSeek-VL2, ChatGPT-4o, and KIMI. Experimental results show that alpha-channel perturbations cause substantial degradation in detection, generation, and multimodal alignment metrics-including mAP, FID, BLEU, METEOR, and CLIP Score-while leaving the visible image content unchanged.To mitigate this invisible yet high-impact risk, we propose a lightweight detection mechanism that integrates histogram overlap and MSE analysis within the alpha channel. The framework achieves an AUC of 0.998, demonstrating strong capability in identifying adversarial samples under real-world constraints.Overall, this study reveals a critical blind spot in modern visual data pipelines and introduces both a novel threat model and an effective defense strategy, contributing to the development of more resilient industrial AI systems.

Introduction: LEGO® based therapy, a social skills program for autistic children and young people, involves collaborative LEGO® building with adult guidance. This paper examines how well the program was delivered in a recent school randomised controlled trial and explores areas for improvement in implementation. The main trial results are published elsewhere.

Methods: The I-SOCIALISE trial investigated LEGO® based therapy for autistic children in schools. Researchers recruited 98 schools and randomly assigned them to deliver LEGO® based therapy or usual care. LEGO® based therapy sessions lasted one hour per week for 12 weeks with groups of 3 children. Schools received a 3-hour training session and a manual. Researchers measured fidelity to the LEGO® based therapy programme using self-reported checklists and video analysis. Research team insight and experience of the delivery of the training and intervention are included in this paper.

Results: LEGO® based therapy was delivered to autistic children in schools with high fidelity according to facilitators and independent reviewers. Most groups (69%) received all 12 sessions, and nearly all groups received the minimum dose of 6 sessions (93%). Sessions typically lasted about an hour and had 1-2 autistic children. Facilitators were mostly teaching assistants with moderate experience in autism. Over 90% of sessions included core elements like group building and social interaction. There were disagreements between facilitators and reviewers on adherence to some program elements like rewards and discussing roles.

Discussion: LEGO® based therapy was delivered with high fidelity in a large school trial, but there were areas for improvement, such as facilitator training and focus on social interaction for and between children. The authors suggest that facilitators may have been more focused on completing LEGO® builds than on facilitating meaningful social interaction and play between children. Three hours of training may not have been enough to prepare facilitators for their role. The study also did not capture young people's experience of the program, which is important for understanding its effectiveness and impact. Future research should explore how to better measure these aspects and develop a stronger theory of how LEGO® based therapy works.

[This corrects the article DOI: 10.1371/journal.pone.0305854.].

Water yam (Dioscorea alata L.) or greater yam, is an essential species of the Dioscoreceae family in tropical and subtropical regions. The wide geographical distribution is owing to its higher tuber yield, storability, and better nutritional and health benefits compared to many other species. Despite these promising characteristics, water yam remains less preferred by consumers for traditional food products, particularly boiled and pounded yam preparations. Fast and efficient development of superior genotypes that meet farmers and end-users needs have been challenging through classical breeding methods. The objective of the study was to use genome-wide associations to assess the genetics of post-harvest tuber quality, mainly targeting the consumer-preferred traits. A panel of 404 water yam genotypes were assessed to decipher the genomic regions associated with traits such as tuber oxidative browning, dry matter content, and boiled and pounded tuber quality. The Multiple Random Mixed Linear Model was employed for marker-trait association analysis using the naive, Q, and Q + K models, followed by gene annotation and marker or allele substitution effects. Fourteen SNP markers were significantly linked with the assessed tuber quality traits and r2 values ranged from 0.62 to 10.02%. The gene annotation analysis revealed presence of 32 putative candidate genes playing crucial roles in enzymatic browning and carbohydrate biosynthesis pathways for dry matter accumulation. The molecular information generated in the present study can be deployed for water yam improvement.