Pioneering a chick embryo model to explore the intrauterine etiology of developmental dysplasia of the hip in oligohydramnios conditions.

OBJECTIVE: To explore the impact of oligohydramnios on fetal movement and hip development, given its association with developmental dysplasia of the hip (DDH) but unclear mechanisms. METHODS: Chick embryos were divided into four groups based on the severity of oligohydramnios induced by amniotic fluid aspiration (control, 0.2 mL, 0.4 mL, 0.6 mL). Fetal movement was assessed by detection of movement and quantification of residual amniotic fluid volume. Hip joint development was assessed by gross anatomic analysis, micro-computed tomography (micro-CT) for cartilage assessment, and histologic observation at multiple time points. In addition, a subset of embryos from the 0.4 mL aspirated group underwent saline reinfusion and subsequent evaluation. RESULTS: Increasing volumes of aspirated amniotic fluid resulted in worsening of fetal movement restrictions (e.g., 0.4 mL aspirated and control group at E10: frequency difference -7.765 [95% CI: -9.125, -6.404]; amplitude difference -0.343 [95% CI: -0.588, -0.097]). The 0.4 mL aspirated group had significantly smaller hip measurements compared to controls, with reduced acetabular length (-0.418 [95% CI: -0.575, -0.261]) and width (-0.304 [95% CI: -0.491, -0.117]) at day E14.5. Histological analysis revealed a smaller femoral head (1.084 ±â€¯0.264 cm) and shallower acetabulum (0.380 ±â€¯0.106 cm) in the 0.4 mL group. Micro-CT showed cartilage matrix degeneration (13.6% [95% CI: 0.6%, 26.7%], P = 0.043 on E14.5). Saline reinfusion resulted in significant improvements in the femoral head to greater trochanter (0.578 [95% CI: 0.323, 0.833], P = 0.001). CONCLUSIONS: Oligohydramnios can cause DDH by restricting fetal movement and disrupting hip morphogenesis in a time-dependent manner. Timely reversal of oligohydramnios during the fetal period may prevent DDH.

Are Generative Pretrained Transformer 4 Responses to Developmental Dysplasia of the Hip Clinical Scenarios Universal? An International Review.

OBJECTIVE: There is increasing interest in applying artificial intelligence chatbots like generative pretrained transformer 4 (GPT-4) in the medical field. This study aimed to explore the universality of GPT-4 responses to simulated clinical scenarios of developmental dysplasia of the hip (DDH) across diverse global settings. METHODS: Seventeen international experts with more than 15 years of experience in pediatric orthopaedics were selected for the evaluation panel. Eight simulated DDH clinical scenarios were created, covering 4 key areas: (1) initial evaluation and diagnosis, (2) initial examination and treatment, (3) nursing care and follow-up, and (4) prognosis and rehabilitation planning. Each scenario was completed independently in a new GPT-4 session. Interrater reliability was assessed using Fleiss kappa, and the quality, relevance, and applicability of GPT-4 responses were analyzed using median scores and interquartile ranges. Following scoring, experts met in ZOOM sessions to generate Regional Consensus Assessment Scores, which were intended to represent a consistent regional assessment of the use of the GPT-4 in pediatric orthopaedic care. RESULTS: GPT-4's responses to the 8 clinical DDH scenarios received performance scores ranging from 44.3% to 98.9% of the 88-point maximum. The Fleiss kappa statistic of 0.113 (P = 0.001) indicated low agreement among experts in their ratings. When assessing the responses' quality, relevance, and applicability, the median scores were 3, with interquartile ranges of 3 to 4, 3 to 4, and 2 to 3, respectively. Significant differences were noted in the prognosis and rehabilitation domain scores (P < 0.05 for all). Regional consensus scores were 75 for Africa, 74 for Asia, 73 for India, 80 for Europe, and 65 for North America, with the Kruskal-Wallis test highlighting significant disparities between these regions (P = 0.034). CONCLUSIONS: This study demonstrates the promise of GPT-4 in pediatric orthopaedic care, particularly in supporting preliminary DDH assessments and guiding treatment strategies for specialist care. However, effective integration of GPT-4 into clinical practice will require adaptation to specific regional health care contexts, highlighting the importance of a nuanced approach to health technology adaptation. LEVEL OF EVIDENCE: Level IV.

Evaluation of large language models in breast cancer clinical scenarios: a comparative analysis based on ChatGPT-3.5, ChatGPT-4.0, and Claude2.

BACKGROUND: Large language models (LLMs) have garnered significant attention in the AI domain owing to their exemplary context recognition and response capabilities. However, the potential of LLMs in specific clinical scenarios, particularly in breast cancer diagnosis, treatment, and care, has not been fully explored. This study aimed to compare the performances of three major LLMs in the clinical context of breast cancer. METHODS: In this study, clinical scenarios designed specifically for breast cancer were segmented into five pivotal domains (nine cases): assessment and diagnosis, treatment decision-making, postoperative care, psychosocial support, and prognosis and rehabilitation. The LLMs were used to generate feedback for various queries related to these domains. For each scenario, a panel of five breast cancer specialists, each with over a decade of experience, evaluated the feedback from LLMs. They assessed feedback concerning LLMs in terms of their quality, relevance, and applicability. RESULTS: There was a moderate level of agreement among the raters (Fleiss' kappa=0.345, P<0.05). Comparing the performance of different models regarding response length, GPT-4.0 and GPT-3.5 provided relatively longer feedback than Claude2. Furthermore, across the nine case analyses, GPT-4.0 significantly outperformed the other two models in average quality, relevance, and applicability. Within the five clinical areas, GPT-4.0 markedly surpassed GPT-3.5 in the quality of the other four areas and scored higher than Claude2 in tasks related to psychosocial support and treatment decision-making. CONCLUSION: This study revealed that in the realm of clinical applications for breast cancer, GPT-4.0 showcases not only superiority in terms of quality and relevance but also demonstrates exceptional capability in applicability, especially when compared to GPT-3.5. Relative to Claude2, GPT-4.0 holds advantages in specific domains. With the expanding use of LLMs in the clinical field, ongoing optimization and rigorous accuracy assessments are paramount.

Does enhanced cognitive performance reduce fracture risk? a Mendelian randomization study.

OBJECTIVE: While observational studies have suggested a link between cognitive performance and fracture risk, the causality and site-specific nature are unclear. We applied Mendelian randomization (MR) to elucidate these associations. METHODS: 147 single-nucleotide polymorphisms (SNPs) tied strongly to cognitive performance (p< 5e-8) were selected. We performed MR analysis to investigate the causal relationship between cognitive performance and fractures at specific sites, including the wrist, upper arm, shoulder, ribs, sternum, thoracic spine, lumbar spine, pelvis, femur, leg, and ankle. The primary estimate was determined using the inverse variance-weighted method. Additionally, we examined heterogeneity using the MR Pleiotropy RESidual Sum Outlier test and Cochran Q, and employed MR-Egger regression to identify horizontal pleiotropy. RESULTS: MR analysis identified a causal association between cognitive performance and fractures at the lumbar-spine-pelvis (odds ratio [OR] = 0.727, 95% CI = 0.552-0.956, p = 0.023), and ribs-sternum-thoracic spine sites (OR = 0.774, 95% CI = 0.615-0.974, p = 0.029). However, no causal association was found for fractures at other sites. CONCLUSIONS: This study provided evidence of a causal connection between cognitive performance and fracture risk at certain locations. These findings underline the potential of cognitive enhancement strategies as innovative and effective methods for fracture prevention.

Causal Link between Gut Microbiota, Neurophysiological States, and Bone Diseases: A Comprehensive Mendelian Randomization Study.

Increasing evidence highlights a robust correlation between the gut microbiota and bone diseases; however, the existence of a causal relationship between them remains unclear. In this study, we thoroughly examined the correlation between gut microbiota and skeletal diseases using genome-wide association studies. Linkage disequilibrium score regression and Mendelian randomization were used to probe genetic causality. Furthermore, the potential mediating role of neuropsychological states (i.e., cognition, depression, and insomnia) between the gut microbiota and bone diseases was evaluated using mediation analysis, with genetic colocalization analysis revealing potential targets. These findings suggest a direct causal relationship between Ruminococcaceae and knee osteoarthritis (OA), which appears to be mediated by cognitive performance and insomnia. Similarly, a causal association was observed between Burkholderiales and lumbar pelvic fractures, mediated by cognitive performance. Colocalization analysis identified a shared causal variant (rs2352974) at the TRAF-interacting protein locus for cognitive ability and knee OA. This study provides compelling evidence that alterations in the gut microbiota can enhance cognitive ability, ameliorate insomnia, and potentially reduce the risk of site-specific fractures and OA. Therefore, strategies targeting gut microbiota optimization could serve as novel and effective preventive measures against fractures and OA.

Intertemporal decision-making as a mediator between personality traits and self-management in type 2 diabetes: a cross-sectional study.

Objectives: The aims to investigate the mediating effect of intertemporal decision-making on the association between personality traits and self-management among individuals with in Type 2 Diabetes (T2DM). Method: Patients with T2DM in the early stages of hospitalization at two tertiary hospitals in Shenyang and Jinzhou, Liaoning Province, May 2022 to January 2023. Questionnaires, including General Demographic, Self-Management, Big Five Personality, and Intertemporal Decision-Making, were administered. Pearson correlation analysis examined relationships between personality traits, intertemporal decision-making, and self-management. Hierarchical regression analysis identified self-management predictors. Mediation analysis used the PROCESS SPSS Macro version 3.3 model 4 to investigate intertemporal decision-making as mediator between personality traits and self-management. Results: Pearson correlation analysis revealed significant associations between self-management scores, personality traits, and intertemporal decision-making. Hierarchical regression revealed that Neuroticism and Conscientiousness accounted for 20.8% of the variance in self-management, while intertemporal decision-making explained 4.5% of the variance. Finally, using the Bootstrap method, the mediation analysis showed that intertemporal decision-making partially mediated the effect of personality traits on self-management. Conclusion: This study emphasizes the importance of intertemporal decision-making in improving self-management behaviors among patients with T2DM. Interventions targeted at modifying intertemporal decision-making preferences could be effective in enhancing self-management behaviors, leading to better health outcomes.