Explore the vast range of titles available.

Background Caring for autistic children becomes challenging and may lead to negative psychological outcomes, even increasing the suicide risk (SR). Researchers have studied the SR among parents of autistic children in Western nations, but little is known about it in China and how it relates to perceived discrimination (PD), affiliate stigma (AS), and social alienation (SA). The current study aimed to reveal the SR prevalence rate among Chinese parents of autistic children, and clarify whether AS and SA may play mediating roles in the association between SR and PD. Methods A total of 645 Chinese parents of autistic children were recruited to complete a series of scales to evaluate SR, SA, AS, and PD using the Suicidal Behaviors Questionnaire-Revised (SBQ-R), Perceived Discrimination Scale for Parents of Children with Autism Spectrum Disorders (PDS-FP), Affiliate Stigma Scale (ASS), and General Social Alienation Scale (GSAS), respectively. Then, the SR prevalence rate among Chinese parents of autistic children was evaluated; and the multiple mediation analysis and structural equation modeling with the bootstrap method were conducted to test the mediating effects of AS and SA in the association between SR and PD. Results 34.6% Chinese parents of autistic children had high SR. In particular, the incidence rate of suicide ideation, suicide plans, suicide attempts, and suicide likelihood during the previous year were 49.8%, 11.9%, 2.5%, and 13.8%, respectively. Additionally, PD was positively associated with SR ( r  = .40, p  < .01); and AS and SA showed significant mediating effects on the association between PD and SR ( p  < .01). Conclusions The current study evaluated the SR prevalence rate among Chinese parents of autistic children, and clarified the mediating effects of AS and SA in the association between SR and PD. Findings might bring new insights and guidance for intervention of suicidality among Chinese parents of autistic children.

To evaluate the feasibility and efficacy of diffusion tensor imaging (DTI) for detecting early brain microstructure alterations in germinal matrix-intraventricular hemorrhage (GMH-IVH) rat model. This study used a postnatal day 5 (PND 5) rat model of GMH-IVH. T2-weighted imaging and DTI were performed during acute (6 h and 24 h) and subacute (3d and 7d) phases after GMH-IVH. Four DTI parameters including fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD) and radial diffusion (RD) were collected in 9 specific brain regions to assess the brain microstructure alterations. Early and long-term neurological function tests were evaluated. Transcriptome sequencing analysis was also performed to investigate possible underlying mechanisms. Regional abnormalities after GMH-IVH were observed in T2-weighted images that showed significant hypointense in striatum region which close to the germinal matrix. DTI parameters also observed changes in striatum region in GMH-IVH. Alterations in other regions of brain including hippocampus, thalamus, external capsule and motor cortex also noted, which were associated with the abnormalities observed in behavioral experiments. Long-term behavioral tests show that compared to sham group, rats in GMH-IVH group caused abnormal motor function. In addition, at 24 h after GMH-IVH, transcriptome analysis results showed that the highly expressed differential genes encode hemoglobin components and down-regulate neurodevelopment-related pathways. DTI imaging allows the early assessment of neurological alteration in GMH-IVH rat pups, and providing great value in evaluating long-term behavioral deficits.

Hypoxic-ischemic encephalopathy (HIE) is a leading cause of neonatal brain injury and neurodevelopmental disorders. Pyroptosis, an inflammatory programmed cell death, may offer new therapeutic targets for HIE by modulating cytokine expression and related pathways. This study aims to identify HIE-associated pyroptosis genes and explore potential drugs and molecular mechanisms. The gene microarray data of hypoxic-ischemic brain damage (HIBD) were obtained from the Gene Expression Omnibus (GEO) database. The Limma package was used to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was performed to find significant expression modules. GO and KEGG analyses were carried out for the pathway enrichment of DEGs, as well as protein-protein interaction (PPI) network analysis were subsequently conducted. Cytohubba software was employed to identify hub genes among DEGs. A random forest (RF) model assessed the pyroptosis-related genes, examining their diagnostic performance. Potential therapeutic drugs or compounds targeting the hub genes were screened through DSigDB, and their binding scores and affinities were evaluated by molecular docking. 96 DEGs with HIBD were identified in our result, including 89 up-regulated genes and 7 down-regulated genes. GO and KEGG results indicated that these DEGs were mostly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway and TNF signaling pathway. Using Cytoscape software and WGCNA-related modules, we identified three hub genes- , and -which were further validated in other transcriptomic datasets, all showing significant differential expression. Random forest analysis demonstrated that these three hub genes had AUC values > 0.75, indicating strong diagnostic performance. Immune infiltration analysis revealed that, compared to the control group, the HIBD group exhibited higher levels of innate immune cells (e.g., macrophages, M0 cells, and dendritic cells) and adaptive immune cells (e.g., CD8 naïve T cells, CD4 follicular helper T cells, and Th1 cells). The ssGSEA algorithm results indicated differences in 25 types of immune cells and 10 immune functions. The hub genes were also validated finally. and may be potential hub pyroptosis-related genes for HIBD. The results of this study could improve the understanding of the mechanisms underlying pyroptosis in HIBD.

Takeda G protein-coupled receptor 5 (TGR5) is the first known G protein-coupled receptor specific for bile acids and is recognized as a new and critical target for type 2 diabetes and metabolic syndrome. It is expressed in many brain regions associated with memory such as the hippocampus and frontal cortex. Here, we hypothesize that activation of TGR5 may ameliorate streptozotocin- (STZ-) induced cognitive impairment. The mouse model of cognitive impairment was established by a single intracerebroventricular (ICV) injection of STZ (3.0 mg/kg), and we found that TGR5 activation by its agonist INT-777 (1.5 or 3.0 μg/mouse, ICV injection) ameliorated spatial memory impairment in the Morris water maze and Y-maze tests. Importantly, INT-777 reversed STZ-induced downregulation of TGR5 and glucose usage deficits. Our results further showed that INT-777 suppressed neuronal apoptosis and improved neurogenesis which were involved in tau phosphorylation and CREB-BDNF signaling. Moreover, INT-777 increased action potential firing of excitatory pyramidal neurons in the hippocampal CA3 and medial prefrontal cortex of ICV-STZ groups. Taken together, these findings reveal that activation of TGR5 has a neuroprotective effect against STZ-induced cognitive impairment by modulating apoptosis, neurogenesis, and neuronal firing in the brain and TGR5 might be a novel and potential target for Alzheimer’s disease.

The rapid advancements in natural language processing, particularly the development of large language models (LLMs), have opened new avenues for managing complex clinical text data. However, the inherent complexity and specificity of medical texts present significant challenges for the practical application of prompt engineering in diagnostic tasks. This paper explores LLMs with new prompt engineering technology to enhance model interpretability and improve the prediction performance of pulmonary disease based on a traditional deep learning model. A retrospective dataset including 2965 chest CT radiology reports was constructed. The reports were from 4 cohorts, namely, healthy individuals and patients with pulmonary tuberculosis, lung cancer, and pneumonia. Then, a novel prompt engineering strategy that integrates feature summarization (F-Sum), chain of thought (CoT) reasoning, and a hybrid retrieval-augmented generation (RAG) framework was proposed. A feature summarization approach, leveraging term frequency-inverse document frequency (TF-IDF) and K-means clustering, was used to extract and distill key radiological findings related to 3 diseases. Simultaneously, the hybrid RAG framework combined dense and sparse vector representations to enhance LLMs' comprehension of disease-related text. In total, 3 state-of-the-art LLMs, GLM-4-Plus, GLM-4-air (Zhipu AI), and GPT-4o (OpenAI), were integrated with the prompt strategy to evaluate the efficiency in recognizing pneumonia, tuberculosis, and lung cancer. The traditional deep learning model, BERT (Bidirectional Encoder Representations from Transformers), was also compared to assess the superiority of LLMs. Finally, the proposed method was tested on an external validation dataset consisted of 343 chest computed tomography (CT) report from another hospital. Compared with BERT-based prediction model and various other prompt engineering techniques, our method with GLM-4-Plus achieved the best performance on test dataset, attaining an F1-score of 0.89 and accuracy of 0.89. On the external validation dataset, F1-score (0.86) and accuracy (0.92) of the proposed method with GPT-4o were the highest. Compared to the popular strategy with manually selected typical samples (few-shot) and CoT designed by doctors (F1-score=0.83 and accuracy=0.83), the proposed method that summarized disease characteristics (F-Sum) based on LLM and automatically generated CoT performed better (F1-score=0.89 and accuracy=0.90). Although the BERT-based model got similar results on the test dataset (F1-score=0.85 and accuracy=0.88), its predictive performance significantly decreased on the external validation set (F1-score=0.48 and accuracy=0.78). These findings highlight the potential of LLMs to revolutionize pulmonary disease prediction, particularly in resource-constrained settings, by surpassing traditional models in both accuracy and flexibility. The proposed prompt engineering strategy not only improves predictive performance but also enhances the adaptability of LLMs in complex medical contexts, offering a promising tool for advancing disease diagnosis and clinical decision-making.

Ultra-deep shale in the Mahu Sag, characterized by difficult-to-drill formations, exhibits high resistivity. This study uses XRD and petrophysical testing on 12 dry core samples (depths 4600–5000 m) to characterize mineral composition and evaluate resistivity-influencing factors. Mineralogical analysis reveals that brittle minerals, dominated by quartz and feldspar (>50%), constitute the primary components of the ultra-deep shale in the Mahu Sag, with quartz, feldspar, and carbonates collectively accounting for ~80%. Clay (~6%) and pyrite (<5%) contents are notably low, resulting in elevated resistivities of 105–107 Ω·m. Resistivity correlates negatively with pyrite (r = −0.588) and feldspar (r = −0.319) but positively with dolomite (r = 0.209), quartz (r = 0.017), and porosity (r = 0.749). At elevated temperatures (100 °C), resistivity declines owing to enhanced ionic conduction. These findings clarify high-resistivity mechanisms, supporting resistivity-based drilling parameter optimization.