Objective: To understand the correlation between autistic traits in children with autism spectrum disorder(ASD) and their parental behaviors, so as to provide evidence for improving social behavior in children with ASD. Methods: A total of 62 children aged 4-7 years diagnosed with ASD at Guangdong Provincial Work Injury Rehabilitation Hospital and Guangzhou Yuexiu District Children s Hospital from October 2021 to May 2025 were selected as the ASD group. A random number table method was used to select 62 healthy children of the same age from 3 ordinary kindergartens in Guangzhou as the control (typically developing,TD) group. The Parental Behavior Inventory and Social Responsiveness Scale were used to evaluate parental behaviors and autistic traits in both groups. The t-test and multiple linear regression were used to analyze the relationship between autistic traits in children with ASD and parental behaviors. Results: Compared with the TD group, the ASD group had significantly higher scores in social awareness, social cognition, social communication, social motivation, autistic behavior patterns, and the total score of social responsiveness, with statistically significant differences ( t =35.83, 46.17, 64.36, 41.45, 49.46,101.15, all P <0.01). The score of parental support/participation in the ASD group (24.61±4.30) was lower than that in the TD group (31.27±4.58), while the score of parental hostility/coercion in the ASD group (25.51±2.72) was higher than that in the TD group (12.75±2.72), with statistically significant differences ( t = -8.34, 26.14, both P <0.01). The total score of the Social Responsiveness Scale was negatively correlated with the dimension score of parental support/participation behaviors, and positively correlated with the dimension score of parental hostility/coercion behaviors ( r =-0.60, 0.91,both P <0.05). After adjusting for covariates such as children s age, gender, and primary caregiver, ASD children with exclusive breastfeeding ( β =-8.79) and parental support/participation behaviors ( β = -0.79 ) had a lower risk of autistic traits, while children with parental hostility/coercion behaviors ( β =4.62) had a higher risk of autistic traits (all P <0.05). Conclusion Autistic traits in children with ASD may be related to their parental behaviors and early feeding mode.

ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

BACKGROUND:Type 2 diabetes mellitus is a secondary causative factor for osteoporosis.As highly heterogeneous innate immune cells,macrophages may be polarized in a hyperglycemic environment,which affects osteogenesis-angiogenesis coupling.This may be a research target for improving bone quality in patients with type 2 diabetic osteoporosis.OBJECTIVE:To explore the role of modulating macrophage M1/M2 polarization to influence osteogenesis-angiogenesis coupling in type 2 diabetic osteoporosis and to summarize the effects of commonly used anti-glucose and anti-osteoporosis drugs and bone biorepair materials on bone osteogenesis-angiogenesis coupling by regulating macrophage M1/M2 polarization.METHODS:The keywords of"macrophage polarization,type 2 diabetes,osteoporosis,osteogenesis-angiogenesis coupling"in Chinese and"macrophages,macrophage polarization,osteogenesis-angiogenesis coupling"in English were used to search for relevant literature in CNKI and PubMed,respectively.Seventy-nine pieces of literature were screened and analyzed.RESULTS AND CONCLUSION:(1)Type 2 diabetes mellitus causes the body to be in a hyperglycemic environment and increases the secretion of inflammatory-related factors in the body,which promotes macrophage polarization towards M1 and decreases the number of M2 macrophages.(2)In type 2 diabetes,promoting M2 macrophage polarization is beneficial for osteogenesis-angiogenesis coupling.(3)Some anti-glycemic drugs,active ingredients in traditional Chinese medicine and bone biorepair materials can improve type 2 diabetic osteoporosis by regulating macrophage M1/M2 polarization,reducing M1/M2 ratio,and promoting osteogenesis-angiogenesis coupling.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver disease worldwide, affecting approximately 32%-38% of the adult population and posing a growing public health burden. MASLD represents a continuous disease spectrum ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH), progressive hepatic fibrosis, cirrhosis, and ultimately hepatocellular carcinoma (HCC). The pathological core of MASLD lies in disruption of hepatic lipid metabolic homeostasis, characterized by an imbalance among de novo lipogenesis, fatty acid β-oxidation, and very-low-density lipoprotein (VLDL)-mediated lipid export. This metabolic disequilibrium subsequently drives inflammatory injury and fibrotic progression. Among the multiple regulatory pathways involved, thyroid hormone (TH) signaling has emerged as a central regulator of hepatic metabolic homeostasis. The liver is a major peripheral target organ of TH action, where TH predominantly exerts its metabolic effects through thyroid hormone receptor β (TRβ). Large-scale epidemiological studies and meta-analyses have demonstrated that hypothyroidism is significantly associated with increased MASLD prevalence, more severe histological injury, and advanced hepatic fibrosis, suggesting that dysregulation of TH signaling may participate throughout the entire MASLD disease spectrum. At the molecular level, TH regulates hepatic lipid metabolism by coordinating suppression of lipogenesis, enhancement of mitochondrial fatty acid oxidation, and promotion of VLDL assembly and secretion through integrated genomic actions of the T3-TRβ axis and non-genomic signaling pathways. Across different stages of MASLD, TH signaling exerts stage-dependent protective effects. In the steatosis stage, TH improves metabolic flexibility by modulating insulin sensitivity, glucose metabolism, and lipid droplet clearance, thereby alleviating early lipotoxic stress. During progression to MASH, TH attenuates inflammatory amplification by improving mitochondrial homeostasis, suppressing activation of the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, and modulating the gut-liver axis microenvironment. In advanced stages, TH signaling influences hepatic stellate cell activation and extracellular matrix deposition, partly through interaction with the transforming growth factor-β (TGF-β)/SMAD pathway, while alterations in intrahepatic TH availability, mediated by dynamic changes in iodothyronine deiodinase 1 (DIO1), contribute to fibrosis progression and hepatocellular dedifferentiation. In hepatocellular carcinoma, coordinated downregulation of TRβ and DIO1 establishes a tumor-associated hypothyroid state that promotes metabolic reprogramming and tumor progression. The clinical relevance of TH signaling in MASLD has been underscored by the recent approval of Resmetirom, a liver-targeted TRβ‑selective agonist, for the treatment of non-cirrhotic MASH with moderate-to-severe fibrosis (F2-F3). This approval represents a landmark transition from mechanistic understanding to metabolism-centered precision therapy in MASLD. Clinical trials have demonstrated that Resmetirom not only improves key histological endpoints, including MASH resolution and fibrosis regression, but also favorably modulates atherogenic lipid profiles, highlighting the therapeutic potential of selectively targeting hepatic TH pathways. This review systematically summarizes the multidimensional regulatory roles of TH across the MASLD disease spectrum and discusses emerging diagnostic and therapeutic implications of TH-based interventions, aiming to inform future mechanistic research and optimize clinical management strategies.

ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

ObjectiveTo investigate the effects of Tianma Goutengyin on the tumor necrosis factor-α （TNF-α）/signal transducer and activator of transcription 3 （STAT3）/α1-antichymotrypsin C-terminal tail fragment （α1ACT） signaling pathway and A1-type astrocytes in a rat model of vascular dementia. MethodsSeventy-two male Sprague-Dawley rats were randomly divided into six groups （n=12 per group）： Sham-operated group， model group， Tianma Goutengyin high-， medium-， and low-dose groups （5.13， 10.26， and 20.52 g·kg^-1）， and a nimodipine group （8.1 mg·kg^-1）. The vascular dementia model was established by permanent bilateral common carotid artery occlusion， followed by 4 weeks of intervention. Learning and memory ability were evaluated using the novel object recognition test， and behavioral performance was assessed using the forced swimming test. Levels of interleukin-6 （IL-6） and C-C motif chemokine ligand 2 （CCL2） in hippocampal tissue were measured by enzyme-linked immunosorbent assay （ELISA）. Hippocampal neuronal morphology was observed by Nissl staining， and apoptosis was detected by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL）. Immunohistochemistry was used to detect positive expression of brain-derived neurotrophic factor （BDNF）， glial fibrillary acidic protein （GFAP）， and myelin basic protein （MBP）. Western blot analysis was performed to measure the protein expression levels of TNF-α， TNF receptor 1 （TNFR1）， phosphorylated STAT3 （p-STAT3）， α1ACT， IL-6， complement component 3 （C3）， BDNF， S100 calcium-binding protein A10 （S100A10）， and GFAP in hippocampal tissue. ResultsCompared with the sham-operated group， the model group showed a significantly reduced relative recognition index in the novel object recognition test （P<0.01）， prolonged immobility time and increased immobility frequency in the forced swimming test （P<0.01）. Hippocampal IL-6 and CCL2 levels were significantly increased （P<0.01）. Nissl staining revealed a marked reduction in neuronal number and loss of Nissl bodies （P<0.01）. MBP-positive expression was significantly decreased （P<0.01）， apoptosis was significantly increased （P<0.01）， BDNF-positive expression was significantly reduced （P<0.05）， and GFAP-positive expression was significantly increased （P<0.01）. In addition， the protein expression levels of TNF-α， TNFR1， p-STAT3， α1ACT， IL-6， and C3 were significantly elevated （P<0.01）， while BDNF and S100A10 expression levels were significantly decreased （P<0.01）. Compared with the model group， all Tianma Gouteng yin dose groups exhibited a significant increase in the relative recognition index （P<0.05）， shortened immobility time and reduced immobility frequency （P<0.05， P<0.01）. IL-6 and CCL2 levels were significantly decreased （P<0.01）， neuronal number was significantly increased （P<0.05， P<0.01）， and MBP-positive expression was significantly enhanced （P<0.01）. Apoptosis was significantly reduced （P<0.01）， BDNF-positive expression was significantly increased （P<0.05）， and GFAP-positive expression was significantly decreased （P<0.01）. Moreover， the protein expression levels of TNF-α， TNFR1， p-STAT3， α1ACT， IL-6， and C3 were significantly decreased （P<0.01）， while BDNF and S100A10 protein expression levels were significantly increased （P<0.01）. ConclusionTianma Goutengyin may inhibit A1-type astrocyte activation in rats with vascular dementia through the TNF-α/STAT3/α1ACT signaling pathway， thereby reducing neuronal apoptosis and improving learning and memory function.