ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

ObjectiveTo investigate the patterns of chronic disease comorbidity among permanent elderly residents in Xuhui District of Shanghai, and to analyze the impact of different comorbidity patterns on the elderly’s ability to perform activities of daily living. MethodsBased on data from the 2015 and 2021 Surveys on Health Status and Health Service Utilization among Permanent Residents in Xuhui District of Shanghai, the study included permanent residents who fully participated in both surveys, were aged 60 years or older at the 2015 survey, self-reported having a chronic disease, and reported no significant changes in their chronic disease status between the two surveys. A prospective cohort study design was adopted, using the 2015 data as the baseline and the 2021 survey data as follow-up data. Physical examinations and questionnaire surveys were conducted by the Shanghai Xuhui District Center for Disease Control and Prevention in the subdistricts and towns within the jurisdiction from April to July in both 2015 and 2021. Latent class analysis (LCA) was used to classify comorbidity patterns across 11 types of chronic diseases. The smaller the values of the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the sample-corrected Bayesian information criterion (aBIC), the better the model fits. The Barthel index (BI) and the Lawton and Brody Instrumental Activities of Daily Living Scale (LB-IADL) were used to assess the participants’ activities of daily living (ADL) and instrumental activities of daily living (IADL). Multiple linear regression and multivariate logistic regression models were used to analyze the association between different comorbidity patterns and ADL and IADL. ResultsA total of 1 608 study participants were enrolled. Among them, 671 (41.73%) self-reported having no chronic diseases. The prevalence of 11 types of chronic diseases was as follows: hypertension in 743 participants (46.21%), heart disease in 148 participants (9.20%), cerebrovascular disease in 37 participants (2.30%), diabetes or abnormal blood glucose in 310 participants (19.28%), other endocrine system diseases (non-diabetic) in 39 participants (2.43%), malignant tumors in 37 participants (2.30%), chronic lung disease in 16 participants (1.00%), gout in 8 participants (0.50%), musculoskeletal disorders in 34 participants (2.11%), gastric or digestive system diseases in 27 participants (1.68%), and dyslipidemia in 15 participants (0.93%). LCA was used to classify comorbidity patterns of chronic diseases among older adults into 1 to 9 latent category combinations. Model 5 (i.e., 5 latent categories) had the lowest BIC (7 112.69) and aBIC (6 909.38), with a relatively low AIC (6 768.20), indicating the best model fit; thus, five latent categories represented the optimal combination in this study. The five latent categories (and their respective proportions of study participants) were the healthy control group (41.73%), the hypertension group (31.28%), the diabetes-hypertension group (19.28%), the severe illness group (5.29%), and the other metabolic diseases group (2.43%). According to multiple linear regression analyses, compared with the healthy control group, the diabetes-hypertension group had significantly lower BI-20 scores (b=-0.952, 95%CI: -1.317‒ -0.587, P<0.001) and lower IADL scores (b=-0.744, 95%CI: -0.999‒ -0.489, P<0.001); the severe illness group (b=-0.644, 95%CI: -1.072‒ -0.217, P=0.003) and the hypertension group (b=-0.344, 95%CI: -0.562‒ -0.125, P=0.002) had lower IADL scores. According to multivariate logistic regression analyses, compared with the healthy control group, older adults in the diabetes-hypertension group had a higher risk of disability (OR=2.835, 95%CI: 1.392‒5.774), impaired ADL function (OR=2.470, 95%CI: 1.637‒3.726), and impaired IADL function (OR=1.739, 95%CI: 1.264‒2.392); older adults in the severe illness group had a higher risk of impaired ADL function (OR=2.206, 95%CI: 1.171‒4.155). ConclusionAmong the elderly participating in the Survey on Health Status and Health Service Utilization of Permanent Residents in Xuhui District of Shanghai, diabetes-hypertension is one of the key chronic disease comorbidity combinations and has adverse effects on both ADL and IADL. Community health management should prioritize the elderly with diabetes-hypertension comorbidity, and strengthen early screening and comprehensive interventions to slow the decline in ADL and IADL.

ObjectiveTo investigate the application of central auditory testing combined with electroencephalographic features in the early screening of mild cognitive impairment due to Alzheimer's disease. MethodsA convenience sampling method was used to conduct a field survey in the Qixia District of Nanjing City, Jiangsu Province, from July to September, 2025. A total of 40 older adults diagnosed as mild cognitive impairment due to Alzheimer's disease and 23 cognitively normal older adults were recruited. Auditory N-back and dichotic listening paradigms were employed for assessment, while the scores of Montreal Cognitive Assessment Beijing Version (MoCA-BJ) and 32-channel electroencephalogram (EEG) data were simultaneously collected. Binary logistic regression analysis was applied to identify predictive indicators, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. ResultsThe total score of dichotic listening was positively correlated with overall cognitive function (r = 0.475, P < 0.01), while right-ear advantage showed negative correlations with overall cognition, visuospatial ability, abstract reasoning, and delayed recall (r < -0.276, P < 0.05). Task performance across all conditions of the N-back task was positively correlated with overall cognitive function (r > 0.249, P < 0.05). Regarding EEG features, δ and θ band power and power peaks were positively correlated with delayed recall (r > 0.258, P < 0.05), whereas (α+θ)/(α+β) and θ/(α+β) were positively correlated with naming ability and visuospatial executive function, respectively (r > 0.261, P < 0.05). The results of the binary logistic regression analysis showed that the total score of binaural dichotic listening, the accuracy of 1-back task, the average power of δ waves, and the ratio of θ/(α+β) were independent influencing factors for mild cognitive impairment due to Alzheimer's disease (P < 0.05). The ROC analysis indicated that the AUC for auditory task performance and EEG features were 0.845 and 0.735 respectively, and when combined, the AUC increased to 0.862. ConclusionThe screening model based on the integration of auditory task performance and EEG features can be used in the early identification of mild cognitive impairment due to Alzheimer's disease.

ObjectiveTo enhance the genetic diagnostic yield for fetal short femur length （FL） and to evaluate the integration of whole exome sequencing （WES） with chromosomal abnormality detection in prenatal diagnosis. MethodsA total of 27 mid-to-late trimester fetuses diagnosed with short FL ［FL < 5th percentile for gestational age or < -2 SD （Standard Deviation）］ at the Prenatal Diagnosis And Medical Genetics Center，Shenshan Medical Center，Memorial Hospital of Sun Yat-Sen University between November 2023 and May 2025 were included. Amniotic fluid or umbilical cord blood samples were collected and subjected to chromosomal karyotyping （19cases）， chromosomal microarray analysis （CMA） or low-coverage whole genome copy number variation（CNV-Seq） （27 cases）， and trio-based whole exome sequencing （trio-WES） （27 cases）. The pathogenicity of genetic variants were interpreted according to the American College of Medical Genetics and Genomics （ACMG） guidelines. ResultsTwenty-six genomic variants were detected in 17 of the 26 fetuses， including numerical chromosomal abnormalities （mosaic type）， copy number variations （CNVs）， single nucleotide variants （SNVs） and insertions-deletions （INDELs）. Genomic variants detected in 11 cases could explain the short femur phenotype.The overall diagnostic rate was 11/27， with pathogenic/likely pathogenic variants explaining 9 cases and variants of uncertain significance （VUS） accounting for 1 cases. In addition Chromosomal karyotype analysis revealed chromosome mosaicism explaining 1 case. The diagnostic rate of CNV detection （CMA/CNV-seq） was 2/27， while WES achieved a diagnostic rate of 10/27， with an incremental yield of 8/27 in cases with negative or inconclusive CNV results. Pathogenic mutations involved genes such as FGFR3， SHOX， DUOX2， CLCN5， and HBA1/HBA2. ConclusionTrio-WES significantly improves the genetic diagnostic rate for fetal short femur length， particularly in cases where chromosomal analysis fails to identify a cause. Karyotyping remains valuable for detecting low-level mosaicism in large-scale chromosomal abnormalities， thereby complementing WES and CNV detection. A stepwise or parallel genetic testing strategy is recommended in prenatal diagnosis to optimize diagnostic efficiency and support clinical decision-making.

It is believed that there is a pathogenesis of yang deficiency in polycystic ovary syndrome （PCOS） infertility， and it is concluded that warm-yang method has a better effect in improving endometrial abnormality， enhancing the quality of follicles， correcting endocrine disorders， and resolving or alleviating clinical symptoms in PCOS infertility. Based on Yanghe Decoction （阳和汤）， a representative traditional Chinese medicine decoction for warming yang， Yanghe Xiaonang Decoction （阳和消囊汤） was formulated， combining with warm medicinals according to symptoms， and aerobic exercise was also advocated to help generate and develop yang qi， in order to provide ideas for clinical treatments.

Objective: To investigate the clinical symptoms, laboratory tests, and treatment strategies of a case of fetal/neonatal alloimmune thrombocytopenia (FNAIT) complicated with piperacillin drug antibody. Methods: The platelet antibodies in the mother were screened and identified by ELISA. The HLA antigens of the newborn were genotyped through PCR-SSO, while the specificity of HLA antibodies in the mother was determined using a Single Antigen kit. The drug antibody was detected by a piperacillin kit. Results: Maternal antibodies against paternally-derived platelet antigens were detected. The HLA genotypes of the newborn were identified as HLA A 33∶03 and HLA B 58∶01. The mother exhibited strong positive antibodies against the specific platelet antigens of the newborn, namely anti-HLA-A33 and anti-HLA-B58 antibodies. The piperacillin antibody was detected in the newborn. Following treatment of continuous intravenous immunoglobulin (IVIG), platelet transfusions, red blood cell transfusions and discontinuation of piperacillin treatment, the platelet count and hemoglobin levels increased in the newborn. Conclusion: The newborn in this case was diagnosed with FNAIT complicated by the presence of anti-HLA-A33 and anti-HLA-B58 antibodies, as well as drug-induced hemolytic anemia caused by piperacillin drug antibody. The condition is more complicated under the influence of dual immune antibodies. Laboratory detection techniques such as platelet antibody and drug antibody tests can assist in early clinical diagnosis. At the same time, more active drug and blood transfusion treatments should be given in clinical practice to improve the prognosis.

Objective: To investigate the clinical symptoms, laboratory tests, and treatment strategies of a case of fetal/neonatal alloimmune thrombocytopenia (FNAIT) complicated with piperacillin drug antibody. Methods: The platelet antibodies in the mother were screened and identified by ELISA. The HLA antigens of the newborn were genotyped through PCR-SSO, while the specificity of HLA antibodies in the mother was determined using a Single Antigen kit. The drug antibody was detected by a piperacillin kit. Results: Maternal antibodies against paternally-derived platelet antigens were detected. The HLA genotypes of the newborn were identified as HLA A 33∶03 and HLA B 58∶01. The mother exhibited strong positive antibodies against the specific platelet antigens of the newborn, namely anti-HLA-A33 and anti-HLA-B58 antibodies. The piperacillin antibody was detected in the newborn. Following treatment of continuous intravenous immunoglobulin (IVIG), platelet transfusions, red blood cell transfusions and discontinuation of piperacillin treatment, the platelet count and hemoglobin levels increased in the newborn. Conclusion: The newborn in this case was diagnosed with FNAIT complicated by the presence of anti-HLA-A33 and anti-HLA-B58 antibodies, as well as drug-induced hemolytic anemia caused by piperacillin drug antibody. The condition is more complicated under the influence of dual immune antibodies. Laboratory detection techniques such as platelet antibody and drug antibody tests can assist in early clinical diagnosis. At the same time, more active drug and blood transfusion treatments should be given in clinical practice to improve the prognosis.

OBJECTIVE: To observe the clinical efficacy of Qihuang needle therapy for autism spectrum disorder (ASD) children with sleep disorder. METHODS: A total of 60 ASD children with sleep disorder were randomly divided into an observation group and a control group, 30 cases in each group. Both groups were treated with structured education intervention, 60 min each time, once a day, 6 times a week. Qihuang needle therapy was applied at Yintang (GV24⁺), Baihui (GV20) and bilateral Jueyinshu (BL14), Xinshu (BL15) in the observation group, multi-direction needling was delivered and without needle retaining. The treatment was given 2 times a week, each treatment was delivered at interval of 2 days at least. Behavioral intervention was adopted in the control group. Treatment for consecutive 12 weeks was required in both groups. Before and after treatment, the scores of children's sleep habits questionnaire (CSHQ), the autism behavior checklist (ABC), the childhood autism rating scale (CARS), and the childhood autism behavior scale (CABS) were observed in the two groups. RESULTS: After treatment, the scores of CSHQ, ABC, CARS and CABS were decreased compared with those before treatment (P<0.01), and the above scores in the observation group were lower than those in the control group (P<0.05). CONCLUSION Qihuang needle therapy can effectively treat ASD with sleep disorder, improve the core symptoms of ASD and the sleep quality.
Humans ; Autism Spectrum Disorder/physiopathology* ; Male ; Female ; Child ; Sleep Wake Disorders/physiopathology* ; Child, Preschool ; Acupuncture Therapy ; Acupuncture Points ; Treatment Outcome ; Sleep ; Needles

Objective This study aims to observe the impact of warm moxibustion on menstrual pain in college students with primary dysmenorrhea and explore its potential mechanism.Methods College students with primary dysmenorrhea were recruited and treated with warm moxibustion at Shenque acupoint for three consecutive menstrual cycles.Healthy subjects were also recruited for comparison.Pain scale,uterine artery hemodynamics,and related inflammatory factors were assessed before and after treatment.Results ①The results of scale study showed that the severity and duration of dysmenorrhea were gradually alleviated with the prolongation of treatment time through the analysis of variance of repeated measurements of the total scores of McGill pain inquiry scale and CMSS dysmenorrhea symptom scale before and after 3 treatments.The results of variance analysis and pairwise comparison of repeated measurements of PRI,VAS and PPI of McGill pain inquiry scale before and after 3 times treatment in warm moxibustion group also showed that each index decreased gradually with the prolongation of treatment time.The comparison of the scores of each item of CMSS scale showed that the severity and duration of low back before treatment were significantly different from those in the healthy group(P<0.001),but the difference was weakened after the third treatment.The severity of vomiting,the duration of vomiting,the severity of diarrhea and the duration of diarrhea were significantly different from those in the healthy group before treatment(P<0.001),but they were still higher than those in the healthy group after the third treatment.but the difference was not statistically significant.②Prior to treatment,PD college students exhibited significantly higher S/D and PI values on both sides compared to healthy subjects,with a statistically significant difference observed for PI on the left side(P<0.001).Following treatment,all aforementioned indexes decreased significantly,particularly PI on the left side which showed a significant difference from pre-treatment levels(P<0.001).③Before treatment,the levels of serum IL-1β,TNF-α,and CRP in PD college students were significantly higher compared to those in the healthy group.The difference in IL-1β level was statistically significant(P<0.001).After treatment,there was a noticeable decrease in the levels of IL-1β,TNF-α,and CRP.Specifically,IL-1β showed a significant reduction(P<0.01),and this time the comparison with the healthy group did not reveal any significant difference in IL-1β levels.Conclusion The application of warm moxibustion at the Shenque acupoint demonstrates a significant improvement in both the dysmenorrhea pain rating index and severity among college students with primary dysmenorrhea,while also alleviating the severity and duration of associated symptoms.These positive effects may be attributed to warm moxibustion's ability to enhance uterine microcirculation in individuals with primary dysmenorrhea,and ameliorating inflammatory conditions.

Objective To compare the node strength in white matter networks between depressive disorder and bipolar depression patients,analyze structural connectivity impairments across brain regions,and assess their diagnostic utility.Methods This longitudinal study initially enrolled 91 patients with a baseline diagnosis of depressive episode.All subjects underwent diffusion tensor imaging at recruitment and white matter structural weighted networks were constructed using deterministic fiber tracking.After≥9 years of naturalistic follow-up,23 patients who maintained a diagnosis of major depressive disorder(MDD group)and 18 patients who maintained a diagnosis of bipolar disorder(BD group),while 30 demographically matched healthy controls(HC group)were included for comparison.The differences in nodal connection strength within the brain white matter networks among the three groups were compared.Furthermore,the receiver operator characteristic(ROC)curve was utilized to evaluate the diagnostic value of the differential brain regions in distinguishing between MDD and BD.Results In the left anterior cingulate gyrus,the node strength was lower in the BD than in the MDD group(3.89±0.76 vs.4.74±0.60).However,the node strength in the right caudate nucleus(4.94±1.26 vs.3.46±0.99)and right globus pallidus(1.98±0.67 vs.1.25±0.29)was higher in the BD than in the MDD group(P<0.01,FWE-corrected).The combined connectivity strengths of three brain regions—the left anterior cingulate gyrus,right caudate nucleus and right globus pallidus—were used to differentiate between MDD and BD,and an ROC curve was plotted,with an area under the curve(AUC)of 0.95(95%CI:0.91～0.99;P<0.001).The sensitivity and the specificity were 0.89 and 0.87,respectively.Conclusion The differences in node strength between patient groups may serve as a potential neuroimaging biomarker.Integration of node connectivity strengths from these differential brain regions can achieve superior discrimination accuracy.