Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Objective: To explore the association between the Internet usage characteristics and suicidal ideation among vocational high school students, so as to provide a theoretical basis for precise intervention of suicide among vocational high school students. Methods: A total of 1 781 students were recruited from three vocational high schools in Wuhan and Xianning in March 2023 by using the cluster random sampling method. The Columbia-Suicide Severity Rating Scale and Revised Chen Internet Addiction Scale were used to measure suicidal ideation and Internet addiction, respectively. LASSO regression model was used to select influential factors related to suicidal ideation, and the gradient boosting decision tree algorithm XGBoost was used to develop prediction models and evaluate predictive performance. By calculating the SHAP values, the contribution of each influential factor was quantified. Results: The prevalence of suicidal ideation among vocational high school students was 42.22% and prevalence of Internet addiction was 26.39%. LASSO regression results indicated that age, gender, experience of being left behind, parental relationship, holding a class cadre position, using the Internet for learning, Internet use during dawn, morning and late night, Internet addiction, and depressive symptoms were all the influential factors of suicidal ideation among vocational high school students ( β= -0.05 , 0.29, 0.09, 0.27, 0.10, -0.01, 0.09, 0.05, 0.24, 0.28, 0.78, all P <0.05). The AUC of the prediction model was 0.75. The results based on SHAP values indicated that all influential factors identified through multivariate analysis contributed positively to the model predictions ( SHAP >0). Among these, depressive symptoms and parental relationship had the greatest impact on suicidal ideation ( SHAP =0.77, 0.26), and the joint effect of features with higher contribution could improve the prediction probability. Conclusions Depressive symptoms, parental relationships, Internet addiction, and time of Internet use are most important risk factors of suicidal behaviors for vocational high school students. Thus, effective interventions should be conducted to reduce their suicidal ideation.

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

OBJECTIVES: To investigate the ethnic origin of Chuanqing people, one of the largest unidentified ethnic groups in Guizhou, China, and analyze its genetic relationships with surrounding populations. METHODS: Based on a self-developed microhaplotype system, we conducted genotyping and analyzed the genetic distribution of microhaplotype loci and their forensic applicability in Chuanqing population in Guizhou Province. Using the microhaplotype data from different intercontinental populations and previously reported data from Han population living in Guizhou Province, we systematically investigated the genetic background of Chuanqing people through population genetic approaches, including genetic distance estimation, principal component analysis, and phylogenetic tree construction. RESULTS: Among the studied population, the number of haplotype per microhaplotype ranged from 6 to 25. The average expected heterozygosity (He), observed heterozygosity (Ho), power of discrimination (PD), and probability of exclusion (PE) were 0.8291, 0.8301, 0.9387, and 0.6593, respectively. The cumulative power of discrimination (CPD) and cumulative probability of exclusion (CPE) for these 33 loci were 1-2.62×10^-41 and 1-7.64×10^-17, respectively. Population genetic analyses revealed that the Chuanqing population had close genetic relationships with the East Asian populations, especially the local Guizhou Han population, Beijing Han population and the Han populations living in southern China. CONCLUSIONS The 33 microhaplotypes exhibit high levels of genetic diversity in the Guizhou Chuanqing population, highlighting their potentials for both forensic identification and parentage testing. The Han populations might have contributed a significant amount of genetic material to the Chuanqing population during the formation and development of the latter.
Humans ; China/ethnology* ; Ethnicity/genetics* ; Forensic Genetics/methods* ; Genetics, Population ; Genotype ; Haplotypes ; Phylogeny ; East Asian People/genetics*

OBJECTIVE: To investigate the incidence of sepsis in Xinjiang Uygur Autonomous Region and the compliance with sepsis diagnosis and treatment guidelines in intensive care unit (ICU) at different levels of hospitals, and to identify the risk factors associated with poor prognosis in patients with sepsis in this region. METHODS: A prospective cross-sectional survey was conducted in ICU of Xinjiang Uygur Autonomous Region Critical Care Medicine Alliance. The survey period was from 10:00 on January 31, 2024, to 09:59 on February 1, 2024. The patients diagnosed with sepsis admitted to the ICU during the study period were included in the analysis. Data on patient demographics, physiology, microbiology, and treatment protocols were collected, with follow-up until the 28th day after ICU admission or death. Baseline characteristics and treatment information of septic patients across different hospital levels were compared, as well as clinical data of septic patients with different 28-day outcomes. Multivariate Cox proportional hazards model was used to identify risk factors for 28-day death in septic patients. RESULTS: A total of 77 units of Xinjiang Uygur Autonomous Region Critical Care Medicine Alliance from 14 prefectures/cities in Xinjiang participated in the survey. On the survey day, 727 patients were admitted to ICU, of whom 179 (24.6%) were diagnosed with sepsis, and 64 (35.8%) died within 28 days, 115 (64.2%) survived. Among the participating institutions, 33 were tertiary hospitals (42.9%), managing 97 septic cases (54.2%), and 44 were secondary hospitals (57.1%), managing 82 septic cases (45.8%). The lactic acid monitoring rate and continuous renal replacement therapy (CRRT) rate for septic patients in tertiary hospitals were significantly higher than those in secondary hospitals [lactic acid monitoring rate: 92.8% (90/97) vs. 82.9% (68/82), CRRT rate: 17.5% (17/97) vs. 3.7% (3/82), both P < 0.05]. No statistically significant differences were observed between tertiary and secondary hospitals in length of ICU stay or 28-day mortality [length of ICU stay (days): 11.0 (16.0) vs. 10.0 (22.0), 28-day mortality: 35.1% (34/97) vs. 36.6% (30/82), both P > 0.05]. Compared with survivors, non-survivors had higher acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, Charlson comorbidity index (CCI) score and lower Glasgow coma scale (GCS) score. Significant differences were noted in vital signs [heart rate, blood pressure, body temperature, pulse oxygen saturation (SpO₂)], laboratory markers [red blood cell count (RBC), white blood cell count (WBC), lymphocyte ratio (LYM%), blood urea nitrogen (BUN), total protein (TP), albumin (Alb), pH value, base excess (BE)], and monitoring, diagnosis and treatment information (invasive blood pressure monitoring, mechanical ventilation, CRRT, usage of norepinephrine). Multivariate Cox proportional hazards model indicated that body temperature [hazard ratio (HR) = 1.416, 95% confidence interval (95%CI) was 1.022-1.961, P = 0.037] and WBC (HR = 1.040, 95%CI was 1.010-1.071, P = 0.009) were independent risk factors for 28-day death in patients with sepsis. CONCLUSIONS Sepsis in Xinjiang Uygur Autonomous Region is characterized by a high mortality. In this region, tertiary hospitals demonstrate better compliance with bundled treatment strategies such as lactic acid monitoring and the usage of CRRT compared to secondary hospitals, yet they do not show significant advantages in clinical outcomes. Body temperature and WBC are independent risk factors for 28-day death in patients with sepsis in this region. However, clinicians should still consider the actual situation of patients, along with more optimal early warning indicators and comprehensive system assessments, to identify and prevent risk factors for adverse outcomes in patients.
Humans ; Sepsis/diagnosis* ; Cross-Sectional Studies ; Prospective Studies ; Risk Factors ; Intensive Care Units ; Prognosis ; China/epidemiology* ; Male ; Female ; Middle Aged ; Aged ; Proportional Hazards Models ; Incidence

Objective: To explore the influencing factors and pathways of suicidal ideation among children and adolescents with severe autism spectrum disorder (ASD), so as to provide references for clarifying the impact intensity and pathways of various factors on suicidal ideation in the population. Methods: A cross sectional study was conducted from June 17, 2024, to January 12, 2025, involving 96 severely affected ASD children and adolescents aged 8-18 years from Tianjin. Participants were assessed using the Puberty Development Scale (PDS), Children s Alexithymia Measure (CAM), Strengths and Difficulties Questionnaire (SDQ), and Positive and Negative Suicide Ideation (PANSI). The random forest Boruta algorithm was employed to screen core variables, and a Bayesian network model was constructed to analyze the influencing factors of suicidal ideation in children and adolescents with severe ASD. Results: Through the screening using the Boruta algorithm, the SDQ scale score, conduct problems, hyperactivity, peer relationship problems and prosocial behavior were identified as the key predictors of suicidal ideation. A Bayesian network model was established with hyperactivity as the central mediating node. The impact of hyperactivity on suicidal ideation exhibited a non linear relationship: compared to the normal state (31.6%, 68.4%), the borderline state of hyperactivity was associated with a higher probability of low risk suicidal ideation (47.1%) and a lower probability of high risk suicidal ideation (52.9%). Suicidal ideation among children and adolescents with severe ASD was closely related to hyperactivity. In the state of hyperactivity, the abnormal peer relationship (95.2%) and the abnormal prosocial behavior (77.0%) were aggravated. Conclusions Suicide ideation among children and adolescents with severe ASD is strongly associated with hyperactivity traits. It is necessary to establish a prevention and control system centered on hyperactivity intervention to reduce this risk.

Objective This paper proposes a novel contour detection method inspired by the surround inhibition mechanism of the primary visual cortex.Methods The method involves simulating the response characteristics of the classical receptive field in the primary visual cortex to external stimuli and constructing a multi-directional two-dimensional Gabor filter model for extracting primary contours.A non-classical receptive subfield surround suppression model is proposed based on the structural characteristics of the non-classical receptive subfield for texture suppression.Additionally,a two-dimensional Gaussian function is used to simulate information processing by ganglion cells,and information is transmitted across levels to improve the response rate.Finally,the characteristics of capturing global information by the human eye are simulated to correct the contours and obtain the final contour map.Results Qualitative and quantitative analysis compared with other existing contour detection algorithms;The average accuracy(AP)of any 200 images in the BSDS500 reached 0.703.Conclusion the results show that the proposed algorithm can more effectively highlight the contour of the subject and suppress the texture background.

Objective To investigate the effect of progressive muscle relaxation training intervention strategy in relieving fatigue of elderly patients with primary hepatocellular carcinoma(HCC)after receiving transcatheter arterial chemoembolization(T ACE),and to analyze its influencing factors.Methods Using convenience sampling method,a total of 150 elderly patients with HCC,who received TACE at a certain grade Ⅲ-A hospital at Peking of China from May 2021 to March 2023,were selected as the subjects of research.The patients were randomly divided into the study group and the control group,and progressive muscle relaxation training intervention strategy and conventional postoperative fatigue care method were employed respectively.The preoperative fatigue status and the postoperative fatigue recovery status were compared between the two groups,and the influencing factors were analyzed.Results In both groups,the postoperative one-day fatigue score was the highest,which was gradually decreased thereafter.The average recovery time of fatigue in the control group was 9.84 days,which in the study group was 6.16 days,the difference between the two groups was statistically significant(P=0.013).The body mass index(BMI),Child-Pugh classification,and preoperative grip strength index had an effect on the postoperative fatigue recovery time after intervention.A BMI of β=-0.953 and a preoperative grip strength index of β=-0.185 were negatively correlated with the postoperative fatigue recovery time after intervention,while a Child-Pugh classification of β=2.177 was positively correlated with the postoperative fatigue recovery time after intervention.Conclusion Progressive muscle relaxation training intervention strategy is helpful for shortening the postoperative fatigue recovery time in elderly patients with HCC after receiving TACE,and it is worth of promotion in clinical practice.The patient's nutrition and physical status such as BMI,hepatic reserve function and grip strength index,are the factors influencing the effectiveness of progressive muscle relaxation training intervention strategy.

Objective To develop a virtual world integration scale for adolescents and test its reliability and validity within the framework of the Virtual Social Ecology Model. Methods A total of 2543 students from four schools in Wuhan and Xianning were recruited from March to October 2023. The preliminary items of the scale were determined by semi-structured interview, literature review, brainstorming and Delphi expert consultation. The compiled scale was analyzed for validity and reliability using critical ration analysis, homogeneity test, confirmatory factor analysis, correlation related validity test , cronbach α coefficient, and split-half coefficient. Results The scale was preliminarily determined to consist of 20 items in 10 dimensions, including virtual self-identity, emotional interaction, virtual social interaction, cyber bullying, community activities, value identity, community participation, community management, network culture and virtual social capital. In the item analysis, the correlation coefficient of homogeneity test ranged from 0.496 to 0.767 (P<0.001), and there was statistical difference in critical ratio analysis (CR= 21.897-53.546, P<0.001). The fits of the confirmatory factor analysis model in validity analysis were: CFI=0.933, NFI=0.951, IFI=0.927, and RMSEA=0.064. The criterion validity showed a significantly positive association (the coefficient was between 0.450 and 0.855, P<0.01). The overall Cronbach α coefficient was 0.929 and the split-half coefficient was 0.846. The Cronbach α coefficients of sub-questionnaires were between 0.719 and 0.900 , and the split-half coefficients were between 0.729 and 0.913. Conclusion The Virtual World Integration Scale has good reliability and validity, and can be used as an assessment tool for the level of adolescents’ integration in virtual world.

Objective:To establish a prediction model for symptomatic radiation pneumonitis (SRP) after radiotherapy for thoracic cancer based on a longitudinal cohort and dose interval variations.Methods:Clinical data of 587 patients who received thoracic radiotherapy in Department of Radiotherapy of Yunnan Cancer Hospital from July 2022 to June 2023 were retrospectively analyzed. The National Cancer Institute common terminology criteria for adverse events (CTCAE) version 5.0 was used to grade radiation pneumonitis, and clinical factors, traditional independent dosimetric characteristics and dose interval variation characteristics were collected. Features used to predict the occurrence of SRP were screened using genetic algorithms and analyzed the correlation between the selected features and SRP occurrence. Predictive models for SRP occurrence were established using the selected features and evaluated, and the optimal predictive model was visualized using a column chart.Results:The incidence of SRP was 35.94%. Five clinical factors, seven independent dosimetric features and six dose interval variation features were screened out by genetic algorithms to effectively predict the occurrence of SRP. The area under ROC curve (AUC) of clinical factors combined with traditional independent dosimetric factors and dose interval variation factors was 76%. The AUC of clinical factors combined with traditional independent dosimetric factors and that of clinical factors combined with dose interval variation factors was 69% and 67%, respectively. The addition of the characteristics of dose interval variation factors significantly improved the effectiveness of the prediction model.Conclusions:The supplement of the characteristics of dose interval variation factors can significantly improve the performance of the SRP prediction model for thoracic tumors after radiotherapy. The SRP prediction model based on dose interval variations can effectively predict the occurrence of SRP.