Based on the interactive ritual chain theory， this paper deeply analyzed the interactive characteristics between doctors， patients， and their families in the palliative care environment， as well as explored the role of emotional resonance， symbolic representation， and situational creation in psychological support. It also sorted out four primary issues currently present in psychological support for palliative care patients， including insufficient recognition of caregivers regarding patients’ psychological needs， limited psychological intervention methods， inadequate psychological support capabilities among medical staff， and an imperfect family and social support system. On this basis， a five-dimensional psychological support mechanism was constructed， encompassing emotional resonance， situational creation， team collaboration， environment building， and technological application. This aimed to provide palliative care patients with comprehensive and continuous psychological intervention by optimizing doctor-patient interaction， strengthening emotional connection， improving physical environment， and utilizing information technology， thereby contributing to alleviating the psychological distress patients confront in the terminal stage and improving their life dignity and quality of life.

BACKGROUND:Spondylolisthesis is a common disease,and there is a lack of effective drugs to treat it.There is still a need to further define the pathogenesis and screen out more suitable therapeutic targets for spondylolisthesis.Mendelian randomization analysis can be used to explore the drugable genes associated with spondylolisthesis and provide valuable guidance for the development of more effective and targeted therapeutic drugs.OBJECTIVE:To explore potential therapeutic targets and effective drugs for spondylolisthesis by means of pharmaceutically available genome-wide Mendelian randomization analysis.METHODS:Using the Finnish database,eQTLGen consortium,drug signature database,drug-gene interaction database,protein-protein interaction database,organic small molecule biological activity database and protein structure database,which contains genome and health information of half a million Finns,data on druggable genes were subjected to two-sample Mendelian randomization analysis and co-localization analysis with data from genome-wide association studies of spondylolisthesis to identify genes highly associated with spondylolisthesis.In addition,GO and KEGG enrichment analysis,protein network construction,drug prediction and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic agents.RESULTS AND CONCLUSION:In this study,we identified 34 potential drug target genes that were significantly associated with spondylolisthesis,particularly the gene APOBEC3G.This gene showed a significant association with spondylolisthesis outcomes through Mendelian analysis and co-localization analysis,suggesting that APOBEC3G may be a priority therapeutic target.As for other potential mechanisms and drugs,we still need to conduct more in-depth research to determine their roles.This study used a database from a European population,which can be used as a reference for the study of population genetics in China.

BACKGROUND:Spondylolisthesis is a common disease,and there is a lack of effective drugs to treat it.There is still a need to further define the pathogenesis and screen out more suitable therapeutic targets for spondylolisthesis.Mendelian randomization analysis can be used to explore the drugable genes associated with spondylolisthesis and provide valuable guidance for the development of more effective and targeted therapeutic drugs.OBJECTIVE:To explore potential therapeutic targets and effective drugs for spondylolisthesis by means of pharmaceutically available genome-wide Mendelian randomization analysis.METHODS:Using the Finnish database,eQTLGen consortium,drug signature database,drug-gene interaction database,protein-protein interaction database,organic small molecule biological activity database and protein structure database,which contains genome and health information of half a million Finns,data on druggable genes were subjected to two-sample Mendelian randomization analysis and co-localization analysis with data from genome-wide association studies of spondylolisthesis to identify genes highly associated with spondylolisthesis.In addition,GO and KEGG enrichment analysis,protein network construction,drug prediction and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic agents.RESULTS AND CONCLUSION:In this study,we identified 34 potential drug target genes that were significantly associated with spondylolisthesis,particularly the gene APOBEC3G.This gene showed a significant association with spondylolisthesis outcomes through Mendelian analysis and co-localization analysis,suggesting that APOBEC3G may be a priority therapeutic target.As for other potential mechanisms and drugs,we still need to conduct more in-depth research to determine their roles.This study used a database from a European population,which can be used as a reference for the study of population genetics in China.

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 Analysis of the effects of relaxation training combined with exercise intervention in patients with colorectal cancer undergoing chemotherapy,aiming to provide reference for clinical nursing practice.Methods Using a convenience sampling method,80 colorectal cancer patients undergoing chemotherapy in the oncology ward of a tertiary A hospital in Changzhou,Jiangsu Province from November 2022 to November 2023 were selected as study subjects.Patients were divided into an experimental group and a control group using a random number table method,with 40 patients in each group.The experimental group received relaxation training combined with exercise interventionin addition to routine care provided to the control group.The control group received routine care.Differences in 6-minute walking distance,anxiety,depression and quality of life scores before and after six chemotherapy cycles were compared between the 2 groups.Results Finally,70 patients completed the intervention,with 35 patients in each group.After the intervention,there were differences in 6-minute walking distance,anxiety scores,depression scores,and overall health status scores between the 2 groups were all statistically significant(P＜0.001).Conclusion Relaxation training combined with exercise intervention can maintain exercise endurance in colorectal cancer patients undergoing chemotherapy and alleviate anxiety and depression to some extent,helping to improve patients' quality of life.

Objective:To explore the prognostic value of a combined model of baseline 18F-FDG PET/CT tumor metabolic parameters and clinical factors for predicting progression-free survival (PFS) in Hodgkin′s lymphoma (HL). Methods:From January 2014 to May 2023, 171 HL patients (102 males, 69 females; median age 40 years) who underwent 18F-FDG PET/CT before treatment at the First Affiliated Hospital of Nanjing Medical University and Nanjing Drum Tower Hospital were retrospectively collected. HL patients from the First Affiliated Hospital of Nanjing Medical University were classified as the training set (101 patients) and HL patients from Nanjing Drum Tower Hospital were classified as the validation set (70 patients). Clinical factors and tumor metabolic parameters associated with PFS were determined by multivariate Cox regression analysis, and then the combined model and the independent model of each factor were constructed respectively. The consistency index (C-index) and AUC were used to evaluate the predictive efficacy of models, and nomogram was constructed based on the optimal model, and calibration curves were used to assess the goodness of fit of the models. The differences in Kaplan-Meier survival curves of the high-risk and low-risk groups were compared using log-rank test. Results:The multivariate Cox regression analysis indicated that the independent prognostic factors associated with PFS were the Lugano staging (hazard ratio ( HR)=3.10, 95% CI: 1.17-8.23, P=0.023), total metabolic tumor volume (TMTV) ( HR=2.65, 95% CI: 1.23-5.74, P=0.014), and maximum distance between tumors ( Dmax) ( HR=2.23, 95% CI: 1.02-4.85, P=0.044). These factors were used to construct the combined model, with the highest prognostic efficacy of the C-index for the training and validation sets of 0.692 and 0.653, and the AUC of 0.732 and 0.697, respectively. The calibration curves demonstrated that the predictions made by the combined model were in high agreement with the actual results in both the training and validation sets. The Kaplan-Meier analysis revealed a significantly lower PFS rate in the high-risk group compared to the low-risk group both in training and validation sets ( χ2 values: 5.88 and 4.52, P values: 0.015 and 0.033). Conclusion:The combined model incorporating tumor metabolic parameters and clinical factors improves prognostic efficacy in predicting PFS in HL patients.