Traditional Chinese medicine has been paid more and more attention in the development of modern healthcare,and its clinical diagnosis and treatment have broad prospects and enormous potential.However,the current traditional Chinese medicine diagnosis and treatment model have serious shortcomings in service capacity and,diagnosis,and treatment effect.The rapid development of big data and artificial intelli-gence technology provides an opportunity for the iterative upgrade of traditional Chinese medicine diagnosis and treatment models.This article reviewed the current situation of artificial intelligence empowering tradi-tional Chinese medicine clinical diagnosis and treatment,clarified the problems and challenges faced by artifi-cial intelligence technology in data integration,data quality,and data analysis in traditional Chinese medicine clinical diagnosis and treatment,and proposed to empower from the aspects of disciplinary integration,data quality optimization,data privacy protection,and promotion and application,so as to provide reference for im-proving the effectiveness of traditional Chinese medicine clinical diagnosis and treatment.

AIM:To investigate whether the expression of perilipin 2(PLIN2)in renal tubular cells could predict a decline in renal function in diabetic kidney disease(DKD)patients,and to explore the potential mechanisms in-volved in renal tubular cell injury induced by PLIN2 during the progression of DKD.METHODS:Control individuals(n=12)and DKD patients(n=51)were enrolled in this retrospective cohort study.Demographic and laboratory data were col-lected.A simplified linear mixed-effects model was applied to assess the estimated glomerular filtration rate(eGFR)slope.The relationship between PLIN2 and renal function decline in DKD patients was predicted by Spearman correlation analysis and a generalized linear model.BKS-db/db diabetic mice and streptozotocin-induced diabetic mice were used.Primary renal tubular cells were treated with glucose and transfected with small interfering RNA or plasmid.Western blot-ting and immunofluorescence staining were used to detect PLIN2 expression.Lipid droplets were stained with oil red O.The oxygen consumption rate(OCR)of mitochondria was measured using an extracellular flux analyser.RESULTS:The expression of PLIN2 was markedly higher in the tubules of DKD patients than in those of control subjects.After 24(12,39)months of follow-up,the eGFR slope of DKD patients was-7.42(-19.77,-2.09)mL/(min·1.73 m2·year).An in-crease in the baseline percentage of PLIN2-positive tubules was significantly associated with the eGFR slope during the fol-low-up period[hazard ratio(HR)=1.90,95%confidence interval(CI):1.00～3.58],indicating that tubular PLIN2 could predict a decrease in renal function in DKD patients.Both the accumulation of lipid droplets and the expression of PLIN2 were markedly greater in the tubules of diabetic mice than in those of control mice.Glucose treatment induced lipid droplet accumulation and PLIN2 expression in renal tubular cells.Knockdown of PLIN2 significantly alleviated glucose-in-duced lipid droplet accumulation,whereas PLIN2 overexpression aggravated glucose-induced lipid droplet accumulation.The decrease in mitochondrial OCR in renal tubular cells induced by glucose treatment was alleviated after PLIN2 knock-down.However,overexpression of PLIN2 directly decreased the mitochondrial OCR.CONCLUSION:The PLIN2 ex-pression in tubules predicts a decline in renal function in patients with DKD.The PLIN2 suppresses mitochondrial aerobic respiration and contributes to the accumulation of lipid droplets in renal tubular cells to promote the progression of DKD.

The regulatory role of pericoronary adipose tissue(PC AT)in cardiovascular diseases is of paramount im-portance.PCAT exerts extensive pathophysiological effects on the cardiovascular system by secreting various bioactive substances,such as adipokines and cytokines.Currently,the attenuation value of PCAT can be detected via coronary computed tomography angiography(CCTA),a method that not only reflects the level of vascular inflammation but also holds significant clinical value in the detection and prognostic assessment of coronary heart disease plaques.Therefore,this article reviews the pathophysiological mechanisms of PCAT and its clinical significance in coronary heart disease.

Objective:To retrospectively analyze the treatment status of tyrosine kinase inhibitors (TKI) in newly diagnosed patients with chronic myeloid leukemia (CML) in China.Methods:Data of chronic phase (CP) and accelerated phase (AP) CML patients diagnosed from January 2006 to December 2022 from 77 centers, ≥18 years old, and receiving initial imatinib, nilotinib, dasatinib or flumatinib-therapy within 6 months after diagnosis in China with complete data were retrospectively interrogated. The choice of initial TKI, current TKI medications, treatment switch and reasons, treatment responses and outcomes as well as the variables associated with them were analyzed.Results:6 893 patients in CP ( n=6 453, 93.6%) or AP ( n=440, 6.4%) receiving initial imatinib ( n=4 906, 71.2%), nilotinib ( n=1 157, 16.8%), dasatinib ( n=298, 4.3%) or flumatinib ( n=532, 7.2%) -therapy. With the median follow-up of 43 ( IQR 22-75) months, 1 581 (22.9%) patients switched TKI due to resistance ( n=1 055, 15.3%), intolerance ( n=248, 3.6%), pursuit of better efficacy ( n=168, 2.4%), economic or other reasons ( n=110, 1.6%). The frequency of switching TKI in AP patients was significantly-higher than that in CP patients (44.1% vs 21.5%, P<0.001), and more AP patients switched TKI due to resistance than CP patients (75.3% vs 66.1%, P=0.011). Multi-variable analyses showed that male, lower HGB concentration and ELTS intermediate/high-risk cohort were associated with lower cytogenetic and molecular responses rate and poor outcomes in CP patients; higher WBC count and initial the second-generation TKI treatment, the higher response rates; Ph + ACA at diagnosis, poor PFS. However, Sokal intermediate/high-risk cohort was only significantly-associated with lower CCyR and MMR rates and the poor PFS. Lower HGB concentration and larger spleen size were significantly-associated with the lower cytogenetic and molecular response rates in AP patients; initial the second-generation TKI treatment, the higher treatment response rates; lower PLT count, higher blasts and Ph + ACA, poorer TFS; Ph + ACA, poorer OS. Conclusion:At present, the vast majority of newly-diagnosed CML-CP or AP patients could benefit from TKI treatment in the long term with the good treatment responses and survival outcomes.

Objective:To identify the influencing factors of postoperative nausea and vomiting (PONV) in the patients undergoing egg retrieval with general anesthesia for in vitro fertilization-embryo transfer (IVF-ET).Methods:This was a case-control study. Medical records from IVF-ET patients undergoing egg retrieval with general anesthesia in the Center for Reproductive Medicine in our hospital from November to December 2020 were retrospectively collected, 52 patients with PONV were identified (group PONV), and 252 patients without PONV were selected as control group (group C). Univariate analysis was performed on the suspicious influencing factors, and the factors with statistically significant differences were included in the logistic regression analysis model to identify the influencing factors of PONV.Results:Compared with group C, statistically significant differences were found in the number of eggs, anesthesia time, and the proportion of PONV history and/or motion sickness history in group PONV ( P<0.05). The results of multivariate logistic regression analysis showed that a large number of eggs, long anesthesia time, and a high proportion of PONV history and/or motion sickness history were independent risk factors for PONV. Conclusions:A large number of eggs, long anesthesia time, and a high proportion of PONV history and/or motion sickness history are independent risk factors for PONV in IVF-ET patients undergoing egg retrieval with general anesthesia.

Objective To analyze the influencing factors of self-management ability of coronary heart disease patients after percutaneous coronary intervention (PCI) and construct a nomogram model. Methods A total of 247 CHD patients who underwent PCI were selected as the research subjects, and all of them were evaluated by Coronary Heart Disease Self-management Scale (CSMS). Multivariate Logistic regression analysis was used to screen out the independent influencing factors of self-management ability of CHD patientsafter PCI, and R software was used to construct a nomogram model. The discrimination, calibration, and goodness-of-fit of the nomogram model were evaluated by receiver operating characteristic (ROC) curve, calibration curve, and Hosmer-Lemeshow goodness-of-fit test. Results The average CSMS score of 247 patients was (86.79±9.22), and these patients were divided into low-score group(89 patients) and high-score group (158 patients)based on the critical value of 86.79 points. There were significant differences in age, education level, monthly per capita family income, and self-efficacy between the low-score group and the high-score group (P < 0.05). Multivariate Logistic regression analysis showed that age, education level, monthly per capita family income, and self-efficacy were independent influencing factors of self-management ability of CHD patients after PCI (P < 0.05). The nomogram model constructed based on the four influencing factors found that age >70 years increased the weight by 86.2 points, education level below high school increased the weight by 87.4 points, monthly per capita family income < 3 500 yuan increased the weight by 82.3 points, and low self-efficacy increased the weight by 99.6 points. The results of ROC curve analysis showed that the area under the curve of the nomogram model for predicting self-management ability ofCHD patients after PCI was 0.842 (95%CI, 0.794 to 0.890). The slope of the calibration curve was close to 1, and the results of goodness-of-fit test was as follows: χ²=10.226, P=0.233. Conclusion Age, education level, monthly per capita family income, and self-efficacy are influencing factors of self-management ability of CHD patients after PCI, and the nomogram model constructed based on these factors has high predictive value for self-management ability.

Objective:To summarize the construction and practice of the compulsory postgraduate course "reproductive genetics in gynecology and obstetrics" in Department of Gynecology and Obstetrics, Peking University, and to evaluate the teaching effect of this course.Methods:A total of 139 postgraduates who studied in Department of Obstetrics and Gynecology, Peking University, from 2019 to 2021 were enrolled as subjects, and a syllabus was constructed through a top-level design based on the "biological-psychological-social medicine pattern", with the teaching objectives of reproductive genetics theory, clinical translation, genetic counseling methods, and research advances. The teaching effect was evaluated by analyzing teaching assessment results, teaching evaluation feedback, and teaching achievements. SPSS 26.0 software was used to perform the t-test and the chi-square test. Results:The written test score, usual performance score, and total score of the postgraduate students in 2021 were higher than those in 2019 and 2020 [(73.50±8.19) vs. (70.94±14.90); (68.60±2.82) vs. (68.22±4.58); (90.58±4.18) vs. (89.49±7.60)], with significant differences in written test score and total score ( P<0.05). There was a high degree of satisfaction with the feedback of teaching, and 85.61% (119/139) of the students selected "great satisfaction"; in particular, there were increases in the degree of satisfaction with expanding research ideas and reflecting the advances in this discipline, but with no significant difference. There was a significant increase in the number of published articles. Conclusion:For the active implementation of the course of "Reproductive Genetics in Obstetrics and Gynecology", improving the teaching process in a planned and step-by-step way through a top-level design in advance can help to expand research ideas for future research work among postgraduates in obstetrics and gynecology and promote the sustainable development and improvement of the teaching of the emerging interdisciplinary discipline of reproductive genetics in obstetrics and gynecology.

【Objective】 To investigate the resource allocation status of blood testing laboratories in 14 blood stations in Gansu Province, explore the impact of differences in basic conditions on the comprehensive testing ability of laboratories, so as to promote the homogenization and standardization of blood screening capacity in blood stations in Gansu and improve blood safety and effectivenes. 【Methods】 An evaluation index system of laboratory resource allocation was constructed and a question-naire was designed. The data of human resources, infrastructure and key equipment of 14 blood stations were collected. The entropy weight -TOPSIS method was used to evaluate and rank the resource allocation of 14 blood stations. 【Results】 In the comprehensive evaluation of blood testing laboratory resource allocation in 14 blood stations in Gansu, the top three were laboratories A, B and I, and the last three were laboratories G, M and J. On the whole, the main issue was unreasonable structure of human resources: most laboratories had unreasonable age structure; except for Laboratory A, there was no personnel with bachelor's degree or above in laboratories; most laboratories had not established a team with intermediate professional titles. In terms of infrastructure, the size of seven laboratories could not meet the needs of modern laboratory testing, and all eight blood stations had no spare nucleic acid laboratories nor a mutual spare laboratory with other blood stations As for the key equipment, 5 laboratories had no automatic blood grouping diagnostic instrument, 5 laboratories only had one set of enzyme immunoassay detection system, 3 laboratories had no spare equipment for the key equipment, which means if the equipment failure could not be repaired in time, the release of results would be affected. 【Conclusion】 There were significant differences in human resources, infrastructure and key equipment of blood testing laboratories in 14 blood stations in Gansu, which had a great impact on laboratory testing capacity and subsequent development. It is suggested that governments at all levels and health administrative departments optimize the input of laboratory resource allocation according to the blood collection volume of blood stations to gradually narrow the differences in resource distribution between different regions, improve the degree of laboratory automation and optimize the personnel structure, so as to build high-quality and efficient blood testing laboratories and ensure the safety of clinical blood use.

Objective:To explore the predictive effect of social support degree on the development trajectory of sleep disorder in patients with peritoneal dialysis after catheterization.Methods:A total of 281 patients who received peritoneal catheterization in Xinxiang Central Hospital from May 2021 to October 2022 were selected by the convenient sampling method. Sleep quality (PSQI) was assessed by using Pittsburgh Sleep Quality Index (PSQI) at the time of enrollment (T 0) and on day 3 (D 3) , day 7 (D 7) , day 14 (D 14) , day 21 (D 21) and day 28 (D 28) after peritoneal catheterization, and then the development trajectory of sleep disorders was analyzed by the latent class growth model. After assessed by Perceived Social Support Scale (PSSS) , patients were divided into low social support group and high social support group, and the differences in the development trajectory distribution of sleep disorders of patients were compared between two groups. Results:There were 134 patients (47.69%) in the low social support group and 147 patients (52.31%) in the high social support group. Three potential categories of sleep disorder development trajectories were identified, including 78 cases without sleep disorder (27.76%) , 79 cases with improved sleep disorder (28.11%) and 124 cases with persistent sleep disorder (44.13%) . The trajectories showed the lowest PSQI score at D 3, followed by D 7 and T 0. In the low social support group, there were 30 cases without sleep disorders (22.39%) , 34 cases with improved sleep disorders (25.37%) and 70 cases with persistent sleep disorders (52.24%) . In the high social support group, there were 48 cases without sleep disorders (32.65%) , 45 cases with improved sleep disorders (30.61%) and 54 cases with persistent sleep disorders (36.73%) . There was a statistically significant difference in the distribution of sleep disorder trajectories between the two groups of patients ( P<0.05) . Conclusions:Sleep disorders are common in patients undergoing peritoneal dialysis after catheterization, with an incidence rate of 72.24%, which is more obvious within one week after surgery. Patients with low social support have lower overall sleep quality and were more likely to have persistent sleep disorders.

Objective:To explore the correlation between socioeconomic status (SES) and diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D).Methods:A total of 276 T2D patients admitted to the Second Affiliated Hospital of Nanjing Medical University from January to June 2020 were enrolled in this cross-sectional study. The estimated glomerular filtration rate (eGFR) was calculated according to the urinary albumin/creatinine ratio (UACR) and the chronic kidney disease epidemiology collaboration equation(CKD-EPI formula) based on serum creatinine. The patients were divided into simple T2D group (184 cases) and DKD group (92 cases). Collect demographic and laboratory examination data, record education, income and occupation, and calculate standardized SES scores. According to SES scores, subjects were divided in three levels: SES≤9, SES≥10-≤12, and SES≥13. Student's t test was used for comparison of measurement data with normal distribution between two groups, and one-way ANOVA was used for comparison between multiple groups. Non-normal distribution was represented by M( Q1, Q3), and rank-sum test was used for comparison between groups. Counting data were expressed as frequency or percentage, and chi-square test was used for comparison between groups. Bofferoni test was further used for pairwise comparison of indicators with statistical significance among multiple groups. Spearman correlation analysis was used to analyze the correlation between variables. The risk factors were analyzed by binary Logistic regression. Results:The age of the subjects was (53.37±10.68) years, men accounted for 55.8% (154/276), the duration of diabetes was 60.00 (12.00, 134.00) months, and eGFR was (97.56±21.15) mL/(min·1.73 m 2). In simple T2D group and DKD group, prevalence of hypertension were 39.7% (73/184) and 57.6% (53/92), systolic blood pressure were (129.43±14.92) mmHg and (139.29±17.61) mmHg, diastolic blood pressure were (81.86±10.06) mmHg and (87.74±11.19) mmHg, serum albumin were (45.74±4.15) g/L and (43.99±5.05) g/L, triglycerides were (1.82±1.24) mmol/L and (2.64±2.92) mmol/L, high density lipoprotein cholesterol were (1.17±0.37) mmol/L and (1.07±0.26) mmol/L, serum uric acid were (298.44±90.73) μmol/L and (336.22±94.01) μmol/L, serum creatinine were (62.83±14.45) μmol/L and (87.75±57.37) μmol/L, eGFR were (102.6±14.28) mL/(min·1.73 m 2) and (87.47±28.04) mL/(min·1.73 m 2), UACR were (7.60 (4.63, 13.15)) mg/g and (93.95 (47.25, 310.25)) mg/g. Prevalence of hypertension, systolic blood pressure, diastolic blood pressure, triglycerides, serum uric acid, serum creatinine, UACR in DKD group were higher than those in simple T2D group. Serum albumin, high density lipoprotein cholesterol and eGFR in DKD group were lower than those in simple T2D group. There was significant difference between the two groups ( χ2=7.95, t values were 4.87, 4.40, 3.04, 3.26, 2.30, 3.22, 5.56, 5.95, Z=13.07, P values were 0.005, <0.001, <0.001, 0.003, 0.001, 0.022, 0.001, <0.001, <0.001, and <0.001, respectively). The number of males in the three groups with SES ≥13 group, SES≥10-≤12 group, SES ≤9 group were 61 (81.3%, 61/75), 55 (59.8%, 55/92), 38 (34.9%, 38/109), respectively. The number of cases with smoking history were 42 (56.0%, 42/75), 41 (44.6%, 41/92), 35 (32.1%, 35/109), respectively. The number of cases with drinking history were 38 (50.7%, 38/75), 32 (34.8%, 32/92), 26 (23.9%, 26/109), respectively. The ages were (47.77±10.76), (52.76±11.22), (57.74±7.96) years old, respectively. Body mass index (BMI) were (26.17±3.87), (24.96±3.93), (24.27±4.89) kg/m 2, respectively. High density lipoprotein cholesterol (HDL) were (1.03±1.03), (1.16±0.41), (1.21±0.32) mmol/L, respectively. Serum uric acid were (336.56±82.05), (293.78±94.78), (307.99±96.53) μmol/L, respectively. EGFR were (105.03±19.72), (99.77±19.44), (90.57±21.49) mL/(min·1.73 m 2),respectively.The difference between groups were statistically significant (χ 2=39.79, 10.55, 14.08, F=22.69, 4.03, 6.20, 4.53, 12.02, P values were <0.001, 0.005, 0.001, <0.001, 0.019, 0.002, 0.012, and <0.001, respectively). Pairwise comparison shows that male and eGFR in SES ≤9 group were lower than those in SES ≥13 group and SES≥10-≤12 group, age in SES ≤9 group was higher than that in SES ≥13 group and SES≥10-≤12 group. The difference was statistically significant (all P<0.05). Smoking history, alcohol history and BMI in SES ≤9 group were lower than those in SES ≥13 group, and the high density lipoprotein cholesterol in SES ≤9 were higher than that in SES ≥13 group. The difference was statistically significant (all P<0.05). Male, alcohol history and serum uric acid in SES≥10-≤12 group were lower than those in SES ≥13 group, and age and high density lipoprotein cholesterol in SES≥10-≤12 group were higher than those in SES ≥13 group. The difference was statistically significant (all P<0.05). Spearman correlation analysis showed that SES in T2D was positively correlated with male, smoking history, alcohol history, BMI, serum uric acid and eGFR ( r values were 0.38, 0.20, 0.24, 0.16, 0.13 and 0.31, P values were <0.001, 0.001, <0.001, 0.008, 0.028, and <0.001, respectively), and negatively correlated with age, high density lipoprotein cholesterol and UACR ( r values were -0.35, -0.24 and -0.14, P values were <0.001, <0.001, and 0.017, respectively). Logistic regression analysis showed that SES (OR=2.71,95% CI:1.10-6.68, P=0.031) was associated with T2DM combined with DKD. The risk of developing DKD increased when the SES was ≤9. Conclusion:The SES in patients with type 2 diabetes is closely related to DKD. Low SES may be a new risk factor for DKD in type 2 diabetic patients.