Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Objective To understand the relationship between sleep status and cognitive function in middle-aged and elderly peo-ple in China,as well as the mediating effect of depression.Methods Selecting middle-aged and elderly people aged 45 years and above from the 2020 China Health and Retirement Longitudinal Survey(CDHARLS),K-means clustering method was used to divide cognitive function into high score group and low score group.Logistic regression analysis was used to analyze the influencing factors of cognitive function,and a restricted cubic spline model was used to explore the dose-response relationship between sleep and cognitive function.A structural equation model was constructed to analyze the mediating effect of depression between sleep status and cognitive function.Results Among 7964 middle-aged and elderly people,the cognitive function score was 13.49±2.99 points,the depression detection rate was 30.8％.After controlling for other confounding factors,the probability of cognitive decline in middle-aged and elderly people with a nighttime sleep duration of＞8hours was 1.4 times that of a nighttime sleep duration of 6-8hours,and the probability of cognitive decline in middle-aged and elderly people with a nap duration of 0-30minutes and≥90minutes was 1.20 times and 1.39 times that of a nap du-ration of 30-60minutes.The probability of cognitive decline in middle-aged and elderly people with depression was 1.52 times that of the non-depression group.The restricted cubic spline model showed that there was a U-shaped and J-shaped correlation between nighttime and afternoon sleep duration and the risk of cognitive decline.The results of the structural equation model showed that sleep sta-tus had a direct effect on cognitive function(β=-0.278,95％CI:-0.376--0.186),while depression has an indirect effect be-tween sleep status and cognitive function(β=0.152,95％CI:0.111-0.201).Conclusion The sleep status,depression,and cogni-tive function of middle-aged and elderly people in China are closely related,and depression mediates the relationship between sleep sta-tus and cognitive decline a certain extent.

Objective To understand the relationship between sleep status and cognitive function in middle-aged and elderly peo-ple in China,as well as the mediating effect of depression.Methods Selecting middle-aged and elderly people aged 45 years and above from the 2020 China Health and Retirement Longitudinal Survey(CDHARLS),K-means clustering method was used to divide cognitive function into high score group and low score group.Logistic regression analysis was used to analyze the influencing factors of cognitive function,and a restricted cubic spline model was used to explore the dose-response relationship between sleep and cognitive function.A structural equation model was constructed to analyze the mediating effect of depression between sleep status and cognitive function.Results Among 7964 middle-aged and elderly people,the cognitive function score was 13.49±2.99 points,the depression detection rate was 30.8％.After controlling for other confounding factors,the probability of cognitive decline in middle-aged and elderly people with a nighttime sleep duration of＞8hours was 1.4 times that of a nighttime sleep duration of 6-8hours,and the probability of cognitive decline in middle-aged and elderly people with a nap duration of 0-30minutes and≥90minutes was 1.20 times and 1.39 times that of a nap du-ration of 30-60minutes.The probability of cognitive decline in middle-aged and elderly people with depression was 1.52 times that of the non-depression group.The restricted cubic spline model showed that there was a U-shaped and J-shaped correlation between nighttime and afternoon sleep duration and the risk of cognitive decline.The results of the structural equation model showed that sleep sta-tus had a direct effect on cognitive function(β=-0.278,95％CI:-0.376--0.186),while depression has an indirect effect be-tween sleep status and cognitive function(β=0.152,95％CI:0.111-0.201).Conclusion The sleep status,depression,and cogni-tive function of middle-aged and elderly people in China are closely related,and depression mediates the relationship between sleep sta-tus and cognitive decline a certain extent.

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Background The population with diabetes in China is increasing year by year. Current research has found that either air pollution or temperature has an impact on the occurrence and development of diabetes, but the interaction between the two is unclear yet. Objective To investigate the effects and the lag effects of air pollutants and temperature on the risk of hospital admission for type 2 diabetes in Hefei, Anhui Province from 2016 to 2019, as well as to verify potential interaction between air pollutants and temperature. Methods This study collected hospital admission data for patients with type 2 diabetes from a tertiary hospital in Hefei, Anhui Province, and the corresponding monitoring data on air pollutants and meteorological factors from 2016 to 2019. Firstly, a distributed lag non-linear model (DLNM) was used to explore the effects of air pollutants and temperature on the risk of hospital admission for type 2 diabetes. Subsequently, a bivariate response surface model was used to explore potential interaction between temperature and various pollutants on frequency of hospital admission due to diabetes. Temperature was further divided into lower, medium, and higher levels by percentiles during the study period, and the potential interaction between air pollutants and temperature strata were verified . Results After controlling long-term trend, seasonal trend, holiday effect, and day of the week effect, the results of single pollutant models showed that for every 10 μg·m⁻³ increase in fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂), the relative risk (RR) values for hospital admission due to type 2 diabetes were 1.032 (95%CI: 1.021, 1.043), 1.018 (95%CI: 1.008, 1.026), and 1.037 (95%CI: 1.016, 1.058), respectively; for every 1 mg·m⁻³ increase in carbon monoxide (CO), the RR value for hospital admission due to type 2 diabetes was 1.319 (95%CI: 1.163, 1.495); the increases in sulfur dioxide (SO₂), ozone (O₃), and daily average temperature showed no statistically significant impact on hospital admission due to type 2 diabetes. The results of bivariate response surface models suggested that daily average temperature and various pollutant levels spontaneously affected the risk of hospital admission for type 2 diabetes, but the stratified analysis did not find significant differences in the effect of PM_2.5 on the risk of hospital admission due to type 2 diabetes across different temperature strata. Conclusion Increases in the concentrations of PM_2.5, PM₁₀, NO₂, and CO elevate the risk of hospital admission for type 2 diabetes. This study could not confirm the interactions between daily average temperature and various pollutants.

Objective To explore the expression level of OTUB1 and its clinical significance in breast cancer.Methods Immunohistochemistry was used to detect the expression level of OTUB1 in 78 cases of breast cancer tissues and 30 cases of normal breast tissue adjacent to carcinoma,and the relationships between OTUB1 and the clinical pathological features of breast cancer were analyzed.Results The positive expression rate of OTUB1 in breast cancer tissues [66.7％ (52/78)] was significantly higher than that in adjacent normal breast tissues [30.0％ (9/30)],with a statistically significant difference (x2 =11.851,P =0.001).OTUB1 expression level was related to the lymph node metastasis (x2 =5.029,P =0.025),postoperative TNM staging (x2 =4.478,P =0.034),expression of human epidermal growth factor receptor-2 (HER-2) (x2 =8.775,P =0.003),expression of P53 (x2 =4.708,P =0.030),expression of estrogen receptor (ER) (x2 =10.364,P =0.001) and molecular subtypes (x2 =10.934,P =0.012).However,OTUB1 expression level in breast cancer was not related to the age (x2 =2.194,P =0.139),menopausal status (x2 =1.843,P =0.175),tumor size (x2 =0.643,P =0.423),histological grade (x2 =3.580,P =0.167),expression of progestin receptor (PR) (x2 =3.371,P =0.066) and expression of Ki-67 (x2 =1.345,P =0.246).Conclusion OTUB1 expression level increases in breast cancer,which is associated with the lymph node metastasis,TNM staging,expressions of HER-2,P53,ER and molecular subtypes of breast cancer.It suggests that the expression of OTUB1 is related to the progression and metastasis of breast cancer.

Objective: To analyze factors influencing antiretroviral therapy (ART) adherence among human immunodeficiency virus (HIV) patients receiving ART at the town level in Ili Kazakh Autonomous Prefecture (Ili) in May 2015 and to document enhanced ART for acquired immunodeficiency syndrome (AIDS) cases. Methods: A cross-sectional survey was conducted using one-on-one interviews and data collection from the system of AIDS follow-up management in three ART services centers at the town level of Ili. The subjects were HIV-infected individuals, aged 18 years or older, who were receiving ART during the survey. The surveys collected demographic characteristics, information related to ART and status of engaging ART, smoking and drinking behavior, depression, and quality of life. Results: A total of 412 participants completed the survey. The age was (41.1±8.0) years (range, 19-67 years). Approximately 60.9% (251) were male and 39.1% (161) were female. The survey showed that 75.0% (309) of participants were in good adherence and the P₅₀ (P₂₅, P₇₅) of quality of life was 56.31 (50.55, 59.42). Females demonstrated better adherence to ART (82.6% (n=133)) than males (70.1% (n=76)) (χ²=8.16, P=0.005). The compliance rate of participants (78.0% (n=54)) with depression was higher than non-depressed participants (63.5% (n=255)) (χ²=7.52, P=0.008). Multivariate logistic regression analyses showed that the probability of good adherence to ART increased with increasing quality of life (OR=1.06, 95%CI:1.02-1.09). Moreover, participants who consumed alcohol or disclosed their HIV infection status to families were less likely to have good adherence to ART (OR=0.26, 95% CI:0.13-0.53 and OR=0.31, 95% CI:0.13-0.72, respectively). Additionally, employed participants were also less likely to have good adherence to ART compared with unemployed participants (OR=0.45, 95% CI:0.21-0.97). Conclusion HIV/AIDS patients primarily showed good adherence to ART. Factors related to ART adherence included alcohol consumption, informing family of infection, work status, and quality of life.

OBJECTIVEThis study aimed to investigate the status of multiple sexual partners and unprotected sexual behaviors and related influencing factors among HIV-positive men who have sex with men (MSM).

METHODSHIV-positive men having sex with men aged 18 years or older, living in Chengdu, Chongqing or Guangzhou were recruited by using the " snowballing" sampling method. Participants completed the questionnaire on computers, after filling in the Informed Consent Form. Content of the study would include social demographic characteristics, number of sexual partners, sexual behaviors, and the symptoms assessment on depression and anxiety.χ(2)-test,t-test and non-conditional Multiple logistic Regression methods were used to examine the risky sexual behaviors with multiple sexual partners among the participants engaged in this project.

RESULTSMean age of the 501 participants was (30.24±7.70) years old. In the past 6 months, 17.4% (87/501) of them had engaged in unprotected sexual behavior with two or more sexual partners. Factors at risk would include: being married (OR=1.93, 95%CI: 0.77-4.84), divorced or widowed (OR=3.94, 95%CI: 1.66-9.36), having primary male sexual partners (OR=5.04, 95%CI: 1.08-23.54) and casual or commercial male sexual partners (OR=2.54, 95%CI: 1.34-4.80) in the past 6 months, drinking alcohol (OR=3.00, 95%CI: 1.37-6.62) or Rush (alkyl nitrite) (OR=3.53, 95%CI: 1.72-7.23) during sexual acts, sharing their HIV-infection status to their partly primary male sexual partners (OR=1.84, 95%CI:0.78-4.33) or not (OR=2.68, 95% CI: 1.25-5.73), and having high sexual sensation seeking scores (OR=1.09, 95%CI: 1.03-1.15).

CONCLUSIONSUnprotected sexual behaviors with multiple sexual partners among HIV-positive MSM played an important role in expediting the HIV transmission. Development of intervention programs to minimize the risk sexual behaviors and setting up efficient medical and biological measures in controlling the HIV transmission were in urgent need.

Adult ; Coitus ; Depression ; Depressive Disorder ; Family Characteristics ; Homosexuality, Male ; psychology ; statistics & numerical data ; Humans ; Infection ; epidemiology ; transmission ; Male ; Marriage ; Middle Aged ; Risk ; Risk-Taking ; Sexual Behavior ; Sexual Partners ; Surveys and Questionnaires ; Unsafe Sex ; psychology ; statistics & numerical data ; Young Adult

Objective To screen the sensing elements for TNT detection in Escherichia coli genome.Methods A genome promoter library with cutting E.coli K-12 MG1655 genome was constructed.Bacterial luciferase luxCDABE was used as a reporter gene during promoter screening.We discovered TNT sensing elements through several rounds of screen-ing.Through analysis of sensitivity, specificity and timeliness, the promoter activity of the elements was evaluated,and the functional sequence of the elements was further confirmed.Results and Conclusion We successfully constructed an E.co-li K-12 MG1655 genome library , from which a TNT sensing element was discovered,which had a good performance in the analysis of sensitivity, specificity and timeliness.In this study, we reported that the topAp4 is a TNT sensing element for the first time.We also verified its excellent promoter activity.