Objective:To investigate the association between social jetlag and depressive symptoms among college students, as well as its potential influencing factors.Methods:A cross-sectional study was conducted through an online questionnaire platform (Wenjuanxing) from March to April 2023, collecting data on social jetlag, depressive symptoms, and other factors from students at Shantou University. Social jetlag time was defined as the absolute difference between the midpoint of sleep time on weekends and weekdays, with a cutoff at the 75th percentile. The presence of social jetlag was defined as social jetlag time≥1 hour. Depressive symptoms were assessed using the Beck Depression Inventory (BDI), with a score of≥10 indicating the presence of depressive symptoms. Participants were divided into depressive symptom group (BDI≥10) and non-depressive symptom group (BDI<10). Linear regression and logistic regression models were used to analyze the relationship between social jetlag and depressive symptoms, with interaction terms and subgroup analyses to explore potential influencing factors.Results:A total of 1 323 college students were included. The social jetlag time (median 0.71 hour vs. 0.50 hour, Z=-3.36, P<0.001) and prevalence of social jetlag (37.64% vs. 30.57%, χ2=7.03, P=0.008) were both higher in the depressive symptom group than in the non-depressive symptom group. The linear regression model showed that each additional hour of social jetlag was associated with an increase of 0.67 points in BDI score (95% CI=0.16-1.18, β=0.06, P=0.010), after adjusting for age, gender, body mass index, being a medical student, smoking, drinking, caffeine intake, physical exercise, anxiety symptoms, insomnia symptoms, and sleep duration. The logistic regression model indicated that social jetlag was a risk factor for depressive symptoms (O R=1.34, 95% CI=1.02-1.76, P=0.036), which was moderated by physical exercise (interaction P=0.033). Among participants without physical exercise, social jetlag was associated with depressive symptoms ( OR=1.71, 95% CI=1.18-2.48, P=0.005), while no such association was found among those with physical exercise ( OR=0.97, 95% CI=0.64-1.47, P=0.892). Conclusion:Social jetlag may be associated with depressive symptoms in college students. This adverse relationship may be improved by enhancing physical exercise.

Objective:To investigate the association between social jetlag and depressive symptoms among college students, as well as its potential influencing factors.Methods:A cross-sectional study was conducted through an online questionnaire platform (Wenjuanxing) from March to April 2023, collecting data on social jetlag, depressive symptoms, and other factors from students at Shantou University. Social jetlag time was defined as the absolute difference between the midpoint of sleep time on weekends and weekdays, with a cutoff at the 75th percentile. The presence of social jetlag was defined as social jetlag time≥1 hour. Depressive symptoms were assessed using the Beck Depression Inventory (BDI), with a score of≥10 indicating the presence of depressive symptoms. Participants were divided into depressive symptom group (BDI≥10) and non-depressive symptom group (BDI<10). Linear regression and logistic regression models were used to analyze the relationship between social jetlag and depressive symptoms, with interaction terms and subgroup analyses to explore potential influencing factors.Results:A total of 1 323 college students were included. The social jetlag time (median 0.71 hour vs. 0.50 hour, Z=-3.36, P<0.001) and prevalence of social jetlag (37.64% vs. 30.57%, χ2=7.03, P=0.008) were both higher in the depressive symptom group than in the non-depressive symptom group. The linear regression model showed that each additional hour of social jetlag was associated with an increase of 0.67 points in BDI score (95% CI=0.16-1.18, β=0.06, P=0.010), after adjusting for age, gender, body mass index, being a medical student, smoking, drinking, caffeine intake, physical exercise, anxiety symptoms, insomnia symptoms, and sleep duration. The logistic regression model indicated that social jetlag was a risk factor for depressive symptoms (O R=1.34, 95% CI=1.02-1.76, P=0.036), which was moderated by physical exercise (interaction P=0.033). Among participants without physical exercise, social jetlag was associated with depressive symptoms ( OR=1.71, 95% CI=1.18-2.48, P=0.005), while no such association was found among those with physical exercise ( OR=0.97, 95% CI=0.64-1.47, P=0.892). Conclusion:Social jetlag may be associated with depressive symptoms in college students. This adverse relationship may be improved by enhancing physical exercise.

OBJECTIVE: To explore the risk factors of acute respiratory distress syndrome (ARDS) in patients with sepsis and to construct a risk nomogram model. METHODS: The clinical data of 234 sepsis patients admitted to the intensive care unit (ICU) of Tianjin Hospital from January 2019 to May 2022 were retrospectively analyzed. The patients were divided into non-ARDS group (156 cases) and ARDS group (78 cases) according to the presence or absence of ARDS. The gender, age, hypertension, diabetes, coronary heart disease, smoking history, history of alcoholism, temperature, respiratory rate (RR), mean arterial pressure (MAP), pulmonary infection, white blood cell count (WBC), hemoglobin (Hb), platelet count (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer, oxygenation index (PaO₂/FiO₂), lactic acid (Lac), procalcitonin (PCT), brain natriuretic peptide (BNP), albumin (ALB), blood urea nitrogen (BUN), serum creatinine (SCr), acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA) were compared between the two groups. Univariate and multivariate Logistic regression were used to analyze the risk factors of sepsis related ARDS. Based on the screened independent risk factors, a nomogram prediction model was constructed, and Bootstrap method was used for internal verification. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the ROC curve (AUC) was calculated to verify the prediction and accuracy of the model. RESULTS: There were no significant differences in gender, age, hypertension, diabetes, coronary heart disease, smoking history, alcoholism history, temperature, WBC, Hb, PLT, PT, APTT, FIB, PCT, BNP and SCr between the two groups. There were significant differences in RR, MAP, pulmonary infection, D-dimer, PaO₂/FiO₂, Lac, ALB, BUN, APACHE II score and SOFA score (all P < 0.05). Multivariate Logistic regression analysis showed that increased RR, low MAP, pulmonary infection, high Lac and high APACHE II score were independent risk factors for sepsis related ARDS [RR: odds ratio (OR) = 1.167, 95% confidence interval (95%CI) was 1.019-1.336; MAP: OR = 0.962, 95%CI was 0.932-0.994; pulmonary infection: OR = 0.428, 95%CI was 0.189-0.966; Lac: OR = 1.684, 95%CI was 1.036-2.735; APACHE II score: OR = 1.577, 95%CI was 1.202-2.067; all P < 0.05]. Based on the above independent risk factors, a risk nomograph model was established to predict sepsis related ARDS (accuracy was 81.62%, sensitivity was 66.67%, specificity was 89.10%). The predicted values were basically consistent with the measured values, and the AUC was 0.866 (95%CI was 0.819-0.914). CONCLUSIONS Increased RR, low MAP, pulmonary infection, high Lac and high APACHE II score are independent risk factors for sepsis related ARDS. Establishment of a risk nomograph model based on these factors may guide to predict the risk of ARDS in sepsis patients.
Humans ; Retrospective Studies ; Alcoholism ; Prognosis ; Respiratory Distress Syndrome ; Pneumonia ; Sepsis ; Intensive Care Units ; Procalcitonin ; Fibrinogen ; ROC Curve

Objective:To explore the current status of gastrointestinal complications in patients after cardiac surgery and analyze its influencing factors.Methods:This study was cross-sectional study. From October 2019 to April 2020, convenience sampling was used to select 152 patients who underwent cardiac surgery in the Department of Adult Cardiac Surgery of a ClassⅢ Grade A hospital in Henan Province as the research object. We collected and recorded whether patients had gastrointestinal complications and its related factors after surgery. Binary Logistics multivariate analysis was used to explore the influencing factors of gastrointestinal complications.Results:The incidence of gastrointestinal complications in patients after cardiac surgery was 9.9% (15/152) . Logistic regression analysis showed that the influencing factor of gastrointestinal complications included the patient's preoperative left ventricular ejection fraction ( OR=0.849) , cardiopulmonary bypass time ( OR=0.974) , aortic block time ( OR=1.056) , intraoperative blood loss ( OR=1.007) , intraoperative blood transfusion ( OR=0.996) , time in Intensive Care Unit ( OR=1.463) , type of surgery [coronary artery bypass grafting (CABG) OR=0.199, valve replacement/valvuloplasty OR=0.105, CABG+ valve replacement/valvuloplasty OR=0.006] ( P<0.05) . Conclusions:There are many factors influencing gastrointestinal complications after cardiac surgery. Medical and nursing staff should strengthen the assessment of risk factors and perform predictive care in order to minimize the occurrence of complications and improve the prognosis of patients.

OBJECTIVETo report on a novel HLA-A allele, A*29:49, identified in a Chinese Han population by sequence based typing (SBT).

METHODSA donor from China Marrow Donor Programme (CMDP) was typed with a bi-allelic PCR-SBT kit, and no full matched result was obtained for the HLA-A locus. The novel HLA allele was verified with an allele-specific amplification SBT kit.

RESULTSA novel HLA-A allele was identified, which has differed by one nucleotide from the closest matched allele, HLA-A*29:01:01:01, at position 368(A→T), codon 99 (TAT→TTT), resulting in an amino acid substitution (Y→F). Another allele was verified as A*02:06:01.

CONCLUSIONA novel HLA-A allele was identified and officially named as HLA-A*29:49 by the WHO Nomenclature Committee for Factors of the HLA System.

Alleles ; Amino Acid Substitution ; genetics ; Base Sequence ; China ; HLA-A Antigens ; genetics ; Humans ; Sequence Analysis, DNA ; methods