Objective To investigate the current status of symptom burden and psychological distress among lung cancer patients in the diagnostic phase, and to explore the chain mediating role of social support and resilience between symptom burden and psychological distress. Methods The patients with lung cancer in the diagnostic phase who were treated in the Department of Thoracic Surgery of the Second Xiangya Hospital of Central South University from October 2022 to June 2023 were investigated by a general information questionnaire using the MD Anderson Symptom Inventory, the Social Support Rating Scale, the Connor-Davidson Resilience Scale, and the Distress Thermometer. The chain mediating role of social support and resilience between symptom burden and psychological distress was analyzed. Results A total of 413 lung cancer patients were enrolled, including 173 males and 240 females, aged (54.69±10.82) years. The detection rate of psychological distress among lung cancer patients in the diagnostic phase was 48.18%, and the average score was (3.84±2.50) points. Psychological distress was positively correlated with symptom burden (P<0.01), and negatively correlated with social support and resilience (P<0.01). The mediating effect of resilience between symptom burden and psychological distress was significant. The chain mediating effect of social support and resilience between symptom burden and psychological distress was also significant. Conclusion Lung cancer patients in the diagnostic phase have a high detection rate of psychological distress. Symptom burden can directly impact psychological distress, and can affect psychological distress through the indirect path of resilience as well as the chain mediating path between social support and resilience among lung cancer patients in the diagnostic phase.

Objective: To compare the intervention effects of sitagliptin combined with metformin and insulin aspart combined with metformin among patients with type 2 diabetes mellitus (T2DM), so as to provide the reference for optimizing blood glucose control strategies among patients with T2DM. Methods: T2DM patients admitted to the department of endocrinology of Jinhua Central Hospital from January 2018 to December 2024 were selected as the research objects. According to the propensity score matching, T2DM patients were divided into sitagliptin combined with metformin group and insulin aspart combined with metformin group at a ratio of 1∶1. The basic information, capillary blood glucose, glycosylated hemoglobin blood glucose (HbA1c), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), triglyceride (TG), body mass index (BMI), systolic blood pressure (SBP) and diastolic blood pressure (DBP) of the two groups were collected by questionnaire survey, physical examination and laboratory test. The generalized estimating equation was used to analyze the changes of various indicators before and after treatment between the two groups of patients, and the intervention effect was compared. Results: There were 69 cases in the sitagliptin combined with metformin group. Among these 42 cases were males, accounting for 60.87%, and 27 cases were females, accounting for 39.13%.Forty-two cases were younger than 60 years, accounting for 60.87%. There were 69 cases in the insulin aspart combined with metformin group. Among these 47 cases were males, accounting for 68.12%, and 22 cases were females, accounting for 31.88%. Forty-five cases were younger than 60 years, accounting for 65.22%. There were no statistically significant differences in gender, age, education level, smoking, drinking, vegetable and fruit intake, and disease duration between the two groups (all P>0.05). After 6 months of treatment, there was an interaction between group and time in capillary blood glucose and HbA1c in the two groups (all P<0.05), and the reduction of capillary blood glucose and HbA1c in the sitagliptin combined with metformin group was greater than that in the insulin aspart combined with metformin group. There were no statistically significant differences between groups and time in TC, HDL-C, TG, BMI, SBP, and DBP, and no interaction effect was found between groups and time (all P>0.05). Conclusion Sitagliptin combined with metformin is more effective than insulin aspart combined with metformin in controlling blood glucose among patients with T2DM.

BACKGROUND: Neuroticism has been associated with numerous health outcomes. However, most research has focused on a single specific disorder and has produced controversial results, particularly regarding mortality risk. Here, we aimed to examine the association of neuroticism with morbidity and mortality and to elucidate how neuroticism affects trajectories from a healthy state, to one or more neuroticism-related disorders, and subsequent mortality risk. METHODS: We included 483,916 participants from the UK Biobank at baseline (2006-2010). Neuroticism was measured using the Eysenck Personality Questionnaire. Three clusters were constructed, including worry, depressed affect, and sensitivity to environmental stress and adversity (SESA). Cox proportional hazards regression and multistate models were used. Linear regression was used to examine the association between neuroticism and immune parameters and neuroimaging measures. RESULTS: High neuroticism was associated with 37 non-overlapping diseases, including increased risk of infectious, cardiometabolic, neuropsychiatric, digestive, and respiratory diseases, and decreased risk of cancer. After adjustment for sociodemographic variables, physical measures, healthy behaviors, and baseline diagnoses, moderate-to-high neuroticism was associated with a decreased risk of all-cause mortality. In multistate models, high neuroticism was associated with an increased risk of transitions from a healthy state to a first neuroticism-related disease (hazard ratio [HR] [95% confidence interval (CI)] = 1.09 [1.05-1.13], P  <0.001) and subsequent transitions to multimorbidity (1.08 [1.02-1.14], P  = 0.005), but was associated with a decreased risk of transitions from multimorbidity to death (0.90 [0.84-0.97], P for trend = 0.006). The leading neuroticism cluster showing a detrimental role in the health-illness transition was depressed affect, which correlated with higher amygdala volume and lower insula volume. The protective effect of neuroticism against mortality was mainly contributed by the SESA cluster, which, unlike the other two clusters, did not affect the balance between innate and adaptive immunity. CONCLUSION This study provides new insights into the differential role of neuroticism in health outcomes and into new perspectives for establishing mortality prevention programs for patients with multimorbidity.
Humans ; Neuroticism/physiology* ; Male ; Female ; Middle Aged ; Aged ; Proportional Hazards Models ; Surveys and Questionnaires ; Adult ; Risk Factors

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective To investigate the impact of social psychological factors on adherence among subjects with chronic diseases during the early phase of new drug clinical trials and propose strategies to optimize trial outcomes.Methods Between December 2020 to December 2023,observational datas were collected from subjects participating in new drug clinical trials in the Frist Hospital of Jiaxing,including customized sociodemographic informations,Eysenck personality questionnaire(EPQ)survey results,symptom checklist 90(SCL-90)scores,Beck anxiety inventory(BAI)scores,Beck depression inventory(BDI)scores.The SCL-90 was further categorized into ten factor scores,and the EPQ was evaluated based on four dimensional standard T-scores.Univariate and multivariate Logistic regression analyses were conducted to identify the related factors.Results Univariate analyses showed that gender,willingness to go out,EPQ_T neuroticism,SCL-90,SCL somatic,SCL obsessive compulsive,SCL interpersonal,SCL depression,SCL anxiety,SCL psychoticism,SCL other,BAI scores,and BDI scores were associated with adherence in subjects with chronic diseases.Multivariate analysis confirmed that a higher willingness to go out,elevated BAI and BDI scores were positively associated with non-adherence risk,whereas an increase in the SCL-90 somatization factor scores were inversely related to adherence risk in subjects with chronic diseases.Conclusion Identifying and managing anxiety and depression among subjects with chronic diseases,as well as understanding their outdoor plans,are crucial for enhancing adherence during the early stages of new drug clinical trials.In certain instances,subjects with chronic diseases heightened awareness of bodily discomfort may paradoxically promote adherence.

Objective To investigate the expression levels of selenoprotein genes in the patients with coronavirus disease 2019(COVID-19)and the possible regulatory mechanisms.Methods The dataset GSE177477 was obtained from the Gene Expression Omnibus,consisting of a symptomatic group(n=11),an asymptomatic group(n=18),and a healthy control group(n=18).The dataset was preprocessed to screen the differentially expressed genes(DEG)related to COVID-19,and gene ontology functional annotation and Kyoto encyclopedia of genes and genomes enrichment analysis were performed for the DEGs.The protein-protein interaction network of DEGs was established,and multivariate Logistic regression was employed to analyze the effects of selenoprotein genes on the presence/absence of symptoms in the patients with COVID-19.Results Compared with the healthy control,the symptomatic COVID-19 patients presented up-regulated expression of GPX1,GPX4,GPX6,DIO2,TXNRD1,SELENOF,SELENOK,SELENOS,SELENOT,and SELENOW and down-regulated ex-pression of TXNRD2 and SELENON(all P＜0.05).The asymptomatic patients showcased up-regulated expres-sion of GPX2,SELENOI,SELENOO,SELENOS,SELENOT,and SELENOW and down-regulated expression of SELP(all P＜0.05).The results of multivariate Logistic regression analysis showed that the abnormally high expression of GPX1(OR=0.067,95％CI=0.005-0.904,P=0.042)and SELENON(OR=56.663,95％CI=3.114-856.999,P=0.006)was the risk factor for symptomatic COVID-19,and the abnormally high expres-sion of SELP was a risk factor for asymptomatic COVID-19(OR=15.000,95％CI=2.537-88.701,P=0.003).Conclusions Selenoprotein genes with differential expression are involved in the regulation of COVID-19 development.The findings provide a new reference for the prevention and treatment of COVID-19.

Objective:To investigate the safety of tocilizumab (TCZ) in the treatment of children with systemic juvenile idiopathic arthritis (sJIA).Methods:Data of children aged 2 to 18 years with the diagnosis of sJIA and treated with TCZ from June 1, 2017 to June 30, 2022 at our hospital were retrospectively collected. The clinical medication characteristics, incidence, severity and outcome of adverse drug reactions (ADR) were statistically analyzed. Univariate and multivariate analysis were used to analyze the risk factors of TCZ-induced ADR. Univariate comparison between groups were compared to the measured data followed by t test for normal distribution, and the counting data were paired with Chi-square test. Binary logistic regression analysis was used for multivariate analysis. Results:A total of 83 eligible children were enrolled. The age at TCZ initiation was (8.5±3.7) years old. Most of the children received oral glucocorticoid (86.8%) and/or methotrexate (72.3%) prior to TCZ treatment. The mean time of TCZ duration was (1.2±0.9) years, the total TCZ exposure was 92.70 patient years. Fifty-five (66.3%) children reported 123 ADR, with a rate of 132.69/100 patient years. Forty-two (50.6%) children reported 103 general ADR, with a rate of 111.11/100 patient years. Eighteen (21.7%) children reported 20 serious ADR, with a rate of 21.57/100 patient years. The results of univariate analysis showed that the dosage of glucocorticoid in ADR group was higher than that in non-ADR group [(0.76±0.50) mg·kg -1·d -1vs. (0.52±0.41) mg·kg -1·d -1, t=2.27, P=0.026], and the difference was statistically significant. However, there were no significant differences in gender [(male 23, female 32) cases vs. (male 9, female 19) cases, χ2=0.73, P=0.392], age at TCZ initiation [(8.5±3.8) years old vs. (9.0±3.1) years old, t=-0.65, P=0.516], duration of TCZ treatment [(1.24±1.00) years vs. (1.05±0.90) years, t=0.87, P=0.385], methotrexate doses weekly [(8.0±5.2) mg/m 2vs. (7.6±5.1) mg/m 2, t=0.39, P=0.696], and history of drug or food allergy (11 cases vs. 5 cases, χ2=0.06, P=0.815) between the two groups. The results of binary logistic regression analysis showed that the combined use of oral glucocorticoids was an independent risk factor for TCZ-induced ADR [ OR (95% CI) =3.05 (1.11, 8.36), P=0.030]. The risk of ADR was 3.05 times higher in the combined daily dose of glucocorticoids ≥0.76 mg/kg prednisone equivalent than that of < 0.76 mg/kg. Common general ADR to TCZ include infections (38.83/100 patient years) and abnormalities in laboratory parameters (37.76/100 patient years) such as elevated glutamic-pyrupiane transaminase (18.34/100 patient years), dyslipidemia (12.94/100 patient years), and hemocytopenia (5.39/100 patient years). The serious ADR included serious infection (9.71/100 patient years) and serious infusion reaction(7.55/100 patient years). All ADR were improved after drug withdrawal or symptomatic treatment, and no deaths occurred. Conclusion:TCZ has a good safety profile in the treatment of sJIA. Serious infections and severe infusion reactions often lead to discontinuation of the drug. The combination of glucocorticoids≥0.76 mg/kg prednisone equivalent is an independent risk factor for TCZ-induced ADR. Monitoring should be strengthened during the application of TCZ, and ADR should be detected and treated as early as possible to reduce the risk of medication related adverse reactions.

The survey on the hesitant status of rubella-containing vaccine (RCV) vaccination was conducted among 2 141 women of childbearing age in four provinces, Liaoning, Jiangsu, Zhejiang, and Anhui, from May 2017 to March 2018. Among the 2 141 study participants, a total of 38.21% (818) of women of childbearing age were aware of rubella and 21.25% (455) of women of childbearing age knew about RCV. Among the 455 women of childbearing age who were aware of RCV, 262 (57.58%) were willing to be vaccinated with RCV; 304 women of childbearing age (66.81%) tended to be vaccinated with RCV free of charge. Among the 193 women of childbearing age who did not want to be vaccinated or did not decide whether they preferred to be vaccinated with RCV, no one around them was vaccinated (89, 46.11%) and fear of vaccine safety (70, 36.27%) were the main reasons for hesitation to receive RCV.

Objective To analyze the value of systemic inflammatory indicators in diagnosing schizo-phrenia and their correlation with disease condition severity and cognitive dysfunction.Methods A total of 47 patients with schizophrenia visited and treated in this hospital from October 2021 to March 2023 were selected as the study subjects,and 47 healthy volunteers during the same period served as the control group.The morn-ing fasting venous blood was collected in all research subjects,and the blood routine and biochemical indicators were measured.The SII,SIRI,NHR and LHR were calculated.The receiver operating characteristic(ROC)curve was used to analyze the diagnostic value of SII,SIRI,NHR and LHR in the patients with schizophrenia.The correlation adopted the Pearson correlation analysis.Results The levels of SII,SIRI,NHR and LHR in the schizophrenia group were significantly increased compared with the control group,and the difference was statistically significant(P＜0.05).The ROC curve analysis demonstrated that the area under the curve(AUC)of SII,SIRI,NHR and LHR detection alone in diagnosing schizophrenia were 0.683,0.686,0.774 and 0.766 respectively.AUC of their combination detection was 0.886.SII was positively correlated with the gen-eral symptoms and the positive and negative symptom scale(PANSS)total score,and negatively correlated with the spatial structure,linguistic function and delayed memory(P＜0.05);SIRI was positively correlated with the negative symptoms,general symptom and PANSS total score,and negatively correlated with the im-mediate memory,spatial structure,linguistic function,attention;NHR was positively correlated with the nega-tive symptoms,positive symptoms,general symptoms and PANSS total score,and negatively correlated with immediate memory,linguistic function,attention and delayed memory(P＜0.05).LHR was positively corre-lated with the positive symptoms,general symptoms and PANSS total score,and negatively correlated with the spatial structure,linguistic function,attention and delayed memory(P＜0.05).Conclusion SII,SIRI,NHR and LHR combined detection has higher diagnostic value for the patients with schizophrenia.Further-more,the SII,SIRI,NHR and LHR levels are closely correlated with the disease condition severity and cogni-tive function impairment degree.

The survey on the hesitant status of rubella-containing vaccine (RCV) vaccination was conducted among 2 141 women of childbearing age in four provinces, Liaoning, Jiangsu, Zhejiang, and Anhui, from May 2017 to March 2018. Among the 2 141 study participants, a total of 38.21% (818) of women of childbearing age were aware of rubella and 21.25% (455) of women of childbearing age knew about RCV. Among the 455 women of childbearing age who were aware of RCV, 262 (57.58%) were willing to be vaccinated with RCV; 304 women of childbearing age (66.81%) tended to be vaccinated with RCV free of charge. Among the 193 women of childbearing age who did not want to be vaccinated or did not decide whether they preferred to be vaccinated with RCV, no one around them was vaccinated (89, 46.11%) and fear of vaccine safety (70, 36.27%) were the main reasons for hesitation to receive RCV.