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Abstract Background The number of chronic diseases has been associated with changes in depressive symptoms over time among middle-aged and older adults. This study aimed to explore the association between the number of chronic diseases and trajectories of depressive symptoms and the role of age in this association. Methods A total of 12,974 middle-aged and older Chinese adults (≥ 45 years) participated in the China Health and Retirement Longitudinal Study (CHARLS) in waves 2011, 2013, 2015, 2018, and 2020. The number of chronic diseases was determined by self-reported hospital diagnosis of hypertension, dyslipidemia, diabetes, cancer, chronic lung diseases, liver disease, heart diseases, stroke, kidney diseases, digestive diseases, emotional, nervous, or psychiatric problems, memory-related disease, arthritis or rheumatism, asthma, and then obtaining the total number of chronic diseases. Depressive symptoms were measured by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). Group-based trajectory modeling (GBTM) was adopted to capture the trajectories of depressive symptoms over time. Multinomial logistic regressions were conducted to examine the association between the number of chronic diseases and trajectories of depressive symptoms and the role of age in this association. Results Four distinct trajectories of depressive symptoms were observed in 34.68% individuals in mild, 40.76% in moderate, 19.41% in increasing, and 5.15% in severe group. Compared to participants without chronic diseases, those with one, two, three or more chronic diseases had a 1.81, 3, and 7.49-fold higher risk of developing severe depressive symptom trajectory, respectively. Moreover, the association between the number of chronic diseases and severe depressive symptoms trajectory differed by age (45–59 and ≥ 60 years) (P for interaction

Abstract Background Although metabolic syndrome (MetS) and depressive symptoms (DS) are predictors of low back pain (LBP), their combined effects and relative contributions to LBP have not been well studied. Using the nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), this study conducted cross-sectional and longitudinal analyses to investigate the impact of MetS on LBP, and the joint effects of MetS and DS on LBP. Methods This study included a cross-sectional analysis of 8957 participants aged at least 45 years from the CHARLS 2011 dataset and a longitudinal follow-up of 3468 participants without LBP from the CHARLS 2011, tracked over 9.25 years (from June 2011 to September 2020) with 4 times LBP assessment in CHARLS 2013, 2015, 2018, and 2020. To explore the association between MetS on LBP and the joint effects of MetS and DS on LBP, multivariable-adjusted multinomial logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Multivariable-adjusted COX proportional hazards regression models were applied to estimate hazard ratios (HRs) and 95% CIs. All statistical analyses were conducted using STATA (version SE16). Results In the cross-sectional analysis, MetS was associated with a lower risk of LBP (adjusted OR = 0.85, 95% CI = 0.74–0.97), while there was no significance for this association in the longitudinal analysis. In the joint association of MetS and DS with LBP, participants with NoMetS + DS (adjusted OR = 2.31, 95% CI = 1.94–2.75), and MetS + DS (adjusted OR = 2.16, 95% CI = 1.81–2.59) were risk factors for LBP events, while those with MetS + NoDS (adjusted OR = 0.75, 95% CI = 0.62–0.90) was a protective factor for LBP events than those with NoMetS + NoDS. During the 9.25 years of follow-up, 1708 cases (49.25%) experienced incident LBP events. In the longitudinal analysis, a significantly negative association was not found in MetS + NoDS for LBP events. Three sensitivity analyses identified the robustness of the associations. Moreover, the nature of cross-sectional associations differed by age (45–64 and 65 + years). Conclusions Our study found that MetS was linked to a lower incidence of LBP, but this effect does not persist over time. Importantly, the combination of MetS and DS significantly increased LBP risk, a joint effect not extensively studied before. These findings underscore the novel contribution of our research, advocating for the joint assessment of MetS and DS to enhance LBP risk stratification and inform prevention strategies.

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic continues to affect the mental health of college students in the post-epidemic era. We assessed the status and related factors of college students who are vulnerable to anxiety and depression during the COVID-19 lockdown. Methods This cross-sectional study was conducted two weeks after the beginning of the COVID-19 lockdown, from November 6, 2022, to December 2, 2022, with 1176 valid samples using convenience sampling. The Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) were used to measure levels of anxiety and depression in college students. Factors related to anxiety, depression, and their comorbidity were analyzed using binary logistic regression. Results The prevalence of anxiety, depression, and their comorbidity were 27.04%, 34.10%, and 25.60%, respectively. There was a higher risk of anxiety, depression, and their comorbidity among those who were currently living in rural areas. Compared with individuals with low monthly living expenses, those with higher monthly living expenses were less prone to anxiety, depression, and their comorbidity. Conclusions High prevalence of anxiety, depression, and their comorbidity were detected among college students during the COVID-19 lockdown. These were most common among college students who were senior students, had abnormal body mass index (BMI), were rural area residents, did study arts and humanities, were one-child in the family, and had low monthly living expenses and poor academic performance. Intervention practitioners and policymakers should formulate individualized prevention and intervention measures during the COVID-19 lockdown in the post-epidemic and possible future pandemics for college students.

BackgroundThe effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.MethodsWe investigate the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients’ overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model’s performance.Results6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software.ConclusionPatients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

Abnormal translate regulation is an important phenomenon in cancer initiation and progression. Eukaryotic translation initiation factor 4A1 (eIF4A1) protein is an ATP-dependent Ribonucleic Acid (RNA) helicase, which is essential for translation and has bidirectional RNA unwinders function. In this review, we discuss the levels of expression, regulatory mechanisms and protein functions of eIF4A1 in different human tumors. eIF4A1 is often involved as a target of microRNAs or long non-coding RNAs during the epithelial-mesenchymal transition, associating with the proliferation and metastasis of tumor cells. eIF4A1 protein exhibits the promising biomarker for rapid diagnosis of pre-cancer lesions, histological phenotypes, clinical staging diagnosis and outcome prediction, which provides a novel strategy for precise medical care and target therapy for patients with tumors at the same time, relevant small molecule inhibitors have also been applied in clinical practice, providing reliable theoretical support and clinical basis for the development of this gene target.

No Poverty is the top priority among 17 Sustainable Development Goals (SDGs). The research perspectives, methods, and subject integration of studies on poverty reduction have been greatly developed with the advance of practice in the 21st century. This paper analyses 2,459 papers on poverty reduction since 2000 using VOSviewer software and R language. Our conclusions show that (1) the 21st century has seen a sharp increase in publications of poverty reduction, especially the period from 2015 to date. (2) The divergence in research quantity and quality between China and Kenya is great. (3) Economic studies focus on inequality and growth, while environmental studies focus on protection and management mechanisms. (4) International cooperation is usually related to geographical location and conducted by developed countries with developing countries together. (5) Research on poverty reduction in different regions has specific sub-themes. Our findings provide an overview of the state of the research and suggest that there is a need to strengthen the integration of disciplines and pay attention to the contribution of marginal disciplines to poverty reduction research in the future.