ObjectiveTo explore the clinical characteristics of epilepsy and depression patients, and provide guidance for clinical intervention of epilepsy and depression patients.MethodsPatients with epilepsy (epilepsy group) were prospectively enrolled in Emeishan People’s Hospital from 2015 to 2017, and healthy controls (control group) were enrolled in the same period. Clinical assessment of depression was conducted and compared between the two groups. In the epilepsy group, the severity and incidence of depression were analyzed and compared among different subgroups according to the epileptic seizure type, frequency and course.ResultsA total of 120 patients and 70 healthy controls were enrolled. The Hamilton Depression Scale score of epilepsy group was higher than that of the control group (t=7.430, P<0.001), and the depression degree of epilepsy group was significantly higher than that of the control group (Z=−4.371, P< 0.001). There was no significant difference in depression rating between convulsive epilepsy patients and partial epilepsy patients (Z=−1.591, P=0.112); there was no significant difference in depression rating among patients with different epilepsy course (χ2=1.943, P=0.584); there was significant difference in depression rating among patients with different seizure frequency (χ2=27.575, P<0.001). Patients with high frequency of seizures were more likely to suffer from depression and severe depression, with the lowest proportion of normal neuropsychological state. Conversely, patients with low frequency of epileptic seizures had a lower proportion of depression and severe depression.ConclusionsThe incidence of depression in epilepsy patients is higher than that in normal people. Timely detection and treatment of depression in clinical work have a positive impact on the prognosis of patients.
ObjectiveTo compare the anxiety, depressive and personality characteristics between diabetes mellitus patients with or without diabetic retinopathy (DR), and look for psychological treatment and corresponding prevention measures. Methods435 diabetic patients were enrolled in this study from April to November 2014 in our hospital, including 178 DR cases (group A) and 257 cases without retinopathy (group B). All the patients completed a questionnaire, the Self-Rating Anxiety (SAS), the Self-Rating Depression Scale (SDS) and the big five personality scale (NEO-FFI), and were scored by eye doctors. According to the score, SAS can be divided into mild anxiety, moderate anxiety, and severe anxiety. SDS is divided into depression, mild depression, moderate depression and major depression. NEO-FFI was scored from emotional stability, outgoing, openness, easy-going and sense of responsibility. Multiple linear regression analysis was used to analyze the DR risk factors in those scores and education level, high blood pressure, age, alcohol consumption, occupation and other factors. ResultsThere were 110 cases of mild anxiety, 57 cases of moderate anxiety, 11 cases of severe anxiety; 74 cases without depression, 53 cases of mild depression, 31 cases of moderate depression, 20 cases with major depression in group A. There were 181 cases of mild anxiety, 53 cases of moderate anxiety, 23 cases of severe anxiety; 177 cases without depression, 44 cases of mild depression, 25 cases of moderate depression, 11 cases with major depression in group B. Group A patients had higher SAS, SDS scores than group B, the difference was statistically significant (P=0.035). Group B patients had higher NEO-FFI score in outgoing, easygoing, responsibility (P=0.022), lower NEO-FFI score in emotional stability (P=0.014) and same NEO-FFI score in openness(P=0.210)compare to Group A patients. Multiple linear regression analysis results showed that education level, high blood pressure, age, weight, drinking, occupation can affect the degree of changes in the retina (P=0.019). ConclusionsCompared with those without retinopathy, DR patients were more prone to anxiety and depression. They also had low score in personality characteristics of outgoing, easygoing, responsibility.
ObjectiveTo investigate the status of quality of life and influencing factors among newly diagnosed epilepsy patients with co-morbid anxiety and depression. MethodsA total of 180 newly diagnosed epilepsy patients from June 2022 to December 2022 in a district of Shanghai were selected as the study subjects. The Quality of Life in Epilepsy-31 (QOLIE-31), Hamilton Depression Rating Scale (HAMD-24), Hamilton Anxiety Rating Scale (HAMA), and Epilepsy Self-Management Scale (ESMS) were used to assess patients' quality of life, depression levels, anxiety levels, and self-management abilities, respectively. Patients were divided into the co-morbid depression group (HAMA≥14 and HAMD>17) and the control group (HAMA<14 and HAMD≤17), and their general characteristics and scale scores were compared. Spearman correlation, Pearson correlation, and multiple linear regression analysis were used to identify influencing factors of quality of life in epilepsy patients with co-morbid depression. ResultsCompared to the control group, the anxiety comorbid with depression group of older adults had a higher proportion, higher unemployment rate, lower personal and family annual income in the past year, higher frequency of epileptic seizures, and lower medication adherence (P<0.05). The correlational analysis revealed a negative correlation between the quality of life abilities of epilepsy patients with comorbid anxiety and depression and the severity of anxiety and depression. (r=−0.589, −0.620, P<0.05). The results of multiple linear regression analysis showed that the frequency of seizures in the past year (β=−1.379, P<0.05), severity of anxiety (β=−0.279, P<0.05), and severity of depression (β=−0.361, P<0.05) have an impact on the ability to quality of life in epilepsy patients with co-morbid anxiety and depression. These factors account for 44.1% of the total variability in quality of life (R2=0.4411, P<0.05). ConclusionThe frequency of seizures in the past year, as well as the severity of anxiety and depression, are important factors that influence the ability to quality of life in epilepsy patients with comorbid anxiety and depression. For these patients, it is crucial to take into account these factors and provide appropriate support and interventions.
ObjectiveTo investigate the fatigue of asthma patients, and to analyze its influencing factors, and provide a reference for clinical intervention.MethodsThe convenience sampling method was adopted to select asthma patients who were in clinic of the First Affiliated Hospital of Guangxi Medical University from November 2018 to March 2019. The patients’ lung function were measured. And questionnaires were conducted, including general data questionnaire, Chinese version of Checklist Individual Strength-Fatigue, Asthma Control Test, Chinese version of Self-rating Depression Scale. Relevant data were collected for multiple stepwise linear regression analysis.ResultsFinally, 120 patients were enrolled. The results of multiple stepwise linear regression analysis showed that age, education level, place of residence, time period of frequent asthma symptoms, degree of small airway obstruction, Asthma Control Test score and degree of depression were the influencing factors of fatigue in asthma patients (P≤0.05). Multivariate linear stepwise regression analysis showed that degree of small airway obstruction, degree of depression and time period of frequent asthma symptoms were the main influencing factors of fatigue in asthma patients, which could explain 51.8% of the variance of fatigue (ΔR2=0.518).ConclusionsThe incidence of fatigue in asthma patients is at a relatively high level. Medical staff should pay attention to the symptoms of fatigue in asthma patients. For asthma patients, it is recommended to strengthen standardized diagnosis and treatment, reduce the onset of symptoms at night and eliminate small airway obstruction. Psychological intervention methods are needed to improve patients’ depression, reduce fatigue symptoms, and improve quality of life.
Objective Depression is a common consequence after stroke and has become a significant issue in clinical practice and research. The aim of this study was to explore associated factors of post-stroke depression among first-ever stroke patients in Hong Kong. Methods A longitudinal study was conducted to collect data in face-to-face interviews and by physical assessment at two time points: T1, within 48 hours of admission to a rehabilitation hospital; and T2, 6 months after the first interview. T2 interviews and assessments were conducted in the participant’s current place of residence. Participants were first-ever stroke patients in Hong Kong. Post-stroke depression was measured using the Center of Epidemiological Study-Depression (CES-D) Scale. Backward linear regression analysis was performed to examine factors associated with level of post-stroke depression at T2. Results Our findings showed that 69% of participants exhibited clinically relevant levels of depressive symptoms at T1 and 48% at T2. Regression analysis revealed complex relationships between the level of depressive symptoms, demographic characteristics and variations in perceived levels of social support. Five variables were found to explain 55% of the variance in depressive symptoms at T2. The variables with significant standardized regression coefficients (β) were: companionship (P=0.001), informational support (P=0.025), baseline level of depressive symptoms (Plt;0.001), ADL dependence level (Plt;0.001) and being a homemaker before the stroke (P=0.039). Conclusions We have followed a group of stroke patients over a 6-month period. Our findings suggest that when screening for post-stroke depression, health professionals must take into consideration of the clinical, socio-personal characteristics that might increase a stroke patient’s vulnerability to develop depression after stroke.
Objective To explore depression-related biomarkers and potential therapeutic drugs in order to alleviate depression symptoms and improve patients’ quality of life. Methods From November 2022 to January 2024, gene expression profiles of depression patients and healthy volunteers were downloaded from the Gene Expression Omnibus database. Differential expression analysis was performed to identify differentially expressed genes. Enrichment analysis of these genes was conducted, followed by the construction of a protein-protein interaction network. Finally, Cytoscape software with the Cytohubba plugin was used to identify potential key genes, and drug prediction was performed. Results Through differential expression analysis, a total of 110 differentially expressed genes (74 upregulated and 36 downregulated) were identified. Protein-protein interaction network identified 10 key genes, and differential expression analysis showed that 8 of these genes (CPA3, HDC, IL3RA, ENPP3, PTGDR2, VTN, SPP1, and SERPINE1) exhibited significant differences in expression levels between healthy volunteers and patients with depression (P<0.05). Enrichment analysis revealed that the upregulated genes were significantly enriched in pathways related to circadian rhythm, niacin and nicotinamide metabolism, and pyrimidine metabolism, while the downregulated genes were primarily enriched in extracellular matrix-receptor interaction and interleukin-17 signaling pathways. Six overlapping verification genes (SALL2, AKAP12, GCSAML, CPA3, FCRL3, and MS4A3) were obtained across two datasets using the Wayn diagram. Single-cell sequencing analysis indicated that these genes were significantly expressed in astrocytes and neurons. Mendelian randomization analysis suggested that the FCRL3 gene might play a critical role in the development of depression. Drug prediction analysis revealed several potential antidepressant agents, such as cefotiam, harmol, lincomycin, and ribavirin. Conclusions Circadian rhythm, nicotinate and nicotinamide metabolism, and pyrimidine metabolism pathways may represent potential pathogenic mechanisms in depression. Harmol may be a potential therapeutic drug for the treatment of depression.
ObjectiveTo systematically review the effect of different psychological intervention methods on depressive symptoms in patients with inflammatory bowel disease. MethodsPubMed, Embase, Cochrane Library, Web of Science, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect randomized controlled trials(RCTs) on psychological interventions on depression of patients with inflammatory bowel disease from inception to January 12, 2023. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Network meta-analysis was then conducted by using software Stata and GeMTC. ResultsA total of 18 articles, 1 567 patients and 6 psychological intervention methods were included. The results of the network meta-analysis showed that, compared with conventional nursing, music therapy, mindfulness therapy and cognitive behavioral therapy had statistically significant differences in the intervention effect of depression in patients with inflammatory bowel disease (P<0.05); Among the six psychological intervention methods included, there was a statistically significant difference in relaxation therapy compared with music therapy, writing expression and mindfulness therapy (P<0.05); The difference between cognitive behavioral therapy and music therapy and mindfulness therapy was statistically significant (P<0.05), while there was no statistically significant difference in other interventions (P>0.05). The SUCRA ranking probability chart showed that music therapy was the best intervention method for depression in patients with inflammatory bowel disease, followed by mindfulness therapy and cognitive behavioral therapy. ConclusionThe current evidence suggests that music therapy has an advantage in relieving depression in patients with inflammatory bowel disease, followed by mindfulness therapy or cognitive behavioral therapy. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.
Objective To systematically review the prevalence of depression and anxiety among health care workers in designated hospitals during the COVID-19 pandemic. Methods The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect cross-sectional studies on the prevalence of depression and anxiety among health care workers from December 2019 to April 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using Stata 14.0 software. Results A total of 21 cross-sectional studies were included, involving 38 372 participants. Meta-analysis results showed that during the COVID-19 epidemic, the prevalence of depression and anxiety among health care workers in designated hospitals were 31.00% (95%CI 0.25 to 0.37) and 44.00% (95%CI 0.34 to 0.53). The results of subgroup analysis showed that individuals of female, married, bachelor degree or above, nurses, junior professional titles, and non-first-line medical staff had higher prevalence of depression and anxiety. Conclusions During the COVID-19 pandemic, the incidence of depression and anxiety among health care workers in designated hospitals remain high. Therefore, more attention should be paid to the mental health of health care workers in designated hospitals. Due to the limited quantity and quality of included studies, more high-quality studies are needed to verify the above conclusions.
For the increasing number of patients with depression, this paper proposes an artificial intelligence method to effectively identify depression through voice signals, with the aim of improving the efficiency of diagnosis and treatment. Firstly, a pre-training model called wav2vec 2.0 is fine-tuned to encode and contextualize the speech, thereby obtaining high-quality voice features. This model is applied to the publicly available dataset - the distress analysis interview corpus-wizard of OZ (DAIC-WOZ). The results demonstrate a precision rate of 93.96%, a recall rate of 94.87%, and an F1 score of 94.41% for the binary classification task of depression recognition, resulting in an overall classification accuracy of 96.48%. For the four-class classification task evaluating the severity of depression, the precision rates are all above 92.59%, the recall rates are all above 92.89%, the F1 scores are all above 93.12%, and the overall classification accuracy is 94.80%. The research findings indicate that the proposed method effectively enhances classification accuracy in scenarios with limited data, exhibiting strong performance in depression identification and severity evaluation. In the future, this method has the potential to serve as a valuable supportive tool for depression diagnosis.
ObjectiveTo explore the risk factors for accompanying depression in patients with community type Ⅱ diabetes and to construct their risk prediction model. MethodsA total of 269 patients with type Ⅱ diabetes accompanied with depression and 217 patients with simple type Ⅱ diabetes from three community health service centers in two streets of Pingshan District, Shenzhen from October 2021 to April 2022 were included. The risk factors were analyzed and screened out, and a logistic regression risk prediction model was constructed. The goodness of fit and prediction ability of the model were tested by the Hosmer-Lemeshow test and the receiver operating characteristic (ROC) curve. Finally, the model was verified. ResultsLogistic regression analysis showed that smoking, diabetes complications, physical function, psychological dimension, medical coping for face, and medical coping for avoidance were independent risk factors for depressive disorder in patients with type Ⅱ diabetes. Modeling group Hosmer-Lemeshow test P=0.345, the area under the ROC curve was 0.987, sensitivity was 95.2% and specificity was 98.6%. The area under the ROC curve was 0.945, sensitivity was 89.8%, specificity was 84.8%, and accuracy was 86.8%, showing the model predictive value. ConclusionThe risk prediction model of type Ⅱ diabetes patients with depressive disorder constructed in this study has good predictive and discriminating ability.