BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*

Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.
Humans ; Magnetoencephalography/methods* ; Brain/physiopathology* ; Depression/diagnosis* ; Electroencephalography ; Magnetic Resonance Imaging

OBJECTIVES: To construct vocal recognition classification models using 6 machine learning algorithms and vocal emotional characteristics of individuals with subthreshold depression to facilitate early identification of subthreshold depression. METHODS: We collected voice data from both normal individuals and participants with subthreshold depression by asking them to read specifically chosen words and texts. From each voice sample, 384-dimensional vocal emotional feature variables were extracted, including energy feature, Meir frequency cepstrum coefficient, zero cross rate feature, sound probability feature, fundamental frequency feature, difference feature. The Recursive Feature Elimination (RFE) method was employed to select voice feature variables. Classification models were then built using the machine learning algorithms Adaptive Boosting (AdaBoost), Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Lasso Regression (LRLasso), and Support Vector Machine (SVM), and the performance of these models was evaluated. To assess generalization capability of the models, we used real-world speech data to evaluate the best speech recognition classification model. RESULTS: The AdaBoost, RF, and LDA models achieved high prediction accuracies of 100%, 100%, and 93.3% on word-reading speech test set, respectively. In the text-reading speech test set, the accuracies of the AdaBoost, RF, and LDA models were 90%, 80%, and 90%, respectively, while the accuracies of the other 3 models were all below 80%. On real-world word-reading and text-reading speech data, the classification models using AdaBoost and Random Forest still achieved high predictive accuracies (91.7% and 80.6% for AdaBoost and 86.1% and 77.8% for Random, respectively). CONCLUSIONS Analyzing vocal emotional characteristics allows effective identification of individuals with subthreshold depression. The AdaBoost and RF models show excellent performance for classifying subthreshold depression individuals, and may thus potentially offer valuable assistance in the clinical and research settings.
Humans ; Machine Learning ; Emotions ; Depression/diagnosis* ; Algorithms ; Voice ; Support Vector Machine ; Male ; Female

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
Humans ; Male ; Female ; Adolescent ; Case-Control Studies ; Depression/diagnosis* ; Early Diagnosis ; Rest ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Models, Psychological ; Reproducibility of Results ; Affect/physiology* ; Photic Stimulation/methods* ; Video Recording ; Brain/physiopathology*

Coronary artery disease (CAD), one of the most common cardiovascular diseases (CVD), poses a serious threat to physical and mental health, resulting in a severe disease burden. Psychocardiology medicine focuses on the vital role of psychological factors in the development, diagnosis, and treatment of CVD. The prevalence of depression and anxiety is high in patients with CAD. Furthermore, there is a vital interplay among depression, anxiety, mental stress-induced myocardial ischemia, cognitive impairment, and delirium. Both cognitive impairment and delirium adversely impact the prognosis of patients with CAD, warranting increasing attention and the development of interventions. To further direct the clinic diagnosis and treatment of cognitive impairment in patients with CAD complicated with depression and anxiety, and to thus improve the prognosis of such patients, the Psychocardiology Medicine Branch of Chinese Medical Association Beijing Branch, and Psychocardiology Education Professional Committee of China Medical Education Association, together with over 40 other organizations, including more than 50 experts from several related fields, have developed the association standards on guidelines for the cognitive clinical diagnosis and treatment of CAD complicated with depression and anxiety under the framework of the China standard association (No.T/CAS 812-2024).
Humans ; Depression/diagnosis* ; Coronary Artery Disease/psychology* ; Anxiety/diagnosis* ; Practice Guidelines as Topic

Objective: To evaluate the effects of sedentary behavior/screen time on mental health of college students by Meta-analysis based on the results of literature retrieval and provide theoretical basis for the improvement of college students' mental health. Methods: The original research literatures about sedentary behavior (including screen time) and college students' mental health published as of 14 July 2022 were retrieved from PubMed, Embase, Cochrane Library, CNKI, VIP and Wanfang data. Data were extracted from the included studies and scored by one author in accordance with the proposed programme, and quality score was reviewed by another author. The literature that met the inclusion criteria was systematically reviewed and Meta-analysis was carried out by software Stata 14.2 based on the data from the literatures. Results: A total of 36 studies met the inclusion criteria, including 35 observational studies and 1 interventional study. There are 4 papers about the effects of sedentary behavior and 9 papers about the effects of screening time on depression in college students and 4 papers about the effects of sedentary behavior/screening time on anxiety in college students were used for a Meta-analysis, and the other studies were also analyzed. The Meta-analysis on the effects of sedentary behavior on depression in college students showed that there was a significant positive correlation between higher level sedentary behavior and increased risk for depression (OR=1.07,95%CI:1.05-1.10). Subgroup analysis indicated that there was no significant correlation between higher level sedentary behavior and depression (OR=1.74, 95%CI:0.93-3.25) in the unadjusted confounding factor model, but there was significance positive correlation after adjusting confounding factors (OR=2.15, 95%CI:1.18-3.90). Meta-analysis on the effects of screen time on depression in college students showed that longer screen time were significantly positively correlated with higher depression level (OR=1.03, 95%CI: 1.02-1.05). The results of subgroup analysis showed that in both unadjusted confounding factor model and adjusted confounding factor model, longer screen time was significantly positively correlated with depression (OR=1.27, 95%CI: 1.13-1.42; OR=1.45, 95%CI: 1.18-1.79) , respectively. Meta-analysis on the effects of sedentary behavior on anxiety showed that longer screen time was significantly positively correlated with increased anxiety risk (OR=1.44, 95%CI: 1.31-1.58). The results of subgroup analysis showed that in both unadjusted confounding factor model and adjusted confounding factor model, there was a significant positive correlation between longer screen time and anxiety (OR=1.47, 95%CI: 1.31-1.65; OR=1.38, 95%CI:1.17-1.62). The analysis for the literatures which were not eligible for Meta-analysis found that sedentary behavior/screen time was significantly associated with stress and other mental health in college students. Conclusions: Sedentary behavior or screen time is significantly negatively correlated with college students' mental health, in particular, resulting in depression and anxiety. These effects might be be different between weekdays and weekend days.
Humans ; Mental Health ; Depression/diagnosis* ; Sedentary Behavior ; Screen Time ; Students/psychology*

Depression syndromes(anxiety and depression), as typical psychological disorders, often coexist with and mutually influence coronary heart disease(CHD). They constitute a psycho-cardiology disease involving both the blood vessels of the heart and the spirit of the heart. Based on the theory of "coexistence of diseases and depression syndromes", it was proposed that CHD and depression syndromes coexisted independently and were causally related. The factors of depression syndromes go through the entire course of CHD and have different causal relationships at different stages, leading to a pathogenic process of "depression causing disease" or "disease causing depression". In the chronic latent period, phlegm predominates, with depression leading to the production of phlegm. Phlegm accumulation and Qi stagnation initiate a mutual damage process of psycho-cardiology, marking the onset of the disease. In the pathological development period, blood stasis becomes predominant. Depression leads to blood stasis, which further obstructs Qi circulation, accelerating disease progression. In the acute attack period, toxicity becomes crucial. Depression transforms into toxicity, damaging Qi and blood, disturbing the balance of the mind, and inducing a sudden and severe exacerbation of the disease. Based on this, the approach of treating phlegm and depression together, treating blood stasis and depression together, and treating toxicity and depression together by stages was established. Research has found that this approach can simultaneously improve organic damage and emotional disorders, and also has a regulating effect on micro-level syndrome indicators, achieving harmonization of psycho-cardiology in the treatment.
Humans ; Medicine, Chinese Traditional ; Depression/diagnosis* ; Coronary Disease/diagnosis* ; Mucus ; Syndrome ; Anxiety

OBJECTIVE: To explore the association between postpartum depression (PPD) and transforming growth factor-β (TGF-β) concentrations in human colostrum. METHODS: Participants were recruited from a maternal and infant cohort established in a tertiary general hospital in Guangdong Province between December, 2020 and September, 2021. In the afternoon of the second postpartum day, the women were evaluated with Edinburgh Postnatal Depression Scale (EPDS) for screening PPD (defined as a score of 10 or higher). The women with PPD were matched at a 1:1 ratio with women without PPD with maternal age difference within 5 years and the same mode of delivery. Colostrum samples were collected in morning on the third postpartum day for measurement of TGF-β concentrations using enzyme-linked immunosorbent assay (ELISA), and the association between EPDS scores and TGF-β concentrations was analyzed in the two groups. RESULTS: A total of 90 women were included in the final analysis. The mean concentrations of TGF-β1, TGF-β2 and TGF-β3 in the colostrum were 684.03 (321.22-859.25) pg/mL, 5116.50±1747.04 pg/mL and 147.84±48.68 pg/mL in women with PPD, respectively, as compared with 745.67 (596.00-964.22) pg/mL, 4912.40±1516.80 pg/mL, and 168.21±48.15 pg/mL in women without PPD, respectively. Compared with women without PPD, the women with PPD had significantly lower concentrations of TGF-β1 (P=0.026) and TGF-β3 (P=0.049) in the colostrum. Spearman correlation analysis revealed that the EPDS scores were negatively associated with the concentrations of TGF-β1 (r=-0.23, P=0.03) and TGF-β3 (r=-0.25, P=0.02) in the colostrum. CONCLUSION PPD is associated with decreased concentrations of TGF-β1 and TGF-β3 in human colostrum, suggesting the need of early PPD screening and interventions during pregnancy and the perinatal period to minimize the impact of PPD on human milk compositions.
Child, Preschool ; Cohort Studies ; Colostrum ; Depression, Postpartum/diagnosis* ; Female ; Humans ; Infant ; Postpartum Period ; Pregnancy ; Transforming Growth Factor beta1 ; Transforming Growth Factor beta2 ; Transforming Growth Factor beta3 ; Transforming Growth Factors

Objective: To screen the physical, psychological and behavioral factors related to patients with temporomandibular disorders (TMD) by using Axis Ⅱ assessment instruments of diagnostic criteria for TMD(DC/TMD). And to provide a reference to establish personalized diagnosis and treatment plans for TMD patients so as to prevent TMD and reduce predisposing factors. Methods: A total of 141 TMD patients, who were admitted in the Department of Oral and Maxillofacial Surgery in School and Hospital of Stomatology, Wuhan University from October 2018 to February 2021 were selected. There were 121 females and 20 males, with an average age of 30 years. A total of 90 healthy people were included as controls. A full-time psychologist conducted relevant questionnaire surveys. The questionnaires include general clinical survey forms and TMD symptom questionnaire. In addition, Axis Ⅱ assessment instruments include graded chronic pain scale, jaw functional limitation scale, oral behaviors checklist, patient health questionnaire-9 (depression), generalized anxiety disorder scale, patient health questionnaire-15 (physical symptoms), etc. The main observational indicators include: pain level, pain impact rates, overall classification of chronic pain, limited chewing function score, limited motor function score, limited communication function score, total jaw function restricted score, depression score, anxiety score, somatic symptom score and oral behavior score.The survey data were imported into SPSS 22.0 software for statistical analysis. Results: In the TMD group 60.3% (85/141) patients had various degrees of pain, 24.1% (34/141) of those with pain effect grades from 1 to 3 and 61.0% (86/141) showed chronic pain overall grades from Ⅰ to Ⅳ. The chewing function restricted score was 2.67(1.17, 4.25), motor function restricted score was 4.25(1.75, 6.12), communication function restricted score was 1.13(1.00, 2.25) and total jaw function restricted score was 2.56(1.47, 4.15) respectively. Patients with mild depression or above accounted for 59.6%(84/141), patients with mild anxiety or above accounted for 56.7%(80/141), 46.1%(65/141) patients had somatization symptoms. Statistical differences (P<0.05) were determined between TMD group and control group in various scores of jaw function, oral behavior grading, depression, anxiety, and physical symptoms. Physical symptoms had significantly statistical difference between different diagnostic classification(P<0.05). Meanwhile, among the different chronic pain levels in the TMD group, there were statistical differences in the various scales of mandibular dysfunction, depression, anxiety, and somatization. In the TMD group, other significant differences were noticed between males and females in terms of the average score of mouth opening, verbal and facial communication, the total score of mandibular dysfunction as well as physical symptoms (P<0.05). Conclusions: Compared with the healthy people, patients with TMD had more abnormal oral behaviors, different restriction of the mandibular functional activities. At the same time, depression, anxiety, and somatization were more serious. Patients with osteoarthritis and subluxation of temporomandibular joint were more likely to suffer physical symptoms. TMD patients suffering from pain had more severe mandibular dysfunction and symptoms of depression, anxiety, and somatization.
Adult ; Depression/diagnosis* ; Facial Pain ; Female ; Humans ; Male ; Mandible ; Somatoform Disorders ; Temporomandibular Joint Disorders/diagnosis* ; Temporomandibular Joint Dysfunction Syndrome

Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of "small world" and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.
Brain ; Brain Mapping ; Depression/diagnosis* ; Electroencephalography ; Humans ; Recognition, Psychology