Objective:To analyze the influencing factors for depression in primary school children aged 9-10 years in Jiangsu Province, and to construct a risk prediction model.Methods:A retrospective study.A total of 1 162 primary school children aged 9-10 years from 3 primary schools in 3 regions of Jiangsu Province were recruited.Their demographic data were collected, and they were surveyed by the Depression Anxiety Stress Scales-21 (DASS-21), the Strengths and Difficulties Questionnaire (SDQ), and the Family Environment Scale (FES). Children were divided into control group (1 059 cases) and depression group (103 cases) based on the depression scores obtained from the DASS-21 scale.Multivariate Logistic regression analysis was used to analyze the influencing factors for depression in primary school students aged 9-10 and construct a risk prediction model. Results:There were significant differences in the economic development region, physical activities, academic performance, student cadres, parents′ education level, frequency of parental quarrels, SDQ and FES dimension scores between control group and depression group (all P<0.05). Among them, economic development areas (Northern Jiangsu and Southern Jiangsu), student cadres, father′s education level (elementary school and below) and intimacy of the FES scale were protective factors for depression in elementary school children; while emotional symptoms, peer problems and the total difficulty score in the SDQ scale, and the conflict in the FES scale were the risk factors for depression in elementary school children.The prediction model was created based on the influencing factors: Logit ( P)=-1.390×economic development area (Northern Jiangsu) -1.508×economic development area (Southern Jiangsu) -1.248×student cadres -2.206×father′s education level (primary school and below) -1.145×father′s education level (junior high school)+ 3.316×emotional symptoms in the SDQ+ 0.979×peer problems in the SDQ+ 2.520×total difficulty score in the SDQ -1.697×cohesion in the FES + 0.760×conflict in the FES -0.678.The area under the curve of receiver operating characteristic was 0.931, with the sensitivity and specificity of 85.42% and 91.83%, respectively. Conclusions:The regional level of economic development, class or school cadres, father′s education level, peer problems, total difficulty score, cohesion and conflict in the family are influencing factors for depression among primary school children aged 9-10 years in Jiangsu Province.The created prediction model can effectively assess the depressive risk factors in this population, which is conductive to achieve the early recognition and intervention of depression in them.

Objective:To explore the speech features of female patients with anhedonic depression and their recognition of pleasure deficient symptoms.Methods:A total of 102 female depression patients who were hospitalized at Nanjing Brain Hospital from September 2020 to October 2021 were selected, including 62 anhedonic depression patients (anhedonic group) and 40 non-anhedonic depression patients (non-anhedonic group). A total of 50 female healthy controls were recruited during the same period.All participants were evaluated by the 17-item Hamilton depression scale (HAMD-17), Snaith-Hamilton pleasure scale (SHAPS), and the temporal experience of pleasure scale (TEPS), as well as voice acquisition.SPSS 23.0 software was used for data processing.Statistical analysis was conducted using one-way ANOVA, non-parametric tests, Logistic regression, and receiver operating characteristic curve.Results:Compared with the non-anhedonic group, the anhedonic group showed significant changes in 15 voice features(all P<0.05), including Mel-frequency cepstral coefficients, formant frequencies, intensity, and energy features.Among these features, Mel-frequency cepstral coefficients exhibited the highest accuracy in identifying anhedonic depression, with sensitivity of 47.5%, specificity of 91.9%, area under curve (AUC) of 0.751, 95% CI=0.686-0.866.Formant frequencies could identify female anhedonic depression, with a sensitivity of 90.0%, a specificity of 40.3%, an AUC of 0.647, and 95% CI=0.605-0.824.Energy features could identify anhedonic deficient depression, with a sensitivity of 60.0%, a specificity of 74.2%, an AUC of 0.679, and 95% CI=0.587-0.804.Intensity features could identify female anhedonic depression, with a sensitivity of 70.0%, a specificity of 58.1%, an AUC of 0.640, and 95% CI=0.554-0.769. Conclusion:Mel-frequency cepstral coefficients, formant frequencies, intensity features, and energy features may have specific changes in female patients with anhedonic depression.The Mel-frequency cepstral coefficients has the highest recognition accuracy for anhedonic symptoms in female depression patients, and is expected to become an objective evaluation index for female anhedonic depression.

Objective:To explore the changes in acoustic features of 9-10-year-old primary school students with depressive symptoms, and based on these features and the random forest (RF) algorithm, construct a model for identifying depressive symptoms in primary school students, so as to provide an intelligent psychological health screening tool for schools and education departments.Methods:This was a case-control study.A total of 1 186 primary school students aged 9-10 from three primary schools in three regions of Jiangsu Province were selected as research subjects for psychological health screening from October 26, 2022 to February 13, 2023.Their demographic data, Depression-Anxiety-Stress Scale (DASS-21) scores, Insomnia Severity Index scores, and voice recordings were collected.Based on the DASS-21 scores, the participants were divided into a control group ( n=1 086) and a depression group ( n=100).Voice recordings were made using the neutral text " The North Wind and the Sun". openSMILE was used to extract 523 acoustic features from the pre-processed voice recordings.Group differences were assessed using independent-samples t-tests or chi-square tests.Pearson correlation analysis was conducted to examine the relationship between acoustic features and depression scores.Depressive symptoms were set as the dependent variable, and the correlated acoustic features were set as the independent variable to construct a classification model using the RF algorithm.The model performance was assessed using the receiver operating characteristic (ROC) curve, the area under the curve (AUC), precision, accuracy, recall, and F1 score. Results:Compared with the control group, the depression group showed significant differences in 105 acoustic features (44 spectral features, 49 source features, and 12 prosodic features) (all P<0.05).Correlation analysis showed that 12 acoustic features (7 spectral features, 4 source features, and 1 prosodic feature) were significantly correlated with the depression score (all P<0.05).Among the RF algorithm-based classification models, the spectral features demonstrated superior performance compared to source features and prosodic features (AUC=0.793), and the performance of the model based on the combination of these features was the best (AUC=0.818). Conclusions:Acoustic features may be an objective indicator to identify the depression of 9-10-year-old primary school students, and the classification model established based on acoustic features can identify the depressed primary school students.