Risk factors for the frequent attendance of older patients at community health service centers in China: a cross-sectional study based on stratified sampling

A Chinese help-seeking model for psychological distress in primary care: An adaptation of Andersen's Behavioral Model of Health Services Use

Help-seeking for depression and anxiety disorders from primary care physicians in Western countries is at three times the rate of China. Western help-seeking models for common mental disorders have limitations in the Chinese settings. This article argues that an adapted model based on Andersen's Behavioral Model of Health Services Use could be an appropriate tool to better understand patients' help-seeking behaviors and improve outcomes. We applied a narrative review approach to integrate research findings from China into Andersen's model to generate a model that fits the Chinese context. We found 39 relevant articles in PubMed, MEDLINE, and Chinese journal databases from 1999 to 2022. Findings were mapped onto predisposing, enabling, and need factors of the model. This model emphasizes that predisposing factors including demographics, social norms, and health beliefs influence help-seeking preferences. Mental health service users in China tend to be older and female. Chinese generally have high concern about psychotropic medications, and social norms that consider psychological distress a personal weakness may discourage help-seeking. However, help-seeking can be enhanced by enabling factors in the health system, including training of primary care physicians, longer consultation time, and continuity of care. Need factors for treatment increase with the severity of distress symptoms, and doctor's skills and attitudes in recognizing psychosomatic symptoms. While predisposing factors are relatively hard to change, enabling factors in the health system and need factors for treatment can be targeted by enhancing the role of family doctors and training in mental health.

Disrupted interhemispheric coordination of sensory-motor networks and insula in major depressive disorder

Objective

Prior researches have identified distinct differences in neuroimaging characteristics between healthy controls (HCs) and patients with major depressive disorder (MDD). However, the correlations between homotopic connectivity and clinical characteristics in patients with MDD have yet to be fully understood. The present study aimed to investigate common and unique patterns of homotopic connectivity and their relationships with clinical characteristics in patients with MDD.

Methods

We recruited 42 patients diagnosed with MDD and 42 HCs. We collected a range of clinical variables, as well as exploratory eye movement (EEM), event-related potentials (ERPs) and resting-state functional magnetic resonance imaging (rs-fMRI) data. The data were analyzed using correlation analysis, support vector machine (SVM), and voxel-mirrored homotopic connectivity (VMHC).

Results

Compared with HCs, patients with MDD showed decreased VMHC in the insula, and increased VMHC in the cerebellum 8/vermis 8/vermis 9 and superior/middle occipital gyrus. SVM analysis using VMHC values in the cerebellum 8/vermis 8/vermis 9 and insula, or VMHC values in the superior/middle occipital gyrus and insula as inputs can distinguish HCs and patients with MDD with high accuracy, sensitivity, and specificity.

Conclusion

The study demonstrated that decreased VMHC in the insula and increased VMHC values in the sensory-motor networks may be a distinctive neurobiological feature for patients with MDD, which could potentially serve as imaging markers to discriminate HCs and patients with MDD.

HPE-GCN: predicting efficacy of tonic formulae via graph convolutional networks integrating traditionally defined herbal properties

Chinese herbal formulae are the heritage of traditional Chinese medicine (TCM) in treating diseases through thousands of years. The formula function is not just a simple herbal efficacy addition, but produces complex and nonlinear relationships between different herbs and their overall efficacy, which brings challenges to the formula efficacy analysis. In our study, we proposed a model called HPE-GCN that combines graph convolutional networks (GCN) with TCM-defined herbal properties (TCM-HPs) to predict formulae efficacy. In addition, to process the unstructured natural language in the formula text, we proposed a weighting calculation method related to herb frequency and the number of herbs in a formula called Formula-Herb dependence degree (FHDD), to assess the dependency degree of a formula with its herbs. In our research, 214 classic tonic formulae from ancient TCM books such as Synopsis of the Golden Chamber, Jingyue's Complete Works and the Golden Mirror of Medicin were collected as datasets. The performance of HPE-GCN on multi-classification of tonic formulae reached the best result compared with classic machine learning models, such as support vector machine, naive Bayes, logistic regression, gradient boosting decision tree, and K-nearest neighbors. The evaluated index Macro-Precision, Macro-Recall, Macro-F1 of HPE-GCN on the test set were 87.70%, 84.08% and 83.51% respectively, increased by 7.27%, 7.41% and 7.30% respectively from second best compared models. GCN has the advantage of low-dimensional feature expression for herbs and formulae, and is an effective analysis tool for TCM research. HPE-GCN integrates TCM-HPs and fits the complex nonlinear mapping relationship between TCM-HPs and formulae efficacy, which provides new ideas for related research.