Support-Seeking Strategies, Family Communication Patterns, and Received Support Among Chinese Women with Postpartum Depression: A Content Analysis of Zhihu Posts.

The incidence of postpartum depression (PPD) among Chinese women surpasses the global average, and this disparity is closely associated with the level of social support they receive. Using Sensitive Interaction Systems Theory and Family Communication Patterns Theory as guiding frameworks, we conducted a content analysis of 705 posts gathered from the hashtag #PPD (chanhou yiyu) on Zhihu, a popular Chinese social media platform. Our findings reveal that postpartum women primarily seek social support from their husbands and mothers-in-law through indirect nonverbal and direct verbal communication strategies. They tend to receive more problem-focused support than emotion-focused solace. Moreover, the use of direct verbal communication strategies promotes potential support providers' problem-solving behaviors, while the use of indirect nonverbal strategies elicits their avoidance behaviors. In addition, the conversation-oriented family communication pattern strengthens the positive association between direct verbal communication and support-providing behaviors, whereas the conformity-oriented family communication pattern weakens this relationship. This study contributes to the applicability of the abovementioned theories in the Chinese context and provides insights for future interventions aimed at addressing PPD among women.

An enhanced machine learning approach for effective prediction of IgA nephropathy patients with severe proteinuria based on clinical data.

IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis more difficult. This study collected data from a sample of adult patients diagnosed with primary IgAN at the First Affiliated Hospital of Wenzhou Medical University, with proteinuria ≥1 g/d at the time of diagnosis. Based on these samples, we propose a machine learning framework based on weIghted meaN oF vectOrs (INFO). An enhanced COINFO algorithm is proposed by merging INFO, Cauchy Mutation (CM) and Oppositional-based Learning (OBL) strategies. At the same time, COINFO and Support Vector Machine (SVM) were integrated to construct the BCOINFO-SVM framework for IgAN diagnosis and prediction. Initially, the proposed enhanced COINFO is evaluated using the IEEE CEC2017 benchmark problems, with the outcomes demonstrating its efficient optimization capability and accuracy in convergence. Furthermore, the feature selection capability of the proposed method is verified on the public medical datasets. Finally, the auxiliary diagnostic experiment was carried out through IgAN real sample data. The results demonstrate that the proposed BCOINFO-SVM can screen out essential features such as High-Density Lipoprotein (HDL), Uric Acid (UA), Cardiovascular Disease (CVD), Hypertension and Diabetes. Simultaneously, the BCOINFO-SVM model achieves an accuracy of 98.56%, with sensitivity at 96.08% and specificity at 97.73%, making it a potential auxiliary diagnostic model for IgAN.