Using machine learning to develop a five-item short form of the children's depression inventory.

BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention. METHODS: Initially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample. RESULTS: The study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach's alpha = 0.72, criterion-related validity = 0.77). CONCLUSIONS: This five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples.

The dynamic nexus: exploring the interplay of BMI before, during, and after pregnancy with Metabolic Syndrome (MetS) risk in Chinese lactating women.

BACKGROUND AND AIM: The health implications of BMI and MetS in lactating women are significant. This study aims to investigate the relationship between risk of Mets in lactation and BMI in four stages: pre-pregnancy, prenatal period, 42 days postpartum, and current lactation. METHODS AND RESULTS: A total of 1870 Lactating Women within 2 years after delivery were included from "China Child and Lactating Mother Nutrition Health Surveillance (2016-2017)". Logistic regression model and Restricted cubic spline (RCS) were used to estimate the relationship between BMI and risk of MetS. ROC analysis was used to determine the threshold for the risk of MetS. Chain mediating effect analysis was used to verify the mediating effect. BMI of MetS group in all stages were higher than non-MetS group (P < 0.0001). There were significant positive correlations between BMI in each stage and ORs of MetS during lactation (P < 0.05). The best cut-off values for BMI in the four stages were 23.47, 30.49, 26.04 and 25.47 kg/m2. The non-linear spline test at BMI in 42 days postpartum, current and MetS in lactation was statistically significant (P non-linear = 0.0223, 0.0003). The mediation effect of all chains have to work through lactation BMI. The total indirect effect accounted for 80.95% of the total effect. CONCLUSIONS: The risk of MetS in lactating women is due to a high BMI base before pregnancy and postpartum. High BMI in all stages of pregnancy and postpartum were risk factors for MetS in lactation. BMI during lactation plays a key role in the risk of MetS.

Developing a Short-Form Buss-Warren Aggression Questionnaire Based on Machine Learning.

For adolescents, high levels of aggression are often associated with suicide, physical injury, worsened academic performance, and crime. Therefore, there is a need for the early identification of and intervention for highly aggressive adolescents. The Buss-Warren Aggression Questionnaire (BWAQ) is one of the most widely used offensive measurement tools. It consists of 34 items, and the longer the scale, the more likely participants are to make an insufficient effort response (IER), which reduces the credibility of the results and increases the cost of implementation. This study aimed to develop a shorter BWAQ using machine learning (ML) techniques to reduce the frequency of IER and simultaneously decrease implementation costs. First, an initial version of the short-form questionnaire was created using stepwise regression and an ANOVA F-test. Then, a machine learning algorithm was used to create the optimal short-form questionnaire (BWAQ-ML). Finally, the reliability and validity of the optimal short-form questionnaire were tested using independent samples. The BWAQ-ML contains only four items, thirty items less than the BWAQ, and its AUC, accuracy, recall, precision, and F1 score are 0.85, 0.85, 0.89, 0.83, and 0.86, respectively. BWAQ-ML has a Cronbach's alpha of 0.84, a correlation with RPQ of 0.514, and a correlation with PTM of -0.042, suggesting good measurement performance. The BWAQ-ML can effectively measure individual aggression, and its smaller number of items improves the measurement efficiency for large samples and reduces the frequency of IER occurrence. It can be used as a convenient tool for early adolescent aggression identification and intervention.

Heat shock protein B7 (HSPB7) inhibits lung adenocarcinoma progression by inhibiting glycolysis.

In the present study, it was aimed to investigate the effects and potential mechanisms of heat shock protein B7 (HSPB7) on lung adenocarcinoma (LUAD). Bioinformatic analysis was performed to explore the association between HSPB7 expression and patients with LUAD. MTT, colony formation, wound healing and Transwell assays were performed to examine the proliferative, migratory and invasive abilities of H1975 and A549 cells. Western blot analysis was conducted to determine the corresponding protein expression. Co­Immunoprecipitation and Chromatin immunoprecipitation assays were carried out to reveal the interaction between HSPB7 and myelodysplastic syndrome 1 and ecotropic viral integration site 1 complex locus (MECOM). In addition, an animal model was conducted by the subcutaneous injection of A549 cells into BALB/c nude mice, and tumor weight and size were measured. HSPB7 was downregulated in LUAD tissues and cells, and its expression level correlated with patient prognosis. Cell functional data revealed that silencing of HSPB7 promoted lung cancer cell proliferation, migration, invasion and epithelial mesenchymal transition (EMT); whereas overexpression of HSPB7 led to the opposite results. Furthermore, bioinformatics analysis showed that HSPB7 inhibited glycolysis. HSPB7 decreased glucose consumption, lactic acid production, and lactate dehydrogenase A, hexokinase 2 and pyruvate kinase muscle isoform 2 protein levels. The results demonstrated that MECOM was a transcription factor of HSPB7. Collectively, these results suggested that HSPB7 is regulated by MECOM, and that HSPB7 attenuates LUAD cell proliferation, migration, invasion and EMT by inhibiting glycolysis.