Demand prediction of medical services in home and community-based services for older adults in China using machine learning.

Background: Home and community-based services are considered an appropriate and crucial caring method for older adults in China. However, the research examining demand for medical services in HCBS through machine learning techniques and national representative data has not yet been carried out. This study aimed to address the absence of a complete and unified demand assessment system for home and community-based services. Methods: This was a cross-sectional study conducted on 15,312 older adults based on the Chinese Longitudinal Healthy Longevity Survey 2018. Models predicting demand were constructed using five machine-learning methods: Logistic regression, Logistic regression with LASSO regularization, Support Vector Machine, Random Forest, and Extreme Gradient Boosting (XGboost), and based on Andersen's behavioral model of health services use. Methods utilized 60% of older adults to develop the model, 20% of the samples to examine the performance of models, and the remaining 20% of cases to evaluate the robustness of the models. To investigate demand for medical services in HCBS, individual characteristics such as predisposing, enabling, need, and behavior factors constituted four combinations to determine the best model. Results: Random Forest and XGboost models produced the best results, in which both models were over 80% at specificity and produced robust results in the validation set. Andersen's behavioral model allowed for combining odds ratio and estimating the contribution of each variable of Random Forest and XGboost models. The three most critical features that affected older adults required medical services in HCBS were self-rated health, exercise, and education. Conclusion: Andersen's behavioral model combined with machine learning techniques successfully constructed a model with reasonable predictors to predict older adults who may have a higher demand for medical services in HCBS. Furthermore, the model captured their critical characteristics. This method predicting demands could be valuable for the community and managers in arranging limited primary medical resources to promote healthy aging.

Thyroid hormone resistance: Mechanisms and therapeutic development.

As an essential primary hormone, thyroid hormone (TH) is indispensable for human growth, development and metabolism. Impairment of TH function in several aspects, including TH synthesis, activation, transportation and receptor-dependent transactivation, can eventually lead to thyroid hormone resistance syndrome (RTH). RTH is a rare syndrome that manifests as a reduced target cell response to TH signaling. The majority of RTH cases are related to thyroid hormone receptor ß (TRß) mutations, and only a few RTH cases are associated with thyroid hormone receptor α (TRα) mutations or other causes. Patients with RTH suffer from goiter, mental retardation, short stature and bradycardia or tachycardia. To date, approximately 170 mutated TRß variants and more than 20 mutated TRα variants at the amino acid level have been reported in RTH patients. In addition to these mutated proteins, some TR isoforms can also reduce TH function by competing with primary TRs for TRE and RXR binding. Fortunately, different treatments for RTH have been explored with structure-activity relationship (SAR) studies and drug design, and among these treatments. With thyromimetic potency but biochemical properties that differ from those of primary TH (T3 and T4), these TH analogs can bypass specific defective transporters or reactive mutant TRs. However, these compounds must be carefully applied to avoid over activating TRα, which is associated with more severe heart impairment. The structural mechanisms of mutation-induced RTH in the TR ligand-binding domain are summarized in this review. Furthermore, strategies to overcome this resistance for therapeutic development are also discussed.

FXR: structures, biology, and drug development for NASH and fibrosis diseases.

The nuclear receptor farnesoid-X-receptor (FXR) plays an essential role in bile acid, glucose, and lipid homeostasis. In the last two decades, several diseases, such as obesity, type 2 diabetes, nonalcoholic fatty liver disease, cholestasis, and chronic inflammatory diseases of the liver and intestine, have been revealed to be associated with alterations in FXR functions. FXR has become a promising therapeutic drug target, particularly for enterohepatic diseases. Despite the large number of FXR modulators reported, only obeticholic acid (OCA) has been approved for primary biliary cholangitis (PBC) therapy as FXR modulator. In this review, we summarize the structure and function of FXR, the development of FXR modulators, and the structure-activity relationships of FXR modulators. Based on the structural analysis, we discuss potential strategies for developing future therapeutic FXR modulators to overcome current limitations, providing new perspectives for enterohepatic and metabolic diseases treatment.