Post-surgery cholecystectomy, hepatectomy, and pancreatectomy patients increase the risk of osteoporotic vertebral fracture.

INTRODUCTION: Data is currently lacking regarding association between the cholecystectomy/hepatectomy/pancreatectomy and the development of osteoporotic fracture. A retrospective cohort study was conducted to investigate the relationship between cholecystectomy/hepatectomy/pancreatectomy and the subsequent risk of developing osteoporotic fracture. MATERIALS AND METHODS: Patients having undergone cholecystectomy, hepatectomy, or pancreatectomy between 2000 and 2012 were selected from the All Population Based Hospitalization File as the surgery cohort (n = 304,081), which was frequency matched with the control cohort (n = 304,081). The Cox proportional hazard model and Kaplan-Meier analysis were applied to measure the hazard ratios and the cumulative incidence of osteoporotic fracture. RESULTS: A total of 1136 patients in the surgery cohort and 1179 patients in the control cohort were newly diagnosed with osteoporotic fracture. The overall osteoporotic fracture risk in the surgery cohort was 1.12-fold higher [95% confidence interval (CI), 1.03-1.21]. Specifically, surgery cohort had higher vertebral fracture risk than non-surgery cohort [adjusted hazard ratio (aHR) 1.12, Cl, 1.03-1.22]. In addition, patients underwent cholecystectomy (includes open and laparoscopic approaches), hepatectomy (only open approach), and pancreatectomy group (only open approach) were 1.10 (95% CI, 1.01-1.19), 1.49 (95% CI, 1.10-2.01), and 1.88 (95% CI, 1.23-2.87) times more likely to develop osteoporotic fracture, respectively. No significant difference of osteoporotic fracture risk was observed between open and laparoscopic cholecystectomy. The risk of osteoporotic fracture was significantly increased in females, patients aged ≥ 40 years old, and patients with some comorbidity. CONCLUSIONS: Patients post cholecystectomy, hepatectomy, or pancreatectomy significantly increased risk of developing osteoporotic fracture, suggesting closer attention in post-operative care is needed.

Application of Neural Network and Cluster Analyses to Differentiate TCM Patterns in Patients With Breast Cancer.

BACKGROUND AND PURPOSE: Pattern differentiation is a critical element of the prescription process for Traditional Chinese Medicine (TCM) practitioners. Application of advanced machine learning techniques will enhance the effectiveness of TCM in clinical practice. The aim of this study is to explore the relationships between clinical features and TCM patterns in breast cancer patients. METHODS: The dataset of breast cancer patients receiving TCM treatment was recruited from a single medical center. We utilized a neural network model to standardize terminologies and address TCM pattern differentiation in breast cancer cases. Cluster analysis was applied to classify the clinical features in the breast cancer patient dataset. To evaluate the performance of the proposed method, we further compared the TCM patterns to therapeutic principles of Chinese herbal medication in Taiwan. RESULTS: A total of 2,738 breast cancer cases were recruited and standardized. They were divided into 5 groups according to clinical features via cluster analysis. The pattern differentiation model revealed that liver-gallbladder dampness-heat was the primary TCM pattern identified in patients. The main therapeutic goals of the top 10 Chinese herbal medicines prescribed for breast cancer patients were to clear heat, drain dampness, and detoxify. These results demonstrated that the neural network successfully identified patterns from a dataset similar to the prescriptions of TCM clinical practitioners. CONCLUSION: This is the first study using machine-learning methodology to standardize and analyze TCM electronic medical records. The patterns revealed by the analyses were highly correlated with the therapeutic principles of TCM practitioners. Machine learning technology could assist TCM practitioners to comprehensively differentiate patterns and identify effective Chinese herbal medicine treatments in clinical practice.

Structure and dynamics of water surrounding the poly(methacrylic acid): a molecular dynamics study.

All-atom molecular dynamics simulations are used to study a single chain of poly(methacrylic acid) in aqueous solutions at various degrees of charge density. Through a combination of analysis on the radial distribution functions of water and snapshots of the equilibrated structure, we observe that local arrangements of water molecules, surrounding the functional groups of COO- and COOH in the chain, behave differently and correlated well to the resulting chain conformation behavior. In general, due to strong attractive interactions between water and charged COO- via the formation of hydrogen bonds, water molecules tend to form shell-like layers around the COO- groups. Furthermore, water molecules often act as a bridging agent between two neighboring COO- groups. These bridged water molecules are observed to stabilize the rodlike chain conformation that the highly charged chain reveals, as they significantly limit torsional and bending degrees of the backbone monomers. In addition, they display different dynamic properties from the bulk water. Both the resulting oxygen and hydrogen spectra are greatly shifted due to the presence of strong H-bonded interactions.