Search

Osteopenia refers to bone density that is not normal but also not as low as that noted in osteoporosis. Osteopenia leads to osteoporosis and increases the risk of fractures. Current research is focused on agents that will prevent or slow the progression of bone loss. On the basis of published evidence, Cissus quadrangularis (CQ) might potentially provide a novel natural treatment for osteopenia.To determine the effect of 24 weeks of consecutive treatment with CQ on delaying bone loss and safety in postmenopausal women (PMW) with osteopenia.This study is a randomized, placebo-controlled trial. Here, 134 enrolled PMW with osteopenia (40 years and having no period for 1-10 years) received CQ at 1.2 (CQ1.2) or 1.6 g/day (CQ1.6) or placebo. The %change in bone mineral density (BMD) at the lumbar spine (L1-L4), femoral neck, and total hip served as the primary outcome. The %change in bone turnover markers (BTMs), including C-terminal telopeptide of type 1 collagen (CTX) and procollagen type 1 amino-terminal propeptide (P1NP), was the secondary outcome. These outcomes were compared between the CQ vs. placebo group at weeks 12 and 24. The least significant change (LSC) was used to monitor clinical changes. The adverse events (AE) were monitored.A total of 108 participants completed this study. The %BMD changes in the CQ-treated groups did not differ at any site after 24 weeks compared to the placebo. Statistically significant differences were detected in CQ1.6 at the lumbar spine (0.011 ± 0.025 g/cmThis is the first clinical report that showed a promising effect on delaying bone loss of orally administration of CQ for 24 weeks, as indicated by a slower bone remodeling process via a reduction in BTMs. However, no change in BMD was observed.

Postoperative adhesions represent a frequent complication of abdominal surgery. Adhesions can result from infection, ischemia, and foreign body reaction, but commonly develop after any surgical procedure. The morbidity caused by adhesions affects quality of life and, therefore, it is paramount to continue to raise awareness and scientific recognition of the burden of adhesions in healthcare and clinical research. This 2021 Global Expert Consensus Group worked together to produce consented statements to guide future clinical research trials and advise regulatory authorities. It is critical to harmonize the expectations of research, to both develop and bring to market improved anti-adhesion therapies, with the ultimate, shared goal of improved patient outcomes.

This research aimed to develop a predictive model of the risk assessment of pregnancy-induced hypertension using a machine learning approach. Pregnancy-induced hypertension is a complication that has a serious impact on pregnant women and fetuses. It is the world’s top three cause of death among pregnant women [1]. Nowadays, the exact cause of pregnancyinduced hypertension is unknown and therefore cannot be prevented. Early detection and received treatment can reduce the severity and danger. A public dataset of Logan (2020) was used in this research [2]. The dataset was collected from a casecontrol study on the determinants of 83 pre-eclampsia and five eclampsia cases among 352 pregnant women delivering in county hospitals in Nairobi, Kenya. According to the dataset, 75 percent of the pregnant women were healthy. Only 25 percent of the pregnant women were pre-eclampsia and eclampsia. Thus, this would result in a problem of an imbalanced classification when one of the two classes had more data than the other class. As such, this problem was resolved with the Synthetic Minority Over-sampling Technique (SMOTE). Risk assessment of pregnancy-induced hypertension was performed on seven machine learning algorithms, which were logistic regression (LR), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), multilayer perceptron neural network(MLP), support vector machines (SVM), and naive Bayes (NB). In the experimental results, RF had the highest accuracy at 89.62 percent compared to other machine learning algorithms.