Curcumin intervention for cognitive function in different types of people: A systematic review and meta-analysis.

Curcumin is a polyphenolic natural compound with diverse and attractive biological activities, which may prevent or ameliorate pathological processes underlying age-related cognitive decline, dementia, or mood disorders. However, clinical trials and animal studies have yielded conflicting conclusions regarding its effectiveness for cognition in different individuals. The aim of this review is to meta-analytically assess the effectiveness of curcumin for cognitive function in different types of people. A preliminary search on PubMed, Embase, Web of Science, ClinicalTrials.gov, Cochrane Library, Chinese National Knowledge Infrastructure, and Wanfang Data and China Biology Medicine disc was performed to identify randomized controlled trials investigating the effect of curcumin on cognition. Six clinical trials with a total of 289 subjects met inclusion criteria for this review. We used a random-effects model to calculate the pooled standardized difference of means (SMD). For older adults who received curcumin, scores on measures of cognitive function (SMD = 0.33, 95% confidence interval [CI] [0.05, 0.62]; p = 0.02), occurrence of adverse events (odds ratio [OR] = 5.59, 95% CI [0.96, 36.80]; p = 0.05), and measures of depression (SMD = -0.29, 95% CI [0.64, 0.05]; p = 0.09) indicated significant memory improvement. In patients with Alzheimer's disease (AD), scores in measures of cognition status (SMD = -0.90, 95% CI [1.48, -0.32]; p = 0.002) indicated that there was a trend for treated subjects to do worse than placebo-treated subjects on the Mini-Mental State Examination. The occurrence of adverse events (OR = 0.87, 95% CI [0.10, 7.51]; p = 0.90) was similar to those who received placebo. Due to insufficient data, it was impossible to provide a narrative account of only the outcomes for schizophrenia. Curcumin appears to be more effective in improving cognitive function in the elderly than in improving symptoms of AD and schizophrenia. Curcumin is also safe and tolerated among these individuals. Because of the small number of studies available, a funnel plot or sensitivity analysis was not possible. Further high-quality trials with larger sample sizes or bioavailability-improved curcumin formulations may be considered for reliable assessment.

Clinical significance of CUL4A in human prostate cancer.

Aberrant expression of the Cullin 4A (CUL4A) is found in many tumor types, but the functions and mechanism of CUL4A in prostate cancer (PCa) development and progression remain largely unknown. The aim of this study was to investigate the possible role of CUL4A in prostate tumorigenesis. Immunohistochemistry was used to examine CUL4A expression in human PCa tissues and BPH tissues. Cell proliferation was assessed by MTT, and migration and invasion were analyzed by Transwell and Matrigel assays after CUL4A knockdown in PCa in vitro. The results showed that CUL4A protein was overexpressed in 86.21 % of PCa tissues. CUL4A knockdown with siRNA in PCa cells decreased cell proliferation, migration, and invasion. Mechanistically, CUL4A could modulate the expression of P53 in PCa cells. Our results indicate that CUL4A overexpression play an oncogenic role in the pathogenesis of PCa, and CUL4A may be a potential therapeutic target for PCa.

Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning.

Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making. Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the "feature_importances" method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model. Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction. Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.

Application of Personal Information Privacy Protection Based on Machine Learning Algorithm.

Based on a machine learning algorithm, this paper deeply explores the privacy protection of personal information. In this paper, the definition of the machine learning algorithm is put forward, the design idea of privacy protection in joint machine learning platform is studied, and the architecture model and model parameter updating strategy of joint machine learning under privacy protection are designed. To protect the privacy of personal information, this paper also proposes a data homomorphic verification mechanism to prevent the global parameters from being tampered with by malicious cloud servers. In order to verify the performance of the models constructed in this paper, the comparative experiments of different models are carried out. The experimental results show that this algorithm has a fast convergence speed, and the average error rate decreases by 4.17% compared with the traditional algorithm. Moreover, the accuracy of this algorithm reaches 95.37%, which is about 8.76% higher than the previous algorithm. This model is applied to the field of personal information privacy protection, which can provide a safe and reliable environment for personal information privacy and effectively protect the privacy of data owners. And, means and reference value is provided for the development direction of privacy protection.