Association of sex hormones with non-alcoholic fatty liver disease: An observational and Mendelian randomization study.

BACKGROUND AND AIMS: Sex-specific associations of sex hormone-binding globulin (SHBG) and bioavailable testosterone (BAT) with NAFLD remain indeterminate. We aimed to explore observational and genetically determined relationships between each hormone and NAFLD. METHODS: We included 187 395 men and 170 193 women from the UK Biobank. Linear and nonlinear Cox regression models and Mendelian randomization (MR) analysis were used to test the associations. RESULTS: During 12.49 years of follow-up, 2209 male and 1886 female NAFLD cases were documented. Elevated SHBG levels were linearly associated with a lower risk of NAFLD in women (HR (95% CI), .71 (.63, .79)), but not in men (a "U" shape, pnon-linear < .001). Higher BAT levels were associated with a lower NAFLD risk in men (HR (95% CI), .81 (.71, .93)) but a higher risk in women (HR (95% CI): 1.25 (1.15, 1.36)). Genetically determined SHBG and BAT levels were linearly associated with NAFLD risk in women (OR (95% CI): .57 (.38, .87) and 2.21 (1.41, 3.26) respectively); in men, an "L-shaped" MR association between SHBG levels and NAFLD risk was found (pnon-linear = .016). The bidirectional MR analysis further revealed the effect of NAFLD on SHBG and BAT levels in both sexes. CONCLUSIONS: Consistently, linear associations of lower SHBG and higher BAT levels with increased NAFLD risk were both conventionally and genetically found in women, while in men, SHBG acts in a nonlinear manner. In addition, NAFLD may affect SHBG and BAT levels.

Comparative Study on Physical Activity in Diabetic and Non-Diabetic Individuals and Influential Factors - China, 2020-2021.

What is already known about this topic?: The majority of Chinese patients with diabetes failed to achieve the level of physical activity recommended by clinical guidelines. What is added by this report?: The prevalence of low-level physical activity was found to be greater in individuals diagnosed with diabetes. It was observed that patients with a protracted duration of diabetes demonstrated a propensity to participate in lower levels of physical activity compared to those with a shorter disease trajectory. The likelihood of engaging in low-level physical activity associated with diabetes was higher in rural inhabitants, those with medium-tier education, employed individuals, and individuals who had longer sleep durations. What are the implications for public health practice?: Developing strategies and interventions to encourage greater involvement of Chinese diabetic patients in physical activity is essential. However, these strategies must take population characteristics into account.

Spatial localization ability of planarians identified through a light maze paradigm.

Spatial localization ability is crucial for free-living animals to fit the environment. As shown by previous studies, planarians can be conditioned to discriminate directions. However, due to their simplicity and primitiveness, they had never been considered to have true spatial localization ability to retrieve locations of objects and places in the environment. Here, we introduce a light maze training paradigm to demonstrate that a planarian worm can navigate to a former recognized place from the start point, even if the worm is transferred into a newly produced maze. This finding identifies the spatial localization ability of planarians for the first time, which provides clues for the evolution of spatial learning. Since the planarians have a primitive brain with simple structures, this paradigm can also provide a simplified model for a detailed investigation of spatial learning.

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.

BACKGROUND: Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny problems. Therefore, it is necessary to consider outliers, imbalanced data, model selection, and parameter tuning when modeling. METHODS: This study used a joint modeling strategy consisting of: outlier detection and removal, data balancing, model fitting and prediction, performance evaluation. We collected medical record data for all ICH patients with admissions in 2017-2019 from Sichuan Province. Clinical and radiological variables were used to construct models to predict mortality outcomes 90 days after discharge. We used stacking ensemble learning to combine logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbors (KNN) models. Accuracy, sensitivity, specificity, AUC, precision, and F1 score were used to evaluate model performance. Finally, we compared all 84 combinations of the joint modeling strategy, including training set with and without cross-validated committees filter (CVCF), five resampling techniques (random under-sampling (RUS), random over-sampling (ROS), adaptive synthetic sampling (ADASYN), Borderline synthetic minority oversampling technique (Borderline SMOTE), synthetic minority oversampling technique and edited nearest neighbor (SMOTEENN)) and no resampling, seven models (LR, RF, ANN, SVM, KNN, Stacking, AdaBoost). RESULTS: Among 4207 patients with ICH, 2909 (69.15%) survived 90 days after discharge, and 1298 (30.85%) died within 90 days after discharge. The performance of all models improved with removing outliers by CVCF except sensitivity. For data balancing processing, the performance of training set without resampling was better than that of training set with resampling in terms of accuracy, specificity, and precision. And the AUC of ROS was the best. For seven models, the average accuracy, specificity, AUC, and precision of RF were the highest. Stacking performed best in F1 score. Among all 84 combinations of joint modeling strategy, eight combinations performed best in terms of accuracy (0.816). For sensitivity, the best performance was SMOTEENN + Stacking (0.662). For specificity, the best performance was CVCF + KNN (0.987). Stacking and AdaBoost had the best performances in AUC (0.756) and F1 score (0.602), respectively. For precision, the best performance was CVCF + SVM (0.938). CONCLUSION: This study proposed a joint modeling strategy including outlier detection and removal, data balancing, model fitting and prediction, performance evaluation, in order to provide a reference for physicians and researchers who want to build their own models. This study illustrated the importance of outlier detection and removal for machine learning and showed that ensemble learning might be a good modeling strategy. Due to the low imbalanced ratio (IR, the ratio of majority class and minority class) in this study, we did not find any improvement in models with resampling in terms of accuracy, specificity, and precision, while ROS performed best on AUC.

A search of only four key databases would identify most randomized controlled trials of acupuncture: A meta-epidemiological study.

Little research has been conducted to assess which specific databases should be searched when performing a systematic review (SR) on acupuncture. The current study aimed to identify key databases and the optimal database combination to retrieve randomized controlled trials (RCTs) on acupuncture for inclusion in SRs. A systematic search for SRs in the field of acupuncture was conducted in order to identify target databases and RCTs were extracted from the SRs that had searched all target databases. The proportions of SRs that had achieved 100%, 95%, or 90% recall of RCTs and the total recall of RCTs in various combinations of target databases were calculated. Sensitivity analysis was performed on those SRs that included 10 or more RCTs. CNKI, WanFang, VIP, PubMed, CENTRAL and Embase were regarded as target databases. A total of 4349 acupuncture RCTs were extracted from 286 SRs. Searching all six target databases retrieved 99.3% of all RCTs while 99.1% were recalled by searching the combination of CNKI, WanFang, PubMed and CENTRAL. There were no significant differences on total recall of RCTs (p = 0.549) or in the proportion of SRs with 90% recall of RCTs (97.2% vs. 97.6%; p = 0.794) between searching the above four and the full six target databases. Most results were similar in the sensitivity analysis. The combined retrieval power of CNKI, WanFang, PubMed and CENTRAL was considered an efficient choice to retrieve acupuncture RCTs included in SRs.

pH-Responsive nanoparticles based on cholesterol/imidazole modified oxidized-starch for targeted anticancer drug delivery.

Various nanoparticles have been developed for tumor-targeted drug delivery. However, nanoparticles with effective targeting and intelligent release capacity are still deficient. Herein, we present new pH-responsive and neutral charged nanoparticles for tumor-targeted anticancer drug delivery. Oxidized starch was synthesized and simultaneously modified by cholesterol and imidazole to obtain amphiphilic cholesterol/imidazole modified oxidized-starch (Cho-Imi-OS). Cho-Imi-OS easily self-assembled into nanoparticles by dialysis. Curcumin was selected as model drug to be encapsulated into the hydrophobic core of nanoparticles. The results showed that curcumin would effectively accumulate in cancer cells by encapsulating into the nanoparticles owing to the nano-sized structure and near neutral charged property of nanoparticles. Curcumin was released faster at pH 5.5 than that at pH 7.4 from the curcumin-loaded nanoparticles (Cur-NPs), indicating the pH-triggered release capacity of Cur-NPs after endocytosis by endosomes since the pH is low to 5.0∼6.0 in endosomes. Naturally, Cur-NPs showed significantly strong inhibitory effect on cancer cells.