Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort.

BACKGROUND: The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability. METHODS: This study employed nine commonly used machine learning methods to construct overweight risk models. The general community are the target of this study, and a total of 10,905 Chinese subjects from Ningde City in Fujian province, southeast China, participated. The best model was selected through appropriate verification and validation and was suitably explained. RESULTS: The overweight risk models employing machine learning exhibited good performance. It was concluded that CatBoost, which is used in the construction of clinical risk models, may surpass previous machine learning methods. The visual display of the Shapley additive explanation value for the machine model variables accurately represented the influence of each variable in the model. CONCLUSIONS: The construction of an overweight risk model using machine learning may currently be the best approach. Moreover, CatBoost may be the best machine learning method. Furthermore, combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Enhanced virulence of genetically engineered Autographa californica nucleopolyhedrovirus owing to accelerated viral DNA replication aided by inserted ascovirus genes.

Genetic engineering technology is an ideal method to improve insecticidal efficiency by combining the advantages of different pathogenic microorganisms. Thus, six ascovirus genes were introduced into the genomic DNA of Autographa californica nucleopolyhedrovirus (AcMNPV) to possibly transfer the intrinsically valuable insecticidal properties from ascovirus to baculovirus. The viral budded virus (BV) production and viral DNA replication ability of AcMNPV-111 and AcMNPV-165 were significantly stronger than that of AcMNPV-Egfp (used as the wild-type virus in this study), whereas AcMNPV-33 had reduced ones. AcMNPV-111 and AcMNPV-165 also exhibited excellent insecticidal efficiency in the in vivo bioassays: AcMNPV-111 showed a 24.1% decrease in the LT50 value and AcMNPV-165 exhibited a 56.3% decrease in the LD50 value compared with AcMNPV-Egfp against the 3rd instar of Spodoptera exigua larvae, respectively. Furthermore, the size of the occlusion bodies (OBs) of AcMNPV-33, AcMNPV-111, and AcMNPV-165 were significantly increased compared to that of AcMNPV-Egfp. AcMNPV-111 and AcMNPV-165 had stable virulence against the 2nd to 4th instars tested larvae and higher OB yield than AcMNPV-Egfp in the 3rd and 4th instar larvae. Correlation and regression analyses indicated that it is better to use 5 OBs/larva virus to infect the 2nd instar larvae to produce AcMNPV-111 and 50 OBs/larva virus to infect the 3rd instar larvae to produce AcMNPV-165. The results of this study obtained recombinant viruses with enhanced virulence and exhibited a diversity of ascovirus gene function based on the baculovirus platform, which provided a novel strategy for the improvement of baculovirus as a biological insecticide.

Machine learning algorithms are comparable to conventional regression models in predicting distant metastasis of follicular thyroid carcinoma.

OBJECTIVE: Distant metastasis often indicates a poor prognosis, so early screening and diagnosis play a significant role. Our study aims to construct and verify a predictive model based on machine learning (ML) algorithms that can estimate the risk of distant metastasis of newly diagnosed follicular thyroid carcinoma (FTC). DESIGN: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. PATIENTS: A total of 5809 FTC patients were included in the data analysis. Among them, there were 214 (3.68%) cases with distant metastasis. METHOD: Univariate and multivariate logistic regression (LR) analyses were used to determine independent risk factors. Seven commonly used ML algorithms were applied for predictive model construction. We used the area under the receiver-operating characteristic (AUROC) curve to select the best ML algorithm. The optimal model was trained through 10-fold cross-validation and visualized by SHapley Additive exPlanations (SHAP). Finally, we compared it with the traditional LR method. RESULTS: In terms of predicting distant metastasis, the AUROCs of the seven ML algorithms were 0.746-0.836 in the test set. Among them, the Extreme Gradient Boosting (XGBoost) had the best prediction performance, with an AUROC of 0.836 (95% confidence interval [CI]: 0.775-0.897). After 10-fold cross-validation, its predictive power could reach the best [AUROC: 0.855 (95% CI: 0.803-0.906)], which was slightly higher than the classic binary LR model [AUROC: 0.845 (95% CI: 0.818-0.873)]. CONCLUSIONS: The XGBoost approach was comparable to the conventional LR method for predicting the risk of distant metastasis for FTC.

Combined T4 + T3 therapy versus T4 monotherapy effect on psychological health in hypothyroidism: A systematic review and meta-analysis.

PURPOSE: To evaluate whether T4 + T3 combination therapy had advantages in improving psychological health compared with T4 monotherapy. METHODS: We searched PubMed, Embase, Cochrane Library, and Web of Science from January 2000 to March 2021, and updated in September 2021. The inclusion criteria (prospective study, published in English, had a T4 + T3 combination therapy test group and a T4 monotherapy control group, patients aged ≥18 years and with overt primary hypothyroidism, and published after January 2000) were applied by two reviewers; any disagreement was resolved by a third reviewer. The two reviewers independently extracted data using a standard data form and assessed the risk of bias using the Cochrane risk of bias tool. Coprimary outcomes included the psychological health measures of depression, fatigue, pain, anxiety and anger, measured using validated and reliable instruments. RESULTS: Eighteen of 2029 studies (883 patients) were included in the meta-analysis. No significant difference was found between T4 + T3 combination therapy and T4 monotherapy with regard to depression (standardized mean difference [SMD]: -0.06, 95% confidence interval [CI]: -0.18; 0.07), fatigue (SMD: 0.06, 95% CI: -0.13; 0.26), pain (SMD: -0.01, 95% CI: -0.24; 0.22), anxiety (SMD: 0.01, 95% CI: -0.15; 0.17) and anger (SMD: 0.05, 95% CI: -0.15; 0.24). Methodological heterogeneity had no influence on the results. The patients preferred combination therapy significantly. CONCLUSIONS: Compared with T4 monotherapy, T4 + T3 combination therapy had no significant advantage in improving psychological health. For patients who are unsatisfied with LT4 monotherapy, the patient and the physician should make a joint decision concerning therapy.