RESUMO
Background: COVID-19 vaccines offer different levels of immune protection but do not provide 100% protection. Vaccinated persons with pre-existing comorbidities may be at an increased risk of SARS-CoV-2 breakthrough infection or reinfection. The aim of this study is to identify the critical variables associated with a higher probability of SARS-CoV-2 breakthrough infection using machine learning. Methods: A dataset comprising symptoms and feedback from 257 persons, of whom 203 were vaccinated and 54 unvaccinated, was used for the investigation. Three machine learning algorithms - Deep Multilayer Perceptron (Deep MLP), XGBoost, and Logistic Regression - were trained with the original (imbalanced) dataset and the balanced dataset created by using the Random Oversampling Technique (ROT), and the Synthetic Minority Oversampling Technique (SMOTE). We compared the performance of the classification algorithms when the features highly correlated with breakthrough infection were used and when all features in the dataset were used. Result: The results show that when highly correlated features were considered as predictors, with Random Oversampling to address data imbalance, the XGBoost classifier has the best performance (F1 = 0.96; accuracy = 0.96; AUC = 0.98; G-Mean = 0.98; MCC = 0.88). The Deep MLP had the second best performance (F1 = 0.94; accuracy = 0.94; AUC = 0.92; G-Mean = 0.70; MCC = 0.42), while Logistic Regression had less accurate performance (F1 = 0.89; accuracy = 0.88; AUC = 0.89; G-Mean = 0.89; MCC = 0.68). We also used Shapley Additive Explanations (SHAP) to investigate the interpretability of the models. We found that body temperature, total cholesterol, glucose level, blood pressure, waist circumference, body weight, body mass index (BMI), haemoglobin level, and physical activity per week are the most critical variables indicating a higher risk of breakthrough infection. Conclusion: These results, evident from our unique data source derived from apparently healthy volunteers with cardiovascular risk factors, follow the expected pattern of positive or negative correlations previously reported in the literature. This information strengthens the body of knowledge currently applied in public health guidelines and may also be used by medical practitioners in the future to reduce the risk of SARS-CoV-2 breakthrough infection.
RESUMO
Breast cancer cells modulate lipid and fatty acid metabolism to sustain proliferation. The role of adipocytes in cancer treatment efficacy remains, however, to be fully elucidated. We investigated whether diet-induced obesity (DIO) affects the efficacy of doxorubicin treatment in a breast tumor-bearing mouse model. Female C57BL6 mice were fed a high fat or low fat diet for the full duration of the study (12 weeks). After 8 weeks, mice were inoculated with E0771 triple-negative breast cancer cells in the fourth mammary gland to develop breast tumor allographs. Tumor-bearing mice received either vehicle (Hank's balanced salt solution) or doxorubicin (chemotherapy). Plasma inflammatory markers, tumor, and mammary adipose tissue fatty acid composition, as well as protein expression of lipid metabolism markers were determined. The high fat diet (HFD) attenuated the treatment efficacy of doxorubicin. Both leptin and resistin concentrations were significantly increased in the HFD group treated with doxorubicin. Suppressed lipogenesis (decreased stearoyl CoA-desaturase-1) and lipolysis (decreased hormone-sensitive lipase) were observed in mammary adipose tissue of the DIO animals, whereas increased expression was observed in the tumor tissue of doxorubicin treated HFD mice. Obesogenic conditions induced altered tissue fatty acid (FA) compositions, which reduced doxorubicin's treatment efficacy. In mammary adipose tissue breast cancer cells suppressed the storage of FAs, thereby increasing the availability of free FAs and favored inflammation under obesogenic conditions.