Indoor air pollution is associated with respiratory symptoms in children in urban Vietnam.

Exposure to indoor air pollution (IAP) is a leading environmental risk for respiratory diseases. We investigated the relationship between respiratory symptoms and polluting indoor activities such as smoking, cooking and contact with pets among children in Ho Chi Minh City (HCMC), Vietnam. A cross-sectional survey applied a multistage sampling method in 24 randomly selected secondary schools across the city. Approximately 15,000 students completed self-administrated questionnaires on risk factors and respiratory health outcomes within the preceding 12 months. Data were analyzed using a multivariable logistic regression model with robust standard errors. Wheeze was the most common respiratory symptom (39.5 %) reported, followed by sneezing and runny nose (28.3 %). A small percentage of students self-reported asthma (8.6 %). Approximately 56 % of participants lived with family members who smoked. A positive association between exposure to indoor secondhand smoke and respiratory symptoms was observed, with adjusted odds ratios (aOR) of 1.41 (95 % CI: 1.25-1.60, p < 0.001) for wheezing and 1.64 (95 % CI: 1.43-1.87, p < 0.001) for sneezing and runny nose, respectively. Using an open stove fuelled by coal, wood, or kerosene for cooking was associated with wheeze (aOR: 1.36, CI 95 %: 1.10-1.68, p = 0.01) and sneezing and runny nose (aOR: 1.36, CI 95 %: 1.09-1.69, p = 0.01). In the present study, IAP was associated with adverse health outcomes, as evidenced by an increase in respiratory symptoms reported within the previous 12 months.

Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning.

In 2016, the World Health Organization (WHO) updated the glioma classification by incorporating molecular biology parameters, including low-grade glioma (LGG). In the new scheme, LGGs have three molecular subtypes: isocitrate dehydrogenase (IDH)-mutated 1p/19q-codeleted, IDH-mutated 1p/19q-noncodeleted, and IDH-wild type 1p/19q-noncodeleted entities. This work proposes a model prediction of LGG molecular subtypes using magnetic resonance imaging (MRI). MR images were segmented and converted into radiomics features, thereby providing predictive information about the brain tumor classification. With 726 raw features obtained from the feature extraction procedure, we developed a hybrid machine learning-based radiomics by incorporating a genetic algorithm and eXtreme Gradient Boosting (XGBoost) classifier, to ascertain 12 optimal features for tumor classification. To resolve imbalanced data, the synthetic minority oversampling technique (SMOTE) was applied in our study. The XGBoost algorithm outperformed the other algorithms on the training dataset by an accuracy value of 0.885. We continued evaluating the XGBoost model, then achieved an overall accuracy of 0.6905 for the three-subtype classification of LGGs on an external validation dataset. Our model is among just a few to have resolved the three-subtype LGG classification challenge with high accuracy compared with previous studies performing similar work.