Machine-learning model for predicting depression in second-hand smokers in cross-sectional data using the Korea National Health and Nutrition Examination Survey.

Objective: Depression among non-smokers at risk of second-hand smoke (SHS) exposure has been a neglected public health concern despite their vulnerability. The objective of this study was to develop high-performance machine-learning (ML) models for the prediction of depression in non-smokers and to identify important predictors of depression for second-hand smokers. Methods: ML algorithms were created using demographic and clinical data from the Korea National Health and Nutrition Examination Survey (KNHANES) participants from 2014, 2016, and 2018 (N = 11,463). The Patient Health Questionnaire was used to diagnose depression with a total score of 10 or higher. The final model was selected according to the area under the curve (AUC) or sensitivity. Shapley additive explanations (SHAP) were used to identify influential features. Results: The light gradient boosting machine (LGBM) with the highest positive predictive value (PPV; 0.646) was selected as the best model among the ML algorithms, whereas the support vector machine (SVM) had the highest AUC (0.900). The most influential factors identified using the LGBM were stress perception, followed by subjective health status and quality of life. Among the smoking-related features, urine cotinine levels were the most important, and no linear relationship existed between the smoking-related features and the values of SHAP. Conclusions: Compared with the previously developed ML models, our LGBM models achieved excellent and even superior performance in predicting depression among non-smokers at risk of SHS exposure, suggesting potential goals for depression-preventive interventions for non-smokers during public health crises.

AMID: Accurate Magnetic Indoor Localization Using Deep Learning.

Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID), an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment.

Heterogeneous target protein expression in synchronous multiple gastric carcinomas.

OBJECTIVE: To explore the target protein expression in separate tumors in a patient with synchronous multiple gastric carcinomas (SMGCs). STUDY DESIGN: Immunohistochemistry for HER2, EGFR, and MET were performed in 282 carcinomas from 141 patients. RESULTS: Of 141 patients with SMGCs, 11.3%, 23.4%, and 14.9% of cases showed HER2, EGFR, and MET protein overexpression, respectively. In SMGC cases with overexpression of target proteins in > 1 tumor, intertumoral heterogeneity was 81.3% (13/16) for HER2, 78.8% (26/33) for EGFR, and 90.5% (19/21) for MET protein. The concordance rate of HER2, EGFR, and MET expression between 2 carcinomas from the same patient was 90.8%, 81.6%, and 86.5%, respectively, with a kappa value below 0.3, indicating slight to fair agreement. CONCLUSION: We found a considerable intertumoral heterogeneity of target protein overexpression in SMGCs. Our findings support a multicentric origin for SMGC and emphasize the need to perform immunohistochemistry for all synchronous lesions.