Detection of statin-induced rhabdomyolysis and muscular related adverse events through data mining technique.

BACKGROUND AND OBJECTIVE: Rhabdomyolysis (RM) is a life-threatening adverse drug reaction in which statins are the one commonly related to RM. The study aimed to explore the association between statin used and RM or other muscular related adverse events. In addition, drug interaction with statins were also assessed. METHODS: All extracted prescriptions were grouped as lipophilic and hydrophilic statins. RM outcome was identified by electronically screening and later ascertaining by chart review. The study proposed 4 models, i.e., logistic regression (LR), Bayesian network (BN), random forests (RF), and extreme gradient boosting (XGBoost). Features were selected using multiple processes, i.e., bootstrapping, expert opinions, and univariate analysis. RESULTS: A total of 939 patients who used statins were identified consisting 15, 9, and 19 per 10,000 persons for overall outcome prevalence, using statin alone, and co-administrations, respectively. Common statins were simvastatin, atorvastatin, and rosuvastatin. The proposed models had high sensitivity, i.e., 0.85, 0.90, 0.95 and 0.95 for LR, BN, RF, and XGBoost, respectively. The area under the receiver operating characteristic was significantly higher in LR than BN, i.e., 0.80 (0.79, 0.81) and 0.73 (0.72, 0.74), but a little lower than the RF [0.817 (95% CI 0.811, 0.824)] and XGBoost [0.819 (95% CI 0.812, 0.825)]. The LR model indicated that a combination of high-dose lipophilic statin, clarithromycin, and antifungals was 16.22 (1.78, 148.23) times higher odds of RM than taking high-dose lipophilic statin alone. CONCLUSIONS: The study suggested that statin uses may have drug interactions with others including clarithromycin and antifungal drugs in inducing RM. A prospective evaluation of the model should be further assessed with well planned data monitoring. Applying LR in hospital system might be useful in warning drug interaction during prescribing.

Deep Learning for Discrimination Between Fungal Keratitis and Bacterial Keratitis: DeepKeratitis.

PURPOSE: Microbial keratitis is an urgent condition in ophthalmology that requires prompt treatment. This study aimed to apply deep learning algorithms for rapidly discriminating between fungal keratitis (FK) and bacterial keratitis (BK). METHODS: A total of 2167 anterior segment images retrospectively acquired from 194 patients with 128 patients with BK (1388 images, 64.1%) and 66 patients with FK (779 images, 35.9%) were used to develop the model. The images were split into training, validation, and test sets. Three convolutional neural networks consisting of VGG19, ResNet50, and DenseNet121 were trained to classify images. Performance of each model was evaluated using precision (positive predictive value), sensitivity (recall), F1 score (test's accuracy), and area under the precision-recall curve (AUPRC). Ensemble learning was then applied to improve classification performance. RESULTS: The classification performance in F1 score (95% confident interval) of VGG19, DenseNet121, and RestNet50 was 0.78 (0.72-0.84), 0.71 (0.64-0.78), and 0.68 (0.61-0.75), respectively. VGG19 also demonstrated the highest AUPRC of 0.86 followed by RestNet50 (0.73) and DenseNet (0.60). The ensemble learning could improve performance with the sensitivity and F1 score of 0.77 (0.81-0.83) and 0.83 (0.77-0.89) with an AUPRC of 0.904. CONCLUSIONS: Convolutional neural network with ensemble learning showed the best performance in discriminating FK from BK compared with single architecture models. Our model can potentially be considered as an adjunctive tool for providing rapid provisional diagnosis in patients with microbial keratitis.

Assessment of temperature-dependent proteomes of Pythium insidiosum by using the SWISS-PROT database.

Pythium insidiosum causes the life-threatening disease, called pythiosis. Information on microbial pathogenesis could lead to an effective method of infection control. This study aims at assessing temperature-dependent proteomes, and identifying putative virulence factors of P. insidiosum. Protein extracts from growths at 25°C and 37°C were analyzed by mass spectrometry and SWISS-PROT database. A total of 1052 proteins were identified. Upon exposure to increased temperature, 219 proteins were markedly expressed, eight of which were putative virulence factors of P. insidiosum. These temperature-dependent proteins should be further investigated for their roles in pathogenesis, and some of which could be potential therapeutic targets.