Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy.

Importance: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed. Objective: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients. Design, Setting, and Participants: This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables. Exposures: One of 7 antiseizure medications. Main Outcomes and Measures: With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models. Results: The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC. Conclusions and Relevance: In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.

Molecular identification of cultivable bacteria in the gut of adult Bactrocera tau (Walker) and their trapping effect.

BACKGROUND: It has been shown that some bacteria can attract their hosts. Our research aimed to identify cultivable bacterial isolates in the guts of sexually mature adult female and male Bactrocera tau and to evaluate their utility in trapping of B. tau. RESULTS: Forty-one strains of bacteria were isolated and identified from B. tau with morphological, physiological, biochemical and 16S rDNA analysis. The dominant bacterial genera shared by both females and males were Enterobacter, Providencia and Serratia. Thirteen bacterial isolates selected from female and male B. tau adults were cultured, and the attractiveness of bacterial fermented liquid and autoclaved supernatants from these strains to B. tau adults was tested. The laboratory test showed that both the autoclaved supernatants and fermented liquid could attract male and female B. tau, and the former was substantially more effective, with the autoclaved supernatants from all strains being significantly more attractive to adult B. tau. BF16, BF(12), BF23 and BF(32) were the most attractive bacteria to 8-day-old and sexually mature B. tau. Furthermore, the results of a subsequent field cage test showed that BF(12), BF23, and BF(32) were significantly more attractive to B. tau adults. CONCLUSION: These results provide useful information for the development of bacterial biocontrol agents and their application as insecticides. © 2018 Society of Chemical Industry.