METHODS: Anopheles gambiae (s.l.) mosquitoes were identified to species level using PCR techniques. Standard WHO insecticide susceptibility bioassays were carried out to detect resistance to deltamethrin (0.05%), DDT (4%) and bendiocarb (0.1%). TaqMan assays were performed on random samples of deltamethrin-resistant phenotyped and pyrethrum spray collected individuals to determine Vgsc-1014 knockdown resistance mutations.
RESULTS: Anopheles arabiensis accounted for 99.9% of any anopheline species collected across all sites. Bioassay screening indicated that mosquitoes remained susceptible to bendiocarb but were resistance to deltamethrin and DDT in all areas. There were significant increases in deltamethrin resistance over the four years, with overall mean percent mortality to deltamethrin declining from 81.0% (95% CI: 77.6-84.3%) in 2011 to 47.7% (95% CI: 43.5-51.8%) in 2014. The rate of increase in phenotypic deltamethrin-resistance was significantly slower in the LLIN + IRS arm than in the LLIN-only arm (Odds ratio 1.34; 95% CI: 1.02-1.77). The frequency of Vgsc-1014F mutation varied spatiotemporally with highest frequencies in Galabat (range 0.375-0.616) and New Halfa (range 0.241-0.447). Deltamethrin phenotypic-resistance correlated with Vgsc-1014F frequency.
CONCLUSION: Combining LLIN and IRS, with different classes of insecticide, may delay pyrethroid resistance development, but the speed at which resistance develops may be area-specific. Continued monitoring is vital to ensure optimal management and control.
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.
METHODS: Validated translated versions of the Hiroshima University-Dental Behavioural Inventory (HU-DBI) questionnaire were administered to 1,096 final-year dental students in 17 countries. Hierarchical cluster analysis was conducted within the data to detect patterns and groupings.
RESULTS: The overall response rate was 72%. The cluster analysis identified two main groups among the countries. Group 1 consisted of twelve countries: one Oceanic (Australia), one Middle-Eastern (Israel), seven European (Northern Ireland, England, Finland, Greece, Germany, Italy, and France) and three Asian (Korea, Thailand and Malaysia) countries. Group 2 consisted of five countries: one South American (Brazil), one European (Belgium) and three Asian (China, Indonesia and Japan) countries. The percentages of 'agree' responses in three HU-DBI questionnaire items were significantly higher in Group 2 than in Group 1. They include: "I worry about the colour of my teeth."; "I have noticed some white sticky deposits on my teeth."; and "I am bothered by the colour of my gums."
CONCLUSION: Grouping the countries into international clusters yielded useful information for dentistry and dental education.