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.
METHODS: We assessed patients from the REMoxTB trial-a randomised controlled trial of tuberculosis treatment that enrolled previously untreated participants with Mycobacterium tuberculosis infection from Malaysia, South Africa, and Thailand. We did whole-genome sequencing and mycobacterial interspersed repetitive unit-variable number of tandem repeat (MIRU-VNTR) typing of pairs of isolates taken by sputum sampling: one from before treatment and another from either the end of failed treatment at 17 weeks or later or from a recurrent infection. We compared the number and location of SNPs between isolates collected at baseline and recurrence.
FINDINGS: We assessed 47 pairs of isolates. Whole-genome sequencing identified 33 cases with little genetic distance (0-6 SNPs) between strains, deemed relapses, and three cases for which the genetic distance ranged from 1306 to 1419 SNPs, deemed re-infections. Six cases of relapse and six cases of mixed infection were classified differently by whole-genome sequencing and MIRU-VNTR. We detected five single positive isolates (positive culture followed by at least two negative cultures) without clinical evidence of disease.
INTERPRETATION: Whole-genome sequencing enables the differentiation of relapse and re-infection cases with greater resolution than do genotyping methods used at present, such as MIRU-VNTR, and provides insights into the biology of recurrence. The additional clarity provided by whole-genome sequencing might have a role in defining endpoints for clinical trials.
FUNDING: Wellcome Trust, European Union, Medical Research Council, Global Alliance for TB Drug Development, European and Developing Country Clinical Trials Partnership.
BACKGROUND: CKD is associated with fluid excess that can be estimated by bioimpedance spectroscopy. We aimed to assess effects of sodium glucose co-transporter 2 inhibition on bioimpedance-derived "Fluid Overload" and adiposity in a CKD population.
METHODS: EMPA-KIDNEY was a double-blind placebo-controlled trial of empagliflozin 10 mg once daily in patients with CKD at risk of progression. In a substudy, bioimpedance measurements were added to the main trial procedures at randomization and at 2- and 18-month follow-up visits. The substudy's primary outcome was the study-average difference in absolute "Fluid Overload" (an estimate of excess extracellular water) analyzed using a mixed model repeated measures approach.
RESULTS: The 660 substudy participants were broadly representative of the 6609-participant trial population. Substudy mean baseline absolute "Fluid Overload" was 0.4±1.7 L. Compared with placebo, the overall mean absolute "Fluid Overload" difference among those allocated empagliflozin was -0.24 L (95% confidence interval [CI], -0.38 to -0.11), with similar sized differences at 2 and 18 months, and in prespecified subgroups. Total body water differences comprised between-group differences in extracellular water of -0.49 L (95% CI, -0.69 to -0.30, including the -0.24 L "Fluid Overload" difference) and a -0.30 L (95% CI, -0.57 to -0.03) difference in intracellular water. There was no significant effect of empagliflozin on bioimpedance-derived adipose tissue mass (-0.28 kg [95% CI, -1.41 to 0.85]). The between-group difference in weight was -0.7 kg (95% CI, -1.3 to -0.1).
CONCLUSIONS: In a broad range of patients with CKD, empagliflozin resulted in a sustained reduction in a bioimpedance-derived estimate of fluid overload, with no statistically significant effect on fat mass.
TRIAL REGISTRATION: Clinicaltrials.gov: NCT03594110 ; EuDRACT: 2017-002971-24 ( https://eudract.ema.europa.eu/ ).