Coverage of tuberculosis and diabetes mellitus screening among household contacts of tuberculosis patients: a household-based cross-sectional survey from Southern Thailand.

BACKGROUND: The comorbid presence of tuberculosis and diabetes mellitus has become an increasingly important public health threat to the prevention and control of both diseases. Thus, household contact investigation may serve a dual purpose of screening for both tuberculosis and diabetes mellitus among household contacts. We therefore aimed to evaluate the coverage of screening for tuberculosis and diabetes mellitus among household contacts of tuberculosis index cases and to determine predictors of tuberculosis screening. METHODS: A household-based survey was conducted in February 2019 in Muang district of Phatthalung Province, Thailand where 95 index tuberculosis patients were newly diagnosed with pulmonary or pleural tuberculosis between October 2017 and September 2018. Household contacts of the index patients were interviewed using a structured questionnaire to ascertain their past-year history of tuberculosis screening and, if appropriate, diabetes mellitus screening. For children, the household head or an adult household member was interviewed as a proxy. Coverage of tuberculosis screening at the household level was regarded as households having all contacts screened for tuberculosis. Logistic regression and mixed-effects logistic regression models were used to determine predictors of tuberculosis screening at the household and individual levels, respectively, with the strengths of association presented as adjusted odds ratios (AOR) and 95% confidence intervals (CI). RESULTS: Of 61 responding households (64%), complete coverage of tuberculosis screening at the household level was 34.4% and among the 174 household contacts was 46.6%. About 20% of contacts did not receive any recommendation for tuberculosis screening. Households were more likely to have all members screened for tuberculosis if they were advised to be screened by a healthcare professional rather than someone else. At the individual level, contacts aged ≥35 years (AOR: 30.6, 95% CI: 2.0-466.0), being an employee (AOR: 0.1, 95% CI: 0.0-0.8) and those who had lived more than 5 years in the same household (AOR: 0.1, 95% CI: 0.0-0.8) were independent predictors for tuberculosis screening. Coverage of diabetes mellitus screening was 80.6% with lack of awareness being the main reason for not being screened. CONCLUSIONS: Compared to diabetes screening, the coverage of tuberculosis screening was low. A better strategy to improve coverage of tuberculosis contact screening is needed.

Density of cannabis outlets vs. cannabis use behaviors and prevalent cannabis use disorder: findings from a nationally-representative survey.

Background: Thailand recently decriminalized (de facto legalized) cannabis use and sales. However, nationally representative data are scarce with regard to cannabis use behaviors and its association with cannabis outlet density. The objectives of this study are: (1) to describe the prevalence of cannabis use behaviors and cannabis use disorder among the general adult population of Thailand; (2) to describe the extent that the density of cannabis outlets is associated with cannabis use behaviors, cannabis use disorder, and the amount of cannabis smoked per day. Methods: We conducted a community-based cross-sectional study in 11 provinces and the Bangkok Metropolitan Area. Participants were residents of sampled communities aged 20 years or older. We requested literate participants to self-administer the questionnaire and interviewed participants who could not read. We analyzed data using descriptive statistics with sampling weight adjustments and multivariate logistic regression analyses. Results: The prevalence of current cannabis use was 15 percent. At a 400-m radius, participants who reported three cannabis outlets had 4.2 times higher odds of being current users than participants who reported no outlet (Adjusted OR = 4.82; 95% CI [3.04-7.63]). We found no association between outlet density and hazardous cannabis use or cannabis use disorder, nor association with the amount of cannabis use among cannabis smokers. Discussion and Conclusion: The patterns of association between outlet density and cannabis use behaviors were inconsistent. Furthermore, limitations regarding outlet density measurement and lack of temporality should be considered as caveats in the interpretation of the study findings.

The KRAS-Mutant Consensus Molecular Subtype 3 Reveals an Immunosuppressive Tumor Microenvironment in Colorectal Cancer.

Colorectal cancers (CRC) with KRAS mutations (KRASmut) are frequently included in consensus molecular subtype 3 (CMS3) with profound metabolic deregulation. We explored the transcriptomic impact of KRASmut, focusing on the tumor microenvironment (TME) and pathways beyond metabolic deregulation. The status of KRASmut in patients with CRC was investigated and overall survival (OS) was compared with wild-type KRAS (KRASwt). Next, we identified CMS, and further investigated differentially expressed genes (DEG) of KRASmut and distinctive pathways. Lastly, we used spatially resolved gene expression profiling to define the effect of KRASmut in the TME regions of CMS3-classified CRC tissues. CRC patients with KRASmut were mainly enriched in CMS3. Their specific enrichments of immune gene signatures in immunosuppressive TME were associated with worse OS. Activation of TGFß signaling by KRASmut was related to reduced pro-inflammatory and cytokine gene signatures, leading to suppression of immune infiltration. Digital spatial profiling in TME regions of KRASmut CMS3-classified tissues suggested up-regulated genes, CD40, CTLA4, ARG1, STAT3, IDO, and CD274, that could be characteristic of immune suppression in TME. This study may help to depict the complex transcriptomic profile of KRASmut in immunosuppressive TME. Future studies and clinical trials in CRC patients with KRASmut should consider these transcriptional landscapes.

Deep learning approaches for challenging species and gender identification of mosquito vectors.

Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.