Search Results (3)

Lao People’s Democratic Republic (Lao PDR) has made significant progress in reducing malaria in recent years. In the Greater Mekong Subregion, forest-going is often a risk factor contributing to continuing malaria transmission. This study assessed forest-going and other potential risk factors for malaria cases in Champasak Province, Lao PDR. Routine passive surveillance data from August 2017 to December 2018 were extracted from health facilities in three districts for a case-control study; at the time of presentation, all fever cases were asked to report any recent forest travel. Multivariable logistic regression was used to assess the relationship between forest-going and malaria infection while controlling for other covariates. Of 2933 fever cases with data available on forest-sleeping and malaria diagnosis from 25 health facilities, 244 (8%) tested positive (cases), and 2689 (92%) tested negative (controls). Compared with spending 0–2 nights in the forest, spending 3–7 nights in the forest was associated with 9.7 times the odds of having a malaria infection (95% CI: 4.67–20.31, p < 0.001) when adjusting for gender, occupation, and season. Forest-going, especially longer trips, is associated with increased risk for confirmed symptomatic malaria in southern Lao PDR, and appropriate and targeted intervention efforts are needed to protect this high-risk population. Full article

Thailand has made substantial progress towards malaria elimination, with 46 of the country’s 77 provinces declared malaria-free as part of the subnational verification program. Nonetheless, these areas remain vulnerable to the reintroduction of malaria parasites and the reestablishment of indigenous transmission. As such, prevention of reestablishment (POR) planning is of increasing concern to ensure timely response to increasing cases. A thorough understanding of both the risk of parasite importation and receptivity for transmission is essential for successful POR planning. Routine geolocated case- and foci-level epidemiological and case-level demographic data were extracted from Thailand’s national malaria information system for all active foci from October 2012 to September 2020. A spatial analysis examined environmental and climate factors associated with the remaining active foci. A logistic regression model collated surveillance data with remote sensing data to investigate associations with the probability of having reported an indigenous case within the previous year. Active foci are highly concentrated along international borders, particularly Thailand’s western border with Myanmar. Although there is heterogeneity in the habitats surrounding active foci, land covered by tropical forest and plantation was significantly higher for active foci than other foci. The regression results showed that tropical forest, plantations, forest disturbance, distance from international borders, historical foci classification, percentage of males, and percentage of short-term residents were associated with the high probability of reporting indigenous cases. These results confirm that Thailand’s emphasis on border areas and forest-going populations is well placed. The results suggest that environmental factors alone are not driving malaria transmission in Thailand; rather, other factors, including demographics and behaviors that intersect with exophagic vectors, may also be contributors. However, these factors are syndemic, so human activities in areas covered by tropical forests and plantations may result in malaria importation and, potentially, local transmission, in foci that had previously been cleared. These factors should be addressed in POR planning. Full article

Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various injuries that may require up to 22 weeks of treatment. In this work, we adopt a wearable approach to detect biceps muscle fatigue during a bicep concentration curl exercise as an example of a gym activity. Our dataset consists of 3000 bicep curls from twenty middle-aged volunteers at ages between 27 to 30 and Body Mass Index (BMI) ranging between 18 to 28. All volunteers have been gym-goers for at least 1 year with no records of chronic diseases, muscle, or bone surgeries. We encountered two main challenges while collecting our dataset. The first challenge was the dumbbell’s suitability, where we found that a dumbbell weight (4.5 kg) provides the best tradeoff between longer recording sessions and the occurrence of fatigue on exercises. The second challenge is the subjectivity of RPE, where we average the reported RPE with the measured heart rate converted to RPE. We observed from our data that fatigue reduces the biceps’ angular velocity; therefore, it increases the completion time for later sets. We extracted a total of 33 features from our dataset, which have been reduced to 16 features. These features are the most overall representative and correlated with bicep curl movement, yet they are fatigue-specific features. We utilized these features in five machine learning models, which are Generalized Linear Models (GLM), Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Feedforward Neural Networks (FNN). We found that using a two-layer FNN achieves an accuracy of 98% and 88% for subject-specific and cross-subject models, respectively. The results presented in this work are useful and represent a solid start for moving into a real-world application for detecting the fatigue level in bicep muscles using wearable sensors as we advise athletes to take fatigue into consideration to avoid fatigue-induced injuries. Full article