The L-LAMP showed 100 % specificity and 92 % sensitivity with respect ELISA and was found better than RT-PCR which showed 100 % specificity and 88 % sensitivity. There was no cross reactivity of primers with other disease like malaria caused by Plasmodium falciparum and P. vivax and with viral disease chikungunya. L-LAMP has dynamic potential as point of care technique.Biomarkers of exposure can be measured at lower and lower levels due to advances in analytical chemistry. Using these sensitive methods, some epidemiology studies report associations between biomarkers and health outcomes at biomarker levels much below those associated with effects in animal studies. While some of these low exposure associations may arise from increased sensitivity of humans compared with animals or from species-specific responses, toxicology studies with drugs, commodity chemicals and consumer products have not generally indicated significantly greater sensitivity of humans compared with test animals for most health outcomes. In some cases, these associations may be indicative of pharmacokinetic (PK) bias, i.e., a situation where a confounding factor or the health outcome itself alters pharmacokinetic processes affecting biomarker levels. Quantitative assessment of PK bias combines PK modeling and statistical methods describing outcomes across large numbers of individuals in simulated populations. Here, we first provide background on the types of PK models that can be used for assessing biomarker levels in human population and then outline a process for considering PK bias in studies intended to assess associations between biomarkers and health outcomes at low levels of exposure. After providing this background, we work through published examples where these PK methods have been applied with several chemicals/chemical classes - polychlorinated biphenyls (PCBs), perfluoroalkyl substances (PFAS), polybrominated biphenyl ethers (PBDE) and phthalates - to assess the possibility of PK bias. Studies of the health effects of low levels of exposure will be improved by developing some confidence that PK bias did not play significant roles in the observed associations.
  Professional pesticides exposure is associated with PD risk, but it remains unclear whether specific products, which strongly depend on farming type, are specifically involved. We performed a nationwide ecological study to examine the association of pesticides expenditures for the main farming types with PD incidence in French farmers.
 
  We used the French National Health Insurance database to identify incident PD cases in farmers (2010-2015). We combined data on pesticides expenditures with the agricultural census to compute pesticides expenditures for nine farming types in 2000 in 3571 French cantons. The association between pesticides expenditures and PD age/sex standardized incidence was examined using multilevel Poisson regression, adjusted for smoking, neurologists' density, and deprivation index.
 
  10,282 incident PD cases were identified. Cantons with the highest pesticides expenditures for vineyards without designation of origin were characterized by 16% (95% CI=6-28%) higher PD incidence (p-trend in these farms should benefit from preventive measures aiming at reducing exposure. Our study highlights the importance of considering farming type in studies on pesticides and PD and the usefulness of pesticides expenditures for exposure assessment.Low-cost sensors (LCSs) are widely acknowledged for bringing a paradigm shift in supplemental traditional air monitoring by air regulatory agencies. However, there is concern regarding its data quality and performance stability, which has greatly restricted its large-scale applications. Knowing the recent techniques, progress, and challenges of LCS calibration is of immense significance to promote the field of environmental monitoring. By summarizing the published evidence, this review shows that the global sensor market is rapidly expanding due to the surging needs, but the calibration efforts have been focused on a limited selection of sensors. Relative humidity correction, regression, and machine learning are the three mainstream calibration techniques. Although there is no one-size-fits-all solution, a feature of the latest research tendency is machine learning. The duration of calibration is largely neglected in the experiment design, but it is found to affect the performance of different calibration methods, especially those that are data-driven. Geographically, China and the United States gained the most research attention in the sensor calibration field, but the spatial mismatch between particulate matter hotspots and calibration sites is quite evident for the rest of the world. Incomplete and unevenly distributed research footprints could limit the large-scale test of method generalizability, as well as diminish the monitoring capacity in underserved areas that suffer greater environmental justice crises. In general, model performance is enhanced by including the key influencing factors, but the degree of improvement is not evidently related to the number of explanatory variables. Overall, studies prove the critical importance of field calibration before sensor deployment, but more studies are needed to establish experiment protocols that can be customized to specific needs.
  Though growing evidence has linked air pollution to Parkinson's disease (PD), the results remain inconsistent. Less is known about the relevance of road proximity and surrounding green. We aimed to investigate the individual and joint associations of air pollution, road proximity and surrounding green with the incidence of PD in a prospective cohort study.
 
  We used data from a prospective cohort of 47,516 participants recruited from July 2015 to January 2018 in Ningbo, China. Long-term exposure to particulate matter with aerodynamic diameter ≤2.5μm (PM
  ) and ≤10μm (PM
  ) and nitrogen dioxide (NO
  ) estimated by land-use regression models, road proximity and surrounding green assessed by Normalized Difference Vegetation Index (NDVI) were calculated based on the residential address for each participant.  https://www.selleckchem.com/products/FK-506-(Tacrolimus).html  Cox proportional hazard models were used to analyze the individual and joint effects of air pollution, road proximity, and surrounding green on PD.
 
  In single-exposure models, PM
  , PM
  , NO
  and road proximity was associated with increased risk of PD (e.