Methods: This was a cross-sectional study among DR-TB patients notified from Dakshina Kannada, Udupi, and Chikamagalur districts of the state of Karnataka. Clinico-demographic details were extracted from treatment cards. The registered addresses of the patients were geocoded (latitude and longitude) using Google Earth. Using the QGIS software, spot map, heat maps and grid maps 25 km2 with more than the expected count of DR-TB patients were constructed.
Results: Of the total 507 patients studied, 376 (74%) were males and the mean (standard deviation) age of the study participants was 41.4 (13.9) years. From 2015 to 2018, the number of patients increased from 85 to 209 per year, the area of aggregation in square kilometers increased from 113.6 to 205.7, and the number of rectangular grids with more than the expected DR-TB patients (> 1) increased from 12 to 47.
Conclusions: The increase in the number of DR-TB patients, area of aggregation, and grids with more than the expected count is a cause for concern. The NTP can use routine programmatic data to develop maps to identify areas of aggregation of disease for targeted TB control activities.
METHODS: Cytochrome b gene sequences (479 bp) generated from India and available at MalAvi database were used to study the avian haemosporidian prevalence and phylogenetic analysis of lineages at local and world levels.
RESULTS: One common (COLL2) and only once in the study (CYOPOL01, CHD01, CYORUB01, EUMTHA01, GEOCIT01) haemosporidian lineages were discovered. 5.88% prevalence of haemosporidian infection was found in 102 samples belonging to 6 host species. Haemoproteus prevalence was 4.90% across five host species (Phylloscopus trochiloides, Cyornis poliogenys, C. hainanus dialilaemus, C. rubeculoides, Eumiyas thalassinus) and Plasmodium prevalence was 0.98% in Geokichla citrina. Spatial phylogeny at the global level showed that COLL2 lineage, found in C. poliogenys in India, was genetically identical to H. pallidus lineages (COLL2) in parts of Africa, Europe, North America, Malaysia, and the Philippines. The Plasmodium lineage (GEOCIT01) was related to PADOM16 in Egypt, but the sequences were only 93.89% alike.
CONCLUSIONS: Four new lineages of Haemoproteus and one of Plasmodium were reported. COLL2 similarity with other H. pallidus lineages may suggest their hosts as possible infection sources.
OBJECTIVE: This study aims to comprehensively explore the diverse mechanisms of cancer drug resistance, assess the evolution of resistance detection methods, and identify strategies for overcoming this challenge. The evolution of resistance detection methods and identification strategies for overcoming the challenge.
METHODS: A comprehensive literature review was conducted to analyze intrinsic and acquired drug resistance mechanisms, including altered drug efflux, reduced uptake, inactivation, target mutations, signaling pathway changes, apoptotic defects, and cellular plasticity. The evolution of mutation detection techniques, encompassing clinical predictions, experimental approaches, and computational methods, was investigated. Strategies to enhance drug efficacy, modify pharmacokinetics, optimizoptimizee binding modes, and explore alternate protein folding states were examined.
RESULTS: The study comprehensively overviews the intricate mechanisms contributing to cancer drug resistance. It outlines the progression of mutation detection methods and underscores the importance of interdisciplinary approaches. Strategies to overcome drug resistance challenges, such as modulating ATP-binding cassette transporters and developing multidrug resistance inhibitors, are discussed. The study underscores the critical need for continued research to enhance cancer treatment efficacy.
CONCLUSION: This study provides valuable insights into the complexity of cancer drug resistance mechanisms, highlights evolving detection methods, and offers potential strategies to enhance treatment outcomes.