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Abstract Background To understand the emergence and spread of drug-resistant parasites in malaria-endemic areas, accurate assessment and monitoring of antimalarial drug resistance markers is critical. Recent advances in next-generation sequencing (NGS) technologies have enabled the tracking of drug-resistant malaria parasites. Methods In this study, we used Targeted Amplicon Deep Sequencing (TADS) to characterise the genetic diversity of the Pfk13, Pfdhfr, Pfdhps, and Pfmdr1 genes among primary school-going children in 15 counties in Kenya (Bungoma, Busia, Homa Bay, Migori, Kakamega, Kilifi, Kirinyaga, Kisii, Kisumu, Kwale, Siaya, Tana River, Turkana, Vihiga and West Pokot). A total of 920 dried blood spot (DBS) samples collected from 121 selected primary schools within the country were used to extract genomic DNA. A nested polymerase chain reaction (PCR) was used to generate amplicons that were sequenced to determine the prevalence of known and novel polymorphisms. Results Pfk13 mutations associated with artemisinin resistance were present as mixed genotype infections for the C469Y mutation in 23 samples (4%), the A675V mutation in 2 samples (1.7%), and the P553L mutation in 7 samples (1.2%). The A578S mutation, was also identified in mixed infections, appearing in 15.2% of the 87 samples analysed. The Pfdhfr 51I and 108 N pyrimethamine-resistance mutations were at fixation (100% frequency), and the Pfmdr1 Y184F mutation, which is linked to reduced susceptibility to several antimalarial drugs, especially those used in combination therapies for malaria treatment, was detected in 97.5% of the samples as mixed-genotype infections. Conclusion The genomic surveillance of asymptomatic school children in Kenya provides an early warning signal of at least 1 of the 3 validated artemisinin resistance mutations circulating in all regions in Western Kenya sampled except Homa Bay and Kisii Counties. These signals in asymptomatic and mixed infections would have been missed without deep sequencing.

In this paper, we have modified the workflow of the traditional light transmission aggregometry (LTA) protocol to characterize tumor cell clusters in vitro in a quantifiable and multifaceted manner. Circulating tumor cell (CTC) clusters have high metastatic potential compared to single tumor cells traveling in the bloodstream. Thus, engineering new therapeutic strategies that specifically target this CTC population is essential. To accomplish this, quantifiable methods to characterize their therapeutic effect on tumor cell clusters is a prerequisite. The method presented here enables the user to precisely quantify the dissociation of cancer cell clusters in the presence of clinically relevant fibrinolytic agents, such as alteplase and tenecteplase. The efficacy of the fibrinolytic agents can be quantified using this in vitro assay, prior to conducting preclinical studies. Here, we have obtained the fibrinolytic activity data in terms of lag time to the initiation of tumor cell dissociation, time to 25% dissociation, and trend of dissociation over time. To validate the assay, cell counts and phase‐contrast microscopy images were recorded over time. Further, we explored an LTA‐assisted preparation of platelet–tumor‐cell clusters of calibrated size for potential downstream testing/applications. To assess whether the assay is applicable to characterize the dissociation of cancer cell clusters in the presence of platelets, we added low (50 000 platelets·μL−1), normal (200 000 platelets·μL−1) and high (450 000 platelets·μL−1) concentrations of platelets to the tumor cell clusters. In addition to dissociation parameters, microcopy images were recorded over time to validate the assay and enabled the enumeration of clusters and single cells. The correlative light electron microscopy (CLEM) technique was utilized to visualize the morphology and composition of platelet–tumor cell clusters.