ABSTRACT
The emergence of drug resistance against the frontline antimalarials is a major challenge in the treatment of malaria. In view of emerging reports on drug-resistant strains of Plasmodium against artemisinin combination therapy, a dire need is felt for the discovery of novel compounds acting against novel targets in the parasite. In this study, we identified a novel series of quinolinepiperazinyl-aryltetrazoles (QPTs) targeting the blood stage of Plasmodium. In vitro anti-plasmodial activity screening revealed that most of the compounds showed IC50 < 10 µM against chloroquine-resistant PfINDO strain, with the most promising lead compounds 66 and 75 showing IC50 values of 2.25 and 1.79 µM, respectively. Further, compounds 64-66, 68, 75-77 and 84 were found to be selective (selectivity index >50) in their action against Pf over a mammalian cell line, with compounds 66 and 75 offering the highest selectivity indexes of 178 and 223, respectively. Explorations into the action of lead compounds 66 and 75 revealed their selective cidal activity towards trophozoites and schizonts. In a ring-stage survival assay, 75 showed cidal activity against the early rings of artemisinin-resistant PfCam3.1R539T. Further, 66 and 75 in combination with artemisinin and pyrimethamine showed additive to weak synergistic interactions. Of these two in vitro lead molecules, only 66 restricted rise in the percentage of parasitemia to about 10% in P. berghei-infected mice with a median survival time of 28 days as compared to the untreated control, which showed the percentage of parasitemia >30%, and a median survival of 20 days. Promising antimalarial activity, high selectivity, and additive interaction with artemisinin and pyrimethamine indicate the potential of these compounds to be further optimized chemically as future drug candidates against malaria.
ABSTRACT
The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.
