Sumit Keshri

ABSTRACT The intracellular protozoan parasite Leishmania donovani causes debilitating human diseases that involve visceral and dermal manifestations. Type 3 interferons (IFNs), also referred to as lambda IFNs (IFNL, IFN-L, or IFN-λ), are known to play protective roles against intracellular pathogens at the epithelial surfaces. Herein, we show that L. donovani induces IFN-λ3 in human as well as mouse cell line-derived macrophages. Interestingly, IFN-λ3 treatment significantly decreased parasite load in infected cells , mainly by increasing reactive oxygen species production. Microscopic examination showed that IFN-λ3 inhibited uptake but not replication, while the phagocytic ability of the cells was not affected. This was confirmed by experiments that showed that IFN-λ3 could decrease parasite load only when added to the medium at earlier time points, either during or soon after parasite uptake, but had no effect on parasite load when added at 24 h post-infection, suggesting that an early event during parasite uptake was targeted. Furthermore, the parasites could overcome the inhibitory effect of IFN-λ3, which was added at earlier time points, within 2-3 days post-infection. BALB/c mice treated with IFN-λ3 before infection led to a significant increase in expression of IL-4 and ARG1 post-infection in the spleen and liver, respectively, and to different pathological changes, especially in the liver, but not to changes in parasite load. Treatment with IFN-λ3 during infection did not decrease the parasite load in the spleen either. However, IFN-λ3 was significantly increased in the sera of visceral leishmaniasis patients, and the IFNL genetic variant rs12979860 was significantly associated with susceptibility to leishmaniasis.

The purpose of this research is to create a disease prediction system that can make precise inferences from user input using machine learning algorithms. The interface of the system will be carefully crafted according to the user requirements and workflow. Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbors are among the efficient machine learning algorithms because of its high accuracy, efficiency, and performance. Patient information will be protected and in line with applicable healthcare rules and guidelines as the system is built according to the security and privacy standards. By giving doctors and nurses an expert-level tool to streamline their processes and improve the quality of care they provide to patients; the proposed system has the potential to dramatically alter the healthcare practices. Better patient outcomes and quality care can be achieved through the use of accurate and timely disease predictions by medical professionals.