Targeting PfProhibitin 2-Hu-Hsp70A1A complex as a unique approach towards malaria vaccine development.

Malaria parasite invasion to host erythrocytes is mediated by multiple interactions between merozoite ligands and erythrocyte receptors that contribute toward the development of disease pathology. Here, we report a novel antigen Plasmodium prohibitin "PfPHB2" and identify its cognate partner "Hsp70A1A" in host erythrocyte that plays a crucial role in mediating host-parasite interaction during merozoite invasion. Using small interfering RNA (siRNA)- and glucosamine-6-phosphate riboswitch (glmS) ribozyme-mediated approach, we show that loss of Hsp70A1A in red blood cells (RBCs) or PfPHB2 in infected red blood cells (iRBCs), respectively, inhibit PfPHB2-Hsp70A1A interaction leading to invasion inhibition. Antibodies targeting PfPHB2 and monoclonal antibody therapeutics against Hsp70A1A efficiently block parasite invasion. Recombinant PfPHB2 binds to RBCs which is inhibited by anti-PfPHB2 antibody and monoclonal antibody against Hsp70A1A. The validation of PfPHB2 to serve as antigen is further supported by detection of anti-PfPHB2 antibody in patient sera. Overall, this study proposes PfPHB2 as vaccine candidate and highlights the use of monoclonal antibody therapeutics for future malaria treatment.

First Report of Anopheles annularis s.l., An. maculatus s.s., and An. culicifacies s.l. as Malaria Vectors and a New Occurrence Record for An. pseudowillmori and An. sawadwongporni in Alipurduar District Villages, West Bengal, India.

A comprehensive entomological survey was undertaken in Alipurduar District, West Bengal, from 2018 to 2020 and in 2022. This study was prompted by reported malaria cases and conducted across nine villages, seven Sub-Centres, and three Primary Health Centres (PHCs). Mosquitoes were hand-collected with aspirators and flashlights from human dwellings and cattle sheds during the daytime. Both morphological and molecular techniques were used for species identification. Additionally, mosquitoes were tested for Plasmodium parasites and human blood presence. Mosquito species such as An. barbirostris s.l., An. hyrcanus s.l., An. splendidus, and An. vagus were morphologically identified. For species like An. annularis s.l., An. minimus s.s., An. culicifacies s.l., and An. maculatus s.s., a combination of morphological and molecular techniques was essential. The mitochondrial cytochrome c oxidase gene subunit 1 (CO1) was sequenced for An. annularis s.l., An. maculatus s.s., An. culicifacies s.l., An. vagus, and some damaged samples, revealing the presence of An. pseudowillmori and An. fluviatilis. The major Anopheles species were An. annularis s.l., An. culicifacies s.l., and An. maculatus s.s., especially in Kumargram and Turturi PHCs. Plasmodium positivity was notably high in An. annularis s.l. and An. maculatus s.s. with significant human blood meal positivity across most species. Morphological, molecular, and phylogenetic analyses are crucial, especially for archived samples, to accurately identify the mosquito fauna of a region. Notably, this study confirms the first occurrence of An. pseudowillmori and An. sawadwongporni in West Bengal and implicates An. maculatus s.s., An. culicifacies s.l., and An. annularis s.l. as significant vectors in the Alipurduar region.

First Report of Rubber Collection Bowls & Plastic and Bamboo Water Containers as the Major Breeding Source of Ae. albopictus with the Indigenous Transmission of Dengue and Chikungunya in Rural Forested Malaria-Endemic Villages of Dhalai District, Tripura, India: The Importance of Molecular Identification.

BACKGROUND: With the reports of indigenous cases of dengue and chikungunya in the forest-covered rural tribal malaria-endemic villages of Dhalai District, Tripura, India, an exploratory study was undertaken to identify the vector breeding sites. METHODS: From June 2021 to August 2022, mosquito larvae were collected from both natural and artificial sources in the villages, house premises, and their nearby forested areas outside of the houses. Other than morphological characterisation, Aedes species were confirmed by polymerase chain reaction targeting both nuclear (ITS2) and mitochondrial genes (COI) followed by bidirectional Sanger sequencing. RESULTS: Aedes albopictus was abundantly found in this area in both natural and artificial containers, whereas Ae. aegypti was absent. Among the breeding sources of molecularly confirmed Ae. albopictus species, rubber collection bowls were found to be a breeding source reported for the first time. Plastic and indigenously made bamboo-polythene containers for storing supply water and harvesting rainwater in the villages with a shortage of water were found to be other major breeding sources, which calls for specific vector control strategies. Natural sources like ponds and rainwater collected on Tectona grandis leaves and Colocasia axil were also found to harbour the breeding, along with other commonly found sources like bamboo stumps and tree holes. No artificial containers as a breeding source were found inside the houses. Mixed breeding was observed in many containers with other Aedes and other mosquito species, necessitating molecular identification. We report six haplotypes in this study, among which two are reported for the first time. However, Aedes aegypti was not found in the area. Additionally, rubber collection bowls, ponds, and water containers also showed the presence of Culex quinquefasciatus and Culex vishnui, known JE vectors from this area, and reported JE cases as well. Different Anopheles vector spp. from this known malaria-endemic area were also found, corroborating this area as a hotbed of several vectors and vector-borne diseases. CONCLUSIONS: This study, for the first time, reports the breeding sources of Aedes albopictus in the forested areas of Tripura, with rubber collection bowls and large water storage containers as major sources. Also, for the first time, this study reports the molecular characterisation of the Ae. albopictus species of Tripura, elucidating the limitations of morphological identification and highlighting the importance of molecular studies for designing appropriate vector control strategies. The study also reports the co-breeding of JE and malaria vectors for the first time in the area reporting these vector-borne diseases.

Evaluating the Viability of a Smartphone-Based Annotation Tool for Faster and Accurate Image Labelling for Artificial Intelligence in Diabetic Retinopathy.

INTRODUCTION: Deep Learning (DL) and Artificial Intelligence (AI) have become widespread due to the advanced technologies and availability of digital data. Supervised learning algorithms have shown human-level performance or even better and are better feature extractor-quantifier than unsupervised learning algorithms. To get huge dataset with good quality control, there is a need of an annotation tool with a customizable feature set. This paper evaluates the viability of having an in house annotation tool which works on a smartphone and can be used in a healthcare setting. METHODS: We developed a smartphone-based grading system to help researchers in grading multiple retinal fundi. The process consisted of designing the flow of user interface (UI) keeping in view feedback from experts. Quantitative and qualitative analysis of change in speed of a grader over time and feature usage statistics was done. The dataset size was approximately 16,000 images with adjudicated labels by a minimum of 2 doctors. Results for an AI model trained on the images graded using this tool and its validation over some public datasets were prepared. RESULTS: We created a DL model and analysed its performance for a binary referrable DR Classification task, whether a retinal image has Referrable DR or not. A total of 32 doctors used the tool for minimum of 20 images each. Data analytics suggested significant portability and flexibility of the tool. Grader variability for images was in favour of agreement on images annotated. Number of images used to assess agreement is 550. Mean of 75.9% was seen in agreement. CONCLUSION: Our aim was to make Annotation of Medical imaging easier and to minimize time taken for annotations without quality degradation. The user feedback and feature usage statistics confirm our hypotheses of incorporation of brightness and contrast variations, green channels and zooming add-ons in correlation to certain disease types. Simulation of multiple review cycles and establishing quality control can boost the accuracy of AI models even further. Although our study aims at developing an annotation tool for diagnosing and classifying diabetic retinopathy fundus images but same concept can be used for fundus images of other ocular diseases as well as other streams of medical science such as radiology where image-based diagnostic applications are utilised.