👤 Arindam Chaudhury

Background & objectives Central TB division facilitated development of a line probe assay (LPA) artificial intelligence (AI) tool. The tool was developed, trained, and validated for performance by collecting more than 18,000 LPA strips across culture and drug susceptibility Testing (C&DST) laboratories. The Indian Council of Medical Research (ICMR)-National Institute for Research in Tuberculosis (NIRT) evaluated the LPAAI tool independently. The objective was to establish and verify an AI-driven system for automatically interpreting LPA strips, which are employed in tuberculosis drug resistance screening, to improve accuracy, consistency, and scalability across diverse laboratory settings. Methods The AI system integrates faster regions convolutional neural network (FR-CNN) for strip detection, detection transformer (DETR) for band localisation, and a hierarchical neural network (HNN) for classification of bands, loci, and drug labels. Independent validation was conducted by ICMR-NIRT using 2810 first-line (FL)-LPA and 241 reflex second-line (SL-LPA) across ten intermediate reference laboratories (IRLs). Results AI comparative models demonstrated an accuracy range of 92-100 per cent, with sensitivity between 80-100 per cent and specificity from 86-100 per cent for the tub, rpoB, katG, InhA, gyrA/gyrB,rrs, and eisgenes. The overall F1 score varies from 0.81 to 1.00, indicating perfect precision and recall. Interpretation & conclusions This AI system offers a novel, modular architecture capable of expert-level interpretation of LPA strips. The AI tool performs at par with expert readers and offers a reliable, scalable solution for LPA interpretation.AI tool adoption can reduce interpretation time, enhance result uniformity, and improve treatment delivery across India's TB programme, supporting national goals for TB elimination. Show less

Synapse loss is an early event in late-onset Alzheimer's disease (LOAD). In this study, we have assessed the capacity of a polygenic risk score (PRS) restricted to synapse-encoding loci to predict LOAD. We used summary statistics from the International Genetics of Alzheimer's Project genome-wide association meta-analysis of 74,046 patients for model construction and tested the "synaptic PRS" in 2 independent data sets of controls and pathologically confirmed LOAD. The mean synaptic PRS was 2.3-fold higher in LOAD than that in controls (p < 0.0001) with a predictive accuracy of 72% in the target data set (n = 439) and 73% in the validation data set (n = 136), a 5%-6% improvement compared with the APOE locus (p < 0.00001). The model comprises 8 variants from 4 previously identified (BIN1, PTK2B, PICALM, APOE) and 2 novel (DLG2, MINK1) LOAD loci involved in glutamate signaling (p = 0.01) or APP catabolism or tau binding (p = 0.005). As the simplest PRS model with good predictive accuracy to predict LOAD, we conclude that synapse-encoding genes are enriched for LOAD risk-modifying loci. The synaptic PRS could be used to identify individuals at risk of LOAD before symptom onset. Show less

Neurodegenerative diseases characterized by aberrant accumulation of undigested cellular components represent unmet medical conditions for which the identification of actionable targets is urgently needed. Here we identify a pharmacologically actionable pathway that controls cellular clearance via Akt modulation of transcription factor EB (TFEB), a master regulator of lysosomal pathways. We show that Akt phosphorylates TFEB at Ser467 and represses TFEB nuclear translocation independently of mechanistic target of rapamycin complex 1 (mTORC1), a known TFEB inhibitor. The autophagy enhancer trehalose activates TFEB by diminishing Akt activity. Administration of trehalose to a mouse model of Batten disease, a prototypical neurodegenerative disease presenting with intralysosomal storage, enhances clearance of proteolipid aggregates, reduces neuropathology and prolongs survival of diseased mice. Pharmacological inhibition of Akt promotes cellular clearance in cells from patients with a variety of lysosomal diseases, thus suggesting broad applicability of this approach. These findings open new perspectives for the clinical translation of TFEB-mediated enhancement of cellular clearance in neurodegenerative storage diseases. Show less