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Amrita Vishwa Vidyapeetham

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  1. Saliency maps for medical image classifiers stop guessing from gradients: unsupervised training lets a frozen classifier check the mask

    Saliency maps for convolutional classifiers on medical images now have a new training route: SalientNet needs no annotated saliency ground truth and instead requires the generated mask to keep enough information for a frozen classifier to reproduce its original prediction within a controlled tolerance. Across BreakHis, Brain MRI ND5 and FracAtlas, it reaches higher faithfulness (Win %) and lower prediction Drop (%) than Grad-CAM, Grad-CAM++ and LIME in nearly all dataset-architecture combinations, and on FracAtlas its maps correlate spatially with practitioner-annotated fracture regions, with a mean ground-truth active fraction of up to 0.96 for ResNet-18.

  2. Review maps indole derivatives targeting mycolic acid enzymes (MmpL3, InhA, KasA/B), their SAR and synthetic routes, and notes most series still lack enzymatic and genetic target validation

    This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.

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