Intra-neuronal misfolding of monomeric tau protein to toxic β-sheet rich neurofibrillary tangles is a hallmark of Alzheimer’s disease (AD). Tau pathology correlates not only with progressive dementia but also with microglia-mediated inflammation in AD. Amyloid-beta (Aβ), another pathogenic peptide involved in AD, has been shown to activate NLRP3 inflammasome (NOD-like receptor family, pyrin domain containing 3), triggering the secretion of proinflammatory interleukin-1β (IL1β) and interleukin-18 (IL18). However, the effect of tau protein on microglia concerning inflammasome activation, microglial polarization, and autophagy is poorly understood. In this study, human microglial cells (HMC3) were stimulated with the unaggregated and aggregated forms of the tau-derived PHF6 peptide (VQIVYK). Modulation of NLRP3 inflammasome was examined by qRT-PCR, immunocytochemistry, and Western blot. We demonstrate that fibrillar aggregates of VQIVYK upregulated the NLRP3 expression at both mRNA and protein levels in a dose- and time-dependent manner, leading to increased expression of IL1β and IL18 in HMC3 cells. Aggregated PHF6-peptide also activated other related inflammation and microglial polarization markers. Furthermore, we also report a time-dependent effect of the aggregated PHF6 on BECN1 (Beclin-1) expression and autophagy. Overall, the PHF6 model system-based study may help to better understand the complex interconnections between Alzheimer’s PHF6 peptide aggregation and microglial inflammation, polarization, and autophagy.

Given the significance of protein aggregation in proteinopathies and the development of therapeutic protein pharmaceuticals, revamped interest in assessing and modelling the aggregation kinetics has been observed.

In spite of rapid evolution of digital technologies, a huge number of applications still rely on the use of paper based medium. This is especially true for processing of bank cheques. The pre-printed account number and cheque number might be easily readable and processed automatically. However, the handwritten texts in cheque are usually processed manually involving important time and cost. An attempt has been made in this paper to develop a bank cheque handwritten text recognition (BCHWTR) system for cheques of Indian banks by recognizing the handwritten characters present in the 'payee name', 'courtesy amount (both in words and figures)' and 'date' fields by using image processing techniques on handwritten cheque images. Images of bank cheques are fed as input to the proposed system. There are four stages in the proposed system: cropping the image at a specific location; segmentation of handwritten lines, words and characters; feature extraction from individual characters and digits using Histogram of Oriented Gradients (HOG) method and Grey Level Co-occurrence Matrix (GLCM) texture features; recognition of isolated characters and digits using the Support Vector Machine (SVM) based classification process that ensures correct recognition. The performance of present BCHWTR system is evaluated on a self-generated dataset of bank cheques and it has provided a promising result.