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Product quality and safety are crucial in the e-commerce market, particularly for regulated sectors like the food industry, for user protection, and it is the responsibility of the merchants who sell their products to ensure these are maintained. Here comes the role of the government, which lays down certain rules and regulations that need to be followed. These rules and regulations are laid down by the central or state government of India. All these rules are enforced by the authorities established under the respective ministries. All these rules and regulations are maintained in the form of government repositories on the websites of the respective government ministries. Accessing data that is present in these repositories is very difficult for users, as these rules, regulations, and amendments are made available in PDF format to the general public and are often in an unstructured format. This initial study validates the system's effectiveness using the specific domain of Indian food safety regulations. Validation of the system on food-specific regulations demonstrated promising performance, achieving a task accuracy between 82 and 89% over 10 runs and an NLP model F-score of 87.17%. The proposed system makes it easy for users to access this data, allowing them to search for rules and regulations related to their product by providing a search engine. The proposed system gathers data scattered across various websites using web scraping. This data is then automatically cleaned and organized using automation scripts before being presented to the users in a structured and informative format. The proposed system website delivers accurate, real-time data with high speed and reliability, ensuring users get up-to-date and precise information instantly. The proposed system provides product-specific output using NLP (Natural Language Processing).•Ensuring product quality and safety in e-commerce is crucial for user protection, and merchants must maintain these standards. The government enforces rules and regulations through various ministries.•Government rules and regulations are often stored in unstructured PDF formats on ministry websites, making it difficult for users to access and interpret the information.•The proposed system uses web scraping to gather regulatory data, cleans it using automated scripts, and stores it in text format. It employs Spacy natural language processing algorithms to provide product-specific search results.

This article presents a reproducible workflow developed in R to process indoor air quality data by integrating wearable multi sensor measurements with tablet-based activity logs. The data come from two field campaigns in four primary schools and involve 20 female maintenance staff members. The method supports occupational exposure assessment under real world conditions, where sensor signals can be incomplete or noisy and self-reported activities may contain timing errors. The workflow organizes raw files by campaign, school and instrument, standardized timestamps (including daylight saving changes), and harmonizes quantitative signals (temperature, relative humidity, CO₂, PM_2.5 and VOC sensors) with qualitative descriptors from activity logs (location, activity, cleaning practice, product, time). It then builds an integrated dataset in which each sensor reading is linked to its activity context, making it possible to distinguish cleaning periods from daytime and night time storage of instruments. The approach produces graphical outputs and summary statistics that support sensor diagnostics, comparison of exposure profiles between micro environments, and communication of semi quantitative exposure patterns to researchers and stakeholders. • Reproducible R workflow integrating multisensor and activity data. • Standardized pipeline for data cleaning, temporal alignment, and data structuring. • Analysis-ready outputs linking exposure levels to activities and contexts.

Immunohistochemistry (IHC) is a widely used technique for detecting proteins in tissue sections; however, its performance is significantly influenced by the quality of antibodies and detection strategies. Conventional IgG or IgM-based antibodies often come with high production costs, variability between batches, and limited penetration into tissues, which can compromise the uniformity and reproducibility of staining. Recombinant single-chain variable fragments (scFvs) offer an attractive alternative due to their smaller size, defined molecular composition, and renewable production capabilities. In this work, we focus on optimizing IHC by chemically biotinylating scFvs, allowing for direct coupling to streptavidin-based detection systems. This method enhances signal sensitivity while eliminating the need for secondary antibodies or additional labeling steps. Biotinylated scFvs can diffuse more easily into tissue sections, streamline the staining workflow, and reduce both assay time and cost. Overall, integrating biotinylated scFvs into IHC protocols presents a robust, scalable, and reproducible strategy that addresses key limitations of conventional antibody-based methods, thus supporting more consistent and efficient protein detection in histological analyses.This protocol allowed the optimization of conventional IHC conditions by using biotinylating scFv to achieve precise, time and cost-efficient antigen visualization.

Early detection of cognitive decline remains one of the major challenges in contemporary neurology. Although event-related potentials associated with cognitive processes, particularly the P300 component, as well as resting-state EEG analysis, have long been investigated as potential functional biomarkers of dementia, their clinical application is often limited by methodological heterogeneity and insufficient transparency of signal processing procedures. In this article, we present a reproducible EEG-ERP methodology based on proprietary software for stimulus generation, synchronization, and deterministic averaging of visual event-related potentials, combined with quantitative analysis of resting-state EEG frequency. The approach integrates precise stimulus timing, transparent offline synchronization using an audio marker recorded on the ECG channel, and a fully deterministic averaging procedure. Pilot clinical data obtained from patients with mild cognitive impairment and dementia are included to illustrate the feasibility of the proposed workflow. • Software-controlled visual stimulation with fixed inter-stimulus intervals and explicit offline synchronization using an audio marker. • Deterministic ERP averaging enabling reliable estimation of P300 latency in routine clinical EEG recordings. • Integration of resting-state EEG frequency and P300 latency as interpretable electrophysiological markers of cognitive decline.

This study tackles the challenge of accurately inverting neural and vasodilatory signals from BOLD-fMRI data. We propose a novel framework that substantially enhances prediction performance through:•Multi-scale dynamic feature extraction to comprehensively characterize BOLD signal properties.•A stacking ensemble architecture that synergistically combines multiple heterogeneous base learners.•Hierarchical model fusion via a meta-learner to robustly integrate predictions and capture complex nonlinear mappings.Evaluated on synthetic data from the Balloon model, our method achieves an R² of 0.92 for the vasodilatory signal and 0.78 for the neural drive signal, outperforming existing benchmarks.Validation on real fMRI data shows successful reconstruction of neural activity, with reconstructed BOLD signals correlating with measured signals at levels up to 0.9931. This provides a new pathway for high-fidelity inversion of microscopic neural activity.

Advanced magnetic resonance imaging (MRI) offers unique opportunities to explore the biological complexity of glioblastoma, the most aggressive brain tumour. The 2021 World Health Organization reclassification of glioblastoma has obscured the interpretation of prior imaging research, necessitating a focussed synthesis to align it with current guidelines. This scoping review protocol aims to systematically map evidence from adult, preoperative, isocitrate dehydrogenase-wildtype glioblastoma to answer the question, "can advanced MRI help understand the biology of glioblastoma?" It will focus on peer-reviewed studies investigating associations between advanced MRI and tumour characteristics, including correlations with histopathological/molecular markers. Five databases will be searched for primary sources using advanced MRI, such as magnetic resonance spectroscopy, diffusion-, perfusion-, chemical exchange saturation transfer- and susceptibility-weighted imaging, to image glioblastoma. Findings will be stratified by biological domains, such as molecular factors, vascularisation and metabolism.•Databases: PubMed, Scopus, Cochrane, EBSCO, Embase.•Methodology: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews framework and Joanna Briggs Institute guidance.•Outcome: Evidence map and narrative synthesis of biological insights derived from advanced MRI, aligning literature with the current classification and highlighting methodological gaps and priorities for validation and clinical translation to guide future study design and standardisation efforts.