EVALUATING THE IMPACT OF BIO-ENZYMATIC SOIL STABILIZATION ON SUSTAINABLE GEOTECHNICAL APPLICATIONS: A COMPREHENSIVE STUDY

Soil stabilization is a critical process in geotechnical engineering to enhance soil properties forconstruction and infrastructure development. Conventional methods often involve environmentallydetrimental materials such as cement and lime, highlighting the need for sustainable alternatives. Thisstudy evaluates the potential of bio-enzymatic soil stabilization as an eco-friendly and cost-effectivesolution. Using bio-enzymes derived from natural organic sources, this method offers improved soilshear strength, reduced permeability, and enhanced durability while minimizing carbon emissions andenergy usage. Laboratory tests and field studies demonstrate significant improvements in soilmechanical properties, showcasing the viability of bio-enzymes for sustainable geotechnicalapplications. The findings suggest that bio-enzymatic stabilization can play a pivotal role inaddressing environmental challenges in modern geotechnics

Development of Multi-Compartment Dielectric Barrier Discharge Plasma Reactor for Innovative Water Treatment

A novel multi-compartment dielectric barrier discharge (MCDBD) plasma rector is developed and tested to produce plasma-activated water (PAW). MCDBD reactor consists of a polycarbonate container with six compartments. The top electrodes are stainless steel needles connected to AC high-voltage power supply. The bottom of each compartment is replaced with a glass slab and stainless-steel mesh electrodes. Cold plasma is generated in all compartments simultaneously to activate water. Experiments conducted by varying treatment time, power levels, gap between electrodes and water surface, and volume of water. Production of reactive oxygen and nitrogen species (ROS and RNS, respectively) in PAW is evaluated as per international standards (APHA/AWWA/IS). The application PAW greatly depends on the ROS and RNS concentration so results of MCDBD reactor are compared with conventional reactors. ROS and RNS concentration in 1800 mL water is measured 0.93 M and 0.52 M, respectively, in the MCDBD reactor. ROS and RNS concentration in 500 mL water is found to be 0.6 M and 0.44 M, respectively, in a conventional reactor. Result shows higher concentration of ROS and RNS produced in large volumes of water using MCDBD reactors. Even though the same amount of power is supplied to both conventional and multicompartment reactors, output in terms of ROS and RNS production is significantly greater in new design. Also, ROS and RNS have longer life in MCDBD reactor which is desirable to deactivate the biofilms and water decontamination. Proposed design is found to be more suitable for wastewater treatments, biomedical and agriculture applications

Scientific perspectives on Guillain-Barre Syndrome (GBS): A comprehensive review for sentience after early 2025 GBS outbreak in an Indian state

Background: Guillain-Barré Syndrome (GBS) is an acute, self-limiting, and rare neurological disorder wherein the body's immune system mistakenly attacks the peripheral nervous system (PNS). A report, published in February 2025 by the Indian newspaper ‘The Times of India’, highlighted a significant outbreak of GBS in the Indian state of Maharashtra, owing to the Campylobacter jejuni (C. jejuni) infection. The surge in cases has been considered as one of the most significant recorded GBS outbreaks globally, which underscores the need to raise GBS awareness. Method: This article provides an in-depth scientific perspective on GBS, drawing on literature from scientific databases such as PubMed and ScienceDirect. It aims to enhance awareness among science-related students, researchers, medical and paramedical professionals, and the general public. Result and discussion: GBS is an acute polyneuropathy characterized by limb weakness with hyporeflexia or areflexia. In severe forms, respiratory and bulbar paralysis can occur, requiring mechanical ventilatory support. It is the commonest cause of acute neuromuscular paralysis. The basic underlying mechanism of the disease is a localized attack against the myelin sheath of the peripheral nerves and nerve roots, with secondary axonal damage. It is believed that the bacterial antigens have a close molecular mimicry with neural antigens. As a result, the response generated against these antigens cross-reacts with the neural cells. Plasma exchange, immunoglobulin infusion, and plasmapheresis are the mainstays of treatment for GBS. Conclusion: A thorough understanding of GBS is essential, including its pathophysiology, underlying causes, risk factors, symptoms, diagnostic methods, treatment strategies, and the latest advancements

Synthesis of Thienopyrimidines and their Antipsychotic Activity

A series of thienopyrimidines and related heterocycles were synthesized by refluxing related imidoyl chloride with primary and secondary amines under microwave irradiation and classical heating. The imidoyl chlorides were synthesized from corresponding cyclic imides with phosphorus oxychlorides under microwave irradiation and classical heating. The structures of the compounds were confirmed by FT-IR, NMR. The synthesized compounds were screened for anti psychotic activity

Review on Classification of Heart Disease using Hidden Pattern Analysis

Prediction of Heart Disease utilizing strategy of Data Mining is successful yet there is loss of Accuracy by utilizing the picture handling as extra preparing for more Accuracy . In Proposed System we are utilizing the calculations like Decision Tree, Nueral Network and the Naive Bayes in the information mining and in the picture Processing we are utilizing the prevalent calculations like Local Binary Pattern. The exploration result indicates forecast precision of 99 Percent. Information mining empower the wellbeing area to foresee designs in the datasets. Here we utilize picture preparing for looking at the ECG, CT examine, Angiography, and so forth, reports and finding the more exact outcomes

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD50)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor variables. A model developed using Estate type fingerprints correctly predicted the LD50 classes for 70 of 94 training set chemicals and 19 of 24 test set chemicals. An individual category was formed for each of the chemicals by extracting its corresponding k-analogs that were identified by k-NN classification. These categories were used to perform the read-across study for prediction of the chronic toxicity, i.e., Lowest Observed Effect Levels (LOEL). We have successfully predicted the LOELs of 54 of 70 training set chemicals (77%) and 14 of 19 test set chemicals (74%) to within an order of magnitude from their experimental LOEL values. Given the success thus far, we conclude that if the k-NN model predicts LD50 classes correctly for a certain chemical, then the k-analogs of such a chemical can be successfully used for data gap filling for the LOEL. This model should support the in silico prediction of repeated dose toxicity

Towards new computational tools for predicting toxicity

The toxicological screening of the numerous chemicals that we are exposed to requires significant cost and the use of animals. Accordingly, more efficient methods for the evaluation of toxicity are required to reduce cost and the number of animals used. Computational strategies have the potential to reduce both the cost and the use of animal testing in toxicity screening. The ultimate goal of this thesis is to develop computational models for the prediction of toxicological endpoints that can serve as an alternative to animal testing. In Paper I, an attempt was made to construct a global quantitative structure-activity relationship (QSAR)model for the acute toxicity endpoint (LD50 values) using the Munro database that represents a broad chemical landscape. Such a model could be used for acute toxicity screening of chemicals of diverse structures. Paper II focuses on the use of acute toxicity data to support the prediction of chronic toxicity. The results of this study suggest that for related chemicals having acute toxicities within a similar range, their lowest observed effect levels (LOELs) can be used in read-across strategies to fill gaps in chronic toxicity data. In Paper III a k-nearest neighbor (k-NN) classification model was developed to predict human ether-a-go-go related gene (hERG)-derived toxicity. The results suggest that the model has potential for use in identifying compounds with hERG-liabilities, e.g. in drug development

Towards new computational tools for predicting toxicity

The toxicological screening of the numerous chemicals that we are exposed to requires significant cost and the use of animals. Accordingly, more efficient methods for the evaluation of toxicity are required to reduce cost and the number of animals used. Computational strategies have the potential to reduce both the cost and the use of animal testing in toxicity screening. The ultimate goal of this thesis is to develop computational models for the prediction of toxicological endpoints that can serve as an alternative to animal testing. In Paper I, an attempt was made to construct a global quantitative structure-activity relationship (QSAR)model for the acute toxicity endpoint (LD50 values) using the Munro database that represents a broad chemical landscape. Such a model could be used for acute toxicity screening of chemicals of diverse structures. Paper II focuses on the use of acute toxicity data to support the prediction of chronic toxicity. The results of this study suggest that for related chemicals having acute toxicities within a similar range, their lowest observed effect levels (LOELs) can be used in read-across strategies to fill gaps in chronic toxicity data. In Paper III a k-nearest neighbor (k-NN) classification model was developed to predict human ether-a-go-go related gene (hERG)-derived toxicity. The results suggest that the model has potential for use in identifying compounds with hERG-liabilities, e.g. in drug development