http://localhost:8081/jspui/handle/123456789/22 2026-10-17T02:48:40Z http://localhost:8081/jspui/handle/123456789/21753 Title: Development of efficient and sensitive whole-cell biosensors Authors: Bhatt, Ankita Abstract: Biosensors are analytical devices that incorporate biological entities, i.e., enzymes, antibodies, nucleic acids (DNA, RNA), and whole cells, such as the sensing moiety. Biosensors have facilitated the detection and quantification of analytes without relying on sophisticated, expensive, and labor-intensive analytical techniques like GC and HPLC. Among the different types of biosensors, whole-cell biosensors (WCBs) stand out as a unique and versatile category. Unlike other biosensors that rely on isolated biomolecules such as enzymes or antibodies, WCBs utilize entire living cells as the sensing element. When microorganisms are used as a whole for biosensing, they are termed microbial whole-cell biosensors (MWCBs). MWCBs utilize the regulatory pathways of microbes, where the DNA sequence elements and regulatory proteins (transcription factors) work in tandem to regulate the expression of genes in the microbial genome. Therefore, the transcription factor, activator or repressor, is pivotal in generating specific and sensitive responses. Although the MWCBs have succeeded in laboratory settings, they have failed to reach the commercial front. However, with the advent of synthetic biology, MWCBs have gained more success by optimizing the sensitivity and specificity of the biosensor. It has opened a plethora of avenues for optimizing the biosensor response, like promoter modifications, reporter protein variation, transcription factor engineering, and changing the RBS of the gene. Also, the chassis for housing the biosensing module can be altered to enhance biosensing attributes like sensitivity, specificity, detection time, etc. MWCBs are widely used in various fields, including medical diagnostics, environmental monitoring, and food safety. 2024-07-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21752 Title: Studies on Valorization of Dairy Waste for Probiotic & Nanobiotic Applications Authors: Hussain, Ajmal Abstract: India, as one of the world’s largest dairy producers, generates approximately five million tons of paneer whey annually. This byproduct, rich in organic compounds, poses severe environmental challenges due to its high biochemical oxygen demand (BOD) and chemical oxygen demand (COD). Currently, a significant portion of whey is disposed of untreated into water bodies, contributing to pollution and ecological imbalance. While some whey is processed into animal feed or low-value products, these methods are limited and do not fully capitalize on whey’s potential for valorization, leading to persistent environmental concerns. This study addresses the need for sustainable whey valorization by transforming paneer whey into valuable bio-products. Leveraging Response Surface Methodology (RSM), we optimized the production of pediocin, an antimicrobial peptide, achieving a peak yield of 56,000 AU/ml. Our results show that the formulated whey medium produced five times more pediocin compared to conventional MRS (de Man–Rogosa–Sharpe) media, with significant validation through growth modeling. Encapsulation of Pediococcus pentosaceus in alginate enabled continuous pediocin production, effectively reducing Listeria monocytogenes by 4 log CFU during co-incubation, thus illustrating the medium's potential for enhanced microbial biomass and bioactive compound production. Furthermore, a Plant Growth-Promoting Bacteria (PGPB) consortium was developed from whey-cultivated Bacillus and Pseudomonas strains, which was tested in field trials on Bok choy plants. The consortium enhanced soil biodiversity, as shown by metagenomic analysis, which revealed increased bacterial richness and the emergence of new metabolic functions in treated soils. These results underscore the consortium’s potential to support sustainable agricultural practices by promoting plant growth and improving soil health. 2025-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21748 Title: STUDY OF MULTISTAGE CHEMICAL HYDROLYSIS OF WHEAT STRAW AND UTILISATION OF HYDROLYSATES BY Lentibacillus salarius BPIITR Authors: Grover, Chetna Abstract: Current study investigates the potential of wheat straw (ws) fractionation coupled with valorization through fermentation by the halophilic bacterium Lentibacillus salarius BPIITR. Initially, the chemical composition of untreated wheat straw was analyzed, the total solid content was 92.9 ± 0.07 % comprising 33.01 ± 0.16% cellulose, 23.88 ± 0.13% hemicellulose, 23.47 ± 0.60% acid-insoluble lignin, 1.82 ± 0.04% acid-soluble lignin, 8.40 ± 0.5 % ash, and remaining are extractives. A fractional factorial design followed by a Box-Behnken design was employed to optimize the alkaline wet oxidative pretreatment (AWOP) and acid hydrolysis conditions for maximum sugar release. The optimal conditions were determined to be 0.048 g/g sodium hydroxide, 127 °C acid hydrolysis temperature, 150 min acid hydrolysis time, and 0.1 g/g sulfuric acid. Under these conditions, a total sugar yield of 251.5 ± 6.91 mg/g ws was achieved, comprising 22.93 ± 1.10 mg/g ws hexose sugars and 228.57 ± 7.74 mg/g ws pentose sugars. This resulted in an 84.23% recovery of hemicellulose, significantly higher than previous studies reporting 43.1-57.12% recovery. The study then investigated the potential of artificial seawater for cellulose hydrolysis using a central composite design. Optimal conditions for artificial seawater-based hydrolysis were identified as 169 °C, 61 min reaction time, and 0.131 g/g acid concentration, yielding total sugar of 310.68 ± 1.88 mg/g of cellulose-rich residual ws. This represented a substantial improvement over the distilled water method, which produced total sugar, 139.36 ± 2.58 mg/g of cellulose-rich residual ws under its optimal conditions (148 °C, 126 min, 0.461 g/g acid concentration). 2024-11-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21747 Title: MACHINE LEARNING AND TELEMEDICINE FOR THE EARLY DIAGNOSIS AND TREATMENT OF CARDIOVASCULAR DISEASES Authors: Srivastava, Shreya Abstract: This thesis examined the application of contemporary technology, such as artificial intelligence and telemedicine, to enhance the existing cardiac patient monitoring and digital diagnostic systems. The research specifically aimed to improve the monitoring of individuals with cardiac problems. The proposed methods enable real-time, remote monitoring of cardiac patients and enhanced arrhythmia prediction abilities. From 271 million in 1990 to 523 million in 2019, cardiovascular diseases (CVDs) have nearly doubled. CVD-related fatalities have grown as well from 12.1 million in 1990 to 20.5 million in 2021, a rise of 53%. This persistent increase in cardiovascular diseases places a substantial strain on the healthcare system, particularly in poorer nations with higher CVD rates due to a lack of financial resources. We developed a mobile application named 'Dhadkan' to minimise the number of hospital visits and expand the availability and cost of renowned doctors to the whole Indian population. The use of automated ECG analysis methods in clinical practise is hampered by a large level of uncertainty resulting from patient variables. In a multi-modal design, none of the ECG annotators that have been created to date account for patient characteristics. We analysed the UCI Arrhythmia dataset using the XGBoost model, correlating patient features to ECG morphological changes. The model recognised patient gender with 87.75% confidence using discriminative ECG characteristics. We propose a multi-modal architecture for ECG analysis and arrhythmia classification that can help defy the variability in ECG owing to patient-specific circumstances. This approach, rECGnition (robust ECG abnormality detection), combines Beat Morphology and Patient Characteristics to generate a discriminative feature map that comprehends the internal correlation of both modalities. Consequently, we developed a self-attentive canonical fusion method for ECG and patient data using DL for effective cardiac diagnostics. Furthermore, DL was also used to develop a programme that digitises 12-lead paper electrocardiograms. This was done so that we could increase both the speed with which ECGs are evaluated and the portability of the data they contain. 2024-06-01T00:00:00Z