Journal of Scientific and Technical Research

Abstract
Chronic Kidney Disease (CKD) is a progressive condition where the kidney becomes damaged and loses its ability to filter blood and waste, leading to a buildup of toxins. This complex organ has intrinsic protective mechanisms that ensure systemic homeostasis in varying physiological and pathological conditions. Loss of these functions can lead to gradual loss of renal function, ending up in chronic kidney disease (CKD) which is a worldwide health problem. The gradual and usually irreversible reduction of glomerular filtration rate advances to end-stage renal disease (ESRD) if not addressed in a timely manner. CKD is almost always accompanied by other etiological factors like diabetes mellitus, hypertension, glomerulonephritis, polycystic kidney disease, obstructive nephropathy, infections, and drug-induced toxicity, all of these resulting in its progression. Owing to its multifactorial etiology, prevention and treatment measures need to be multidimensional. Foremost are the early detection, dietary and lifestyle changes, ideal pharmacologic treatment, and the use of nephroprotective foods, fruits, and herbal supplements having antioxidative, anti-inflammatory, and reno-protective effects. This article emphasizes the subtleties of CKD pathogenesis and presents a survey of evidence-based remedial interventions in an attempt to reduce progression and preserve renal function

Abstract
This paper explores in detail the operational, internal, and external challenges of Indian Micro, Small, and Medium Enterprises (MSME), which contribute significantly to India’s GDP and play a very important role in the country’s economic growth, based on original survey responses from one of the essential and fast-growing Packaging sectors. The survey was carried out on more than 70 small and medium corrugated box manufacturers spread all over the country. Key areas investigated include shortages of skilled workforce, machine maintenance, retention, technological, and financial difficulties, and compliance. Relevant literature is cited, pointing out the challenges, and quantitative and qualitative data are used to show that these challenges are multidimensional. The actionable recommendations that emerge from this study will enhance the efficiency of MSMEs in India

Abstract
Somatosensation refers to the body’s ability to sense and perceive physical sensations like touch, pain, pressure, temperature, and body position. The present study examined somatosensory deficits among teaching professionals, a population frequently subjected to prolonged standing and recurrent postural stresses. Thirty healthy university faculty members aged between 28-40 years participated in the study. The vibration, pressure, proprioception, and graphesthesia components of somatosensation were evaluated by Cumulative Somatosensory Impairment Scale (CSIS). The results indicated that the absence of vibration sensation was found to be the highest (63.30%) among teachers, followed by graphesthesia (43.30%), pressure (46.60%), and proprioception (30%) sensation. On contrary, the maximum percentage of normal responses (36.60%) was observed in graphesthesia, indicating a higher-level sensory processing. The results suggest a differential susceptibility of somatosensory modalities, indicating that pressure and vibration sensations are more vulnerable to occupational stress. These, in turn can affect the fine motor performance and postural regulation among teachers making them prone to musculoskeletal diseases and fatigue. The findings emphasize the importance of regular sensory assessments among teachers for preventing long-term functional loss. Overall, this study addresses a critical gap by highlighting somatosensory variability in teaching professionals and its implications for motor control and work-related functional efficiency.

Abstract
The growing demand for plant-based meat analogues (PBMAs) has driven the need for sustainable and stable products with extended shelf life. A cost-effective, soy-free PBMA was developed from rice bran and oyster mushroom protein isolates (1:6 ratio) using a steaming process. Samples were packed under vacuum and normal atmospheric conditions, and stored at low (4±1 °C) and room (25±2 °C) temperatures. Rapid microbial growth and quality loss were observed at room temperature, limiting shelf life to 3 and 7 days under normal and vacuum conditions, respectively. In contrast, low-temperature vacuum storage minimized spoilage, maintained desirable composition (moisture 4.28%, protein 42.45%, fat 4.65%, ash 5.02%), and preserved color, texture, and water and oil holding capacities. Shelf life of 14 days under normal and 28 days under vacuum conditions were observed under low temperature storage. The study emphasizes the importance of packaging, temperature, and further strategies in ensuring PBMA quality during storage

Abstract
Background: Many psychological and physical health problems are instigated due to existence of stress because of chaotic lifestyle. It is a noteworthy hazardous factor for several cerebral disorders and hence, impacts the superiority of life. To manage the stress, it is important to practice the meditation and yoga. Electroencephalogram (EEG) is the chosen analytical tool in this research as it is economical, non-invasive and easy to operate. This study mainly focuses on Deep Learning (DL) technique for recognition of brain state linked with Isha Shoonya meditation. To obtain the best outcomes, the task was accomplished for identifying meditative brain state with varying High Pass Filter (HPF) frequencies. Purpose: The performance of the model depends upon various factors like model designing, hyperparameter tuning, Independent Components Analysis (ICA), and HPF. This study emphasized on one important factor how the high pass filtering impacts the accuracy of the model. The filter setting was done to dissimilar frequencies: 0.1 Hz, 0.5 Hz, 1 Hz, and 2 Hz to investigate the varying impacts of HPF on the presented Hybrid model. The performance was systematically assessed by varying the filter settings. Methods: Hybrid model was designed and examined how the high pass filtering impacts the accuracy of the model. Results and Conclusion: Accuracy of 68.93% with filter setting at 0.1 Hz, 87.50% at 0.5 Hz, 96.41% at 1 Hz, and 93.76% at 2 Hz was attained. The maximum accuracy of 96.41% was achieved for Hybrid model at 1 Hz for Isha Shoonya meditation. HPF at 1 Hz gave decent outcomes.

Abstract

Abstract
Blood flow restriction (BFR) training uses partial vascular occlusion alongside reduced mechanical loading to stimulate muscular adaptations. The present study compared between the effect of blood flow restricted resistance training (BFRRT) and conventional resistance training on peak lower body strength in sixty healthy young males aged 18-26 years. Participants were allocated to experimental (BFR) and control groups (conventional) (n=30 each). Both groups performed identical four-week squat-based training comprising of three sessions/week for four weeks. Peak lower body strength assessment was done using one-repetition maximum at baseline and postintervention. Statistical evaluation was done using paired and unpaired t-tests for intra-group and inter-group comparisons. Significant strength improvements were noticed in both training groups. In BFR group, peak lower body strength increased from 88.17 ± 9.60 kg to 95.33 ± 10.74 kg. In control group, it improved from 90.17 ± 9.33 kg to 96.50 ± 9.75 kg (p < 0.001). Inter-group comparison revealed no significant difference in post-training strength outcomes (p = 0.661), despite numerically superior gains in the blood flow restriction group. Both BFR and conventional resistance training produced substantial peak lower body strength enhancement. Although blood flow restriction training yielded marginally greater absolute improvements, statistical equivalence between protocols suggests comparable efficacy.

Abstract
Artificial Intelligence (AI) has been increasingly applied in the field of food safety, offering solutions to improve food yield, quality, nutrition, safety, and traceability, while also reducing resource consumption and food waste. This review highlights the potential of AI for improving food safety across the entire food production process, from precision agriculture to precision nutrition. It also identifies research hotspots and future trends, providing valuable insights for researchers, practitioners, and policymakers in the field. In conclusion, AI technologies have shown promising potential in enhancing food safety and quality. The ongoing research and development in this field are expected to bring about significant improvements in food quality and safety management

Abstract
Teleorthodontics is an emerging field that leverages digital technology to provide remote orthodontic consultations, treatment monitoring, and patient communication. With the increasing adoption of telehealth, teleorthodontics offers a convenient and efficient solution for both patients and practitioners by reducing the need for inperson visits while maintaining effective treatment oversight. Through mobile applications, artificial intelligence (AI), and digital imaging, orthodontists can assess dental conditions, track treatment progress, and provide timely guidance. This approach enhances accessibility to orthodontic care, particularly for patients in remote areas or those with busy schedules. However, challenges such as limitations in accurate diagnosis, the need for physical interventions, and data security concerns must be addressed. As technology advances, integrating teleorthodontics with traditional orthodontic practices can optimize patient care, making treatment more efficient and patient-centered.

Abstract
Adaptive Radiotherapy (ART) is a fundamental shift from static dosimetry to a dynamic, customized procedure that is determined by the patient’s daily biological and anatomical changes. Organs at risk (OARs) may become more toxic, or therapy efficacy may be compromised if these inter-fractional discrepancies are not addressed. AI-driven solutions are the most important part of making things useful. They make it possible how to adapt (Triggered OART) and cut down on the main problem, manual demarcation. To get around the problems with CBCT for proton treatment, employ Robust Planning or Deep Learning-based Synthetic CT (sCT). Dosimetric efficacy is consistently shown; highlevel clinical evidence connecting these benefits to enhanced patient outcomes is still developing. Future research must concentrate on large-scale Randomized Controlled Trials (RCTs) and the inclusion of biological and functional MRI biomarkers.

Abstract
Triple negative breast cancer (TNBC) an aggressive and diverse subtype of breast cancer. Lack of three crucial receptors estrogen, progesterone, and HER2 expression make untreatable now yet, many approaches utilized but no satisfactory results came to manage TNBC. In numbers of treatments strategy chemotherapy, a promising option, while chemoresistance major challenge. In this study, we investigated natural compounds as possible inhibitors of PI3KG, a crucial protein associated with cancer growth and therapy resistance. Molecular docking research revealed that all five tested natural compounds exhibited enhanced binding affinity for PI3KG. Among them Phosmidosine B had the highest affinity, with a docking score of -10.01 kcal/mol and a Glide energy of -60.45 kcal/mol. Emodacidamide E (9.69 kcal/ mol; -56.91 kcal/mol), Emodacidamide H (-9.62 kcal/mol; -52.49 kcal/mol), Methylinoscavin D (-9.57 kcal/mol; -58.71 kcal/mol), and 1-hydroxymethyl-8-hydroxy-anthraquinone- 3-carboxylic acid (-9.54 kcal/mol; -43.78 kcal/mol) followed. ADME analysis and cytotoxicity prediction showed that all of the chosen compounds had good drug like attributes and safety profiles, which supports their drug properties and low toxicity potential. In conclusion, the significant binding affinities, stable interaction energies, and good ADME and cytotoxicity profiles of these natural compounds, show that they could be good PI3KG inhibitors. Nevertheless, extensive experimental validation is essential to facilitate their further therapeutic advancement

Abstract
Patients with pulmonary tuberculosis (PTB) often experience persistent mucus retention, bronchiectasis, and systemic inflammation even after microbiological cure, resulting in impaired quality of life and exercise capacity. This randomized controlled trial compared the efficacy of the Lung Flute (acoustic vibration device) and Acapella (oscillatory positive expiratory pressure device) with standard care in microbiologically cured PTB patients with residual respiratory symptoms. Sixty patients with daily sputum production were randomized (1:1:1) into Lung Flute, Acapella, or control groups and followed for 8 weeks. Primary outcomes were changes in six-minute walk distance (6MWD) and St. George’s Respiratory Questionnaire (SGRQ) total score. Secondary outcomes included sputum volume, modified Medical Research Council (mMRC) dyspnea scale score, maximal inspiratory pressure (MIP), C-reactive protein (CRP), interleukin-6 (IL-6), erythrocyte sedimentation rate (ESR), and exacerbation/readmission rates. Both devices significantly outperformed standard care. The Lung Flute group showed the greatest improvements: 6MWD +72.4 ± 31.2 m, SGRQ −12.3 ± 5.1 points, daily sputum volume +18.2 ± 7.1 mL, ESR −28.4 ± 11.2 mm/h, CRP −42 ± 18%, and IL-6 −48 ± 22% (all p<0.01 vs. control; most p<0.05 vs. Acapella). Reductions in inflammatory markers strongly correlated with clinical improvements, particularly in the Lung Flute group (r = −0.68 for ΔESR vs. Δ6MWD, p<0.001). Six-month readmission rates were 0.3, 0.5, and 0.9 per patient-year for the Lung Flute, Acapella, and control groups, respectively (log-rank p=0.05). No adverse events were reported. The Lung Flute is superior to Acapella and standard care in reducing systemic inflammation and improving functional outcomes in PTB patients with residual mucus hypersecretion.

Abstract
Metallo-Polyester Nanocomposite (MPN) has played a pivotal role in environmental remediation. These hybrid nanomaterials have demonstrated photocatalytic activity in degrading organic pollutants and killing microorganisms, thereby mitigating environmental pollution. In the present study, we have utilised a novel bimetallic-semi-aromatic polyester nanocomposite (NC) composed of poly(t BGE-alt-PA) copolymer and Zinc Ferrite nanoparticles (ZnFe2O4 NPs). The NC was physicochemically characterized and tested for in vitro and in vivo toxicity studies in our previous study. The ZnFe2O4@poly(t-BGEalt- PA) composite was fabricated in NP form and then tested for photocatalytic action on methylene blue (MB) dye under sunlight. As a result, the ZnFe2O4@poly(t-BGE-alt- PA) composite degraded 91.73% MB dye in 150 minutes, compared with ZnFe2O4 NPs, which degraded only 83.10% MB dye. Additionally, the ZnFe2O4@poly(t-BGE-alt-PA) composite was found to be reusable and more stable during the recycling study.

Abstract
Quality recognition of structural areas on printed documents is vital in other downstream processes like optical character recognition, document retrieval and digital archiving. Devanagari script has further problems that include the close morphology, headline writing and multi-tiered character constructions which often undermine the efficiency of generic document-analytic models. In this paper, Authors presented a deep learning model that is founded on YOLOv8 to identify structural elements in Devanagari printed texts. The method is trained and tested on PubLayNet dataset which is a large scale benchmark that consists of a variety of document structures and later adjusted to Devanagari script using targeted fine-tuning and regionspecific annotations. The model is very precise in detecting fundamental structural components of text block, titles, table, lists, and figures and it shows high generalization in complicated pages of Devanagari. The experimental findings prove that YOLOv8 is an appropriate solution to detect document-structure quickly and accurately and provide a solid basis of larger Indic-language document processing pipelines. The structure helps to achieve high efficiency in the process of document digitization and structural preservation that is needed to support high-quality script-specific OCR systems.