Exploring and Evaluating the impact of Blended Learning Outcomes through LMS in Rural Higher Education
This study explores and evaluates the impact of blended learning through Learning Management Systems (LMS) on the educational outcomes of undergraduate students from rural backgrounds in higher education institutions, specifically in the context of Andhra Pradesh. By utilizing pre-test, post-test, mid-term, and end-semester grades, our study incorporates statistical analyses of data collected from 214 undergraduate students over two consecutive semesters. The investigation highlights the efficacy of eLearning via LMS, demonstrating significant improvements in teaching and learning performance compared to traditional instructional methods. The research underscores the relevance of these findings for higher education institutions, particularly in developing regions, as they consider adopting digital technologies to enhance educational delivery. The study also highlights the flexibility and independent learning opportunities provided by blended learning approaches, with over 85% of surveyed students expressing positive feedback. This approach, combining asynchronous and synchronous learning activities, has shown to effectively support students' academic progress and potential, particularly in challenging circumstances such as the COVID-19 pandemic. Results indicate that tailored teaching strategies using LMS platforms, such as classroom teaching, peer learning activities, group-wise experimental activities, and adoption of LMS, can significantly improve learning outcomes.
Blended Learning, Learning Management Systems (LMS), Rural Education, Higher Education, eLearning.
GAKS Rajeev Kumar, Vinay P, Venkata Krishna Reddy Vadisena, Chandra Sekhar Kolli, Siddi Someshwar, Nichenametla Rajesh
Effect of Hydrocortisone on Outcome of Post-Cardiac Arrest Between Road Traffic Accident Poly-Trauma Patients
Background: Post-cardiac arrest carrying a very poor outcome especially after road traffic accident (RTA) between polytrauma patients. Corticosteroid used recently by many authors to improve outcome. Objective: This study aimed to assess the effect of hydrocortisone on outcomes of cardiac arrest between RTA polytrauma patients. Methods: This prospective, double-blinded, randomized controlled study enrolled 200 patients admitted to our ICU as post-cardiac arrest following RTA poly trauma. Patients were randomly allocated to either group A(n=100), received resuscitation according to advanced cardio-vascular life support (ACLS) protocol, group B (n=100) received same protocol with administration of hydrocortisone 200 mg intravenous (IV) state and 50 mg IV every 6 hours for 3 days. Number of patients died, re-arrested and revived, showed stunning heart on echocardiogram (echo), return of spontaneous circulation (ROSC) with vasopressor support, ROSC without vasopressor, had improvement in their Glasgow coma scale (GCS), showed normal tissue perfusion, developed coagulopathy were collected and compared. Results: Patients in Groups B showed a significant improvement in all outcome parameters measured after cardiac arrest between RTA poly-trauma patients at the end of the studied period. As there was significant lower number of patients died, lower number of patients who developed re-arrest, lower number of patients who need high dose of inotropes to support ROSC and developed coagulopathy. And significant higher number of patients who showed improvement in their GCS and restored normal tissue perfusion. Conclusion: This study high light and clarify the effect of hydrocortisone in improving all outcome parameters measured after cardiac arrest between RTA poly-trauma patients as regard lowering number of patients died, re-arrest, showed post-arrest stunning myocardium, developed post-arrest coagulopathy and increasing number of patients who showed improved conscious level and restores normal tissue perfusion.
Hydrocortisone, Outcome, Cardiac Arrest, Road Traffic Accident, Poly-Trauma Patients.
Mohamed Gaber Ibrahim Mostafa Allam, Ahmed Gamal Salah Elsawy, Ahmed Maher Abouelnasr Khalifa, Mohamed A. Elbadawy, Mohamed Mohamed Hegab, Ayman Salah Emara Abouelnour, Mahmoud Elsayed Abdelmawla Elsayed, Abdelkarem Hussiny Ismail Elsayed, Mohamed Alshaha
Ethical AI Systems for Bias-Resistant Decision Making in Business and Healthcare
This paper gives an overview of how ethical AI systems can be integrated within business and healthcare decision-making processes, with an emphasis on bias detection, transparency, and the imposition of fairness limitations. The study explores models that allow AI systems to detect bias in themselves, justify decision-making processes, and enforce formal fairness conditions when making high-stakes transactions like lending, pricing, approving contracts, and supporting clinical decisions. The paper discusses successful and unsuccessful AI implementations in the case studies of finance, healthcare, and recruitment to identify the effects of biased decision-making. Among the key findings, it is indicated that the use of AI systems can improve the accuracy and efficiency of decision-making, but the issue of bias has also become a major concern. This paper highlights the importance of open, self-correcting artificial intelligence that will be just and accountable. The potential implications of the research are that the study might allow improving business and clinical practices, increasing consumer trust, and creating common frameworks of ethical AI implementation in industries.
AI fairness, bias detection, ethical AI, business decision-making, healthcare decision support, transparency constraints, bias mitigation.
Transdermal Drug Delivery Systems: Progress, Obstacles, And Future Prospectives
Transdermal patches offer a comfortable and non-invasive method for administering medication, marking a significant shift from traditional pills and injections. This review provides a detailed exploration of their core principles, from the structure of the skin and the variables influencing drug absorption to the various patch designs like matrix, reservoir, and drug-in-adhesive systems. It also assesses modern production methods, including solvent casting and hot-melt extrusion, weighing their practicality for large-scale manufacturing. The discussion also covers key innovations designed to improve drug absorption, such as chemical agents (e.g., terpenes and fatty acids) and physical approaches like iontophoresis and microneedles. To ensure safety and efficacy, the text details essential testing protocols, from in vitro release and skin irritation studies to stability assessments that adhere to ICH standards. Furthermore, the real-world impact of this technology is illustrated with case studies of established transdermal products, including fentanyl for pain, oestradiol for hormone therapy, and patches for nicotine cessation. The field continues to evolve dynamically, with emerging innovations like patches that deliver proteins, integrate glucose sensors, or use thermo-responsive smart polymers. Despite clear benefits such as avoiding first-pass metabolism and enhancing patient compliance, transdermal systems still face challenges like skin irritation and limitations on drug molecule size. Given that transdermal products now constitute 40% of investigational delivery systems and their market is expanding at a 25% CAGR, this review offers critical insights for the development of next-generation patches. The future of this progress lies in the integration of nanotechnology and novel biocompatible materials, which are set to unlock new therapeutic frontiers.
Transdermal drug delivery, skin permeability, drug release, patches, controlled release, pharmacokinetics, polymer matrix.
Suhani Saxena, Deepti Seth, Anmol Singh, N. G. Raghavendra Rao, Anuj Pathak
Modular Deep Learning Framework for Ovarian Ultrasound Image Segmentation Using Enhanced U-Net Architecture and BCE–Dice Optimization
Ovarian cancer diagnosis relies heavily on precise lesion segmentation from ultrasound images, which remains a challenging task due to speckle noise, low contrast, and anatomical variability. This study proposes a Modular Deep Learning Framework for Ovarian Ultrasound Image Segmentation Using an Enhanced U-Net Architecture and Binary Cross-Entropy (BCE) Dice Optimization, designed to improve segmentation accuracy, computational efficiency, and generalization. The framework integrates five modular algorithms covering dataset preprocessing, splitting, model definition, training, and evaluation. The methodology begins by preprocessing 1469 MMOTU ultrasound image–mask pairs, resizing them to 256×256 pixels and normalizing intensities for consistency. The dataset is divided into 80% for training and 20% for validation using efficient PyTorch DataLoaders. The enhanced U-Net model employs convolutional, batch normalization, and ReLU layers with skip connections for spatial preservation, is trained using the Adam optimizer (learning rate = 1×10⁻³), and uses a composite BCE–Dice loss function to enhance overlap precision. Quantitative results demonstrate a Dice coefficient of 0.96, IoU of 0.90, Precision of 0.94, and Recall of 0.92, outperforming existing GAN-based and hybrid segmentation models such as MGGAN + U-Net. Furthermore, image quality evaluation metrics achieved SSIM = 0.9834, FID = 12.85, and LPIPS = 0.03457, confirming improved perceptual fidelity and structural consistency. The proposed framework significantly reduces computational complexity and training time while maintaining superior accuracy.
Ovarian Ultrasound, U-Net, Dice Coefficient, BCE Loss, Image Segmentation, MMOTU Dataset, Deep Learning, Medical Imaging.
Mathematical and Computational Models in Prostate Cancer: From Early Detection to Precision Therapy
This study critically analyses the biases impacting scientific credibility and equality while looking into the main data sources used in prostate can- cer research. It examines important repositories and describes their func- tion in epidemiological and clinical analysis, including The Cancer Genome Atlas (TCGA), Surveillance, Epidemiology, and End Results (SEER), and GLOBOCAN. In particular, the under-representation of minorities and low- and middle-income groups, as well as structural biases resulting from elec- tronic health records and diagnostic access, are highlighted in the paper as limits in data completeness, representativeness, and annotation.These prob- lems limit the generalisability of sophisticated analytical techniques like ma- chine learning and spread algorithmic bias, as it is discussed. In order to improve scientific rigour and equity, the study investigates solutions such as external validation, synthetic data production, and standardised protocols. Results show that in order to facilitate thorough, equitable, and globally ap- plicable prostate cancer research, inclusive data collecting and transparent annotation are essential.
Babli, Sonu Mehla, Mina Kumari, Pooja Nagpal
High Prevalence of Left Ventricular Dysfunction in End-Stage Renal Disease Patients on Maintenance Hemodialysis: A Cross-Sectional Echocardiographic Study
Background: Cardiovascular disease is the leading cause of mortality in end-stage renal disease (ESRD) patients, with left ventricular (LV) dysfunction being a common manifestation. Limited data exist on the prevalence of cardiac dysfunction among Indian ESRD patients on maintenance hemodialysis. Objective: To assess the prevalence of LV systolic and diastolic dysfunction in ESRD patients on maintenance hemodialysis and identify associated clinical and laboratory parameters. Methods: This cross-sectional observational study included 79 ESRD patients aged ≥18 years on maintenance hemodialysis at a tertiary care hospital in Mangalore, India, from September 2023 to August 2025. Two-dimensional transthoracic echocardiography was performed to assess LV function. Systolic dysfunction was defined as ejection fraction <50%, and diastolic dysfunction was graded using standard criteria. Pearson correlation analysis examined associations between cardiac parameters and clinical variables. Results: The mean age was 60.4 ± 7.3 years with equal gender distribution. LV systolic dysfunction was present in 28 patients (35.44%), including 15.19% with severe dysfunction. Diastolic dysfunction affected 36 patients (45.57%), with Grade II being most common (15.19%). Overall cardiac dysfunction prevalence was 81.01%. Significant negative correlations were observed between LV ejection fraction and age (r = -0.42, p < 0.001), ESRD duration (r = -0.38, p = 0.001), and serum phosphorus (r = -0.33, p = 0.003). E/A ratio showed strong negative correlation with age (r = -0.56, p < 0.001). Conclusion: ESRD patients on maintenance hemodialysis demonstrate exceptionally high prevalence of LV dysfunction, emphasizing the need for systematic cardiovascular screening and early intervention in this high-risk population
End-Stage Renal Disease, Hemodialysis, Left Ventricular Dysfunction, Echocardiography, Cardiovascular Disease, Systolic Dysfunction, Diastolic Dysfunction.
Rahul S. Bakale, Hanumanthakari Prudhvirajrana, Dr. B. Devadas Rai
Molecular-Genetic Profile of Nephroblastoma in Children in Uzbekistan: Clinical-Morphological Correlations and the Prognostic Significance of K-RAS Mutations.
Relevance. Nephroblastoma remains one of the leading malignant tumors of childhood. In a subset of patients, despite standard SIOP/COG protocols, early relapses and metastases occur, which are associated with unfavorable molecular profiles, including activation of the RAS/MAPK cascade and K-RAS mutations. Data on the pediatric population of Uzbekistan have previously been unavailable. Objective of the study. To assess the frequency of K-ras gene mutations in children with nephroblastoma in Uzbekistan and to determine their association with tumor stage, organ invasion, metastasis, and treatment characteristics Materials and methods. A retrospective analysis was conducted on 230 patients under 15 years of age with histologically confirmed nephroblastoma who were treated at the Republican Specialized Scientific and Practical Medical Center of Oncology and Radiology and its branches. Clinical and morphological parameters, Enneking stage, surgical volume, therapy regimens, recurrences, and regional and distant metastases were evaluated. K-ras mutations (codons 12 and 13) were identified using Sanger sequencing and/or NGS. Statistical analysis included χ² testing, with p < 0.05 considered significant. Results. K-ras mutations were detected in 23 (10.0%) of the 230 patients. Their frequency increased with age and tumor stage, reaching 76.5% in stage IV. Mutations were significantly more common in cases of relapse (33.3% vs. 2.3%), in the presence of regional (15.7% vs. 6.6%) and distant metastases (36.4% vs. 3.8%), as well as in patients who received only chemotherapy and palliative interventions. Conclusion. K-ras mutation is a rare but highly informative marker of aggressive nephroblastoma, associated with advanced stages, organ invasion, relapse, and metastasis. Incorporating K-ras analysis into the risk stratification system for children with nephroblastoma in Uzbekistan may improve individualized treatment and the accuracy of prognostic assessment.
Nephroblastoma, Wilms Tumor, K-Ras, Children, Organ Invasion, Metastasis, Relapse, Uzbekistan..
Mustafayev Tojiddin Qurbonovich, Nishanov Daniyar Anarbayevich
Role Of Family-Centered Educational Approach In Medication Adherence
Anaemia is defined as haemoglobin (Hb) below two standard deviations of average for age and gender. The common type is iron deficiency Anemia (IDA), which shows microcytic and hypochromic red cells on the peripheral smear. Management includes treating underlying causes and iron supplementation. Iron supplementation is most commonly used in oral form, but rarely requires intravenous iron. Around 25% of people have anaemia globally. Iron deficiency is responsible for 50% of all anaemias. The prevalence of IDA in men under 50 is 1%. In women of childbearing age, the rate is 10% due to losses from menstruation, and one-third of children aged 12 to 36 develop anaemia. Low-income families are particularly at high risk of IDA.1-2 Successful treatment implies 2-g/dL Hb raise in 3-4 weeks. So, taking 70-mg tablet of elemental iron, three times a day, is one of the best ways to treat IDA as per previous studies.Hence, we considered checking for Hb levels after a minimum of 3 weeks of oral iron therapy in the current study. 3-4 Hence, we considered
Dr Ramya Rachamanti, Ms M Dayana Ratnam, Dr Manam Manisrikanth, Ms. Deepya Mallika Perumalla
The Forgetten Axis-Hidden Neuroendocrine Sequelae Of Aneurysmal Subarachnoid Hemorrhage
Background and Aim: In the treatment of Aneurysmal Subarachnoid Hemorrhage (aSAH), endocrine dysfunction is rarely given adequate attention. Nevertheless, neuroendocrine disturbances, particularly involving the pituitary gland, occur frequently after aSAH and can cause lasting issues such as impaired cognition and diminished quality of life. While hypopituitarism following SAH may present with vague or subtle signs, missing the diagnosis can lead to serious health risks. Methods and Materials/Patients: This prospective study aimed to investigate the frequency and characteristics of neuroendocrine changes in patients with acute aneurysmal subarachnoid hemorrhage (aSAH). It included 35 patients who presented within seven days of symptom onset. Comprehensive endocrine evaluations were conducted to assess potential dysfunction across various pituitary axes, including the somatotropic, gonadotropic, corticotropic, thyrotropic axes, as well as prolactin levels. Results: A total of 35-SAH cases (10 males and 25 females; mean age was 56.24 years) were included in the study. Aneurysms were more commonly found in the anterior circulation (n=30) than in the posterior circulation (n=5). Most of the patients presented with the Hunt-Hess grade of 1, followed by grades 3, 2, and 4, respectively. Growth hormone deficiency (48%) was the most common pituitary dysfunction, followed by adrenocorticotrophic hormone (24%), gonadotropins (FSH & LH) (24%), and thyroid stimulating hormone (16%) deficiencies and prolactin deficiency (10%) respectively. Single pituitary axis neuroendocrine dysfunction was noted in 14 patients (40%) and multiple pituitary axes dysfunction was observed in 11 patients (31.5%). Overall, 25 patients (71.5%) had neuroendocrine dysfunction in single or multiple pituitary hormone axes. Conclusion: Neuroendocrine dysfunction in acute aSAH is 71.5%. Accordingly, 40% of the participants had single-axis pituitary dysfunction and 31.5% had multiple axes pituitary dysfunction. The most common endocrine dysfunction was growth hormone deficiency (48%), followed by adrenocorticotrophic hormone, gonadotropins (LH & FSH), and thyroid stimulating hormone. Therefore, it is suggested to include hormonal evaluation in the management of acute SAH for better clinical outcomes.
There is an increased incidence of neuroendocrine dysfunction following aneurysmal SAH (aSAH), mostly during the acute phase. Multiple pituitary hormone deficiencies and single-axis pituitary dysfunction were noted in 36% and 32% of patients, respectively. The most common pituitary hormone deficiency following aSAH was growth hormone (48%), followed by adreno-corticotrophic hormone, gonadotropins (LH/FSH), and thyroid stimulating hormone and prolactin deficiency. In the clinical management of aSAH, clinical outcome benefits from the evaluation of hormonal status
Prof(Dr.) Basanta Kumar Baishya, Dr. Prabhakar Narayan
Lateral Supraorbital Craniotomy: Neurosurgical Approach for Anterior Cranial Fossa Lesion
Background: The lateral supraorbital (LSO) craniotomy is a limited-exposure frontal–basal approach used for anterior cranial fossa (ACF) lesions. Evidence evaluating effectiveness and safety from Indian tertiary centres remains sparse. Objective: To evaluate effectiveness and safety of LSO for ACF pathology in a single-centre cohort, reporting radiological, peri-operative, and functional outcomes. Methods: We conducted a retrospective observational cohort at a tertiary teaching hospital. Consecutive adults (18–75 years) with imaging-confirmed ACF lesions selected for LSO were included. Data were abstracted from operative records, imaging, and clinic follow-up. Outcomes were extent of resection (EOR: gross-total resection [GTR] vs subtotal resection [STR]), predefined complications (e.g., CSF leak, infection, transient deficits), ICU/hospital stay, and functional status (Karnofsky Performance Status [KPS], Glasgow Outcome Scale [GOS]). Results: The study included 120 patients (mean age 52.4 ± 12.1 years; 56.7% male). Early postoperative imaging showed GTR in 81.7% (98/120) and STR in 18.3% (22/120). Complications were infrequent: CSF leak 4.2% (5/120), infection 2.5% (3/120), transient neurological deficits 7.5% (9/120); revision surgery occurred in 1.7% (2/120). Mean operative time was 145.2 ± 30.6 minutes, and mean hospital stay 5.8 ± 2.3 days. Functional outcomes (KPS, GOS) were captured at discharge and follow-up; values will be reported in structured tables based on the master dataset. Conclusions: In this single-centre experience, LSO provided effective access to ACF lesions with high rates of gross-total resection, low early complication frequencies, and short hospitalization. Findings support LSO as a practical skull-base option in resource-aware settings when case selection and postoperative imaging are standardized. Prospective multicentre studies with uniform patient-reported outcomes and volumetric measures are warranted.
Lateral supraorbital craniotomy; anterior cranial fossa; skull-base surgery; gross-total resection; postoperative complications; Karnofsky Performance Status; Glasgow Outcome Scale; radiological outcomes; hospital length of stay.
Dr. Angirash Bhattacharyya, Prof (Dr) Basanta Kumar Baishya
Food Industries Organizational Performance attributed impact on Eco-Efficient Supply Chain Management and Sustainability
It is a well-known fact that the food business significantly backs for improvement of green supply chain management (GSCM) practices in digital age. Experimental examination grounded on participant comments received for the same, within an Indian backdrop, is not so exhaustive. To address the issues, the study, focus to do a factual analysis and how the GSCM practices improve the sustainability and impact of its influence over the profitability analysed in this paper in more detail which is not pointed out in earlier studies. A test study was conducted over various states of India on a standardized questionnaire among 168 participants. Out of 168, 155 participants responded to the survey. The participants were chosen by sampling (snowball) method by contacting their dealers, suppliers, partners etc. This research study uses percentage analysis to determine participant perspectives and reality about GSCM strategies for sustainability. Regression analysis was used to ascertain the direct correlation between the company's profitability and its use of green supply chain management strategies. The regression line equation Ŷ = 6.2561 - 0.6636X. The result reveals GSCM Practices will impact profitability and improve workable aspects. Relationship, The R is -0.5126, indicating that profitability will be significantly have a negative effect achieving the sustainability. The review has shown that an indication of the GSCM may account for 26.3% of the variation in GSCM practices that impact profitability, rendering to the R2 of 0.2627.
Green Supply Chain Management, Food Industry, Sustainability, Management Strategies, Organization performance.
Design of Novel Quinazoline–Thiadiazolidinedione Hybrids: Synthetic Approach and Comprehensive Spectroscopic Analysis
A recent Research Study on Dipeptidyl Peptidase-4 (DPP-IV) enzyme inhibitors has made it possible to treat type 2 diabetes mellitus (T2DM) with minimal side effects. Dipeptidyl peptidase-4 (DPP-4) is a target for the treatment of type 2 diabetes mellitus. As DPP-4 inhibitors can increase insulin levels and prolong the activity of glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP), they are effective for glycemic control. Quinazoline-based heterocycles and thiadiazolidinedione scaffolds are widely explored for their diverse pharmacological properties. In the present study, a new series of quinazolinone–thiadiazolidinedione derivatives coded as (S1R1-S1R11) was rationally designed & synthesised to investigate their structural features and potential biological relevance. The target compounds were obtained through a multistep synthetic route involving the formation of the quinazolinone core, followed by its condensation with substituted thiadiazolidinedione moieties under optimised reaction conditions. The synthesised derivatives were purified using chromatographic techniques, and their structures were confirmed through spectral analyses, including FT-IR, MASS, ¹H NMR and ¹³C NMR. The spectral data consistently supported the successful incorporation of both quinazolinone and thiadiazolidinedione rings within the final molecular framework. The study provides a reliable synthetic approach for generating new hybrid heterocycles, which may serve as promising candidates for further pharmacological evaluation.
T2DM, DPP-4 inhibitors, Quinazolinone, Thiadiazolidinedione, Synthesis, Structural characterisation.
Endovascular Treatment Approaches In Mesenteric Ischemia: A Prospective Study.
Background: Mesenteric ischemia is a life-threatening vascular emergency marked by reduced intestinal blood flow, and delayed intervention often leads to bowel necrosis and high mortality. Endovascular treatments have emerged as less invasive, rapid, and effective options compared to open surgical revascularization. This study evaluates the outcomes of various endovascular approaches in patients presenting with mesenteric ischemia. Aims & Objectives: To evaluate the effectiveness, safety, and early clinical outcomes of endovascular treatment in 30 patients with mesenteric ischemia. Materials and Methods: A prospective observational study was conducted on 30 consecutive patients diagnosed with acute or chronic mesenteric ischemia at a tertiary care center. CT angiography confirmed diagnosis in all cases. Endovascular procedures included balloon angioplasty, primary stenting, aspiration thrombectomy, and catheter-directed thrombolysis. Primary endpoints were technical success, clinical improvement, complication rates, need for laparotomy, and 30-day mortality. Patients were followed clinically and radiologically for 30 days post-procedure. Results: Among the 30 patients (mean age 59 years), 18 presented with acute and 12 with chronic mesenteric ischemia. Technical success was achieved in 90% (27/30) of cases, and 80% (24/30) demonstrated significant symptom relief and lactate improvement. Four acute cases required laparotomy for non-viable bowel. Minor complications occurred in three patients, with no major procedure-related adverse events. The 30-day mortality rate was 10%, largely attributable to delayed presentation. Conclusion: Endovascular therapy provides a high success rate, low complication profile, and favourable early outcomes, making it a preferred first-line treatment for mesenteric ischemia when performed promptly.
Mesenteric ischemia; Endovascular treatment; Angioplasty; Stenting; Thrombectomy; Thrombolysis; Vascular intervention; Acute abdomen.
sadyojata Bhavani, rahul Kenawadekar, roshan Naik
Real-Time Aquatic Biodiversity Monitoring through IoT and Machine Learning Integration.
The integration of the Internet of Things (IoT) with machine learning (ML) has emerged as a transformative approach for environmental monitoring, particularly in aquatic ecosystems where biodiversity assessment requires high temporal and spatial resolution. This study explores an IoT-enabled, machine-learning-based framework for real-time monitoring of aquatic biodiversity, emphasizing the detection and analysis of biotic indicators such as plankton, fish populations, and water quality parameters. The system employs a network of smart sensors measuring temperature, pH, dissolved oxygen, turbidity, and nitrate concentration, transmitting continuous data through low-power wide-area networks (LPWANs) to a cloud-based analytics platform. Machine learning algorithms, including Random Forest and Convolutional Neural Networks (CNNs), are utilized to identify patterns, anomalies, and species distribution trends. Field implementation was conducted across selected freshwater lakes and estuaries in South India, integrating both in-situ sensor data and satellite imagery for validation. Results demonstrate a significant improvement in accuracy and responsiveness over conventional manual sampling, reducing detection latency and enhancing ecological forecasting. The proposed architecture provides a scalable and cost-efficient solution for environmental agencies, policymakers, and conservationists, enabling proactive biodiversity management and early warning against habitat degradation and species loss through continuous, intelligent observation of aquatic ecosystems.
IoT-based monitoring, Machine learning, Aquatic biodiversity, Real-time data analytics, Environmental sensing, Ecological forecastin
Rakesh K Kadu , Dr.Govind Hanmantrao Balde, Sriperambuduri Vinay Kumar , Dr. Suchita Yogesh shelke
The 4-Day Workweek: Global Case Studies and Long-Term Viability.
The global shift toward flexible work models has accelerated interest in the four-day workweek as a strategic reform for productivity, employee wellbeing, and economic resilience. This study evaluates the long-term viability of the four-day workweek by synthesizing evidence from global pilot programs, national policy trials, and large-scale organizational transformations. Using comparative case analysis across regions including Europe, Asia, and North America, the research examines productivity outcomes, employee satisfaction, burnout reduction, operational efficiency, and sector-specific impacts. Findings from international trials consistently show that reduced weekly hours, when paired with workflow redesign and outcome-based performance metrics, maintain or increase productivity while significantly improving worker wellbeing. However, challenges remain in sectors requiring continuous service delivery, shift-based operations, or labor-intensive workflows. The study highlights the importance of transition frameworks, digital enablement, leadership adaptability, and long-term cost–benefit balancing. Ultimately, global evidence suggests that the four-day workweek is viable for a broad range of industries, but its sustainability depends on policy support, organizational redesign, and cultural acceptance of output-focused work norms. The paper positions the four-day workweek as a transformative labor innovation with measurable economic, social, and psychological benefits, offering a scalable model for future-ready work environments.
Four-day workweek, global case studies, productivity, workforce wellbeing, flexible work models, long-term viability
Ms. Mariyam Ahmed, Dr Angelin Fiona J, Dr. N. Arunfred
Smart Aquaculture: IoT, Cloud Computing, and AI for Sustainable Fisheries.
The integration of advanced digital technologies such as the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) has transformed traditional aquaculture into a data-driven, automated, and sustainable practice. Smart aquaculture systems leverage IoT-enabled sensors to continuously monitor key environmental parameters including temperature, pH, dissolved oxygen, turbidity, and ammonia levels, providing real-time insights into pond conditions. These data streams are transmitted through cloud-based platforms for centralized storage, remote access, and intelligent analysis. AI algorithms further enhance system performance by predicting fish behaviour, optimizing feeding schedules, detecting disease outbreaks, and improving resource utilization efficiency. The synergy of these technologies not only reduces operational costs and environmental stress but also ensures higher yield consistency and ecological balance. This paper presents a comprehensive framework for sustainable fisheries management through IoT–AI–Cloud integration, emphasizing scalability, cost-effectiveness, and data-driven decision support. The proposed approach demonstrates how intelligent sensing and predictive analytics can revolutionize aquaculture practices, ensuring sustainability and resilience in a rapidly changing climate and growing global seafood demand..
Smart aquaculture, Internet of Things (IoT), Cloud computing, Artificial intelligence, Sustainable fisheries, Data analytics, Real-time monitoring, Predictive modeling, Environmental sustainability, Aquaculture automation
Dr. Pankaj Mudholkar , Dr. Megha Mudholkar , Dr. Kadam Jagannath Jijaba , Dr.Jyoti Prasad Kalita
Neonatal Sepsis Prediction Using AI-Based Vital Sign Analysis in NICUs: A Multicenter Retrospective Study.
Neonatal sepsis remains one of the leading causes of morbidity and mortality in intensive care units, yet early diagnosis continues to be a major clinical challenge due to nonspecific symptoms and rapidly evolving physiological instability. This study develops and evaluates an AI-based vital-sign prediction framework capable of detecting early signatures of sepsis using continuous physiological monitoring data from multiple NICUs. A multicenter retrospective dataset comprising heart rate, respiratory rate, oxygen saturation, temperature, blood pressure, and derived variability metrics was analyzed across three tertiary hospitals. After preprocessing, imputation, and artifact suppression, machine learning and deep learning models including Random Forest, Gradient Boosting, and a hybrid LSTM network were trained to capture nonlinear temporal patterns indicative of sepsis onset. The proposed LSTM-based model demonstrated strong predictive ability, identifying sepsis up to 6–12 hours before clinical diagnosis with improved sensitivity and reduced false alarms compared to conventional scoring systems. Key physiological precursors included abnormal heart-rate variability, intermittent desaturations, rising temperature instability, and increased respiratory fluctuations. The findings confirm that AI-guided prediction can enhance real-time surveillance, support timely clinical interventions, and reduce progression to severe outcomes. This multicenter analysis underscores the potential of intelligent vital-sign analytics as a scalable decision-support tool in modern NICU environments.
Neonatal Sepsis, NICU, Artificial Intelligence, Vital Sign Analysis, LSTM, Machine Learning, Early Prediction, Clinical Decision Support, Multicenter Study, Time-Series Data
Sudheer Nidamanuri , Yogesh H. Bhosale,Dr. Kadam Jagannath Jijaba , Boddepalli Kiran Kumar, Dr. L. Malathi
An Integrated Digital Forensic Response To Ransomware Threats Leveraging Cve Exploits And Threat Intelligence
Ransomware has become a major threat to safety, attacking both public and private sectors in more and more complex ways. This research shows a complete digital forensic reaction system created to look into and stop ransomware attacks, especially those that use well-known Common Vulnerabilities and Exposures (CVEs) like EternalBlue (CVE-2017-0144). The system combines memory forensics, disc imaging, network traffic analysis, and threat intelligence matching to quickly find ransomware attacks, analyse them in detail, and stop them from spreading. Open-source tools like Volatility, Autopsy, Wireshark, and YARA are used in a Kali Linux system by the suggested model to do multi-layered forensic investigations. The framework can find infection pathways, recover encrypted artefacts, extract in-memory encryption keys, and attribute threats based on behavioural patterns and Indicators of Compromise (IOCs). This is shown in real-world and simulated case studies, such as a WannaCry outbreak in a hospital network. The study shows the quantitative results show that they were very good at finding threats (93.2%), getting rid of memory artefacts (92.5%), and recovering some data (up to 87.4%). In addition, the system helps with legal paperwork and following foreign rules like GDPR and the Budapest Convention. This study offers a scalable and proactive way to fight modern ransomware threats by connecting technical forensics with strategic threat intelligence and legal ready. The paper stresses how important it is to always be ready for forensic situations, especially in business settings where quick responses and keeping proof safe are important for keeping operations running smoothly and for legal action.
Ransomware, Digital Forensics, CVE Exploits, Threat Intelligence, Memory Analysis, Incident Response.
Sivaji Patingrao Patil, Dr. Kamesh V.N.,
A Multi-Modal Deep Learning Framework Combining Histopathological Imaging and Gene Expression for Automated Cancer Detection
Cancer subtyping diagnosis is essential for personalized medicine and is conventionally facilitated by histopathological imaging OR genetic assessment diagnosis. Such a method of diagnosis compromises biological development as genetic component considerations are neglected OR a histology approach is taken with subjective visual interpretation and a non-confirmatory genetic component. Here, a Multi-Modal Deep Learning Framework is established for image diagnosis via Convolutional Neural Networks (CNN) and gene expression profiles via a Multi-Layer Perceptron (MLP), and Cooperative Multi-Colony Ant Optimization (MCO) attunes the dimensionality complications of both data streams. This framework is the first to engage a Cooperative Multi-Colony effort where heterogeneous colonies operate independently, but collaboratively throughout the optimization process - one colony for spatial feature selection (CNN) and another for genomic feature selection (MLP) - and a third "Master Colony" features the assessment of both to optimally tune the fused layer. Performance results on TCGA-based multimodal datasets (Lung, Breast, and Colorectal) suggest that this cooperative multi-colony operation outperforms the single colony/single modality counterparts for convergence time and classification accuracy.
Multi-Colony Optimization, Multi-Modal Learning, CNN, Gene Expression, Feature Fusion, Heterogeneous Ant Colonies.
Kantilal P Rane, Prof. (Dr.) Chandra Kumar Dixit, Dr. Sreemoy
Design and Optimization of Brain-Targeted Nasal Liposomes for Enhanced Delivery of Galantamine
The present study demonstrates the potential of intranasal liposomal delivery of galantamine as an advanced therapeutic strategy for vascular dementia (VaD). Conventional routes of galantamine administration, including oral and intravenous delivery, are limited by low bioavailability, systemic side effects, and poor blood–brain barrier penetration. In contrast, liposomal encapsulation significantly enhanced stability, entrapment efficiency, and sustained release of galantamine, ensuring higher brain concentrations. Ex-vivo and in-vivo studies confirmed improved permeation through the nasal mucosa and efficient targeting of brain regions implicated in cognitive decline. The optimized formulation (F3) achieved the highest drug targeting efficiency and direct transport percentage, highlighting its superiority in nose-to-brain delivery. Behavioral studies further validated enhanced cognitive performance, consistent with the cholinergic hypothesis underlying VaD pathology. Safety evaluations showed intact nasal mucosa, high cell viability, and absence of behavioral abnormalities, confirming the biocompatibility of liposomal formulations. Importantly, this approach addresses a major gap in dementia therapeutics by specifically focusing on VaD, which has been overshadowed by Alzheimer’s disease in drug delivery research. Overall, the findings emphasize that intranasal liposomal galantamine offers a non-invasive, patient-friendly, and efficient drug delivery system with strong translational potential for dementia management. In conclusion, intranasal liposomal galantamine represents a novel, efficient, and safe therapeutic strategy for vascular dementia, with the potential to redefine management of cognitive disorders by integrating advanced nanotechnology with patient-centered care.
Vascular dementia, Galantamine, Liposomes, Intranasal delivery, Nose-to-brain transport, Cognitive improvement.
Kathir A S, Amudhavalli Victor , Jyothirmayee Devineni, Aniruddha Mukherjee, Swathi. S, Josh Praveen, Dhanshri Borawake, Shraddha Dingare
Ingestion Of Omega-6 Fatty Acid And Increased Risk Of Colorectal Cancer- A Systematic Review
Background: Colorectal cancer is a major disease affecting mankind, with an increasing incidence rate. While extensive research has validated the protective role of omega-3 fatty acids in tumorigenesis, the role of omega6 fatty acids remain ambiguous. Aim- This systematic review aims to determine whether omega-6 fatty acids acid ingestion has increased risk of colorectal cancer Objectives- To perform a literature review of the effects of omega-6 fatty acids on colorectal cancer tumorigenesis Materials and methods- PRISMA analysis was done. A total of 11 articles were included for the study based on inclusion and exclusion criteria to evaluate the evidences from multiple prospective and cohort studies to assess the relationship between omega-6 fatty acid ingestion and increased risk of colorectal cancer. Result- Evidence shows that there is increased risk of colorectal cancer with excess consumption of omega-6fatty acids and modest consumption of combination of omega-6 and omega-3 counteracts the deleterious effects of omega-6 fatty acids on genesis colorectal cancer. Conclusion- Several studies included in this review reported association between higher intake of omega-6 fatty acids and increased risk of colorectal cancer. Conversely, some studies report no significant or even inverse associations between omega-6 fatty acid consumption and colorectal cancer risk. This systematic review emphasizes the importance of balancing omega-6 with omega-3 fatty acids, rather than suggesting that omega-6 alone is a significant independent risk factor for colorectal cancer.
Colorectal cancer, Omega6 fatty acids, Polyunsaturated fatty acids, Dietary assessments.
Sandeep Krishna. R, Sindhu. R, Gibbson Derick D, Lubna Fathima, Prabu D, Rajmohan M, Dinesh Dhamodhar,
Heart Disease Prediction Using Parallel Ensemble Deep Learning Techniques
To guarantee proper categorization and allow cardiac specialists to treat patients effectively, it is essential that heart disease be diagnosed and prognoses. Implementation of machine learning in the medical field have expanded as a result of its capacity to recognize patterns in data. The objective of this research is to decrease the amount of deaths from heart disease by creating a model that can accurately forecast these issues. The five primary stages of this study are preprocessing, clustering, extraction of feature, feature selection, and classification. Initially the min-max normalization is used for pre-processing. The K-Means Clustering (KMC) technique is used for clustering with the goal of improving classifier accuracy. This work suggests using a Principal Component Analysis (PCA) to efficiently extract features. After that, the feature selection process is finished using the modified whale optimization algorithm method. To choose the most pertinent and significant characteristics from the cardiovascular illness dataset, it computes the optimal fitness value. Finally, the classification is done using parallel ensemble deep learning model as VGG16, Inceptionv2, ResNet50 and used the Mutation Albatross Optimization Algorithm (MAO) for hyperparameter optimization. To improve health outcomes and lessen the demand on healthcare, these models can be used to create future cardiovascular disease detection systems.
Heart disease data, k-means clustering, Principal Component Analysis (PCA), parallel ensemble deep learning model and Mutation Albatross Optimization Algorithm (MAO), VGG16, Inceptionv2, ResNet50.
The Use Of Titanium Structures With Modification Of The Surface Of The Rutileom In Reconstructive Operations In The Maxillofacial Region
Titanium alloys are known for their biological, mechanical and chemical properties, which have successfully expanded their use in the maxillofacial field. The internal fixation using titanium miniplates and screws offer a new perspective for the treatment of trauma and in orthognathic surgery and maxillofacial oncology. Although, titanium is highly recommended for its excellent biocompatibility, recent research has focused on identifying the potential local and general implications of the interactions between the human tissue and the metallic particles. This present review aims to outline the existing tissue changes, cellular alterations and future perspectives regarding the use of titanium-based alloys as osteosynthesis materials, taking into consideration the existing present debate whether the routinely removal of these materials should be an indication.
Titanium; Alloys; Maxillofacial Fractures; Osteosynthesis; Miniplates; Biocompatibility.
Shaihaliev Astemir Ikramovich, Mohammed Abbas Jasim
Pecto-Intercostal Fascial Block and Inflammatory Mechanism (TNF-Α) in Post-Surgical Heart Pain: a Review Article
Background: Postoperative discomfort subsequent to cardiac surgery predominantly originates from sternotomy and heightened inflammatory activation, characterized by elevated concentrations of tumour necrosis factor-α (TNF-α). The pecto-intercostal fascial block (PIFB) technique has emerged as a potent regional analgesic modality for mitigating sternotomy-related pain and is purported to exert a modulatory influence on inflammatory responses. Objective: This literature review aims to explain the relationship between PIFB, inflammatory mechanisms involving TNF-α, and post-cardiac surgery pain. Results: The literature shows that PIFB can reduce post-sternotomy pain intensity, decrease opioid requirements, and accelerate extubation. Although changes in TNF-α levels vary between studies, there is a scientific basis that facial blocks reduce nociceptor activation and may influence inflammatory pathways associated with TNF-α production. This review integrates the latest findings on PIFB and TNF-α and provides a physiological explanation of the potential relationship between the two in the context of post-cardiac surgery pain. Conclusion: PIFB is an effective analgesic technique with the potential to modulate the inflammatory response, although specific evidence regarding changes in TNF-α is still limited and requires further research
Pecto-Intercostal Fascial Block, TNF-α, Post-Sternotomy Pain, Inflammation, Cardiac Surgery
Fadhiel Yudistiro, Philia Setiawan, Christrijogo Soemartono Waloejo
The Role of CD44 and CD24 as a Marker in Breast Cancer Metastasis
Background: CD44 and CD24 are important factors in stimulating metastasis, treatment failure and recurrence in breast cancer. The aim of this research is to explore the manifestation of these CD44 and CD24 in females diagnosed with breast cancer. Methods and Materials: In this descriptive-analytical study, 40 patients with breast cancer after mastectomy and 40 healthy people were included as a control group in this study in medical care at Rizgary teaching hospital of Erbil were included in the study in the period from November 2021 to October 2022, the patients were divided into two groups. including 40 patients and 40 healthy people. After completing the consent form, clinical information about all the samples was taken. EDTA blood samples were gathered from patients and healthy individual, and RNA was obtained using the RNX-Plus kit (Sinaclon, Iran). Following the extraction of total RNA and the creation of complementary DNA, we assessed relative gene expression using a quantitative real-time PCR technique. Serum was also prepared for ELISA analysis, levels of CD44 and CD24 in serum were measured using ELISA kits(eBioscience, USA) according to manufacturer instructions Finally, the results was examined by statistical analysis. Results: The results of examining the changes in the expression of CD44 and CD24 showed that the expression of CD44 and CD24 increased significantly in patients compared in healthy women (P≤0.001), Conversely, the gene expression was demonstrated to increase further with an extended cultivation time. Furthermore, there was a distinct rise in gene expression observed in breast tumors undergoing metastasis. The results showed that the plasma of the patients contained 34.3 and 3.15 respectively of CD44 and CD24. The results of serum level of CD44 and CD24 increased significantly in patients compared in healthy women ,The mean ± SD of CD44 in the control group was 177.35 ± 31.55 (ng/ml) and in the patient group was 725.22 ± 289.998 (ng/ml) and The mean ± SD of CD24 in the control group was 98.21 ± 12.36 (ng/ml) and in the patient group was 504.25 ± 96.357 (ng/ml), and this difference in gene expression between the two groups was statistically significant (P≤0.001) . Conclusions: The findings from the present research illustrate the substantial role that CD44 and CD24 play in regulating the biological processes of cancer cells. CD44 and CD24 in particular were found to be substantially expressed in the patients’ samples.
Breast cancer, CD44, CD24, metastasis, ELISA
Conchopexy of middle turbinate Versus Bolgerization in Endoscopic Sinus Surgery..
Background: Middle turbinate (MT) lateralization remains a frequent postoperative complication of functional endoscopic sinus surgery (FESS), potentially leading to synechiae, impaired ventilation, and surgical failure. This study aimed to compare the efficacy of Conchopexy and Bolgerization techniques in preventing MT lateralization and promoting postoperative healing. Methods: A prospective comparative interventional study was conducted on 45 patients with chronic rhinosinusitis undergoing bilateral FESS at Rizgari Teaching Hospital from January to December 2023. Patients were randomized into two groups: Group A (Conchopexy, n = 23) underwent MT–septal fixation using an absorbable suture, while Group B (Bolgerization, n = 22) received controlled mucosal abrasion to create a synechia. The MT position and mucosal healing (POSE score) were assessed at 12 weeks postoperatively. Statistical analysis was performed using SPSS v27 with a significance level of p < 0.05. Results: MT lateralization occurred in 13.1% of Conchopexy cases and 9.1% of Bolgerization cases (p = 0.67). The mean POSE score was 2.33 ± 1.17 for Conchopexy and 2.08 ± 1.43 for Bolgerization (p = 0.54), indicating comparable mucosal healing. No major complications or follow-up losses were reported. Conclusion: Conchopexy and Bolgerization are equally safe and effective in maintaining MT medialization and supporting mucosal recovery following FESS. Bolgerization offers simplicity and shorter operative time, whereas Conchopexy provides secure fixation and mucosal preservation. The choice of technique should depend on intraoperative anatomy and surgeon preference.
Conchopexy, Bolgerization, Middle turbinate, Endoscopic sinus surgery, Chronic rhinosinusitis, POSE score.,
Ganj Mahmood Asaad, Arsalan Awlla Mustafa Shem, Lana Al-Dabbagh
Biogenic Synthesis And Characterization Of Copper Oxide And Zinc Oxide Nanoparticles By Using Chlorella Vulgaris And Its Applications.
The present study investigates the biogenic synthesis of copper oxide (CuO) and Zinc oxide (ZnO) nanoparticles using the green microalga Chlorella vulgaris as a Reducing and stabilizing agent1. Biogenic nanoparticle synthesis has emerged as an Eco-friendly and sustainable alternative to conventional chemical and physical Methods. In this study, aqueous extracts of Chlorella vulgaris were used to Synthesize CuO and ZnO nanoparticles by reducing copper sulfate and zinc nitrate Precursors, respectively2. The formation of nanoparticles was initially indicated by Color changes in the reaction mixtures, and further confirmed using UV-visible Spectroscopy. The characteristic absorbance peaks observed for CuO and ZnO Nanoparticles confirmed their formation and stability3This study highlights the potential of Chlorella vulgaris in the green Synthesis of metal oxide nanoparticles paving the way for applications in biomedical and environmental fields.
Green synthesis, Chlorella vulgaris, copper oxide, zinc oxide, UV-Vis Spectroscopy, SEM Analysis and applications.
Dr .Mubarakunnisa Md, Dr. Shahul Hussain, Dr. N. Deepa, Dr. Suleman Shareef
Detection and Management of Signals Associated with Adverse Drug Reactions from Atorvastatin and Rosuvastatin: An Observational Study
Background: Statins are the mainstay of dyslipidaemia treatment, but individual agents display variable adverse drug reaction (ADR) profiles. Pharmacovigilance signal detection facilitates early identification of safety concerns and guides risk minimization. Objective: To identify and compare ADR signals associated with atorvastatin and rosuvastatin and to evaluate management strategies in real-world settings. Methods: Patients receiving atorvastatin or rosuvastatin at Teerthanker Mahaveer University were monitored for suspected ADRs. Causality was determined using WHO–UMC criteria, and seriousness per ICH E2A definitions. Signal detection employed disproportionality methods Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR). Results: Signal detection analysis for system organ class was done by using frequentist disproportionality analysis (cluster-based disproportionality) method for the following organ classes like musculoskeletal disorder, gastrointestinal disorders and general disorders. Conclusion: Comparative signal detection highlights distinct safety profiles for atorvastatin and rosuvastatin, underscoring the role of structured pharmacovigilance and individualized clinical management.
Pharmacovigilance, Signal Detection, Atorvastatin, Rosuvastatin, Adverse Drug Reactions, Disproportionality Analysis.
Nanodentistry: A Review on the Future of Dental Care
Aim: The current review sought to examine and critically evaluate the progress, applications, and prospective developments of nanotechnology across diverse dental disciplines. It concentrated on assessing the role of nanomaterials in enhancing restorative, preventive, and regenerative results. The goal was to show how nanodentistry is changing the way dental care is done in clinics. This review also aimed to pinpoint current challenges and research deficiencies in clinical translation. Methodology: A thorough literature review was performed in accordance with PRISMA guidelines, utilising databases such as PubMed, Scopus, and Web of Science. We looked through studies that were published between 2000 and 2025 using specific keywords related to nanodentistry. Out of 168 articles that were first found, 45 peer-reviewed studies were chosen after using JBI criteria to check their quality. We took data out and looked at it qualitatively to put nanomaterials into groups based on their types, uses, and performance outcomes. Results: The results showed that nanotechnology greatly improves the mechanical strength, antibacterial effectiveness, and biocompatibility of dental materials. Silver, zirconia, and nano-hydroxyapatite nanoparticles exhibited significant advantages in restorative and regenerative applications. Regenerative scaffolds that included bioactive nanoparticles increased osteogenic activity and osseointegration by 25–30%. In vitro and preclinical data show promise, but there aren't many large-scale studies on people yet. Conclusion: Nanodentistry is a new and exciting field that is changing the way we diagnose and treat dental problems and make new materials. The use of nanomaterials improves clinical accuracy, biointegration, and tissue regeneration, which helps SDG 3 (Good Health and Well-being) by making oral health outcomes better. At the same time, research and industry development driven by nanotechnology support SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). To make sure that safety, affordability, and accessibility last over time, we need to keep working together across disciplines, as SDG 17 (Partnerships for the Goals) says. Nanodentistry is on track to become an important part of next-generation, patient-centered dental care as more research and clinical trials are done.
Nanodentistry; Nanoparticles; Nano-hydroxyapatite; Regenerative dentistry; Nanorobotics; Biomimetic materials; SDG 3, 8, 9, 17
Dr. Sangeetha R, Dr. Bennete Fernandes, Dr. Karthick Arumugam, Dr. Khushboo Rastogi, Dr. Nitheash P., Dr. Darshana Sachin Nayak