2026-05-28T07:05:17Z https://ijerams.org/index.php/files/oai

oai:ojs2.ijerams.org:article/3 2026-02-18T06:01:04Z files:ART

Optimizing IoT-Based Smart Grids with AI and Blockchain: A New Approach for Real-Time Energy Management Ramesh Bingi Iot, Smart Grids, Artificial Intelligence, Blockchain Real-Time Energy Management The appearance of the Internet of Things (IoT) and the advancement of the energy systems provided a chance to the emergence of the smart grids which allow to arrange the effective management of the energy in real-time. Despite the smart grids being useful when the energy consumption distribution is optimized, they demand substantial challenges in terms of their scalability, security and effective analysis of millions of participated devices-generated data. Blockchain and Artificial Intelligence (AI) have been advanced as the remedy to such concerns. Blockchain can be effectively combined with AI in the work of smart grids with its decentralized immutable traceability and ability to forecast, suggest, and identify anomalies, and with data security, transparency, and integrity, on another level, making smart grids capable of functioning autonomously and efficiently. The feasibility of integrating AI and Blockchain to optimise smart grids on a platform of IoT with the view of employing them in the real-time management of energy is discussed in the paper. The study gives the benefits of using the AI in predictive maintenance, management and management of demand, and demand-response forecasting and demand-response loads, and the Blockchain in safe transaction relating to safe exchange of data and transparent transaction. A conceptual scheme of the way these technologies could be applied in smart grid systems is proposed, and after it, the discussion of the potential use of these technologies, their challenges, and solutions is presented. Based on the study, smart grids that rely on the IoT will be more efficient, secure, and scalable and combining them with the AI and the Blockchain may offer a chance of attaining sustainability in energy management within smart cities. International Journal of Emerging Research in Applied Medical Sciences 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/3 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 9-16 3108-2599 eng https://ijerams.org/index.php/files/article/view/3/2 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/4 2026-02-18T06:02:01Z files:ART

Towards a Unified Blockchain and AI Architecture for IoT-Enabled Autonomous Vehicles: A Machine Learning Approach Banthi lal Bhukya Autos Vehicles, Block Challange, AI, IoT, Machine Learning, Security and V2V communication AVs are coming into the future of transportation in which the utilization of IoT devices which have the capabilities to drive self-driving cars in live time determining all the collection, decision-making and communication with all the other vehicles as well as the infrastructure is extremely essential. However, security, privacy, data integrity and scalability are key variables of the IoT-enabled AV systems. It is decentralized, secure and transparent manner of the Blockchain technology to attend to these problems and there is the requirement of the artificial intelligence (AI) that provides the necessary functionality of the real-time decision-making and optimization. The paper makes the proposal of the integration of Blockchain and AI to reach one architecture to enhance the performance, security and efficiency of the IoTs that have automated vehicles. The specified architecture is suggested to be applied with the support of the Machine Learning (ML) technique to manage data processing and consensus mechanism in the Blockchain so that it was able to comply with the avenues of scalability and real-time aspects of the AVs. In this research a case will be made of the advantages and limitations and the potential application of such an integrated strategy in autonomous vehicle systems like safe data sharing to autonomous decision making to vehicle to vehicle (V2V) communication. Results of experimental research and case studies prove the effectiveness of such combined work to increase the safety and expandability and efficiency of unmanned auto vehicles running. International Journal of Emerging Research in Applied Medical Sciences 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/4 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 17-24 3108-2599 eng https://ijerams.org/index.php/files/article/view/4/3 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/5 2026-02-18T06:02:52Z files:ART

A Novel Machine Learning Algorithm for Enhancing Blockchain Consensus Mechanisms in IoT-Enabled Smart Cities Saddam Hussain MD Contracts Consensus Mechanisms, Internet of Things (IoT), Smart Cities, Scalability, Energy Efficiency, Transaction Speed, Blockchain Optimization, IoT Data Handling, Smart The need to have more effective and secure Blockchain consensus mechanisms has been brought about by the fact that the Internet of Things (IoT) devices have become increasingly deployed in the smart cities. Though Blockchain brings a decentralized, non-reversible platform to implement the IoT applications, the conventional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS) are questionable in its scalability, energy expense, and latency particularly in the IoT systems with extremely large number of devices. To address these challenging concerns in IoT-powered smart cities, this paper proposes a new Machine Learning (ML) algorithm that would generate the best Blockchain consensus mechanism. The ML algorithm is dynamically flexible with time, and modifies the consensus strategy based on the real-time network health, way in which the IoT appliances behave, and load due to transactions giving the solution more scalability, which is more energyefficient and permits to handle transactions at a faster rate. The proposed approach is addressed on the simulated case of a smart city where data, such as those transmitted by smart meters, traffic control sensors, and environmental sensors, are collected on a realtime basis using the IoT technology. The experimental results confirm that the integrated combination of ML and the Blockchain agreement protocols provides the opportunity of refining the functioning IoT systems in the smart cities in relation to the resources optimization, the reduction of latency, the safety of transaction, and the transparency. With the aid of the Internet of Things, the study introduces an easy answer to the issue of scalability and efficiency that the current models of Blockchain pose to smart cities framework in the sense of being able to scale. International Journal of Emerging Research in Applied Medical Sciences 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/5 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 25-31 3108-2599 eng https://ijerams.org/index.php/files/article/view/5/4 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/6 2026-02-18T06:04:56Z files:ART

AI-Driven IoT Security Enhancements Using Blockchain Technology: A DataDriven Approach Sai prasanna Pilli AI, Blockchain, IoT, Security, and Anomaly detection, Data Integrity. The utilities of the IoT devices that industries are currently using are increasing at a very alarming rate based on the latest statistics meaning that very large figures of security issues are raised in terms of management and safeguarding of information. The traditional security systems would fail in an IoT network whereby more and more objects would connect to each other to generate massive amount of data. The paper will discuss the ways in which embracing Artificial Intelligence (AI) and Blockchain can enhance security of the IoT. Artificial Intelligence can identify bug lapses, prevent infiltrations and provide predictive security claims and Blockchain can secure messages and guarantee data continuity and decentralized loyalty. The AI based and blockchain used security protection comes with the security protection provided by the proposed IoT security protection that gives the real time identification and efficacy of the IoT security threats in the IoT systems. In this context, the use of data-driven approach can be implemented and this aspect in itself makes this framework dynamic as pertains to the security environment. This way, it provides a decent cover to a variety of hazards. The paper now looks into realities on how this holistic approach can be applied in the real life of actual implementation of the same in the real life of actual implementation of the IoT system and how effective it can go to the point of making the IoT systems more efficient in the realms of enhancing the security and scalability of the IoT systems. Any case on information theft, unauthorized access to the systems and vulnerability of systems are traced the answer as the results of the experiment show that the AI and Blockchain will be able to provide powerful tool in the securing of IoT devices. International Journal of Emerging Research in Applied Medical Sciences 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/6 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 32-38 3108-2599 eng https://ijerams.org/index.php/files/article/view/6/5 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/8 2026-02-18T06:10:38Z files:ART

AI-Powered Blockchain for Decentralized IoT Applications: Enhancing Security and Efficiency in Edge Computing Dasari Karthik Kumar AI, Blockchain, IoT, Edge computing, Security, decentralization The Internet of Things (IoT) disrupted all the industries that allowed controlling the processes of all businesses in the real-time regime using automatization and optimization. Security issues, centralized control as well as inefficient processing of data on the other hand may face the IoT networks. On edge computing, this is aggravated by the fact that the data being processed is done near the source to minimize latency and bandwidths utilized by the IoT devices. This can be solved because the decentralized blockchain technology is able to offer security and transparency to the process of using the IoT. Besides it, the Artificial Intelligence (AI) had an opportunity to enhance the quality of decision-making and create opportunities to predict and identify anomalies. The paper will address the combination of AI and Blockchain as one of the ways of making the decentralized IoT system much safer and faster in an edge computing environment. The strategy therefore helps in ensuring that the IoT applications will be secure and more productive whereby; BlockChain will be used to handle the data securely and; AI will be used to process and make real-time decisions on how the data will be used. The paper suggests the theoretical framework considering the presence of such technologies in the decentralised IoT platforms and the possible implications to the security, scalability as well as the effectiveness. The possible future applications and trends as well as the real world applications are provided, especially of the smart cities, healthcare and industrial IoT. International Journal of Emerging Research in Applied Medical Sciences 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/8 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 1-7 3108-2599 eng https://ijerams.org/index.php/files/article/view/8/6 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/9 2026-02-18T06:16:18Z files:ART

Blockchain and IoT Integration for Secure and Transparent Supply Chain Management Athram Mahesh Athram Mahesh Block chain, IoT supply chain controls, information Security, transparencies Even the tone of proposing the idea of Internet of Things (IoT) to the supply chain management process has already made quite a difference in the sense of promising a more efficient process in the operations process, inventory management and actual tracking of various goods in real time. But with regards to data security, data privacy and data transparency of the data, the question of the data appears in the case of IoT system in the supply chain. The block chain technology has been one of the solutions to such issues as it is an immutable transparent and decentralised technology. As it is a collaborative process, data integrity, traceability, transparent and secure transaction along the supply chain with use of IoT and the Blockchain can be contributed even by the organization. The paper has raised the issue of the combination of the Blockchain and IOT into the supply chain management especially its secure and transparent supply chain management and how blockchain can be used as a means of mesh-up of exchanges, the provenance of products and security of regulatory compliance. The condition of the application of the IoT devices in the context of the data collection and data transfer and the method that Blockchain can be employed in the context of the security of the collected data and its real-time transparency is also stated in the paper. The case studies and simulation represented by the document demonstrate that, it is possible to combine both Blockchain and IoT in a bid to enhance an effectiveness of the operations, and eliminate frauds, as well as allow general visibility of the supply chain. It also looks through the problems that are stepped on along with the implementation of such collective actions, and it is basing on it that answers are given to the questions on how to win the problems. International Journal of Emerging Research in Applied Medical Sciences 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/9 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 8-14 3108-2599 eng https://ijerams.org/index.php/files/article/view/9/7 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/11 2026-02-18T06:21:22Z files:ART

Blockchain-Enabled AI Frameworks for Predictive Analytics in IoT-Driven Environmental Monitoring Systems Deekonda pranaykumar Deekonda pranaykumar Blockchain, AI, Internet things, Environmental Monitoring, predictive analytics, Security. Monitoring of the environment systems that have emerged on the basis of the Internet of Things (IoT) give in real time some of the environmental parameters like the temperature, humidity, air and pollution quality. Nevertheless, there are certain issues associated with the presence of the systems due to massive real-time data streaming through the devices of the IoT and they are data integrity, security, and scaling. Blockchain has a read-only access to a distributed ledger; a ledger whose data management under a distributed ledger is both secure, transparent and cannot be mutilated. Completely to the opposite, with the help of the Artificial Intelligence ( AI ), it is possible to make the anomalies and decisions out of the data with the help of the predictions and anomalies. The article gives a clue not only how to integrate Blockchain and AI into the system containing the track of IoT-driven solutions but also how to enhance the procedure monitoring IoT-driven solutions, including those of them encouraged to turn to predictive analytics. The model will also improve the level of precision, safety and efficiency of an environment observation by using a rather advanced technology-Blockchain that helps to store and transport safe data; Artificial intelligence (AI) to predict, generate appearance and optimize it. All these challenges that the alliance will have to encounter and all the applications in the future related to an association such as this and the benefits related to the alliance have all been given out in the paper. It is possible to use an idea of conceptual framework and use of case study simulation that is employed in the current paper in utilisation of blockchain-based artificial intelligence in the two ways; creating some add on case studies as model in prediction of the environmental risk, a model to simulate the use of case study. International Journal of Emerging Research in Applied Medical Sciences 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/11 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 15-21 3108-2599 eng https://ijerams.org/index.php/files/article/view/11/8 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/12 2026-02-18T06:32:32Z files:ART

Blockchain-Integrated Machine Learning for Autonomous IoT Networks: A Paradigm Shift in Data Security Adigoppula Ashwitha Machine Learning, Blockchain, Internet of things, Data security, Autonomous system. With the introduction of the idea of the IoT (the Internet of Things), new opportunities and the issues that come along with it in the area of data safety are created. The greater the number of devices connected to the Internet of Things network, the more complex and bulky the field of securing data becomes. The Blockchain and Machine Learning (ML) technology can provide a solution to that end in order to eliminate such challenges. Blockchain offers privacy, transparency and scalable features through which data integrity is improved, and the ML brain is a solution to facilitate autonomous decision and detecting anomalies inside the IoT network. In this paper, the author explains how Blockchain and ML may be used to create a secure and stable IoT ecosystem. It talks about how the integration can maximize data security and the optimum resources and scalability of independent operations of the networks IoT. The study also speaks about the possible benefits, difficulties and the future consequences of such paradigm shift on the security of the data. Through its critical investigation, the following paper is going to provide an outline of future studies and potential application of Machine Learning uses Blockchain in self-managing internet-of-things networks. International Journal of Emerging Research in Applied Medical Sciences 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/12 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 22-29 3108-2599 eng https://ijerams.org/index.php/files/article/view/12/10 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/13 2026-02-18T06:41:41Z files:ART

Exploring Blockchain-Based Trust Models in IoT-Driven Healthcare Systems: A Machine Learning Approach Karabathula Keerthi Machine learning, ( IoT ) Healthcare, Blockchain, Trust Models, Healthcare. The IoT devices are experiencing growth in the healthcare industry as they are being used to track the health status of patients, to treat them and also to perform other functions. However, the IoT-based healthcare system has enormous problems regarding data security, confidentiality, and trust, especially when salient medical information is being transferred across various networks. One of the potential answers to these hiccups is the blockchain technology that is not centralized, immutable and transparent. Together with that, trust models could be enhanced by utilising the capabilities of Machine Learning (ML), through predictive analytics, anomaly detection, and real-time decision-making, which will further increase the security and efficiency of the IoT healthcare system. The article explains the process of the integration of such tools as Blockchain-based trust models, and Machine Learning used in IoT-powered healthcare environments. This research paper aims at developing an efficient solution on how to protect the IoT health networks, open data sharing, and develop confidence between the devices and the healthcare provider and, patients. Using the case studies and simulations, we represent how Blockchain and ML can be applied jointly so that to develop safe, effective, and scalable health applications. The paper also disadvantages and provides a glimpse of what will be done on the future research of using the technologies to enhance the delivery of the healthcare. International Journal of Emerging Research in Applied Medical Sciences 2025-09-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/13 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 2, September 2025; 30-35 3108-2599 eng https://ijerams.org/index.php/files/article/view/13/11 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/16 2026-02-18T05:59:20Z files:ART

The Utilization of Machine Learning and Blockchain in the Safe Handling of a Supply Chain in IoT Ecosystems Avinash Gurram blockchain, Machine Learning, IoT Ecosystems, SCM, security The revolution in the management of supply chain through introduction of the Internet of Things (IOT) devices will be pushed by automation since the devices can be tracked and monitored in real time. Storing much information that is highly secured however is a very hectic process. The databases that were established with the old systems of the supply chain are of a centralized variety and can be easily tampered with, hence, susceptible to in-efficient processes besides being vulnerable to security risk. The given paper is devoted to the domain of the supply chain security, transparency, and efficiency in regard to the opportunities of the Blockchain and Machine Learning (ML). Blockchain has also been termed as irreconcilable decent and distributed ledger that creates certain transactions that bode well in respect to the other end of the scale is data quality and ML that provides an idea on the supply chain, tracking anomaly in addition to intelligent decision-making. The two technologies are capable of coming up with an empowering technology that will further increase the level of trust the company will enjoy, reduce the importance of the money used in the course of operation and the outcome of it will be the real time decision making. The hypothesis of the paper posits the descriptions of the application, the perceived gains and the challenges and how they are going to be incorporated in the current supply chains of the technologies. The work integrates the possibilities of the Machine Learning and the Blockchain phenomenon and explains the conceptual model, and after that, the synergetic dilation can be an adequate, efficient, and extensive solution to the problem of supply chain management in the contemporary world. International Journal of Emerging Research in Applied Medical Sciences 2025-08-08 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/16 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 1, August 2025; 1-8 3108-2599 eng https://ijerams.org/index.php/files/article/view/16/12 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/19 2026-02-18T07:32:30Z files:ART

Cognitive Outcomes in Post-COVID-19 Patients: A Cross-Sectional Neurological Assessment Medi Jyothi SARS-CoV-2, Post-infection cognitive dysfunction., Neurocognitive assessment, Urban Indian population, Neuropsychological testing, Neurological long-term COVID The cognitive impairment, also known as the brain fog, is also one of the severe consequences of post-acute COVID-19. Although, there are international data illustrating the high effect of neurocognitive, evidence shows that there is a shortage of evidence which illustrates the effect in the Indian populations. This paper was intended to compare cognitive functioning among COVID-19 epidemic survivors living in urban India, define and quantify the frequency, size and predictors of cognitive dysfunction using conventional neuropsychological tests. It was a cross-sectional study conducted in three tertiary hospitals in the time frame of January 2023 to March 2024 in Mumbai, Delhi and Bengaluru. They included four hundred and fifty-one COVID-19 recovered adults (at least 12 weeks after their infections) with a mean age of 1865 years. It was assessed using the cognitive ability which included Montreal Cognitive Assessment (MoCA), Digit Span Test and Trail Making Test (TMT). The severity of the disease, comorbidity and information on hospitalization together with demographics were received. To determine the variables that can be used to predict cognitive impairment, statistical tests were undertaken using t-tests, analytical one way ANOVA and multiple linear regression. The percentage of those whose thinking was impaired, mildly (MoCA < 26), was 38.7 percent. The patients who had the severe cases of COVID-19 recorded very low scores in the MoCA values (23.5 ± 2.8) than those who had mild cases (27.1 ± 1.9, p < 0.001). Predictive significant factors of cognitive decline were hospitalization, hypoxia, old age, and comorbidities. The areas of most prominent impairments were in the areas of attention, executive functioning and memory. Long-term cognitive dysfunction occurs at a high rate among post-COVID-19 patients in urban India several months post-recovery. It might also be significant to make sure that the neurological morbidity is delayed through routine cognitive screening and timely rehabilitation. International Journal of Emerging Research in Applied Medical Sciences 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/19 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 1-6 3108-2599 eng https://ijerams.org/index.php/files/article/view/19/14 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/20 2026-02-18T07:32:30Z files:ART

Prevalence of Multidrug-Resistant Urinary Tract Infections and Their Antibiotic Susceptibility Patterns in Tertiary Care Hospitals Shaik Shukoor UTI, antimicrobial resistance, MDR bacteria, antimicrobial susceptibility, tertiary care hospital. UTIs are some of the most frequent cases of bacterial infections that are experienced in the community and in hospitals. The rising occurrence of multidrug-resistant (MDR) pathogens makes the process of treatment more complicated and contributes to morbidity. To establish the prevalence, bacterial profile and antimicrobial resistance patterns of the pathogens responsible of urinary tract infection in the tertiary care hospitals. The study was a crosssectional one conducted during six months (January to June 2024) and on 500 patients diagnosed with UTIs clinically. Middle urines were collected and cultured. Standard biochemical tests were used to identify bacterial isolates and antibiotic susceptibility testing was done using KirbyBauer disk diffusion method according to CLSI 2024 guidelines. Among 500 samples, 310 (62) of them had a significant bacteria growth. The most common isolate was Escherichia coli (48), then Klebsiella pneumoniae (22), Enterococcus spp. (15), Pseudomonas aeruginosa (8) and Proteus mirabilis (7). Forty six percent of the isolates were detected with MDR, mostly Gram-negative bacteria. The resistance was high against the Ceftriaxone (72%), Ciprofloxacin (64%), and Ampicillin (80%), but Nitrofurantoin (88%), and Imipenem (90%) were effective. The analysis demonstrates that MDR urinary pathogens are present at a very elevated rate, and the regular monitoring of antimicrobials usage and the reasonable use of antibiotics are necessary to avoid the further development of resistance. International Journal of Emerging Research in Applied Medical Sciences 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/20 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 7-10 3108-2599 eng https://ijerams.org/index.php/files/article/view/20/15 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/21 2026-02-18T07:32:30Z files:ART

Prevalence and Antimicrobial Resistance Patterns of Hospital-Acquired Infections in Tertiary Care Centers Jarupla Aravind Hospital-acquired infections, Antimicrobial resistance, Tertiary care, MDR organisms, Infection control. HAIs are among the major global health problems, in terms of morbidity, mortality, and health care spending. The growing incidences of the multi-drug-resistant (MDR) organisms also make it difficult to manage and control infections among patients. The study aimed to determine the occurrence rates of HAIs, microbial profile and resistance to antimicrobials of HAIs in tertiary care units. It was cross-sectional observational study that was conducted in three tertiary hospitals. The clinical samples included inpatients who contracted an infection not less than 48 hours after admission: urine, sputum, pus, and blood. The bacterial isolates were identified by conventional microbiological techniques and antibiotic susceptibility with the assistance of the KirbyBauer disk diffusion technique as per the CLSI guidelines of 2024. Among inpatients (n=1000) surveyed, 286 (28.6) of them later suffered HAIs. The most common sites of infection were urinary tract (35%), surgical wounds (25) and respiratory tract (22). The gram negative bacteria (72 percent) were the most predominant, with Klebsiella pneumoniae (24 percent), Escherichia coli (18 percent), and Pseudomonas aeruginosa (16 percent) being the other common bacteria species. It was found that all the resistance rates were high against the cephalosporins (6578%), fluoroquinolones (60%), and colistin and carbapenems were still sensitive (>80%). The high rates and alarming rates of HAIs indicate the urgent need to possess antibiotic stewardship, strengthened infection control measures and surveillance programs to assist in the reduction of the spread of the resistant pathogens. International Journal of Emerging Research in Applied Medical Sciences 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/21 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 11-15 3108-2599 eng https://ijerams.org/index.php/files/article/view/21/16 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/22 2026-02-18T07:32:30Z files:ART

Evaluating the Impact of Early Screening on the Prognosis of Type 2 Diabetes in Urban Populations of India Shameer shaik Type 2 Diabetes, Early screening, India, Prognosis, Urban Population, HbA1c, Complications. Type 2 Diabetes Mellitus (T2DM) is a rapidly increasing issue of the Indian population health and is particularly common in the urban areas of India where lifestyle causes encourage the development and progression of type 2 diabetes. Early screening plays a significant role in enhancing the outcome of the patient better since it offers timely interventions. This study was aimed at examining the impact of early screening on the clinical outcome and complications prevalence of T2DM in urban India. The design of the study was cross-sectional cohort study that was conducted in 3 big urban cities, i.e., Mumbai, Delhi, and Bengaluru. The patients were recruited (600 patients aged 30- 60 years in total) in two subgroups early-screened (diagnosed during the course of a regular screening before the symptoms appear, n = 300) and late-diagnosed (diagnosed when the symptoms appeared, n = 300). Medical records, laboratory findings, and structured interviews were used to obtain the data. The variables employed in the assessment of prognosis included the levels of HbA1c, complications, and adherence to the lifestyle modification programs. These statistical tests were chi-square tests, t-tests and multivariate logistic regression. The screened group showed significantly lower values of the mean HbA1c level (6.8 ± 0.7) as compared to the late-diagnosed group (8.3 ± 1.1, p = 0.001). The cases of complications such as neuropathy and retinopathy were significantly reduced in the early-screened cohort (12% vs. 28 p < 0.01). Multivariate analysis revealed that screening at a young age reduced the risk of acquiring complications by 45 percent (OR: 0.55; 95 percent CI: 0.38 -0.79). Diagnosis at an early stage is a significant contribution to the management of glycemia and a reduction in the rates of complications among urban Indian individuals with T2DM. Public health policies which promote periodic screening on the high-risk groups of individuals will bring about a substantial change in the long-run. International Journal of Emerging Research in Applied Medical Sciences 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/22 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 16-22 3108-2599 eng https://ijerams.org/index.php/files/article/view/22/17 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/23 2026-02-18T07:32:30Z files:ART

Vitamin D Deficiency: The regularity and bivariate relationship with Anemia among Adults in Urban Populations: a cross sectional study Shanmukhi Bejagam Vitamin D deficiency, Anemia, Hemoglobin, 25(OH)D, Nutritional deficiency, Urban population. The most common diseases that give rise to nutritional diseases that are likely to attack the general society include the vitamin D deficiency and anemia that are more common in the developing states. The other process is the calcium metabolism which incorporates the vitamin D in addition to the erythropoiesis process which involves the regulation of the erythropoietin as well as the activity of the bone marrow. Nevertheless, they have not done adequate correlation of the amount of Vitamin D and anemia on the adult population in the urban population. The study aim was to identify the status of Vitamin D deficiency and association with anemia in adults in a tertiary care unit in an urban population. The research design will be cross-sectional study and based on a population of forty one thousand and one hundred adults between the age of 18-60 years who visit the outpatient departments of a tertiary hospital in the period between January and June of 2024. The quantities of hemoglobin (Hb) were explained utilizing the chemiluminescent immunoassay and the hematology automated analyser to quantify the amounts of serum 25- hydroxyvitamin D [25(OH)D] and the hematology automated analyser respectively. The anemia was within the WHO. Pearson correlation coefficient and regression analysis helped to consider the statistical correlation of Vitamin D and hemoglobin levels. Among the whole group of the study participants (260/118), 260 (65) and 118 ( 29.5) were not only ill of Vitamin D (<20 ng/mL), but also anaemic, respectively. This was also established to be the case since, the Vitamin deficient individuals had 85 (32.7) anaemic individuals and 33 (17.5) Vitamin D adequate individuals (p = 0.004). They have not demonstrated a statistically significant difference of the mean levels of Vitamin D (16.4 + 5.3 ng/mL in anemic and 24.2 + 7.6 ng/mL in non-anemic). There was a positive correlation between Vitamin D (Serum Vitamin D) and hemoglobin (r = 0.36, p < 0.001). The deficiency of vitamin D is also excessive in the case of the urban adults themselves and it becomes directly adopted against the anemia. The other diagnostic and therapeutic application that can be made is screening of anemic patients in vitamin D. The interventional studies are required further to provide the solution to consumption and treatment outcome. International Journal of Emerging Research in Applied Medical Sciences 2026-02-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/23 International Journal of Emerging Research in Applied Medical Sciences; JERAMS: Vol 1, Issue 3, October 2025; 23-28 3108-2599 eng https://ijerams.org/index.php/files/article/view/23/18 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijerams.org:article/27 2026-02-18T07:35:19Z files:ART

Seroprevalence and Risk Factors of Hepatitis B and C Virus Infections among Pregnant Women in Kaduna State, Nigeria Sarah Nuhu Kase Christy Chinyere Fredrick Amaechi Dennis Esther Luka Abdulkarim Garba Ninani Jonah Danladi Haruna Danlami Sambo Magdaline Joseph Kwaji Jamila Ibrahim Suleiman Theophilus I. Ojemudia Mercy Kure Hepatitis, HBV, HCV and Pregnant women Background: Hepatitis is caused by Viruses that are all contagious and the infection can be transmitted from one person to another. Estimating the prevalence of hepatitis B and C viral infections among pregnant women will help reduce mortality and morbidity rates in these subjects. Methodology: A total of three hundred (300) blood samples were collected from three Hospitals in Saminaka, Lere and Gure based on the availability of samples. The sera samples were assayed using in-vitro diagnostic kit (dipsticks/strips) to detect the hepatitis B surface antigen (HBsAg) and antibodies to hepatitis C virus (anti-HCV). Results: Test strip revealed that 18/330 (6.00%) of pregnant women tested positive for HBV and 15/300 (5.00%) were infected with HCV. The Enzyme Linked Immunosorbent Assay (ELISA) kit confirmed 25/300 (8.33%) of HBV and 21/300 (7.00%) of HCV in the study population.. Conclusion: There is need for proper screening of blood before transfusion as most women that had blood transfusion once were found to be infected also there is need for proper sensitization about the disease as many participants were not aware of its capacity to spread and cause life threatening infection. International Journal of Emerging Research in Applied Medical Sciences 2026-11-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijerams.org/index.php/files/article/view/27 International Journal of Emerging Research in Applied Medical Sciences; IJERAMS: Vol 1, Issue 4, November 2025; 1-12 3108-2599 eng https://ijerams.org/index.php/files/article/view/27/19 https://creativecommons.org/licenses/by/4.0