Prevalence and Antimicrobial Resistance Patterns of Hospital-Acquired Infections in Tertiary Care Centers

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

Cognitive Outcomes in Post-COVID-19 Patients: A Cross-Sectional Neurological Assessment

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

Vitamin D Deficiency: The regularity and bivariate relationship with Anemia among Adults in Urban Populations: a cross sectional study

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

Seroprevalence and Risk Factors of Hepatitis B and C Virus Infections among Pregnant Women in Kaduna State, Nigeria

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

Prevalence of Multidrug-Resistant Urinary Tract Infections and Their Antibiotic Susceptibility Patterns in Tertiary Care Hospitals

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

Evaluating the Impact of Early Screening on the Prognosis of Type 2 Diabetes in Urban Populations of India

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

AI-Driven IoT Security Enhancements Using Blockchain Technology: A DataDriven Approach

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

The Utilization of Machine Learning and Blockchain in the Safe Handling of a Supply Chain in IoT Ecosystems

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

Towards a Unified Blockchain and AI Architecture for IoT-Enabled Autonomous Vehicles: A Machine Learning Approach

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

Blockchain-Enabled AI Frameworks for Predictive Analytics in IoT-Driven Environmental Monitoring Systems

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