Drug utilization and prescribing pattern in the treatment of urolithiasis: a perspective on World Health Organization recommendations

Background: Drug utilization research (DUR) is essential in promoting rational use of medicine, aimed at understanding the patterns of prescription, administration, and utilization of medications. It provides valuable insights into the actual drug usage patterns for specific disease conditions. To evaluate the current utilization pattern of drugs in patients of urolithiasis in the Department of General Medicine and Surgery at Integral Institute of Medical Science and Research Hospital, Lucknow. Methods: Following the approval of the institutional ethics committee, a prospective observational study was conducted at Integral Institute of Medical Science and Research Department of general medicine and surgery over a six-month period. Urolithiasis patients’ prescriptions were analyzed to study the prescribing patterns. Information about patient demographics, co-morbidities, and the number and types of medications prescribed were collected and analyzed. Results: Out of 102 patients studied, a female preponderance over male patients was observed. The co-morbidities that are encountered most commonly were hydronephrosis, cystitis, and renal cyst. There is averaged 7 medicines per prescription, 15.25% of medicines written by the generic name, 83.33% of patients receiving antibiotics, 54.70% of patients receiving injections, and 83% of drugs prescribed are mentioned in the essential medicine list. Analgesics, antibiotics, nutritional supplements, antiemetic, alkalizing agents, and antispasmodics were among the class of medicines given. Conclusions: This study highlights the current use of medicines and drug utilization in urolithiasis management. The findings show important insights for healthcare professionals to enhance medication therapy, encourage cost-effective healthcare delivery and improve quality of patient in urolithiasis management

UAVs and Blockchain Synergy: Enabling Secure Reputation-based Federated Learning in Smart Cities

Unmanned aerial vehicles (UAVs) can be used as drones’ edge Intelligence to assist with data collection, training models, and communication over wireless networks. UAV use for smart cities is rapidly growing in various industries, including tracking and surveillance, military defense, managing healthcare delivery, wireless communications, and more. In traditional machine learning techniques, an enormous amount of sensor data from UAVs must be shared to central storage to perform model training, which poses serious privacy risks and risks of misuse of information. The federated learning technique (FL), which can be applied to UAVs, is a promising means of collaboratively training a global model while retaining local access to sensitive raw data. Despite this, FL is a significant communication burden for battery-constrained UAVs due to local model training and global synchronization frequency. In this article, we address the major challenges associated with UAV-based FL for smart cities, including single-point failure, privacy leakage, scalability, and global model verification. To tackle these challenges, we present a differentially private federated learning framework based on Accumulative Reputation-based Selection (ARS) for the edge-aided UAV network that utilizes blockchains to prevent single-point failures where we switched from central control to decentralized control, Interplanetary File System (IPFS) for off-chain model storage and their respective hash-keys on-chain to ensure model integrity. Due to IPFS, the size of the blockchain will be reduced, and local differential privacy will be applied to prevent privacy leakages. In the proposed framework, an aggregator will be selected based on its ARS score and model verification by the validators. After most validators approve it, it will be available for use. Several parameters are taken into consideration during evaluation, including accuracy, precision, recall, F1-score, and time consumption. It also evaluates the number of edge computers vs test accuracy, the number of edge computers vs time consumption for global model convergence, and the number of rounds vs test accuracy. This is done by considering two benchmark datasets: MNIST and CIFAR-10. The results show that the proposed work preserves privacy while achieving high accuracy. Moreover, it is scalable to accommodate many participants

Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm

Recommender systems are intelligent data mining applications that deal with the issue of information overload significantly. The available literature discusses several methodologies to generate recommendations and proposes different techniques in accordance with users’ needs. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information. A context is a vast term that may consider various aspects; for example, a user’s social circle, time, mood, location, weather, company, day type, an item’s genre, location, and language. Typically, the rating behavior of users varies under different contexts. From this line of research, we have proposed a new algorithm, namely Kernel Context Recommender System, which is a flexible, fast, and accurate kernel mapping framework that recognizes the importance of context and incorporates the contextual information using kernel trick while making predictions. We have benchmarked our proposed algorithm with pre- and post-filtering approaches as they have been the favorite approaches in the literature to solve the context-aware recommendation problem. Our experiments reveal that considering the contextual information can increase the performance of a system and provide better, relevant, and meaningful results on various evaluation metrics

TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart devices within these networks are employed to handle sensitive information. However, the scale and openness of IoT networks make them highly vulnerable to security breaches and attacks, such as eavesdropping, weak authentication, and malicious payloads. Hence, there is a need for advanced machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS). Existing ML-based IoT-IDSs face several limitations in effectively detecting malicious activities, mainly due to imbalanced training data. To address this, this study introduces a transformer neural network-based intrusion detection system (TNN-IDS) specifically designed for MQTT-enabled IoT networks. The proposed approach aims to enhance the detection of malicious activities within these networks. The TNN-IDS leverages the parallel processing capability of the Transformer Neural Network, which accelerates the learning process and results in improved detection of malicious attacks. To evaluate the performance of the proposed system, it was compared with various IDSs based on ML and DL approaches. The experimental results demonstrate that the proposed TNN-IDS outperforms other systems in terms of detecting malicious activity. The TNN-IDS achieved optimum accuracies reaching 99.9% in detecting malicious activities

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Laparoscopic Cholecystectomy: A New Era in the Field of Surgery in Bangladesh

Laparoscopic cholecystectomy which is the latest development in the field of general surgery has recently been introduced in Bangladesh. We performed this less invasive surgery successfully on two cases of cholelithiasis for the first time in Bangladesh, one in BIRDEM Hospital and another in lPGMR Hospital in December, 1991. Though for those demonstrative cases, the set–up was difficult in a new place but there was no major technical problem during the procedure. After that there was a long gap and our team has started this procedure from April, 1993 regularly at two hospitals in Dhaka. By September, 1993 we have already performed over 100 cases of laparoscopic cholecystectomy successfully. This paper will describe the patient selection, preoperative investigation, operative procedure and postoperative care of laparoscopic cholecystectomy, the newest treatment modality for gallbladder diseases