Intravenous tirofiban in acute ischemic stroke patients not receiving reperfusion treatments: a systematic review and meta-analysis of randomized controlled trials

BackgroundReperfusion treatments with intravenous thrombolysis and endovascular thrombectomy after acute ischemic stroke (AIS) can improve patients’ outcomes significantly. Yet, a substantial portion of patients miss the opportunity to receive reperfusion treatments. In this study, we aimed to assess the role of intravenous tirofiban in this specific population.MethodsA search was performed in Embase, Cochrane Central Register of Controlled Trials, Medline, and Web of Science databases from inception until August 2024. The random-effects model was used to calculate odds ratios (ORs) with their corresponding 95% confidence intervals (CIs). Efficacy endpoints included excellent (modified Rankin scale of 0–1) and good (modified Rankin scale of 0–2) functional outcomes at 90 days. Safety outcomes included symptomatic intracerebral hemorrhage (sICH), any ICH, and 90-day mortality.ResultsFour randomized clinical trials, including a total of 1,199 patients, were included. Of these, 599 patients (50%) received tirofiban. The meta-analysis demonstrated that tirofiban was associated with significantly higher rates of both excellent (OR 1.63 [95% CI, 1.24–2.13]; I2 = 0) and good (OR 1.65 [95% CI, 1.19–2.29]; I2 = 0) functional outcomes at 90 days. No significant differences were observed in sICH, any ICH, or 90-days mortality.ConclusionTreatment with intravenous tirofiban can be beneficial without increased risk in patients with AIS who are not eligible for reperfusion treatment. Further studies are still needed to validate the generalizability of these findings.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024590097, CRD42024590097

Decision-Making Taxonomy of DevOps Success Factors Using Preference Ranking Organization Method of Enrichment Evaluation

Due to multitudes factors like rapid change in technology, customer needs, and business trends, the software organizations are facing pressure to deliver quality software on time. To address this concern, the software industry is continually looking the solution to improve processing timeline. Thus, the Development and Operations (DevOps) has gained a wide popularity in recent era, and several organizations are adopting it, to leverage its perceived benefits. However, companies are facing several problems while executing the DevOps practices. The objective of this work is to identify the DevOps success factors that will help in DevOps process improvement. To accomplish this research firstly, a systematic literature review is conducted to identify the factors having positive influence on DevOps. Secondly, success factors were mapped with DevOps principles, i.e., culture, automation, measurement, and sharing. Thirdly, the identified success factors and their mapping were further verified with industry experts via questionnaire survey. In the last step, the PROMETHEE-II method has been adopted to prioritize and investigate logical relationship of success factors concerning their criticality for DevOps process. This study’s outcomes portray the taxonomy of the success factors, which help the experts design the new strategies that are effective for DevOps process improvement.</jats:p

Decision-Making Framework of Requirement Engineering Barriers in the Domain of Global Healthcare Information Systems

The smart healthcare information system offers several benefits including effectively reducing the cost and risk of medical procedures, improving the utilization efficiency of medical resources, promoting exchanges and cooperation in different regions, pushing the development of telemedicine and self-service medical care, and ultimately connecting people (patients and medical teams). The development of smart healthcare information systems in a globally distributed environment is a swiftly followed paradigm in the recent era. The development and implementation of a global healthcare information system (GHIS) are complicated as it faces several important problems that are more related to requirement engineering (RE). The ultimate aim of this research study is to explore and prioritize the barriers that would hinder the actual RE process in GHIS. To meet the research objective, 17 barriers of RE in GHIS have been identified through literature review and verified with practitioners using a questionnaire survey approach. Moreover, the analytical hierarchy process (AHP) approach was applied to rank the investigated barriers considering their significance for GHIS. The outcomes revolved that coordination is the most critical category for RE barriers in GHIS. Emotions and personal values, scope change, and creep are considered, and ad hoc exchange management and the absence of traceability are analyzed as the high-priority barriers to RE in GHIS. The findings of this study provide a robust framework that is beneficial for researchers and practitioners to consider the most critical barriers on a priority basis and develop the new strategies for RE process success in GHIS.</jats:p

Comprehensive evaluation and performance analysis of machine learning in heart disease prediction

Abstract Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model’s robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model’s predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system’s sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes

Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients

&lt;abstract&gt; &lt;p&gt;By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.&lt;/p&gt; &lt;/abstract&gt;</jats:p

A wireless controlled intelligent healthcare system for diplegia patients

&lt;abstract&gt; &lt;p&gt;Rehabilitation engineering is playing a more vital role in the field of healthcare for humanity. It is providing many assistive devices to diplegia patients (The patients whose conditions are weak in terms of muscle mobility on both sides of the body and their paralyzing effects are high either in the arms or in the legs). Therefore, in order to rehabilitate such types of patients, an intelligent healthcare system is proposed in this research. The electric sticks and chairs are also a type of this system which was used previously to facilitate the diplegia patients. It is worth noting that a voice recognition system along with wireless control feature has been integrated intelligently in the proposed healthcare system in order to replace the common and conventional assistive tools for diplegia patients. These features will make the proposed system more user friendly, convenient and comfortable. The voice recognition system has been used for movements of system in any desired direction along with the ultrasonic sensor and light detecting technology. These sensors detect the obstacles and low light environment intelligently during the movement of the wheelchair and then take the necessary actions accordingly.&lt;/p&gt; &lt;/abstract&gt;</jats:p