Prognostic Role of Neutrophil to Lymphocyte Ratio in Contrast-Induced Nephropathy: A Systematic Review and Meta-analysis.

This meta-analysis assessed the use of the neutrophil-to-lymphocyte ratio (NLR) as a means of early detection of contrast-induced nephropathy (CIN) following diagnostic or therapeutic procedures. We used Web of Science, PubMed, and Scopus to conduct a systematic search. There was no limitation regarding language or date of publication. We reported standardized mean difference (SMD) with a 95% confidence interval (CI). Due to high heterogeneity, a random-effects model was used, and the Newcastle-Ottawa scale was used for quality assessment. Thirty-one articles were included in the analysis. Patients in the CIN group had elevated levels of NLR compared with those in the non-CIN group (SMD = 0.78, 95% CI = 0.52-1.04, P < .001). Similar results were observed in either prospective (SMD = 1.03, 95% CI = 0.13-1.93, P = .02) or retrospective studies (SMD = 0.70, 95% CI = 0.45-0.96, P < .001). The pooled sensitivity of NLR was 74.02% (95% CI = 66.54%-81.02%), and the pooled specificity was 60.58% (95% CI = 53.94%-66.84%). NLR shows potential as a cost-effective biomarker for predicting CIN associated with contrast-involved treatments. This could help implement timely interventions to mitigate CIN and improve outcomes.

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service.

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.

The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things.

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.

Systematic review of the diagnostic role of neutrophil to lymphocyte ratio in sarcoidosis.

BACKGROUND AND AIM: To outline the observations of studies evaluating the prominence of Neutrophil to Lymphocyte Ratio (NLR) in sarcoidosis. METHODS: The search was performed on PubMed, Scopus, and web of science up until November 21, 2021. Eventually, a number of 17 papers were incorporated into this review. RESULTS: The results of this analysis showed no significant difference of NLR values between sarcoidosis patients and tuberculosis patients (SMD=-0.36, 95% CI= -0.92-0.21). The results showed high heterogeneity (I2=90.83%, p<0.001). So, we used random-effects model. However, NLR can be utilized to identify the radiological severity and staging of pulmonary sarcoidosis due to statistically significant variations. An elevation in NLR values may assist both sarcoidosis diagnosis and lung parenchyma involvement. Also, extra-pulmonary involvement was just more probable to be found in individuals diagnosed with sarcoidosis inhibiting high rates of NLR. High NLR levels were found to be associated with an accelerated rate of progression, revealing that NLR might be used to detect Pulmonary Hypertension (PH) as a complication of sarcoidosis. CONCLUSIONS: In the visualizations of the disease, NLR was revealed to be a beneficial and straightforward fundamental laboratory biomarker connected to disease severity and requirement for therapy.

Quality of the Systematic Reviews in Cochrane Gynecological Cancer Group and Their Understudied RCTs.

Purpose: Gynecological cancers are common neoplasms in clinical settings with a high impact on the economy of communities. The medical literature is an essential resource to guide clinical decision-making, and misconduct in researches undermines the credibility and integrity of research in general. We aimed to evaluate the quality of Cochrane gynecological cancers reviews and their understudies RCTS among the different biases dimensions. Methods: This cross-sectional analytical study was performed on 118 systematic reviews published by the Cochrane gynecological cancers Group up to June 2021. The risk of bias was assessed in each Cochrane survey using the Joanna Bridges Institute (JBI) critical assessment tool consisting of 11 questions. The JBI checklist for systematic reviews and research syntheses is available at https://jbi.global/critical-appraisal-tools. After a systematic critical evaluation of the reviews and meta-analysis, we extracted a different bias from all of their understudied RCTs examined in these systematic reviews, which were evaluated by systematic review authors using a standard bias risk tool developed by the Cochrane Group. Results: Cochrane gynecological cancers reviews had high quality based on appraise results using the JBI appraisal checklist. In addition, all of the included studies used PRISMA standards for reporting their results. However, in their understudied RCTs, the most prevalent risk of bias was unclear selection bias (allocation concealment) and performance bias (blinding of participants and personnel). Also, the highest risk of bias was blinding participants and personnel (performance bias) and incomplete outcome data (attrition bias). Our results showed that the lowest risk of bias was incomplete outcome data (attrition bias) and random sequence generation (selection bias). Conclusion: Although most Cochrane gynecological cancers reviews had high quality, unclear performance bias was the highest in their understudied RCTs, indicating structural deficiencies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13224-022-01655-6.