Fairness-aware predictive graph learning in social networks

Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data. © 2022 by the authors

Sentiment Analysis of Semantically Interoperable Social Media Platforms Using Computational Intelligence Techniques

Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability. This article proposes an improved social media sentiment analytics technique to predict the individual state of mind of social media users and the ability of users to resist profound effects. The proposed estimation function tracks the counts of the aversion and satisfaction levels of each inter- and intra-linked expression. It tracks down more than one ontologically linked activity from different social media platforms with a high average success rate of 99.71%. The accuracy of the proposed solution is 97% satisfactory, which could be effectively considered in various industrial solutions such as emo-robot building, patient analysis and activity tracking, elderly care, and so on

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

Funding Information: Funding Statement: This work was funded by the Researchers Supporting Project Number (RSP2023R 509) King Saud University, Riyadh, Saudi Arabia. This work was supported in part by the Higher Education Sprout Project from the Ministry of Education (MOE) and National Science and Technology Council, Taiwan, (109-2628-E-224-001-MY3), and in part by Isuzu Optics Corporation. Dr. Shih-Yu Chen is the corresponding author. Publisher Copyright: © 2023 Tech Science Press. All rights reserved.Peer reviewedPublisher PD

F-LSTM: Federated learning-based LSTM framework for cryptocurrency price prediction

In this paper, a distributed machine-learning strategy, i.e., federated learning (FL), is used to enable the artificial intelligence (AI) model to be trained on dispersed data sources. The paper is specifically meant to forecast cryptocurrency prices, where a long short-term memory (LSTM)-based FL network is used. The proposed framework, i.e., F-LSTM utilizes FL, due to which different devices are trained on distributed databases that protect the user privacy. Sensitive data is protected by staying private and secure by sharing only model parameters (weights) with the central server. To assess the effectiveness of F-LSTM, we ran different empirical simulations. Our findings demonstrate that F-LSTM outperforms conventional approaches and machine learning techniques by achieving a loss minimal of $2.3 \times 10^{-4}$. Furthermore, the F-LSTM uses substantially less memory and roughly half the CPU compared to a solely centralized approach. In comparison to a centralized model, the F-LSTM requires significantly less time for training and computing. The use of both FL and LSTM networks is responsible for the higher performance of our suggested model (F-LSTM). In terms of data privacy and accuracy, F-LSTM addresses the shortcomings of conventional approaches and machine learning models, and it has the potential to transform the field of cryptocurrency price prediction

Surgical Management of Thermal Injury: Narrative Review

Extensive burn care advanced over the past few decades to the point where burn victims can now often live. The goal of treating a severely burned patient nowadays is to help them return to their communities, families, and places of employment as fully participating members of society, rather than only preserving their life and ability to function. Burns are a common and difficult critical care issue. Specialized hospitals prioritize achieving optimal functional recovery, infection prevention, and patient stabilization. Over the past few decades, researches on burns have attracted a lot of attention. A number of significant discoveries have improved patient stability and reduced mortality, particularly in the case of younger patients and those with intermediate-degree burns. The presence of dead tissue over a burn wound hinders the healing process and serves as a breeding ground for bacteria. Consequently, clearing the eschar as soon as possible and getting a clean wound bed as soon as possible, can be regarded as the main objective to initiate the process of wound healing, either through autografting or spontaneous epithelization. This review article provides a comprehensive overview of the surgical management of thermal injuries. The article also discusses the importance of early surgical intervention, including debridement, skin grafting, and other surgical techniques. Additionally, it explores the latest advancements in surgical management and the potential future directions in this field. Overall, this review aims to provide a valuable resource for healthcare professionals involved in the care of patients with thermal injuries

The Level of Information Security Awareness among Academic Staff in IHL

IS security awareness plays a significant role in the process of the overall information security of any organisation. Based on an empirical study of 368 academic staff in three institutions of higher learning (IHL), we found that the level of information security awareness can be considered good, but it can certainly be improved further. Employees need further training in this area mainly at institutions which only recently received the ISO/IEC 27001:2013 certification. Our sample seems to suggest that demographics such as the age of the respondents contributed to their information security risk tolerance and adherence behaviour

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

The efficacy and safety of prokinetic agents in critically ill patients receiving enteral nutrition: a systematic review and meta-analysis of randomized trials.

BACKGROUND: Intolerance to enteral nutrition is common in critically ill adults, and may result in significant morbidity including ileus, abdominal distension, vomiting and potential aspiration events. Prokinetic agents are prescribed to improve gastric emptying. However, the efficacy and safety of these agents in critically ill patients is not well-defined. Therefore, we conducted a systematic review and meta-analysis to determine the efficacy and safety of prokinetic agents in critically ill patients. METHODS: We searched MEDLINE, EMBASE, and Cochrane Library from inception up to January 2016. Eligible studies included randomized controlled trials (RCTs) of critically ill adults assigned to receive a prokinetic agent or placebo, and that reported relevant clinical outcomes. Two independent reviewers screened potentially eligible articles, selected eligible studies, and abstracted pertinent data. We calculated pooled relative risk (RR) for dichotomous outcomes and mean difference for continuous outcomes, with the corresponding 95 % confidence interval (CI). We assessed risk of bias using Cochrane risk of bias tool, and the quality of evidence using grading of recommendations assessment, development, and evaluation (GRADE) methodology. RESULTS: Thirteen RCTs (enrolling 1341 patients) met our inclusion criteria. Prokinetic agents significantly reduced feeding intolerance (RR 0.73, 95 % CI 0.55, 0.97; P = 0.03; moderate certainty), which translated to 17.3 % (95 % CI 5, 26.8 %) absolute reduction in feeding intolerance. Prokinetics also reduced the risk of developing high gastric residual volumes (RR 0.69; 95 % CI 0.52, 0.91; P = 0.009; moderate quality) and increased the success of post-pyloric feeding tube placement (RR 1.60, 95 % CI 1.17, 2.21; P = 0.004; moderate quality). There was no significant improvement in the risk of vomiting, diarrhea, intensive care unit (ICU) length of stay or mortality. Prokinetic agents also did not significantly increase the rate of diarrhea. CONCLUSION: There is moderate-quality evidence that prokinetic agents reduce feeding intolerance in critically ill patients compared to placebo or no intervention. However, the impact on other clinical outcomes such as pneumonia, mortality, and ICU length of stay is unclear

The Saudi Critical Care Society practice guidelines on the management of COVID-19 in the ICU: Therapy section

BACKGROUND: The rapid increase in coronavirus disease 2019 (COVID-19) cases during the subsequent waves in Saudi Arabia and other countries prompted the Saudi Critical Care Society (SCCS) to put together a panel of experts to issue evidence-based recommendations for the management of COVID-19 in the intensive care unit (ICU). METHODS: The SCCS COVID-19 panel included 51 experts with expertise in critical care, respirology, infectious disease, epidemiology, emergency medicine, clinical pharmacy, nursing, respiratory therapy, methodology, and health policy. All members completed an electronic conflict of interest disclosure form. The panel addressed 9 questions that are related to the therapy of COVID-19 in the ICU. We identified relevant systematic reviews and clinical trials, then used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach as well as the evidence-to-decision framework (EtD) to assess the quality of evidence and generate recommendations. RESULTS: The SCCS COVID-19 panel issued 12 recommendations on pharmacotherapeutic interventions (immunomodulators, antiviral agents, and anticoagulants) for severe and critical COVID-19, of which 3 were strong recommendations and 9 were weak recommendations. CONCLUSION: The SCCS COVID-19 panel used the GRADE approach to formulate recommendations on therapy for COVID-19 in the ICU. The EtD framework allows adaptation of these recommendations in different contexts. The SCCS guideline committee will update recommendations as new evidence becomes available