Using artificial intelligence to improve body iron quantification: A scoping review

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.Open Access funding provided by the Qatar National Library.Scopu

The lived experiences of frontline nurses during the coronavirus disease 2019 (COVID-19) pandemic in Qatar: A qualitative study

This study aims to explore the lived experiences of frontline nurses providing nursing care for COVID-19 patients in Qatar. Qualitative, Phenomenological. Nurses were recruited from a designated COVID-19 facility using purposive and snowball sampling. The participants were interviewed face-to-face using semi-structured interview questions from 6 September-10 October 2020. The interviews were transcribed and analyzed using Colaizzi's phenomenological method. A total of 30 nurses were interviewed; (76.7%) were deployed for >6 months. Three major themes were drawn from the analysis: (a) Challenges of working in a COVID-19 facility (subthemes: working in a new context and new working environment, worn out by the workload, the struggle of wearing protective gear, fear of COVID-19, witnessing suffering); (b) Surviving COVID-19 (subthemes: keeping it safe with extra measures, change in eating habits, teamwork and camaraderie, social support); and (c) Resilience of Nurses (subthemes: a true calling, a sense of purpose).This study was funded by the Medical Research Center at Hamad Medical Corporation (MRC-01-20-423

The safety, health, and well-being of healthcare workers during COVID-19: A scoping review

The outbreak of the COVID-19 pandemic has affected the safety and well-being of healthcare workers. A scoping review was conducted to highlight the impact of COVID-19 on the safety, health, and well-being of healthcare workers and to shed light on the concerns about their perceived safety and support systems. A literature search was conducted in three different databases from December 1, 2019, through July 20, 2022, to find publications that meet the aim of this review. Using search engines, 3087 articles were identified, and after a rigorous assessment by two reviewers, 30 articles were chosen for further analysis. Two themes emerged during the analysis: safety and health and well-being. The primary safety concern of the staff was mostly about contracting COVID-19, infecting family members, and caring for patients with COVID-19. During the pandemic, the health care workers appeared to have anxiety, stress, uncertainty, burnout, and a lack of sleep. Additionally, the review focused on the suggestions of health care providers to improve the safety and well-being of workers through fair organizational policies and practices and timely, individualized mental health care

Quality of life, sleep quality, depression, anxiety, stress, eating habits, and social bounds in nurses during the coronavirus disease 2019 pandemic in qatar (The PROTECTOR study): A cross-sectional, comparative study

There have been numerous concerns regarding the physical and mental health of nurses during the COVID-19 pandemic. Stress, sleep deprivation, anxiety, and depression potentiated nurses’ vulnerability to poor eating habits. Aims and Objectives: The purpose of this study was to explore the differences between nurses’ characteristics with COVID-19 facility designation, and sleep quality, depression, anxiety, stress, eating habits, social bonds, and quality of life. Design: A cross-sectional, comparative study. Methods: An online survey was sent using the corporation’s email to nurses working in three hospitals in Qatar from September to December 2020. One of them is a designated COVID-19 facility. The sleep quality, depression, eating habits, social bonds, and quality of life were measured using The Insomnia Severity Index (ISI), Depression Anxiety and Stress Scale 21 (DASS-21), Emotional Eater Questionnaire (EEQ), Oslo Social Support Scale (OSSS-3), and the World Health Organization Quality of Life (WHOQOL-BREF), respectively. Results: A total of 200 nurses participated in the study (RR: 13.3%). No statistically significant association was found between designated facility (COVID-19 vs. not COVID-19) or nurses’ characteristics and ISI categories (OR 1.15; 95% CI 0.54, 2.44). Nurses working in COVID-19 facilities had increased odds of having higher EEQ categories by 2.62 times (95% CI 1.18, 5.83). Similarly, no statistically significant associations were found between any of the nurses’ characteristics and OSSS-3 categories. On the other hand, no statistically significant associations were found between any of the nurses’ characteristics and QOL domains except for the gender and social relationships’ domain. Conclusion: Overall, the quality of life of nurses in Qatar is on a positive level whether they are assigned to a COVID-19 facility or not. Although no significant difference was found with regard to the sleep quality, stress, anxiety, depression, and eating habits between nurses in a COVID-19 facility and in a non-COVID-19 facility, special interventions to diminish stressors need to be implemented and maintained.This study was funded by the Medical Research Center at Hamad Medical Corporation (MRC-01-20-392)

Predictive value of tyrosine phosphatase receptor gamma for the response to treatment tyrosine kinase inhibitors in chronic myeloid leukemia patients.

Protein tyrosine phosphatase receptor gamma (PTPRG) is a member of the receptor-like family protein tyrosine phosphatases and acts as a tumor suppressor gene in different neoplasms. Recent studies reported the down-regulation of PTPRG expression levels in Chronic Myeloid Leukemia disease (CML). In addition, the BCR-ABL1 transcript level is currently a key predictive biomarker of CML response to treatment with Tyrosine Kinase Inhibitors (TKIs). The aim of this study was to employ flow cytometry to monitor the changes in the expression level of PTPRG in the white blood cells (WBCs) of CML patients at the time of diagnosis and following treatment with TKIs. WBCs from peripheral blood of 21 CML patients were extracted at diagnosis and during follow up along with seven healthy individuals. The PTPRG expression level was determined at protein and mRNA levels by both flow cytometry with monoclonal antibody (TPγ B9-2) and RT-qPCR, and BCR-ABL1 transcript by RT-qPCR, respectively. PTPRG expression was found to be lower in the neutrophils and monocytes of CML patients at time of diagnosis compared to healthy individuals. Treatment with TKIs nilotinib and Imatinib Mesylate restored the expression of PTPRG in the WBCs of CML patients to levels observed in healthy controls. Moreover, restoration levels were greatest in optimal responders and occurred earlier with nilotinib compared to imatinib. Our results support the measurement of PTPRG expression level in the WBCs of CML patients by flow cytometry as a monitoring tool for the response to treatment with TKIs in CML patients

Professional Self-Concept and Self-Confidence for Nurses Dealing with COVID-19 Patients

Purpose: To identify the impact of dealing with COVID-19 patients in clinical areas on nurses’ professional self-concept and self-confidence. Background: Professional self-concept is considered a critical factor in the recruitment/retention process in nursing, nursing shortage, career satisfaction, and academic achievements. Professional self-confidence is also a crucial determinant in staff satisfaction, reducing turnover, and increasing work engagement. Design: Descriptive, comparative study. Methods: The study was conducted between February to May 2021 by utilizing a convenience sampling technique. A total of 170 nurses from two facilities were recruited from two COVID-19- and non-COVID-19-designated facilities. The level of professional self-concept and self-confidence was assessed by utilizing the Nurses’ Self-Concept Instrument and Self-Confidence Scale. Results: The professional self-concept level among the group exposed to COVID-19 patients was lower than the comparison group, while the professional self-confidence level among the exposed group to COVID-19 patients was similar to the comparison group. On the other hand, the satisfied staff and those who received professional training in dealing with COVID-19 patients reported a higher level of professional self-concept. Conclusions: Dealing with COVID-19 patients has an impact on professional self-concept; the exposure group was lower than those who did not deal with COVID-19 patients, while the professional self-confidence level among the exposed group was similar to the comparison group. Getting professional training in dealing with COVID-19 patients and being satisfied at work were significant factors in improving professional self-concept. Policymakers should create strategies that target the improvement of professional training in dealing with COVID-19 patients

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke

Abstract Background Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. Methods We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew’s correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. Results The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. Conclusion This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient’s clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings

Machine learning-based prognostication of mortality in stroke patients

Objectives: Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods: Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. Results: The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. Conclusions: This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments

Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors

The acceptability, appropriateness, and feasibility of implementing supportive supervision within humanitarian contexts: A qualitative study

Supervision is considered integral to quality mental health and psychosocial support (MHPSS) interventions and features as a key recommendation in all major international guidelines. Despite this, provision of supervision remains a gap within MHPSS programming and is often one of the most challenging aspects and unmet needs in programme implementation within humanitarian contexts.The Danish Red Cross Psychosocial Centre (PSC) and Trinity Centre for Global Health (TCGH) have developed the Integrated Model for Supervision (IMS) Handbook and accompanying training guidelines using participatory action research approaches. The IMS reflects input from a range of stakeholders, ensuring a user-centered design. The IMS was piloted within four organizations in Afghanistan, Nigeria, Jordan, and Ukraine.The acceptability, appropriateness, and feasibility of the IMS were assessed through (a) surveys of training participants (n-40), (b) in-session feedback gathered during training workshops (n ​= ​2 workshops), and (c) in depth interviews with workshop participants (n ​= ​8). Interview participants were supervisors, supervisees and leadership and management who had completed the IMS training and workshops.Results point to the acceptability and appropriateness of the IMS Handbook and its accompanying training for use within humanitarian contexts; with participants indicating that the IMS addressed issues and challenges that were present in their organizations. Preliminary data suggests that training in the IMS is associated with improvements in knowledge of MHPSS and perceived supervision practices, as well as reductions in secondary traumatic stress and burnout. Themes emerging from the qualitative analysis suggest that the IMS training promotes buy-in for supervision practices within organizations, and that the IMS training meet the need for providing emotional support within organizations. Participants indicate that the follow-up support and practical guidance offered as part of the IMS training is essential for effective supervision. This study bolsters evidence for the importance of strengthening support structures for human resources for mental health programming