A bibliometric analysis of cystic fibrosis transmembrane conductance regulators.

Cystic fibrosis (CF), a multisystem disease primarily affecting the lungs, arises due to pathogenic mutations in the CF transmembrane conductance regulator (CFTR) gene. This study embarked on a bibliometric analysis to survey the use of CFTR modulators in CF treatment. Utilizing the Scopus database, a comprehensive search was executed, incorporating terms related to CF and CFTR modulators. Various document types up to July 19, 2023, were included, with citation counts forming the basis of our analyses. Trends, contributor countries, leading institutions, top authors, journals, keywords, and annual citation trends were evaluated. Our search retrieved 2317 records, predominantly articles. The United States dominated in both publications and citations, followed by the United Kingdom. The University of Alabama, Birmingham, and Vertex Pharmaceuticals, Boston, were among the top institutions. Rowe S.M. was identified as a top-cited author. The Journal of Cystic Fibrosis emerged as the leading journal in terms of publication volume, while the New England Journal of Medicine had the highest citation count. The most-cited article addressed a CFTR potentiator's efficacy in patients with the G551D mutation. The keyword "Cystic fibrosis" appeared most frequently. This bibliometric analysis underscores the significant research focus on CF, especially concerning CFTR modulators. The results highlight the pivotal role of certain countries, institutions, authors, and journals in the progression of CF research, offering insights into current trends and future research directions.

Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar.

BACKGROUND: Public health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (FWC22). This study estimated the empirical probability of EIOS detecting signals of public health relevance. It also looked at the factors responsible for discerning a moderate-high risk signal during a mass gathering event. METHODS: This cross-sectional descriptive study used data collected between November 8th and December 25th, 2022, through an EIOS dashboard that filtered open-source articles using specific keywords. Triage criteria and scoring scheme were developed to capture signals and these were maintained in MS Excel. EIOS' contribution to epidemic intelligence was assessed by the empirical probability estimation of relevant public health signals. Chi-squared tests of independence were performed to check for associations between various hazard categories and other independent variables. A multivariate logistic regression evaluated the predictors of moderate-high risk signals that required prompt action. RESULTS: The probability of EIOS capturing a signal relevant to public health was estimated at 0.85 % (95 % confidence interval (CI) [0.82 %-0.88 %]) with three signals requiring a national response. The hazard category of the signal had significant association to the region of occurrence (χ2 (5, N = 2543) = 1021.6, p < .001). The hazard category also showed significant association to its detection during matchdays of the tournament (χ2 (5, N = 2543) = 11.2, p < .05). The triage criteria developed was able to discern between low and moderate-high risk signals with an acceptable discrimination (Area Under the Curve=0.79). CONCLUSION: EIOS proved useful in the early warning of public health threats.

Vitamin B12 deficiency in diabetic patients treated with metformin: A narrative review.

Metformin is the most prescribed oral hypoglycemic drug and is considered by many health practitioners as the first-line treatment for non-insulin-dependent diabetes mellitus (T2DM). It is used either as a monotherapy or adjuvant to other anti-hyperglycemic agents. Most of its side effects are usually mild and self-limiting. However, several studies have shown an association between the use of metformin and low vitamin B12 levels in diabetic patients. The current review aimed to provide a literature review of the current published reports on the association, the possible mechanisms, and the related individualized risk factors that might lead to this incidence. The most accepted mechanism of the effect of metformin on vitamin B12 level is related to the absorption process where metformin antagonism of the calcium cation and interference with the calcium-dependent IF-vitamin B12 complex binding to the ileal cubilin receptor. In addition, many risk factors have been associated with the impact of metformin on vitamin B12 levels in diabetic patients such as dose and duration where longer durations showed a greater prevalence of developing vitamin B12 deficiency. Male patients showed lower levels of vitamin B12 compared to females. Black race showed a lower prevalence of vitamin B12 deficiency in metformin-treated patients. Moreover, chronic diseases including T2DM, hyperlipidemia, coronary artery disease, polycystic ovary disease (PCOD), obesity, and metformin therapy were significantly associated with increased risk of vitamin B12 deficiency.

Qatar's Silent Epidemic: A Comprehensive Meta-analysis on the Prevalence of Metabolic Syndrome.

BACKGROUND: Metabolic syndrome comprises various conditions like abdominal obesity, insulin resistance, elevated triglyceride levels, reduced HDL, and high blood pressure, which pose significant health challenges globally. It's imperative to determine its prevalence in specific populations to formulate effective preventive measures. OBJECTIVE: This systematic review and meta-analysis aimed to determine the prevalence of metabolic syndrome in the Qatari population. METHODS: Using the PRISMA guidelines, a systematic search was executed on PubMed until July 2023 with keywords "Metabolic syndrome" and "Qatar." Eligibility criteria included human subjects, studies assessing metabolic syndrome components, and research conducted in Qatar or on Qatari subjects. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS). Pooled prevalence rates were calculated using the inverse variance weighting metaanalysis. RESULTS: Out of 237 studies, 14 met our inclusion criteria, with a combined sample size of 14,772 from the Qatari population. The overall pooled prevalence of metabolic syndrome was 26%. The ATP III and IDF criteria exhibited significant differences in prevalence rates, with the IDF criteria showing a higher prevalence. Age ≥ 40 years demonstrated a higher prevalence compared to the younger group. Studies post-2018 reported a decreasing trend in metabolic syndrome prevalence. CONCLUSION: The prevalence of metabolic syndrome in the Qatari population is comparable to rates in the Middle East. The study underscores the need for tailored interventions and strategies, especially targeting the older age group. Continuous research and monitoring are essential to track and understand the disease's progression in Qatar.

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review.

PURPOSE OF REVIEW: This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to understand the role of AI and ML in healthcare, specifically in medical diagnosis, statistics, and precision medicine, and to explore their applications in predicting and managing sudden cardiac arrest outcomes, especially in the context of prehospital emergency care. RECENT FINDINGS: The role of AI and ML in healthcare is expanding, with applications evident in medical diagnosis, statistics, and precision medicine. Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There's a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). Furthermore, the combination of AI with automated external defibrillators (AEDs) shows potential in better detecting shockable rhythms during cardiac arrest incidents. AI and ML hold immense promise in revolutionizing the prediction and management of sudden cardiac arrest, hinting at improved survival rates and more efficient healthcare interventions in the future. Sudden cardiac arrest (SCA) continues to be a major global cause of death, with survival rates remaining low despite advanced first responder systems. The ongoing challenge is the prediction and prevention of SCA. However, with the rise in the adoption of AI and ML tools in clinical electrophysiology in recent times, there is optimism about addressing these challenges more effectively.

Migrated Hem-o-lok clip in the bladder: A rare cause of bladder stone for cystolitholapaxy.

The bladder is the most common site of foreign bodies in the urinary tract. Most foreign bodies are self-inserted via the urethra due to exotic impulses, psychometric problems, or sexual curiosity. Here we present a rare case of bladder stones due to the migration of the Heme-o-lok clip. We present a case of a 76-year-old male with hematuria for 4 days. An abdominal computed tomography (CT) scan showed a 15 mm calculus noted in the urinary bladder. The patient underwent cystolitholapaxy which was successful. Foreign bodies inserted in the bladder pose a significant challenge and require timely intervention.

Biomaterials in Traumatic Brain Injury: Perspectives and Challenges.

Traumatic brain injury (TBI) is a leading cause of mortality and long-term impairment globally. TBI has a dynamic pathology, encompassing a variety of metabolic and molecular events that occur in two phases: primary and secondary. A forceful external blow to the brain initiates the primary phase, followed by a secondary phase that involves the release of calcium ions (Ca2+) and the initiation of a cascade of inflammatory processes, including mitochondrial dysfunction, a rise in oxidative stress, activation of glial cells, and damage to the blood-brain barrier (BBB), resulting in paracellular leakage. Currently, there are no FDA-approved drugs for TBI, but existing approaches rely on delivering micro- and macromolecular treatments, which are constrained by the BBB, poor retention, off-target toxicity, and the complex pathology of TBI. Therefore, there is a demand for innovative and alternative therapeutics with effective delivery tactics for the diagnosis and treatment of TBI. Tissue engineering, which includes the use of biomaterials, is one such alternative approach. Biomaterials, such as hydrogels, including self-assembling peptides and electrospun nanofibers, can be used alone or in combination with neuronal stem cells to induce neurite outgrowth, the differentiation of human neural stem cells, and nerve gap bridging in TBI. This review examines the inclusion of biomaterials as potential treatments for TBI, including their types, synthesis, and mechanisms of action. This review also discusses the challenges faced by the use of biomaterials in TBI, including the development of biodegradable, biocompatible, and mechanically flexible biomaterials and, if combined with stem cells, the survival rate of the transplanted stem cells. A better understanding of the mechanisms and drawbacks of these novel therapeutic approaches will help to guide the design of future TBI therapies.