Noor Ul Ain Saleem

Background and Aims: Diabetes mellitus (DM) is a major global health concern, particularly in resource‐constrained settings. Incarcerated populations in low‐ and middle‐income countries (LMICs) face increased health risks and limited care access, yet remain understudied. No prior systematic review has synthesized prevalence data for this group. This review aims to estimate diabetes prevalence among incarcerated individuals in LMICs. Methods: We conducted a systematic review and meta‐analysis per PRISMA and GATHER guidelines. PubMed, Scopus, Embase, Web of Science and regional databases were searched till May 2025. Random‐effects models with Freeman‐Tukey double arcsine transformation were applied in R (version 4.5.1). Publication bias was assessed using DOI plots. Study quality was evaluated with the Joanna Briggs Institute tool, and certainty of evidence was appraised using an adapted GRADE approach. Results: Twenty‐three studies (107,473 participants) were included. Mean participant ages varied from 29 ± 9.6 to 40.5 ± 9.8 years. The tentative pooled estimate of diabetes prevalence was 3.7% (95% CI: [2.5%–5.1%], Prediction Interval [0%–12.2%]), with high heterogeneity ( I ² = 95.4%). The pooled prevalence estimate was highest in Asia (5.2%), particularly Pakistan (9.8%), and lowest in South America (2.3%). The DOI plot revealed major asymmetry, indicating a high possibility of publication bias. Overall certainty of evidence was rated as very low for diabetes prevalence due to risk of bias and publication bias. Interpretation of the pooled estimate is limited by substantial heterogeneity and limitations in the primary studies, including differences in diabetes ascertainment methods. Conclusion: Diabetes is an important health concern among incarcerated populations in LMICs. Differences in diabetes ascertainment methods, participant characteristics and correctional healthcare systems may have potentially contributed to the variability of the pooled estimates. A hypothesis is that there may also be potentially limited detection or underdiagnosis which possibly could have contributed to the observed prevalence estimates.

Background: Amiodarone and lidocaine are antiarrhythmic agents recommended for advanced cardiac life support in shockable ventricular arrhythmias during cardiac arrest, yet their comparative effectiveness in in-hospital (IHCA) and out-of-hospital cardiac arrest (OHCA) remains uncertain. Methods: From 930 records identified in PubMed, ScienceDirect, Embase, and Cochrane Library, 14 studies were included. Outcomes were analyzed using random-effects meta-analysis in R with IHCA and OHCA subgroup analyses. Bayesian meta-analysis using the bayesmeta package assessed result credibility. Results: Amiodarone did not improve return of spontaneous circulation (R0SC) compared with lidocaine (Risk ratio (RR) 0.96, p = 0.40). In IHCA, amiodarone was associated with lower survival to hospital discharge (RR 0.88, p < 0.0001), lower 24 h survival (RR 0.93, p < 0.0001), worse neurological recovery (RR 0.83, p < 0.0001), and longer arrest duration (Mean difference (MD) 7.46 min, p = 0.002). Termination of shockable rhythms favored lidocaine only after sensitivity analysis (RR 0.51, p = 0.02). In OHCA, amiodarone did not improve survival to discharge (RR 0.93, p = 0.44), while return of spontaneous circulation favored lidocaine after sensitivity analysis (RR 0.88, p = 0.002). No consistent benefit was observed across outcomes. Bayesian analysis showed overlapping credible intervals and low probabilities of benefit, indicating no clear superiority. Conclusions: No survival difference was observed between amiodarone and lidocaine arrest. Although lidocaine showed more favorable in-hospital outcomes, long-term survival was similar, suggesting agents may be selected based on availability and practice.

Background: The gut microbiome has long been recognized as a critical regulator of host metabolism, yet the fungal component the mycobiome remains comparatively understudied. Emerging evidence implicates gut fungi in endocrine and metabolic homeostasis through distinct mechanisms including bile acid modification, immune modulation, and cross-kingdom interactions with bacteria. Objective: This narrative review synthesizes current evidence on the role of the gut mycobiome in obesity, insulin resistance (IR), and polyendocrine metabolic ovarian syndrome (PMOS), with emphasis on mechanistic pathways, clinical implications, and therapeutic potential. Key Findings: The healthy gut mycobiome is characterized by dominance of Candida , Saccharomyces , Cladosporium , and Malassezia genera, though considerable variability exists based on geography, diet, age, and sex. Fungal dysbiosis typically manifesting as reduced diversity and overgrowth of pro-inflammatory taxa such as Candida albicans has been consistently documented in obesity, IR, and PMOS. Mechanistically, fungi influence host metabolism through: (1) production of bioactive metabolites (short-chain fatty acids, tryptophan derivatives); (2) modulation of bile acid pools and farnesoid X receptor (FXR) signaling; (3) immune priming via β-glucan-mediated Th17 responses; and (4) regulation of gut barrier integrity through fungal-bacterial crosstalk. In PMOS specifically, mycobiome alterations correlate with hyperandrogenemia, insulin resistance, and chronic inflammation, with emerging evidence implicating the gut-brain-ovary axis. Therapeutic strategies under investigation include probiofungals ( Saccharomyces boulardii ), dietary modulation, and selective antifungal interventions. Conclusion: The gut mycobiome represents a novel, modifiable determinant of metabolic health with particular relevance to obesity, IR, and PMOS. Future research priorities include longitudinal cohort studies, mechanistic validation in gnotobiotic models, and randomized controlled trials of mycobiome-targeted interventions. Integration of mycobiome assessment into clinical practice may enable personalized approaches to metabolic disease management.

Background and objective: The global rise in the incidence of diabetes mellitus necessitates innovative management strategies to mitigate the severity of the disease. The mainstay of diabetes treatment is still insulin therapy, although there are drawbacks, such as the need to maintain ideal glycemic control while lowering the risk of hypoglycemia and user fatigue. The development, clinical usefulness, and constraints of insulin delivery systems are examined in this narrative review, with particular attention to sensor-augmented and closed-loop technologies. Methods: A thorough electronic database search was conducted to obtain pertinent data on major insulin delivery devices, including sensor-augmented and closed-loop systems. We studied advancements in insulin delivery systems over time and their impact on the management of diabetes mellitus. We thoroughly examined both qualitative and quantitative data to summarize our conclusions. Key content and findings: Closed-loop insulin delivery systems, which combine insulin pumps, advanced control algorithms, and continuous glucose monitoring, are the next step forward in the treatment of diabetes. These systems autonomously regulate blood glucose levels, reducing the need for user intervention. Closed-loop devices outperform sensor-augmented pump (SAP) treatment in reducing hypoglycemia, extending the time-in-range, and lowering self-management responsibilities. In order to provide real-time control, closed-loop systems dynamically modify insulin delivery in response to glucose variations by using algorithms powered by artificial intelligence. Because SAP therapy depends more on user input and may not be as successful in providing consistent glycemic control, this capability helps overcome its drawbacks. Further improving overall safety and efficacy, the closed-loop technique decreases blood glucose variability. Conclusion: The two primary developments in diabetes care are sensor-augmented and closed-loop insulin devices, which offer better glucose control and lower hypoglycemia rates compared to traditional therapies. Closed-loop insulin delivery systems seem to be a more clinically successful option for controlling diabetes mellitus than SAP therapy.

Gastrointestinal obstruction is an important cause of morbidity and mortality in the United States, but full descriptions of time trends and disparities are poorly defined. This study aims to examine trends in mortality from gastrointestinal obstruction over a span of 25 years in various demographics and geographic locations within the United States from 1999 to 2023. We conducted a retrospective analysis using CDC WONDER multiple cause-of-death data. Deaths were identified when gastrointestinal obstruction was listed as either the underlying or a contributing cause of death. Age-adjusted mortality rates (AAMR) per 1,00,000 population were calculated and stratified by sex, race/ethnicity, census region, urbanization level, and state. Joinpoint regression analysis identified inflection points and estimated annual percent changes (APC) and average annual percent changes. Between 1999 and 2023, 5,20,045 deaths from gastrointestinal obstruction were identified. Overall, AAMR declined from 10.76 per 1,00,000 in 1999 to 8.56 in 2014, followed by an increase to 9.95 in 2023. Joinpoint analysis revealed 3 distinct phases: significant decline (1999–2012, APC −1.78%, P < .001), plateau (2012–2018, APC −0.09%, P = .862), and significant increase (2018–2023, APC 3.43%, P < .001). Males consistently exhibited higher mortality than females throughout the study period (2023: 10.44 vs 9.59 per 1,00,000). Substantial racial disparities persisted, with non-Hispanic Black individuals demonstrating the highest AAMR (12.95 in 2023), 1.32-fold higher than non-Hispanic white individuals. Rural areas maintained consistently higher mortality than urban areas; data from 2020 are presented for urbanization comparisons, as this was the most recent year with complete rural-urban classification data available from CDC WONDER at the time of analysis (2020: 10.99 vs 8.54 per 1,00,000). State-level age-adjusted mortality rates varied nearly 2-fold, ranging from 6.86 in Florida to 13.15 in Vermont. Most deaths occurred in medical facilities (66.8%). After a decade of declining gastrointestinal obstruction mortality, rates have increased significantly since 2018 across nearly all demographic groups. Persistent disparities by race, geography, and urbanization underscore the need for targeted interventions and equitable access to timely surgical care. The reversal of favorable trends warrants urgent investigation into contributing factors and implementation of prevention strategies.

Introduction Aortic stenosis (AS) and hypertension (HTN) frequently co-exist in older adults, worsening disease progression and increasing mortality risk compared to either condition alone. Aim To examine mortality trends and demographic-geographic disparities from comorbid AS and HTN in the U.S. from 1999–2020. Methods Data were extracted from the CDC WONDER Multiple Cause of Death database. Crude and age-adjusted mortality rates (CMRs, AAMRs) per 100,000 were calculated. Joinpoint regression estimated annual (APC) and average annual percent change (AAPC). Trends were projected to 2030 using an autoregressive integrated moving average (ARIMA) model. Results Between 1999 and 2020, 100,507 deaths were attributed to comorbid AS and HTN. The overall AAMR significantly increased (AAPC: 5.01%), with a steep rise from 1999–2001 (APC: 21.95%) and consistent increase from 2001–2020 (APC: 3.46%). AAMRs were higher in men (11.6) than women (10.1). NH Whites had the highest AAMR (11.4), followed by Hispanic/Latino and NH Black (7.4), and NH Asian/Pacific Islanders (6.2). AAMRs also varied by region (West: 13.5; Midwest: 11.5; Northeast: 10.7; South: 8.6). Most deaths occurred in medical facilities (34.4%) or at home (31.5%). Vermont, Minnesota, New Hampshire, California, and Nebraska had the highest AAMRs. Non-metropolitan areas had higher AAMRs than metropolitan (11.4 vs. 10.6). Adults ≥ 85 years had the highest CMR (53.6). ARIMA projected AAMR to reach 25.4 by 2030 (22.0 in females, 28.3 in males). Conclusion Mortality from concurrent AS and HTN is rising, with notable demographic and geographic disparities. Early detection, integrated care, and targeted public health strategies are critical. Graphical Abstract The alternative text for this image may have been generated using AI.

Pediatric trauma is a leading cause of childhood mortality worldwide, with a disproportionate burden in low- and middle-income countries (LMICs) where up t

Purpose of Review: Pediatric airway management in the Emergency Department is a critical intervention with high stakes and significant complication rates, including adverse events in 16–25% of intubations and first-attempt success rates of approximately 75%. This review examines the application of advanced digital technologies—artificial intelligence (AI), machine learning (ML), computer vision, deep learning, and predictive analytics—to address key gaps: unreliable prediction of difficult pediatric airways (with first-pass failure rates of 30–40%), variability in device selection and technique, and the absence of robust real-time decision support for clinicians. Recent Findings: Emerging AI tools demonstrate promise across three phases of airway management. For prediction, machine learning models analyzing patient data can identify difficult airways with accuracy approaching that of expert clinicians. During performance, deep learning algorithms applied to video laryngoscopy automatically recognize anatomical landmarks (e.g., vocal cords) and guide laryngoscope positioning, improving visualization and potentially first-attempt success. For monitoring, AI-based systems analyze real-time physiological signals - including heart rate, oxygen saturation, and respiratory patterns - to detect subtle signs of airway difficulty or patient decompensation, showing improved hypoxia detection compared to standard monitoring. Summary: Integrating AI and advanced technologies could enable a future of “precision airway management,” tailoring interventions to individual patient anatomy, physiology, and risk factors to reduce failed intubations and complications. Realizing this potential requires ensuring equitable access to avoid disparities between centers, mitigating algorithmic bias, and fundamentally incorporating AI education—covering advanced airway technologies and human-AI collaboration—into Pediatric Emergency Medicine and anesthesia training programs.

Ozempic (semaglutide) was first authorized to treat type 2 diabetes, but because of its appetite-suppressing properties, it has become popular off-label for weight loss. According to clinical investigations, GLP-1 significantly decreases blood sugar and helps people lose weight. Approximately 15 million persons in the United States are currently taking GLP-1 drugs. Off-label use is becoming more common, which raises worries about potential health hazards and significant side effects such as non-arteritic anterior ischemic optic neuropathy (NAION). According to a study, 6.7% of people who were overweight and 8.9% of those with diabetes who took semaglutide had NAION. The FDA issued a warning regarding counterfeit and compounded products while approving semaglutide for weight loss under the Wegovy ^® brand. Unsupervised usage and problems with diabetes accessibility raise ethical questions.