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Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Abstract Background Pathogenesis of Scrub typhus (ST), a tick-borne acute zoonotic disease caused by the bacterium Orientia tsutsugamushi, involves infection of vascular endothelial cells, triggering a potent cytokine host response that causes organ damage. Methods Prospective study in a tertiary care hospital in North-west India in 30 cases of Scrub typhus. Measurements of cytokines IL-17A, INF-γ, TNF-α, IL-10, IL-6, IL-4, and IL-2 were carried out at admission in all 30 patients and a control group of 10 healthy persons using BDTM Cytometric Bead Array, BD Biosciences, San Diego, California, USA. Results Mean age of the entire cohort was 36 years, and the male-female ratio was 2:1. The most common symptoms were dyspnea 87%, altered sensorium 23%, abdominal pain 20%, decreased urine output 10% and myalgia in 10%. On clinical examination, eschar was present in 40%. Investigations revealed anemia in 80% cases, leukocytosis in 46.6%, thrombocytopenia in 90%, jaundice in 53.3% and elevated serum transaminase levels in 84% cases. Of all complications, acute respiratory distress syndrome occurred in 90% patients, acute kidney injury in 37% patients, acute meningoencephalitis syndrome in 30%, and myocarditis in 23%. The mean APACHE 2 score of the entire cohort was 12 . Multi-organ dysfunction syndrome (MODS) was seen in 67% of cases, and the overall mortality rate was 20%. On Fisher's Exact Correlation analysis, presentation with meningoencephalitis, myocarditis, systemic acidosis, and a high APACHE 2 score correlated with mortality. The serum levels of cytokines were elevated in all patients compared to the healthy control group (Table 1), but did not significantly differ between survivors and non-survivors. Furthermore, the elevated levels of TNF-α (p=0.004) and IL-10 (p=0.029) strongly correlated with multi-organ dysfunction syndrome (MODS). Conclusion In Scrub Typhus, the strong correlation of serum levels of TNF-α and IL-10 with multi-organ dysfunction syndrome (MODS) suggests their involvement in disease progression. The high prevalence of acute respiratory distress syndrome and MODS, alongside a mortality rate of 20%, highlights the critical nature of scrub typhus and the necessity for early recognition of complicated disease and intervention. Disclosures All Authors: No reported disclosures

Abstract Superior mesenteric artery (SMA) thrombosis is rare yet critical illness that can result in lethal bowel ischemia and infarction. This report details a case of SMA thrombosis with associated superior mesenteric vein (SMV) narrowing, secondary to mesenteric torsion in a 26-year-old female with history of Roux-en-Y gastric bypass surgery. The patient’s presenting symptoms were severe epigastric pain, vomiting, and significant anemia. Imaging confirmed SMA thrombosis with early signs of bowel ischemia. Interventional radiology promptly performed an SMA angiogram and initiated thrombolysis with a tenecteplase infusion, which led to near-complete resolution of the thrombus. This case draws attention to the necessity for early diagnosis and swift management of SMA thrombosis, particularly in post-surgical patients with altered gastrointestinal anatomy. The resolution of this case demonstrates the value of a collaborative care, combining interventional radiology and general surgery to prevent bowel ischemia.

PURPOSE The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning–/deep learning–based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

Background: Telehealth use was previously limited by strict regulations and financial constraints. However, the pandemic prompted some countries to ease their telehealth laws. Thus, we conducted a bibliometric analysis and network visualization to gauge changes in tele-neuropsychology (Tele-NP) and tele-mental Health (Tele-MH) trends with the onset of the COVID-19 pandemic. Materials and methods: The authors conducted a literature search on SCOPUS and included relevant articles pertaining to Tele-NP and Tele-MH, which were published before the COVID-19 pandemic (2017–2019) and during the COVID-19 pandemic (2020–2022). The authors presented publication patterns on psychiatric disorders, mode of administration, journals, active countries, authors, affiliations, funding sponsors, keywords, publication, and citation output. Results: Three hundred forty-one articles were included in our study with 80 articles before the pandemic and 261 during the pandemic. Our analysis revealed the greatest increase in publications and citations output was from the year 2020 to 2021. A greater number of journals published tele-NP and tele-MH-related research, with higher frequency, during the COVID-19 pandemic with Telemedicine and E-health leading the way. WHO regional analysis revealed that the Region of the Americas (AMRO) was the leading contributor in terms of publications. Harvard Medical Center remained the number one contributor both before and during the COVID-19- pandemic. Publications by funding sponsors, particularly by those that were US-based, increased twice as much during the pandemic. Conclusion: The increase in research output following the COVID-19 outbreak reflects the growing interest and relevance of telemedicine for the delivery of mental health services.

Background: Soluble urokinase plasminogen activator receptor (suPAR) is an inflammatory biomarker that is used to predict mortality, readmission, early discharge, and LOS, thus, serves as a useful tool for ED physicians. Our study aims to analyze the efficacy of suPAR in predicting these prognostic markers in ED. Methods: We performed a comprehensive search on 6 databases from the inception to 30th November 2022, to select the following eligibility criteria; a) observation or triage trial studies investigating the role of suPAR levels in predicting: 30 day and 90-day mortality, 30-day readmission, early discharge (within 24hr), and LOS in patients coming to AMU. Results: A total of 13 studies were included, with a population size of 35,178, of which 52.9% were female with a mean age of 62.93 years. Increased risk of 30-day mortality (RR = 10.52; 95% CI = 4.82–22.95; I2 = 38%; P < .00001), and risk of 90-day mortality (RR = 5.76; 95% CI = 3.35–9.91; I2 = 36%; P < .00001) was observed in high suPAR patients. However, a slightly increased risk was observed for 30-day readmission (RR = 1.50; 95% CI = 1.16–1.94; I2 = 54%; P = .002). More people were discharged within 24hr in the low suPAR level group compared to high suPAR group (RR = 0.46; 95% CI = 0.40–0.53; I2 = 41%; P < .00001). LOS was thrice as long in high suPAR level patients than in patients with low suPAR (WMD = 3.20; 95% CI = 1.84–4.56; I2 = 99%; P < .00001). Conclusion: suPAR is proven to be a significant marker in predicting 30-day and 90-day mortality in ED patients.

AbstractBackgroundRecent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk‐prediction model (called SPLC‐RAT) was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. The predictive performance of SPLC‐RAT was evaluated in a hospital‐based cohort of lung cancer survivors.MethodsThe authors analyzed data from 8448 ever‐smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997–2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC‐RAT and further explored the potential of improving SPLC detection through risk model‐based surveillance using SPLC‐RAT versus existing clinical surveillance guidelines.ResultsOf 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person‐years. The application of SPLC‐RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10‐year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC‐RAT development cohort), the observed SPLC incidence was significantly elevated in the high‐risk versus low‐risk subgroup (13.1% vs. 1.1%, p < 1 × 10–6). The risk‐based surveillance through SPLC‐RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow‐ups needed to detect one SPLC (162 vs. 202).ConclusionIn a large, hospital‐based cohort, the authors validated the predictive performance of SPLC‐RAT in identifying high‐risk survivors of SPLC and showed its potential to improve SPLC detection through risk‐based surveillance.Plain Language Summary Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence‐based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk‐prediction model was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. Using a large, real‐world cohort of lung cancer survivors, we showed the high predictive accuracy and risk‐stratification ability of the SPLC risk‐prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model‐based surveillance strategies compared to the existing consensus‐based clinical guidelines, including the National Comprehensive Cancer Network.

We investigated the link between enhancement of SI (by hyperinsulinemic–euglycemic clamp) and muscle metabolites after 12 weeks of aerobic (high-intensity interval training [HIIT]), resistance training (RT), or combined training (CT) exercise in 52 lean healthy individuals. Muscle RNA sequencing revealed a significant association between SI after both HIIT and RT and the branched-chain amino acid (BCAA) metabolic pathway. Concurrently with increased expression and activity of branched-chain ketoacid dehydrogenase enzyme, many muscle amino metabolites, including BCAAs, glutamate, phenylalanine, aspartate, asparagine, methionine, and γ-aminobutyric acid, increased with HIIT, supporting the substantial impact of HIIT on amino acid metabolism. Short-chain C3 and C5 acylcarnitines were reduced in muscle with all three training modes, but unlike RT, both HIIT and CT increased tricarboxylic acid metabolites and cardiolipins, supporting greater mitochondrial activity with aerobic training. Conversely, RT and CT increased more plasma membrane phospholipids than HIIT, suggesting a resistance exercise effect on cellular membrane protection against environmental damage. Sex and age contributed modestly to the exercise-induced changes in metabolites and their association with cardiometabolic parameters. Integrated transcriptomic and metabolomic analyses suggest various clusters of genes and metabolites are involved in distinct effects of HIIT, RT, and CT. These distinct metabolic signatures of different exercise modes independently link each type of exercise training to improved SI and cardiometabolic risk. Article Highlights We aimed to understand the link between skeletal muscle metabolites and cardiometabolic health after exercise training. Although aerobic, resistance, and combined exercise training each enhance muscle insulin sensitivity as well as other cardiometabolic parameters, they disparately alter amino and citric acid metabolites as well as the lipidome, linking these metabolomic changes independently to the improvement of cardiometabolic risks with each exercise training mode. These findings reveal an important layer of the unique exercise mode–dependent changes in muscle metabolism, which may eventually lead to more informed exercise prescription for improving SI.