Tag: pubmed

J Cardiovasc Med (Hagerstown). 2025 Apr 7. doi: 10.2459/JCM.0000000000001719. Online ahead of print.

ABSTRACT

AIMS: While timely reperfusion is known to reduce mortality, the extent to which the severity of heart failure, as classified by the Killip system, influences treatment delays remains unclear. Our study aims to address the existing gap in evidence regarding the relationship between Killip classification at presentation and treatment times in ST-elevation myocardial infarction (STEMI) patients.

METHODS: We conducted a correlative analysis using data from patients treated in our hospital and enrolled in the FITT-STEMI Register from 2009 to 2022. We focused on the relation of treatment times allocating patients into the four Killip classes and used an ANOVA test (significance level: P < 0.05). Killip class and intrahospital mortality were studied via binary logistic regression.

RESULTS: In total, 1264 patients were identified. Door-to-balloon time among Killip I patients was 54 (±35) min (mean ± SD) and 53 (±26) min among Killip II and prolonged up to 77.5 (±46) min for class III and 79.7 (±45) min for class IV (overall P-value < 0.001). This remained statistically significant even after the exclusion of patients with out-of-hospital cardiac arrest (OHCA) (overall P-value: <0.001).Post hoc analysis showed a significant difference between Killip II and III classes for both all-comers (P = 0.014) as well as after the exclusion of OHCA patients (P = 0.012).Intrahospital mortality increased from <5% for classes I and II up to 10.3% for class III and 35.4% for class IV.

CONCLUSION: The severity of heart failure among STEMI patients significantly affects the duration of treatment times. Patients presenting with Killip class III and IV demonstrate high intrahospital mortality rates.

PMID:40203294 | DOI:10.2459/JCM.0000000000001719

PLoS One. 2025 Apr 9;20(4):e0316648. doi: 10.1371/journal.pone.0316648. eCollection 2025.

ABSTRACT

Active tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) bacteria and is characterized by multiple phases of infection, leading to difficulty in diagnosing and treating infected individuals. Patients with latent tuberculosis infection (LTBI) can reactivate to the active phase of infection following perturbation of the dynamic bacterial and immunological equilibrium, which can potentially lead to further Mtb transmission. However, current diagnostics often lack specificity for LTBI and do not inform on TB reactivation risk. We hypothesized that immune profiling readily available QuantiFERON-TB Gold Plus (QFT) plasma supernatant samples could improve LTBI diagnostics and infer risk of TB reactivation. We applied a whispering gallery mode, silicon photonic microring resonator biosensor platform to simultaneously quantify thirteen host proteins in QFT-stimulated plasma samples. Using machine learning algorithms, the biomarker concentrations were used to classify patients into relevant clinical bins for LTBI diagnosis or TB reactivation risk based on clinical evaluation at the time of sample collection. We report accuracies of over 90% for stratifying LTBI + from LTBI- patients and accuracies reaching over 80% for classifying LTBI + patients as being at high or low risk of reactivation. Our results suggest a strong reliance on a subset of biomarkers from the multiplexed assay, specifically IP-10 for LTBI classification and IL-10 and IL-2 for TB reactivation risk assessment. Taken together, this work introduces a 45-minute, multiplexed biomarker assay into the current TB diagnostic workflow and provides a single method capable of classifying patients by LTBI status and TB reactivation risk, which has the potential to improve diagnostic evaluations, personalize treatment and management plans, and optimize targeted preventive strategies in Mtb infections.

PMID:40203284 | DOI:10.1371/journal.pone.0316648

Aesthet Surg J. 2025 Apr 9:sjaf059. doi: 10.1093/asj/sjaf059. Online ahead of print.

ABSTRACT

Buttock augmentation has become one of the most sought-after cosmetic procedures, but concerns over fat embolism-related fatalities have raised significant safety issues. Guidelines emphasize that fat grafting should remain in the subcutaneous layer, avoiding intramuscular injection. This systematic review and meta-analysis assess the efficacy and safety of ultrasound-guided gluteal fat grafting. A systematic search of PubMed, Cochrane Central Register of Controlled Trials, and Embase was conducted until July 2024, analyzing patient satisfaction, complication rates, mortality, fat embolism, fat necrosis, infection, and seroma. Statistical analyses, including the Freeman-Tukey Double Arcsine Transformation, were performed using R version 4.1.2. Four studies with a total of 6,235 female patients (mean age 34 years, BMI 30.1 kg/m²) met the inclusion criteria. The pooled analysis showed no reported mortality (0.00 per 100, 95% CI: 0.00-0.00) or fat embolism (0.00 per 100, 95% CI: 0.00-0.00). Minor complications occurred at a rate of 6.32 per 100 (95% CI: 3.23-10.27), with seroma at 2.94 per 100 (95% CI: 0.97-5.75), infection at 0.23 per 100 (95% CI: 0.00-0.96), and fat necrosis at 0.09 per 100 (95% CI: 0.01-0.23; I² = 0). The findings indicate that ultrasound-guided gluteal fat grafting is associated with low complication rates and no reported serious adverse events such as death or fat embolism, reinforcing its role as a safer technique for buttock augmentation.

PMID:40203280 | DOI:10.1093/asj/sjaf059

PLoS One. 2025 Apr 9;20(4):e0319074. doi: 10.1371/journal.pone.0319074. eCollection 2025.

ABSTRACT

Causal mediation analysis decomposes the total effect of an exposure on an outcome into: 1. the indirect effect through a mediator and 2. the remaining “direct” effect through all other pathways. When the outcome is a time-to-event/survival time, censoring makes identifying the indirect and direct effects on the expected value scale untenable. We propose a semi-parametric estimator of the indirect and direct effects on the restricted mean survival time (RMST) scale using the pseudo-value approach for estimating conditional RMSTs. The pseudo-value approach is generalizable to various forms of outcome censoring. We demonstrate the use of the pseudo-value based estimator to right and interval censored data. Our estimator applies to any set of identification assumptions that lead to the Mediation Formula, including natural, organic, randomized and separable indirect and direct effects. A simulation study demonstrates the performance of the estimators for right and interval censored outcomes under various scenarios. The methodology is applied to an HIV cure example with the intention of estimating the indirect effect of a putative treatment on time-to-viral rebound mediated through the viral reservoir.

PMID:40203275 | DOI:10.1371/journal.pone.0319074

Am J Public Health. 2025 May;115(5):716-725. doi: 10.2105/AJPH.2024.307964.

ABSTRACT

Objectives. To understand the landscape of the nonfederal governmental public health workforce and to identify replicable methods for future enumerations. Methods. This enumeration of the state and local public health workforce was conducted from 2023 to 2024 and triangulated the National Association of County and City Health Officials (NACCHO) Profile 2022 and the Association of State and Territorial Health Officials (ASTHO) Profile 2022. We utilized Public Health Workforce Interests and Needs Survey (PH WINS) data from 2021 to assess demographic distributions across Department of Health and Human Services (HHS) regions in the United States. Results. A total of 239 000 staff were employed in state and local health departments in 2022, a 2% increase since 2012. Sixteen states-including 6 in the Southeast-lost staff relative to population growth. Conclusions. An uneven landscape of public health workforce density reflects chronic underinvestment in public health. The process of enumeration itself was also fraught with pitfalls and data limitations. Public Health Implications. We recommend building on federal investments to develop dedicated funding streams for state and local public health. We also recommend amending federal efforts around enumeration to include governmental public health at all levels. (Am J Public Health. 2025;115(5):716-725. https://doi.org/10.2105/AJPH.2024.307964).

PMID:40203264 | DOI:10.2105/AJPH.2024.307964

Am J Public Health. 2025 May;115(5):673-677. doi: 10.2105/AJPH.2025.308031.

NO ABSTRACT

PMID:40203261 | DOI:10.2105/AJPH.2025.308031

Am J Public Health. 2025 May;115(5):689-692. doi: 10.2105/AJPH.2025.308009.

ABSTRACT

Increasing numbers of individuals experiencing unsheltered homelessness and substance use disorder are living in tent encampments in cities across the United States. In response, the City of Boston, Massachusetts employed a public health approach comprising four implementation components: centralized leadership with cross-agency collaboration, creation of low-threshold spaces, person-centered engagement, and stakeholder-driven long-term planning. These steps led to the equitable transition of unsheltered individuals into harm reduction spaces and formed the foundation for future planning and encampment response in the city. (Am J Public Health. 2025;115(5):689-692. https://doi.org/10.2105/AJPH.2025.308009).

PMID:40203250 | DOI:10.2105/AJPH.2025.308009

Annu Rev Biomed Data Sci. 2025 Apr 9. doi: 10.1146/annurev-biodatasci-103123-095750. Online ahead of print.

ABSTRACT

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data’s increasing complexity-as text or images in electronic health records-calls for complex models, such as machine learning. Here, we review how machine learning can be used to inform decisions for individualized interventions, a causal question. Going from prediction to causal effects is challenging, as no individual is seen as both treated and not. We detail how some data can support some causal claims and how to build causal estimators with machine learning. Beyond variable selection to adjust for confounding bias, we cover the broader notions of study design that make or break causal inference. As the problems span across diverse scientific communities, we use didactic yet statistically precise formulations to bridge machine learning to epidemiology.

PMID:40203240 | DOI:10.1146/annurev-biodatasci-103123-095750

Annu Rev Biomed Data Sci. 2025 Apr 9. doi: 10.1146/annurev-biodatasci-090624-022951. Online ahead of print.

ABSTRACT

In recent decades, there has been an explosion of data streams spanning the entire spectrum of biomedicine, opening novel opportunities to tackle biological and medical research questions, increasing our ability to provide effective and efficient health care. In parallel, augmented computational power has allowed the development and deployment of quantitative approaches at unprecedented scales. To effectively take advantage of this progress, it is important to invest in the training of a new generation of biomedical data scientists. Designing a graduate curriculum in the backdrop of a rapidly changing landscape of data, methods, and computing power demands flexibility and openness to adaptation. At the same time, we strive to ensure that the students acquire foundational competencies that might fuel productive and evolving careers, without being constrained to and defined by a niche trendy topic. We offer here a view of graduate training in biomedical data science from the standpoint of our experience at Stanford University. We conclude with a series of open challenges, the answers to which we believe will shape training in biomedical data science.

PMID:40203230 | DOI:10.1146/annurev-biodatasci-090624-022951

PLOS Glob Public Health. 2025 Apr 9;5(4):e0004394. doi: 10.1371/journal.pgph.0004394. eCollection 2025.

ABSTRACT

BACKGROUND: The retention of under-5 mortality (U5M) in various ramifications has dire policy implications. The varying impacts of this inequality is very important and has been researched in many rural-urban settings. In spite of many studies that have examined rural-urban inequalities, very little has been researched with respect to low middle-income countries. In this study, we utilized an innovative statistical method to examine and explain the socio-economic determinants and rural-urban differences of mortality in some selected low- and middle-income (LMIC) countries.

METHODS: Using secondary data from the Demographic Health Survey (DHS), we utilized a Fairlie decomposition analysis to enumerate the differences amongst under-5 populations across 59 low-middle income countries in four continents. Death of any child within 0 – 59 months of life was our dependent variable while some selected individual and neighborhood factors constituted the explanatory variables.

RESULTS: Study findings revealed significant pro-rural and pro-non-rural inequities across the 59 countries. Pro-rural inequities were more commonly found in the African regions. Except for the Maldives, pro-non-rural inequities were largely associated in the remaining four continents. Some factors, unemployed status, ever married or single status, female household head, insurance cover, unimproved water sources, clean fuel were associated with a higher risk of Under-5 mortality.

CONCLUSION: The results from this study are pertinent to health system reforms needed to tackle the menace of under-5 mortalities in LMICs and worldwide. Consolidation of existing maternal and child health programs supported by a resolute and firm re-evaluation of political will considerably help to control the surge of U5MR in the countries studied.

PMID:40203228 | DOI:10.1371/journal.pgph.0004394