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Prenatal Exposure to Metals Is Associated with Placental Decelerated Epigenetic Gestational Age in a Sex-Dependent Manner in Infants Born Extremely Preterm
Get PDFPrenatal exposure to metals can influence fetal programming via DNA methylation and has been linked to adverse birth outcomes and long-term consequences. Epigenetic clocks estimate the biological age of a given tissue based on DNA methylation and are potential health biomarkers. This study leveraged the Extremely Low Gestational Age Newborn (ELGAN) study (n = 265) to evaluate associations between umbilical cord tissue concentrations of 11 metals as single exposures as well as mixtures in relation to (1) placental epigenetic gestational age acceleration (eGAA) and the (2) methylation status of the Robust Placental Clock (RPC) CpGs. Linear mixed effect regression models were stratified by infant sex. Both copper (Cu) and manganese (Mn) were significantly associated with a decelerated placental eGA of −0.98 (95% confidence interval (CI): −1.89, −0.07) and −0.90 weeks (95% CI: −1.78, −0.01), respectively, in male infants. Cu and Mn levels were also associated with methylation at RPC CpGs within genes related to processes including energy homeostasis and inflammatory response in placenta. Overall, these findings suggest that prenatal exposures to Cu and Mn impact placental eGAA in a sex-dependent manner in ELGANs, and future work could examine eGAA as a potential mechanism mediating in utero metal exposures and later life consequences
Correction to “Spatiotemporal patterns of Lyme disease in North Carolina: 2010–2020”
Get PDFCorrection to “Spatiotemporal patterns of Lyme disease in North Carolina: 2010–2020
Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations
Get PDFBackground/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables non-invasive, layer-specific visualization of the retinal vasculature, facilitating more precise identification of early microvascular changes. Concurrently, advancements in artificial intelligence (AI), particularly deep learning (DL) architectures such as convolutional neural networks (CNNs), attention-based models, and Vision Transformers (ViTs), have revolutionized image analysis. These AI-driven tools substantially enhance the sensitivity, specificity, and interpretability of DR screening. Methods: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. Results: A total of 32 studies were included in this systematic review. In comparison to conventional ML techniques, our results indicated that DL algorithms significantly improve the accuracy, sensitivity, and specificity of DR screening. Multi-branch CNNs, ensemble architectures, and ViTs were among the sophisticated models with remarkable performance metrics. Several studies reported that accuracy and area under the curve (AUC) values were higher than 99%. Conclusions: This systematic review underscores the transformative potential of integrating advanced DL and machine learning (ML) algorithms with OCTA imaging for DR screening. By synthesizing evidence from 32 studies, we highlight the unique capabilities of AI-OCTA systems in improving diagnostic accuracy, enabling early detection, and streamlining clinical workflows. These advancements promise to enhance patient management by facilitating timely interventions and reducing the burden of DR-related vision loss. Furthermore, this review provides critical recommendations for clinical practice, emphasizing the need for robust validation, ethical considerations, and equitable implementation to ensure the widespread adoption of AI-OCTA technologies. Future research should focus on multicenter studies, multimodal integration, and real-world validation to maximize the clinical impact of these innovative tools
Management approaches for primary hepatic lymphoma: 10 year institutional experience with comprehensive literature review
Get PDFPurpose/objective: Primary hepatic lymphomas (PHL) are an extremely rare form of non-Hodgkin Lymphoma (NHL) for which there are no established treatment guidelines, with available literature largely comprised of small case reports. Therefore, we evaluate our institutional experience treating PHL within the context of existing literature to better understand treatment modalities, role of radiotherapy (RT), and outcomes. Materials/methods: We conducted a single institutional retrospective study of all patients with PHL diagnosed from 2000-2021, defined as a biopsy-proven liver lesion in the absence of other lymphomatous solid organ involvement, except for concurrently diagnosed hepatosplenic lymphomas. Subgroup analysis was performed for diffuse large B-cell lymphoma (DLBCL) and indolent lymphomas, which included marginal zone lymphoma (MZL), Grade 1-2 follicular lymphoma (FL), and low-grade B-cell lymphoma (BCL), NOS. Univariable (UVA) and multivariable analysis (MVA) for overall survival (OS) were performed using the Cox proportional hazards model. A literature review was conducted using key words “liver”, “lymphoma”, and “treatment” to identify relevant literature. Results: We identified 30 patients with PHL within the institutional cohort and 192 patients from comprehensive literature review. Subgroup analysis of DLBCL included 15 patients. On MVA for OS, only ECOG score (p=0.02) and Lugano stage (p=0.04) remained significant. Subgroup analysis of the indolent lymphoma group included 9 patients. On MVA for OS, only age remained significant. Systemic therapy was the most common treatment modality overall (20 patients; 67%) with surgery, radiation and observation utilized in 4 patients (13%) each. Seventeen (57%) of patients were alive at the time of data collection, with 8 (27%) deceased and 5 (17%) lost to follow-up. Conclusion: PHL are an extremely rare subtype of NHL for which there is no clear treatment consensus. Primary hepatic DLBCL appears to be treated mostly with chemotherapy with good disease control. For indolent PHL, low-dose RT appears to have good overall disease control with minimal toxicity. Our RT data is limited by the short duration of follow-up for patients receiving RT compared to those who received chemotherapy, surgery or observation. However, our results are encouraging for the use of RT for appropriate patients with indolent PHL
The next SABV—stress as a biological variable
Get PDFThe 2015 policy to incorporate sex as a biological variable (SABV) enhanced biomedical research and allowed for better predictions to be made regarding clinical outcomes and environmental health risks. This review aims to make a case for the next SABV—stress as a biological variable. While the body is equipped to respond to acute stress, chronic stress can overwork physiologic systems, leading to allostatic load, or progressive wear and tear on the brain and body. Allostatic load has many implications on immune, cardiovascular, and metabolic function, and alters xenobiotic metabolism of environmental and pharmaceutical chemicals. However, historically disadvantaged communities and populations are at an increased risk of harm due to elevated exposure to psychosocial stressors and environmental pollutants. Therefore, the unique biological responses among populations that experience this double hit should be considered in toxicology risk assessments. Among current approaches, allostatic load measurements are optimal as a framework that captures health disparities and a tool that quantifies cumulative stress burdens that can be integrated into health data for better risk predictions
Urban Refugee Youth's Recommendations for Sexual and Mental Health Promotion: Qualitative Insights From Kampala, Uganda.
Get PDFRefugee youth in Kampala, Uganda, face a unique sexual reproductive health and mental health risk environment requiring focused interventions. Resource limitations and access barriers complicate the provision of relevant supports. Few studies have engaged refugee youth's recommendations for satisfying their sexual and mental health needs. This cross-sectional, qualitative study aimed to identify urban refugee youth's sexual and mental health promotion preferences. We administered a structured survey to refugee youth in Kampala (n = 54) between July and November 2023 using the qualitative data collection platform, Sensemaker and analyzed survey responses using inductive thematic analysis (ITA). Two themes emerged: health promotion and youth empowerment. Participants proposed sexual and mental health promotion recommendations directed at distinct stakeholder groups including policymakers/service providers and peers. There were several points of overlap between the sexual and mental health promotion recommendations, including calls for counseling services, health education, and employment opportunities. Youth empowerment was a central theme underwriting both sexual and mental health recommendations. By differentiating between recommendations directed at distinct stakeholder groups, this study identified opportunities for non-governmental actors to contribute to promoting the sexual and mental health of refugee youth in Kampala. Participant insights show how engagement with urban refugee youth's health promotion recommendations can empower youth and ensure that service design and delivery is consistent with their knowledge, needs, and preferences
IMPROVING CHILD FOOD SECURITY WITH SCHOOL MEAL PROGRAMS IN GRANVILLE AND VANCE COUNTIES, NORTH CAROLINA
Get PDFThis report is generated by the economic group for the commissioner’s office and examines the critical issue of child food insecurity in Granville and Vance Counties in North Carolina. The contextual analysis evaluates the current and historical socio-economic factors that contribute to food insecurity, a high-priority social determinant of health that has a long-lasting impact on children’s mental and physical health. A multifaceted approach was utilized for stakeholder mapping and engagement, analysis of past policies, and development of new potential policies. The design process utilized quality tools to develop the change idea, focusing on and enhancing the school meal program and enhancing local food access. The design process emphasized the feasibility, sustainability, and scalability of the school meal program by establishing a steering committee to oversee the implementation and iteration process. This comprehensive assessment aims to inform policy and practice to reduce child food insecurity and improve health outcomes.Master of Public Healt
Survival Prediction with Machine Learning: Addressing Unequal Probability of Selection in Survey Data
Get PDFAccurate all-cause mortality prediction is essential for assessing population health and guiding targeted public health interventions. Traditional survival models, such as the Cox proportional hazards model, are widely used for statistical inference but may potentially struggle with prediction due to their limited ability to capture nonlinear relationships and complex interactions. Machine learning approaches offer a promising alternative by providing greater flexibility in modeling complex survival data. However, their application to survey-based epidemiological studies is challenging, particularly when accounting for unequal probability of selection. Motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), this work explores the methodological challenges of applying machine learning to survival prediction under such conditions. We review existing mortality prediction models, discuss machine learning methods suitable for handling unequal selection probabilities, and propose a structured workflow for implementing these approaches. By addressing key methodological considerations, this work lays the foundation for improving survival prediction in studies with unequal sampling designs and supports the development of more generalizable risk prediction models
Six-Month Outcomes in the Long-Term Outcomes After the Multisystem Inflammatory Syndrome in Children Study
Get PDFMultisystem inflammatory syndrome in children (MIS-C) is a life-threatening complication of COVID-19 infection. Data on midterm outcomes are limited. To characterize the frequency and time course of cardiac dysfunction (left ventricular ejection fraction [LVEF] <55%), coronary artery aneurysms (z score ≥2.5), and noncardiac involvement through 6 months after MIS-C. This cohort study enrolled participants between March 2020 and January 2022 with a follow-up period of 2 years. Participants were recruited from 32 North American pediatric hospitals, and all participants met the 2020 Centers for Disease Control and Prevention case definition of MIS-C. MIS-C after COVID-19 infection. Outcomes included echocardiography core laboratory (ECL) assessments of LVEF and maximum coronary artery z scores (zMax); data collection on cardiac and noncardiac sequelae during hospitalization and at 2 weeks, 6 weeks, and 6 months after discharge; and age-appropriate Patient-Reported Outcomes Measurement Information Systems (PROMIS) Global Health Instruments at follow-up. Descriptive statistics, linear regression models, and Kaplan-Meier analysis were used. Of 1204 participants (median [IQR] age, 9.1 [5.6-12.7] years; 724 male [60.1%]), 325 self-identified with non-Hispanic Black race (27.0%) and 324 with Hispanic ethnicity (26.9%). A total of 548 of 1195 participants (45.9%) required vasoactive support, 17 of 1195 (1.4%) required extracorporeal membrane oxygenation, and 3 (0.3%) died during hospitalization. Of participants with echocardiograms reviewed by the ECL (n = 349 due to budget constraints), 131 of 322 (42.3%) had LVEF less than 55% during hospitalization; of those with follow-up, all but 1 normalized by 6 months. Black race (vs other/unknown race), higher C-reactive protein level, and abnormal troponin level were associated with lowest LVEF (estimate [SE], −3.09 [0.98]; R2 = 0.14; P =.002). Fifteen participants had coronary artery z scores of 2.5 or greater at any time point; 1 participant had a large/giant aneurysm. Of the 13 participants with z scores of 2.5 or greater during hospitalization, 12 (92.3%) had normalized by 6 months. Return to greater than 90% of pre–MIS-C health status (energy, sleep, appetite, cognition, and mood) was reported by 711 of 824 participants (86.3%) at 2 weeks, increasing to 548 of 576 (95.1%) at 6 months. Fatigue was the most common symptom reported at 2 weeks (141 of 889 [15.9%]), falling to 3.4% (22 of 638) by 6 months. PROMIS Global Health parent/guardian proxy median T scores for fatigue, global health, and pain interference improved significantly from 2 weeks to 6 months (fatigue, 56.1 vs 48.9; global health, 48.8 vs 51.3; pain interference, 53.0 vs 43.3; P < .001) and by the 6-week visit were at least equivalent to prepandemic population norms. Results of this cohort study suggest that although children and young adults with MIS-C can have severe disease during the acute phase, most recovered quickly and had a reassuring midterm prognosis