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CyanoHABs and CAPs: assessing community-based monitoring of PM2.5 with regional sources of pollution in rural, northeastern North Carolina
Get PDFUnderserved rural communities in northeastern North Carolina (NC), surrounding the Albemarle Sound, have faced degraded environmental quality from various sources of air and water pollution. However, access to local air quality data is regionally scarce due to a lack of state-run monitoring stations, which has motivated local community science efforts. In January 2022, we co-developed a community-led study to investigate the relationship between fine particulate matter (PM2.5) and sources of regional air pollution, with a specific focus on previously identified emissions from cyanobacterial harmful algal blooms (CyanoHABs). Using low-cost PurpleAir air quality sensors to quantify PM2.5 mass, satellite-derived indicators of CyanoHABs, and other publicly available atmospheric and meteorological data, we assessed environmental drivers of PM2.5 mass in the airshed of the Albemarle Sound estuary during 2022–2023. We found that bias-corrected PurpleAir PM2.5 mass concentrations aligned with composite data from the three nearest federal reference equivalent measurements within 1 μg m−3 on average, and that the temporal variation in PM2.5 was most closely associated with changes in criteria air pollutants. Ultimately, satellite-based indicators of CyanoHABs (Microcystis spp. equivalent cell counts and bloom spatial extent) were not strongly associated with ambient/episodic increases in PurpleAir PM2.5 mass during our study period. For the first time, we provide local PM2.5 measurements to rural communities in northeastern NC with an assessment of environmental drivers of PM2.5 pollution events. Additional compositional analyses of PM2.5 are warranted to further inform respiratory risk assessments for this region of NC. Despite the lack of correlation between CyanoHABs and PM2.5 observed, this work serves to inform future studies that seek to employ widely available and low-cost approaches to monitor both CyanoHAB aerosol emissions and general air quality in rural coastal regions at high spatial and temporal resolutions.We evaluated the use of low-cost air quality sensors (PurpleAir) and satellite-derived indicators of ocean color (CyAN) in the study of aerosol emissions from cyanobacterial blooms
Defining the regulatory logic of breast cancer using single-cell epigenetic and transcriptome profiling
Get PDFAnnotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [scRNA-seq]) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell of origin for subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. This work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of cancer cells
Understanding the Graduate-Level Addiction Counselor Workforce: Differences in Educational Standards, Scope of Practice, and Supervisory Opportunities Across the United States
Get PDFBackground: Graduate-level licensed addiction counselors are a critical component of the substance use disorder workforce, yet their scope of practice, education and training requirements, and credentialing varies in the U.S. Objectives: To better understand the roles and functions of the graduate-level addiction counselor workforce across the U.S., this state-by state analysis sought to identify the titles, minimum education and training requirements, scope of practice, and supervisory opportunities for graduate-level addiction counselors. Design: This project conducted systematic abstraction and descriptive analysis of U.S. state Practice Acts, certification board rules, materials from behavioral health professional organizations, and state Medicaid plans, fee schedules, and provider manuals. Methods: Descriptive summaries were produced to describe trends across states. Tables synthesized aggregated data across the workforce domains of education and training, regulation and credentialing, supervision, and payment for graduate-level addiction counselors only. Results: Forty-one states offer a graduate-level addiction counselor credential, 18 of which offer multiple credentials for a total of 69 credentials. States varied in services permissible by scope of practice definition, including assessments (41 states), psychotherapy (28 states), telehealth (28 states), and diagnosis (16 states). Only 26 states allow for independent practice. States required an average of 2,887 practice hours and 143 post-graduate supervision hours. Sixteen states permitted all tiers of graduate-level addiction counselors to supervise others, and 10 states specifically offer a graduate-level supervisory credential. Conclusion: State graduate-level addiction counselor credentials widely vary, suggesting that states utilize this workforce differently based on differing training criteria, required competencies, scope of practice, and supervision. Strategies to support growth of this workforce include alignment of training competencies, additional substance use disorder training in behavioral health graduate programs, and expansion of supervisory pathways and credentials
Feasibility and usability of a voice-based dietary recall tool in older adults: A pilot comparison with ASA-24
Get PDFObjective Dietary assessment is important for identifying patterns that can influence an older adult's medical conditions. Existing assessments are dependent on the recall limit of the current use of dietary tools. This pilot study aimed to compare the short-term usability and acceptability of a novel voice-based dietary recall tool (DataBoard) to the traditional Automated Self-Administered Dietary Assessment Tool (ASA-24) in older adults. Methods Participants aged over 65 years old, meeting specific criteria, were recruited through Research For Me and Research Match across a six-month period in 2023–2024. During the session on Zoom, they were randomly assigned to complete either a voice-based recall via DataBoard or use ASA-24 first, followed by a semi-structured interview. DataBoard enables survey completion using speech input through shared links. We obtained data on meal choices, participant feedback, and preferences for either method using a 1–10 rating scale (low to high agreement). Descriptive statistics and qualitative coding were conducted. Results We recruited 20 participants (mean age 70.5 ± 4.26 years, 55% female and 35% non-White). Feasibility and acceptability of DataBoard's voice-based recall were rated as 7.95/10 and 7.6/10. Participants rated the overall performance of DataBoard as easier than the ASA-24, with an average rating of 6.7/10. Participants preferred using DataBoard; they felt it could be used more frequently to report food than ASA-24 (mean 7.2/10). Dedoose analysis revealed preferences, challenges, and usability insights for DataBoard. Conclusion Older adults supported voice-based recall as a means to evaluate dietary intake. Further evaluation with larger cohorts of older adults could provide additional opportunities to create a better tool for food recall
Medication Use – Biomarker Home Exam
Get PDFThis document summarizes the rationale, equipment, measurement, and protocol procedures for the medication inventories collected during the Wave VI Biomarker home exam. It also documents the protocol for assigning therapeutic classes to those medications. Whenever possible, data collection and methods in Wave VI mirrored those of Wave V to ensure comparability of data between waves. This document is one in a set of Wave VI user guides.
Adaptation Process of Social Cognition and Interaction Training (SCIT) in an Indian Context for Persons with Schizophrenia.
Get PDFInterventions aimed at enhancing social cognition deficits in individuals with schizophrenia are globally supported by evidence demonstrating improvements in various functional outcomes. The Social Cognition and Interaction Training (SCIT) intervention was adapted for use in the Indian context for individuals with schizophrenia using the Reporting Cultural Adaptation in Psychological Trials (RECAPT) guidelines, informed by expert consultations. This included contextually relevant changes in the resource materials (print, photographs, and video) and the development of additional resources. Changes in the intervention delivery process included the use of individual sessions with adjunctive group sessions. Initial feasibility was assessed via a pilot tryout of the adapted SCIT on three persons diagnosed with schizophrenia. This informed additional changes for future applications of the adapted SCIT, such as structured involvement of family members as practice partners and modifications in the intervention delivery format. Content validation process for the final adapted intervention modules was carried out by four mental health practitioners. The experiences, challenges, and decision-making process involved in the adaptation are outlined, along with implications for future research and contextually tailored intervention strategies
Reliability of artificial intelligence algorithms in automated age estimation using orthopantomograms: A scoping review.
Get PDFThis study aims to evaluate the efficiency of AI (artificial intelligence) algorithms for automated age estimation using orthopantomograms (OPGs) and to determine whether these models can effectively replace conventional age estimation techniques.Three independent literature searches were conducted in PubMed, Scopus, and Embase. Studies published in the English language were considered, focusing on age estimation using AI. A total of 1519 articles were screened, and 24 articles were included in the study. The data was extracted in a standardized, predefined manner. After finalizing the search, the data collected was tabulated, interpreted, and verified. The selected studies were analyzed for methodological rigor, algorithmic performance, and comparative effectiveness against traditional age estimation methods.AI-based models, especially deep learning architectures like convolutional neural networks, EfficientNet, DenseNet, and hybrid models such as Age-Net, demonstrated superior accuracy, precision, and reliability compared to traditional age estimation methods. These AI-driven models show promising results in reducing human error, increasing efficiency, and enhancing forensic and clinical decision-making.AI-driven age estimation using OPGs represents a transformative advancement with considerable forensic and clinical potential. Although these AI models may not yet fully replace conventional techniques, they offer a substantial value as complementary tools, improving both accuracy and operational efficiency. To foster wider adoption and improve reliability, ongoing research and the development of standardized protocols are essential for integrating these methods into forensic odontology and related fields
Anticipation of a therapeutic odyssey following predictive testing for autism.
Get PDFBrain-based tools are being developed to identify infants at ultra-high likelihood for developing autism and enable presymptomatic intervention, though such interventions are not yet clinically available. Given persistent challenges in accessing autism services, we sought to understand how families might use early predictive results to seek support. We analyzed 55 interviews with parents of infants aged 6-13 months; one group had experience parenting an older autistic child (n = 30), the other had no prior autism parenting experience (n = 25). All parents were asked what steps they would take if told their infant was likely to develop autism. Both groups described an intent to find appropriate services; parents with prior autism experience provided more specifics based on prior knowledge. The groups diverged in their anticipated supports and information sources. Parents with autism experience anticipated seeking financial support via insurance and disability benefits; those without autism experience reported they would consult their pediatrician for information or search online. This qualitative study was conducted with a sample of parents selected for their specific life experiences, but likely does not capture the full range of potential responses to biomarker testing in infancy. Given that most services and benefits require a formal diagnosis, families receiving predictive results in infancy will likely face challenges finding appropriate services. Prior to implementing predictive testing in the first year of life, researchers should consider their obligation to support families who receive predictive results
Risk of food insecurity and its association with social determinants of health among hospitalized patients in Lebanon
Get PDFBackground Food insecurity is a growing concern globally, particularly in conflict-affected settings. In these contexts, hospitalized patients face heightened risks of poor health outcomes. The present study aims to assess the risk of food insecurity among hospitalized patients in Lebanon and investigate its association with social determinants of health (SDH) amidst multiple crises. Methods A cross-sectional observational study was conducted from May to October 2021 on a random sample of adult hospitalized patients in five large hospitals across different districts in Lebanon. A structured survey was used to collect socio-demographic characteristics, sources of health coverage, and medical history among study participants. In addition, survey included analysis of four indicators considered as integral part of SDH criteria: (1) area of residence and household size, (2) level of education, (3) employment status and type of employment, (4) healthcare access and coverage. Risk of food insecurity among praticipants was screened by a validated two-question tool, adapted from the US Department of Agriculture Household Food Security Survey. Associations between the SDH and risk of food insecurity were explored using logistic regression analysis using STATA V13.1. Results Among the 343 participants, the majority (79.5%) were identified as being at risk of food insecurity with 62.4% classified as experiencing mild food insecurity, 15% as moderate, and 2.1% living with severe food insecurity. Higher odds of food insecurity were observed among residents of of predominantly rural areas mainly in the North of Lebanon (OR = 6.59, CI [1.79; 24.32], p = 0.005) and Bekaa (OR = 2.55, CI [0.92; 7.05], p = 0.071) districts. Additionally, participants with higher levels of education, particularly those with high school degree or higher, had lower odds of food insecurity (p < 0.05). Employment status, household size, and healthcare coverage were not found to be significant predictors of food insecurity among hospitalized patients in the multiple logistic regression analysis in the study sample. Conclusion The study highlights the critical role of SDH, including educational level and geographical residence on experience of food insecurity among hospitalized patients. Screening for risk of food insecurity and associated determinants in health care settings are critical to design adequate programs and interventions to mitigate the risk of food and nutrition insecurity among vulnerable groups, particularly in conflict-affected settings
A Healthy Food Availability Program to Increase Food Security in Robeson County, North Carolina
Get PDFFood insecurity in Robeson County, North Carolina particularly among Lumbee tribal members exceeds state and national averages and contributes to high rates of obesity, diabetes, and chronic disease. This capstone adapts the evidence-based OPREVENT model to improve food access, nutrition knowledge, and culturally relevant dietary practices. The multilevel intervention includes three components: (1) healthy retail initiatives that provide stipends and support for local stores to stock and promote fresh produce; (2) a school-based nutrition curriculum emphasizing traditional foods and practical skills; and (3) community workshops led by community health workers and elders. To evaluate the first objective, A quasi-experimental pre/post design will use exit intercept surveys with receipt verification to evaluate changes in purchasing behavior among 200 adult shoppers. Expected outcomes include increased produce purchasing and improved daily fruit and vegetable intake. This program aims to strengthen food sovereignty and offer a scalable approach for Native and rural communities.Master of Public Healt