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Mapping Violence: Police Use of Force and Gender-Based Violence in New York City
Get PDFSpatially concentrated policing practices play a key role in the public face of mass incarceration, prompting inquiry into potential negative impact on rates of gender-based violence (GBV) in targeted communities. Using administrative data on police encounters (stops, use of force, and violence) and GBV incidents in New York City, linear regression shows that spatially clustered police encounters predict higher GBV rates, with stronger effects as police use of force intensifies. Community racial composition moderates this relationship such that the association between police use of force and GBV is stronger in communities with an above-average proportion of Black residents
Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: a machine learning modelling study from the N3C and RECOVER consortia
Get PDFBackground In 2021, we used the National COVID Cohort Collaborative (N3C) as part of the National Institutes of Health RECOVER Initiative to develop a machine learning pipeline to identify patients with a high probability of having post-acute sequelae of SARS-CoV-2 infection or long COVID. However, the increased home testing, missing documentation, and reinfections that characterise the pandemic beyond 2022 necessitated the re-engineering of our original model to account for these changes in the COVID-19 research landscape. Methods Trained on 72 745 patient records (36 238 with long COVID and 36 507 with no evidence of long COVID), our updated XGBoost model gathered data for each patient in overlapping 100-day periods that progressed through time and issued a probability of long COVID for each 100-day period. We ran the model on patients in N3C (n=5 875 065) who met at least one of the following criteria from Jan 1, 2020, to June 22, 2023: a U07·1 (COVID-19) diagnosis code; a positive SARS-CoV-2 test; a U09·9 (post-acute sequelae of SARS-CoV-2 infection) diagnosis code; a prescription for nirmatrelvir–ritonavir or remdesivir; or an M35·81 (multisystem inflammatory syndrome in children [MIS-C]) diagnosis code. Each patient was given a model score that predicted long COVID status for each 100-day window in which they were aged ≥18 years. If a patient had known acute COVID-19 during any 100-day window (including reinfections), we censored the data from 7 days before the diagnosis or positive test date to 28 days after. We ran the model on controls selected from pre-2020 data to assess the likelihood of false positives. Findings The updated model had an area under the receiver operating characteristic curve of 0·90. Precision and recall could be adjusted according to a given use case, depending on whether greater sensitivity or specificity was warranted. Using our model, we estimate the overall prevalence of long COVID among the COVID-19 positive cohort within N3C repository to be 10.4%. Interpretation By eschewing the COVID-19 index date as an anchor point for analysis, we can assess the probability of long COVID among patients who might have tested at home, or with suspected (but untested) cases of COVID-19, or multiple SARS-CoV-2 reinfections. We view this exercise as a model for maintaining and updating any machine learning pipeline used for clinical research and operations. Funding National Institutes of Health RECOVER Initiative
Former Solar Fuels Research Center (SERC) Website
No full textThis WARC file was generated using Conifer, and is a snapshot of the former SERC website http://solarfuels.web.unc.edu/, that is no longer active. The new SERC website is https://serc.web.unc.edu/.
Encoding Cognitive Maps: Investigating Topological Representation of Networks Through Right Angle Bias
No full textMy project investigated how we use topology, a branch of mathematics describing relations between objects, to represent space. We did this by conducting a simple memory task in which participants are asked to draw stimuli from memory. By measuring both the topological features present and the degree of right angle bias in the drawings, we found that topological relations play an essential role in mentally representing and recreating objects in space
Comparative Analysis of Chitosan, Lipid Nanoparticles, and Alum Adjuvants in Recombinant SARS-CoV-2 Vaccine: An Evaluation of Their Immunogenicity and Serological Efficacy
Get PDFBackground: Chitosan, a family of polysaccharides composed of glucosamine and N-acetyl glucosamine, is a promising adjuvant candidate for eliciting potent immune response. Methods: This study compared the adjuvant effects of chitosan to those of empty lipid nanoparticles (eLNPs) and aluminum hydroxide (alum) following administration of recombinant SARS-CoV-2 spike immunogen in adult mice. Mice received the adjuvanted recombinant protein vaccine in a prime-boost regimen with four weeks interval. Subsequent analyses included serological assessment of antibody responses, evaluation of T cell activity, immune cell recruitment and cytokine profiles at injection site. Results: Compared to alum, chitosan induced a more balanced Th1/Th2 response, akin to that observed with eLNPs, demonstrating its ability to modulate both the humoral and cellular immune pathways. Chitosan induced a different proinflammatory cytokine (e.g., IL-1⍺, IL-2, IL-6, and IL-7) and chemokine (e.g., Eotaxin, IP-10, MIP-1a) profile compared to eLNPs and alum at the injection site and in the draining lymph nodes. Moreover, chitosan potentiated the recruitment of innate immune cells, with neutrophils accounting for about 40% of the infiltrating cells in the muscle, representing a ~10-fold increase compared to alum and a comparable level to eLNPs. Conclusions: These findings collectively indicate that chitosan has the potential to serve as an effective adjuvant, offering comparable, and potentially superior, properties to those of currently approved adjuvants
Editorial: Oral health care for vulnerable and underserved populations
Get PDFIn the shadow of modern dentistry’s technological achievements lies a troubling reality: millions of vulnerable individuals face significant barriers to basic oral healthcare, which in turns have a direct effect on their quality of life. These disparities represent not just a clinical challenge but a social justice issue demanding our collective attentio
Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.
Get PDFBackground: A majority (>80%) of maternal deaths in the United States are preventable. Using machine learning (ML) models that are generated from electronic medical records (EMRs) may be a promising approach to predict the risk of adverse maternal outcomes and enable proactive intervention to prevent maternal mortality. Current evidence syntheses of such ML approaches either focus only on specific maternal outcomes, aspects other than risk prediction, or do not consider the full pipeline of studies from the development to implementation in clinical practice. Objective: The goal of this scoping review is to document evidence for the use of ML models for predicting the risk of maternal morbidity and mortality outcomes (research objective [RO1]), the translation of such models into applications for clinical use by providers (RO2), and factors associated with the implementation of clinical applications in practice (RO3). Methods: The review was limited to studies in health care settings, using data from EMRs. A detailed search string was developed in collaboration with a health sciences librarian and implemented on February 20, 2023, on PubMed, CINAHL Plus, Scopus, Embase, and IEEE Xplore. Two reviewers independently reviewed titles and abstracts for inclusion, and a third reviewer resolved conflicts. Only full-length journal articles published in English were included. Studies using non-EMR data exclusively were excluded. Two reviewers independently reviewed full texts for inclusion, and a third reviewer resolved conflicts. A structured template was used for data extraction, and findings were summarized descriptively. Results: From 480 deduplicated studies identified from the search, 142 studies were included for full-text review, and 39 studies were included in the review. More than half of the included studies were conducted in 2022, and 34 studies were from just 3 countries (United States, China, and Israel). More studies focused on identifying the risk of pregnancy and delivery outcomes compared with postpartum outcomes. The top 3 most common outcomes for risk prediction were cardiovascular risks and hypertensive disorders of pregnancy (9 studies), gestational diabetes (7 studies), and postpartum hemorrhage (6 studies). Data were labeled with computable phenotypes in 30 studies, and the most often used method in ML models was boosting methods (18 studies). The most common metric used to assess model performance was area under the precision-recall curve (AUPRC; 33 studies). No studies described clinical applications of ML models for providers (RO2) or associated implementation factors (RO3). Conclusions: Key recommendations for future research and practice include expanding efforts to study maternal morbidity and mortality outcomes in the postpartum period, increasing transparency and reproducibility of studies through use of reporting checklists, and expanding efforts to implement ML models in clinical practice
Everyday Discrimination and Vulnerability to HIV Transmission Among Sexual and Gender Minorities of Color in Raleigh-Durham, North Carolina
Get PDFSexual and gender minority (SGM) individuals of color face disproportionate HIV burdens in the United States, partly due to the effects of discrimination. Discrimination may drive behaviors linked to HIV risk, including increased sexual activity and substance use, but these relationships remain underexplored in the Southern U.S. We conducted a cross-sectional analysis using data from a social network survey of adult persons of color assigned male sex at birth (AMAB) who have sex with other AMAB individuals in Raleigh-Durham, North Carolina. Everyday discrimination was measured using the five-item Everyday Discrimination Scale (EDS), summed by the number of situations reported. Participants were categorized by sexual activity level—high (≥ 3 partners in the past 6 months) or low (0–2 partners). Multivariable logistic regression was used to assess associations between EDS scores, substance use, and sexual activity. Among 100 participants (median age 32), 79% identified as Black/African American, 22% as Latinx, 55% were living with HIV, and 10% identified as gender diverse. Most (87%) reported experiencing at least one type of everyday discrimination in the past year. EDS scores were significantly higher among those with high sexual activity (median 4 vs. 3, p = 0.007). In adjusted models, both EDS (OR 1.76; 95% CI 1.25–2.61) and recreational drug use (OR 4.69; 95% CI 1.59–15.5) were associated with high sexual activity. Discrimination and substance use are significantly associated with elevated sexual activity among SGM of color. Multilevel interventions addressing these factors are needed to improve HIV prevention outcomes in this population
Determinants of Healthy Food Choice: Evidence from Pricing, Labeling, and Behavioral Nudges
No full textThis presentation was prepared for the Summer Undergraduate Research Conference (SARC) at UNC-Chapel Hill as part of the PSYC395 independent research project. The study investigates key determinants of healthy food choice, focusing on pricing strategies, information labeling, dietary substitutions, visual cues, and behavioral nudges. The slide deck summarizes prior research, theoretical frameworks, and ongoing analyses, highlighting how behavioral economics can inform interventions that encourage healthier dietary decisions
Same-Race Social Media Exposure & Body Image of Adolescents from Minoritized Ethnic/Racial Backgrounds
No full textThis project was completed as part of the 2025 Summer Undergraduate Research Fellowship, titled “Same-Race Social Media Exposure & Body Image of Adolescents from Minoritized Ethnic/Racial Backgrounds,” under the mentorship of Dr. Annie Maheux. My research examined how exposure to same-race peers on social media relates to adolescents’ body image, with attention to differences across gender. This work contributes to a growing understanding of how social media can serve as both a risk and protective factor for youth well-being