eScholarship@UMassChan
eScholarship@UMassChan is a digital archive for UMass Chan Medical School's research and scholarship, including journal articles, theses, datasets and more. We welcome submissions from our faculty, staff, and students. eScholarship@UMassChan is a service of the Lamar Soutter Library, Worcester, MA, USA. See also our open access journal publishing services.
Questions? See the Help menu or contact escholarship@umassmed.edu.
Communities in eScholarship@UMassChan
Select a community to browse its collections.
Recent Publications
Publication Open Access FAVOR 2.0: A reengineered functional annotation of variants online resource for interpreting genomic variation(2025-12-03)The Functional Annotation of Variants Online Resource (FAVOR), http://favor.genohub.org, is a whole genome variant annotation database and portal that provides comprehensive variant functional annotations of all possible variants across the genome. It can facilitate the analysis of whole-genome sequencing studies, support the interpretation of variant functional impacts, and help prioritize causal variants of diseases or traits. To support the growing popularity and expand the scope of FAVOR, we present here a substantial platform update. The new release features dramatically expanded annotations, a completely redesigned infrastructure powered by a newly implemented application programming interface (FAVOR-API), and a revamped web interface with advanced data-visualization capabilities and enhanced query performance. Key expansions include much more comprehensive variant annotations, including global, tissue- and cell-type-specific variant annotations; gene and protein annotations; support for both hg38 and hg19 reference genomes; and an interactive genome-browser for visualization of multi-faceted variant annotations. The updated platform also includes FAVOR-GPT, a large language model-powered interface for navigating the FAVOR database and interpreting results. FAVOR continues to evolve to keep pace with advances in research on interpreting the functional and phenotypic impact of genomic variation.Publication Open Access Factors influencing immediate post-angiographic occlusion outcomes in intracranial aneurysms treated with the woven endobridge device: a multi-center analysis and predictive model from the WorldWideWEB consortium(2025-12-02)The Woven EndoBridge (WEB) device treats wide-necked bifurcation aneurysms, but occlusion rates vary. This study aims to identify factors associated with immediate WEB device occlusion. Data from patients treated with WEB devices across 36 sites were analyzed. Machine learning algorithms and ordinal regression models were developed to predict immediate incomplete occlusion for ruptured and unruptured aneurysms. The study included 1565 patients, with 436 ruptured and 1129 unruptured aneurysms. Immediate complete occlusion was achieved in 38.3% of ruptured and 32.8% of unruptured aneurysms. For ruptured aneurysms, the CatBoost classifier achieved an AUROC of 0.69. Key predictors of incomplete occlusion included pretreatment mRS, aneurysm diameter, and MCA location. Ordinal regression revealed that smoking history (OR: 1.95, p < 0.001), neck diameter (Odds Ratio [OR]: 1.50, p < 0.001), and presence of a branch from the aneurysm (OR: 2.06, p = 0.016) were associated with incomplete, while bifurcation aneurysms (OR: 0.55, p = 0.017) were associated with complete immediate occlusion. For unruptured aneurysms, the CatBoost classifier achieved an AUROC of 0.68. Significant predictors of immediate incomplete occlusion included aneurysm neck width, MCA location, and presence of daughter sac. Ordinal regression revealed that smoking history (OR: 1.29, p = 0.032), neck diameter (OR: 1.24, p < 0.001), and presence of a daughter sac (OR: 1.53, p = 0.005) were associated with incomplete, while bifurcation aneurysms (OR: 0.71, p = 0.02) and posterior circulation location (OR: 0.68, p = 0.01) were associated with complete immediate occlusion. Careful evaluation of patient demographics and specific aneurysm characteristics may help improve the outcomes of intracranial aneurysms treated with WEB device.Publication Open Access UMCCTS Newsletter, December 2025(UMass Chan Medical School, 2025-12-01)This is the December 2025 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest.Publication Open Access Presenteeism Among Health Care Personnel With COVID-19(2025-12-01)IMPORTANCE: Presenteeism-defined as continuing to work during an illness-poses a public health risk in the workplace and is especially hazardous within health care institutions where vulnerable patients may be exposed to nosocomial infections. Understanding the frequency and characteristics of health care personnel (HCP) who report presenteeism while ill with COVID-19 may help mitigate SARS-CoV-2 spread in hospitals and other health care institutions. OBJECTIVES: To determine the frequency of presenteeism among HCP with symptomatic COVID-19, and to evaluate the demographic, occupational, and clinical factors associated with it. DESIGN, SETTING, AND PARTICIPANTS: This is an observational cohort study that uses data from the Preventing Emerging Infections Through Vaccine Effectiveness Testing (PREVENT) project: a test-negative, case-control vaccine effectiveness study that enrolled HCP who had COVID-19 symptoms at 24 academic medical centers from December 2020 through April 2024. EXPOSURE: Exposures include demographic, occupational, and clinical characteristics of participants. MAIN OUTCOMES AND MEASURES: Having confirmed symptomatic COVID-19 infection and reporting presenteeism; overall frequency of presenteeism through the study period and the association of the exposure characteristics with presenteeism, adjusting for confounders using 3 multivariable models. Presenteeism was defined as HCP who did not stop working during their illness, but the study did not differentiate whether they continued working remotely. RESULTS: A total of 3721 HCP were included in the analysis (2842 [76.4%] aged 18-49 years; 2993 [80.4%] female; 278 [7.5%] Asian, 406 [10.9%] Black, and 2912 [78.3%] White). Overall, 293 (7.9%) reported presenteeism during the study period, and the frequency of presenteeism increased each year of the study period (from 1 of 73 [1.4%] in 2020 to 16 of 105 [15.2%] in 2024). Presenteeism was associated with HCP who have minimal patient contact (adjusted odds ratio [aOR], 3.73; 95% CI, 2.39-4.37), a graduate or professional degree (aOR, 1.90; 95% CI, 1.45-2.50), and income over $100 000 (aOR, 1.74; 95% CI, 1.12-2.69). CONCLUSION AND RELEVANCE: In this observational cohort study of 3721 HCP, there was an increasing frequency of presenteeism from 2020 through 2024, and job role and socioeconomic factors were associated. More studies are needed to understand the rationale behind the decision to continue working and the exact causes of presenteeism's rising incidence among HCP with COVID-19.Publication Open Access GrantCheck-an AI Solution for Guiding Grant Language to New Policy Requirements: Development Study(2025-11-27)BACKGROUND: Academic institutions face increasing challenges in grant writing due to evolving federal and state policies that restrict the use of specific language. Manual review processes are labor-intensive and may delay submissions, highlighting the need for scalable, secure solutions that ensure compliance without compromising scientific integrity. OBJECTIVE: This study aimed to develop a secure, artificial intelligence-powered tool that assists researchers in writing grants consistent with evolving state and federal policy requirements. METHODS: GrantCheck (University of Massachusetts Chan Medical School) was built on a private Amazon Web Services virtual private cloud, integrating a rule-based natural language processing engine with large language models accessed via Amazon Bedrock. A hybrid pipeline detects flagged terms and generates alternative phrasing, with validation steps to prevent hallucinations. A secure web-based front end enables document upload and report retrieval. Usability was assessed using the System Usability Scale. RESULTS: GrantCheck achieved high performance in detecting and recommending alternatives for sensitive terms, with a precision of 1.00, recall of 0.73, and an F-score of 0.84-outperforming general-purpose models including GPT-4o (OpenAI; F=0.43), Deepseek R1 (High-Flyer; F=0.40), Llama 3.1 (Meta AI; F=0.27), Gemini 2.5 Flash (Google; F=0.58), and even Gemini 2.5 Pro (Google; F=0.72). Usability testing among 25 faculty and staff yielded a mean System Usability Scale score of 85.9 (SD 13.4), indicating high user satisfaction and strong workflow integration. CONCLUSIONS: GrantCheck demonstrates the feasibility of deploying institutionally hosted, artificial intelligence-driven systems to support compliant and researcher-friendly grant writing. Beyond administrative efficiency, such systems can indirectly safeguard public health research continuity by minimizing grant delays and funding losses caused by language-related policy changes. By maintaining compliance without suppressing scientific rigor or inclusivity, GrantCheck helps protect the pipeline of research that advances biomedical discovery, health equity, and patient outcomes. This capability is particularly relevant for proposals in sensitive domains-such as social determinants of health, behavioral medicine, and community-based research-that are most vulnerable to evolving policy restrictions. As a proof-of-concept development study, our implementation is tailored to one institution's policy environment and security infrastructure, and findings should be interpreted as preliminary rather than universally generalizable.