Assessing Health Concerns & Obstacles to Diesel Exposure Reduction in Vermont Diesel Vehicle Operators

Background and Objectives: Diesel vehicle idling reduction is an important national environmental and legislative issue. Exposure to diesel exhaust is associated with significant morbidity and mortality, including: • Lung & esophageal cancer • Asthma • Cardiovascular disease • Neurotoxicity • Decreased sperm count & testosterone deficiency Drivers of diesel vehicles have specifically been shown to have increased incidence and death from lung cancer. Diesel engines emit a number of known hazardous chemicals, including carbon monoxide, nitric oxide, sulfur dioxide, benzene, formaldehyde, and acrolein, into the air supply. While public health efforts to reduce diesel idling in Vermont and elsewhere have identified employers’ significant financial incentives in fuel conservation, perhaps there is also a role for appealing to drivers themselves: the people who are incurring the most direct exposure. It is unknown, however, whether Vermont diesel vehicle operators are aware of the health effects of diesel exhaust – or, more significantly, whether they are concerned about it. In order to identify potential targets for future interventions to reduce diesel idling in Vermont, this study aims to probe the following: • Have Vermont drivers been educated about exhaust exposure? • Are they concerned about potential health effects of diesel? • Are they satisfied with their understanding of the health impact of diesel fuel? • What are their health concerns, more generally? • What resources for health information do they respect? • What are their specific obstacles to idling reduction?https://scholarworks.uvm.edu/comphp_gallery/1046/thumbnail.jp

Disparities in Mortality Among Acute Myeloid Leukemia-Related Hospitalizations

Racial and socioeconomic disparities have become apparent in acute myeloid leukemia (AML) outcomes. We conducted a retrospective cohort study of hospitalizations for adults with a diagnosis of AML from 2009 to 2018 in the Nationwide Inpatient Sample (NIS). We categorized patients\u27 ages in groups of≥60 years and stratified them by reported race/ethnicity. Exposures of interest were patient sociodemographics, hospital characteristics, and Elixhauser-comorbidity Index. Outcome of interest was in-hospital death. Statistical analyses included survey logistic regression to generate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) to quantify the independent associations between patient characteristics and mortality. Of 622,417 AML-related hospitalizations, 57.6% were in patients ≥60 years. The overall rate of in-hospital death was 9.4%. Compared to patient

Timely delivery of PORT for head and neck squamous cell carcinoma in a county hospital

Abstract Objectives The objective of this study was to compare the rate of post‐operative radiation therapy (PORT) initiation within 6 weeks for head and neck squamous cell carcinoma patients treated at a safety net, academic institutio between 2019 and 2021 versus those treated in 2022 after implementation of a new clinical pathway. Methods A retrospective case–control study was performed at a single tertiary care, safety‐net, academic institution. Patient demographics, tumor characteristics, dates of surgery, and other treatment dates were collected from the electronic medical record. The time from surgery to PORT was calculated. Patients who started radiation treatment within 42 days of surgery were regarded as having started PORT on time. The demographics, tumor characteristics, and rate of timely PORT for the two cohorts of patients were compared. Results From 2018 to 2021, our rate of PORT initiation within 6 weeks of surgery was 12% (n = 57). In 2022, our rate of timely PORT was 88% (n = 16), p < 0.5. Patient demographics and characteristics were similar with the exception of marital status and use of free‐flap reconstruction. The 2022 cohort was more likely to be single (p < 0.5), and all patients underwent free‐flap reconstruction in 2022 (p < 0.05). Conclusion Early referrals, frequent communication, and use of a secure registry were the key to the success found by our group despite the socioeconomic challenges of our underserved, safety‐net hospital patient population. The changes made at our institution should serve as a template for other institutions seeking to improve the quality of care for their HNSCC patients

Incidental detection of FGFR3 fusion via liquid biopsy leading to earlier diagnosis of urothelial carcinoma.

The rising utilization of circulating tumor DNA (ctDNA) assays in Precision Oncology may incidentally detect genetic material from secondary sources. It is important that such findings are recognized and properly leveraged for both diagnosis and monitoring of response to treatment. Here, we report a patient in whom serial cell-free DNA (cfDNA) monitoring for his known prostate adenocarcinoma uncovered the emergence of an unexpected FGFR3-TACC3 gene fusion, a BRCA1 frameshift mutation, and other molecular abnormalities. Due to the rarity of FGFR3 fusions in prostate cancer, a workup for a second primary cancer was performed, leading to the diagnosis of an otherwise-asymptomatic urothelial carcinoma (UC). Once UC-directed treatment was initiated, the presence of these genetic abnormalities in cfDNA allowed for disease monitoring and early detection of resistance, well before radiographic progression. These findings also uncovered opportunities for targeted therapies against FGFR and BRCA1. Overall, this report highlights the multifaceted utility of longitudinal ctDNA monitoring in early cancer diagnosis, disease prognostication, therapeutic target identification, monitoring of treatment response, and early detection of emergence of resistance

Enhancing prognostic power in multiple myeloma using a plasma cell signature derived from single-cell RNA sequencing

Abstract Multiple myeloma (MM) is a heterogenous plasma cell malignancy, for which the established prognostic models exhibit limitations in capturing the full spectrum of outcome variability. Leveraging single-cell RNA-sequencing data, we developed a novel plasma cell gene signature. We evaluated and validated the associations of the resulting plasma cell malignancy (PBM) score with disease state, progression and clinical outcomes using data from five independent myeloma studies consisting of 2115 samples (1978 MM, 65 monoclonal gammopathy of undetermined significance, 35 smoldering MM, and 37 healthy controls). Overall, a higher PBM score was significantly associated with a more advanced stage within the spectrum of plasma cell dyscrasias (all p < 0.05) and a shorter overall survival in MM (hazard ratio, HR = 1.72; p < 0.001). Notably, the prognostic effect of the PBM score was independent of the International Staging System (ISS) and Revised ISS (R-ISS). The downstream analysis further linked higher PBM scores with the presence of cytogenetic abnormalities, TP53 mutations, and compositional changes in the myeloma tumor immune microenvironment. Our integrated analyses suggest the PBM score may provide an opportunity for refining risk stratification and guide decisions on therapeutic approaches to MM

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Abstract Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images