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Objectives: This county-level epidemiological study evaluated the travel distance to the nearest otolaryngologist for continental US communities and identified socioeconomic differences between low- and high-access regions., Methods: Geospatial analysis of publicly available 2015-2022 NPI records was combined with US census data to identify geospatial gaps in otolaryngologist distribution. Moran's index geospatial clustering in distance to the nearest county with an otolaryngologist was used as the core metric for differential access determination. Univariate logistic analysis was conducted between low- and high-access counties for 20 socioeconomic and demographic variables., Results: Nationally, the average person was 22 miles from an otolaryngologist. 444 counties were identified as geospatially "low access" with increased travel distance in the Midwest, Great Planes, and Nevada with a median of 47 miles. 1231 counties in the Eastern United States and Western Coast were identified as "high access" with a 3-mile median travel distance. Areas of low access to otolaryngological care had smaller median populations (12,963 vs. 558,306), had smaller percent Black and Asian populations (2% vs. 11%, 1% vs. 5%, respectively), had a greater percent American Indian population (2% vs. 1%), were less densely populated (8 vs. 907 people per square mile), had fewer percent college graduates (20% vs. 34%), and fewer otolaryngologists per county (median: 0.01-20)., Conclusion: These findings highlight disparity in otolaryngology care in the United States and the need for otolaryngology funding initiatives in the Midwest and Great Plains regions., Level of Evidence: Level 3., Competing Interests: The authors declare no conflicts of interest to disclose., (© 2024 The Authors. Laryngoscope Investigative Otolaryngology published by Wiley Periodicals LLC on behalf of The Triological Society.)

Objective: To investigate the geographic clustering of ambulatory surgical center (ASC) utilization in otolaryngology to determine hot spot areas of high utilization and cold spot areas of low utilization and socioeconomic factors that correlate with these hot spots and cold spots., Study Design: To develop a national epidemiologic study of ASC utilization in otolaryngology in the United States., Setting: United States of America., Methods: Multiple county-level national databases were reviewed including Center for Medicare Services (CMS) physician billing data, CMS Medicare demographic data, and US Census socioeconomic data. The analysis was conducted using the average of all Medicare billing information from 2015 to 2019. Whether a procedure was performed in an ASC was extracted from CMS data using the CMS definition of an ASC. The percentage ASC billing was calculated as the fraction of CMS payments that were performed in ASCs for the top ENT procedures. A Python-based script for database building and GeoDa, Moran's I clustering coefficient, and a 1-way analysis of variance was utilized to chart and analyze demographic, geographic, and socioeconomic trends., Results: Hot spots of utilization, with an average ASC billing of 80.13%, were seen in Southern California, Florida, Mid-Atlantic, and clusters throughout the Deep South. Cold spot clusters, with an average ASC billing of 2.21%, were located in large swaths of New England, Ohio, and the Deep South with clusters bisecting the Midwest. Cold spots had a higher percentage of poverty and percent eligible for Medicaid., Conclusion: ASC utilization is best used to improve cost-effectiveness and accessibility of care but what is seen is that ASC use is currently highest in cities in coastal areas which already have high levels of care access and are making the most proportional money compared to their rural counterparts., Competing Interests: The authors deny any financial or ethical conflicts of interest. Specifically, no authors have financial ties or ownership of surgical centers., (© 2023 The Authors. OTO Open published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngology–Head and Neck Surgery Foundation.)

Objective: To evaluate the existence of statistically significant clusters of Cesarean section rates at the county level and assess the relationship of such clusters with previously implicated socioeconomic factors., Results: County-level obstetrics data was extracted from March of Dimes, originally sourced from National Center for Health Statistics. County-level demographic data were extracted from the US Census Bureau. Access to obstetricians was extracted from National Provider Identifier records. Rural counties were identified using Rural Urban Commuting Area codes developed by the department of agriculture. The dataset was geospatially analyzed using Moran's I statistic, a metric of local spatial autocorrelation, to identify clusters of increased or decreased Cesarean section rates. The American South, especially the Deep South, is a major cluster of increased Cesarean section rates. As a general but not absolute pattern, the American West and Midwest had lower Cesarean section rates than the Northeast. Focal areas of increased Cesarean section rates included the Kansas-Nebraska border, Michigan's upper peninsula, and the New York City metropolitan area. The gross geospatial differences were not explained by rurality, obstetric access, or ethnic and racial factors alone., (© 2022. The Author(s).)

Treatment response assessment for patients with advanced solid tumors is complex and existing methods require greater precision. Current guidelines rely on imaging, which has known limitations, including the time required to show a deterministic change in target lesions. Serial changes in whole-genome (WG) circulating tumor DNA (ctDNA) were used to assess response or resistance to treatment early in the treatment course. Ninety-six patients with advanced cancer were prospectively enrolled (91 analyzed and 5 excluded), and blood was collected before and after initiation of a new, systemic treatment. Plasma cell-free DNA libraries were prepared for either WG or WG bisulfite sequencing. Longitudinal changes in the fraction of ctDNA were quantified to retrospectively identify molecular progression (MP) or major molecular response (MMR). Study endpoints were concordance with first follow-up imaging (FFUI) and stratification of progression-free survival (PFS) and overall survival (OS). Patients with MP ( n = 13) had significantly shorter PFS (median 62 days vs. 310 days) and OS (255 days vs. not reached). Sensitivity for MP to identify clinical progression was 54% and specificity was 100%. MP calls were from samples taken a median of 28 days into treatment and 39 days before FFUI. Patients with MMR ( n = 27) had significantly longer PFS and OS compared with those with neither call ( n = 51). These results demonstrated that ctDNA changes early after treatment initiation inform response to treatment and correlate with long-term clinical outcomes. Once validated, molecular response assessment can enable early treatment change minimizing side effects and costs associated with additional cycles of ineffective treatment., (©2020 American Association for Cancer Research.)

Unlike most macromolecules that are homogeneously distributed in the bacterial cell, mRNAs that encode inner-membrane proteins can be concentrated near the inner membrane. Cotranslational insertion of the nascent peptide into the membrane brings the translating ribosome and the mRNA close to the membrane. This suggests that kinetic properties of translation can determine the spatial organization of these mRNAs and proteins, which can be modulated through posttranscriptional regulation. Here we use a simple stochastic model of translation to characterize the effect of mRNA properties on the dynamics and statistics of its spatial distribution. We show that a combination of the rate of translation initiation, the availability of secretory apparatuses, and the composition of the coding region determines the abundance of mRNAs near the membrane, as well as their residence time. We propose that the spatiotemporal dynamics of mRNAs can give rise to protein clusters on the membrane and determine their size distribution., Competing Interests: The authors declare no conflict of interest., (Copyright © 2017 the Author(s). Published by PNAS.)

Background: Sort-seq is an effective approach for simultaneous activity measurements in a large-scale library, combining flow cytometry, deep sequencing, and statistical inference. Such assays enable the characterization of functional landscapes at unprecedented scale for a wide-reaching array of biological molecules and functionalities in vivo. Applications of sort-seq range from footprinting to establishing quantitative models of biological systems and rational design of synthetic genetic elements. Nearly as diverse are implementations of this technique, reflecting key design choices with extensive impact on the scope and accuracy the results. Yet how to make these choices remains unclear. Here we investigate the effects of alternative sort-seq designs and inference methods on the information output using mathematical formulation and simulations., Results: We identify key intrinsic properties of any system of interest with practical implications for sort-seq assays, depending on the experimental goals. The fluorescence range and cell-to-cell variability specify the number of sorted populations needed for quantitative measurements that are precise and unbiased. These factors also indicate cases where an enrichment-based approach that uses a single sorted population can offer satisfactory results. These predications of our model are corroborated using re-analysis of published data. We explore implications of these results for quantitative modeling and library design., Conclusions: Sort-seq assays can be streamlined by reducing the number of sorted populations, saving considerable resources. Simple preliminary experiments can guide optimal experiment design, minimizing cost while maintaining the maximal information output and avoiding latent biases. These insights can facilitate future applications of this highly adaptable technique.

Small regulatory RNAs (sRNAs) in bacteria regulate many important cellular activities under normal conditions and in response to stress. Many sRNAs bind to the mRNA targets at or near the 5' untranslated region (UTR) resulting in translation inhibition and accelerated degradation. Often the sRNA-binding site is adjacent to or overlapping with the ribosomal binding site (RBS), suggesting a possible interplay between sRNA and ribosome binding. Here we combine quantitative experiments with mathematical modeling to reveal novel features of the interaction between small RNAs and the translation machinery at the 5'UTR of a target mRNA. By measuring the response of a library of reporter targets with varied RBSs, we find that increasing translation rate can lead to increased repression. Quantitative analysis of these data suggests a recruitment model, where bound ribosomes facilitate binding of the sRNA. We experimentally verified predictions of this model for the cell-to-cell variability of target expression. Our findings offer a framework for understanding sRNA silencing in the context of bacterial physiology., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Two decades into the genomics era the question of mapping sequence to function has evolved from identifying functional elements to characterizing their quantitative properties including, in particular, their specificity and efficiency. Here, we use a large-scale approach to establish a quantitative map between the sequence of a bacterial regulatory RNA and its efficiency in modulating the expression of its targets. Our approach generalizes the sort-seq method, introduced recently to analyze promoter sequences, in order to accurately quantify the efficiency of a large library of sequence variants. We focus on two small RNAs (sRNAs) in E. coli, DsrA and RyhB, and their regulation of both repressed and activated targets. In addition to precisely identifying functional elements in the sRNAs, our data establish quantitative relationships between structural and energetic features of the sRNAs and their regulatory activity, and characterize a large set of direct and indirect interactions between nucleotides. A core of these interactions supports a model where specificity can be enhanced by a rigid molecular structure. Both sRNAs exhibit a modular design with limited cross-interactions, dividing the requirements for structural stability and target binding among modules., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)