The rural mortality penalty in U.S. hospital patients with COVID-19.

BACKGROUND: The COVID-19 pandemic brought greater focus to the rural mortality penalty in the U.S., which describes the greater mortality rate in rural compared to urban areas. Although it is widely thought that issues such as access to care, age structure of the population, and differences in behavior are likely drivers of the rural mortality penalty, few studies have attempted to tie delayed access to care in rural populations to healthcare outcomes quantitatively. Therefore, it is critical to try and understand these factors to enable more effective public health policy. METHODS: We performed a cross-sectional analysis of a population of patients with COVID-19 who were admitted to hospitals in the United States between 3/1/2020 and 2/26/2023 to better understand factors leading to outcome disparities amongst groups that all had some level of access to hospital care. Nevertheless, it is widely thought that rural populations often experience delayed access to care, due to transportation and other constraints. Therefore, we hypothesized that deteriorated patient condition at admission likely explained some of the observed difference in mortality between rural and urban populations. RESULTS: Our results supported our hypothesis, showing that the rural mortality penalty persists in this population and that by multiple measures, rural patients were likely to be admitted in worse condition, had worse overall health, and were older. CONCLUSIONS: Although the pandemic threw the rural mortality penalty into sharp relief, it is important to remember that it existed prior to the pandemic and will continue to exist until effective interventions are implemented. This study demonstrates the critical need to address the underlying factors that resulted in rural-dwelling patients being admitted to the hospital in worse condition than their urban-dwelling counterparts during the COVID-19 pandemic, which likely affected other healthcare outcomes as well.

AMEND: active module identification using experimental data and network diffusion.

BACKGROUND: Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein-protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions. RESULTS: To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets. CONCLUSION: The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND .

On the rocks: can urologists identify stone composition based on endoscopic images alone? A worldwide survey of urologists.

PURPOSE: As part of the management of nephrolithiasis, determination of chemical composition of stones is important. Our objective in this study is to assess urologists' accuracy in making visual, intraoperative determinations of stone composition. MATERIALS AND METHODS: We conducted a REDCap survey asking urologists to predict stone composition based on intraoperative images of 10 different pure-composition kidney stones of 7 different types: calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), calcium phosphate (CP) apatite, CP brushite, uric acid (UA), struvite (ST) and cystine (CY). To evaluate experience, we examined specific endourologic training, years of experience, and number of ureteroscopy (URS) cases/week. A self-assessment of ability to identify stone composition was also required. RESULTS: With a response rate of 26% (366 completed surveys out of 1,370 deliveries), the overall accuracy of our cohort was 44%. COM, ST, and COD obtained the most successful identification rates (65.9%, 55.7%, and 52.0%, respectively). The most frequent misidentified stones were CP apatite (10.7%) and CY (14.2%). Predictors of increased overall accuracy included self-perceived ability to determine composition and number of ureteroscopies per week, while years of experience did not show a positive correlation. CONCLUSIONS: Although endoscopic stone recognition can be an important tool for surgeons, it is not reliable enough to be utilized as a single method for stone identification, suggesting that urologists need to refine their ability to successfully recognize specific stone compositions intraoperatively.

pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples.

Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, "bridging samples", to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M, and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE, a simulation-based approach to estimate the bridging sample size, M, in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE, we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer's disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.

Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE).

Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called "bridge samples", to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinalComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.

The need to study rural cancer outcome disparities at the local level: a retrospective cohort study in Kansas and Missouri.

BACKGROUND: Rural residence is commonly thought to be a risk factor for poor cancer outcomes. However, a number of studies have reported seemingly conflicting information regarding cancer outcome disparities with respect to rural residence, with some suggesting that the disparity is not present and others providing inconsistent evidence that either urban or rural residence is associated with poorer outcomes. We suggest a simple explanation for these seeming contradictions: namely that rural cancer outcome disparities are related to factors that occur differentially at a local level, such as environmental exposures, lack of access to care or screening, and socioeconomic factors, which differ by type of cancer. METHODS: We conducted a retrospective cohort study examining ten cancers treated at the University of Kansas Medical Center from 2011 to 2018, with individuals from either rural or urban residences. We defined urban residences as those in a county with a U.S. Department of Agriculture Urban Influence Code (UIC) of 1 or 2, with all other residences defines a rural. Inverse probability of treatment weighting was used to create a pseudo-sample balanced for covariates deemed likely to affect the outcomes modeled with cumulative link and weighted Cox-proportional hazards models. RESULTS: We found that rural residence is not a simple risk factor but rather appears to play a complex role in cancer outcome disparities. Specifically, rural residence is associated with higher stage at diagnosis and increased survival hazards for colon cancer but decreased risk for lung cancer compared to urban residence. CONCLUSION: Many cancers are affected by unique social and environmental factors that may vary between rural and urban residents, such as access to care, diet, and lifestyle. Our results show that rurality can increase or decrease risk, depending on cancer site, which suggests the need to consider the factors connected to rurality that influence this complex pattern. Thus, we argue that such disparities must be studied at the local level to identify and design appropriate interventions to improve cancer outcomes.

Equivalent change enrichment analysis: assessing equivalent and inverse change in biological pathways between diverse experiments.

BACKGROUND: In silico functional genomics have become a driving force in the way we interpret and use gene expression data, enabling researchers to understand which biological pathways are likely to be affected by the treatments or conditions being studied. There are many approaches to functional genomics, but a number of popular methods determine if a set of modified genes has a higher than expected overlap with genes known to function as part of a pathway (functional enrichment testing). Recently, researchers have started to apply such analyses in a new way: to ask if the data they are collecting show similar disruptions to biological functions compared to reference data. Examples include studying whether similar pathways are perturbed in smokers vs. users of e-cigarettes, or whether a new mouse model of schizophrenia is justified, based on its similarity in cytokine expression to a previously published model. However, there is a dearth of robust statistical methods for testing hypotheses related to these questions and most researchers resort to ad hoc approaches. The goal of this work is to develop a statistical approach to identifying gene pathways that are equivalently (or inversely) changed across two experimental conditions. RESULTS: We developed Equivalent Change Enrichment Analysis (ECEA). This is a new type of gene enrichment analysis based on a statistic that we call the equivalent change index (ECI). An ECI of 1 represents a gene that was over or under-expressed (compared to control) to the same degree across two experiments. Using this statistic, we present an approach to identifying pathways that are changed in similar or opposing ways across experiments. We compare our approach to current methods on simulated data and show that ECEA is able to recover pathways exhibiting such changes even when they exhibit complex patterns of regulation, which other approaches are unable to do. On biological data, our approach recovered pathways that appear directly connected to the condition being studied. CONCLUSIONS: ECEA provides a new way to perform gene enrichment analysis that allows researchers to compare their data to existing datasets and determine if a treatment will cause similar or opposing genomic perturbations.

A Bayesian framework for identifying consistent patterns of microbial abundance between body sites.

Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.

pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS).

BACKGROUND: When designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS. RESULTS: The current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (Δß), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user settings. CONCLUSION: Both under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in the design and planning of EWAS. AVAILABILITY: The web interface is written in the R statistical programming language using Shiny (RStudio Inc., 2016) and is available at https://biostats-shinyr.kumc.edu/pwrEWAS/ . The R package for pwrEWAS is publicly available at GitHub ( https://github.com/stefangraw/pwrEWAS ).

Perception, knowledge, and interest of urologic surgery: a medical student survey.

BACKGROUND: Although only a limited number of medical schools require a formal educational rotation in urologic surgery, urology as a medical specialty continues to attract a large number of students into the match each year. The purpose of this study was to describe medical student awareness, perception, and knowledge of urology, to determine factors influencing students' consideration of urology as a career, and to determine if prior urology clerkship experience is associated with differences in these variables. METHODS: In this cross-sectional study, medical students were electronically surveyed in 07/2016. Self-reported and question-based knowledge of urology were determined. A total of 25 factors were assessed with a five-point Likert scale to determine their influence on students' consideration of urology as a career. Data analysis was performed using R. RESULTS: The survey was completed by 114 students (13.5% of all medical students). A total of 11(9.65%)students had previously participated in a urology clerkship. All students reported awareness of urology; however, only 74 students (64.9%) correctly identified the training pathway and job duties of urologists. Self-perceived knowledge of urology was poor but improved with increased medical school training. Question-based assessment also demonstrated increased knowledge with advanced medical school training (27% per year; p < 0.01). Prior urology clerkship experience appeared to be associated with increased urologic knowledge; however, this was confounded by year in medical school training. When assessing factors impacting students' consideration of a career in urology, 'combination of medicine and surgery' was the most positively influential and 'competitiveness of the specialty' was the most negatively influential. CONCLUSIONS: Although medical students are aware of urology as a specialty, they perceive their knowledge of urology as poor. However, knowledge of urology increases throughout medical school training. Multiple factors influence students' consideration of urology as a career choice. Additional studies are needed to further explore how participation in a formal urology experience alters students' perceptions and influences their consideration of urology as a career choice. TRIAL REGISTRATION: Retrospectively registered.

Health Sciences Patron Preference for Library Spaces: A Multisite Observational Study.

Health sciences libraries are often challenged to make decisions regarding physical space allocation without quantitative data to support specific user preferences. This multisite, longitudinal study sought to answer the following questions related to academic health sciences libraries: (1) Which library spaces are popular with health sciences patrons? (2) How does time of day and allocated seating space affect patron choices? (3) What similarities and differences occur in space usage across four different health sciences libraries? Results suggest health sciences libraries must develop a nuanced understanding of their patrons' preferences to best serve patrons' needs regarding space allocation. Libraries can benefit from these types of methodological studies that target specific populations, supporting more informed space allocation decision making.

Pan-Cancer Analysis Reveals Differential Susceptibility of Bidirectional Gene Promoters to DNA Methylation, Somatic Mutations, and Copy Number Alterations.

Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by the Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration.

National Estimates of and Risk Factors for Inpatient Revision Surgeries for Orofacial Clefts.

OBJECTIVE: To provide national estimates of the number and cost of primary and revision cleft lip and palate surgeries in the U.S. and to determine patient and hospital characteristics associated with disproportionate use of revision surgery. DESIGN: Retrospective cross-sectional study using data obtained from the 2003, 2006, and 2009 Kids' Inpatient Database. SETTING: Inpatient. PATIENTS: Children with CL, CP, or CLP undergoing inpatient cleft lip and/or palate surgery. INTERVENTIONS: Inpatient cleft lip and/or palate surgery. MAIN OUTCOME MEASURES: Orofacial cleft surgery estimates, estimates of primary versus revision surgeries, and estimated inflation-adjusted hospitalization costs. RESULTS: In 2009, there were a total of 2824 and 5431 hospitalizations for cleft lip and palate surgeries, respectively. Revision surgery accounted for 24.2% of cleft lip surgeries and 36.8% of cleft palate surgeries. Children with CLP (OR 1.87, 95% CI: 1.48-2.38), a syndromic diagnosis (OR 1.47, 95% CI: 1.16-1.87), or private insurance (OR 1.71, 95% CI: 1.41-2.09) were more likely to undergo cleft lip revision surgery. Similar risk factors were found for children undergoing cleft palate revision. Mean cost per hospitalization ranged from $7564 to $8393 in 2009, depending on surgery type, and did not change significantly (in 2009 U.S. $) between 2003 and 2009. CONCLUSIONS: Interventions to reduce revision surgery by improving results of primary surgery should be targeted in the population of identified high-risk (e.g., syndromic) patients. In addition, the association of health insurance status with revision surgery highlights the need to understand and address the impact of economic disparities on cleft care delivery.

Urologist Access for the Male Population: An 18-Year Study on Population Trends.

INTRODUCTION: Our study examines the factors associated with urologist availability for younger and older men across the country over a period of 18 years from 2000 to 2018. METHODS: The Area Health Resource Files and US Census Data were analyzed from 2000, 2010, and 2018. The younger male population was defined as men aged 20 to 49, and the older male population was defined as ages 50 to 79. Urologist availability was determined by county at all time points. Logistic regression analysis and geographically weighted regression was completed. RESULTS: Over an 18-year period, overall urologist availability decreased for men by 19.6%. Access to urologist availability for men in metropolitan and rural counties decreased by 9.4% and 29.5%, respectively. Among the younger male cohort, urologist availability increased in metropolitan counties by 4%, but decreased by 16% in rural counties. There was an overall decrease in urologist availability of 28% and 43% in metropolitan and rural counties in the older male population. Multiple logistic regression analysis demonstrated that metropolitan status was the most significant factor associated with urologist availability for both male populations. The odds of each independent factor predicting urologist availability for the younger and older male population is dependent on geography. CONCLUSIONS: The majority of the male population has seen a decline in urologist availability. This is especially true for the older male residing in a rural county. Predictors of urologist availability depend on geographical regions, and understanding these regional drivers may allow us to better address disparities in urological care.

Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Introduction: Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance. Methods: The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate. Results: Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935. Conclusions: This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

Multi-Omics after O-GlcNAc Alteration Identifies Cellular Processes Working Synergistically to Promote Aneuploidy.

Pharmacologic or genetic manipulation of O-GlcNAcylation, an intracellular, single sugar post-translational modification, are difficult to interpret due to the pleotropic nature of O-GlcNAc and the vast signaling pathways it regulates. To address this issue, we employed either OGT (O-GlcNAc transferase), OGA (O-GlcNAcase) liver knockouts, or pharmacological inhibition of OGA coupled with multi-Omics analysis and bioinformatics. We identified numerous genes, proteins, phospho-proteins, or metabolites that were either inversely or equivalently changed between conditions. Moreover, we identified pathways in OGT knockout samples associated with increased aneuploidy. To test and validate these pathways, we induced liver growth in OGT knockouts by partial hepatectomy. OGT knockout livers showed a robust aneuploidy phenotype with disruptions in mitosis, nutrient sensing, protein metabolism/amino acid metabolism, stress response, and HIPPO signaling demonstrating how OGT is essential in controlling aneuploidy pathways. Moreover, these data show how a multi-Omics platform can discern how OGT can synergistically fine-tune multiple cellular pathways.

An R package for Survival-based Gene Set Enrichment Analysis.

Functional enrichment analysis is usually used to assess the effects of experimental differences. However, researchers sometimes want to understand the relationship between transcriptomic variation and health outcomes like survival. Therefore, we suggest the use of Survival-based Gene Set Enrichment Analysis (SGSEA) to help determine biological functions associated with a disease's survival. We developed an R package and corresponding Shiny App called SGSEA for this analysis and presented a study of kidney renal clear cell carcinoma (KIRC) to demonstrate the approach. In Gene Set Enrichment Analysis (GSEA), the log-fold change in expression between treatments is used to rank genes, to determine if a biological function has a non-random distribution of altered gene expression. SGSEA is a variation of GSEA using the hazard ratio instead of a log fold change. Our study shows that pathways enriched with genes whose increased transcription is associated with mortality (NES > 0, adjusted p-value < 0.15) have previously been linked to KIRC survival, helping to demonstrate the value of this approach. This approach allows researchers to quickly identify disease variant pathways for further research and provides supplementary information to standard GSEA, all within a single R package or through using the convenient app.

The rural mortality penalty in U.S. hospital patients with COVID-19.

Background: The COVID-19 pandemic brought greater focus to the rural mortality penalty in the U.S., which describes the greater mortality rate in rural compared to urban areas. Although it is understood that issues such as access to care, age structure of the population, and differences in behavior are likely drivers of the rural mortality penalty, it is critical to try and understand these factors to enable more effective public health policy. Methods: We performed a cross-sectional analysis of a population of patients with COVID-19 who were admitted to hospitals in the United States between 3/1/2020 and 2/26/2023 to better understand factors leading to outcome disparities amongst groups that all had some level of access to hospital care, hypothesizing that deteriorated patient condition at admission likely explained some of the observed difference in mortality between rural and urban populations. Results: Our results supported our hypothesis, showing that the rural mortality penalty persists in this population and that by multiple measures, rural patients were likely to be admitted in worse condition, had worse overall health, and were older. Conclusions: Although the pandemic threw the rural mortality penalty into sharp relief, it is important to remember that it existed prior to the pandemic and will continue to exist until effective interventions are implemented. This study demonstrates the critical need to address the underlying factors that resulted in rural-dwelling patients being admitted to the hospital in worse condition than their urban-dwelling counterparts during the COVID-19 pandemic, which likely affected other healthcare outcomes as well.

Comprehensive exploration of JQ1 and GSK2801 targets in breast cancer using network pharmacology and molecular modeling approaches.

JQ1 and GSK2801 are bromo domain inhibitors (BDI) known to exhibit enhanced anti-cancer activity when combined with other agents. However, the underlying molecular mechanisms behind such enhanced activity remain unclear. We used network-pharmacology approaches to understand the shared molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 when used together to treat breast cancer (BC). The gene targets of JQ1 and GSK2801 were intersected with known BC-targets and their putative targets against BC were derived. The key genes were explored through gene-ontology-enrichment, Protein-Protein-Interaction (PPI) networking, survival analysis, and molecular modeling simulations. The genes, CTSB, MAPK14, MET, PSEN2 and STAT3, were found to be common targets for both drugs. In total, 49 biological processes, five molecular functions and 61 metabolic pathways were similarly enriched for JQ1 and GSK2801 BC targets among which several terms are related to cancer: IL-17, TNF and JAK-STAT signaling pathways. Survival analyses revealed that all five putative synergistic targets are significantly associated with survival in BC (log-rank p < 0.05). Molecular modeling studies showed stable binding of JQ1 and GSK2801 against their targets. In conclusion, this study explored and illuminated the possible molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 against BC and suggests synergistic action through their similar BC-targets and gene-ontologies.

Image Distortion During Flexible Ureteroscopy: A Laboratory Model Comparing Super Pulsed Thulium Fiber Laser vs High-Power Ho:YAG Laser.

Purpose: Digital ureteroscopes employ "chip-on-the-tip" technology that allows for significant improvement in image resolution. However, image distortion often occurs during laser lithotripsy owing to acoustic wave production. We sought to compare image distortion using different laser power settings and distances from the laser fiber tip to the scope for the Super Pulsed Thulium Fiber (SPTF) laser and high-power Holmium:YAG (Ho:YAG) laser. Materials and Methods: Ureteroscopy was simulated using a silicon kidney-ureter-bladder model fitted with a 12F/14F access sheath and the Lithovue™ (Boston Scientific), disposable digital flexible ureteroscope. At defined laser parameters (10, 20, 30 and 40 W, short pulse), a 200-µm laser fiber was slowly retracted toward the tip of the ureteroscope during laser activation. Image distortion was identified, and distance from the laser tip to the scope tip was determined. Data from the two lasers were compared utilizing t-tests. Results: After controlling for frequency, power, and laser mode, utilizing 1.0 J of energy was significantly associated with less feedback than 0.5 J (-0.091 mm, p ≤ 0.05). Increased power was associated with larger feedback distance (0.016 mm, p ≤ 0.05); however, increase in frequency did not have a significant effect (-0.001 mm, p = 0.39). The SPFT laser had significantly less feedback when compared with all Holmium laser modes. Conclusions: Increased total power results in image distortion occurring at greater distances from the tip of the ureteroscope during laser activation. Image distortion occurs further from the ureteroscope with Ho:YAG laser than with SPTF fibers at the same laser settings. In clinical practice, the tip of the laser fiber should be kept further away from the tip of the scope during ureteroscopy as the power increases as well as when utilizing the Ho:YAG system compared with the SPTF laser platform. The SPTF laser may have a better safety profile in terms of potential scope damage.