Molecular Hallmarks of Prostate-specific Membrane Antigen in Treatment-naïve Prostate Cancer.

BACKGROUND AND OBJECTIVE: We characterized tumor prostate-specific membrane antigen (PSMA) levels as a reflection of cancer biology and treatment sensitivities for treatment-naïve prostate cancer. METHODS: We first correlated PSMA positron emission tomography (PET) maximum standardized uptake values (SUVmax) in primary prostate cancer with tumor FOLH1 (PSMA RNA abundance) to establish RNA as a proxy (n = 55). We then discovered and validated molecular pathways associated with PSMA RNA levels in two large primary tumor cohorts. We validated those associations in independent cohorts (18 total; 5684 tumor samples) to characterize the pathways and treatment responses associated with PSMA. KEY FINDINGS AND LIMITATIONS: PSMA RNA abundance correlates moderately with SUVmax (ρ = 0.41). In independent cohorts, androgen receptor signaling is more active in tumors with high PSMA. Accordingly, patients with high PSMA tumors experienced longer cancer-specific survival when managed with androgen deprivation therapy for biochemical recurrence (adjusted hazard ratio [AHR] 0.54 [0.34-0.87]; n = 174). PSMA low tumors possess molecular markers of resistance to radiotherapy. Consistent with this, patients with high PSMA tumors experience longer time to recurrence following primary radiotherapy (AHR 0.50 [0.28-0.90]; n = 248). In the SAKK09/10 trial (n = 224), patients with high PSMA tumors who were managed with salvage radiotherapy experienced longer time to progression in the 64-Gy arm (restricted mean survival time [RMST] +7.60 [0.05-15.16]), but this effect was mitigated in the 70-Gy arm (RMST 3.52 [-3.30 to 10.33]). Limitations include using PSMA RNA as a surrogate for PET SUVmax. CONCLUSIONS AND CLINICAL IMPLICATIONS: PSMA levels in treatment-naïve prostate cancer differentiate tumor biology and treatment susceptibilities. These results warrant validation using PET metrics to substantiate management decisions based on imaging.

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline.

Summary: DNA sequencing is becoming more affordable and faster through advances in high-throughput technologies. This rise in data availability has contributed to the development of novel algorithms to elucidate previously obscure features and led to an increased reliance on complex workflows to integrate such tools into analyses pipelines. To facilitate the analysis of DNA sequencing data, we created metapipeline-DNA, a highly configurable and extensible pipeline. It encompasses a broad range of processing including raw sequencing read alignment and recalibration, variant calling, quality control and subclonal reconstruction. Metapipeline-DNA also contains configuration options to select and tune analyses while being robust to failures. This standardizes and simplifies the ability to analyze large DNA sequencing in both clinical and research settings. Availability: Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is freely available at https://github.com/uclahs-cds/metapipeline-DNA.

De-escalation of Monitoring in Active Surveillance for Prostate Cancer: Results from the GAP3 Consortium.

BACKGROUND AND OBJECTIVE: There is no consensus on de-escalation of monitoring during active surveillance (AS) for prostate cancer (PCa). Our objective was to determine clinical criteria that can be used in decisions to reduce the intensity of AS monitoring. METHODS: The global prospective AS cohort from the Global Action Plan prostate cancer AS consortium was retrospectively analyzed. The 24656 patients with complete outcome data were considered. The primary goal was to develop a model identifying a subgroup with a high ratio of other-cause mortality (OCM) to PCa-specific mortality (PCSM). Nonparametric competing-risks models were used to estimate cause-specific mortality. We hypothesized that the subgroup with the highest OCM/PCSM ratio would be good candidates for de-escalation of AS monitoring. KEY FINDINGS AND LIMITATIONS: Cumulative mortality at 15 yr, accounting for censoring, was 1.3% for PCSM, 11.5% for OCM, and 18.7% for death from unknown causes. We identified body mass index (BMI) >25 kg/m2 and <11% positive cores at initial biopsy as an optimal set of criteria for discriminating OCM from PCSM. The 15-yr OCM/PCSM ratio was 34.2 times higher for patients meeting these criteria than for those not meeting the criteria. According to these criteria, 37% of the cohort would be eligible for de-escalation of monitoring. Limitations include the retrospective nature of the study and the lack of external validation. CONCLUSIONS: Our study identified BMI >25 kg/m2 and <11% positive cores at initial biopsy as clinical criteria for de-escalation of AS monitoring in PCa. PATIENT SUMMARY: We investigated factors that could help in deciding on when to reduce the intensity of monitoring for patients on active surveillance for prostate cancer. We found that patients with higher BMI (body mass index) and lower prostate cancer volume may be good candidates for less intensive monitoring. This model could help doctors and patients in making decisions on active surveillance for prostate cancer.

Limitations of Clustering with PCA and Correlated Noise.

It is now common to have a modest to large number of features on individuals with complex diseases. Unsupervised analyses, such as clustering with and without preprocessing by Principle Component Analysis (PCA), is widely used in practice to uncover subgroups in a sample. However, in many modern studies features are often highly correlated and noisy (e.g. SNP's, -omics, quantitative imaging markers, and electronic health record data). The practical performance of clustering approaches in these settings remains unclear. Through extensive simulations and empirical examples applying Gaussian Mixture Models and related clustering methods, we show these approaches (including variants of kmeans, VarSelLCM, HDClassifier, and Fisher-EM) can have very poor performance in many settings. We also show the poor performance is often driven by either an explicit or implicit assumption by the clustering algorithm that high variance features are relevant while lower variance features are irrelevant, called the variance as relevance assumption. We develop practical pre-processing approaches that improve analysis performance in some cases. This work offers practical guidance on the strengths and limitations of unsupervised clustering approaches in modern data analysis applications.

Weight Loss Outcomes in a Veterans Affairs Pharmacotherapy-based Weight Management Clinic.

Context: Despite a high prevalence of obesity in the veteran population, antiobesity medications (AOMs) have been underused in the Veterans Health Administration. Real-world reports on outcomes when AOMs have been used in veterans is limited. Objective: To analyze weight loss outcomes from a local Veterans Health Administration pharmacotherapy-based weight management clinic (WMC). Methods: This was a retrospective cohort study of veterans enrolled in a local WMC for 15 months from August 2016 through September 2018 and followed through November 2019. Patients were offered 1 of 5 available AOMs based on their comorbidities. Factors associated with weight loss (5% or more weight loss) were assessed. Key results: A total of 159 patients were seen in a WMC, 149 (93.7%) veterans were prescribed an AOM, and 129 returned for follow-up. Overall, 61/129 (47%) patients achieved 5% or greater weight loss and 28/129 (22%) achieved 10% or greater weight loss within 15 months. Clinically significant weight loss (%) over the first 15 months was achieved with phentermine/topiramate ER (-6.3%) and liraglutide (-7.5%), but not with orlistat (-3.9%) and lorcaserin (-3.6%). Comorbid obstructive sleep apnea was negatively associated with achieving ≥5% weight loss. Conclusion: Phentermine/topiramate ER and liraglutide were found to be effective AOMs among veterans. Further work is needed to mitigate barriers to AOM initiation given the continued rise in obesity.

Anthropometric, dietary, and lifestyle factors and risk of advanced thyroid cancer: The NIH-AARP diet and health cohort study.

BACKGROUND: Most patients diagnosed with thyroid cancer have low-risk disease, but some have a higher risk for persistent or recurrent disease and even death from thyroid cancer. Few studies have evaluated potential anthropometric, lifestyle, or dietary risk factors for advanced or aggressive types of thyroid cancer. METHODS: Using data from a large US cohort study, we examined associations for high-risk thyroid cancer (HRTC) and, separately, low-risk thyroid cancer (LRTC) in relation to anthropometric factors, diet, smoking, and alcohol consumption. The National Institutes of Health-American Association of Retired Persons (NIH-AARP) Diet and Health Study included 304,122 participants (124,656 women and 179,466 men) without a history of cancer who completed a mailed questionnaire in 1996-1997 and were followed for cancer incidence through 2011 via linkages with state cancer registries. Hazard ratios (HRs) for anthropometric, dietary, and lifestyle factors in relation to HRTC or LRTC, defined using guidance from the American Thyroid Association initial risk of recurrence classification, were calculated using multivariable-adjusted Cox proportional hazards regression models. RESULTS: During follow-up (median = 10.1 years), 426 participants were diagnosed with HRTC (n = 95) or LRTC (n = 331). In models combining men and women, baseline waist circumference (per 5 cm, HR = 1.13, 95% confidence interval [CI] 1.01-1.27) and weight gain from age 18 years to baseline age (per 5 kg, HR = 1.14, 95% CI 1.02-1.28) were positively associated with risk of HRTC but not LRTC. In contrast, vegetable intake (per cup equivalents/day, HR = 1.15, 95% CI 1.01-1.30), cigarette smoking (current vs. never, HR = 0.39, 95% CI 0.23-0.68), and alcohol consumption (per drink/day, HR = 0.83, 95% CI 0.70-0.97) were associated with risk of LRTC but not HRTC. The association of LRTC risk with vegetable intake was limited to men, and that of current smoking was more pronounced in women. CONCLUSIONS: Our findings suggest that greater waist circumference and adulthood weight gain are associated with thyroid cancers at higher risk for recurrence. These results may have implications for the primary prevention of advanced thyroid cancer.

Serrate RNA Effector Molecule (SRRT) Is Associated with Prostate Cancer Progression and Is a Predictor of Poor Prognosis in Lethal Prostate Cancer.

Arsenite-resistance protein 2, also known as serrate RNA effector molecule (ARS2/SRRT), is known to be involved in cellular proliferation and tumorigenicity. However, its role in prostate cancer (PCa) has not yet been established. We investigated the potential role of SRRT in 496 prostate samples including benign, incidental, advanced, and castrate-resistant patients treated by androgen deprivation therapy (ADT). We also explored the association of SRRT with common genetic aberrations in lethal PCa using immunohistochemistry (IHC) and performed a detailed analysis of SRRT expression using The Cancer Genome Atlas (TCGA PRAD) by utilizing RNA-seq, clinical information (pathological T category and pathological Gleason score). Our findings indicated that high SRRT expression was significantly associated with poor overall survival (OS) and cause-specific survival (CSS). SRRT expression was also significantly associated with common genomic aberrations in lethal PCa such as PTEN loss, ERG gain, mutant TP53, or ATM. Furthermore, TCGA PRAD data revealed that high SRRT mRNA expression was significantly associated with higher Gleason scores, PSA levels, and T pathological categories. Gene set enrichment analysis (GSEA) of RNAseq data from the TCGA PRAD cohort indicated that SRRT may play a potential role in regulating the expression of genes involved in prostate cancer aggressiveness. Conclusion: The current data identify the SRRT's potential role as a prognostic for lethal PCa, and further research is required to investigate its potential as a therapeutic target.

Single-Cell Analysis in Lung Adenocarcinoma Implicates RNA Editing in Cancer Innate Immunity and Patient Prognosis.

RNA editing modifies single nucleotides of RNAs, regulating primary protein structure and protein abundance. In recent years, the diversity of proteins and complexity of gene regulation associated with RNA editing dysregulation has been increasingly appreciated in oncology. Large-scale shifts in editing have been observed in bulk tumors across various cancer types. However, RNA editing in single cells and individual cell types within tumors has not been explored. By profiling editing in single cells from lung adenocarcinoma biopsies, we found that the increased editing trend of bulk lung tumors was unique to cancer cells. Elevated editing levels were observed in cancer cells resistant to targeted therapy, and editing sites associated with drug response were enriched. Consistent with the regulation of antiviral pathways by RNA editing, higher editing levels in cancer cells were associated with reduced antitumor innate immune response, especially levels of natural killer cell infiltration. In addition, the level of RNA editing in cancer cells was positively associated with somatic point mutation burden. This observation motivated the definition of a new metric, RNA editing load, reflecting the amount of RNA mutations created by RNA editing. Importantly, in lung cancer, RNA editing load was a stronger predictor of patient survival than DNA mutations. This study provides the first single cell dissection of editing in cancer and highlights the significance of RNA editing load in cancer prognosis. SIGNIFICANCE: RNA editing analysis in single lung adenocarcinoma cells uncovers RNA mutations that correlate with tumor mutation burden and cancer innate immunity and reveals the amount of RNA mutations that strongly predicts patient survival. See related commentary by Luo and Liang, p. 351.

Creating an algorithm to identify indices of sleep quantity and quality from a wearable armband in adults.

Objective: To develop an algorithm to quantify indices of sleep quantity and quality using the SenseWear armband (SWA) and to compare indices of sleep from this novel algorithm to standard wrist actigraphy (Actiwatch 2; AW2) under free-living conditions. Material and Methods: Thirty participants (47±10 years; 33.0±4.8kg/m2) wore the SWA and AW2 for seven consecutive days. Participants self-reported bedtime and waketime across these 7 days. Bedtime, sleep onset, sleep offset, waketime, total sleep time (TST), time in bed (TIB), sleep effciency (SE), sleep onset latency (SOL), wake after sleep onset (WASO), sleep fragmentations (SF), sleep regularity (calculated as SD of waketime), and mid-point of sleep were calculated using each device. Results: There was significant evidence for equivalence of means (or mean ranks) for bedtime, sleep onset, sleep offset, waketime, TST, TIB, SOL, WASO, and midpoint of sleep measured by the SWA and AW2 (p<0.05). There was insuffcient evidence for equivalence of means in SF (SW: 25±6 vs. AW2: 10±3 events; p=1.0), mean ranks in sleep regularity (SW: 58±33 vs. AW2: 68±40 min; p=0.11), and mean ranks in SE (SW: 84.7±5.1% vs. AW2: 86.3±5.5%; p=0.05). When comparing minute-by-minute sleep/wake status, the sensitivity and specificity of the SWA were 0.94 (95%CI: 0.93, 0.95) and 0.88 (95%CI: 0.85, 0.90), respectively, using AW2 as the criterion measure. Conclusion: The algorithm developed for the SWA produced relatively accurate and consistent measurements of sleep quantity, timing, and quality compared to the AW2 under free-living conditions. Thus, the SWA is a viable alternative to standard wrist actigraphy.

Effect of sleep on weight loss and adherence to diet and physical activity recommendations during an 18-month behavioral weight loss intervention.

BACKGROUND/OBJECTIVES: To examine the association between indices of sleep quantity and quality with dietary adherence, physical activity adherence, and weight loss during a behavioral weight loss intervention. METHODS: Adults (n = 156) with overweight and obesity (40 ± 9 years, 84% female, BMI: 34.4 ± 4.2 kg/m2) participated in an 18-month behavioral weight loss intervention which prescribed a reduced calorie diet (1200-1800 kcal/d) and increased physical activity (300 min/wk). Body weight, indices of sleep (SenseWear armband; SWA), energy intake (EI, 3-day food records), and moderate-to-vigorous physical activity (SWA) were measured at baseline, 6, 12, and 18 months. Linear mixed effects models examined the association between sleep and weight change over time. Additional models were adjusted for covariates including age, BMI, sex, race, ethnicity, study completion, randomization, EI, and physical activity. Secondary analyses examined the association between sleep and adherence to diet and physical activity recommendations. RESULTS: Mean weight loss was 7.7 ± 5.4, 8.4 ± 7.9, and 7.1 ± 9.0 kg at 6, 12, and 18 months, respectively. Lower sleep efficiency, higher wake after sleep onset (WASO), more awakenings, and higher sleep onset latency (SOL) were significantly associated with attenuated weight loss (p < 0.05). Lower sleep efficiency, more awakenings, and higher SOL remained significantly associated with blunted weight loss after adjustment for covariates (p < 0.05). Later waketime, longer time in bed, longer sleep duration, higher WASO, more awakenings, and higher SOL were associated with lower odds of achieving ≥300 min/wk of moderate-to-vigorous physical activity, adjusted for covariates (FDR p < 0.05). CONCLUSIONS: Future studies should evaluate whether incorporating strategies to improve sleep health within a behavioral weight loss intervention leads to improved adherence to diet and physical activity recommendations and enhanced weight loss. CLINICAL TRIALS IDENTIFIER: NCT01985568.

Clinical phenotyping in sarcoidosis using cluster analysis.

BACKGROUND: Most phenotyping paradigms in sarcoidosis are based on expert opinion; however, no paradigm has been widely adopted because of the subjectivity in classification. We hypothesized that cluster analysis could be performed on common clinical variables to define more objective sarcoidosis phenotypes. METHODS: We performed a retrospective cohort study of 554 sarcoidosis cases to identify distinct phenotypes of sarcoidosis based on 29 clinical features. Model-based clustering was performed using the VarSelLCM R package and the Integrated Completed Likelihood (ICL) criteria were used to estimate number of clusters. To identify features associated with cluster membership, features were ranked based on variable importance scores from the VarSelLCM model, and additional univariate tests (Fisher's exact test and one-way ANOVA) were performed using q-values correcting for multiple testing. The Wasfi severity score was also compared between clusters. RESULTS: Cluster analysis resulted in 6 sarcoidosis phenotypes. Salient characteristics for each cluster are as follows: Phenotype (1) supranormal lung function and majority Scadding stage 2/3; phenotype (2) supranormal lung function and majority Scadding stage 0/1; phenotype (3) normal lung function and split Scadding stages between 0/1 and 2/3; phenotype (4) obstructive lung function and majority Scadding stage 2/3; phenotype (5) restrictive lung function and majority Scadding stage 2/3; phenotype (6) mixed obstructive and restrictive lung function and mostly Scadding stage 4. Although there were differences in the percentages, all Scadding stages were encompassed by all of the phenotypes, except for phenotype 1, in which none were Scadding stage 4. Clusters 4, 5, 6 were significantly more likely to have ever been on immunosuppressive treatment and had higher Wasfi disease severity scores. CONCLUSIONS: Cluster analysis produced 6 sarcoidosis phenotypes that demonstrated less severe and severe phenotypes. Phenotypes 1, 2, 3 have less lung function abnormalities, a lower percentage on immunosuppressive treatment and lower Wasfi severity scores. Phenotypes 4, 5, 6 were characterized by lung function abnormalities, more parenchymal abnormalities, an increased percentage on immunosuppressive treatment and higher Wasfi severity scores. These data support using cluster analysis as an objective and clinically useful way to phenotype sarcoidosis subjects and to empower clinicians to identify those with more severe disease versus those who have less severe disease, independent of Scadding stage.

Multicenter Study Evaluating Nitrous Oxide Use for Labor Analgesia at High- and Low-Altitude Institutions.

BACKGROUND: Nitrous oxide (N2O) has been used nationally as an analgesic in many clinical settings. While neuraxial analgesia is still the most commonly used labor analgesic in the United States, there is increasing use of N2O in labor. Given the reduction in the partial pressure of gases at a higher altitude, N2O has been reported to have reduced analgesic properties. However, there is no study to date evaluating the impact of altitude on labor analgesia and N2O. METHODS: We conducted a multicenter retrospective data analysis of a N2O registry collected from 4 institutions over a 3-year period. We compared the impact of altitude on 50% N2O administration for labor analgesia, conversion rates to another analgesic modality, as well as collected side effect frequencies and conversion predictors. Multivariable regression models were used to compare clinical characteristics and outcomes between parturients at high and low altitudes, while adjusting for race, ethnicity, education, and age (logistic and linear regressions for categorical and quantitative outcomes, respectively). RESULTS: A total of 1856 laboring parturients (age 18-50) were included in the analysis. The odds of converting from 50% N2O to another analgesic modality had no statistically significant difference between high- versus low-altitude institutions (adjusted odds ratio [aOR], 1.13; 95% confidence interval [CI], 0.90-1.42; P = .3). Yet, when parturients at low altitude converted from N2O, they were more likely (aOR, 3.03; 95% CI, 1.59-5.88) to choose neuraxial analgesia instead of another analgesic modality when compared to high-altitude parturients. This is possibly due to higher epidural rates at the low-altitude institutions. When parturients at high altitude did convert into another modality, they were more likely (aOR, 2.19; 95% CI, 1.14-4.21) to convert due to inadequate pain relief compared to low-altitude parturients; however, missing data may have affected this finding. Laboring individuals at low altitude were significantly more likely to experience side effects (aOR, 2.13; 95% CI, 1.45-3.12). Those requiring labor augmentation, assisted vaginal, or cesarean delivery converted to neuraxial analgesia significantly more often than those that delivered via spontaneous vaginal delivery (P < .05) in both high- and low-altitude groups. CONCLUSIONS: This is the first study evaluating 50% N2O as a labor analgesic at high altitude. As expected, we found lower side effects at high altitude, likely due to the lower partial pressure of N2O. However, there was not a statistically significant increase in conversion from N2O to another analgesic modality at high altitude and no clinically significant differences in neonatal outcomes.

Association of Sputum Neutrophil Extracellular Trap Subsets With IgA Anti-Citrullinated Protein Antibodies in Subjects at Risk for Rheumatoid Arthritis.

OBJECTIVE: Mechanisms leading to anti-citrullinated protein antibody (ACPA) generation in rheumatoid arthritis (RA) are hypothesized to originate in the lung. We undertook this study to understand associations between neutrophil extracellular trap (NET) formation in the lung and local ACPA generation in subjects at risk of developing RA. METHODS: Induced sputum was collected from 49 subjects at risk of developing RA, 12 patients with RA, and 18 controls. Sputum neutrophils were tested for ex vivo NET formation, and sputum-induced NET formation of control neutrophils was measured using immunofluorescence imaging. Sputum macrophages were tested for ex vivo endocytosis of apoptotic and opsonized cells. Levels of ACPA, NET remnants, and inflammatory proteins were quantified in sputum supernatant. RESULTS: Spontaneous citrullinated histone H3 (Cit-H3)-expressing NET formation was higher in sputum neutrophils from at-risk subjects and RA patients compared to controls (median 12%, 22%, and 0%, respectively; P < 0.01). In at-risk subjects, sputum IgA ACPA correlated with the percentage of neutrophils that underwent Cit-H3+ NET formation (r = 0.49, P = 0.002) and levels of Cit-H3+ NET remnants (r = 0.70, P < 0.001). Reduced endocytic capacity of sputum macrophages was found in at-risk subjects and RA patients compared to controls. Using a mediation model, we found that sputum inflammatory proteins were associated with sputum IgA ACPA through a pathway mediated by Cit-H3+ NET remnants. Sputum-induced Cit-H3+ NET formation also correlated with sputum levels of interleukin-1ß (IL-1ß), IL-6, and tumor necrosis factor in at-risk subjects, suggesting a causal relationship. CONCLUSION: These data support a potential mechanism for mucosal ACPA generation in subjects at risk of developing RA, whereby inflammation leads to increased citrullinated protein-expressing NETs that promote local ACPA generation.

Predictors of long-term weight loss trajectories during a behavioral weight loss intervention: An exploratory analysis.

BACKGROUND: Substantial interindividual variability in response to behavioral weight loss interventions remains a critical challenge in obesity treatment. An improved understanding of the complex factors that contribute to this variability may improve obesity treatment outcomes. OBJECTIVE: To identify weight change trajectories during a behavioral weight loss intervention and to explore differences between trajectory groups in sociodemographic, biologic, behavioral, and psychosocial factors. METHODS: Adults (n = 170, 40 ± 9 years, BMI 34 ± 4 kg/m2, 84% female) participated in an 18-month behavioral weight loss intervention. Weight was measured at 0, 3, 6, 9, 12, 15, 18, and 24 months. Among participants with at least two weights after baseline (n = 140), clusters of longitudinal trajectories of changes in weight were identified using a latent class growth mixture model. The association between baseline factors or changes in factors over time and trajectory group was examined. RESULTS: Two weight change trajectories were identified: "weight regainers" (n = 91) and "weight loss maintainers" (n = 49). Black participants (90%, 19/21) were more likely than non-Black participants to be regainers versus maintainers (p < 0.01). Maintainers demonstrated greater increases in device-measured physical activity, autonomous motivation for exercise, diet self-efficacy, cognitive restraint, and engagement in weight management behaviors and greater reductions in barriers for exercise, disinhibition, and depressive symptoms over 24 months versus regainers (p < 0.05). CONCLUSION: Maintainers and regainers appear to be distinct trajectories that are associated with specific sociodemographic, behavioral, and psychosocial factors. Study results suggest potential targets for more tailored, multifaceted interventions to improve obesity treatment outcomes.

Predicting olfactory loss in chronic rhinosinusitis using machine learning.

OBJECTIVE: Compare machine learning (ML)-based predictive analytics methods to traditional logistic regression in classification of olfactory dysfunction in chronic rhinosinusitis (CRS-OD) and identify predictors within a large multi-institutional cohort of refractory CRS patients. METHODS: Adult CRS patients enrolled in a prospective, multi-institutional, observational cohort study were assessed for baseline CRS-OD using a smell identification test (SIT) or brief SIT (bSIT). Four different ML methods were compared to traditional logistic regression for classification of CRS normosmics versus CRS-OD. RESULTS: Data were collected for 611 study participants who met inclusion criteria between 2011 April and 2015 July. Thirty-four percent of enrolled patients demonstrated olfactory loss on psychophysical testing. Differences between CRS normosmics and those with smell loss included objective disease measures (CT and endoscopy scores), age, sex, prior surgeries, socioeconomic status, steroid use, polyp presence, asthma, and aspirin sensitivity. Most ML methods performed favorably in terms of predictive ability. Top predictors include factors previously reported in the literature, as well as several socioeconomic factors. CONCLUSION: Olfactory dysfunction is a variable phenomenon in CRS patients. ML methods perform well compared to traditional logistic regression in classification of normosmia versus smell loss in CRS, and are able to include numerous risk factors into prediction models. Several actionable features were identified as risk factors for CRS-OD. These results suggest that ML methods may be useful for current understanding and future study of hyposmia secondary to sinonasal disease, the most common cause of persistent olfactory loss in the general population.

Comparing Statistical Tests for Differential Network Analysis of Gene Modules.

Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a "module" (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results.

Graduate nursing student stressors during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic profoundly impacted graduate nursing students at work, home, and school. Stress can influence the ability to focus, study, and may delay continuation in graduate school. PURPOSE: The purpose of this study was to identify the stressors of graduate nursing students during the pandemic. METHOD: A prospective, descriptive, online survey design was used to identify graduate nursing student stressors during the pandemic at one educational institution in the United States. Questions related to employment, COVID-19 exposure, institutional support, future graduate plans were summarized for the total sample and stratified by program (MS, DNP, PhD). Changes in a total stress score were evaluated pre and post onset of the COVID-19 pandemic. RESULTS: A total of 222 graduate nursing students completed the survey. The vast majority of students were employed before the pandemic and a significant decrease in employment occurred during the pandemic (97.3% to 90.1%, p < .001). Overall stress increased (p < .001). The increased total stress was associated with students participating in clinical rotations (q = 0.024) and having a change in work hours (q = 0.022). CONCLUSIONS: Nursing schools need to address graduate student concerns during the pandemic, including having clear communication platforms and offering support services.

Clinical Nurse Specialists in the United States Registered With a National Provider Identifier.

PURPOSE/AIMS: This study illuminates the workforce of the clinical nurse specialist (CNS), using data from the National Provider Identifier (NPI) registry. DESIGN: This is a descriptive exploratory study using CNSs' self-reported data within the NPI registry through December 2019. METHODS: Tabulations for frequencies of available demographics, population foci/specialty, and tests for differences in urban/rural locations were conducted. Clinical nurse specialists practicing as nurse practitioners were also counted and identified. Run charts were used to assess quarterly trends for CNSs obtaining new NPIs. RESULTS: Ten thousand CNSs were registered with an NPI, and 9470 reported 1 population foci/specialty. Furthermore, 1932 CNSs (19.3%) also report working as a nurse practitioner. Only 2 CNS specialties differed by more than 4% between urban and rural areas (respectively): family health (7.7% and 15.5%) and CNSs without a specialty (17.1% and 12.6%). Run charts identified 5 specialties with changing trends in new NPI enumerations (family health, adult-gero, postacute care, pediatrics, and women's health). CONCLUSIONS: Of the estimated 89 122 CNSs in the United States, only 11.2% of the CNS workforce was described and accounted for within the NPI registry. Clinical nurse specialists are encouraged to register for an NPI. Further research is necessary to describe the location and type of work CNSs are performing within the US healthcare system.

A Survey of Mental Health in Graduate Nursing Students during the COVID-19 Pandemic.

BACKGROUND: Elevated levels of stress, anxiety, and depression are common symptoms in graduate students pursuing a degree in the health care professions. The onset of the COVID-19 pandemic likely complicated these existing issues. PURPOSE: To confirm this hypothesis, researchers created a survey to examine the experiences of graduate nursing students during COVID-19. METHODS: Graduate nursing students (n = 222) completed the survey, which included 2 instruments: the Depression, Anxiety and Stress Scale (DASS-21) and the Impact of Events Scale (IES-R). RESULTS: Nearly 25% of students expressed moderate to extremely severe levels of negative emotional states on the DASS-21, and 23.8% of students scored within the area of clinical concern for the presence of posttraumatic stress disorder on the IES-R. CONCLUSIONS: Understanding levels of mental health and associated factors that may contribute to changes can assist administration, faculty, and staff in targeting resources and interventions to support graduate nursing students to continue their education.

tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R.

BACKGROUND: The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. RESULTS: We developed the R package "tidyMicro" to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts' control over workflow. A complete vignette is provided to aid new users in analysis workflow. CONCLUSIONS: tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets.