Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model.

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension.

BACKGROUND: Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead electrocardiogram (ECG). METHODS: The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%), and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). Performance was also tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS: Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test set at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test set. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION: The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.

PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records. METHODS: In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity. RESULTS: For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time. CONCLUSION: Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.

The Effects of Wastewater Reuse on Smallmouth Bass (Micropterus dolomieu) Relative Abundance in the Shenandoah River Watershed, USA.

Municipal and industrial wastewater effluent is an important source of water for lotic systems, especially during periods of low flow. The accumulated wastewater effluent flows-expressed as a percentage of total streamflow (ACCWW%)-contain chemical mixtures that pose a risk to aquatic life; fish may be particularly vulnerable when chronically exposed. Although there has been considerable focus on individual-level effects of exposure to chemical mixtures found in wastewater effluent, scaling up to population-level effects remains a challenging component needed to better understand the potential consequences of exposure in wild populations. This may be particularly important under a changing climate in which wastewater reuse could be essential to maintain river flows. We evaluated the effects of chronic exposure to wastewater effluent, as measured by ACCWW%, on the relative abundance of young-of-year (YOY), juvenile, and adult smallmouth bass (Micropterus dolomieu) populations in the Shenandoah River Watershed (USA). We found that increases in ACCWW% in the previous year and during the prespawn period were negatively correlated with the relative abundance of YOY, resulting in an average 41% predicted decrease in abundance (range = 0.5%-94% predicted decrease in abundance). This lagged effect suggests that adult fish reproductive performance may be compromised by chemical exposure during periods of high ACCWW%. No relationships between ACCWW% and juvenile or adult relative abundance were found, suggesting that negative effects of ACCWW% on YOY abundance may be offset due to compensatory mechanisms following higher ACCWW% exposure. Understanding the effects of wastewater effluent exposure at multiple levels of biological organization will help in the development of management strategies aimed at protecting aquatic life. Environ Toxicol Chem 2024;43:1138-1148. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Medicare Part D Coverage of Drugs Selected for the Drug Price Negotiation Program.

This cross-sectional study describes and historically benchmarks Medicare Part D coverage in 2019 and 2023 for the first 10 drugs selected for negotiation.

Silver Lining Study: Missileer Fatigue Mitigation During 2020 Coronavirus Pandemic.

INTRODUCTION: The U.S. Air Force's Intercontinental Ballistic Missile (ICBM) force stands ready to launch weapons 365 days per year. Since its inception, missileers vigilantly operate launch consoles on a 3-day cycle: minimum 24-hour alert-shift/24-hour travel-admin/24-hour off, leading to concerns that health, morale, and alertness are chronically impacted. In 2020, a Missileer Occupational Health Assessment (OHA) revealed 76% of respondents struggle with being rested for duty and 29% of respondents never feel adequately rested for duty. Later that year, 20th Air Force initiated long-duration operations to safeguard from the SARS CoV-2 (COVID-19) Pandemic, resulting in increased operations tempo, and exacerbating crew fatigue.341st Operations Group and 341st Medical Group at Malmstrom Air Force Base enacted interventions to mitigate crew fatigue and support continued readiness during pandemic operations. They recorded, analyzed, and compiled findings in this report, including recommendations for long-term ICBM operations at Missile Wings. MATERIALS AND METHODS: All participants were Nuclear and Missile Operations Officers, or missileers, were continuously evaluated with qualitative and quantitative measures to ensure safety of the force during a period of unprecedented change. Interventions implemented and evaluated during the 9-month period included: environmental modifications, scheduling changes, and crew education on fatigue management, nutrition, anticipatory sleep preparation, and fitness. Most notably, the 341st Operations Group examined various 3-person and 4-person shift-length and alert duration schedules. Psychomotor vigilance testing results validated safety of operators and delta between pre- and post-shift measurements. Crew force readiness trends were analyzed for force-health awareness. Pre- and post-OHA results were compared for subjective changes. Fatigue and health-related outcomes were collected from a safety monitoring effort during standard and COVID-19 operations at 341st Missile Wing. RESULTS: Findings from qualitative and quantitative data indicate the optimal schedule is a 3-week cycle:7-day alert/7-day recovery/7-day training-administrative utilizing 4-member or 3-member crews for low tempo operations. Crews experimented with shift-lengths of 24hrs-on/24hrs-off, 16hrs-on/8hrs-off, and 12hrs-on/12hrs-off. Maximum safe alert duration is 7 days due to task fatigue onset between 8 and 10 days. Short and long duration Duties Not to Include Flight (DNIF) (also known as Duties Not to Include Alert (DNIA) among missileers) rates decreased from the first to last month of the period by 74.6% and 79.2%, respectively. The number of alerts missed per month decreased 86% from baseline. The 2021 OHA found a 7% decline in members seeking separation, and absence of sleep, fatigue, and physical or mental health as missileer concerns. CONCLUSIONS: This analysis has identified a sustainable alert rotation of 7/7/7 with emphasis on protected recovery and training time and has been continued after concluding pandemic operations, creating consistent schedule stability where there once was none. If executed properly, this alert rotation, regardless of shift-length selected, has potential to improve trust between crews and leadership, provides adequate recovery time between alerts to maintain health, and improves wellness, family stability, morale, unit cohesion, and crew force retention. Notably, all Air Force Global Strike Missile Operations Groups adjusted scheduling practices to align with these findings.

Retrospective study of propionic acidemia using natural language processing in Mayo Clinic electronic health record data.

BACKGROUND: Propionic acidemia (PA) is a rare autosomal recessive organic acidemia that classically presents within the first days of life with a metabolic crisis or via newborn screening and is confirmed with laboratory tests. Limited data exist on the natural history of patients with PA describing presentation, treatments, and clinical outcomes. OBJECTIVE: To retrospectively describe the natural history of patients with PA in a clinical setting from a real-world database using both structured and unstructured electronic health record (EHR) data using novel data extraction techniques in a unique care setting. DESIGN/METHODS: This retrospective study used EHR data to identify patients with PA seen at the Mayo Clinic. Unstructured clinical text (medical notes, pathology reports) were analyzed using augmented curation natural language processing models to enhance analysis of data extracted by structured data fields (International Classification of Diseases 9th or 10th revision [ICD-9/-10] codes, Current Procedural Terminology [CPT] codes, and medication orders). De-identified health records were also manually reviewed by clinical scientists to ensure data accuracy and completeness. The index date was defined as the patient's date of PA diagnosis at the Mayo Clinic. Results were reported as aggregate descriptive statistics relative to patients' index dates. Complications, therapeutic interventions, laboratory tests, procedures, and hospitalization encounters related to PA were described at and within 6 months of the patient's index date, and from medical history available before the index date. RESULTS: In total, 13 patients with PA were identified, with visits occurring from 1998 to 2022. Age at diagnosis ranged from birth to 3 years; age at initial evaluation at the Mayo Clinic ranged from 3 days to 28 years. The mean number of Mayo Clinic outpatient visits was 31 (median duration of care, 2 years). PA-related complications were documented in 85% of patients and included nutritional difficulties (46%), metabolic decompensation events (MDEs; 38%), neurologic abnormalities (38%), and cardiomyopathy (7%). One pair of affected siblings had mild symptoms and no complications or MDEs. All 5 patients with a history of MDEs presented with developmental delays. Among patients with MDEs, the mean frequency of outpatient clinical care visits was 10 per year, and 3 patients required inpatient hospitalization (mean duration, 16 days). The incidence of severe complications was higher among patients with MDEs than those without MDEs. Of the patients with MDEs, 2 experienced crises while receiving treatment at the Mayo Clinic, with 9 total MDEs occurring between the 2 patients. Symptoms at presentation included hyperammonemia (78%), fever and/or decreased nutritional intake (67%), hyperglycemia/hypoglycemia (56%), intercurrent upper respiratory infection and/or lethargy (44%), constipation (33%), altered mental status (33%), and cough (33%). CONCLUSIONS: This study highlights the range and frequency of clinical outcomes experienced by patients with PA and demonstrates the clinical burden of MDEs.

Per- and polyfluoroalkyl substances (PFAS) in United States tapwater: Comparison of underserved private-well and public-supply exposures and associated health implications.

Drinking-water quality is a rising concern in the United States (US), emphasizing the need to broadly assess exposures and potential health effects at the point-of-use. Drinking-water exposures to per- and poly-fluoroalkyl substances (PFAS) are a national concern, however, there is limited information on PFAS in residential tapwater at the point-of-use, especially from private-wells. We conducted a national reconnaissance to compare human PFAS exposures in unregulated private-well and regulated public-supply tapwater. Tapwater from 716 locations (269 private-wells; 447 public supply) across the US was collected during 2016-2021 including three locations where temporal sampling was conducted. Concentrations of PFAS were assessed by three laboratories and compared with land-use and potential-source metrics to explore drivers of contamination. The number of individual PFAS observed ranged from 1 to 9 (median: 2) with corresponding cumulative concentrations (sum of detected PFAS) ranging from 0.348 to 346 ng/L. Seventeen PFAS were observed at least once with PFBS, PFHxS and PFOA observed most frequently in approximately 15% of the samples. Across the US, PFAS profiles and estimated median cumulative concentrations were similar among private wells and public-supply tapwater. We estimate that at least one PFAS could be detected in about 45% of US drinking-water samples. These detection probabilities varied spatially with limited temporal variation in concentrations/numbers of PFAS detected. Benchmark screening approaches indicated potential human exposure risk was dominated by PFOA and PFOS, when detected. Potential source and land-use information was related to cumulative PFAS concentrations, and the number of PFAS detected; however, corresponding relations with specific PFAS were limited likely due to low detection frequencies and higher detection limits. Information generated supports the need for further assessments of cumulative health risks of PFAS as a class and in combination with other co-occurring contaminants, particularly in unmonitored private-wells where information is limited or not available.

Landscape transcriptomics as a tool for addressing global change effects across diverse species.

Landscape transcriptomics is an emerging field studying how genome-wide expression patterns reflect dynamic landscape-scale environmental drivers, including habitat, weather, climate, and contaminants, and the subsequent effects on organismal function. This field is benefitting from advancing and increasingly accessible molecular technologies, which in turn are allowing the necessary characterization of transcriptomes from wild individuals distributed across natural landscapes. This research is especially important given the rapid pace of anthropogenic environmental change and potential impacts that span levels of biological organization. We discuss three major themes in landscape transcriptomic research: connecting transcriptome variation across landscapes to environmental variation, generating and testing hypotheses about the mechanisms and evolution of transcriptomic responses to the environment, and applying this knowledge to species conservation and management. We discuss challenges associated with this approach and suggest potential solutions. We conclude that landscape transcriptomics has great promise for addressing fundamental questions in organismal biology, ecology, and evolution, while providing tools needed for conservation and management of species.

Predicting climate change impacts on poikilotherms using physiologically guided species abundance models.

Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.

Clinical Administration Characteristics of Subcutaneous and Intravenous Administration of Daratumumab in Patients With Multiple Myeloma at Mayo Clinic Infusion Centers.

PURPOSE: Median duration of daratumumab (DARA) administration for treatment of multiple myeloma is 3-7 hours for the intravenous formulation (DARA IV) and 3-5 minutes for the subcutaneous formulation (DARA SC). Here, we describe clinical administration characteristics of DARA using a novel method for data extraction from electronic health records. METHODS: Time-based measurements were extracted using a scheduling/pharmacy software program that tracked patient movement through appointments for patients initiating DARA in Mayo Clinic infusion centers from April 5, 2017, to October 14, 2021. Cohorts included patients who received DARA IV or DARA SC, or converted from DARA IV to DARA SC. The DARA SC cohort was further analyzed before (DARA SC initial) and after (DARA SC shortened) a reduction in the postadministration observation time mandated by the treatment plan. Events associated with administration-related reactions (ARRs) were also identified. RESULTS: Median total clinic times were 2.7-3.0 hours shorter for DARA SC versus DARA IV. Median clinic times were highest at dose 1 and decreased with subsequent doses. Median total chair times were 2.7-2.8 hours shorter for DARA SC versus DARA IV. Incidences of ARR-related events with DARA SC were low across doses. CONCLUSION: Reduced clinic times were observed with DARA SC, indicating that use of DARA SC as a treatment option results in time savings that may free clinic resources. Furthermore, novel methods of electronic health record data extraction can provide insights that may help inform clinic resource optimization.

Estrogenic activity response to best management practice implementation in agricultural watersheds in the Chesapeake Bay watershed.

Best management practices (BMPs) have been predominantly used throughout the Chesapeake Bay watershed (CBW) to reduce nutrients and sediments entering streams, rivers, and the bay. These practices have been successful in reducing loads entering the estuary and have shown the potential to reduce other contaminants (pesticides, hormonally active compounds, pathogens) in localized studies and modeled load estimates. However, further understanding of relationships between BMPs and non-nutrient contaminant reductions at regional scales using sampled data would be beneficial. Total estrogenic activity was measured in surface water samples collected over a decade (2008-2018) in 211 undeveloped NHDPlus V2.1 watersheds within the CBW. Bayesian hierarchical modeling between total estrogenic activity and landscape predictors including landcover, runoff, BMP intensity, and a BMP*agriculture intensity interaction term indicates a 96% posterior probability that BMP intensity on agricultural land is reducing total estrogenic activity. Additionally, watersheds with high agriculture and low BMPs had a 49% posterior probability of exceeding an effects-based threshold in aquatic organisms of 1 ng/L but only a 1% posterior probability of exceeding this threshold in high-agriculture, high-BMP watersheds.

Introducing LGBTQIA+ Patient Care to Pharmacy Students Through Laboratory-Based Exercises.

Objective. To assess the impact of novel skills-based laboratory exercises on first-, second-, and third-year pharmacy students' confidence and knowledge regarding care for people identifying as lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other (LGBTQIA+).Methods. An LGBTQIA+ lecture discussing pronouns, common terminology, health disparities, health screenings, and gender-affirming hormone therapy was presented to students. During laboratory sessions, students applied lecture topics via a learning level-specific activity. Students completed a pre- and post-activity survey assessing their knowledge, confidence, and activity experience.Results. Seventy-nine students (N=348) completed both the pre- and post-activity survey. Students' overall increase in knowledge scores was significant, with improvement in four out of six questions among each cohort. A significant increase was seen in students understanding of the role of the pharmacist, their confidence in caring for LGBTQIA+ patients, and their comfort with using appropriate terminology. Most students (92%) agreed or strongly agreed that learning about LGBTQIA+ patient care was a positive experience, while 74% agreed that additional education on LGBTQIA+ patients is needed within their pharmacy curriculum.Conclusion. After a brief skills-based laboratory course, students' knowledge and confidence in caring for LGBTQIA+ patients improved; however, students agreed that more exposure was necessary. Future studies will follow students as they progress through the curriculum to determine the impact of exposure to LGBTQIA+ content across all three didactic years.

Using the Business Model Canvas to Guide Doctor of Pharmacy Students in Building Business Plans.

Objective. To review the use of the business model canvas, a one-page visual description of a business initiative, as a tool for teaching pharmacy students about entrepreneurship and business planning in pharmacy practice settings.Findings. Students often struggle to develop the mindset, skillset, and toolset to effectively apply business modeling and planning processes to pharmacy practice settings. Over years of experimentation and various iterations in a pharmacy practice management class, a new business model canvas was developed and refined. The canvas contains 13 sections which emphasize key terms, concepts, and ideas crucial for achieving entrepreneurial competencies. Using the zone of proximal development as a framework, the course structure offered a range of supportive activities that guided students to independent competence. The business model canvas formed a framework around which assigned course readings, exercises, and group assignments helped pharmacy students build confidence and competence in completing a capstone business plan assignment.Summary. This paper provides recommendations and examples of how to structure a course in the Doctor of Pharmacy curricula using an entrepreneurial tool, the business model canvas, to help students master business competencies. Recommendations and lessons-learned are provided.

The statistical power to detect regional temporal trends in riverine contaminants in the Chesapeake Bay Watershed, USA.

Chemical contamination of riverine ecosystems is largely a result of urbanization, industrialization, and agricultural activities occurring on adjacent terrestrial landscapes. Land management activities (e.g., Best Management Practices) are an important tool used to reduce point and non-point sources of pollution. However, the ability to confidently make inferences about the efficacy of land management activities on reducing in-stream chemical concentrations is poorly understood. We estimated regional temporal trends and components of variation for commonly used herbicides (atrazine and metolachlor), total estrogenicity, and riverine sediment concentrations of total PCBs for rivers in the Chesapeake Bay Watershed, USA. We then used the estimated variance components to perform a power analysis and evaluated the statistical power to detect regional temporal trends under different monitoring scenarios. Scenarios included varying the magnitude of the annual contaminant decline, the number of sites sampled each year, the number of years sampled, and sampling frequency. Monitoring for short time periods (e.g., 5 years) was inadequate for detecting regional temporal trends, regardless of the number of sites sampled or the magnitude of the annual declines. Even when monitoring over a 20-year period, sampling a relatively large number of sites each year was required (e.g., >50 sites) to achieve adequate statistical power for smaller trend magnitudes (declines of 5-7%/year). Annual sampling frequency had little impact on power for any monitoring scenario. All sampling scenarios were underpowered for sediment total PCBs. Power was greatest for total estrogenicity, suggesting that this aggregate measure of estrogenic activity may be a useful indicator. This study provides information that can be used to help (1) guide the development of monitoring programs aimed at detecting regional declines in riverine chemical contaminant concentrations in response to land management actions, and (2) set expectations for the ability to detect changes over time.

Analysis of the Effectiveness of the Ad26.COV2.S Adenoviral Vector Vaccine for Preventing COVID-19.

Importance: Continuous assessment of the effectiveness and safety of the US Food and Drug Administration-authorized SARS-CoV-2 vaccines is critical to amplify transparency, build public trust, and ultimately improve overall health outcomes. Objective: To evaluate the effectiveness of the Johnson & Johnson Ad26.COV2.S vaccine for preventing SARS-CoV-2 infection. Design, Setting, and Participants: This comparative effectiveness research study used large-scale longitudinal curation of electronic health records from the multistate Mayo Clinic Health System (Minnesota, Arizona, Florida, Wisconsin, and Iowa) to identify vaccinated and unvaccinated adults between February 27 and July 22, 2021. The unvaccinated cohort was matched on a propensity score derived from age, sex, zip code, race, ethnicity, and previous number of SARS-CoV-2 polymerase chain reaction tests. The final study cohort consisted of 8889 patients in the vaccinated group and 88 898 unvaccinated matched patients. Exposure: Single dose of the Ad26.COV2.S vaccine. Main Outcomes and Measures: The incidence rate ratio of SARS-CoV-2 infection in the vaccinated vs unvaccinated control cohorts, measured by SARS-CoV-2 polymerase chain reaction testing. Results: The study was composed of 8889 vaccinated patients (4491 men [50.5%]; mean [SD] age, 52.4 [16.9] years) and 88 898 unvaccinated patients (44 748 men [50.3%]; mean [SD] age, 51.7 [16.7] years). The incidence rate ratio of SARS-CoV-2 infection in the vaccinated vs unvaccinated control cohorts was 0.26 (95% CI, 0.20-0.34) (60 of 8889 vaccinated patients vs 2236 of 88 898 unvaccinated individuals), which corresponds to an effectiveness of 73.6% (95% CI, 65.9%-79.9%) and a 3.73-fold reduction in SARS-CoV-2 infections. Conclusions and Relevance: This study's findings are consistent with the clinical trial-reported efficacy of Ad26.COV2.S and the first retrospective analysis, suggesting that the vaccine is effective at reducing SARS-CoV-2 infection, even with the spread of variants such as Alpha or Delta that were not present in the original studies, and reaffirm the urgent need to continue mass vaccination efforts globally.

Optimization of a suite of flathead catfish (Pylodictis olivaris) microsatellite markers for understanding the population genetics of introduced populations in the northeast United States.

OBJECTIVE: Flathead catfish are rapidly expanding into nonnative waterways throughout the United States. Once established, flathead catfish may cause disruptions to the local ecosystem through consumption and competition with native fishes, including species of conservation concern. Flathead catfish often become a popular sport fish in their introduced range, and so management strategies must frequently balance the need to protect native and naturalized fauna while meeting the desire to maintain or enhance fisheries. However, there are currently few tools available to inform management of invasive flathead catfish (Pylodictis olivaris). We describe a suite of microsatellite loci that can be used to characterize population structure, predict invasion history, and assess potential mitigation strategies for flathead catfish. RESULTS: Our panel of 13 microsatellite loci were polymorphic and appear to be informative for population genetic studies of flathead catfish. We found moderate levels of diversity in four nonnative collections of flathead catfish in the Pennsylvania and Maryland sections of the Susquehanna River and the Schuylkill River, Pennsylvania. Analyses suggested patterns of genetic differentiation within- and among-rivers, highlighting the utility of this marker panel for understanding the structure and assessing the degree of connectivity among flathead catfish populations.

Real-time analysis of a mass vaccination effort confirms the safety of FDA-authorized mRNA COVID-19 vaccines.

BACKGROUND: As the coronavirus disease 2019 (COVID-19) vaccination campaign unfolds, it is important to continuously assess the real-world safety of Food and Drug Administration (FDA)-authorized vaccines. Curation of large-scale electronic health records (EHRs) enables near-real-time safety evaluations that were not previously possible. METHODS: In this retrospective study, we deployed deep neural networks over a large EHR system to automatically curate the adverse effects mentioned by physicians in over 1.2 million clinical notes between December 1, 2020 and April 20, 2021. We compared notes from 68,266 individuals who received at least one dose of BNT162b2 (n = 51,795) or mRNA-1273 (n = 16,471) to notes from 68,266 unvaccinated individuals who were matched by demographic, geographic, and clinical features. FINDINGS: Individuals vaccinated with BNT162b2 or mRNA-1273 had a higher rate of return to the clinic, but not the emergency department, after both doses compared to unvaccinated controls. The most frequently documented adverse effects within 7 days of each vaccine dose included myalgia, headache, and fatigue, but the rates of EHR documentation for each side effect were remarkably low compared to those derived from active solicitation during clinical trials. Severe events, including anaphylaxis, facial paralysis, and cerebral venous sinus thrombosis, were rare and occurred at similar frequencies in vaccinated and unvaccinated individuals. CONCLUSIONS: This analysis of vaccine-related adverse effects from over 1.2 million EHR notes of more than 130,000 individuals reaffirms the safety and tolerability of the FDA-authorized mRNA COVID-19 vaccines in practice. FUNDING: This study was funded by nference.

Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems.

Dimethyl sulfide (DMS) serves as an anti-greenhouse gas, plays multiple roles in aquatic ecosystems, and contributes to the global sulfur cycle. The chlorophyll a (CHL, an indicator of phytoplankton biomass)-DMS relationship is critical for estimating DMS emissions from aquatic ecosystems. Importantly, recent research has identified that the CHL-DMS relationship has a breakpoint, where the relationship is positive below a CHL threshold and negative at higher CHL concentrations. Conventionally, mean regression methods are employed to characterize the CHL-DMS relationship. However, these approaches focus on the response of mean conditions and cannot illustrate responses of other parts of the DMS distribution, which could be important in order to obtain a complete view of the CHL-DMS relationship. In this study, for the first time, we proposed a novel Bayesian change point quantile regression (BCPQR) model that integrates and inherits advantages of Bayesian change point models and Bayesian quantile regression models. Our objective was to examine whether or not the BCPQR approach could enhance the understanding of shifting CHL-DMS relationships in aquatic ecosystems. We fitted BCPQR models at five regression quantiles for freshwater lakes and for seas. We found that BCPQR models could provide a relatively complete view on the CHL-DMS relationship. In particular, it quantified the upper boundary of the relationship, representing the limiting effect of CHL on DMS. Based on the results of paired parameter comparisons, we revealed the inequality of regression slopes in BCPQR models for seas, indicating that applying the mean regression method to develop the CHL-DMS relationship in seas might not be appropriate. We also confirmed relationship differences between lakes and seas at multiple regression quantiles. Further, by introducing the concept of DMS emission potential, we found that pH was not likely a key factor leading to the change of the CHL-DMS relationship in lakes. These findings cannot be revealed using piecewise linear regression. We thereby concluded that the BCPQR model does indeed enhance the understanding of shifting CHL-DMS relationships in aquatic ecosystems and is expected to benefit efforts aimed at estimating DMS emissions. Considering that shifting (threshold) relationships are not rare and that the BCPQR model can easily be adapted to different systems, the BCPQR approach is expected to have great potential for generalization in other environmental and ecological studies.

Plasma IL-6 levels following corticosteroid therapy as an indicator of ICU length of stay in critically ill COVID-19 patients.

Intensive care unit (ICU) admissions and mortality in severe COVID-19 patients are driven by "cytokine storms" and acute respiratory distress syndrome (ARDS). Interim clinical trial results suggest that the corticosteroid dexamethasone displays better 28-day survival in severe COVID-19 patients requiring ventilation or oxygen. In this study, 10 out of 16 patients (62.5%) that had an average plasma IL-6 value over 10 pg/mL post administration of corticosteroids also had worse outcomes (i.e., ICU stay >15 days or death), compared to 8 out of 41 patients (19.5%) who did not receive corticosteroids (p-value = 0.0024). Given this potential association between post-corticosteroid IL-6 levels and COVID-19 severity, we hypothesized that the glucocorticoid receptor (GR or NR3C1) may be coupled to IL-6 expression in specific cell types that govern cytokine release syndrome (CRS). Examining single-cell RNA-seq data from BALF of severe COVID-19 patients and nearly 2 million cells from a pan-tissue scan shows that alveolar macrophages, smooth muscle cells, and endothelial cells co-express NR3C1 and IL-6, motivating future studies on the links between the regulation of NR3C1 function and IL-6 levels.