Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling.

Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tickborne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapularis L.) in the southeastern US. Eight years (2014-2021) of tick encounter data were used to fit environmental niche and generalized linear models to predict where and when ticks are likely to be encountered. Our results indicate temporal and environmental partitioning of the three species. Ixodes scapularis were more likely to be encountered in the autumn and winter seasons and associated with soil organic matter, vegetation indices, evapotranspiration, temperature, and gross primary productivity. By contrast, A. americanum and D. variabilis were more likely to be encountered in spring and summer seasons and associated with elevation, landcover, temperature, dead belowground biomass, vapor pressure, and precipitation. Regions in the southeast least suitable for encountering ticks included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Florida Coastal Plain, whereas suitable regions included the Interior Plateau, Central Appalachians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Spatial and temporal patterns of different tick species can inform outdoorsmen and the public on tick avoidance measures, reduce tick populations by managing suitable tick habitats, and monitoring areas with unsuitable tick habitat for potential missed encounters.

Medication Treatment for Youth in Substance Use Disorder Residential Treatment.

OBJECTIVE: Residential is a common treatment setting for youth with high-severity substance use disorders (SUD). This study evaluated the prevalence of psychiatric symptoms and medication for youth in residential SUD treatment. METHODS: Youth in Massachusetts state licensed and funded SUD residential programs completed questionnaires assessing demographics, primary substance of use, and psychopathology symptoms (Youth Self Report [YSR]/Adult Self Report [ASR]). De-identified medication lists were provided by the programs. Descriptive statistics were used to describe the sample. RESULTS: Among the 47 youth who participated, 51.1% were male, 72.3% white, 83% non-Hispanic, mean age 20.7 years. Opioids were the most common primary substance identified by youth (51.1%), and 75% had at least one clinically elevated subscale on the YSR/ASR. Most youth were prescribed at least one medication (89.4%) with a mean of 2.9 medications. CONCLUSION: Youth in SUD residential treatment frequently have clinically elevated psychiatric symptoms, and psychotropic medication was commonly prescribed.

Active contact proximity to the cerebellothalamic tract predicts initial therapeutic current requirement with DBS for ET: an application of 7T MRI.

Objective: To characterize how the proximity of deep brain stimulation (DBS) active contact locations relative to the cerebellothalamic tract (CTT) affect clinical outcomes in patients with essential tremor (ET). Background: DBS is an effective treatment for refractory ET. However, the role of the CTT in mediating the effect of DBS for ET is not well characterized. 7-Tesla (T) MRI-derived tractography provides a means to measure the distance between the active contact and the CTT more precisely. Methods: A retrospective review was conducted of 12 brain hemispheres in 7 patients at a single center who underwent 7T MRI prior to ventral intermediate nucleus (VIM) DBS lead placement for ET following failed medical management. 7T-derived diffusion tractography imaging was used to identify the CTT and was merged with the post-operative CT to calculate the Euclidean distance from the active contact to the CTT. We collected optimized stimulation parameters at initial programing, 1- and 2-year follow up, as well as a baseline and postoperative Fahn-Tolosa-Marin (FTM) scores. Results: The therapeutic DBS current mean (SD) across implants was 1.8 mA (1.8) at initial programming, 2.5 mA (0.6) at 1 year, and 2.9 mA (1.1) at 2-year follow up. Proximity of the clinically-optimized active contact to the CTT was 3.1 mm (1.2), which correlated with lower current requirements at the time of initial programming (R2 = 0.458, p = 0.009), but not at the 1- and 2-year follow up visits. Subjects achieved mean (SD) improvement in tremor control of 77.9% (14.5) at mean follow-up time of 22.2 (18.9) months. Active contact distance to the CTT did not predict post-operative tremor control at the time of the longer term clinical follow up (R2 = -0.073, p = 0.58). Conclusion: Active DBS contact proximity to the CTT was associated with lower therapeutic current requirement following DBS surgery for ET, but therapeutic current was increased over time. Distance to CTT did not predict the need for increased current over time, or longer term post-operative tremor control in this cohort. Further study is needed to characterize the role of the CTT in long-term DBS outcomes.

A pragmatic clinical effectiveness trial of a novel alternative to punishment for school-based substance use infractions: study protocol for the iDECIDE curriculum.

Background: Adolescents who use alcohol and other drugs on school campuses are at heightened risk for adverse consequences to their health and wellbeing. Schools have historically turned to punitive approaches as a first-line response to substance use. However, punishment is an ineffective deterrent for substance use and may cause harm and increase inequities. iDECIDE (Drug Education Curriculum: Intervention, Diversion, and Empowerment) was developed as a scalable and youth-centered drug education and diversion program that can be used as a skills-based alternative to punishment. We aim to evaluate the effectiveness of the iDECIDE curriculum as an alternative to punishment (ATP) for school-based substance use infractions in the context of a large pragmatic clinical effectiveness study. Methods: We will conduct a Type 1, hybrid effectiveness-implementation trial. Using a stepped wedge design with approximately 90 middle and high schools in Massachusetts, we will randomly allocate the timing of implementation of the iDECIDE curriculum compared to standard disciplinary response over approximately 36 months. We will test the overarching hypothesis that student-level outcomes (knowledge of drug effects and attitudes about substance use; frequency of substance use; school connectedness) improve over time as schools transition from a standard disciplinary response to having access to iDECIDE. The secondary aims of this trial are to (1) explore whether change in student-level outcomes vary according to baseline substance use, number of peers who use alcohol or other drugs, age, gender, and school urbanicity, and (2) determine the acceptability and feasibility of the iDECIDE curriculum through qualitative stakeholder interviews. Discussion: Substance use continues to be a major and rapidly evolving problem in schools. The importance of moving away from punishment to more restorative approaches is widely accepted; however, scalable alternatives have not yet been identified. This will be the first study to our knowledge to systematically evaluate an ATP for students who violate the school substance use policy and is well poised to have important implications for policy making.

Air pollution and the sequelae of COVID-19 patients: A multistate analysis.

IMPORTANCE: Recent evidence links air pollution to the severity COVID-19 symptoms and to death from the disease. To date, however, few studies have assessed whether air pollution affects the sequelae to more severe states or recovery from COVID-19 in a cohort with individual data. OBJECTIVE: To assess how air pollution affects the transition to more severe COVID-19 states or to recovery from COVID-19 infection in a cohort with detailed patient information. DESIGN AND OUTCOMES: We used a cohort design that followed patients admitted to hospital in the Kaiser Permanente Southern California (KPSC) Health System, which has 4.7 million members with characteristics similar to the general population. Enrollment began on 06/01/2020 and ran until 01/30/2021 for all patients admitted to hospital while ill with COVID-19. All possible states of sequelae were considered, including deterioration to intensive care, to death, discharge to recovery, or discharge to death. Transition risks were estimated with a multistate model. We assessed exposure using chemical transport model that predicted ambient concentrations of nitrogen dioxide, ozone, and fine particulate matter (PM2.5) at a 1 km scale. RESULTS: Each increase in PM2.5 concentration equivalent to the interquartile range was associated with increased risk of deterioration to intensive care (HR of 1.16; 95% CI: 1.12-1.20) and deterioration to death (HR of 1.11; 95% CI: 1.04-1.17). Results for ozone were consistent with PM2.5 effects, but ozone also affected the transition from recovery to death: HR of 1.24 (95% CI: 1.01-1.51). NO2 had weaker effects but displayed some elevated risks. CONCLUSIONS: PM2.5 and ozone were significantly associated with transitions to more severe states while in hospital and to death after discharge from hospital. Reducing air pollution could therefore lead to improved prognosis for COVID-19 patients and a sustainable means of reducing the health impacts of coronaviruses now and in the future.

An Urgent Need for School-Based Diversion Programs for Adolescent Substance Use: A Statewide Survey of School Personnel.

PURPOSE: There has been growing interest in reserving punishment as a last resort to address substance use in schools. However, there is significant variability in adoption of alternative approaches. This study reported school personnel's perceptions of diversion programs, identified characteristics of schools/districts that currently have diversion programs, and defined barriers of implementation of diversion programs. METHODS: One hundred fifty six school stakeholders from Massachusetts' K-12 schools, including district administrators, principals and vice principals, school resource officers, guidance counselors, and nurses, completed a web-based survey in May-June 2020. Participants were recruited through e-mail distributed via professional listservs, direct school outreach, and community coalitions. The web survey queried beliefs, attitudes, and actions that schools take regarding substance use infractions as well as perceived barriers to implementing diversion programs. RESULTS: Participants endorsed strong beliefs that punishment was an appropriate school response for student substance use, particularly for nontobacco-related infractions. Despite these personal beliefs, diversion programs were rated as more effective but less commonly used than punitive approaches (37% of respondents reported having diversion programs in their schools/districts vs. 85% used punitive approaches) (p < .03). Punishment was more likely to be used to respond to cannabis, alcohol, and other substances compared to tobacco (p < .02). Primary barriers of implementing diversion programs included funding, staff training, and parental support. DISCUSSION: Based on school personnel perceptions, these findings lend further support for a transition away from punishment and toward more restorative alternatives. However, barriers to sustainability and equity were identified that warrant consideration when implementing diversion programs.

Machine learning versus regression for prediction of sporadic pancreatic cancer.

BACKGROUND/OBJECTIVES: There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. METHODS: This retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed. RESULTS: The KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively. CONCLUSIONS: The three models complement each other, but each has unique contributions.

The membrane androgen receptor ZIP9 (SCL39A9) maintains ovarian homeostasis by mediating post-ovulatory follicle breakdown in zebrafish.

ZIP9 was recently characterized as a membrane androgen receptor in Atlantic croaker granulosa/theca (G/T) cells where it mediates androgen-induced apoptosis in vitro, but the physiological significance of this action has remained unclear. In the current study, we utilized ZIP9 knockout (zip9-/-) zebrafish to investigate the role of ZIP9-mediated androgen-induced G/T cell apoptosis in vivo. We first confirmed ZIP9 mediates apoptosis of zebrafish G/T cells in vitro. Testosterone increased apoptosis, intracellular free zinc, and expression of pro-apoptotic members bax and p53 in wildtype and zip9+/+ zebrafish G/T cells, but not in ZIP9 knockout and knockdown cell models. We hypothesized ZIP9-mediated G/T cell apoptosis may be involved in post-ovulatory follicle (POF) breakdown in vivo. Post ovulation, zip9, bax, and p53 were upregulated in zip9+/+ but not in zip9-/- ovaries. Immunoreactivity of cleaved caspase 3 was also higher in POFs from zip9+/+ ovaries compared to zip9-/-, and POF breakdown was significantly delayed in zip9-/- fish compared to zip9+/+ counterparts. To determine the detrimental consequences of delayed POF breakdown in the zip9-/- model, fish were challenged with repeated ovulation induction. After the challenge, zip9-/- fish exhibited abnormal ovarian lesions that contained debris consistent with atretic or necrotic cellular material. However, no abnormalities were observed in zip9+/+ fish ovaries, indicating that the abnormal phenotype is due to the loss of ZIP9. This study demonstrates an important role for ZIP9 in mediating POF breakdown and maintaining tissue remodeling and homeostasis in the teleost ovary and indicates a role for the ZIP9-mediated androgen-induced apoptotic response in vivo.

Assessment of Disruptive Life Events for Individuals Diagnosed With Schizophrenia or Bipolar I Disorder Using Data From a Consumer Credit Reporting Agency.

Importance: There is a dearth of population-level data on major disruptive life events (defined here as arrests by a legal authority, address changes, bankruptcy, lien, and judgment filings) for patients with bipolar I disorder (BPI) or schizophrenia, which has limited studies on mental health and treatment outcomes. Objective: To conduct a population-level study on disruptive life events by using publicly available data on disruptive life events, aggregated by a consumer credit reporting agency in conjunction with electronic health record (EHR) data. Design, Setting, and Participants: This study used EHR data from 2 large, integrated health care systems, Kaiser Permanente Southern California and Henry Ford Health. Cohorts of patients diagnosed from 2007 to 2019 with BPI or schizophrenia were matched 1:1 by age at analysis, age at diagnosis (if applicable), sex, race and ethnicity, and Medicaid status to (1) an active comparison group with diagnoses of major depressive disorder (MDD) and (2) a general health (GH) cohort without diagnoses of BPI, schizophrenia, or MDD. Patients with diagnoses of BPI or schizophrenia and their respective comparison cohorts were matched to public records data aggregated by a consumer credit reporting agency (98% match rate). Analysis took place between November 2020 and December 2022. Main Outcomes and Measures: The differences in the occurrence of disruptive life events among patients with BPI or schizophrenia and their comparison groups. Results: Of 46 167 patients, 30 008 (65%) had BPI (mean [SD] age, 42.6 [14.2] years) and 16 159 (35%) had schizophrenia (mean [SD], 41.4 [15.1] years). The majoriy of patients were White (30 167 [65%]). In addition, 18 500 patients with BPI (62%) and 6552 patients with schizophrenia (41%) were female. Patients with BPI were more likely to change addresses than patients in either comparison cohort (with the incidence ratio being as high as 1.25 [95% CI, 1.23-1.28]) when compared with GH cohort. Patients with BPI were also more likely to experience any of the financial disruptive life events with odds ratio ranging from 1.15 [95% CI, 1.07-1.24] to 1.50 [95% CI, 1.42-1.58]). The largest differences in disruptive life events were seen in arrests of patients with either BPI or schizophrenia compared with GH peers (3.27 [95% CI, 2.84-3.78] and 3.04 [95% CI, 2.57-3.59], respectively). Patients with schizophrenia had fewer address changes and were less likely to experience a financial event than their matched comparison cohorts. Conclusions and Relevance: This study demonstrated that data aggregated by a consumer credit reporting agency can support population-level studies on disruptive life events among patients with BPI or schizophrenia.

Lateral cerebellothalamic tract activation underlies DBS therapy for Essential Tremor.

BACKGROUND: While deep brain stimulation (DBS) therapy can be effective at suppressing tremor in individuals with medication-refractory Essential Tremor, patient outcome variability remains a significant challenge across centers. Proximity of active electrodes to the cerebellothalamic tract (CTT) is likely important in suppressing tremor, but how tremor control and side effects relate to targeting parcellations within the CTT and other pathways in and around the ventral intermediate (VIM) nucleus of thalamus remain unclear. METHODS: Using ultra-high field (7T) MRI, we developed high-dimensional, subject-specific pathway activation models for 23 directional DBS leads. Modeled pathway activations were compared with post-hoc analysis of clinician-optimized DBS settings, paresthesia thresholds, and dysarthria thresholds. Mixed-effect models were utilized to determine how the six parcellated regions of the CTT and how six other pathways in and around the VIM contributed to tremor suppression and induction of side effects. RESULTS: The lateral portion of the CTT had the highest activation at clinical settings (p < 0.05) and a significant effect on tremor suppression (p < 0.001). Activation of the medial lemniscus and posterior-medial CTT was significantly associated with severity of paresthesias (p < 0.001). Activation of the anterior-medial CTT had a significant association with dysarthria (p < 0.05). CONCLUSIONS: This study provides a detailed understanding of the fiber pathways responsible for therapy and side effects of DBS for Essential Tremor, and suggests a model-based programming approach will enable more selective activation of lateral fibers within the CTT.

Risk Prediction of Pancreatic Cancer in Patients With Recent-onset Hyperglycemia: A Machine-learning Approach.

BACKGROUND: New-onset diabetes (NOD) has been suggested as an early indicator of pancreatic cancer. However, the definition of NOD by the American Diabetes Association requires 2 simultaneous or consecutive elevated glycemic measures. We aimed to apply a machine-learning approach using electronic health records to predict the risk in patients with recent-onset hyperglycemia. MATERIALS AND METHODS: In this retrospective cohort study, health plan enrollees 50 to 84 years of age who had an elevated (6.5%+) glycated hemoglobin (HbA1c) tested in January 2010 to September 2018 with recent-onset hyperglycemia were identified. A total of 102 potential predictors were extracted. Ten imputation datasets were generated to handle missing data. The random survival forests approach was used to develop and validate risk models. Performance was evaluated by c -index, calibration plot, sensitivity, specificity, and positive predictive value. RESULTS: The cohort consisted of 109,266 patients (mean age: 63.6 y). The 3-year incidence rate was 1.4 (95% confidence interval: 1.3-1.6)/1000 person-years of follow-up. The 3 models containing age, weight change in 1 year, HbA1c, and 1 of the 3 variables (HbA1c change in 1 y, HbA1c in the prior 6 mo, or HbA1c in the prior 18 mo) appeared most often out of the 50 training samples. The c -indexes were in the range of 0.81 to 0.82. The sensitivity, specificity, and positive predictive value in patients who had the top 20% of the predicted risks were 56% to 60%, 80%, and 2.5% to 2.6%, respectively. CONCLUSION: Targeting evaluation at the point of recent hyperglycemia based on elevated HbA1c could offer an opportunity to identify pancreatic cancer early and possibly impact survival in cancer patients.

Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California.

BACKGROUND: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models. METHODS: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients. FINDINGS: We found significant associations between COVID-19 death and several air pollution exposures, including: PM2.5 mass, PM0.1 mass, PM2.5 nitrates, PM2.5 elemental carbon, PM2.5 on-road diesel, and PM2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM2.5 mass and PM2.5 nitrate to HR âˆ¼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects. INTERPRETATION: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer.

INTRODUCTION: There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records. METHODS: This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry. RESULTS: The Kaiser Permanente Southern California cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PDAC cases. The 18-month incidence rate of PDAC was 0.77 (95% confidence interval 0.73-0.80)/1,000 person-years. The final main model contained age, abdominal pain, weight change, HbA1c, and alanine transaminase change (c-index: mean = 0.77, SD = 0.02; calibration test: P value 0.4, SD 0.3). The final early detection model comprised the same features as those selected by the main model except for abdominal pain (c-index: 0.77 and SD 0.4; calibration test: P value 0.3 and SD 0.3). The VA testing cohort consisted of 2.7 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30)/1,000 person-years. The recalibrated main and early detection models based on VA testing data sets achieved a mean c-index of 0.71 (SD 0.002) and 0.68 (SD 0.003), respectively. DISCUSSION: Using widely available parameters in electronic health records, we developed and externally validated parsimonious machine learning-based models for detection of PC. These models may be suitable for real-time clinical application.

Development and validation of the COVID-19 Hospitalized Patient Deterioration Index.

OBJECTIVES: To develop a COVID-19-specific deterioration index for hospitalized patients: the COVID Hospitalized Patient Deterioration Index (COVID-HDI). This index builds on the proprietary Epic Deterioration Index, which was not developed for predicting respiratory deterioration events among patients with COVID-19. STUDY DESIGN: A retrospective observational cohort was used to develop and validate the COVID-HDI model to predict respiratory deterioration or death among hospitalized patients with COVID-19. Deterioration events were defined as death or requiring high-flow oxygen, bilevel positive airway pressure, mechanical ventilation, or intensive-level care within 72 hours of run time. The sample included hospitalized patients with COVID-19 diagnoses or positive tests at Kaiser Permanente Southern California between May 3, 2020, and October 17, 2020. METHODS: Machine learning models and 118 candidate predictors were used to generate benchmark performance. Logit regression with least absolute shrinkage and selection operator and physician input were used to finalize the model. Split-sample cross-validation was used to train and test the model. RESULTS: The area under the receiver operating curve was 0.83. COVID-HDI identifies patients at low risk (negative predictive value [NPV] > 98.5%) and borderline low risk (NPV > 95%) of an event. Of all patients, 74% were identified as being at low or borderline low risk at some point during their hospitalization and could be considered for discharge with or without home monitoring. A high-risk group with a positive predictive value of 51% included 12% of patients. Model performance remained high in a recent cohort of patients. CONCLUSIONS: COVID-HDI is a parsimonious, well-calibrated, and accurate model that may support clinical decision-making around discharge and escalation of care.

Vasopressin mediates nonapeptide and glucocorticoid signaling and social dynamics in juvenile dominance hierarchies of a highly social cichlid fish.

Early-life social experience can strongly affect adult behavior, yet the behavioral mechanisms underlying developmental trajectories are poorly understood. Here, we use the highly social cichlid, Burton's Mouthbrooder (Astatotilapia burtoni) to investigate juvenile social status and behavior, as well as the underlying neuroendocrine mechanisms. We placed juveniles in pairs or triads and found that they readily establish social status hierarchies, with some group structural variation depending on group size, as well as the relative body size of the group members. Next, we used intracerebroventricular injections to test the hypothesis that arginine vasopressin (AVP) regulates juvenile social behavior and status, similar to adult A. burtoni. While we found no direct behavioral effects of experimentally increasing (via vasotocin) or decreasing (via antagonist Manning Compound) AVP signaling, social interactions directed at the treated individual were significantly altered. This group-level effect of central AVP manipulation was also reflected in a significant shift in whole brain expression of genes involved in nonapeptide signaling (AVP, oxytocin, and oxytocin receptor) and the neuroendocrine stress axis (corticotropin-releasing factor (CRF), glucocorticoid receptors (GR) 1a and 1b). Further, social status was associated with the expression of genes involved in glucocorticoid signaling (GR1a, GR1b, GR2, mineralocorticoid receptor), social interactions with the dominant fish, and nonapeptide signaling activity (AVP, AVP receptor V1aR2, OTR). Together, our results considerably expand our understanding of the context-specific emergence of social dominance hierarchies in juveniles and demonstrate a role for nonapeptide and stress axis signaling in the regulation of social status and social group dynamics.

The active role of interpreters in medical discourse - An observational study in emergency medicine.

OBJECTIVE: To study communicative tasks executed and related strategies used by patients, health professionals, and medical interpreters. METHODS: English proficient and limited English proficient emergency department patients were observed. The content of patient-hospital staff communication was documented via pen and paper. Key themes and differences across interpreter types were established through qualitative analysis. Themes and differences across interpreter type were vetted and updated through member checking interviews. RESULTS: 6 English proficient and 9 limited English proficient patients were observed. Key themes in communicative tasks included: establishing, maintaining, updating, and repairing understanding and rapport. All tasks were observed with English proficient and limited English proficient patients. The difference with limited English proficient patients was that medical interpreters played an active role in completing communicative tasks. Telephone-based interpreters faced challenges in facilitating communicative tasks based on thematic comparisons with in-person interpreters, including issues hearing and lost information due to the lack of visual cues. CONCLUSIONS: Professional interpreters play an important role in communication between language discordant patients and health professionals that goes beyond verbatim translation. PRACTICAL IMPLICATIONS: Training for interpreters and health professionals, and the design of tools for facilitating language discordant communication, should consider the role of interpreters beyond verbatim translation.

Rapid Discovery and Detection of Haemaphysalis longicornis through the Use of Passive Surveillance and Collaboration: Building a State Tick-Surveillance Network.

Between March 2019 and February 2020, Asian long-horned ticks (Haemaphysalis longicornis Neumann, 1901) were discovered and collected for the first time in one middle and seven eastern Tennessee counties, facilitated by a newly developed passive and collaborative tick-surveillance network. Network collaborators included federal, state, county, university, and private resource personnel working with companion animals, livestock, and wildlife. Specimens were collected primarily from dogs and cattle, with initial detections of female adult stage ticks by stakeholders associated with parasitology positions (e.g., entomologists and veterinary parasitologists). Initial county tick detections were confirmed with morphological and molecular identifications, and then screened for the presence of animal-associated pathogens (Anaplasma marginale, Babesia species, Ehrlichia species, and Theileria orientalis), for which all tests were negative. Herein, we describe the identification and confirmation of these tick specimens as well as other results of the surveillance collaboration.

Discovery of Icenticaftor (QBW251), a Cystic Fibrosis Transmembrane Conductance Regulator Potentiator with Clinical Efficacy in Cystic Fibrosis and Chronic Obstructive Pulmonary Disease.

Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) ion channel are established as the primary causative factor in the devastating lung disease cystic fibrosis (CF). More recently, cigarette smoke exposure has been shown to be associated with dysfunctional airway epithelial ion transport, suggesting a role for CFTR in the pathogenesis of chronic obstructive pulmonary disease (COPD). Here, the identification and characterization of a high throughput screening hit 6 as a potentiator of mutant human F508del and wild-type CFTR channels is reported. The design, synthesis, and biological evaluation of compounds 7-33 to establish structure-activity relationships of the scaffold are described, leading to the identification of clinical development compound icenticaftor (QBW251) 33, which has subsequently progressed to deliver two positive clinical proofs of concept in patients with CF and COPD and is now being further developed as a novel therapeutic approach for COPD patients.

Validation of the Enriching New-Onset Diabetes for Pancreatic Cancer Model in a Diverse and Integrated Healthcare Setting.

BACKGROUND: The risk of pancreatic cancer is elevated among people with new-onset diabetes (NOD). Based on Rochester Epidemiology Project Data, the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) model was developed and validated. AIMS: We validated the END-PAC model in a cohort of patients with NOD using retrospectively collected data from a large integrated health maintenance organization. METHODS: A retrospective cohort of patients between 50 and 84 years of age meeting the criteria for NOD in 2010-2014 was identified. Each patient was assigned a risk score (< 1: low risk; 1-2: intermediate risk; ≥ 3: high risk) based on the values of the predictors specified in the END-PAC model. Patients who developed pancreatic ductal adenocarcinoma (PDAC) within 3 years were identified using the Cancer Registry and California State Death files. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated. RESULTS: Out of the 13,947 NOD patients who were assigned a risk score, 99 developed PDAC in 3 years (0.7%). Of the 3038 patients who had a high risk, 62 (2.0%) developed PDAC in 3 years. The risk increased to 3.0% in white patients with a high risk. The AUC was 0.75. At the 3+ threshold, the sensitivity, specificity, PPV, and NPV were 62.6%, 78.5%, 2.0%, and 99.7%, respectively. CONCLUSIONS: It is critical that prediction models are validated before they are implemented in various populations and clinical settings. More efforts are needed to develop screening strategies most appropriate for patients with NOD in real-world settings.

Operative Treatment of Acute Patellar Tendon Ruptures.

BACKGROUND: The gold standard for patellar tendon repair is a transosseous technique. Suture anchor repair has gained popularity, with recent biomechanical studies demonstrating significantly less gap formation during cyclic loading and significantly higher ultimate failure loads as compared with transosseous repair. These findings have not been substantiated in a large clinical cohort. PURPOSE: To report demographic and epidemiologic data, clinical and surgical findings, and outcomes and complications of anchor and transosseous suture repairs of acute patellar tendon ruptures. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: All patients who underwent a primary repair of a traumatic patellar tendon rupture within 45 days of injury between 2006 and 2016 were retrospectively reviewed. Surgery was performed at a multisurgeon (120 surgeons) multicenter (14 centers) community-based integrated health care system. Patient demographic information, repair type, complications, and time from surgery to release from medical care were recorded. RESULTS: A total of 361 patients (374 knees) met our inclusion criteria. The mean age was 39.8 years (range, 9-86 years), and 91.7% were male. There were 321 transosseous and 53 anchor repairs. There was no significant difference in the mean age (P = .27), sex (P = .79), tourniquet time (P = .93), or body mass index (P = .78) between the groups. There was a significant difference in rerupture rate between transosseous and anchor repairs (7.5% vs 0%, respectively; P = .034). Based on logistic regression, transosseous repair had 3.24 times the odds of reoperation verseus anchor repair (95% CI, 0.757-13.895; P = .1129). The infection rate was 1.6% for transosseous repair and 7.5% for anchor repair (P = .160). There was no difference in time to release from medical care: 18.4 weeks for anchor and 17.1 weeks for transosseous repairs (P = .92). CONCLUSION: Anchor repair demonstrated a significant decrease in rerupture rate when compared with transosseous repair. There was no difference in reoperation rate, infection rate, or time to release from medical care.