Unique and conserved aspects of gut development in zebrafish

AbstractAlthough the development of the digestive system of humans and vertebrate model organisms has been well characterized, relatively little is known about how the zebrafish digestive system forms. We define developmental milestones during organogenesis of the zebrafish digestive tract, liver, and pancreas and identify important differences in the way the digestive endoderm of zebrafish and amniotes is organized. Such differences account for the finding that the zebrafish digestive system is assembled from individual organ anlagen, whereas the digestive anlagen of amniotes arise from a primitive gut tube. Despite differences of organ morphogenesis, conserved molecular programs regulate pharynx, esophagus, liver, and pancreas development in teleosts and mammals. Specifically, we show that zebrafish faust/gata-5 is a functional ortholog of gata-4, a gene that is essential for the formation of the mammalian and avian foregut. Further, extraembryonic gata activity is required for this function in zebrafish as has been shown in other vertebrates. We also show that a loss-of-function mutation that perturbs sonic hedgehog causes defects in the development of the esophagus that parallel those associated with targeted disruption of this gene in mammals. Perturbation of sonic hedgehog also affects zebrafish liver and pancreas development, and these effects occur in a reciprocal fashion, as has been described during mammalian liver and ventral pancreas development. Together, these data define aspects of digestive system development necessary for the characterization of zebrafish mutants. Given the similarities of teleost and mammalian digestive physiology and anatomy, these findings have implications for developmental and evolutionary studies as well as research of human diseases, such as diabetes, liver cirrhosis, and cancer

Distinguishing Death from Disenrollment in Claims Data Using a Readily Implemented Machine Learning Algorithm

Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims based research. If not an outcome, death is a competing risk and poses a threat to validity when treated as non-informative right censoring. Objectives: We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database. Methods: We identified adults (18+ years) with at least 2 years of continuous enrollment prior to disenrollment between 01/2007 and 01/2018. Leveraging unique linkages in addition to data that are typically unavailable in the publicly licensed data, we ascertained date of death from the Social Security Death Index, inpatient discharge status, and death indicators in the administrative data. Models including candidate predictors for age, sex, Census region, month of disenrollment, year of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set. Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity, specificity, and ROC associated with various thresholds of predicted probability to classify death at disenrollment were calculated. Results: Overall, we identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the year of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97. At the same prediction threshold, the weighted algorithm classified death with a sensitivity of .947 and a specificity of 0.898 (ROC=.973). Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment with high specificity and varying sensitivity depending on the chosen prediction threshold. Users can easily implement the algorithm and can choose the prediction threshold (balancing sensitivity and specificity) to meet the needs of the specific study at hand

Impact of Sleep and Circadian Disruption on Energy Balance and Diabetes: A Summary of Workshop Discussions

A workshop was held at the National Institute for Diabetes and Digestive and Kidney Diseases with a focus on the impact of sleep and circadian disruption on energy balance and diabetes. The workshop identified a number of key principles for research in this area and a number of specific opportunities. Studies in this area would be facilitated by active collaboration between investigators in sleep/circadian research and investigators in metabolism/diabetes. There is a need to translate the elegant findings from basic research into improving the metabolic health of the American public. There is also a need for investigators studying the impact of sleep/circadian disruption in humans to move beyond measurements of insulin and glucose and conduct more in-depth phenotyping. There is also a need for the assessments of sleep and circadian rhythms as well as assessments for sleep-disordered breathing to be incorporated into all ongoing cohort studies related to diabetes risk. Studies in humans need to complement the elegant short-term laboratory-based human studies of simulated short sleep and shift work etc. with studies in subjects in the general population with these disorders. It is conceivable that chronic adaptations occur, and if so, the mechanisms by which they occur needs to be identified and understood. Particular areas of opportunity that are ready for translation are studies to address whether CPAP treatment of patients with pre-diabetes and obstructive sleep apnea (OSA) prevents or delays the onset of diabetes and whether temporal restricted feeding has the same impact on obesity rates in humans as it does in mice

External validation of a machine learning algorithm to distinguish death from disenrollment in claims data

Poster presentation from the 38th International Conference on Pharmacoepidemiology & Therapeutic Risk Managemen

Distinguishing Death from Disenrollment: Applying a Predictive Algorithm to Reduce Bias in Estimating the Risk of Rehospitalization

Background: The inability to identify dates of death in several insurance claims data sources can result in biased estimates when death is a competing event. To address this issue, an algorithm to predict when plan disenrollment is due to death was developed and validated using the MarketScan insurance claims data. Objectives: We illustrate the bias introduced when estimating the risk of rehospitalization within 90-days of acute myocardial infarction (AMI) if death is not accounted for as a competing event. We demonstrate how this validated algorithm can be used to reduce this bias. Methods: We use a 20% sample of Medicare claims (2007–2017) to identify patients with an incident admission for AMI. Patients were required to be 66+ years of age with employer-sponsored supplemental insurance. We compare 3 methods of estimating the risk of 90-day rehospitalization. The first method uses the true death data available in the Medicare enrollment data. We used cumulative incidence functions to estimate the risk of rehospitalization, accounting for death as a competing risk. The second method mimics scenarios where death data are unavailable, and patients are disenrolled from insurance coverage shortly after death. We used Kaplan Meier curves to estimate the risk of rehospitalization, treating death as non-informative censoring at the time of disenrollment. The third method applies the validated predictive algorithm to the Medicare claims where death date has been obscured. We used a predicted probability threshold of 0.99 to distinguish between plan disenrollment and death (sensitivity = 0.92, specificity = 0.90). We estimated the risk of rehospitalization accounting for predicted death as a competing risk. Results: We identified 12 753 patients with an index hospitalization for AMI (mean age = 77.8 years). When accounting for death as a competing risk using validated death dates, the estimated 90-day risk of rehospitalization was 21.6% (20.8%, 22.3%). When mimicking a scenario where death is treated as non-informative censoring at the time of disenrollment, the estimated 90-day risk was 24.8% (23.9%, 25.6%). When using the algorithm to distinguish between death and disenrollment and accounting for predicted death as a competing risk, the estimated 90-day risk was 21.7% (21.0%, 22.4%). Conclusions: When estimating the risk of rehospitalization following AMI in a cohort of Medicare patients, applying a claims-based algorithm to predict death resulted in estimates that closely mirrored the estimates using validated death data. Alternatively, failure to account for death as a competing risk resulted in an estimate that was biased upwards

1962: Abilene Christian College Bible Lectures - Full Text

THE RESTORATION PRINCIPLE” Being the Abilene Christian College Annual Bible Lectures 1962 Price: $3.95 Published by ABILENE CHRISTIAN COLLEGE STUDENTS EXCHANGE ACC Station Abilene, Texa

In vivo modeling of metastatic human high-grade serous ovarian cancer in mice

Metastasis is responsible for 90% of human cancer mortality, yet it remains a challenge to model human cancer metastasis in vivo. Here we describe mouse models of high-grade serous ovarian cancer, also known as high-grade serous carcinoma (HGSC), the most common and deadliest human ovarian cancer type. Mice genetically engineered to harbor Dicer1 and Pten inactivation and mutant p53 robustly replicate the peritoneal metastases of human HGSC with complete penetrance. Arising from the fallopian tube, tumors spread to the ovary and metastasize throughout the pelvic and peritoneal cavities, invariably inducing hemorrhagic ascites. Widespread and abundant peritoneal metastases ultimately cause mouse deaths (100%). Besides the phenotypic and histopathological similarities, mouse HGSCs also display marked chromosomal instability, impaired DNA repair, and chemosensitivity. Faithfully recapitulating the clinical metastases as well as molecular and genomic features of human HGSC, this murine model will be valuable for elucidating the mechanisms underlying the development and progression of metastatic ovarian cancer and also for evaluating potential therapies