Predicting High-Grade Glioma Response to Chemoradiation via MRI-Calibrated

https://openworks.mdanderson.org/sumexp21/1085/thumbnail.jp

Diversity in health professional education scholarship: a document analysis of international author representation in leading journals

Objectives The global distribution of health professionals and associated training programmes is wide but prior study has demonstrated reported scholarship of teaching and learning arises from predominantly Western perspectives. Design We conducted a document analysis to examine authorship of recent publications to explore current international representation. Data sources The table of contents of seven high-impact English-language health professional education journals between 2008 and 2018 was extracted from Embase. Eligibility criteria The journals were selected according to highest aggregate ranking across specific scientific impact indices and stating health professional education in scope; only original research and review articles from these publications were included for analysis. Data extraction and synthesis The table of contents was extracted and eligible publications screened by independent reviewers who further characterised the geographic affiliations of the publishing research teams and study settings (if applicable). Results A total 12 018 titles were screened and 7793 (64.8%) articles included. Most were collaborations (7048, 90.4%) conducted by authors from single geographic regions (5851, 86%). Single-region teams were most often formed from countries in North America (56%), Northern Europe (14%) or Western Europe (10%). Overall lead authorship from Asian, African or South American regions was less than 15%, 5% and 1%, respectively. Geographic representation varied somewhat by journal, but not across time. Conclusions Diversity in health professional education scholarship, as marked by nation of authors’ professional affiliations, remains low. Under-representation of published research outside Global North regions limits dissemination of novel ideas resulting in unidirectional flow of experiences and a concentrated worldview of teaching and learning.Scopu

Remodelling of human atrial K+ currents but not ion channel expression by chronic β-blockade

Chronic β-adrenoceptor antagonist (β-blocker) treatment in patients is associated with a potentially anti-arrhythmic prolongation of the atrial action potential duration (APD), which may involve remodelling of repolarising K+ currents. The aim of this study was to investigate the effects of chronic β-blockade on transient outward, sustained and inward rectifier K+ currents (ITO, IKSUS and IK1) in human atrial myocytes and on the expression of underlying ion channel subunits. Ion currents were recorded from human right atrial isolated myocytes using the whole-cell-patch clamp technique. Tissue mRNA and protein levels were measured using real time RT-PCR and Western blotting. Chronic β-blockade was associated with a 41% reduction in ITO density: 9.3 ± 0.8 (30 myocytes, 15 patients) vs 15.7 ± 1.1 pA/pF (32, 14), p < 0.05; without affecting its voltage-, time- or rate dependence. IK1 was reduced by 34% at −120 mV (p < 0.05). Neither IKSUS, nor its increase by acute β-stimulation with isoprenaline, was affected by chronic β-blockade. Mathematical modelling suggested that the combination of ITO- and IK1-decrease could result in a 28% increase in APD90. Chronic β-blockade did not alter mRNA or protein expression of the ITO pore-forming subunit, Kv4.3, or mRNA expression of the accessory subunits KChIP2, KChAP, Kvβ1, Kvβ2 or frequenin. There was no reduction in mRNA expression of Kir2.1 or TWIK to account for the reduction in IK1. A reduction in atrial ITO and IK1 associated with chronic β-blocker treatment in patients may contribute to the associated action potential prolongation, and this cannot be explained by a reduction in expression of associated ion channel subunits

Cerebral Blood Volume Changes During Radiotherapy May Predict Pseudoprogression versus Disease Progression for Patients with High Grade Glioma

https://openworks.mdanderson.org/sumexp21/1040/thumbnail.jp

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Symposium review: uncertainties in enteric methane inventories,measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes