An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: application in primary health care centres

Background Gestational diabetes mellitus (GDM) is one of the critical causes of adverse perinatal outcomes. A reliable estimate of GDM in early pregnancy would facilitate intervention plans for maternal and infant health care to prevent the risk of adverse perinatal outcomes. This study aims to build an early model to predict GDM in the first trimester for the primary health care centre. Methods Characteristics of pregnant women in the first trimester were collected from eastern China from 2017 to 2019. The univariate analysis was performed using SPSS 23.0 statistical software. Characteristics comparison was applied with Mann-Whitney U test for continuous variables and chi-square test for categorical variables. All analyses were two-sided with p < 0.05 indicating statistical significance. The train_test_split function in Python was used to split the data set into 70% for training and 30% for test. The Random Forest model and Logistic Regression model in Python were applied to model the training data set. The 10-fold cross-validation was used to assess the model’s performance by the areas under the ROC Curve, diagnostic accuracy, sensitivity, and specificity. Results A total of 1,139 pregnant women (186 with GDM) were included in the final data analysis. Significant differences were observed in age (Z=−2.693, p=0.007), pre-pregnancy BMI (Z=−5.502, p<0.001), abdomen circumference in the first trimester (Z=−6.069, p<0.001), gravidity (Z=−3.210, p=0.001), PCOS (χ2=101.024, p<0.001), irregular menstruation (χ2=6.578, p=0.010), and family history of diabetes (χ2=15.266, p<0.001) between participants with GDM or without GDM. The Random Forest model achieved a higher AUC than the Logistic Regression model (0.777±0.034 vs 0.755±0.032), and had a better discrimination ability of GDM from Non-GDMs (Sensitivity: 0.651±0.087 vs 0.683±0.084, Specificity: 0.813±0.075 vs 0.736±0.087). Conclusions This research developed a simple model to predict the risk of GDM using machine learning algorithm based on pre-pregnancy BMI, abdomen circumference in the first trimester, age, PCOS, gravidity, irregular menstruation, and family history of diabetes. The model was easy in operation, and all predictors were easily obtained in the first trimester in primary health care centres.

Powered flight in hatchling pterosaurs: evidence from wing form and bone strength

Competing views exist on the behaviour and lifestyle of pterosaurs during the earliest phases of life. A ‘flap-early’ model proposes that hatchlings were capable of independent life and flapping flight, a ‘fly-late’ model posits that juveniles were not flight capable until 50% of adult size, and a ‘glide-early’ model requires that young juveniles were flight-capable but only able to glide. We test these models by quantifying the flight abilities of very young juvenile pterosaurs via analysis of wing bone strength, wing loading, wingspan and wing aspect ratios, primarily using data from embryonic and hatchling specimens of Pterodaustro guinazui and Sinopterus dongi. We argue that a young Sinopterus specimen has been mischaracterised as a distinct taxon. The humeri of pterosaur juveniles are similar in bending strength to those of adults and able to withstand launch and flight; wing size and wing aspect ratios of young juveniles are also in keeping with powered flight. We therefore reject the ‘fly-late’ and ‘glide-early’ models. We further show that young juveniles were excellent gliders, albeit not reliant on specialist gliding. The wing forms of very young juveniles differ significantly from larger individuals, meaning that variation in speed, manoeuvrability, take-off angle and so on was present across a species as it matured. Juveniles appear to have been adapted for flight in cluttered environments, in contrast to larger, older individuals. We propose on the basis of these conclusions that pterosaur species occupied distinct niches across ontogeny.

Consolidating the Circuit Model for Addiction

Addiction is a disease characterized by compulsive drug seeking and consumption observed in 20–30% of users. An addicted individual will favor drug reward over natural rewards, despite major negative consequences. Mechanistic research on rodents modeling core components of the disease has identified altered synaptic transmission as the functional substrate of pathological behavior. While the initial version of a circuit model for addiction focused on early drug adaptive behaviors observed in all individuals, it fell short of accounting for the stochastic nature of the transition to compulsion. The model builds on the initial pharmacological effect common to all addictive drugs—an increase in dopamine levels in the mesolimbic system. Here, we consolidate this early model by integrating circuits underlying compulsion and negative reinforcement. We discuss the genetic and epigenetic correlates of individual vulnerability. Many recent data converge on a gain-of-function explanation for circuit remodeling, revealing blueprints for novel addiction therapies. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Design in Egypt. A Historic and Theoretical Context

This study is observing design in Egypt to articulate its theoretical framework, which developed since the establishments of the early model of vocational school in 1839. The school formed to provide much needed skilled workers for the modern industrial productions capabilities progressively established at that time. The school transformed during the postcolonial era to take the current educational institution of applied arts. Building on Egypt’s geopolitical superiority and cultural influences in the region, its design education model spread across many neighbouring countries. Egypt is one of the ancient centres of skilled craftsmanship, traditional crafts as socio-cultural context and practices continue to influence the applied arts teaching pedagogy. The study argues the need to consider the role of design in the social context and move it outside its traditional context and practices of making and ornamenting material objects.

The Affects of Celebrity Brand

This chapter explores how Trump’s political identity builds on his status as a celebrity and as a brand. Trump has long utilized media technologies of spectacle in order to enhance his brand identity. As a commercial assemblage, Trump’s business model has been to combine the affective dimensions of celebrity and brand in a way that guarantees the flows of capital and finance that his business enterprise requires. The first chapter provides this context for how we have been socialized by these technologies of post-truth reality during these years and how they came to infect political culture. Trump was gaining celebrity status in the 1980s when President Ronald Reagan was putting similar techniques to work in the White House, providing an early model for Trump to later reinvent.

Development of a Complication- and Treatment-Aware Prediction Model for Favorable Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning

BACKGROUND Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by being primarily based on patient and disease characteristics on admission. OBJECTIVE To develop and validate a complication- and treatment-aware outcome prediction tool in aSAH. METHODS This cohort study included data from an ongoing prospective nationwide multicenter registry on all aSAH patients in Switzerland (Swiss SOS [Swiss Study on aSAH]; 2009-2015). We trained supervised machine learning algorithms to predict a binary outcome at discharge (modified Rankin scale [mRS] ≤ 3: favorable; mRS 4-6: unfavorable). Clinical and radiological variables on admission (“Early” Model) as well as additional variables regarding secondary complications and disease management (“Late” Model) were used. Performance of both models was assessed by classification performance metrics on an out-of-sample test dataset. RESULTS Favorable functional outcome at discharge was observed in 1156 (62.0%) of 1866 patients. Both models scored a high accuracy of 75% to 76% on the test set. The “Late” outcome model outperformed the “Early” model with an area under the receiver operator characteristics curve (AUC) of 0.85 vs 0.79, corresponding to a specificity of 0.81 vs 0.70 and a sensitivity of 0.71 vs 0.79, respectively. CONCLUSION Both machine learning models show good discrimination and calibration confirmed on application to an internal test dataset of patients with a wide range of disease severity treated in different institutions within a nationwide registry. Our study indicates that the inclusion of variables reflecting the clinical course of the patient may lead to outcome predictions with superior predictive power compared to a model based on admission data only.

Internet of Things, cybersecurity and governing wicked problems: learning from climate change governance

The implementation of the Internet of Things (IoT) is central to what the World Economic Forum has coined the ‘Fourth Industrial Revolution’; a technological revolution built upon cyber-physical systems that will blur the lines between the physical, digital and biological spheres. Novel interconnections will emerge as a result, challenging traditional relations and modes of governance. However, a central feature of the IoT is that the implications of cyber (in)security are no longer abstract. The IoT also returns us to the world of kinetic effects in international relations; more familiar territory for IR. The resulting cooperation and coordination challenges are transboundary in nature, occur at multiple levels across sectors, between institutions, and will impact all actors, both public and private, in complex, often highly politicised ways. In this article we argue that advances in global climate governance appear to be offering an early model of a consensual rules-based approach within the existing international order that provides space for advancing agility, flexibility, and polycentrism to meet the demands of ‘wicked problems’ like the cybersecurity of the IoT. Perhaps one of the most important lessons to be drawn across from climate governance is the role of robust mechanisms for knowledge exchange – specifically between the technical and policy communities.