Estimating Risk of Cardiovascular Disease Among Long-Term Colorectal Cancer Survivors: A Nationwide Cohort Study

BackgroundConcerns about a growing number of colorectal cancer survivors have emerged regarding cardiovascular disease (CVD) risks. However, there is not yet a predictive tool that can estimate CVD risk and support the management of healthcare as well as disease prevention in terms of CVD risk among long-term colorectal cancer survivors.AimTo develop predictive tools to estimate individualized overall and each subtype of CVD risk using a nationwide cohort in South Korea.Methods and ResultsA total of 4,709 newly diagnosed patients with colorectal cancer who survived at least 5 years in the National Health Insurance System were analyzed. Cox proportional hazard regression was used for the identification of independent risk factors for the derivation of predictive nomograms, which were validated in an independent cohort (n = 3,957). Age, fasting serum glucose, γ-glutamyl transpeptidase, Charlson comorbidity index, household income, body mass index, history of chemotherapy, cigarette smoking, and alcohol consumption were identified as independent risk factors for either overall CVD or each subtype of CVD subtype. Based on the identified independent risk factors, six independent nomograms for each CVD category were developed. Validation by an independent cohort demonstrated a good calibration with a median C-index of 0.687. According to the nomogram-derived median score, relative risks of 2.643, 1.821, 4.656, 2.629, 4.248, and 5.994 were found for overall CVD, ischemic heart disease, myocardial infarction, total stroke, ischemic stroke, and hemorrhage stroke in the validation cohort.ConclusionsThe predictive tools were developed with satisfactory accuracy. The derived nomograms may support the estimation of overall and individual CVD risk for long-term colorectal cancer survivors.

Coupled Assessment of Fire Behavior and Firebrand Dynamics

The hazards associated with firebrands have been well documented. However, there exist few studies that allow for the hazard from a given fire to be quantified. To develop predictive tools to evaluate this hazard, it is necessary to understand the conditions that govern firebrand generation and those that affect firebrand deposition. A method is presented that allows for time-resolved measurements of fire behavior to be related to the dynamics of firebrand deposition. Firebrand dynamics were recorded in three fires undertaken in two different ecosystems. Fire intensity is shown to drive firebrand generation and firebrand deposition—higher global fire intensities resulting in the deposition of more, larger firebrands at a given distance from the fire front. Local firebrand dynamics are also shown to dominate the temporal firebrand deposition with periods of high fire intensity within a fire resulting in firebrand shower at deposition sites at times commensurate with firebrand transport. For the range of conditions studied, firebrand deposition can be expected up to 200 m ahead of the fire line based on extrapolation from the measurements.

Accelerating pre-formulation investigations in early drug product lifecycle using predictive methodologies and computational algorithms

Precisely developed computational methodologies can allow the drug product lifecycle process to be time-efficient, cost-effective and reliable through a thorough fundamental understanding at the molecular level. Computational methodologies include computational simulations, virtual screening, mathematical modeling and predictive tools. In light of current trends and increased expectations of product discovery in early pharmaceutical development, we have discussed different case studies. These case studies clearly demonstrate the successful application of predictive tools alone or in combination with analytical techniques to predict the physicochemical properties of drug substances and drug products, thereby shortening research and development timelines. The overall goal of this report is to summarize unique predictive methodologies, which can assist pharmaceutical scientists in achieving time-sensitive research goals and avoiding associated risks that can potentially affect the drug product quality.

InflamNat: Web-Based Database and Predictor of Anti-Inflammatory Natural Products

Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. Experimental results demonstrated that the proposed predictive tools achieved the desired performance in terms of AUC.

The Role of Emerging Predictive IT Tools in Effective Migration Governance

Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in-depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these tools’ scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short- or longer-term migration patterns, or to assess and estimate migration uncertainties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance.