Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion

Potential drug-drug interactions (DDI) can lead to adverse drug reactions (ADR), and DDI prediction can help pharmacy researchers detect harmful DDI early. However, existing DDI prediction methods fall short in fully capturing drug information. They typically employ a single-view input, focusing solely on drug features or drug networks. Moreover, they rely exclusively on the final model layer for predictions, overlooking the nuanced information present across various network layers. To address these limitations, we propose a multi-scale dual-view fusion (MSDF) method for DDI prediction. More specifically, MSDF first constructs two views, topological and feature views of drugs, as model inputs. Then a graph convolutional neural network is used to extract the feature representations from each view. On top of that, a multi-scale fusion module integrates information across different graph convolutional layers to create comprehensive drug embeddings. The embeddings from the two views are summed as the final representation for classification. Experiments on two real-world datasets demonstrate that MSDF achieves higher accuracy than state-of-the-art methods, as the dual-view, multi-scale approach better captures drug characteristics

Unique closed-form solutions of portfolio selection subject to mean-skewness-normalization constraints

This paper originally proposes two unique closed-form solutions, respectively to risky assets only and a risk-free asset existing situations, of the mean-variance-skewness (MVS) optimization model subject to mean-sknewness-normalization constraints for portfolio selection. The efficient frontier and capital allocation surface (CAS) respectively derived from the two solutions are two hyperboloids, and tangent to each other at one hyperbola referred to as the market portfolio curve. Moreover, this curve intersects the mean-skewness plane of the portfolio return wtih zero-variance (zero-risk) at a line. Calculating the distance between a point on the coincident curve with the vertex of the CAS, we present a novel ratio to measure the performance of the risk-adjusted returns of market portfolio. The ratio is similar to the Sharpe ratio, moreover, under the more realistic assumption that portfolio returns follow a skew-normal distribution, the novel ratio can quantify the degree (or absence) of market portfolio exuberance

C1 inhibitor, a multi-functional serine protease inhibitor

SummaryC1 inhibitor (C1INH) is a serpin that regulates both complement and contact (kallikrein-kinin) system activation. It consists of a serpin domain that is highly homologous to other serpins and an amino terminal non-serpin mucin-like domain. Deficiency of C1INH results in hereditary angioedema, a disease characterised by episodes of angioedema of the skin or the mucosa of the gastrointestinal tract or the oropharynx. Although early data suggested that angioedema was mediated via complement system activation, the preponderance of the data indicate that bradykinin is the mediator. In the past few years, it has become apparent that C1INH has additional anti-inflammatory functions independent of protease inhibition. These include interactions with leukocytes that may result in enhanced phagocytosis, with endothelial cells via Eand P-selectins that interfere with leukocyte rolling and in turn results in suppression of transmigration of leukocytes across the endothelium, and interactions with extracellular matrix components that may serve to concentrate C1INH at sites of inflammation. In addition, C1INH suppresses gram negative sepsis and endotoxin shock, partly via direct interaction with endotoxin that interferes with its interaction with macrophages, thereby suppressing tumour necrosis factor-α and other inflammatory mediators. C1INH treatment improves outcome in a number of disease models, including sepsis and other bacterial infections, possibly malaria, ischaemia-reperfusion injury (intestinal, hepatic, muscle, cardiac, brain), hyper-acute transplant rejection, and other inflammatory disease models. Recent data suggest that this effectiveness is the result of mechanisms that do not require protease inhibition, in addition to both complement and contact system activation.</jats:p

Determination of optimal system parameters to characterize the wrinkle recovery of fabrics by an integrated shape retention evaluation system

Measurement methods based on the visual testing principle have been widely used for evaluating the wrinkle recovery property of fabrics; however, the cumbersome testing process and poor adaptability for various fabrics are the main shortcomings of these methods. Here, a facile mechanical testing method named the integrated shape retention evaluation system (ISRES) was developed, providing an alternative approach to assess the wrinkle recovery, as well as the compression recovery and elastic recovery of fabrics. The optimal system parameters of the ISRES in measuring the wrinkle recovery angle were determined using an orthogonal experiment design based on correlation analysis between the curve parameters from the force–displacement curves of the ISRES and the wrinkle recovery angles tested by a standard Shirley crease recovery tester. Moreover, the sensitivity of the ISRES for the differences of the wrinkle recovery of fabrics was analyzed. The results showed that the selected optimal system parameters were a good combination, and the ISRES with the optimal system parameters provided a feasible method to differentiate the wrinkle recovery of fabrics. </jats:p