Novel Dissymmetric Formal Quinoidal Molecules End-Capped by Dicyanomethylene and Triphenylphosphonium.

A number of quinoidal molecules with symmetric end-capping groups, particularly dicyanomethylene units, have been synthesized for organic optoelectronic materials. In comparison, dissymmetric quinoidal molecules, characterized by end-capping with different groups, are less explored. In this paper, we present the unexpected formation of new formal quinoidal molecules, which are end-capped with both dicyanomethylene and triphenylphosphonium moieties.

Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patients.

We explored whether a model based on contrast-enhanced computed tomography radiomics features and clinicopathological factors can evaluate preoperative lymphovascular invasion (LVI) in patients with gastric cancer (GC) with Lauren classification. Based on clinical and radiomic characteristics, we established three models: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore and a combined model. The relationship between Lauren classification and LVI was analyzed using a histogram.

A new nomogram for assessing complete response (CR) in gastric diffuse large B-cell lymphoma (DLBCL) patients after chemotherapy.

Purpose: Achieving complete response (CR) after first-line chemotherapy in gastric DLBCL patients often results in longer disease-free survival. We explored whether a model based on imaging features combined with clinicopathological factors could assess the CR to chemotherapy in patients with gastric DLBCL.

Methods: Univariate (P < 0.

A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.

Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature.

Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.

Retinal age gap as a predictive biomarker for mortality risk.

Aim: To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.

Methods: A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation.

Dynamic Changes in Brain Glucose Metabolism and Neuronal Structure in Rats with Heart Failure.

Patients with heart failure (HF) are more susceptible to cognitive impairment, but the mechanism is still unclear. This study aimed to observe the dynamic changes in brain glucose metabolism and neuronal structure in different stages of HF. An HF rat model was established by ligating the anterior descending branch of the left coronary artery.

[Transcriptional regulation of genes involved in liver-selective cell communication].

Objective: To explore the mechanism of transcription regulation of the liver-selective genes responsible for cell communication.

Methods: Tissue-selective Affymetrix probe sets (3919 probes in total) were clustered by functional categories. Liver-selective cell communication (LSCC) genes were selected for further analysis.