Association of elevated baseline bilirubin caused by preadmission statin use with cardiovascular prognosis in patients undergoing elective percutaneous coronary intervention.

Bilirubin is widely recognized to possess antioxidant and anti-inflammatory characteristics. However, the relationship between bilirubin and coronary artery disease (CAD) remains controversial, particularly in individuals receiving Percutaneous Coronary Intervention (PCI). Given that statins may enhance the production of heme oxygenase-1 (HO-1) and bilirubin, we investigated the long-term cardiovascular prognostic role of bilirubin levels elevated by statin use in patients undergoing PCI.

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

Background: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer.

Causal effect of lipoprotein(a) level on chronic kidney disease of European ancestry: a two-sample Mendelian randomization study.

Introduction: Chronic kidney disease is a growing health issue, and the options of prevention and therapy remain limited. Although a number of observational studies have linked higher Lp(a) [lipoprotein(a)] levels to the kidney impairment, the causal relationship remains to be determined. The purpose of this study was to assess the causal association between Lp(a) levels and CKD.

Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

Background: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive.

Purpose: The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers.

ROS-responsive & scavenging NO nanomedicine for vascular diseases treatment by inhibiting endoplasmic reticulum stress and improving NO bioavailability.

Vascular diseases seriously threaten human life and health. Exogenous delivery of nitric oxide (NO) represents an effective approach for maintaining vascular homeostasis during pathological events. However, the overproduction of reactive oxygen species (ROS) at vascular injury sites would react with NO to produce damaging peroxynitrite (ONOO) species and limit the therapeutic effect of NO.

Aiming at early-stage vulnerable plaques: A nanoplatform with dual-mode imaging and lipid-inflammation integrated regulation for atherosclerotic theranostics.

The vulnerable plaques in atherosclerosis can cause severe outcome with great danger of acute cardiovascular events. Thus, timely diagnosis and treatment of vulnerable plaques in early stage can effectively benefit the clinical management of atherosclerosis. In this work, a targeting theranostic strategy on early-stage vulnerable plaques in atherosclerosis is realized by a LAID nanoplatform with X-CT and fluorescent dual-mode imaging and lipid-inflammation integrated regulation abilities.

Immune patterns of cuproptosis in ischemic heart failure: A transcriptome analysis.

Cuproptosis is a recently discovered programmed cell death pattern that affects the tricarboxylic acid (TCA) cycle by disrupting the lipoylation of pyruvate dehydrogenase (PDH) complex components. However, the role of cuproptosis in the progression of ischemic heart failure (IHF) has not been investigated. In this study, we investigated the expression of 10 cuproptosis-related genes in samples from both healthy individuals and those with IHF.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes.

Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel.

Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data.

Purpose: To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods.

Methods: Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2).

Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network.

Accurate segmentation of rectal tumors is the most crucial task in determining the stage of rectal cancer and developing suitable therapies. However, complex image backgrounds, irregular edge, and poor contrast hinder the related research. This study presents an attention-based multi-modal fusion module to effectively integrate complementary information from different MRI images and suppress redundancy.

Combined effects of physical activity and sedentary behavior on all-cause mortality in heart failure patients: A cohort study of national health and nutrition examination survey analysis.

Background: Physical activity and sedentary behavior are independently related to the risk of cardiovascular disease. Physical activity is recognized as having a protective effect, while being sedentary seems to be adverse. Nonetheless, the interactions between physical activity and sedentary behavior and the combined effect on the prognosis of heart failure patients remain unclear.

Targeted delivery of liver X receptor agonist to inhibit neointimal hyperplasia by differentially regulating cell behaviors.

Restenosis induced by neointimal hyperplasia is one of the key reasons limiting the long-term success of cardiovascular interventional therapy. However, it remains a serious challenge to completely overcome restenosis because of the dilemma of simultaneously activating human umbilical vein endothelial cells (HUVECs) and inhibiting human aortic smooth muscle cells (HASMCs). Herein, we developed a targeted nanomedicine encapsulating the liver X receptor (LXR) agonist, T0901317, for differentially regulating the behaviors of HUVECs and HASMCs.

Association between electronic cigarettes use and whole blood cell among adults in the USA-a cross-sectional study of National Health and Nutrition Examination Survey analysis.

Electronic cigarettes (E-cigarettes) use is an emerging public health problem. Trying to assess the independent associations between E-cigarettes use and whole blood cell in a nationally representative sample of the US adults is very important for the smoking population. Using E-cigarettes data from NHANES (National Health and Nutrition Examination Survey) 2013-2018, 17,180 adults were included in this cross-sectional analysis.

Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma.

Purpose: To investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).

Methods: One hundred and twenty-two HCC patients (objective response, = 63; non-response, = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort ( = 85) and a validation cohort ( = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted.