Two Levels of High Ligation of the Inferior Mesenteric Artery Based on Three Anatomical Fasciae for Colorectal Cancer (with Video).

Background: The ligation of the inferior mesenteric artery (IMA) is the primary procedure during surgeries of the left colon, sigmoid colon, and rectal cancer. Despite the ongoing debate on high or low ligation of the IMA, high ligation (HL) is now preferred by most of the surgeons. However, there is still a lack of consistency in the exact position of HL among surgical videos or introductions presented by different teams, causing confusion to new learners.

Identifying Cancer Driver Genes Using a Neural Network Framework with Cross-attention Mechanism.

Identifying cancer driver genes can accelerate the discovery of drug targets and the development of cancer therapies. Recent research methods improve the accuracy of identifying cancer driver genes by using deep learning framework. However, due to ignore the connection among learned features, they usually have weak feature representations that limits further improvement in the accuracy of identifying cancer driver genes.

scPEGSSC:Proximity Enhanced Graph Convolutional Sparse Subspace Clustering Method for scRNA-seq Data.

The identification of cell types by clustering singlecell RNA sequencing (scRNA-seq) data is a fundamental step in the downstream analysis of single-cell data. However, great challenges remain owing to the inherent characteristics of scRNAseq data, including high dimensionality, high noise, and high sparsity. In this study, we propose a proximity enhanced graph convolutional sparse subspace clustering method scPEGSSC for scRNA-seq data.

Using Multi-Feature Weak Consensus Model to Discover Essential Proteins.

Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features.

A Deep Learning Framework for Identifying Cancer Driver Genes Based on Transformer and Graph Convolutional Network.

Correct identification of cancer driver genes plays a significant role in cancer research. The advancement of graph neural network (GNN) research has led to the emergence of many high-performance cancer driver gene prediction methods. However, GNN-based methods frequently overlook the importance of capturing global information.

scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.

It is a significant step for single cell analysis to identify cell types through clustering single-cell RNA sequencing (scRNA-seq) data. However, great challenges still remain due to the inherent high-dimensionality, noise, and sparsity of scRNA-seq data. In this study, scPEDSSC, a deep sparse subspace clustering method based on proximity enhancement, is put forward.

MNMO: discover driver genes from a multi-omics data based-multi-layer network.

Motivation: Cancer as a public health problem is driven by genomic variations in "cancer driver" genes. The identification of driver genes is critical for the discovery of key biomarkers and the development of personalized therapy.

Results: We propose a prediction method MNMO: a multi-layer network model based on multi-omics data.

Integrated analysis of transcriptome, sRNAome, and degradome involved in the drought-response of maize Zhengdan958.

Drought is a major abiotic stress in restricting the growth, development, and yield of maize. As a significant epigenetic regulator, small RNA also functions in connecting the transcriptional and post-transcriptional regulatory network. Further to help comprehending the molecular mechanisms underlying drought adaptability and tolerance of maize, an integrated multi-omics analysis of transcriptome, sRNAome, and degradome was performed on the seedling roots of an elite hybrid Zhengdan958 under drought stress.

Physicochemical Properties, Antioxidant Capacity and Bioavailability of Whey Protein Concentrate-Based Coenzyme Q10 Nanoparticles.

Coenzyme Q10 (CoQ10) is a powerful antioxidant. However, the poor water solubility and low bioavailability still remain challenges for its application. An embedded delivery system of CoQ10 based on whey protein concentrate (WPC) and polymerized whey protein concentrate (PWPC) was prepared, and the physicochemical properties, antioxidant capacity and bioavailability were characterized in this study.

Adipose-derived mesenchymal stem cells combined with platelet-rich plasma are superior options for the treatment of osteoarthritis.

Background: There is currently no definitive treatment for osteoarthritis. We examined the therapeutic effects and underlying mechanisms of platelet-rich plasma (PRP) and adipose-derived mesenchymal stem cells (ADSCs), individually or in combination, in a rat model of anterior cruciate ligament-induced degenerative osteoarthritis (OA) of the knee. This study seeks to advance clinical approaches to OA treatment.

SIGRN: Inferring Gene Regulatory Network with Soft Introspective Variational Autoencoders.

Gene regulatory networks (GRNs) exhibit the complex regulatory relationships among genes, which are essential for understanding developmental biology and uncovering the fundamental aspects of various biological phenomena. It is an effective and economical way to infer GRNs from single-cell RNA sequencing (scRNA-seq) with computational methods. Recent researches have been done on the problem by using variational autoencoder (VAE) and structural equation model (SEM).

Identification of Cancer Driver Genes based on Dynamic Incentive Model.

Cancer is a complex genomic mutation disease, and identifying cancer driver genes promotes the development of targeted drugs and personalized therapies. The current computational method takes less consideration of the relationship among features and the effect of noise in protein-protein interaction(PPI) data, resulting in a low recognition rate. In this paper, we propose a cancer driver genes identification method based on dynamic incentive model, DIM.

Early-stage voiding function following uni- versus bilateral inferior vesical vessel resection during therapeutic lateral lymph node dissection with autonomic nerve sparing for advanced low rectal cancer (with video).

Introduction: Lateral lymph node dissection (LLND) has now been widely accepted as the optimal procedure to minimize lateral local recurrence (LLR) for selected cases with advanced lower rectal cancer in Asian countries. However, there is still controversy over the preservation or resection of the inferior vesical vessels (IVVs) during LLND due to concerns of impaired post-operative urinary function. Moreover, the standardized procedure for autonomic nerve preservation has not yet been established.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.

Background: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.

Methods: Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers.

Identification of cancer driver genes based on hierarchical weak consensus model.

Unlabelled: Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well.

Comparison of lipidome profiles in human milk from Chinese Han and Korean ethnic groups based on high-throughput lipidomic techniques.

The composition of milk lipids varies across different ethnic sources. The lipidome profiles of Chinese Han human milk (HHM) and Chinese Korean human milk (KHM) were investigated in this study. A total of 741 lipids were identified in HHM and KHM.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention.

Mdwgan-gp: data augmentation for gene expression data based on multiple discriminator WGAN-GP.

Background: Although gene expression data play significant roles in biological and medical studies, their applications are hampered due to the difficulty and high expenses of gathering them through biological experiments. It is an urgent problem to generate high quality gene expression data with computational methods. WGAN-GP, a generative adversarial network-based method, has been successfully applied in augmenting gene expression data.

Curcumin-mediated photodynamic treatment extends the shelf life of salmon (Salmo salar) sashimi during chilled storage: Comparisons of preservation effects with five natural preservatives.

The impact of curcumin-mediated photodynamic treatment (PDT) on the microbiological, physicochemical and sensory qualities of salmon sashimi has not been explored. Herein, this study aimed to evaluate the effects of PDT on the shelf-life quality of ready-to-eat salmon fillets during chilled storage (4 °C) in comparison with five widely investigated natural extracts, including cinnamic aldehyde, rosmarinic acid, chlorogenic acid, dihydromyricetin and nisin. From a microbial perspective, PDT exhibited outstanding bacterial inhibition, the results of total viable counts, total coliform bacteria, psychrotrophic bacteria, Pseudomonas spp.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer.

Objective: This study aimed to explore the genes regulating lymph node metastasis in colorectal cancer (CRC) and to clarify their relationship with tumor immune cell infiltration and patient prognoses.

Methods: The data sets of CRC patients were collected through the Cancer Gene Atlas database; the differentially expressed genes (DEGs) associated with CRC lymph node metastasis were screened; a protein-protein interaction (PPI) network was constructed; the top 20 hub genes were selected; the Gene Ontology functions and the Kyoto Encyclopedia of Genes and Genomes pathways were enriched and analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to further screen the characteristic genes associated with CRC lymph node metastasis in 20 hub genes, exploring the correlation between the characteristic genes and immune cell infiltration, conducting a univariate COX analysis on the characteristic genes, obtaining survival-related genes, constructing a risk score formula, conducting a Kaplan-Meier analysis based on the risk score formula, and performing a multivariate COX regression analysis on the clinical factors and risk scores.