Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data.

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter.

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.

CoIn: Correlation Induced Clustering for Cognition of High Dimensional Bioinformatics Data.

Analysis of high dimensional biomedical data such as microarray gene expression data and mass spectrometry images, is crucial to provide better medical services including cancer subtyping, protein homology detection, etc. Clustering is a fundamental cognitive task which aims to group unlabeled data into multiple clusters based on their intrinsic similarities. However, for most clustering methods, including the most widely used K-means algorithm, all features of the high dimensional data are considered equally in relevance, which distorts the performance when clustering high-dimensional data where there exist many redundant variables and correlated variables. In this paper, we aim at addressing the problem of the high dimensional bioinformatics data clustering and propose a new correlation induced clustering, CoIn, to capture complex correlations among high dimensional data and guarantee the correlation consistency within each cluster. We evaluate the proposed method on a high dimensional mass spectrometry dataset of liver cancer tumor to explore the metabolic differences on tissues and discover the intra-tumor heterogeneity (ITH). By comparing the results of baselines and ours, it has been found that our method produces more explainable and understandable results for clinical analysis, which demonstrates the proposed clustering paradigm has the potential with application to knowledge discovery in high dimensional bioinformatics data.

CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines.

Cellular Thermal Shift Assay (CETSA) has been widely used in drug discovery, cancer cell biology, immunology, etc. One of the barriers for CETSA applications is that CETSA experiments have to be conducted on various cell lines, which is extremely time-consuming and costly. In this study, we make an effort to explore the translation of CETSA features cross cell lines, i.e., known CETSA feature of a given protein in one cell line, can we automatically predict the CETSA feature of this protein in another cell line, and vice versa? Inspired by pix2pix and CycleGAN, which perform well on image-to-image translation cross various domains in computer vision, we propose a novel deep neural network model called CycleDNN for CETSA feature translation cross cell lines. Given cell lines A and B, the proposed CycleDNN consists of two auto-encoders, the first one encodes the CETSA feature from cell line A into Z in the latent space [Formula: see text], then decodes Z into the CETSA feature in cell line B., Similarly, the second one translates the CETSA feature from cell line B to cell line A through the latent space [Formula: see text]. In such a way, the two auto-encoders form a cyclic feature translation between cell lines. The reconstructed loss, cycle-consistency loss, and latent vector regularization loss are used to guide the training of the model. The experimental results on a public CETSA dataset demonstrate the effectiveness of the proposed approach.

ADAN: An Adversarial Domain Adaptation Neural Network for Early Gastric Cancer Prediction.

Gastric cancer is a highly prevalent cancer world-wide. Accurate diagnosis of Early Gastric Cancer (EGC) is of great significance to improve the treatment and survival rate of patients. However, EGC and gastric ulcers have similar en-doscopic image characteristics, resulting in a high misdiagnosis rate. Most existing deep learning and machine learning models for EGC recognition have the disadvantages of cumbersome pre-processing steps and high leakage ratios. To address the above challenges, we propose an end-to-end Adversarial Do-main Adaptation Neural network (ADAN) for EGC prediction on endoscopic images. ADAN network consists of a source domain feature extractor, a source domain classifier, two target domain feature extractors, a target domain classifier, and a domain discriminator. A source domain feature extractor is designed to train the model on public gastrointestinal datasets, which effectively solves the problem of insufficient training data. In addition, an adaptive source-target domain mapping classifier is added to each target domain feature extractor for automatically adjusting the number of classification categories in the target domain. Experimental results show that the proposed ADAN network is superior to the most advanced methods and can accurately predict EGC in clinical practice. Clinical relevance-In this study, the EGC diagnosis model based on the adversarial domain adaptive construction will be more applicable to the real clinical scenario, with higher accuracy and sensitivity and assist the endoscopist to make more accurate diagnosis for EGC and reduce the workload.

Residual Channel Attention Network for Brain Glioma Segmentation.

A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of inter-class ambiguities in tumor regions. Recently, deep learning approaches have achieved outstanding performance in the automatic segmentation of brain glioma. However, existing al-gorithms fail to exploit channel-wise feature interdependence to select semantic attributes for glioma segmentation. In this study, we implement a novel deep neural network that integrates residual channel attention modules to calibrate intermediate features for glioma segmentation. The proposed channel at-tention mechanism adaptively weights feature channel-wise to optimize the latent representation of gliomas. We evaluate our method on the established dataset BraTS2017. Experimental results indicate the superiority of our method. Clinical relevance - While existing glioma segmentation approaches do not leverage channel-wise feature dependence for feature selection our method can generate segmentation masks with higher accuracies and provide more insights on graphic patterns in brain MRI images for further clinical reference.

Malignancy Suspicious Region Guided Deep Neural Networks for Gastric Ulcer Classification.

Malignant transformation of gastric ulcer can result in gastric cancer, hence an accurate gastric ulcer classification method is of vital importance. Despite marvelous progress has been achieved in recent years, there are still many challenges in diagnosis of gastric ulcer. In this paper, we propose a mechanism to mimic gastroenterologist's behaviours based on deep learning techniques, by integrating the segmented malignancy suspicious masks with gastroscopic images for gastric ulcer classification, which instructs the model to focus on the area where symptoms occur for gastric ulcer diagnosis. Specifically, a U-Net-type deep neural network is built to segment the suspicious pathological regions from gastroscopic images, then the segmented regions are treated as an attention channel of gastroscopic images for the gastric ulcer classification by a ResNet-type deep neural network. Experiments on a real gastroscopic dataset with 900+ patient cases demonstrate that our proposed approach achieves much better performance for gastric ulcer diagnosis, compared with standard method with only gastroscopic images.

Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation.

Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNNs are computationally expensive and memory intensive. To address these issues, we first propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs, thereby improving the model performance. Moreover, we design a 2.5D light-weight nnU-Net from a network perspective, in which, depthwise separable convolutions are adopted to improve the efficiency. Extensive experiments on the LiTS dataset have demonstrated the superiority of the proposed method.Clinical relevance- The proposed method can effectively segment livers and tumors from CT scans with low complexity, which can be easily implemented into clinical practice.

CRISPR-Cas12a-based efficient electrochemiluminescence biosensor for ATP detection.

CRISPR-Cas12a system exhibits tremendous potential in accurate recognition and quantitation of nucleic acids and non-nucleic-acid targets thanks to the discovery of its cleavage capability toward single-stranded DNA (ssDNA). In this study, we developed an efficient electrochemiluminescence (ECL) sensing platform based on CRISPR-Cas12a for the analysis of adenosine triphosphate (ATP). In the presence of the target, the successful release of the DNA activator is specially recognized by Cas12a-crRNA duplex and activates the cleavage of ferrocene (Fc) labeled-ssDNA (Fc-ssDNA) modified on the cathode of bipolar electrode (BPE), resulting in a decrease of ECL intensity of [Ru(bpy)3]2+/TPrA in the anodic cell of BPE. By means of the unique combination of Cas12a with ECL technique based on BPE, it can convert the recognition of target ATP into a detectable ECL signal. The detection limit of ATP was determined to be 0.48 nM under the optimal conditions. This work will expand the application of CRISPR-Cas detection system and propose a potential method for the analysis of non-nucleic-acid targets.

Retrievable inferior vena cava filter to prevent pulmonary embolism in patients with fractures and deep venous thrombosis of lower extremities: a single-center experience.

OBJECTIVE: To evaluate the effectiveness of inserting a retrievable inferior vena cava filter (IVCF) to prevent pulmonary embolism (PE) in patients with bone fractures and acute deep venous thrombosis (DVT) before major orthopedic surgery. METHODS: Clinical data of patients with fractures and acute DVT who underwent IVCF insertion were analyzed. The patients were divided into above-knee DVT (AKDVT), popliteal vein thrombosis (PVT), and below-knee DVT (BKDVT) groups. RESULTS: An IVCF was successfully implanted in 964 patients, among whom 929 were followed up (335, 470, and 124 in AKDVT, PVT, and BKDVT groups, respectively). There was no significant difference in the incidence of filter thrombosis among the groups (11.04%, 11.70%, and 8.06%, respectively). No symptomatic PE occurred during follow-up. The mean filter indwelling time was 18.4 ± 4.3 days, and the total filter removal rate was 76.87%. There was no significant difference in the rate of filter implantation, retrieval, complications, or mortality among the groups. CONCLUSIONS: Retrievable filters can effectively prevent PE before orthopedic surgery in patients with fractures and acute DVT of the lower limbs. AKDVT more readily forms a ≥1-cm thrombus in the IVCF than does BKDVT, and PVT more readily forms a <1-cm thrombus than does AKDVT.

Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Considering intra-tumour heterogeneity (ITH) in HCC, multi-region sampling technology is implemented to generate the dataset for experiments. Experimental results demonstrate the effectiveness of the proposed model.

Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma.

Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.

High-Throughput, Rapid Quantification of Phthalic Acid Esters and Alkylphenols in Fish Using a Coated Direct Inlet Probe Coupled with Atmospheric Pressure Chemical Ionization.

Intake of endocrine-disrupting chemicals (EDCs) by humans could disturb the metabolism of hormones, induce cancer, and damage the liver and other organs. Phthalate acid esters (PAEs) and alkylphenols (APs) are important EDCs and environmental contaminants. With the increasing use of plastics and nonionic surfactants worldwide, PAEs and APs have entered environmental water and accumulated in edible fish, which are finally consumed by humans. In this study, a coated direct inlet probe (CDIP) based on an atmospheric solid analysis probe, which can rapidly and simultaneously extract both PAEs and APs in fish, was developed. Twelve PAEs and APs were quantified by using a stable-isotope-labeled internal standard. Standard curves of the PAEs and APs having correlation coefficients of R2 ≥ 0.9837 were obtained. The limit of detection of the PAEs and APs was distributed from 0.01 to 40 ng g-1. The relative recovery of the method was 78-120% between low, medium, and high spiked levels. Combined with principal component analysis, PAE- and AP-contaminated Carassius auratus from different habitats could be identified. Multiple sample analysis mode allowed the extraction of up to 12 samples at once, and the total analysis time (including sample pretreatment, extraction, and analysis time) was less than 10 min per sample, which indicates that CDIP is useful for rapid quantitative analysis.

A Novel Peptide from Soybean Protein Isolate Significantly Enhances Resistance of the Organism under Oxidative Stress.

Recent studies have indicated that protein hydrolysates have broad biological effects. In the current study we describe a novel antioxidative peptide, FDPAL, from soybean protein isolate (SPI). The aim of this study was to purify and characterize an antioxidative peptide from SPI and determine its antioxidative mechanism. LC-MS/MS was used to isolate and identify the peptide from SPI. The sequence of the peptide was determined to be Phe-Asp-Pro-Ala-Leu (FDPAL, 561 Da). FDPAL can cause significant enhancement of resistance to oxidative stress both in cells as well as simple organisms. In Caenorhabditis elegans (C. elegans), FDPAL can up-regulate the expression of certain genes associated with resistance. The antioxidant activity of this peptide can be attributed to the presence of a specific amino acid sequence. Results from our work suggest that FDPAL can facilitate potential applications of proteins carrying this sequence in the nutraceutical, bioactive material and clinical medicine areas, as well as in cosmetics and health care products.

Comparison of high ligation and stripping of the great saphenous vein combined with foam sclerotherapy versus conventional surgery for the treatment of superficial venous varicosities of the lower extremity.

The aim of this study was to compare the results of high ligation and stripping of the great saphenous vein (GSV) trunk combined with foam sclerotherapy with conventional surgery for the treatment of superficial venous varicosities of the lower extremity. One hundred and thirty eight patients with primary or secondary superficial venous varicosities of the lower extremity were included. 60 underwent conventional surgery and 78 were treated with high ligation and stripping of the GSV trunk and foam sclerotherapy of GSV branches, spider veins, and reticular veins. Surgical time and amount of bleeding of single limb, recurrence of varicose vein, complications and patients satisfactory were recorded. Compared with the conventional surgery group, the GSV trunk stripping and foam sclerotherapy group had a significantly lower surgical time (P < 0.05), amount of bleeding and duration of hospital stays (P < 0.01). No statistically significant difference with respect to the wound infection, local discomfort, postoperative recurrence rates of varicosity and patients satisfaction score was observed (P > 0.05). GSV trunk stripping and foam sclerotherapy group at a 6 months of follow up had a higher recurrence rate of varicosity as compared to the conventional surgery group (P < 0.05). High ligation and GSV trunk stripping combined with foam sclerotherapy prior to conventional surgery for patients with superficial venous varicosities of the lower extremity with a shorter surgical time, fewer bleeding, duration of hospital stays and higher patients satisfactory scores.

Dysregulated miR1254 and miR579 for cardiotoxicity in patients treated with bevacizumab in colorectal cancer.

Methods for detecting circulating microRNAs (miRNAs), small RNAs that control gene expression, at high sensitivity and specificity in the blood have been reported in recent studies. The goal of this study was to determine if detectable levels of specific miRNAs are released into the circulation for bevacizumab-induced cardiotoxicity. A miRNA array analysis was performed using RNA isolated from 10 control patients in bevacizumab treatment, and n=10 patients have been confirmed to have bevacizumab-induced cardiotoxicity. From the array, we selected 19 candidate miRNA for a second validation study in 90 controls and 88 patients with bevacizumab-induced cardiotoxicity. Consistent with the data obtained from the microRNA array, circulating levels of five miRNAs were significantly increased in patients with bevacizumab-induced cardiotoxicity compared with controls. To confirm these data, we compared selected miRNAs in the plasma of patients with bevacizumab-induced cardiotoxicity with those of 66 patients with acute myocardial infarction (AMI). Moreover, we went on to analyze what factors may influence the levels of potential biomarker miRNAs. Consistent with the data obtained from the microRNA array, circulating levels of five miRNAs were significantly increased in patients with bevacizumab-induced cardiotoxicity compared with those of healthy bevacizumab treatment controls. However, only miRNA1254 and miRNA579 showed high specificity in the validation experiments. Moreover, we went on to analyze what factors may influence the levels of potential biomarker miRNAs. We identify two miRNAs that are specifically elevated in patients with bevacizumab-induced cardiotoxicity, miR1254 and miRNA579, and miRNA1254 shows the strongest correlation to the clinical diagnosis of bevacizumab-induced cardiotoxicity.