Patterns and outcomes of early and late recurrence after hepatectomy for hepatocellular carcinoma with microvascular invasion: a multicenter study in China.

Background: A few studies focus on the long-term outcomes and surveillance strategies for patients with hepatocellular carcinoma (HCC) and microvascular invasion (MVI) who experience postoperative recurrence. The aim of this study was to explore the patterns and prognosis of early and late recurrence (ER and LR) after hepatectomy of such patients.

Methods: Consecutive patients with HCC and MVI after hepatectomy from 26 centers in China from 2009 to 2020 were included.

Prognostic Significance of CDK1 in Ovarian and Cervical Cancers.

Ovarian cancer (OC) and cervical cancer (CC) are the leading causes of death among women. Therefore, identifying markers for early detection and treatment is critical. CDK1 governs the G2/M transition of the cell cycle and is a significant regulatory protein of the cycle.

Optimization of enzymatic hydrolysate process of Volvariella volvacea and analysis of volatile flavor compounds in Maillard reaction.

Background: Edible fungi seasoning not only enhances umami flavor but also improves nutritional value in the production of traditional Chinese cuisine. Volvariella volvacea is a typical tropical and subtropical tasty edible fungus, with an annual production in China reaching 330, 000 tons, accounting for 80% of the global yield. Therefore, the high-value utilization of V.

YBX1 is required for assembly of viral replication complexes of chikungunya virus and replication of multiple alphaviruses.

Chikungunya virus (CHIKV), an enveloped positive-sense RNA virus, is a member of the alphaviruses and cause fever and arthralgia in humans. We performed genome-wide CRISPR/Cas9-based screens and identified Y-box binding protein 1 (YBX1) as an essential cellular factor for CHIKV. Deficiency of YBX1 inhibited CHIKV RNA replication and impaired virus production.

CPMKG: a condition-based knowledge graph for precision medicine.

Personalized medicine tailors treatments and dosages based on a patient's unique characteristics, particularly its genetic profile. Over the decades, stratified research and clinical trials have uncovered crucial drug-related information-such as dosage, effectiveness, and side effects-affecting specific individuals with particular genetic backgrounds. This genetic-specific knowledge, characterized by complex multirelationships and conditions, cannot be adequately represented or stored in conventional knowledge systems.

The heterogeneity of cellular metabolism in the tumour microenvironment of hepatocellular carcinoma with portal vein tumour thrombus.

Given the growing interest in the metabolic heterogeneity of hepatocellular carcinoma (HCC) and portal vein tumour thrombus (PVTT). This study comprehensively analysed the metabolic heterogeneity of HCC, PVTT, and normal liver samples using multi-omics combinations. A single-cell RNA sequencing dataset encompassing six major cell types was obtained for integrated analysis.

A simple and effective deep neural network based QRS complex detection method on ECG signal.

The QRS complex is the most prominent waveform within the electrocardiograph (ECG) signal. The accurate detection of the QRS complex is an essential step in the ECG analysis algorithm, which can provide fundamental information for the monitoring and diagnosis of the cardiovascular diseases. Seven public ECG datasets were used in the experiments.

High expression ITGA2 affects the expression of MET, PD-L1, CD4 and CD8 with the immune microenvironment in pancreatic cancer patients.

Purpose: Pancreatic cancer is characterized by a grim prognosis and is regarded as one of the most formidable malignancies. Among the genes exhibiting high expression in different tumor tissues, ITGA2 stands out as a promising candidate for cancer therapy. The promotion of cancer in pancreatic cancer is not effective.

Body mass index, C-reactive protein, and pancreatic cancer: A Mendelian randomization analysis to investigate causal pathways.

Aim: To explore whether C-reactive protein (CRP) mediates the risk of body mass index (BMI) in pancreatic cancer (PC) and calculate the mediate proportion of CRP in this possible mechanism.

Methods: Based on two-sample Mendelian randomization (TSMR), a two-step Mendelian randomization (TM) model was conducted to determine whether CRP was a mediator of the causal relationship between BMI and PC. The multivariable Mendelian randomization (MVMR) study was designed for mediating analysis and to calculate the mediating proportion mediated by CRP.

Assessing the causality between thyroid and breast neoplasms: A bidirectional Mendelian randomization study.

Aim: This study aimed to evaluate the association between thyroid neoplasms (TN) and the risk of developing breast neoplasms (BN) by assessing data on single nucleotide polymorphisms (SNPs) obtained from the Deutsches Krebsforschungszentrum (DKFZ) and Breast Cancer Association (BCAC).

Methods: Data on SNPs associated with TN and BN were obtained from DKFZ and BCAC, respectively. Secondary data analysis of all pooled data from genome-wide association studies (GWAS) was performed to identify the genetic loci closely associated with TN or BN as instrumental variables (IVs).

Mex3B inhibits DC-STAMP mRNA level and osteoclastogenesis.

Bone homeostasis is maintained through continuous remodeling by osteoclast-driven bone resorption and osteoblast-mediated bone formation. Osteoclasts are multinucleated giant cells (MNCs) differentiated from myeloid progenitors of the monocytic lineage. During osteoclast maturation, DC-STAMP (dendritic cell specific transmembrane protein) has been shown as a master determinant of osteoclast cell fusion.

A Weighted Graph Attention Network Based Method for Multi-label Classification of Electrocardiogram Abnormalities.

The multi-label electrocardiogram (ECG) classification is to automatically predict a set of concurrent cardiac abnormalities in an ECG record, which is significant for clinical diagnosis. Modeling the cardiac abnormality dependencies is the key to improving classification performance. To capture the dependencies, we proposed a multi-label classification method based on the weighted graph attention networks.

Deep Multi-instance Networks for Bundle Branch Block Detection from Multi-lead ECG.

Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training.

A Novel Method for Calibration-Based Cuff-Less Blood Pressure Estimation.

Cuff-less blood pressure estimation technology is useful for cardiovascular disease monitoring. However, without calibration, cuff-less blood pressure estimation is hard to achieve clinical acceptable performance. The traditional methods are always calibrated with retraining.

Novel Deep Convolutional Neural Network for Cuff-less Blood Pressure Measurement Using ECG and PPG Signals.

Cuff-less blood pressure (BP) is a potential method for BP monitoring because it is undisturbed and continuous monitoring. Existing cuff-less estimation methods are easily influenced by signal noise and non-ideal signal morphology. In this study we propose a novel well-designed Convolutional Neural Network (CNN) model named Deep-BP for BP estimation task.

An Electrocardiogram Delineator via Deep Segmentation Network.

Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac diseases. We treat the ECG delineation task as an one-dimensional segmentation problem, and propose a novel end-to-end deep learning method to segment sections of ECG signal. Our neural network consists of two parts: a segmentation network composed of multiple 1D Convolutional Neural Networks (CNN) and a postprocessing network composed of a sequential Conditional Random Field (CRF).

Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal.

The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat.

A Novel Detection Method of Bundle Branch Block from Multi-lead ECG.

Bundle branch block (BBB) is a common conduction block disease and can be diagnosed using electrocardiogram (ECG) signal in clinical practice. In this paper, a novel method was proposed to detect two types of BBB: right BBB (RBBB) and left BBB (LBBB) based on the combination of deep features and several kinds of expert features. We evaluated the proposed method on the MIT-BIH Arrhythmia database (AR) and China Physiological Signal Challenge 2018 database (CPSC).

SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG.

EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model.

A Review of Emotion Recognition Using Physiological Signals.

Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

Overexpression of Malonyl-CoA: ACP Transacylase in Schizochytrium sp. to Improve Polyunsaturated Fatty Acid Production.

Polyunsaturated fatty acids (PUFAs) have been widely applied in the food and medical industry. In this study, malonyl-CoA: ACP transacylase (MAT) was overexpressed through homologous recombination to improve PUFA production in Schizochytrium. The results showed that the lipid and PUFA concentration were increased by 10.