Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is known for its aggressive nature, lack of effective diagnostic tools and treatments, and generally poor prognosis. The objective of this study was to investigate metabolic changes in TNBC using metabolomics approaches and explore the underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were first examined between 18 TNBC patients and 21 healthy control (HC) subjects using liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly differential metabolites (DMs).

Alpha sensory stimulation modulates theta phase during speech-print associative learning.

Applying 10 Hz (α-rate) sensory stimulation, not 5 Hz (θ-rate), prior to introducing novel speech-print pairs can reset the phase of θ oscillations and enhance associative learning. This rapid gain indicates coordinated mechanisms to regulate attentional/cognitive resources (α oscillations) and facilitate memory storage (θ oscillations) early in learning. The present findings may inform educational practices for children with reading difficulties.

Advancing Underlying Cause of Death Inference Through Wide and Deep Model.

Introduction: Accurately filling out death certificates is essential for death surveillance. However, manually determining the underlying cause of death is often imprecise. In this study, we investigate the Wide and Deep framework as a method to improve the accuracy and reliability of inferring the underlying cause of death.

LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer.

Background: Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach.

Methods: In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30).

Machine learning-based classification of deubiquitinase USP26 and its cell proliferation inhibition through stabilizing KLF6 in cervical cancer.

Objective: We aim to accurately distinguish ubiquitin-specific proteases (USPs) from other members within the deubiquitinating enzyme families based on protein sequences. Additionally, we seek to elucidate the specific regulatory mechanisms through which USP26 modulates Krüppel-like factor 6 (KLF6) and assess the subsequent effects of this regulation on both the proliferation and migration of cervical cancer cells.

Methods: All the deubiquitinase (DUB) sequences were classified into USPs and non-USPs.

Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks.

The complexes of long non-coding RNAs bound to proteins can be involved in regulating life activities at various stages of organisms. However, in the face of the growing number of lncRNAs and proteins, verifying LncRNA-Protein Interactions (LPI) based on traditional biological experiments is time-consuming and laborious. Therefore, with the improvement of computing power, predicting LPI has met new development opportunity.

AIEgens-Doped Photonic Crystals for High Sensitivity Fluorescence Detection of Tumor Markers.

Detection of tumor markers is of great significance to preliminarily judge whether patients have malignant tumors. Fluorescence detection (FD) is an effective means to achieve sensitive detection of tumor markers. Currently, the increased sensitivity of FD has attracted research interest worldwide.

Electrochemical Microneedles: Innovative Instruments in Health Care.

As a significant part of drug therapy, the mode of drug transport has attracted worldwide attention. Efficient drug delivery methods not only markedly improve the drug absorption rate, but also reduce the risk of infection. Recently, microneedles have combined the advantages of subcutaneous injection administration and transdermal patch administration, which is not only painless, but also has high drug absorption efficiency.

BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism.

Background: RNA Secondary Structure (RSS) has drawn growing concern, both for their pivotal roles in RNA tertiary structures prediction and critical effect in penetrating the mechanism of functional non-coding RNA. Computational techniques that can reduce the in vitro and in vivo experimental costs have become popular in RSS prediction. However, as an NP-hard problem, there is room for improvement that the validity of the prediction RSS with pseudoknots in traditional machine learning predictors.

Improvement of Rimocidin Biosynthesis by Increasing Supply of Precursor Malonyl-CoA via Over-expression of Acetyl-CoA Carboxylase in Streptomyces rimosus M527.

Precursor engineering is an effective strategy for the overproduction of secondary metabolites. The polyene macrolide rimocidin, which is produced by Streptomyces rimosus M527, exhibits a potent activity against a broad range of phytopathogenic fungi. It has been predicted that malonyl-CoA is used as extender units for rimocidin biosynthesis.

Emerging 3D Printing Strategies for Enzyme Immobilization: Materials, Methods, and Applications.

As the strategies of enzyme immobilization possess attractive advantages that contribute to realizing recovery or reuse of enzymes and improving their stability, they have become one of the most desirable techniques in industrial catalysis, biosensing, and biomedicine. Among them, 3D printing is the emerging and most potential enzyme immobilization strategy. The main advantages of 3D printing strategies for enzyme immobilization are that they can directly produce complex channel structures at low cost, and the printed scaffolds with immobilized enzymes can be completely modified just by changing the original design graphics.

A Novel Nomogram for Predicting the Risk of Short-Term Recurrence After Surgery in Glioma Patients.

Objective: The aim of this study was to establish a nomogram model for predicting the risk of short-term recurrence in glioma patients.

Methods: The clinical data of recurrent glioma patients were summarized and analyzed in this study. Univariate and multivariate logistic regression analyses were performed to analyze the correlation between clinical data and the risk of short-term recurrence after operation.

Predicting subcellular location of protein with evolution information and sequence-based deep learning.

Background: Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins.

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China.

To distinguish Methicillin-Resistant Staphylococcus aureus (MRSA) from Methicillin-Sensitive Staphylococcus aureus (MSSA) in the protein sequences level, test the susceptibility to antibiotic of all Staphylococcus aureus isolates from Quanzhou hospitals, define the virulence factor and molecular characteristics of the MRSA isolates. MRSA and MSSA Pfam protein sequences were used to extract feature vectors of 188D, n-gram and 400D. Weka software was applied to classify the two Staphylococcus aureus and performance effect was evaluated.

Intranasal oxytocin decreases self-oriented learning.

Rationale: Oxytocin has been found to play an important role in human social cognition and social interaction. Over the last two decades, surge studies have been conducted to investigate how oxytocin impacts other-oriented processes, such as trust and generosity (Zak et al. in PLoS ONE 2(11):e1128, 2007); however, the examination of the effect of oxytocin on self-related processes was relatively inadequate.

Identification of a gene from Streptomyces rimosus M527 negatively affecting rimocidin biosynthesis and morphological differentiation.

The polyene macrolide rimocidin, produced by Streptomyces rimosus M527, was found to be highly effective against a broad range of fungal plant pathogens. Current understanding of the regulatory mechanism of rimocidin biosynthesis and morphological differentiation in S. rimosus M527 is limited.

Cloning and Overexpression of the Cluster for Titer Improvement of Toyocamycin in .

The nucleoside antibiotic toyocamycin (TM) is a potential fungicide that can control plant diseases, and it has become an attractive target for research. 1628, a TM-producing strain, was isolated by our laboratory and was considered to be a potent industrial producer of TM. Recently, the putative TM biosynthetic gene cluster ( cluster) in 1628 was found by genome sequencing.

Chemoenzymatic conversion of Sorghum durra stalk into furoic acid by a sequential microwave-assisted solid acid conversion and immobilized whole-cells biocatalysis.

In this study, chemoenzymatic conversion of Sorghum durra stalk (SDS) into furoic acid was developed by a sequential microwave-assisted solid acid conversion and immobilized whole-cells biocatalysis method. Dry dewaxed SDS (75 g/L) was catalyzed into furfural at 57.8% yield with heterogeneous Sn-argil (2.

Development and optimization of an intergeneric conjugation system and analysis of promoter activity in Streptomyces rimosus M527.

An efficient genetic transformation system and suitable promoters are essential prerequisites for gene expression studies and genetic engineering in streptomycetes. In this study, firstly, a genetic transformation system based on intergeneric conjugation was developed in Streptomyces rimosus M527, a bacterial strain which exhibits strong antagonistic activity against a broad range of plant-pathogenic fungi. Some experimental parameters involved in this procedure were optimized, including the conjugative media, ratio of donor to recipient, heat shock temperature, and incubation time of mixed culture.

Iterative feature representations improve N4-methylcytosine site prediction.

Motivation: Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications.

MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification.

Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A, formerly known as PGC-1a) is a transcriptional coactivator and metabolic regulator. Previous studies are mainly focused on the association between PPARGC1A and hepatoma. However, the regulatory mechanism remains unknown.