Novel Gene Clusters for Secondary Metabolite Synthesis in Mesophotic Sponge-Associated Bacteria.

Mesophotic coral ecosystems (MCEs) host a diverse array of sponge species, which represent a promising source of bioactive compounds. Increasing evidence suggests that sponge-associated bacteria may be the primary producers of these compounds. However, cultivating these bacteria under laboratory conditions remains a significant challenge.

MWFormer: Estimation of Molecular Weights from Electron Ionization Mass Spectra for Improved Library Searching.

Molecular weight (MW) is a crucial property to improve the accuracy of multidimensional compound identification. In this study, we have developed MWFormer, a novel method that predicts MWs solely from spectra of electron ionization mass spectrometry (EI-MS) based on a Transformer encoder. MWFormer achieves a mean absolute error (MAE) of 6.

Graphene Oxide/TiO Nanocomposite-Assisted Two-Step Ambient Liquid Extraction Mass Spectrometry Imaging for Comprehensively Enhancing Lipid Coverage in Spatial Lipidomics.

It is challenging to have comprehensive spatial lipidomic analysis by mass spectrometry imaging (MSI) due to the strong ion suppression and peak interference from high-abundance polar lipids to low-abundance poorly ionizable lipids. In this work, we proposed a new MSI approach via ambient liquid extraction techniques assisted by a new mixed-mode adsorptive material, graphene oxide (GO)/TiO nanocomposite. The material combines chelation affinity from TiO and hydrophobic interaction from GO.

MSBERT: Embedding Tandem Mass Spectra into Chemically Rational Space by Mask Learning and Contrastive Learning.

Tandem mass spectrometry (MS/MS) is a powerful technique for chemical analysis in many areas of science. The vast MS/MS spectral data generated in liquid chromatography-mass spectrometry (LC-MS) experiments require efficient analysis and interpretation methods for the following compound identification. In this study, we propose MSBERT based on self-supervised learning strategies to embed MS/MS spectra into reasonable embeddings for efficient compound identification.

Phylogenomic analysis uncovers an unexpected capacity for the biosynthesis of secondary metabolites in Pseudoalteromonas.

Pseudoalteromonas is a genus of marine bacteria and a promising source of natural products with antibacterial, antifungal, and antifouling bioactivities. To accelerate the exploration of new compounds from this genus, we applied the gene-first approach to study 632 public Pseudoalteromonas genomes. We identified 3968 biosynthetic gene clusters (BGCs) involved in the biosynthesis of secondary metabolites and classified them into 995 gene cluster families (GCFs).

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the GEO database and then merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA).

Andrographolide suppresses SARS-CoV-2 infection by downregulating ACE2 expression: A mechanistic study.

Angiotensin-converting enzyme 2 (ACE2) is the receptor that enables SARS-CoV-2 to invade host cells. Previous studies have reported that reducing ACE2 expression may have an anti-SARS-CoV-2 effect. In this study, we constructed a pGL4.

Network analysis to explore the anti-senescence mechanism of Jinchan Yishen Tongluo Formula (JCYSTLF) in diabetic kidneys.

Background: The Jinchan Yishen Tongluo Formula (JCYSTLF) has the effect of delaying senescence in diabetic kidneys. However, the mechanism is not clear.

Purpose: Combination methods to investigate the anti-senescence mechanism of JCYSTLF in diabetic kidneys.

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography-Mass Spectrometry.

Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC-MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data.

A novel lncRNA TCONS_00071187 upregulated by activated GSK3β promotes high glucose-induced mesangial cell proliferation in the diabetic nephropathy.

Inhibiting mesangial cell proliferation is one of the strategies to control the early progression of diabetic nephropathy (DN). GSK3β is closely related to cell apoptosis as well as the development of DN, but whether it acts on the proliferation of mesangial cells is unclear. This study aimed to elucidate the role and mechanism of GSK3β-mediated lncRNA in high glucose-induced mesangial cell proliferation.

Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers.

The practically infinite chemical and morphological space of polymers makes them pervasive with applications in materials science but challenges the rational discovery of new materials with favorable properties. Polymer informatics aims to accelerate materials design through property prediction and large-scale virtual screening. In this study, a new method (Lieconv-Tg) has been developed to predict glass-transition temperature (Tg) values from repeating units of polymers based on Lieconv, which is equivariant with transformations from any specified Lie group.

The influence of resourcefulness on the family functioning of young- and middle-aged lymphoma patients in China: a cross-sectional study.

Background: Lymphoma has become 1 of the 10 most common cancers with increased prevalence in young- and middle-aged adults in China. This poses a tremendous burden on patients and their families and brings great challenges to maintaining the balance of family functioning in young- and middle-aged patients.

Objective: This cross-sectional study aimed to analyse the influence of resourcefulness on the family functioning of Chinese young- and middle-aged lymphoma patients.

Lewis Acidic Metal-Organic Framework Assisted Ambient Liquid Extraction Mass Spectrometry Imaging for Enhancing the Coverage of Poorly Ionizable Lipids in Brain Tissue.

The spatial distribution of lipidomes in tissues is of great importance in studies of living processes, diseases, and therapies. Mass spectrometry imaging (MSI) has become a critical technique for spatial lipidomics. However, MSI of low-abundance or poorly ionizable lipids is still challenging because of the ion suppression from high-abundance lipids.

Photocatalytic degradation-based ambient mass spectrometry imaging for enhancing detection coverage of poorly-ionizable lipidomes.

Localization of lipidomes and tracking their spatial changes in tissues by mass spectrometry imaging (MSI) plays an important role in unveiling the mechanisms of living processes, diseases and therapeutic treatments. However, it is always challenging to achieve direct MSI of poorly-ionizable lipids, such as glycolipids and glycerolipids, due to the strong ion suppression and isobaric peaks interference from high-abundance phosphatidylcholines (PCs) in tissues. Here we developed a photocatalytic degradation-based ambient liquid extraction MSI method to largely enhance the detection coverage of poorly-ionizable lipids by rapid online removal of PCs in MSI.

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors.

Establishment of a model for predicting preterm birth based on the machine learning algorithm.

Background: The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future.

Methods: This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis.

Pulled Flowprobe for Ambient Liquid Extraction-Based High Spatial Resolution Mass Spectrometry Imaging with Enhanced Sensitivity and Stability.

Ambient liquid extraction techniques enable direct mass spectrometry imaging (MSI) under ambient conditions with minimal sample preparation. However, currently an integrated probe for ambient liquid extraction-based MSI with high spatial resolution, high sensitivity, and stability is still lacking. In this work, we developed a new integrated probe made of pulled coaxial capillaries, named pulled flowprobe, and compared it with the previously reported single-probe.

Highly accurate and large-scale collision cross sections prediction with graph neural networks.

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values.

Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning.

Feature extraction is the most fundamental step when analyzing liquid chromatography-mass spectrometry (LC-MS) datasets. However, traditional methods require optimal parameter selections and re-optimization for different datasets, thus hindering efficient and objective large-scale data analysis. Pure ion chromatogram (PIC) is widely used because it avoids the peak splitting problem of the extracted ion chromatogram (EIC) and regions of interest (ROIs).

Combined SERS Microfluidic Chip with Gold Nanocone Array for Effective Early Lung Cancer Prognosis in Mice Model.

Introduction: As the most common malignant tumor in the world, the prognosis of patients with advanced lung cancer remains poor even after treatment. There are many prognostic marker assays available, but there is still more room for the development of high-throughput and sensitive detection of circulating tumor DNA (ctDNA). Surface-enhanced Raman spectroscopy (SERS), a spectroscopic detection method that has received wide attention in recent years, can achieve exponential amplification of Raman signals by using different metallic nanomaterials.

Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library.

Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established.

Gut microbiota-derived trimethylamine N-oxide is associated with the risk of all-cause and cardiovascular mortality in patients with chronic kidney disease: a systematic review and dose-response meta-analysis.

Background: Trimethylamine N-oxide (TMAO) derived from gut microbiota causes kidney-heart damage in chronic kidney disease (CKD) patients. However, it is controversial whether CKD patients with higher TMAO are associated with a higher risk of death. We aimed to assess the correlation between circulating TMAO concentration and the risk of all-cause and cardiovascular death in CKD patients of different dialysis statuses and different races by dose-response analyses, and the underlying mechanisms were also explored by analyzing the correlations of TMAO with glomerular filtration rate (GFR) and inflammation.