Preliminary evaluation of ex vivo and in vivo skin permeability of aromatic amines.

A potential link has been reported between skin exposure to aromatic amines, such as ortho-toluidine (OT) and 3,3'-dichloro-4,4'-diaminodiphenylmethane (MOCA), and bladder cancer cases observed in Japanese chemical factories. To evaluate this association, we explored the permeability of OT and MOCA through pig skin and investigated the subsequent changes in plasma and urine concentrations in rats following percutaneous exposure. Employing Yucatan micropig skin, we first executed a permeability test by affixing the skin to a diffusion cell and applying 14C-labeled OT or MOCA.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies.

Background: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods: We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets.

Study on the Synergistic Effect of SiO and HO on Oxidative Coupling of Methane over Mn-NaWO/SiO Catalyst.

Mn-NaWO-based catalysts with different supports were prepared using the incipient wetness impregnation method and evaluated for their oxidative coupling of methane (OCM) reaction performance. The results demonstrated that the SiO- supported catalyst exhibited the best catalytic performance, and the introduction of HO further enhanced its activity. Under the conditions of a feed gas mixture of CH/O/HO = 6:1:24 at 800 °C and atmospheric pressure, the CH conversion and C selectivity over the Mn-NaWO/SiO catalyst increased from 28.

Disease gene prioritization with quantum walks.

Motivation: Disease gene prioritization methods assign scores to genes or proteins according to their likely relevance for a given disease based on a provided set of seed genes. This scoring can be used to find new biologically relevant genes or proteins for many diseases. Although methods based on classical random walks have proven to yield competitive results, quantum walk methods have not been explored to this end.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis.

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes.

Rationale: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear.

Objectives: Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods: We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities.

Cluster analysis identifies novel real-world lung disease-pulmonary hypertension subphenotypes: implications for treatment response.

Background: Clinical trials repurposing pulmonary arterial hypertension (PAH) therapies to patients with lung disease- or hypoxia-pulmonary hypertension (PH) (classified as World Health Organization Group 3 PH) have failed to show a consistent benefit. However, Group 3 PH clinical heterogeneity suggests robust phenotyping may inform detection of treatment-responsive subgroups. We hypothesised that cluster analysis would identify subphenotypes with differential responses to oral PAH therapy.

Exploring Intrinsic Discrimination and Consistency for Weakly Supervised Object Localization.

Weakly supervised object localization (WSOL) is a challenging and promising task that aims to localize objects solely based on the supervision of image category labels. In the absence of annotated bounding boxes, WSOL methods must employ the intrinsic properties of the image classification task pipeline to generate object localizations. In this work, we propose a WSOL method for exploring the Intrinsic Discrimination and Consistency in the image classification task pipeline, and call it as IDC.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection.

[Difference of lipid-lowering efficacy of "Xinjianqu" before and after fermentation and its mechanism based on LKB1-AMPK pathway and 16S rDNA sequencing technology].

On the basis of establishing the prescription of Xinjianqu and clarifying the increase of the lipid-lowering active ingredients of Xinjianqu by fermentation, this paper further compared the differences in the lipid-lowering effects of Xinjianqu before and after fermentation, and studied the mechanism of Xinjianqu in the treatment of hyperlipidemia. Seventy SD rats were randomly divided into seven groups, including normal group, model group, positive drug simvastatin group(0.02 g·kg~(-1)), and low-dose and high-dose Xinjianqu groups before and after fermentation(1.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations.

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19.

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases.

Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome.

Uncovering common pathobiological processes between COVID-19 and pulmonary arterial hypertension by integrating Omics data.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID-19. SARS-CoV-2 infection can cause pulmonary vascular dysfunction.

Proline and glucose metabolic reprogramming supports vascular endothelial and medial biomass in pulmonary arterial hypertension.

In pulmonary arterial hypertension (PAH), inflammation promotes a fibroproliferative pulmonary vasculopathy. Reductionist studies emphasizing single biochemical reactions suggest a shift toward glycolytic metabolism in PAH; however, key questions remain regarding the metabolic profile of specific cell types within PAH vascular lesions in vivo. We used RNA-Seq to profile the transcriptome of pulmonary artery endothelial cells (PAECs) freshly isolated from an inflammatory vascular injury model of PAH ex vivo, and these data were integrated with information from human gene ontology pathways.

Analysis of Chemical Constituents of Traditional Chinese Medicine Jianqu before and after Fermentation Based on LC-MS/MS.

Objective: To detect the chemical constituents in Jianqu samples under different fermentated states by using UPLC-QTOF-MS/MS technology, to conduct preliminary analyses, and to establish an HPLC method for the simultaneous determination of hesperidin and naringenin in Jianqu, and the variation of the two components during fermentation were compared.

Methods: Waters ACQUITYTM UPLC HSST3 column (2.1 mm × 100 mm, 1.

A viscoelastic-viscoplastic constitutive model for polymer bonded explosives under low impact loading.

Viscoplastic work is very important to explosive ignition under impact loading. At present, a large number of constitutive models only consider the viscoelastic and damage behavior of explosives, ignoring the plastic effect under low impact loading. A new viscoelastic-viscoplastic (VE-VP) model was developed and studied to describe the dynamic mechanical behaviors of polymer-bonded explosives (PBXs).