Analysis of environmental pollutant Bisphenol F elicited prostate injury targets and underlying mechanisms through network toxicology, molecular docking, and multi-level bioinformatics data integration.

Bisphenol F (BPF) has gained prominence as an alternative to bisphenol A (BPA) in various manufacturing applications, yet being detected in diverse environments and posed potential public health risk. This research aims to elucidate the putative toxic targets and underlying molecular mechanisms of prostate injury induced by exposure to BPF through multi-level bioinformatics data, integrating network toxicology and molecular docking. Systematically leveraging multilevel databases, we determined 276 targets related to BPF and prostate injury.

Effect of pyrolysis temperature on the binding characteristics of DOM derived from livestock manure biochar with Cu(II).

Biochar-derived dissolved organic matter (BDOM) has the potential to influence the environmental application of biochar and the behavior of heavy metals. In this study, the binding properties of BDOM derived from livestock manure biochar at different pyrolysis temperatures with Cu(II) were investigated based on a multi-analytical approach. The results showed that the DOC concentration, aromatics, and humification degree of BDOM were higher in the process of low pyrolysis of biochar.

Multisite clinical cross-validation and variant interpretation of a next generation sequencing panel for lymphoid cancer prognostication.

Aims: Genomic sequencing of lymphomas is under-represented in routine clinical testing despite having prognostic and predictive value. Clinical implementation is challenging due to a lack of consensus on reportable targets and a paucity of reference samples. We organised a cross-validation study of a lymphoma-tailored next-generation sequencing panel between two College of American Pathologists (CAP)-accredited clinical laboratories to mitigate these challenges.

Chronic Lymphocytic Leukemia IGHV Somatic Hypermutation Detection by Targeted Capture Next-Generation Sequencing.

Background: Somatic hypermutation (SHM) status of the immunoglobulin heavy variable (IGHV) gene plays a crucial role in determining the prognosis and treatment of patients with chronic lymphocytic leukemia (CLL). A common approach for determining SHM status is multiplex polymerase chain reaction and Sanger sequencing of the immunoglobin heavy locus; however, this technique is low throughput, is vulnerable to failure, and does not allow multiplexing with other diagnostic assays.

Methods: Here we designed and validated a DNA targeted capture approach to detect immunoglobulin heavy variable somatic hypermutation (IGHV SHM) status as a submodule of a larger next-generation sequencing (NGS) panel that also includes probes for ATM, BIRC3, CHD2, KLHL6, MYD88, NOTCH1, NOTCH2, POT1, SF3B1, TP53, and XPO1.

Efficient analysis of toxicity and mechanisms of environmental pollutants with network toxicology and molecular docking strategy: Acetyl tributyl citrate as an example.

The study aims to promote network toxicology strategy to efficiently investigate the putative toxicity and underlying molecular mechanisms of environmental pollutants through an example of exploring brain injury induced by ATBC exposure. By utilizing ChEMBL, STITCH, GeneCards, and OMIM databases, we identified 213 potential targets associated with ATBC exposure and brain injury. Further refinements via STRING and Cytoscape software highlight 23 core targets, including AKT1, CASP3, and HSP90AA1.

A novel strategy for the study on molecular mechanism of prostate injury induced by 4,4'-sulfonyldiphenol based on network toxicology analysis.

The study aimed to investigate the underlying molecular mechanisms of prostate injury induced by 4,4'-sulfonyldiphenol (BPS) exposure and propose a novel research strategy to systematically explore the molecular mechanisms of toxicant-induced adverse health effects. By utilizing the ChEMBL, STITCH, and GeneCards databases, a total of 208 potential targets associated with BPS exposure and prostate injury were identified. Through screening the potential target network in the STRING database and Cytoscape software, we determined 21 core targets including AKT1, EGFR, and MAPK3.

Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+Â breast cancer.

Background: Human epidermal growth receptor 2-positive (HER2+) breast cancer (BC) is a heterogeneous subgroup. Estrogen receptor (ER) status is emerging as a predictive marker within HER2+ BCs, with the HER2+/ER+ cases usually having better survival in the first 5 years after diagnosis but have higher recurrence risk after 5 years compared to HER2+/ER-. This is possibly because sustained ER signaling in HER2+ BCs helps escape the HER2 blockade.

Bioinformatics driven discovery of small molecule compounds that modulate the FOXM1 and PPARA pathway activities in breast cancer.

Our previous studies demonstrated that the FOXM1 pathway is upregulated and the PPARA pathway downregulated in breast cancer (BC), and especially in the triple negative breast cancer (TNBC) subtype. Targeting the two pathways may offer potential therapeutic strategies to treat BC, especially TNBC which has the fewest effective therapies available among all BC subtypes. In this study we identified small molecule compounds that could modulate the PPARA and FOXM1 pathways in BC using two methods.

Facile Fabrication of 1-Methylimidazole/Cu Nanozyme with Enhanced Laccase Activity for Fast Degradation and Sensitive Detection of Phenol Compounds.

Facile construction of functional nanomaterials with laccase-like activity is important in sustainable chemistry since laccase is featured as an efficient and promising catalyst especially for phenolic degradation but still has the challenges of high cost, low activity, poor stability and unsatisfied recyclability. In this paper, we report a simple method to synthesize nanozymes with enhanced laccase-like activity by the self-assembly of copper ions with various imidazole derivatives. In the case of 1-methylimidazole as the ligand, the as-synthesized nanozyme (denoted as Cu-MIM) has the highest yield and best activity among the nanozymes prepared.

Predicting breast cancer drug response using a multiple-layer cell line drug response network model.

Background: Predicting patient drug response based on a patient's molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data.

Erb-b2 Receptor Tyrosine Kinase 2 (ERBB2) Promotes ATG12-Dependent Autophagy Contributing to Treatment Resistance of Breast Cancer Cells.

The epidermal growth factor receptor (EGFR) family member erb-b2 receptor tyrosine kinase 2 () is overexpressed in many types of cancers leading to (radio- and chemotherapy) treatment resistance, whereas the underlying mechanisms are still unclear. Autophagy is known to contribute to cancer treatment resistance. In this study, we demonstrate that ERBB2 increases the expression of different autophagy genes including (autophagy-related 12) and promotes ATG12-dependent autophagy.

The treatment of SARS-CoV2 with antivirals and mitigation of the cytokine storm syndrome: the role of gene expression.

In the absence of a vaccine, the treatment of SARS-CoV2 has focused on eliminating the virus with antivirals or mitigating the cytokine storm syndrome (CSS) that leads to the most common cause of death: respiratory failure. Herein we discuss the mechanisms of antiviral treatments for SARS-CoV2 and treatment strategies for the CSS. Antivirals that have shown in vitro activity against SARS-CoV2, or the closely related SARS-CoV1 and MERS-CoV, are compared on the enzymatic level and by potency in cells.

An integrative deep learning framework for classifying molecular subtypes of breast cancer.

Classification of breast cancer subtypes using multi-omics profiles is a difficult problem since the data sets are high-dimensional and highly correlated. Deep neural network (DNN) learning has demonstrated advantages over traditional methods as it does not require any hand-crafted features, but rather automatically extract features from raw data and efficiently analyze high-dimensional and correlated data. We aim to develop an integrative deep learning framework for classifying molecular subtypes of breast cancer.

Cardiac-specific methylation patterns of circulating DNA for identification of cardiomyocyte death.

Background: Correct detection of human cardiomyocyte death is essential for definitive diagnosis and appropriate management of cardiovascular diseases. Although current strategies have proven utility in clinical cardiology, they have some limitations. Our aim was to develop a new approach to monitor myocardial death using methylation patterns of circulating cell-free DNA (cf-DNA).

DTF: Deep Tensor Factorization for predicting anticancer drug synergy.

Motivation: Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs.

Deep graph embedding for prioritizing synergistic anticancer drug combinations.

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies.

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules.

Synchronous multiple ground-glass nodules (SM-GGNs) are a distinct entity of lung cancer which has been emerging increasingly in recent years in China. The oncogenesis molecular mechanisms of SM-GGNs remain elusive. We investigated single nucleotide variations (SNV), insertions and deletions (INDEL), somatic copy number variations (CNV), and germline mutations of 69 SM-GGN samples collected from 31 patients, using target sequencing (TRS) and whole exome sequencing (WES).

Integrative Analysis Reveals Subtype-Specific Regulatory Determinants in Triple Negative Breast Cancer.

Different breast cancer (BC) subtypes have unique gene expression patterns, but their regulatory mechanisms have yet to be fully elucidated. We hypothesized that the top upregulated (Yin) and downregulated (Yang) genes determine the fate of cancer cells. To reveal the regulatory determinants of these Yin and Yang genes in different BC subtypes, we developed a lasso regression model integrating DNA methylation (DM), copy number variation (CNV) and microRNA (miRNA) expression of 391 BC patients, coupled with miRNA-target interactions and transcription factor (TF) binding sites.

An electrodialytic potassium hydroxide eluent generator suited to small bore ion chromatography.

An electrodialytic potassium hydroxide eluent generator (EDG) well-suited to small-bore (2 mm i.d.) ion chromatography (sIC) system is described.

Online Gas-Free Electrodialytic KOH Eluent Generator for Ion Chromatography.

An online gas-free KOH electrodialytic eluent generator (EDG) with two-membrane configuration is described for ion chromatography (IC). A central eluent channel is separated from two outer regenerant channels bearing KOH solution (or one is water) by stacked cation-exchange membranes (sCEMs) and a bipolar membrane (BPM) plus stacked CEMs (BPM-sCEMs), in which the anion-exchange membrane (AEM) of BPM is facing the central channel (the anode direction). Independent fluid input or output ports address all channels.

Overexpression of lncRNA LINC01793 acts as a potential predictor for progression and poor prognosis of gastric cancer.

Objective: Accumulating evidence has shown that differentially expressed long non-coding RNA (lncRNA) is closely related to the development of gastric cancer. This study aims to explore the role of potential lncRNAs in the development of gastric cancer.

Methods: TCGA database of gastric cancer were analyzed by bioinformatics.

Dysregulated Up-Frameshift Protein 1 Promotes Ulcerative Colitis Pathogenesis Through the TNFR1-NF-κB/MAPKs Pathway.

Background: Ulcerative colitis (UC) is an idiopathic colonic mucosal disease, and its pathogenesis has not been fully understood. Up-frameshift protein 1 (UPF1) is a potential molecule for UC predicted by a computational approach.

Aim: The present study aimed to validate the underlying mechanism of UPF1 in UC.

Genes and functions from breast cancer signatures.

Background: Breast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively studied in the past decades and more than 30 different signatures have been reported. A major concern is the minimal overlap of genes among the reported signatures.

Distance Calibration between Reference Plane and Screen in Direct Phase Measuring Deflectometry.

The recently developed direct phase measuring deflectometry (DPMD) method can directly measure the three-dimensional (3D) shape of specular objects with discontinuous surfaces, but requires a calibrated distance between a reference plane and liquid crystal display screen. Because the plane and screen are different distances from the imaging device, they cannot be clearly captured given the limited depth of field (DOF) of the lens. Therefore, existing machine vision-based methods cannot be used to effectively calibrate a DPMD system.