Occurrence, ecological risk and estrogenic effect of 19 bisphenol analogues in the surface water used for drinking water in Shanghai, China.

Bisphenol analogues (BPs), a prominent group of endocrine-disrupting compounds, are widely used in the production of epoxy resins and polycarbonate plastics, leading to their inevitable release into aquatic environments. However, limited data exists on the occurrence of BPs in drinking water sources and upstream rivers. In this study, we developed and validated a solid-phase extraction method coupled with ultra-performance liquid chromatography-tandem mass spectrometry for the trace-level detection and simultaneous quantification of 19 BPs in surface water.

One-step microbial cultivated bacterial cellulose membrane with 1D/2D nanochannels for efficient osmotic energy conversion.

Osmotic energy conversion based on bio-inspired layered membranes has garnered significant interest. However, traditional biomass ion-selective membranes suffer from complex preparation, uneconomic nature, poor selectivity, and low power density. Here, we introduce scalable one-step in situ culture for nanofluidic membrane materials (GO/C-BC) composed of graphene oxide (GO), carboxymethyl cellulose sodium salt (CMC), and bacterial cellulose (BC).

Corrigendum: NLR, a convenient early-warning biomarker of fatal outcome in patients with severe fever with thrombocytopenia syndrome.

[This corrects the article DOI: 10.3389/fmicb.2022.

Harnessing an MMP-Independent NIR Probe Unveiling the Different Mitochondrial Cristae Changes during Mitophagy and Ferroptosis under STED Microscopy.

Mitochondrial cristae remain dynamic structures in order to adapt various physiopathologic processes (e.g., mitophagy and ferroptosis); thus, visualizing and tracking different changes of cristae are crucial for a deeper understanding of these processes.

Hailey-Hailey disease successfully treated with naloxone: 2 case reports and Review of the literature on efficacy of opioid receptor antagonist in Hailey-Hailey disease patients.

Background: Hailey-Hailey disease (HHD), a genetic blistering disease, is caused by a mutation in a calcium transporter protein in the Golgi apparatus encoded by the gene. Clinically, HHD is characterized by flaccid vesicles, blisters, erosions, fissures, and maceration mainly in intertriginous regions. Some patients remain refractory to conventional treatments.

Identification of Novel Iodinated Polyfluoroalkyl Ether Acids and Other Emerging PFAS in Soils Using a Nontargeted Molecular Network Approach.

Despite advancements in high-resolution screening techniques, the identification of novel perfluoroalkyl and polyfluoroalkyl substances (PFAS) remains challenging without prior structural information. In view of this, we proposed and implemented a new data-driven algorithm to calculate spectral similarity among PFAS, facilitating the generation of molecular networks to screen for unknown compounds. Using this approach, 81 PFAS across 12 distinct classes were identified in soil samples collected near an industrial park in Shandong Province, China, including the first reported occurrence of 12 iodine-substituted PFAS.

Self-Reinforced Cathode Interface to Prolong the Cyclic Stability of Zn-MnO Batteries.

Rechargeable aqueous zinc-ion batteries (RAZIBs) are attracting increasing attention due to their advantages in safety, cost, and energy density. However, the choice of electrode binders when using aqueous electrolytes faces many constraints, as nontoxic and low-cost hydrophilic binders, such as sodium alginate (SA), can lead to electrode damage due to excessive swelling in aqueous solution. Here, by employing double-network hydrogel electrolytes formed by poly(vinyl alcohol) and alginate, the content and activity of water molecules at the electrode-electrolyte interface are reduced, and the mechanical stability of the electrode is reinforced, thereby affording reliable protection to the cathode bonded with SA.

A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.

Identifying potential cancer biomarkers is a key task in biomedical research, providing a promising avenue for the diagnosis and treatment of human tumors and cancers. In recent years, several machine learning-based RNA-disease association prediction techniques have emerged. However, they primarily focus on modeling relationships of a single type, overlooking the importance of gaining insights into molecular behaviors from a complete regulatory network perspective and discovering biomarkers of unknown types.

A multi-task prediction method based on neighborhood structure embedding and signed graph representation learning to infer the relationship between circRNA, miRNA, and cancer.

Motivation: Research shows that competing endogenous RNA is widely involved in gene regulation in cells, and identifying the association between circular RNA (circRNA), microRNA (miRNA), and cancer can provide new hope for disease diagnosis, treatment, and prognosis. However, affected by reductionism, previous studies regarded the prediction of circRNA-miRNA interaction, circRNA-cancer association, and miRNA-cancer association as separate studies. Currently, few models are capable of simultaneously predicting these three associations.

X-LDA: An interpretable and knowledge-informed heterogeneous graph learning framework for LncRNA-disease association prediction.

The identification of disease-related long noncoding RNAs (lncRNAs) is beneficial to unravel the intricacies of gene expression regulation and epigenetic signatures. Computational methods provide a cost-effective means to explore lncRNA-disease associations (LDAs). However, these methods often lack interpretability, leaving their predictions less convincing to biological and medical researchers.

Highly Selective Artificial K Transporters Reverse Liver Fibrosis In Vivo.

Liver fibrosis is a life-threatening disease that currently lacks clinically effective therapeutic agents. Given the close correlation between dysregulated intracellular K homeostasis and the progression of liver fibrosis, developing artificial K transporters mimicking the essential function of their natural counterparts in regulating intracellular K levels might offer an appealing yet unexplored treatment strategy. Here, we present an unconventional class of artificial K transporters involving the "motional" collaboration between two K transporter molecules.

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations.

Multi-Omics Profiling Unveils the Complexity and Dynamics of Immune Infiltrates in Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a highly heterogeneous malignancy. The reasons behind the global rise in the incidence of ICC remain unclear, and there exists limited knowledge regarding the immune cells within the tumor microenvironment (TME). In this study, a more comprehensive analysis of multi-omics data was performed using machine learning methods.

Dynamic glycolytic reprogramming effects on dendritic cells in pancreatic ductal adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma tumors exhibit resistance to chemotherapy, targeted therapies, and even immunotherapy. Dendritic cells use glucose to support their effector functions and play a key role in anti-tumor immunity by promoting cytotoxic CD8 T cell activity. However, the effects of glucose and lactate levels on dendritic cells in pancreatic ductal adenocarcinoma are unclear.

Arctigenin inhibits the progression of colorectal cancer through epithelial-mesenchymal transition via PI3K/Akt/mTOR signaling pathway.

Background: Colorectal cancer (CRC) is a significant disease worldwide, with high mortality rates. Conventional treatment methods often lead to metastasis and drug resistance, highlighting the need to explore new drugs and their potential molecular mechanisms. In this study, we investigated the effects of arctigenin on CRC cell proliferation, migration, invasion, apoptosis, and related protein expression, as well as its potential molecular mechanisms.

Exposure and potential risks of thirteen endocrine- disrupting chemicals in pharmaceuticals and personal care products for breastfed infants in China.

Ingestion of breast milk represents the primary exposure pathway for endocrine-disrupting chemicals (EDCs) in newborns. To elucidate the associated risks, it is essential to quantify EDC levels in both breast milk and infant urine. This study measured the concentrations of 13 EDCs, including parabens (methyl paraben (MP), ethyl paraben (EP), propyl paraben (PP), iso-propyl paraben, butyl paraben, and iso-butyl paraben), bisphenols (bisphenol A (BPA), bisphenol F, bisphenol S, bisphenol AF, and bisphenol Z), triclosan (TCS), and triclocarban, in breast milk and infant urine to assess their potential health effects and endocrine disruption risks.

Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.

Exploring associations between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is crucial for disease prevention, diagnosis and treatment. While determining these relationships experimentally is resource-intensive and time-consuming, computational methods have emerged as an attractive way. However, existing computational methods tend to focus on single tasks, neglecting the benefits of leveraging multiple biomolecular interactions and domain-specific knowledge for multi-task prediction.

TAK1 Promotes an Immunosuppressive Tumor Microenvironment through Cancer-Associated Fibroblast Phenotypic Conversion in Pancreatic Ductal Adenocarcinoma.

Purpose: We aim to clarify the precise function of TGFβ1-activated kinase 1 (TAK1) in cancer-associated fibroblasts (CAF) within human pancreatic ductal adenocarcinoma (PDAC) by investigating its role in cytokine-mediated signaling pathways.

Experimental Design: The expression of TAK1 in pancreatic cancer was confirmed by The Cancer Genome Atlas data and human pancreatic cancer specimens. CAFs from freshly resected PDAC specimens were cultured and used in a three-dimensional model for direct and indirect coculture with PDAC tumors to investigate TAK1 function.

Interactions of Potential Endocrine-Disrupting Chemicals with Whole Human Proteome Predicted by AlphaFold2 Using an In Silico Approach.

Binding with proteins is a critical molecular initiating event through which environmental pollutants exert toxic effects in humans. Previous studies have been limited by the availability of three-dimensional (3D) protein structures and have focused on only a small set of environmental contaminants. Using the highly accurate 3D protein structure predicted by AlphaFold2, this study explored over 60 million interactions obtained through molecular docking between 20,503 human proteins and 1251 potential endocrine-disrupting chemicals.

A novel health monitoring system for vital signs using IoT.

The current research looked at how to use the Internet of Things (IoT) to create a vital sign health monitoring system. Eight indications are employed to get critical patient information. Therefore, the number of nodes of the IoT embedded in the human body is 8, which have been worked on in different places of the body.

ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention.

The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation.