Tumor microenvironment-regulated nanoplatform for enhanced chemotherapy, cuproptosis and nonferrous ferroptosis combined cancer therapy.

Therapeutic approaches combining various treatments have attracted intensive interests for tumor therapy. Nevertheless, these strategies still face many obstacles, such as overexpressed GSH and hypoxia, owing to the intricate tumor microenvironment (TME). Herein, a versatile nanoplatform, CeO@CuO@DOX-RSL3@HA (CCDRH), was initially constructed for promoting the antitumor efficiency regulation of the TME.

PBPK model of pegylated liposomal doxorubicin to simultaneously predict the concentration-time profile of encapsulated and free doxorubicin in tissues.

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict the concentrations of encapsulated and free doxorubicin in plasma and tissues in mice after intravenous injection of PEGylated liposomes (Doxil). The PBPK model used in this study contains liposomes and free doxorubicin disposition components. The free doxorubicin disposition component was used to simulate the disposition of free doxorubicin produced by mononuclear phagocyte system (MPS)-degrading liposomes.

A high hydrophobic moment arginine-rich peptide screened by a machine learning algorithm enhanced ADC antitumor activity.

Cell-penetrating peptides (CPPs) with better biomolecule delivery properties will expand their clinical applications. Using the MLCPP2.0 machine algorithm, we screened multiple candidate sequences with potential cellular uptake ability from the nuclear localization signal/nuclear export signal database and verified them through cell-penetrating fluorescent tracing experiments.

A novel method for multiple phenotype association studies based on genotype and phenotype network.

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN).

Saponin and Ribosome-Inactivating Protein Synergistically Trigger Lysosome-Dependent Apoptosis by Inhibiting Lysophagy: Potential to Become a New Antitumor Strategy.

The susceptibility of lysosomal membranes in tumor cells to cationic amphiphilic drugs (CADs) enables CADs to induce lysosomal membrane permeabilization (LMP) and trigger lysosome-dependent cell death (LDCD), suggesting a potential antitumor therapeutic approach. However, the existence of intrinsic lysosomal damage response mechanisms limits the display of the pharmacological activity of CADs. In this study, we report that low concentrations of QS-21, a saponin with cationic amphiphilicity extracted from tree, can induce LMP but has nontoxicity to tumor cells.

Prevalence and genomic-based antimicrobial resistance analysis of Avibacterium paragallinarum isolates in Guangdong Province, China.

Infectious coryza (IC) is an acute infectious respiratory disease in chickens that is caused by Avibacterium paragallinarum (A. paragallinarum). A.

Generation and characterization of a nanobody against the avian influenza virus H7 subtype.

Avian influenza virus (AIV) H7N9 diseases have been recently reported, raising concerns about a potential pandemic. Thus, there is an urgent need for effective therapeutics for AIV H7N9 infections. Herein, camelid immunization and yeast two-hybrid techniques were used to identify potent neutralizing nanobodies (Nbs) targeting the H7 subtype hemagglutinin.

Magnetic field filtering of the boundary supercurrent in unconventional metal NiTe-based Josephson junctions.

Topological materials with boundary (surface/edge/hinge) states have attracted tremendous research interest. Additionally, unconventional (obstructed atomic) materials have recently drawn lots of attention owing to their obstructed boundary states. Experimentally, Josephson junctions (JJs) constructed on materials with boundary states produce the peculiar boundary supercurrent, which was utilized as a powerful diagnostic approach.

Treatment of Depression with Acupuncture Based on Pathophysiological Mechanism.

Depression is a prevalent mental disorder and has a profound impact on an individual's psychological and physical well-being. It is characterized by a persistently depressed mood, loss of interest, energy loss, and cognitive dysfunction. In recent years, more and more people have changed to mental diseases, such as depression, anxiety, mania and so on.

CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost.

Integrating External Controls by Regression Calibration for Genome-Wide Association Study.

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.

Unlabelled: Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence.

Improving combination cancer immunotherapy by manipulating dual immunomodulatory signals with enzyme-triggered, cell-penetrating peptide-mediated biomodulators.

Immunosuppressive tumor microenvironments challenge the effectiveness of protein-based biopharmaceuticals in cancer immunotherapy. Reestablishing tumor cell immunogenicity by enhancing calreticulin (CRT) exposure is expected to improve tumor immunotherapy. Given that CRT translocation is inherently modulated by phosphorylated eIF2α, the selective inhibition of protein phosphatase 1 (PP1) emerges as an effective strategy to augment tumor immunogenicity.

MiR-203a-3p attenuates apoptosis and pyroptosis of chondrocytes by regulating the MYD88/NF-κB pathway to alleviate osteoarthritis progression.

Background: Osteoarthritis (OA) is a degenerative joint disease that imposes a significant socioeconomic burden worldwide. Our previous studies revealed a down-regulation of miR-203a-3p in the knee tissues of OA patients. However, the underlying mechanism through which miR-203a-3p mediates the pathological process of OA remains unknown.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability.

Exploring biomarkers associated with severity of knee osteoarthritis in Southern China using widely targeted metabolomics.

Background: Metabolomics is a tool to study the pathogenesis of diseases and their associated metabolites, but there are still insufficient metabolomic studies on severe knee osteoarthritis.To investigate the differences in serum metabolites between healthy populations and knee osteoarthritis (KOA) patients in Southern China using widely targeted metabolomics, and to explore biomarkers and their metabolic pathways that could be associated with the severity of KOA.

Methods: There were 10 healthy individuals in the control group and 32 patients with KOA.

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network.

Motivation: Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites.

Transplantation of MiR-28-5p-Modified BMSCs Promotes Functional Recovery After Spinal Cord Injury.

Traumatic spinal cord injury (TSCI) is a prevalent central nervous system condition that imposes a significant burden on both families and society, affecting more than 2 million people worldwide. Recently, there has been increasing interest in bone marrow mesenchymal stem cell (BMSC) transplantation as a promising treatment for spinal cord injury (SCI) due to their accessibility and low immunogenicity. However, the mere transplantation of BMSCs has limited capacity to directly participate in the repair of host spinal cord nerve function.

TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization.

Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver.

A polymer dot-based NADH-sensitive electrochemiluminescence biosensor for analysis of metabolites in serum.

Nicotinamide adenine dinucleotide (NADH) plays a pivotal role in metabolism. Convenient detection of NADH and its related metabolites has the pursuit of point-of-care and clinical analysis. Here, we propose a polymer dots (Pdots)-based NADH-sensitive electrochemiluminescence (ECL) biosensor for detection of NADH and three metabolites.