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. The liver, spleen, kidneys, and lungs contain an additional MPS subcompartment. These compartments are interconnected through blood and lymphatic circulation. The model was validated strictly by four doses of external observed plasma and tissue concentration-time profiles. The fold error (FE) values were almost all within threefold. The sensitivity analysis revealed that the MPS-related parameters greatly influenced the model. The predicted in vivo distribution characteristics of the doxorubicin liposomes and doxorubicin solution were consistent with the observed values. The PBPK model was established based on the physiological mechanism and parameters of practical significance that can be measured in vitro. Thus, it can be used to study the pharmacokinetic properties of liposomes. This study also provides a reference for the establishment of liposome PBPK model.

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

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. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

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 peptide (NCR) derived from the Rev protein of the caprine arthritis-encephalitis virus exhibited efficient cell-penetrating activity, delivering over four times more EGFP than the classical CPP TAT, allowing it to accumulate in lysosomes. Structural and property analysis revealed that a high hydrophobic moment and an appropriate hydrophobic region contribute to the high delivery activity of NCR. Trastuzumab emtansine (T-DM1), a HER2-targeted antibody-drug conjugate, could improve its anti-tumor activity by enhancing targeted delivery efficiency and increasing lysosomal drug delivery. This study designed a new NCR vector to non-covalently bind T-DM1 by fusing domain Z, which can specifically bind to the Fc region of immunoglobulin G and effectively deliver T-DM1 to lysosomes. MTT results showed that the domain Z-NCR vector significantly enhanced the cytotoxicity of T-DM1 against HER2-positive tumor cells while maintaining drug specificity. Our results make a useful attempt to explore the potential application of CPP as a lysosome-targeted delivery tool.

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). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

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 Quillaja Saponaria tree, can induce LMP but has nontoxicity to tumor cells. QS-21 and MAP30, a type I ribosome-inactivating protein, synergistically induce apoptosis in tumor cells at low concentrations of both. Mechanistically, QS-21-induced LMP helps MAP30 escape from endosomes or lysosomes and subsequently enter the endoplasmic reticulum, where MAP30 downregulates the expression of autophagy-associated LC3 proteins, thereby inhibiting lysophagy. The inhibition of lysophagy results in the impaired clearance of damaged lysosomes, leading to the leakage of massive lysosomal contents such as cathepsins into the cytoplasm, ultimately triggering LDCD. In summary, our study showed that coadministration of QS-21 and MAP30 amplified the lysosomal disruption and can be a new synergistic LDCD-based antitumor therapy.

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. First, we evaluated the binding specificity and hemagglutination inhibition activity of the screened Nbs against the H7 subtype hemagglutinin. Nb-Z77, with high hemagglutination inhibition activity was selected from the screened Nbs to optimize the yeast expression conditions and construct oligomeric forms of Nb-Z77 using various ligation methods. The oligomers Nb-Z77-DiGS, Nb-Z77-TriGS, Nb-Z77-Fc and Nb-Z77-Foldon were successfully constructed and expressed. Nb-Z77-DiGS and Nb-Z77-Foldon exhibited considerably greater activity than did Nb-Z77 against H7 subtype hemagglutinin, with median effective concentrations of 384.7 and 27.33 pM and binding affinity values of 213 and 5.21 pM, respectively. Nb-Z77-DiGS and Nb-Z77-Foldon completely inhibited the hemagglutination activity of the inactivated virus H7-Re1 at the lowest concentration of 0.938 µg/mL. This study screened a strain of Nb with high hemagglutination inhibition activity and enhanced its antiviral activity through oligomerization, which may have great potential for developing effective agents for the prevention, diagnosis, and treatment of AIV H7 subtype infection.

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. paragallinarum poses a significant threat to poultry health due to its virulence and multidrug resistance. This study isolated and identified 21 A. paragallinarum isolates from Guangdong between 2022 and 2023. Biochemical tests showed that 100% of A. paragallinarum isolates fermented glucose but did not ferment alginate and galactose, and only YZ18 was nicotinamide adenine dinucleotide independent. To determine the genetic relatedness between these isolates and NCBI reference strains, whole-genome-based phylogenetic analysis was employed. In addition, analysis of the 2,000 bp-length hmtp210 gene showed that the hmtp210 gene was strongly associated with A. paragallinarum serotypes. Meanwhile, a PCR assay for serotyping A. paragallinarum was developed based on the hmtp210 gene, this assay has high sensitivity and specificity. The antimicrobial susceptibility of isolates was assessed using the disk diffusion method. The antibiotic resistance genes of isolates were analyzed using the genomic method. Phenotypic resistance to ampicillin (95.2%), streptomycin (95.2%), methotrexate-sulfamethoxazole (90.5%), and tetracycline (85.7%) was most frequent among the isolates. All of the isolates exhibited resistance to multiple drugs, and furthermore, the isolates possessed a collective total of 14 genes associated with antibiotic resistance. This study will contribute to advancing our knowledge of A. paragallinarum antibiotic resistance and provide a scientific basis for the prophylaxis and treatment of IC, and the subsequent rational design of potential clinical therapeutics.

Efficient markerless genetic manipulation of Pasteurella multocida using lacZ and pheSm as selection markers.

Pasteurella multocida is a zoonotic conditional pathogen that infects multiple livestock species, causing substantial economic losses in the animal husbandry industry. An efficient markerless method for gene manipulation may facilitate the investigations of P. multocida gene function and pathogenesis of P. multocida. Herein, a temperature-sensitive shuttle vector was constructed using lacZ as a selection marker, and markerless glgB, opa, and hyaE mutants of P. multocida were subsequently constructed through blue-white colony screening. The screening efficiency of markerless deletion strains was improved by the lacZ system, and the method could be used for multiple gene deletions. However, the fur mutant was unavailable via this method. Therefore, we constructed a pheSm screening system based on mutated phenylalanine tRNA synthetase as a counterselection marker to achieve fur deletion mutant. The transformed strain was sensitive to 20 mM p-chloro-phenylalanine, demonstrating the feasibility of pheSm as a counter-selective marker. The pheSm system was used for markerless deletions of glgB, opa, and hyaE as well as fur that could not be screened by the lacZ system. A comparison of screening efficiencies of the system showed that the pheSm counterselection system was more efficient than the lacZ system and broadly applicable for mutant screening. The methods developed herein may provide valuable tools for genetic manipulation of P. multocida.IMPORTANCEPasteurella multocida is a highly contagious zoonotic pathogen. An understanding of its underlying pathogenic mechanisms is of considerable importance and requires efficient species-specific genetic tools. Herein, we propose a screening system for P. multocida mutants using lacZ or pheSm screening markers. We evaluated the efficiencies of both systems, which were used to achieve markerless deletion of multiple genes. The results of this study support the use of lacZ or pheSm as counterselection markers to improve counterselection efficiency in P. multocida. This study provides an effective genetic tool for investigations of the virulence gene functions and pathogenic mechanisms of P. multocida.

Magnetic field filtering of the boundary supercurrent in unconventional metal NiTe2-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. Here, we report the observations of boundary supercurrent in NiTe2-based JJs. Particularly, applying an in-plane magnetic field along the Josephson current can rapidly suppress the bulk supercurrent and retain the nearly pure boundary supercurrent, namely the magnetic field filtering of supercurrent. Further systematic comparative analysis and theoretical calculations demonstrate the existence of unconventional nature and obstructed hinge states in NiTe2, which could produce hinge supercurrent that accounts for the observation. Our results reveal the probable hinge states in unconventional metal NiTe2, and demonstrate in-plane magnetic field as an efficient method to filter out the bulk contributions and thereby to highlight the hinge states hidden in topological/unconventional materials.

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. In the incidence of depression, covering all ages, but still mainly young and middle-aged women. Traditional treatments for depression mainly rely on medication and psychotherapy, but these methods are not effective for all patients and are often accompanied by certain side effects. Therefore, finding safe and effective alternative or adjuvant treatments has become a priority. Here we highlight the research progress of acupuncture in the treatment of depression and to explore the mechanism of acupuncture in the treatment of depression. Acupuncture treatment of depression is an ancient and effective method, the mechanism involves multiple biological pathways, for example, by regulating neurotransmitter levels, regulating the neuroendocrine axis, improving neuroplasticity, anti-inflammatory and other effects, improving emotional state and play an antidepressant role. To provide evidence to support the widespread use of acupuncture in clinical practice. We hope to provide new treatment ideas and methods for patients with depression, and even reduce the incidence of depression.

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

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. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). CONCLUSIONS: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

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. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

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. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning is employed, which maximizes the mutual information between different types of omics. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Finally, a Softmax classifier is employed to perform multi-omics data classification. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicate that our proposed CLCLSA produces promising results in multi-omics data classification using both complete and incomplete multi-omics data.

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. To harness the PP1-disrupting potential of GADD34-derived motifs and address their limited intracellular delivery, we integrated these sequences into an enzyme-triggered, cell-penetrating peptide-mediated chimeric protein scaffold. This design not only facilitates efficient cytoplasmic delivery of these immunostimulatory motifs to induce "eat-me" signaling, but also provides a versatile platform for combination immunotherapy. Fabrication of biomodulators with cytotoxic BLF1 provides additional "eat-me" signaling through phosphatidylserine exposure or that with an immunomodulatory designed ankyrin repeat protein disables "don't-find-me" signaling by antagonizing PD-L1. Notably, these bifunctional biomodulators exhibit remarkable ability to induce macrophage phagocytosis, dendritic cell maturation, and CD8+ T activation, ultimately substantially inhibiting tumor growth. This study presents a modular genetic coding strategy for PP1-centered therapies that enables seamless integration of immunostimulatory sequences into protein-based anti-tumor cocktail therapies, thereby offering novel alternatives for improving antitumor efficacy.

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. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

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. MiR-28-5p, identified as a key differentially expressed miRNA in spinal cord ischemia-reperfusion injury, exhibits differential expression and regulation in various neurological diseases. Nevertheless, its involvement in this process and its specific regulatory mechanisms in SCI remain unclear. Therefore, this study aimed to investigate the potential mechanisms through which miR-28-5p promotes the neuronal differentiation of BMSCs both in vivo and in vitro. Our results indicate that miR-28-5p may directly target Notch1, thereby facilitating the neuronal differentiation of BMSCs in vitro. Furthermore, the transplantation of lentivirus-mediated miR-28-5p-overexpressed BMSCs into SCI rats effectively improved footprint tests and Basso, Beattie, and Bresnahan (BBB) scores, ameliorated histological morphology (hematoxylin-eosin [HE] and Nissl staining), promoted axonal regeneration (MAP2 and growth-associated protein 43 [GAP43]), and facilitated axonal remyelination (myelin basic protein [MBP]). These findings may suggest that miR-28-5p-modified BMSCs could serve as a therapeutic target to enhance the behavioral and neurological recovery of SCI rats.

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. According to the Kellgren-Lawrence (KL) grading system, KOA was further divided into mild (n = 13, KL grade 1 and 2) and severe (n = 19, KL grade 3 and 4). Serum samples from all participants were collected and analyzed metabolomics based on ultra-performance liquid chromatography/electrospray ionization/tandem mass spectrometry. We screened for differential metabolites between patients and controls, and between mild and severe KOA. We explored the metabolic pathways involved in differential metabolism using the Kyoto Encyclopedia of Genes and Genomes database. RESULTS: Sixty-one metabolites were differentially expressed in the sera of the patient group compared with the control group (45 upregulated and 16 downregulated). Analysis of the mild and severe KOA groups showed a total of 12 differential metabolites. Receiver operating characteristic curve analysis showed N-alpha-acetyl-L-asparagine was a good predictor of advanced osteoarthritis(OA).Differential metabolites are enriched in multiple pathways such as arachidonic acid metabolism. CONCLUSION: Widely targeted metabolomics found that upregulation of the amino acid metabolite N-α-acetyl-L-asparagine was significantly associated with severe KOA and could be a biomarker for predicting severity of KOA. Arachidonic acid metabolism may play an important role in patients with severe KOA.

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. Thus, we aimed to determine the effects of miR-203a-3p in the progression of OA. METHODS: Rat primary chondrocytes were stimulated with 10 µg/mL lipopolysaccharide (LPS) for 24 hours, followed by transfection with 50 nM miR-203a-3p mimic, inhibitor, and siRNA for MYD88 or consistent negative controls for 48 hours. To evaluate the effects of miR-203a-3p on cartilage matrix degradation, oxidative stress, apoptosis, and pyroptosis in chondrocytes, various techniques such as immunofluorescence staining, biochemical analysis, Western blotting, and the TUNEL staining were utilized. In the rat OA model, all rats were randomly divided into four groups: Sham, OA, OA+Agomir negative control (NC), and OA+Agomir. They received intra-articular injections of 25 nmol miR-203a-3p agomir, agomir NC, or normal saline twice a week for the duration of 8 weeks after OA induction. Immunofluorescence staining was performed to evaluate the effects of miR-203a-3p on cartilage matrix degradation in rats. RESULTS: MiR-203a-3p was down-regulated in LPS-treated rat chondrocytes and OA cartilage, and directly targeted MYD88. Moreover, miR-203a-3p significantly inhibited LPS-induced cartilage matrix degradation, oxidative stress, apoptosis, and pyroptosis of chondrocytes via targeting MYD88. Mechanistically, miR-203a-3p exerted protective effects via the inhibition of the MYD88/NF-κB pathway. In the rat OA model, intra-articular injections of miR-203a-3p agomir also significantly inhibited cartilage matrix degradation, thereby alleviating OA progression. Furthermore, the miR-203a-3p agomir-treated arthritic rat dramatically exhibited better articular tissue morphology and lower OARSI scores. CONCLUSIONS: MiR-203a-3p plays a role in alleviating the progression of OA by regulating the MYD88/NF-κB pathway, thereby inhibiting cartilage matrix degradation, oxidative stress, apoptosis, and pyroptosis of chondrocytes. It highlights the potential significance of miR-203a-3p as an important regulator of OA.

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. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Results: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

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. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP. RESULTS: We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with. AVAILABILITY AND IMPLEMENTATION: https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O.