Publications by authors named "Wenxiang Cai"

Article Synopsis
  • Bone marrow mesenchymal stem cells (BMSCs) are important for regenerative medicine, but their effectiveness is limited by aging due to prolonged culture.
  • This study explored using electromagnetic fields (EMF) to rejuvenate aged BMSCs, demonstrating that EMF treatment can partly reverse aging effects and improve stem cell properties.
  • The research found that EMF enhances autophagy in aged BMSCs, ultimately promoting better bone formation when transplanted, suggesting a new method for improving stem cell therapy in tissue engineering.
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Aluminum (Al) is usually added to solid propellants to improve the combustion performance, however the condensed combustion products (CCPs) especially the large agglomerates generated from aluminum combustion can reduce the specific impulse of the engine, and result in two-phase loss, residue accumulation and throat liner ablation. Al and ammonium perchlorate (AP), as important components of NEPE propellants, can affect the formation process of the CCPs of aluminized NEPE propellants. To clarify the effect of Al and AP particle sizes on the properties of the CCPs of aluminized NEPE propellants, a constant-pressure quench vessel was adopted to collect the combustion products of four different formulations of NEPE propellants.

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Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a bidirectional two-sample Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386,577).

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Background: The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear.

Aims: To investigate the contribution of HLMs to human evolution through post-transcriptional modification.

Methods: We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity.

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Recently, there has been growing interest in using cell therapy through core decompression (CD) to treat osteonecrosis of the femoral head (ONFH). Our study aimed to investigate the effectiveness and mechanism of human umbilical cord mesenchymal stem cells (hUCMSCs) in treating steroid-induced ONFH. We constructed a steroid-induced ONFH rabbit model as well as dexamethasone (Dex)-treated bone microvascular endothelial cells (BMECs) model of human femoral head.

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Background: It has previously been proven that circadian rhythm disruption is associated with the incidence and deterioration of several tumors, which potentially leads to increased tumor susceptibility and a worse prognosis for tumor-bearing patients. However, their potential role in osteosarcoma has yet to be sufficiently investigated.

Methods: Transcriptomic and clinical data of 84 osteosarcoma samples and 70 normal bone tissue samples were obtained from the TARGET and GTEx databases, circadian rhythm-related genes were obtained from Genecards, and circadian rhythm-related lncRNAs (CRLs) were obtained by Pearson correlation analysis, differential expression analysis, and protein-protein interaction (PPI) analysis.

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Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx.

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Osteoarthritis (OA) is a frequently seen degenerative joint disease in the elderly. Its pathogenesis is highly related to the local inflammatory reaction and autophagy. Tizoxanide (Tiz), the main active metabolite of nitazoxanide, has proved its anti-inflammatory properties in several diseases.

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Article Synopsis
  • Scientists studied how lactate (a substance produced during exercise) affects a type of bone cancer called osteosarcoma.
  • They looked at data from numerous bone samples and found special types of genes related to lactate that could help predict how the disease progresses.
  • Their research showed that these genes influence both the cancer's growth and response to certain drugs, which could help improve treatments for patients.
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Several researchers have focused on understanding the pathogenesis and treatment strategies for osteoarthritis (OA). Gastrodin (GAS) is a potential anti-inflammatory agent. In this study, we constructed an in vitro OA chondrocyte model by treating chondrocytes with IL-1β.

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Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control.

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Sphingolipid metabolism (SM) fuels tumorigenesis and the malignant progression of osteosarcoma (OS), which leads to an unfavorable prognosis. Elucidating the molecular mechanisms underlying SM in osteosarcoma and developing a SM-based prognostic signature could be beneficial in the clinical setting. This study included 88 frozen OS samples to recognize the vital SM-relevant genes in the development of OS utilizing univariate Cox regression.

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Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks.

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Objective: Osteoarthritis (OA) is characterized by cartilage degeneration and inflammation. Procyanidin B2 (PCB2), a natural flavonoid compound, exhibits potential anti-inflammatory and anti-oxidative effects against several diseases. However, its curative effects on OA remain unclear.

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Schizophrenia (SCZ) is a severe mental disorder that may result in hallucinations, delusions, and extremely disordered thinking. How each cell type in the brain contributes to SCZ occurrence is still unclear. Here, we leveraged the human dorsolateral prefrontal cortex bulk RNA-seq data, then used the RNA-seq deconvolution algorithm CIBERSORTx to generate SCZ brain single-cell RNA-seq data for a comprehensive analysis to understand SCZ-associated brain cell types and gene expression changes.

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Background: We aimed to evaluate the potential role of antagonistic selection in polygenic diseases: if one variant increases the risk of one disease and decreases the risk of another disease, the signals of genetic risk elimination by natural selection will be distorted, which leads to a higher frequency of risk alleles.

Methods: We applied local genetic correlations and transcriptome-wide association studies to identify genomic loci and genes adversely associated with at least two diseases. Then, we used different population genetic metrics to measure the signals of natural selection for these loci and genes.

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Obsessive-compulsive disorder (OCD) is a chronic anxiety disorder with a substantial genetic basis and a broadly undiscovered etiology. Recent studies of de novo mutation (DNM) exome-sequencing studies for OCD have reinforced the hypothesis that rare variation contributes to the risk. We performed, to our knowledge, the first whole-genome sequencing on 53 parent-offspring families with offspring affected with OCD to investigate all rare de novo variants and insertions/deletions.

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The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer's Disease (AD).

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Chromosome 22q11.21 copy number variant (CNV) is a vital risk factor that can be a genetic predisposition to neurodevelopmental disorders (NDD). As 22q11.

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Both SETD2-mediated H3K36me3 and miRNAs play critical epigenetic roles in inflammatory bowel disease (IBD) and involve in the dysfunctional intestinal barrier. However, little is known about cross-talk between these two types of regulators in IBD progression. We performed small RNA sequencing of Setd2 epithelium-specific knockout mice (Setd2) and wild-type controls, both with DSS-induced colitis, and designed a framework for integrative analysis.

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Recurrent deletions and duplications of chromosome 7q11.23 copy number variants (CNVs) are associated with several psychiatric disorders. Although phenotypic abnormalities have been observed in patients, causal genes responsible for CNV-associated diagnoses and traits are still poorly understood.

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