Publications by authors named "Chengkai Yan"

The retina exhibits conserved structural and functional features across species, yet the evolutionary mechanisms underlying color vision remain unclear. Here, we employ a single-cell prediction (scPred) model to construct a cross-species single-cell retinal atlas from 24 species. We identify conserved retinal cell types and expression patterns, with photoreceptor cells, especially rods, showing evolutionary shifts.

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Background: The HECT E3 ubiquitin ligase Nedd4 has been shown to positively regulate T cell responses, but its role in T helper (Th) cell differentiation and autoimmunity is unknown. Th17 cells are believed to play a pivotal role in the development and pathogenesis of autoimmune diseases. Nevertheless, the regulation of RORγt activation during Th17 cell differentiation by TCR signaling is yet to be elucidated.

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Multiple sclerosis (MS) is a debilitating demyelinating disease characterized by remyelination failure attributed to inadequate oligodendrocyte precursor cells (OPCs) differentiation and aberrant astrogliosis. A comprehensive cell atlas reanalysis of clinical specimens brings to light heightened clusterin (CLU) expression in a specific astrocyte subtype links to active lesions in MS patients. Our investigation reveals elevated astrocytic CLU levels in both active lesions of patient tissues and female murine MS models.

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Erythrocytes are the most abundant type of cells in the blood and have a relatively simple structure when mature; they have a long life-span in the circulatory system. The primary function of erythrocytes is as oxygen carriers; however, they also play an important role in the immune system. Erythrocytes recognize and adhere to antigens and promote phagocytosis.

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E3 ubiquitin ligase Cbl-b is involved in the maintenance of a balance between immunity and tolerance. Mice lacking Cbl-b are highly susceptible to experimental autoimmune encephalomyelitis (EAE), a Th17-mediated autoimmune disease. However, how Cbl-b regulates Th17 cell responses remains unclear.

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Background: Minimal manifestation status (MMS) is an important landmark in the treatment of myasthenia gravis (MG), and predictors of MMS induction have rarely been identified in previous studies.

Objective: The objective of this study is to evaluate the clinical factors associated with MMS induction among patients with MG.

Design: This two-step retrospective cohort study with a single center investigated the factors that may be associated with MMS induction and retested these predictors in a test cohort.

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Myasthenia gravis (MG) is a rare autoimmune disease. Although the impact of immune cell disorder in MG has been extensively studied, little is known about the transcriptomes of individual cells. Here, we assessed the transcriptional profiles of 39,243 cells by single-cell sequencing and identified 13 major cell clusters, along with 39 subgroups of cells derived from patients with new-onset myasthenia gravis and healthy controls.

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Introduction: Disease evaluation and long-term follow-up of myasthenia gravis (MG) patients rely on disease-specific measures. We evaluated four widely used MG-specific assessments, and compared the response to disease change in different MG subgroups.

Methods: We used the Cronbach's α coefficient to test reliability, Pearson correlation coefficients to test construct validity, as well as one-way ANOVA and independent-sample t-tests to access discriminant validity.

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Article Synopsis
  • The most common cause of fungal infections in humans is candidiasis, which is especially deadly in hospital settings and often leads to bloodstream infections with high mortality rates.
  • Research indicates that Nedd4 is crucial for proper signaling through immune receptors Dectin-1 and Dectin-2/3, affecting the immune response against fungal infections.
  • Mice lacking Nedd4 show increased susceptibility to systemic infections and impaired immune responses, indicating that Nedd4 is necessary for effective antifungal immunity by regulating specific signaling pathways in immune cells.
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Aberrant NLRP3 inflammasome activation contributes to the development of endotoxemia. The importance of negative regulation of NLRP3 inflammasomes remains poorly understood. Here, we show that the E3 ubiquitin ligase Cbl-b is essential for preventing endotoxemia induced by a sub-lethal dose of LPS via a caspase-11/NLRP3-dependent manner.

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Purpose: Tacrolimus is a novel effective immunosuppressant for myasthenia gravis (MG) patients. However, the narrow therapeutic window, and high inter- and intrapatient variation in bioavailability largely limited its clinical application. This article intended to find the SNPs influencing clinical outcome and discover the possible mechanisms.

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Introduction: The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers.

Methods: According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions.

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Recent studies have suggested that genomic diversity may play a key role in different clinical outcomes, and the importance of SNPs is becoming increasingly clear. In this article, we summarize the bioactivity of SNPs that may affect the sensitivity to or possibility of drug reactions that occur among the signaling pathways of regularly used immunosuppressants, such as glucocorticoids, azathioprine, tacrolimus, mycophenolate mofetil, cyclophosphamide and methotrexate. The development of bioinformatics, including machine learning models, has enabled prediction of the proper immunosuppressant dosage with minimal adverse drug reactions for patients after organ transplantation or for those with autoimmune diseases.

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