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Although the mammalian intestinal epithelium manifests robust regenerative capacity after various cytotoxic injuries, the underlying mechanism has remained unclear. Here we identify the cyclin-dependent kinase inhibitor p57 as a specific marker for a quiescent cell population located around the +4 position of intestinal crypts. Lineage tracing reveals that the p57 + cells serve as enteroendocrine/tuft cell precursors under normal conditions but dedifferentiate and act as facultative stem cells to support regeneration after injury. Single-cell transcriptomics analysis shows that the p57 + cells undergo a dynamic reprogramming process after injury that is characterized by fetal-like conversion and metaplasia-like transformation. Population-level analysis also detects such spatiotemporal reprogramming widely in other differentiated cell types. In intestinal adenoma, p57 + cells manifest homeostatic stem cell activity, in the context of constitutively activated spatiotemporal reprogramming. Our results highlight a pronounced plasticity of the intestinal epithelium that supports maintenance of tissue integrity in normal and neoplastic contexts. Rapid turnover and regeneration of intestinal epithelium requires distinct intestinal stem cell (ISC) populations. Here the authors show p57 marks quiescent ISCs, and that differentiated cells revert to stem cell state after injury, through dynamic reprogramming characterized by fetal- and metaplastic-like changes.

This multi-center, retrospective study aimed to clarify the factors affecting drug retention of the Janus kinase inhibitors (JAKi) including baricitinib (BAR) and tofacitinib (TOF) in patients with RA. Patients were as follows; females, 80.6%; age, 60.5 years; DAS28-ESR, 4.3; treated with either BAR (n = 166) or TOF (n = 185); bDMARDs- or JAKi-switched cases (76.6%). The reasons for drug discontinuation were classified into four major categories. The drug retention was evaluated at 24 months using the Kaplan–Meier method and multivariate Cox proportional hazards modelling adjusted by confounders. Discontinuation rates for the corresponding reasons were as follows; ineffectiveness (22.3%), toxic adverse events (13.3%), non-toxic reasons (7.2%) and remission (0.0%). Prior history of anti-interleukin-6 receptor antibody (aIL-6R) ineffectiveness significantly increased the risk of treatment discontinuation due to ineffectiveness (p = 0.020). Aging (≥ 75 years) (p = 0.028), usage of PSL ≥ 5 mg/day (p = 0.017) and female sex (p = 0.041) significantly increased the risk of treatment discontinuation due to toxic adverse events. Factors not associated with treatment discontinuation were: number of prior bDMARDs or JAKi, concomitant MTX usage, difference of JAKi, and prior use of TNF inhibitor, CTLA4-Ig or other JAKi.

The duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA positivity will be important to prevent the spread of coronavirus disease 2019 (COVID-19). A systematic review and meta-analysis were conducted following PRISMA to determine the duration from several parts of the body and clinical characteristics affecting it. PubMed, Web of Science, Scopus, and CENTRAL were searched for original studies reporting the duration from COVID-19 onset to the disappearance of viral RNA. Of the 1682 studies identified, 100 met the selection criteria and 13,431 patients were included in this study. The duration of SARS-CoV-2 RNA positivity was 18.29 [95% confidence interval: 17.00-19.89] days in the upper respiratory tract samples, 23.79 [20.43-27.16] days in the sputum, 14.60 [12.16-17.05] days in the blood, and 22.38 [18.40-26.35] days in the stool. Sensitivity analysis revealed that the duration was positively correlated with age, comorbidities, severity, and usage of glucocorticoid. Subgroup analysis indicated that the presence or absence of complications had the greatest impact on the difference in DSRP. The duration of SARS-CoV-2 RNA positivity was 18.29 days in the upper respiratory tract samples. The duration in the sputum and the stool was longer, while that in the blood was shorter. The duration in the upper respiratory tract samples was longer in older, with any comorbidities, severer, and treated with glucocorticoid. These results provide the basic data for the duration of SARS-CoV-2 RNA positivity, and in the future, the effect of vaccination against SARS-CoV-2 and the SARS-CoV-2 variants on the duration of RNA positivity should be assessed.

Background This work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice. Methods We collected 4691 X-ray images of the cervical spine of the 906 patients with RA. Among these images, 3480 were used for training the deep learning model, 803 were used for validating the model during the training process, and the remaining 408 were used for testing the performance of the trained model. The two-dimensional key points’ detection model of Deep High-Resolution Representation Learning for Human Pose Estimation was adopted as the base convolutional neural network model. The model inferred four coordinates to calculate the atlantodental interval (ADI) and space available for the spinal cord (SAC). Finally, these values were compared with those by clinicians to evaluate the performance of the model. Results Among the 408 cervical images for testing the performance, the trained model correctly identified the four coordinates in 99.5% of the dataset. The values of ADI and SAC were positively correlated among the model and two clinicians. The sensitivity of AAS diagnosis with ADI or SAC by the model was 0.86 and 0.97 respectively. The specificity of that was 0.57 and 0.5 respectively. Conclusions We present the development of a deep learning model for the evaluation of cervical lesions of patients with RA. The model was demonstrably shown to be useful for quantitative evaluation.

Background Undifferentiated arthritis (UA) often develops into rheumatoid arthritis (RA), but predicting disease progression from seronegative UA remains challenging because seronegative RA often does not meet the classification criteria. This study aims to build a machine learning (ML) model to predict the progression from seronegative UA to RA using clinical and laboratory parameters. Methods KURAMA cohort (training dataset) and ANSWER cohort (validation dataset) were utilized. Patients with seronegative UA were selected based on specific inclusion and exclusion criteria. Clinical and laboratory parameters, including demographic data, acute phase reactants, autoantibodies, and physical examination findings, were collected. Various ML models, including a Feedforward Neural Network (FNN), were developed and compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and other metrics. SHapley Additive exPlanations (SHAP) values were computed to interpret the importance of variables. Results KURAMA cohort included 210 patients with seronegative UA, of whom 57 (27.1%) progressed to RA. The FNN model demonstrated the highest predictive performance with an AUC of 0.924 and a sensitivity of 80.7% in the training dataset. Validation with ANSWER cohort (140 patients; 32.1% progressed to RA) showed an AUC of 0.777, sensitivity of 77.8%. MMP-3 had the highest impact on the model. Conclusions The FNN model exhibited robust performance in predicting the progression of RA from seronegative UA and maintained substantial sensitivity in an independent validation cohort. This model using only clinical and laboratory parameters has potential for predicting RA progression in patients with seronegative UA.

ObjectivesTo evaluate the ability of geldanamycin to modulate two opposing TNFα/TNFR1-triggered signals for inflammation and cell death.MethodsThe effects of geldanamycin on TNFα-induced proinflammatory cytokine production, apoptosis, NF-κB activation, caspase activation, and necroptosis in a human rheumatoid synovial cell line (MH7A) were evaluated via ELISA/qPCR, flow cytometry, dual-luciferase reporter assay, and western blotting assay, respectively. In addition, therapeutic effects on murine collagen-induced arthritis (CIA) were also evaluated.ResultsGeldanamycin disrupted RIPK1 in MH7A, thereby inhibiting TNFα-induced proinflammatory cytokine production and enhancing apoptosis. TNFα-induced NF-κB and MLKL activation was inhibited, whereas caspase 8 activation was enhanced. Recombinant RIPK1 restored the geldanamycin-mediated inhibition of TNFα-induced NF-κB activation. In addition, GM showed more clinical effectiveness than a conventional biologic TNF inhibitor, etanercept, in murine CIA and significantly attenuated synovial hyperplasia, a histopathological hallmark of RA.ConclusionsGM disrupts RIPK1 and selectively inhibits the TNFR1-triggered NF-κB activation signaling pathway, while enhancing the apoptosis signaling pathway upon TNFα stimulation, thereby redressing the balance between these two opposing signals in a human rheumatoid synovial cell line. Therapeutic targeting RIPK1 may be a novel concept which involves TNF inhibitor acting as a TNFR1-signal modulator and have great potential for a more fundamental, effective, and safer TNF inhibitor.Key Points• Geldanamycin (GM) disrupts RIPK1 and selectively inhibits the TNFR1-triggered NF-κB activation signaling pathway while enhancing the apoptosis signaling pathway upon TNFα stimulation, thereby redressing the balance between these two opposing signals in a human rheumatoid synovial cell line, MH7A.• GM showed more clinical effectiveness than a conventional biologic TNF-inhibitor, etanercept, in murine collagen-induced arthritis (CIA), and significantly attenuated synovial hyperplasia, a histopathological hallmark of RA.• Therapeutic targeting RIPK1 may be a novel concept which involves TNF inhibitor acting as a TNFR1-signal modulator and have great potential for a more fundamental, effective, and safer TNF-inhibitor.

The defining biology that distinguishes neutrophil extracellular traps (NETs) from other forms of cell death is unresolved, and techniques which unambiguously identify NETs remain elusive. Raman scattering measurement provides a holistic overview of cell molecular composition based on characteristic bond vibrations in components such as lipids and proteins. We collected Raman spectra from NETs and freeze/thaw necrotic cells using a custom built high-throughput platform which is able to rapidly measure spectra from single cells. Principal component analysis of Raman spectra from NETs clearly distinguished them from necrotic cells despite their similar morphology, demonstrating their fundamental molecular differences. In contrast, classical techniques used for NET analysis, immunofluorescence microscopy, extracellular DNA, and ELISA, could not differentiate these cells. Additionally, machine learning analysis of Raman spectra indicated subtle differences in lipopolysaccharide (LPS)-induced as opposed to phorbol myristate acetate (PMA)-induced NETs, demonstrating the molecular composition of NETs varies depending on the stimulant used. This study demonstrates the benefits of Raman microscopy in discriminating NETs from other types of cell death and by their pathway of induction.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to severe coronavirus disease 2019 (COVID-19), which is characterized by cytokine storm and organ dysfunction. The spike S1 subunit induces inflammatory cytokine production, but the immune cell subsets that respond to S1 stimulation and contribute to disease severity remain unclear. We analyzed serum samples and peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 (moderate: n = 7; severe: n = 25) and healthy controls (n = 38). Using mass cytometry (cytometry by time-of-flight; CyTOF), we analyzed immune cell responses to S1 subunit stimulation in PBMCs from healthy donors and patients with COVID-19. We examined correlations among identified cell populations, serum cytokine levels, and clinical parameters. Serum S1 subunit levels correlated with disease severity and inflammatory cytokine concentrations. S1 subunit stimulation induced dose-dependent cytokine production from PBMCs, predominantly from myeloid cells. CyTOF analysis identified classical monocytes with high CD147 expression (CD147hi cMono) as the primary source of S1-induced cytokines. The proportion of CD147hi cMono increased significantly in severe COVID-19 and decreased with clinical improvement. The frequency of CD147hi cMono showed a stronger positive correlation with clinical severity markers in younger patients compared to older patients. CD147hi cMono are the primary cellular source of S1-induced inflammatory cytokines and may serve as potential biomarkers for monitoring COVID-19 severity and treatment response.

Consecutive mRNA vaccinations against SARS-CoV-2 reinforced both innate and adaptive immune responses. However, it remains unclear whether the enhanced innate immune responses are mediated by epigenetic regulation and, if so, whether these effects persist. Using mass cytometry, RNA-Seq, and ATAC-Seq, we show that BNT162b2 mRNA vaccination upregulated antiviral and IFN-stimulated gene expression in monocytes with greater effects after the second vaccination than those after the first vaccination. Transcription factor–binding motif analysis also revealed enriched IFN regulatory factors and PU.1 motifs in accessible chromatin regions. Importantly, although consecutive BNT162b2 mRNA vaccinations boosted innate immune responses and caused epigenetic changes in isolated monocytes, we show that these effects occurred only transiently and disappeared 4 weeks after the second vaccination. Furthermore, single-cell RNA-Seq analysis revealed that a similar gene signature was impaired in the monocytes of unvaccinated patients with COVID-19 with acute respiratory distress syndrome. These results reinforce the importance of the innate immune response in the determination of COVID-19 severity but indicate that, unlike adaptive immunity, innate immunity is not unexpectedly sustained even after consecutive vaccination. This study, which focuses on innate immune memory, may provide novel insights into the vaccine development against infectious diseases.

ObjectivesThe aim of this multicenter, retrospective study was to clarify the retention rates of sarilumab (SAR), baricitinib (BAR), and tofacitinib (TOF) in patients with rheumatoid arthritis (RA).MethodsPatients treated with either SAR (n = 62), BAR (n = 166), or TOF (n = 185) (females, 80.9%; age, 61.0 years; disease duration, 11.1 years; rheumatoid factor positivity, 84.4%; Disease Activity Score in 28 joints using erythrocyte sedimentation rate, 4.3; concomitant prednisolone dose, 5.3 mg/day [47.0%] and methotrexate dose, 8.8 mg/week [58.4%]; biologics- or Janus kinase inhibitors-switched cases 78.4%) were included. The reasons for drug discontinuation were classified into 4 major categories (lack of effectiveness, toxic adverse events, non-toxic reasons, and remission) by each attending physician. The drug retention rate was estimated at 18 months using the Kaplan–Meier method and adjusted for potential confounders by Cox proportional hazards modeling.ResultsThe discontinuation rates of SAR, BAR, and TOF for the corresponding reasons were as follows, respectively: lack of effectiveness (15.7%, 15.6%, and 21.5%; P = 0.84), toxic adverse events (15.8%, 12.1%, and 12.3%; P = 0.35), non-toxic reasons (10.9%, 7.7%, and 6.8%; P = 0.35), and remission (0.0%, 2.8%, and 0.0%; P = 1.0). The overall retention rates excluding non-toxic reasons and remission were as follows: 68.8% for SAR, 72.5% for BAR, and 66.7% for TOF (P = 0.54).ConclusionsAfter adjustment by potent confounders, SAR, BAR, and TOF showed similar discontinuation rates due to lack of effectiveness and toxic adverse events. Key Points• This is the first retrospective multicenter study that aimed to clarify the retention rates and reasons for discontinuation of SAR, BAR, and TOF in patients with RA.