Multiple Signaling Roles of CD3ε and Its Application in CAR-T Cell Therapy.

A T cell receptor (TCR) mediates antigen-induced signaling through its associated CD3ε, δ, γ, and ζ, but the contributions of different CD3 chains remain elusive. Using quantitative mass spectrometry, we simultaneously quantitated the phosphorylation of the immunoreceptor tyrosine-based activation motif (ITAM) of all CD3 chains upon TCR stimulation. A subpopulation of CD3ε ITAMs was mono-phosphorylated, owing to Lck kinase selectivity, and specifically recruited the inhibitory Csk kinase to attenuate TCR signaling, suggesting that TCR is a self-restrained signaling machinery containing both activating and inhibitory motifs. Moreover, we found that incorporation of the CD3ε cytoplasmic domain into a second-generation chimeric antigen receptor (CAR) improved antitumor activity of CAR-T cells. Mechanistically, the Csk-recruiting ITAM of CD3ε reduced CAR-T cytokine production whereas the basic residue rich sequence (BRS) of CD3ε promoted CAR-T persistence via p85 recruitment. Collectively, CD3ε is a built-in multifunctional signal tuner, and increasing CD3 diversity represents a strategy to design next-generation CAR.

Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.

We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n = 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.

Disulfiram bolsters T-cell anti-tumor immunity through direct activation of LCK-mediated TCR signaling.

Activation of the T-cell antigen receptor (TCR)-CD3 complex is critical to induce the anti-tumor response of CD8+ T cells. Here, we found that disulfiram (DSF), an FDA-approved drug previously used to treat alcohol dependency, directly activates TCR signaling. Mechanistically, DSF covalently binds to Cys20/Cys23 residues of lymphocyte-specific protein tyrosine kinase (LCK) and enhances its tyrosine 394 phosphorylation, thereby promoting LCK kinase activity and boosting effector T cell function, interleukin-2 production, metabolic reprogramming, and proliferation. Furthermore, our in vivo data revealed that DSF promotes anti-tumor immunity against both melanoma and colon cancer in mice by activating CD8+ T cells, and this effect was enhanced by anti-PD-1 co-treatment. We conclude that DSF directly activates LCK-mediated TCR signaling to induce strong anti-tumor immunity, providing novel molecular insights into the therapeutic effect of DSF on cancer.

Elp3-mediated codon-dependent translation promotes mTORC2 activation and regulates macrophage polarization.

Macrophage polarization is a process whereby macrophages acquire distinct effector states (M1 or M2) to carry out multiple and sometimes opposite functions. We show here that translational reprogramming occurs during macrophage polarization and that this relies on the Elongator complex subunit Elp3, an enzyme that modifies the wobble uridine base U34 in cytosolic tRNAs. Elp3 expression is downregulated by classical M1-activating signals in myeloid cells, where it limits the production of pro-inflammatory cytokines via FoxO1 phosphorylation, and attenuates experimental colitis in mice. In contrast, alternative M2-activating signals upregulate Elp3 expression through a PI3K- and STAT6-dependent signaling pathway. The metabolic reprogramming linked to M2 macrophage polarization relies on Elp3 and the translation of multiple candidates, including the mitochondrial ribosome large subunit proteins Mrpl3, Mrpl13, and Mrpl47. By promoting translation of its activator Ric8b in a codon-dependent manner, Elp3 also regulates mTORC2 activation. Elp3 expression in myeloid cells further promotes Wnt-driven tumor initiation in the intestine by maintaining a pool of tumor-associated macrophages exhibiting M2 features. Collectively, our data establish a functional link between tRNA modifications, mTORC2 activation, and macrophage polarization.

Self-programmed dynamics of T cell receptor condensation.

A common event upon receptor-ligand engagement is the formation of receptor clusters on the cell surface, in which signaling molecules are specifically recruited or excluded to form signaling hubs to regulate cellular events. These clusters are often transient and can be disassembled to terminate signaling. Despite the general relevance of dynamic receptor clustering in cell signaling, the regulatory mechanism underlying the dynamics is still poorly understood. As a major antigen receptor in the immune system, T cell receptors (TCR) form spatiotemporally dynamic clusters to mediate robust yet temporal signaling to induce adaptive immune responses. Here we identify a phase separation mechanism controlling dynamic TCR clustering and signaling. The TCR signaling component CD3ε chain can condensate with Lck kinase through phase separation to form TCR signalosomes for active antigen signaling. Lck-mediated CD3ε phosphorylation, however, switched its binding preference to Csk, a functional suppressor of Lck, to cause the dissolvement of TCR signalosomes. Modulating TCR/Lck condensation by targeting CD3ε interactions with Lck or Csk directly affects T cell activation and function, highlighting the importance of the phase separation mechanism. The self-programmed condensation and dissolvement is thus a built-in mechanism of TCR signaling and might be relevant to other receptors.

Development of the fetal brain cortico-cortical structural network during the second-to-third trimester based on diffusion MRI.

During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the inter-wired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and post-mortem fetal brains, the in-utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in-utero dMRI data from human fetuses of both sexes between 26 to 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intra-hemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for the Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.Significance Statement We studied the normal development of intra-hemispheric cortico-cortical structural connectivity networks (SCNs) of the fetal brain from 26 to 38 gestational weeks using in-utero diffusion MRI data. Graph-theory-based analysis revealed significant enhancement in network efficiency and clustering, as well as persisted small-worldness with age, revealing balanced integration and segregation in the fetal brain SCN during the studied period, supported by regional developmental patterns. Leftward lateralization in network efficiency, clustering coefficient and small-worldness was observed. Regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge. We also summarized the challenges of investigating the fetal brain SCN development, and provided suggestions for future studies.

Structure-preserved dimension reduction using joint triplets sampling for multi-batch integration of single-cell transcriptomic data.

Dimension reduction (DR) plays an important role in single-cell RNA sequencing (scRNA-seq), such as data interpretation, visualization and other downstream analysis. A desired DR method should be applicable to various application scenarios, including identifying cell types, preserving the inherent structure of data and handling with batch effects. However, most of the existing DR methods fail to accommodate these requirements simultaneously, especially removing batch effects. In this paper, we develop a novel structure-preserved dimension reduction (SPDR) method using intra- and inter-batch triplets sampling. The constructed triplets jointly consider each anchor's mutual nearest neighbors from inter-batch, k-nearest neighbors from intra-batch and randomly selected cells from the whole data, which capture higher order structure information and meanwhile account for batch information of the data. Then we minimize a robust loss function for the chosen triplets to obtain a structure-preserved and batch-corrected low-dimensional representation. Comprehensive evaluations show that SPDR outperforms other competing DR methods, such as INSCT, IVIS, Trimap, Scanorama, scVI and UMAP, in removing batch effects, preserving biological variation, facilitating visualization and improving clustering accuracy. Besides, the two-dimensional (2D) embedding of SPDR presents a clear and authentic expression pattern, and can guide researchers to determine how many cell types should be identified. Furthermore, SPDR is robust to complex data characteristics (such as down-sampling, duplicates and outliers) and varying hyperparameter settings. We believe that SPDR will be a valuable tool for characterizing complex cellular heterogeneity.

Age-dependent functional development pattern in neonatal brain: An fMRI-based brain entropy study.

The relationship between brain entropy (BEN) and early brain development has been established through animal studies. However, it remains unclear whether the BEN can be used to identify age-dependent functional changes in human neonatal brains and the genetic underpinning of the new neuroimaging marker remains to be elucidated. In this study, we analyzed resting-state fMRI data from the Developing Human Connectome Project, including 280 infants who were scanned at 37.5-43.5 weeks postmenstrual age. The BEN maps were calculated for each subject, and a voxel-wise analysis was conducted using a general linear model to examine the effects of age, sex, and preterm birth on BEN. Additionally, we evaluated the correlation between regional BEN and gene expression levels. Our results demonstrated that the BEN in the sensorimotor-auditory and association cortices, along the 'S-A' axis, was significantly positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively. Meanwhile, the BEN in the right rolandic operculum correlated significantly with both GA and PNA. Preterm-born infants exhibited increased BEN values in widespread cortical areas, particularly in the visual-motor cortex, when compared to term-born infants. Moreover, we identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2), which were involved in protein folding, synaptic vesicle transportation and cell division. These findings suggest that the fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes.

Sialic Acid-Functionalized Pyroptosis Nanotuner for Epigenetic Regulation and Enhanced Cancer Immunotherapy.

The efficacy of immune checkpoint blockade (ICB) in promoting an immune response against tumors still encounters challenges such as low response rates and off-target effects. Pyroptosis, an immunogenic cell death (ICD) mechanism, holds the potential to overcome the limitations of ICB by activating and recruiting immune cells. However, the expression of the pyroptosis-related protein Gasdermin-E(GSDME) in some tumors is limited due to mRNA methylation. To overcome this obstacle, sialic acid-functionalized liposomes coloaded with decitabine, a demethylation drug, and triclabendazole, a pyroptosis-inducing drug are developed. This nanosystem primarily accumulates at tumor sites via sialic acid and the Siglec receptor, elevating liposome accumulation in tumors up to 3.84-fold at 24 h and leading to the upregulation of pyroptosis-related proteins and caspase-3/GSDME-dependent pyroptosis. Consequently, it facilitates the infiltration of CD8+ T cells into the tumor microenvironment and enhances the efficacy of ICB therapy. The tumor inhibition rate of the treatment group is 89.1% at 21 days. This study highlights the potential of sialic acid-functionalized pyroptosis nanotuners as a promising approach for improving the efficacy of ICB therapy in tumors with low GSDME expression through epigenetic alteration and ICD.

Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.

Developments of single-cell RNA sequencing (scRNA-seq) technologies have enabled biological discoveries at the single-cell resolution with high throughput. However, large scRNA-seq datasets always suffer from massive technical noises, including batch effects and dropouts, and the dropout is often shown to be batch-dependent. Most existing methods only address one of the problems, and we show that the popularly used methods failed in trading off batch effect correction and dropout imputation. Here, inspired by the idea of causal inference, we propose a novel propensity score matching method for scRNA-seq data (scPSM) by borrowing information and taking the weighted average from similar cells in the deep sequenced batch, which simultaneously removes the batch effect, imputes dropout and denoises data in the entire gene expression space. The proposed method is testified on two simulation datasets and a variety of real scRNA-seq datasets, and the results show that scPSM is superior to other state-of-the-art methods. First, scPSM improves clustering accuracy and mixes cells of the same type, suggesting its ability to keep cell type separation while correcting for batch. Besides, using the scPSM-integrated data as input yields results free of batch effects or dropouts in the differential expression analysis. Moreover, scPSM not only achieves ideal denoising but also preserves real biological structure for downstream gene-based analyses. Furthermore, scPSM is robust to hyperparameters and small datasets with a few cells but enormous genes. Comprehensive evaluations demonstrate that scPSM jointly provides desirable batch effect correction, imputation and denoising for recovering the biologically meaningful expression in scRNA-seq data.

Drug resistance prediction and resistance genes identification in Mycobacterium tuberculosis based on a hierarchical attentive neural network utilizing genome-wide variants.

Prediction of antimicrobial resistance based on whole-genome sequencing data has attracted greater attention due to its rapidity and convenience. Numerous machine learning-based studies have used genetic variants to predict drug resistance in Mycobacterium tuberculosis (MTB), assuming that variants are homogeneous, and most of these studies, however, have ignored the essential correlation between variants and corresponding genes when encoding variants, and used a limited number of variants as prediction input. In this study, taking advantage of genome-wide variants for drug-resistance prediction and inspired by natural language processing, we summarize drug resistance prediction into document classification, in which variants are considered as words, mutated genes in an isolate as sentences, and an isolate as a document. We propose a novel hierarchical attentive neural network model (HANN) that helps discover drug resistance-related genes and variants and acquire more interpretable biological results. It captures the interaction among variants in a mutated gene as well as among mutated genes in an isolate. Our results show that for the four first-line drugs of isoniazid (INH), rifampicin (RIF), ethambutol (EMB) and pyrazinamide (PZA), the HANN achieves the optimal area under the ROC curve of 97.90, 99.05, 96.44 and 95.14% and the optimal sensitivity of 94.63, 96.31, 92.56 and 87.05%, respectively. In addition, without any domain knowledge, the model identifies drug resistance-related genes and variants consistent with those confirmed by previous studies, and more importantly, it discovers one more potential drug-resistance-related gene.

Fast simulation for interacting four-level Rydberg atoms: electromagnetically induced transparency and Autler-Townes splitting.

Quantum sensing using Rydberg atoms is an emerging technology for precise measurement of electric fields. However, most existing computational methods are all based on a single-particle model and neglect Rydberg-Rydberg interaction between atoms. In this study, we introduce the interaction term into the conventional four-level optical Bloch equations. By incorporating fast iterations and solving for the steady-state solution efficiently, we avoid the computation of a massive 4N × 4N dimensional matrix. Additionally, we apply the Doppler frequency shift to each atom used in the calculation, eliminating the requirement for an additional Doppler iteration. These schemes allow for the calculation of the interaction between 7000 atoms around one minute. Based on the many-body model, we investigate the Rydberg-Rydberg interaction of Rydberg atoms under different atomic densities. Furthermore, we compare our results with the literature data of a three-level system and the experimental results of our own four-level system. The results demonstrate the validity of our model, with an effective error of 4.59% compared to the experimental data. Finally, we discover that the many-body model better predicts the linear range for measuring electric fields than the single-particle model, making it highly applicable in precise electric field measurements.

Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma.

OBJECTIVES: To develop a high-accuracy MRI-based deep learning method for predicting cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion status in isocitrate dehydrogenase (IDH)-mutant astrocytoma. METHODS: Multiparametric brain MRI data and corresponding genomic information of 234 subjects (111 positives for CDKN2A/B homozygous deletion and 123 negatives for CDKN2A/B homozygous deletion) were obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) respectively. Two independent multi-sequence networks (ResFN-Net and FN-Net) are built on the basis of ResNet and ConvNeXt network combined with attention mechanism to classify CDKN2A/B homozygous deletion status using MR images including contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI). The performance of the network is summarized by three-way cross-validation; ROC analysis is also performed. RESULTS: The average cross-validation accuracy (ACC) of ResFN-Net is 0.813. The average cross-validation area under curve (AUC) of ResFN-Net is 0.8804. The average cross-validation ACC and AUC of FN-Net is 0.9236 and 0.9704, respectively. Comparing all sequence combinations of the two networks (ResFN-Net and FN-Net), the sequence combination of CE-T1WI and T2WI performed the best, and the ACC and AUC were 0.8244, 0.8975 and 0.8971, 0.9574, respectively. CONCLUSIONS: The FN-Net deep learning networks based on ConvNeXt network achieved promising performance for predicting CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma. CLINICAL RELEVANCE STATEMENT: A novel deep learning network (FN-Net) based on preoperative MRI was developed to predict the CDKN2A/B homozygous deletion status. This network has the potential to be a practical tool for the noninvasive characterization of CDKN2A/B in glioma to support personalized classification and treatment planning. KEY POINTS: • CDKN2A/B homozygous deletion status is an important marker for glioma grading and prognosis. • An MRI-based deep learning approach was developed to predict CDKN2A/B homozygous deletion status. • The predictive performance based on ConvNeXt network was better than that of ResNet network.

Role of alternative splicing in fish immunity.

Alternative splicing serves as a pivotal source of complexity in the transcriptome and proteome, selectively connecting various coding elements to generate a diverse array of mRNAs. This process encodes multiple proteins with either similar or distinct functions, contributing significantly to the intricacies of cellular processes. The role of alternative splicing in mammalian immunity has been well studied. Remarkably, the immune system of fish shares substantial similarities with that of humans, and alternative splicing also emerges as a key player in the immune processes of fish. In this review, we offer an overview of alternative splicing and its associated functions in the immune processes of fish, and summarize the research progress on alternative splicing in the fish immunity. Furthermore, we review the impact of alternative splicing on the fish immune system's response to external stimuli. Finally, we present our perspectives on future directions in this field. Our aim is to provide valuable insights for the future investigations into the role of alternative splicing in immunity.

Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.

BACKGROUND: Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD: In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS: We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS: Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION: We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

Summary of best evidence for prevention and management of frailty.

BACKGROUND: Frailty in older people can seriously affect their quality of life and increase the demand for long-term care and health care expenses. Aims of this study are to provide an evidence-based basis for clinical practice of frailty in older people by systematically searching for the best current evidence on interventions for the prevention and management of frailty. METHODS: According to the '6S' evidence resource model, evidence retrieval is searched from the top-down and collected relevant guidelines, best practices, evidence summaries, systematic reviews and expert consensus. The retrieval time limit was from the database establishment to 20 March 2023. Two reviewers independently screened and evaluated the literature, and then extracted and summarised the evidence according to the JBI grading of evidence and recommendation system. RESULTS: A total of 44 publications were finally included, including 12 guidelines, 5 best practices, 4 expert consensus, 5 evidence summaries and 18 systematic reviews. Through the induction and integration of the evidence, the evidence was finally summarised from eight aspects: frailty screening, frailty assessment, exercise intervention, nutrition intervention, multi-domain intervention, drug administration, social support and health education, and 43 best evidences were formed. CONCLUSIONS: This study summarised the best evidence for the prevention and management of frailty from eight aspects, which can provide guidance for clinical or community medical staff to develop and apply frailty intervention and practice programmes for older people and improved the clinical outcome and quality of life of older people.

Interface-driven structural evolution on diltiazem as novel uPAR inhibitors: from in silico design to in vitro evaluation.

The urokinase-type plasminogen activator receptor (uPAR) emerges as a key target for anti-metastasis owing to its pivotal role in facilitating the invasive and migratory processes of cancer cells. Recently, we identified the uPAR-targeting anti-metastatic ability of diltiazem (22), a commonly used antihypertensive agent. Fine-tuning the chemical structures of known hits represents a vital branch of drug development. To develop novel anti-metastatic drugs, we performed an interface-driven structural evolution strategy on 22. The uPAR-targeting and anti-cancer abilities of this antihypertensive drug wereidentified by us recently. Based on in silico strategy, including extensive molecular dynamics (MD) simulations, hierarchical binding free energy predictions, and ADMET profilings, we designed, synthesized, and identified three new diltiazem derivatives (221-8, 221-57, and 221-68) as uPAR inhibitors. Indeed, all of these three derivatives exhibited uPAR-depending inhibitory activity against PC-3 cell line invasion at micromolar level. Particularly, derivatives 221-68 and 221-8 showed enhanced uPAR-dependent inhibitory activity against the tumor cell invasion compared to the original compound. Microsecond timesclae MD simulations demonstrated the optimized moiety of 221-68 and 221-8 forming more comprehensive interactions with the uPAR, highlighting the reasonability of our strategy. This work introduces three novel uPAR inhibitors, which not only pave the way for the development of effective anti-metastatic therapeutics, but also emphasize the efficacy and robustness of an in silico-based lead compound optimization strategy in drug design.

The diagnosis and treatment of pediatric clinical myocarditis in China: a multicenter retrospective study.

This study aims to examine the clinical characteristics and outcomes of clinical myocarditis in pediatric patients in China. This is a multicenter retrospective study. Children diagnosed with clinical myocarditis from 20 hospitals in China and admitted between January 1, 2015, and December 30, 2021, were enrolled. The clinical myocarditis was diagnosed based on the "Diagnostic Recommendation for Myocarditis in Children (Version 2018)". The clinical data were collected from their medical records. A total of 1210 patients were finally enrolled in this study. Among them, 45.6% had a history of respiratory tract infection. An abnormal electrocardiogram was observed in 74.2% of patients. Echocardiography revealed that 32.3% of patients had a left ventricular ejection fraction of less than 50%. Cardiac MRI was performed in 4.9% of children with clinical myocarditis, of which 61% showed localized or diffuse hypersignal on T2-weighted images. Serum levels of cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and N-terminal B-type natriuretic peptide (NT-proBNP) were higher in patients with fulminant myocarditis than in patients with myocarditis, making them potential risk factors for fulminant myocarditis. Following active treatment, 12.1% of patients were cured, and 79.1% were discharged with improvement. CONCLUSION: Clinical myocarditis in children often presents with symptoms outside the cardiovascular system. CK-MB, cTnI, and NT-proBNP are important indicators for assessing clinical myocarditis. The electrocardiogram and echocardiogram findings in children with clinical myocarditis exhibit significant variability but lack specificity. Cardiac MRI can be a useful tool for screening clinical myocarditis. Most children with clinical myocarditis have a favorable prognosis. WHAT IS KNOWN: • Pediatric myocarditis presents complex clinical manifestations and exhibits varying degrees of severity. Children with mild myocarditis generally have a favorable prognosis, while a small number of children with critically ill myocarditis experience sudden onset, hemodynamic disorders, and fatal arrhythmias. Therefore, early diagnosis and timely treatment of myocarditis are imperative. WHAT IS NEW: • To the best of our knowledge, this multicenter retrospective study is the largest ever reported in China, aiming to reveal the clinical characteristics and outcomes of pediatric clinical myocarditis in China. We provided an extensive analysis of the clinical characteristics, diagnosis, treatment, prognosis, and factors impacting disease severity in pediatric clinical myocarditis in China, which provides insights into the epidemiological characteristics of pediatric clinical myocarditis.

The Association between Serum Level of Vitamin D and Inflammatory Biomarkers in Hospitalized Adult Patients: A Cross-Sectional Study Based on Real-World Data.

Objective: The association between vitamin D status and inflammation remains unclear in hospitalized patients. Materials and Methods: We performed the current study based on real-world data from two teaching hospitals. Serum level of vitamin D (assessed by 25-hydroxyvitamin D) was evaluated within 2 days after admission. All the patients were further classified into three groups: deficiency (<12 ng/mL), insufficiency (12-20 ng/mL), and adequate (≥20 ng/mL). White blood cell (WBC) count, serum level of C-reactive protein (CRP), and procalcitonin were also measured and used to evaluate inflammation. Other potential covariates were abstracted from medical records. Charlson comorbidity index (CCI) was calculated to assess the severity of disease. Results: A total number of 35,528 hospitalized adult patients (21,171 men and 14,357 women) were included. The average age and BMI were 57.5 ± 16.2 years and 23.4 ± 3.7 kg/m2, respectively, while medium vitamin D level was 16.1 ng/mL (interquartile range: 11.4 ng/mL, 21.6 ng/mL) and median CCI was one point (interquartile range: 0 point, two points). The prevalence of deficiency and insufficiency was 28.0% and 40.5%. Multivariate linear regression model showed that serum level of vitamin D was significantly associated with WBC and CRP but not associated with procalcitonin. Each standard deviation (≈7.4 ng/mL) increase in vitamin D was associated with a decrease in WBC by 0.13 × 109/mL (95% CI: 0.2 × 109/mL, 0.06 × 109/mL) and 0.62 mg/L (95% CI: 0.88 mg/L, 0.37 mg/L) for CRP. Subgroup analysis and sensitivity analysis (excluding those whose eGFR <60 ml/min/1.73 m2, those whose daily calorie intake <1,000 kcal, and those who were recruited from Xin Hua hospital) generated similar results. Conclusions: The deficiency and insufficiency of vitamin D in the hospitalized adult patients was very common. However, the results should be interpreted with caution for limited representation of the whole inpatients. Low level of vitamin D was associated with inflammatory biomarkers, which provide the evidences to early intervention for lower the risk of infection.