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The structural components of the thymus are essential for guiding T cell development, but a thorough spatial view is still absent. Here we develop the TSO-his tool, designed to integrate multimodal data from single-cell and spatial transcriptomics to decipher the intricate structure of human thymus. Specifically, we characterize dynamic changes in cell types and critical markers, identifying ELOVL4 as a mediator of CD4+ T cell positive selection in the cortex. Utilizing the mapping function of TSO-his, we reconstruct thymic spatial architecture at single-cell resolution and recapitulates classical cell types and their essential co-localization for T cell development; additionally, previously unknown co-localization relationships such as that of CD8αα with memory B cells and monocytes are identified. Incorporating VDJ sequencing data, we also delineate distinct intermediate thymocyte states during αß T cell development. Overall, these insights enhance our understanding of thymic biology and may inform therapeutic interventions targeting T cell-mediated immune responses.

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BACKGROUND: Analyse the effects of Bifidobacterium BB-12 on intestinal metabolites and serum inflammatory factors in premature infants. METHODS: 71 premature infants at gestational age of ≤32 weeks were randomly divided into the probiotic (n = 36) and control (n = 35) groups. Faecal and blood samples were collected from the two groups of premature infants at the 2nd and 4th week of life for intestinal metabolite detection and assessment of the level of the serum inflammatory markers TLR4, NF- κ B, IL-1ß, and TNF- α. RESULTS: Compared to the control group, the probiotic group contained more amino acids, these elements were enriched on multiple amino acid metabolic pathways, and the probiotic group showed significantly lower levels of the serum inflammatory markers TLR4, NF-κB, IL-1ß, and TNF-α. Finally, the probiotic group showed a lower incidence of feeding intolerance. CONCLUSIONS: The administration of Bifidobacterium BB-12 is associated with increasing the levels of glutamine, glutamic acid, and kynurenine in the gut of premature infants, and associated with reducing the levels of TLR4 and NF-κB in the serum, further decreasing the secretion of the pro-inflammatory factors IL-1ß and TNF-α, and alleviating systemic inflammatory reactions, thereby reducing the incidence of feeding intolerance. IMPACT: 1. The use of Bifidobacterium BB-12 in premature infants can increase the levels of amino acids in the intestine. 2. Increases in Bifidobacterium BB-12 may decrease the serum levels of TLR4, NF-κB, IL-1ß, and TNF-α. 3. Kynurenine may improve the prognosis of preterm infants by reducing inflammation. 4. Bifidobacterium BB-12 may improve the feeding tolerance of premature infants, thus reducing the incidence of feeding intolerance.

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BACKGROUND: Small cell lung carcinoma (SCLC) is highly susceptible to metastasis in the early stages of the disease. However, the stomach is an uncommon site of metastasis in SCLC, and only a few cases of this type of metastasis have been reported. Therefore, SCLC gastric metastases have not been systematically characterized and are easily missed and misdiagnosed. CASE SUMMARY: We report three cases of gastric metastasis from SCLC in this article. The first patient presented primarily with cough, hemoptysis, and epigastric fullness. The other two patients presented primarily with abdominal discomfort, epigastric distension, and pain. All patients underwent gastroscopy and imaging examinations. Meanwhile, the immunohistochemical results of the lesions in three patients were suggestive of small cell carcinoma. Finally, the three patients were diagnosed with gastric metastasis of SCLC through a comprehensive analysis. The three patients did not receive appropriate treatment and died within a short time. CONCLUSION: Here, we focused on summarizing the characteristics of gastric metastasis of SCLC to enhance clinicians' understanding of this disease.

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Background: Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. Methods: This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. Results: The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

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Introduction: Tertiary lymphoid structures (TLSs) are ectopic lymphoid formations that arise in non-lymphoid tissues due to chronic inflammation. The pivotal function of TLSs in regulating tumor invasion and metastasis has been established across several cancers, such as lung cancer, liver cancer, and melanoma, with a positive correlation between increased TLS presence and improved prognosis. Nevertheless, the current research about the clinical significance of TLSs in breast cancer remains limited. Methods: In our investigation, we discovered TLS-critical genes that may impact the prognosis of breast cancer patients, and categorized breast cancer into three distinct subtypes based on critical gene expression profiles, each exhibiting substantial differences in prognosis (p = 0.0046, log-rank test), with Cluster 1 having the best prognosis, followed by Cluster 2, and Cluster 3 having the worst prognosis. We explored the impact of the heterogeneity of these subtypes on patient prognosis, the differences in the molecular mechanism, and their responses to drug therapy and immunotherapy. In addition, we designed a machine learning-based classification model, unveiling highly consistent prognostic distinctions in several externally independent cohorts. Results: A notable marker gene CXCL13 was identified in Cluster 3, potentially pivotal in enhancing patient prognosis. At the single-cell resolution, we delved into the adverse prognosis of Cluster 3, observing an enhanced interaction between fibroblasts, myeloid cells, and basal cells, influencing patient prognosis. Furthermore, we identified several significantly upregulated genes (CD46, JAG1, IL6, and IL6R) that may positively correlate with cancer cells' survival and invasive capabilities in this subtype. Discussion: Our study is a robust foundation for precision medicine and personalized therapy, presenting a novel perspective for the contemporary classification of breast cancer.

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During the progression of acute lung injury (ALI), oxidative stress and inflammatory responses always promote each other. The datasets analyzed in this research were acquired from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-expression Network Analysis (WGCNA) and limma package were used to obtain the ALI-related genes (ALIRGs) and differentially expressed genes (DEGs), respectively. In total, two biological markers (Gch1 and Tnfaip3) related to oxidative stress were identified by machine learning algorithms, Receiver Operator Characteristic (ROC), and differential expression analyses. The area under the curve (AUC) value of biological markers was greater than 0.9, indicating an excellent power to distinguish between ALI and control groups. Moreover, 15 differential immune cells were selected between the ALI and control samples, and they were correlated to biological markers. The transcription factor (TF)-microRNA (miRNA)-Target network was constructed to explore the potential regulatory mechanisms. Finally, based on the quantitative reverse transcription polymerase chain reaction (qRT-PCR), the expression of Gch1 and Tnfaip3 was significantly higher in ALI lung tissue than in healthy controls. In conclusion, the differences in expression profiles between ALI and normal controls were found, and two biological markers were identified, providing a research basis for further understanding the pathogenesis of ALI.

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BACKGROUND: Neuroblastoma (NB) is a childhood cancer originating from immature nerve cells in the sympathetic nervous system. Current clinical and molecular subtyping methods for NB have limitations in providing accurate prognostic information and guiding treatment decisions. RESULTS: To overcome these challenges, we explored the microenvironment of NB based on the knowledge-based functional gene expression signatures (Fges), which revealed heterogeneous subtypes. Consensus clustering of Fges activity scores identified three subtypes (Cluster 1, Cluster 2, and Cluster 3) that demonstrated significant differences in prognosis compared to mainstream subtypes. We assessed the immune infiltration, immunogenicity, CD8T cytotoxicity, and tumor purity of these subtypes, uncovering their distinct biological functions. Cluster 1 and Cluster 2 exhibited higher immunoreactivity, while Cluster 3 displayed higher tumor purity and poor prognosis. Gene ontology annotation and pathway analysis identified immune activation in Cluster 1, epithelial-mesenchymal transition (EMT) in Cluster 2, and cell cycle processes in Cluster 3. Notably, the impact of EMT activity on prognosis may vary across NB subtypes. A classification model using XGBoost accurately predicted subtypes in independent NB cohorts, with significant prognostic differences. GPR125, CDK4, and GREB1 emerged as potential therapeutic targets in Cluster 3. CD4K inhibitors showed subtype-specific responses, suggesting tailored treatment strategies. Single-cell analysis highlighted unfavorable clinical features in Cluster 3, including high-risk classification and reduced cytotoxicity. Suppressed interactions between monocytes, macrophages, and regulatory T cells were observed, affecting immune regulation and patient prognosis. CONCLUSION: To summarize, we have identified a new independent prognostic factor in NB that underscores the significant correlation between tumor phenotype and immune contexture. These findings deepen our understanding of NB subtypes and immune cell interactions, paving the way for more effective treatment approaches.

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Vegetation restoration after the abandonment of sloping farmland can effectively promote the sequestration of soil organic carbon (SOC), with soil aggregates playing a pivotal role. However, in abandoned farmlands in karst regions with varying degrees of rocky desertification, the relationship between soil aggregates, aggregate-associated organic carbon (AAOC), and total SOC content remains unclear. Taking abandoned sloping farmlands (5 years, 10 years, and 15 years) with different levels of rocky desertification (no rocky desertification, potential rocky desertification, slight rocky desertification, and moderate rocky desertification) in a typical karst area as research objects, this study investigated the dynamic characteristics of the particle size distribution of soil aggregates, total SOC, and AAOC. The results indicated that total SOC content in the 0-20 cm soil layer increased after abandonment in all levels of rocky desertification, peaking after 15 years. The abandoned sloping farmland with moderate desertification showed the best recovery effect. Post-abandonment vegetation restoration increased the content of 5-10 mm soil aggregates, but decreased those of 1-2 mm and < 0.25 mm aggregates. Particularly for 5-10 mm aggregates, the contribution of AAOC to total SOC significantly increased over time. Moreover, a strong correlation was observed between >1 mm aggregates and total SOC (p < 0.05). The increase in total SOC was primarily driven by the growth of AAOC in 5-10 mm aggregates. In general, vegetation restoration is an effective approach for enhancing total SOC content in abandoned sloping farmland with varying degrees of rocky desertification.

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Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

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Colorectal cancer (CRC) is the third most common cancer in the world. Other than adenocarcinomas, exceptional tumors of the colon and rectum represent a neglected clinical issue due to their rarity. Signet ring cell carcinoma (SRCC) is a rare subtype of CRC and has an extremely poor prognosis due to its advanced stage at diagnosis. Here we report a rare case of colorectal SRCC manifested as recurrent intestinal obstruction with a negative colonoscopy. Finally, he was diagnosed with signet ring cell carcinoma of the colon by postoperative pathology. It emphasized the special feature of intramural tumor growth without penetrating the mucosa in SRCC, which requires timely surgical intervention to avoid delay in diagnosis and treatment.

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Recently, various novel drug delivery systems have been developed to overcome ocular barriers in order to improve drug efficacy. We have previously reported that montmorillonite (MT) microspheres (MPs) and solid lipid nanoparticles (SLNs) loaded with the anti-glaucoma drug betaxolol hydrochloride (BHC) exhibited sustained drug release and thus intraocular pressure (IOP) lowering effects. Here, we investigated the effect of physicochemical particle parameters on the micro-interactions with tear film mucins and corneal epithelial cells. Results showed that the MT-BHC SLNs and MT-BHC MPs eye drops significantly prolonged the precorneal retention time due to their higher viscosity and lower surface tension and contact angle compared with the BHC solution, with MT-BHC MPs exhibiting the longest retention due to their stronger hydrophobic surface. The cumulative release of MT-BHC SLNs and MT-BHC MPs was up to 87.78% and 80.43% after 12 h, respectively. Tear elimination pharmacokinetics study further confirmed that the prolonged precorneal retention time of the formulations was due to the micro-interaction between the positively charged formulations and the negatively charged tear film mucins. Moreover, the area under the IOP reduction curve (AUC) of MT-BHC SLNs and MT-BHC MPs was 1.4 and 2.5 times that of the BHC solution. Accordingly, the MT-BHC MPs also exhibit the most consistent and long-lasting IOP-lowering effect. Ocular irritation experiments showed no significant toxicity of either. Taken together, MT MPs may have the potential for more effective glaucoma treatment.

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The increasing rates of antibiotic resistance in Helicobacter pylori (H. pylori) are a major concern of the decreasing eradication rate. Large-scale and long-period studies on antimicrobial susceptibility of H. pylori in children are limited. This study aimed to describe the temporal changes of antibiotic resistance among children in southeast China. Gastric biopsies obtained from children were cultured for H. pylori from 2015 to 2020. Susceptibility to clarithromycin (CLA), amoxicillin (AML), metronidazole (MTZ), furazolidone (FZD), tetracycline (TET) and levofloxacin (LEV) was tested. Data from 2012 to 2014 reported previously were obtained for comparing the change in temporal trends of antibiotic resistance. A total of 1638 (52.7%) H. pylori strains were isolated from 3111 children recruited. The resistance rates to CLA, MTZ and LEV were 32.8%, 81.7% and 22.8%, respectively. There were 52.9% strains resistant to single resistance, 28.7% to double resistance, and 9.0% to triple resistance. The total resistance rate and resistance rates to CLA, MTZ, LEV, CLA + LEV and CLA + MTZ + LEV increased annually in a linear manner. All resistant patterns except single resistance increased obviously from 2015 to 2017 and 2018 to 2020 compared to that from 2012 to 2014. Double resistance to CLA + MTZ increased significantly with age. The resistance rate to CLA and triple resistance to CLA, MTZ and LEV increased in children with prior H. pylori treatment than that from children without prior treatment. The antibiotic resistance rates of H. pylori were high in a large pediatric population in southeast China from 2015 to 2020. Individual treatment based on susceptibility test is imperative and optimal regimens should be chosen in H. pylori eradication therapy.

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Background: The clinical outcomes of breast cancer (BC) are unpredictable due to the high level of heterogeneity and complex immune status of the tumor microenvironment (TME). When set up, multiple long non-coding RNA (lncRNA) signatures tended to be employed to appraise the prognosis of BC. Nevertheless, predicting immunotherapy responses in BC is still essential. LncRNAs play pivotal roles in cancer development through diverse oncogenic signal pathways. Hence, we attempted to construct an oncogenic signal pathway-based lncRNA signature for forecasting prognosis and immunotherapy response by providing reliable signatures. Methods: We preliminarily retrieved RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database and extracted lncRNA profiles by matching them with GENCODE. Following this, Gene Set Variation Analysis (GSVA) was used to identify the lncRNAs closely associated with 10 oncogenic signaling pathways from the TCGA-BRCA (breast-invasive carcinoma) cohort and was further screened by the least absolute shrinkage and selection operator Cox regression model. Next, an lncRNA signature (OncoSig) was established through the expression level of the final 29 selected lncRNAs. To examine survival differences in the stratification described by the OncoSig, the Kaplan-Meier (KM) survival curve with the log-rank test was operated on four independent cohorts (n = 936). Subsequently, multiple Cox regression was used to investigate the independence of the OncoSig as a prognostic factor. With the concordance index (C-index), the time-dependent receiver operating characteristic was employed to assess the performance of the OncoSig compared to other publicly available lncRNA signatures for BC. In addition, biological differences between the high- and low-risk groups, as portrayed by the OncoSig, were analyzed on the basis of statistical tests. Immune cell infiltration was investigated using gene set enrichment analysis (GSEA) and deconvolution tools (including CIBERSORT and ESTIMATE). The combined effect of the Oncosig and immune checkpoint genes on prognosis and immunotherapy was elucidated through the KM survival curve. Ultimately, a pan-cancer analysis was conducted to attest to the prevalence of the OncoSig. Results: The OncoSig score stratified BC patients into high- and low-risk groups, where the latter manifested a significantly higher survival rate and immune cell infiltration when compared to the former. A multivariate analysis suggested that OncoSig is an independent prognosis predictor for BC patients. In addition, compared to the other four publicly available lncRNA signatures, OncoSig exhibited superior predictive performance (AUC = 0.787, mean C-index = 0.714). The analyses of the OncoSig and immune checkpoint genes clarified that a lower OncoSig score meant significantly longer survival and improved response to immunotherapy. In addition to BC, a high OncoSig score in several other cancers was negatively correlated with survival and immune cell infiltration. Conclusions: Our study established a trustworthy and discriminable prognostic signature for BC patients with similar clinical profiles, thus providing a new perspective in the evaluation of immunotherapy responses. More importantly, this finding can be generalized to be applicable to the vast majority of human cancers.

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Background: To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods: Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results: Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions: Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

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Vitamin D (VD) plays an important role in infectious and autoimmune diseases. We investigated the association between the serum VD levels and active human cytomegalovirus (HCMV) infections in pediatric systemic lupus erythematosus (SLE) patients. From January 2015 to June 2021, 120 children were diagnosed with SLE, and 100 healthy children were enrolled. The serum 25-hydroxyvitamin D [25(OH)D] levels were detected using ELISA. The serum anti-HCMV IgM antibodies were measured by a chemiluminescence immunoassay. Comparisons of the 25(OH)D levels between SLE patients and healthy children were performed, as well as subgroups of SLE patients with or without active HCMV infections. The serum 25(OH)D levels of SLE patients were significantly lower than those of healthy children (35.3 ± 12.9 vs. 49.3 ± 15.3, P < 0.001). The VD deficiency ratio was higher in SLE patients (89.2%) than that in healthy children (52.0%). The serum 25(OH)D levels in the positive anti-HCMV IgM group were significantly lower than those of the negative anti-HCMV IgM group (30.6 ± 12.3 vs. 38.2 ± 12.5, P < 0.001). A severe VD deficiency ratio was significantly higher in HCMV-IgM(+)-SLE patients (42.2%) than that in HCMV-IgM(-)-SLE patients (13.3%). This study suggested that the serum VD level is associated with active HCMV infections in pediatric SLE patients.

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Mechanical energy derived from friction is a kind of clean energy which is ubiquitous in nature. In this research, two-dimensional graphite carbon nitride (g-C3N4) is successfully applied to the conversion of nitrogen (N2) fixation through collecting the mechanical energy generated from the friction between a g-C3N4 catalyst and a stirring rod. At the stirring speed of 1000 r/min, the tribocatalytic ammonia radical (NH4+) generation rate of g-C3N4 can achieve 100.56 µmol·L-1·g-1·h-1 using methanol as a positive charge scavenger, which is 3.91 times higher than that without any scavengers. Meanwhile, ammonia is not generated without a catalyst or contact between the g-C3N4 catalyst and the stirring rod. The tribocatalytic effect originates from the friction between the g-C3N4 catalyst and the stirring rod which results in the charges transfer crossing the contact interface, then the positive and negative charges remain on the catalyst and the stirring rod respectively, which can further react with the substance dissolved in the reaction solution to achieve the conversion of N2 to ammonia. The effects of number and stirring speed of the rods on the performance of g-C3N4 tribocatalytic N2 fixation are further investigated. This excellent and efficient tribocatalysis can provide a potential avenue towards harvesting the mechanical energy in a natural environment.

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Using bipolar redox-active molecules (BRMs) as active materials is a practical way to address electrolyte crossover and resultant unpredictable side reactions in redox-flow batteries. However, the development of BRMs is greatly hindered by difficulties in finding new molecules from limited redox-active moieties and in achieving high cell voltage to compete with existing flow battery chemistries. This study proposes a strategy for design of high-voltage BRMs using fused conjugation that regulates the redox potential of integrated redox-active moieties. As a demonstration, quaternary N and ketone redox moieties are used to construct a new BRM that shows a prominent voltage gap with good electrochemical stability. A symmetrical redox-flow cell based on this molecule exhibits a high voltage of 2.5 V and decent cycling stability. This study provides a general strategy for designing new BRMs that may enrich the cell chemistries of organic redox-flow batteries.

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BACKGROUND: Human respiratory syncytial virus (HRSV) is the leading pathogens causing acute respiratory infections (ARI) in children under five years old. We aimed to investigate the distribution of HRSV subtypes and explore the relationship between viral subtypes and clinical symptoms and disease severity. METHODS: From November 2016 to April 2017, 541 children hospitalized because of ARI were included in the study. Throat swabs were collected for analysis and all samples were tested by multiplex one-step qRT-PCR for quantitative analysis and typing of HRSV. Patients' demographics, clinical symptoms as well as laboratory and imaging results were retrieved from medical records. RESULTS: HRSV was detected in 19.6% of children hospitalized due to ARI. HRSV-positive children were younger (P < 0.001), had a higher frequency of wheezing and pulmonary rales (P < 0.001; P = 0.003), and were more likely to develop bronchopneumonia (P < 0.001). Interleukin (IL) 10、CD4/CD8 (below normal range) and C-reactive protein levels between subtypes A and B groups were significantly different (P = 0.037; P = 0.029; P = 0.007), and gender differences were evident. By age-stratified analysis between subtypes A and B, we found significant differences in fever frequency and lymphocyte ratio (P = 0.008; P = 0.03) in the 6-12 months age group, while the 12. 1-36 months age group showed significant differences in fever days and count of leukocytes, platelets, levels aspartate aminotransferase, IL-6, lactate dehydrogenase and proportion CD4 positive T cells(P = 0.013; P = 0.018; P = 0.016; P = 0.037; P = 0.049; P = 0.025; P = 0.04). We also found a positive correlation between viral load and wheezing days in subtype A (P < 0.05), and a negative correlation between age, monocyte percentage and LDH concentration in subtype B (P < 0.05). CONCLUSIONS: HRSV is the main causative virus of bronchopneumonia in infants and children. The multiplex one-step qRT-PCR not only provides a rapid and effective diagnosis of HRSV infection, but also allows its typing. There were no significant differences in the severity of HRSV infection between subtypes A and B, except significant gender-specific and age-specific differences in some clinical characteristics and laboratory results. Knowing the viral load of HRSV infection can help understanding the clinical features of different subtypes of HRSV infection.

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OBJECTIVE: The present study aimed to assess the diagnostic significance of serum bone metabolic parameters in children with growing pains (GPs). METHODS: All patients diagnosed with GP and healthy controls matched with age and gender were recruited at the outpatient clinic of Children's Hospital at Zhejiang University School of Medicine from August 2016 to August 2021. In all subjects, serum levels of calcium (Ca), phosphorus (P), procollagen type-I N-terminal (PINP), parathormone (PTH), 25-hydroxyvitamin D (25-(OH)D), osteocalcin (OC), N-terminal cross-linked telopeptides of type-I collagen (CTX), and tartrate-resistant acid phosphatase type 5b (TRACP5b) were investigated. The univariate analysis, multivariate logistic regression analysis, and receiver operating characteristic (ROC) curve were used to identify the bone metabolic parameters factors for diagnosing GP. RESULTS: We enrolled 386 children with GP and 399 healthy controls in present study. The mean age of GP group was 5.319 years, and, primarily, the subjects were preschool-age children. The gender ratio (male-to-female) was 1.27 in GP group. After adjusting for age and gender, we identified that the serum levels of Ca (p < 0.001, OR: 25.039), P (p = 0.018, OR: 2.681), PINP (p < 0.001, OR: 1.002), and PTH (p = 0.036, OR: 0.988) were independent diagnostic factors associated with GP. Area under curve (AUC) of the ROC curves was in the order: PINP (0.612) > Ca (0.599) > P (0.583) > PTH (0.541). A combination of independent diagnostic factors and multivariable logistic regression analysis provided a refined logistic regression model to improve the diagnostic potential, of which the AUC had reached 0.655. CONCLUSIONS: Serum levels of Ca, P, PINP, and PTH could be independent diagnostic factors associated with GP. The logistic model was significantly superior to bone metabolic parameters for diagnosing GP.

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The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.

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