Metalated covalent organic frameworks as efficient catalysts for multicomponent tandem reactions.

Multicomponent tandem reactions have become indispensable synthetic methods due to their economic advantages and efficient usage in natural products and drug synthesis. The emergence of metalated covalent organic frameworks (MCOFs) has opened up new opportunities for the advancement of multicomponent tandem reactions. In contrast to commonly used homogeneous transition metal catalysts, MCOFs possess regular porosity, high crystallinity, and rich metal chelation sites that facilitate the uniform distribution and anchoring of metals within their cavities. Thus, they show extremely high activity and have recently been widely employed as catalysts for multicomponent tandem reactions. It is timely to conduct a review of MCOFs in multicomponent tandem reactions, in order to offer guidance and assistance for the synthesis of MCOF catalysts and their application in multicomponent tandem reactions. This review provides a comprehensive overview of the design and synthesis of MCOFs, their application and progress in multicomponent tandem reactions, and the primary challenges encountered during their current development with the aim of contributing to the promotion of the field.

Early change of retinal nerve fiber layer in children with type 1 diabetes mellitus in northern China.

OBJECTIVES: This study aimed to identify discrepancies in the retinal nerve fiber layer (RNFL) between type 1 diabetes mellitus (T1DM) children without retinopathy and healthy subjects in northern China. METHODS: This was a cross-sectional hospital-based study carried out from Jan 2019 until Jul 2021 at the department of pediatrics in Tianjin medical university general hospital. Children with T1DM but no retinal disease were screened. RNFL thickness was obtained via spectral domain optical coherence tomography. Disease duration, HbA1c, 25-hydroxyvitamin D level, insulin regimen, and diet control status were also collected. RESULTS: A total of 20 children with T1DM and 20 matched health participants were enrolled. The mean age in the T1DM group was 10.3 ± 2.8 years, and the median duration of diabetes was 1 (range 1-3) year. Children with T1DM had thinner average RNFL than control subjects (105 ± 6 vs. 110 ± 11 µm, p=0.008), especially in temporal and nasal parts. There was a significant negative association between HbA1c levels and the RNFL thickness in the T1DM group (B (95 % confidence interval): -4.313 (-7.055 to -1.571); p=0.005). CONCLUSIONS: In our study, the decreased thickness of RNFL was negatively associated with elevated HbA1c in children with early stages of T1DM.

Causal relationship between immune cells and telomere length: mendelian randomization analysis.

BACKGROUND: The causal relationship between immune cells and telomere length remains controversial. METHODS: Data on the immune cells were obtained from a previous study with 3,757 participants. Data on telomere length were obtained from the OpenGWAS database. Genome-Wide Association Study (GWAS) data were obtained and screened for eligible instrumental variables (IVs) using the TwoSampleMR package and the Phenoscanner database. To investigate the genetic causality between immune cells and telomere length, Mendelian randomization (MR) analysis and Bayesian weighted Mendelian randomization (BWMR) analysis were used. RESULTS: MR analysis showed that there is indeed a genetic causal relationship between immune cells and telomere length. A total of 16 immune cells were successfully validated. A positive correlation was found between telomere length and immune cells such as CD28 + CD45RA + CD8br %CD8br (OR = 1.002, 95%CI: 1.000-1.003). A negative correlation was found between telomere length and immune cells such as Transitional AC (OR = 0.991, 95%CI: 0.984-0.997) (P < 0.05). Reverse MR analysis similarly confirmed that telomere length can affect four types of immune cells, including CD25 on IgD + CD24- (OR = 1.291, 95%CI: 1.060-1.571), at the genetic level. CONCLUSION: There is indeed a mutual genetic causality between immune cells and telomere length, which will provide theoretical basis and support for more subsequent clinical studies.

Construction of Multifunctional Covalent Organic Frameworks for Photocatalysis.

Covalent organic frameworks (COFs) have recently drawn intense attention due to their potential applications in photocatalysis. Herein, we report a multifunctional COF which consists of triphenylamine (TPA) and 2,2'-bipyridine (2, 2'-bipy) entities. The obtained TAPA-BPy-COF is a heterogeneous photocatalyst and can efficiently catalyze the oxidative coupling of thiols to disulfides. In addition, TAPA-BPy-COF can be further metalated by Pd(II) via 2,2'-bipy-metal coordination. The generated Pd@TAPA-BPy-COF can highly promote photocatalytic synthesis of 3-cyanopyridines via cascade addition/cyclization of arylboronic acids with γ-ketodinitriles in heterogeneous way. This work has demonstrated the way for the rational design and preparation of more efficient photoactive COFs for photocatalysis.

Cuproptosis-Related Genes MTF1 and LIPT1 as Novel Prognostic Biomarker in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a life-threatening hematologic malignant disease with high morbidity and mortality in both adults and children. Cuproptosis, a novel mode of cell death, plays an important role in tumor development, but the functional mechanisms of cuproptosis-related genes (CRGs) in AML are unclear. The differential expression of CRGs between tumors such as AML and normal tissues in UCSC XENA, TCGA and GTEx was verified using R (version: 3.6.3). Lasso regression, Cox regression and Nomogram were used to screen for prognostic biomarkers of AML and to construct corresponding prognostic models. Kaplan-Meier analysis, ROC analysis, clinical correlation analysis, immune infiltration analysis and enrichment analysis were used to further investigate the correlation and functional mechanisms of CRGs with AML. The ceRNA regulatory network was used to identify the mRNA-miRNA-lncRNA regulatory axis. Cuproptosis-related genes LIPT1, MTF1, GLS and CDKN2A were highly expressed in AML, while FDX1, LIAS, DLD, DLAT, PDHA1, SLC31A1 and ATP7B were lowly expressed in AML. Lasso regression, Cox regression, Nomogram and calibration curve finally identified MTF1 and LIPT1 as two novel prognostic biomarkers of AML and constructed the corresponding prognostic models. In addition, all 12 CRGs had predictive power for AML, with MTF1, LIAS, SLC31A1 and CDKN2A showing more reliable results. Further analysis showed that ATP7B was closely associated with mutation types such as FLT3, NPM1, RAS and IDH1 R140 in AML, while the expression of MTF1, LIAS and ATP7B in AML was closely associated with immune infiltration. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) revealed that biological functions such as metal ion transmembrane transporter activity, haptoglobin binding and oxygen carrier activity, pathways such as interferon alpha response, coagulation, UV response DN, apoptosis, hypoxia and heme metabolism all play a role in the development of AML. The ceRNA regulatory network revealed that 6 lncRNAs such as MALAT1, interfere with MTF1 expression through 6 miRNAs such as hsa-miR-32-5p, which in turn affect the development and progression of AML. In addition, APTO-253 has the potential to become an AML-targeted drug. The cuproptosis-related genes MTF1 and LIPT1 can be used as prognostic biomarkers in AML. A total of six lncRNAs, including MALAT1, are involved in the expression and regulation of MTF1 in AML through six miRNAs such as hsa-miR-32-5p.

Integrative analysis of immune-related signature profiles in eosinophilic chronic rhinosinusitis with nasal polyposis.

Eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP) is a subtype of chronic rhinosinusitis (CRS) that is associated with the nasal cavity and sinus polyps, elevated levels of eosinophils, and dysregulated immune responses to environmental triggers. The underlying cause of ECRSwNP is not well understood, and few studies have focused on the unique features of this subtype of CRS. Our study integrated proteomic and transcriptomic data with multi-omic bioinformatics analyses. We collected nasal polyps from three ECRSwNP patients and three control patients and identified 360 differentially expressed (DE) proteins, including 119 upregulated and 241 downregulated proteins. Functional analyses revealed several significant associations with ECRSwNP, including focal adhesion, hypertrophic cardiomyopathy, and extracellular matrix (ECM)-receptor interactions. Additionally, a protein-protein interaction (PPI) network revealed seven hub proteins that may play crucial roles in the development of ECRSwNP. We also compared the proteomic data with publicly available transcriptomic data and identified a total of 1077 DE genes. Pathways enriched by the DE genes involved angiogenesis, positive regulation of cell motility, and immune responses. Furthermore, we investigated immune cell infiltration and identified biomarkers associated with eosinophil and M2 macrophage infiltration using CIBERSORT and Weighted Gene Correlation Network Analysis (WGCNA). Our results provide a more complete picture of the immune-related mechanisms underlying ECRSwNP, which could contribute to the development of more precise treatment strategies for this condition.

Mendelian randomization analysis to analyze the genetic causality between different levels of obesity and different allergic diseases.

BACKGROUND: The causal relationship between obesity and different allergic diseases remains controversial. METHODS: The Two Sample MR package and Phenoscanner database were used to obtain and filter Genome-Wide Association Study (GWAS) data from the Open GWAS database. Mendelian randomization (MR) analysis was used to study the causal relationship between different levels of obesity and different allergic diseases. The data sets related to obesity and asthma were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by the limma package. Cluster Profiler and GO plot packages were used for enrichment analysis to verify the results of MR analysis. RESULTS: Two-sample MR analysis showed a causal relationship between obesity and childhood allergy (age < 16), allergic asthma and atopic dermatitis (P < 0.05). In addition, there was also a causal relationship between allergic asthma and obesity (P < 0.05), while there was no genetic causal relationship between obesity and allergic rhinitis, eczema, lactose intolerance and so on (P > 0.05). Subgroup analysis revealed a causal relationship between both class 1 and class 2 obesity and childhood allergy (age < 16) (P < 0.05). Obesity class 1 was associated with allergic asthma, while obesity class 3 was associated with atopic dermatitis (P < 0.05). Bioinformatics analysis shows that there were common DEGs between obesity and allergic asthma. CONCLUSION: Obesity is a risk factor for childhood allergy (age < 16), allergic asthma and atopic dermatitis, while allergic asthma is also a risk factor for obesity. Class 1 and class 2 obesity are both causally associated with childhood allergy (age < 16). In addition, there is a causal relationship between milder obesity and allergic asthma, while heavier obesity is causally related to atopic dermatitis.

R-Mixup: Riemannian Mixup for Biological Networks.

Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-MIXUP, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-MIXUP leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the swelling effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R-MIXUP with five real-world biological network datasets on both regression and classification tasks. Besides, we derive a commonly ignored necessary condition for identifying the SPD matrices of biological networks and empirically study its influence on the model performance. The code implementation can be found in Appendix E.

Hashimoto's thyroiditis, vitiligo, anemia, pituitary hyperplasia, and lupus nephritis-A case report of autoimmune polyglandular syndrome type III C + D and literature review.

Objective: This study aims to summarize the clinical characteristics of one teenager with autoimmune polyglandular syndrome (APS) type III C + D to improve the understanding of APS III C + D and its effect of thyroid function. Methods: This article reported the clinical manifestations, laboratory examinations, treatment methods, and outcomes of an adolescent with anemia admitted to the Pediatrics Department of Tianjin Medical University General Hospital in July 2020 and reviewed the literature. Results: A girl, aged 13 years and 1 month, was admitted to the hospital due to anemia for more than 4 years and episodic abdominal pain for 1 week. Four years ago, the girl went to a local hospital for "vitiligo", and a routine blood test revealed anemia. The lowest hemoglobin (HGB) was 61 g/L, and the blood test revealed iron deficiency anemia. She had no menstrual cramps for 2 months. Urine routine showed protein 3+∼4+ and 258 red blood cells (RBCs)/high-power field. Urine protein was 3,380 mg/24 h. Free thyroxine was low, thyroid-stimulating hormone was >100 uIU/ml, thyroid peroxidase antibody was >1,000 IU/ml, and thyroglobulin antibody and thyrotropin receptor antibody were negative. Pituitary magnetic resonance imaging showed a mass in the sellar region with a uniform signal and a maximum height of about 15.8 mm. The result of the antinuclear antibody was 1:80 homogeneous type, and anti-dsDNA and anticardiolipin antibodies IgA and IgM were slightly higher. Thyroxine and iron were given for 1 month, menstruation resumed, and urine protein and RBC count decreased. After 5 months of treatment, free thyroid function, HGB, RBCs in urine, and pituitary returned to normal. Later, a renal biopsy showed changes in focal proliferative glomerulonephritis, and the girl was diagnosed with lupus glomerulonephritis type III. After 3 days of shock therapy with methylprednisolone, prednisone, mycophenolate mofetil, and other treatments were administrated for 1 year. At the time of writing, urine protein was 280 mg/24 h. Conclusion: Co-occurrence of Hashimoto's thyroiditis, vitiligo, anemia, pituitary hyperplasia, and lupus nephritis is rare. It is very important to pay attention to the screening of thyroid function.

IGF2BP3 Regulates TMA7-mediated Autophagy and Cisplatin Resistance in Laryngeal Cancer via m6A RNA Methylation.

Translation machinery associated 7 homolog (TMA7) is closely related to proliferation-related diseases. However, the function and regulatory mechanism of TMA7 in laryngeal squamous cell carcinoma (LSCC) remain unclear. The present study aimed to investigate the effect of TMA7 on the occurrence and development of LSCC and to study the mechanism of TMA7. TMA7 is upregulated in LSCC tissues and associated with poor prognosis. After TMA7 downregulation, the autophagy level was increased, and the proliferation, migration, and invasion of LSCC cells were inhibited. The m6A methylated reader IGF2BP3 enhanced the stability of TMA7 and reduced the level of autophagy. TMA7 interacted directly with UBA2. Furthermore, the activation of the IGF2BP3-regulated TMA7-UBA2-PI3K pathway is the primary mechanism by which TMA7 inhibits autophagy and promotes the progression of LSCC. The current study revealed that IGF2BP3-mediated TMA7 m6A modification promotes LSCC progression and cisplatin-resistance through UBA2-PI3K pathway, providing new insights into the autophagy-related mechanism, potential biomarkers, and therapeutic targets for LSCC.

BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks.

Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

DEEP DAG LEARNING OF EFFECTIVE BRAIN CONNECTIVITY FOR FMRI ANALYSIS.

Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopted for fMRI analysis with superior performance. Unfortunately, traditional functional brain networks are mainly constructed based on similarities among region of interests (ROIs), which are noisy and can lead to inferior results for GNN models. To better adapt GNNs for fMRI analysis, we propose DABNet, a Deep DAG learning framework based on Brain Networks for fMRI analysis. DABNet adopts a brain network generator module, which harnesses the DAG learning approach to transform the raw time-series into effective brain connectivities. Experiments on two fMRI datasets demonstrate the efficacy of DABNet. The generated brain networks also highlight the prediction-related brain regions and thus provide interpretations for predictions.

Neighborhood-Regularized Self-Training for Learning with Few Labels.

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. We further stabilize self-training via aggregating the predictions from different rounds during sample selection. Experiments on eight tasks show that our proposed method outperforms the strongest self-training baseline with 1.83% and 2.51% performance gain for text and graph datasets on average. Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8% and saves 57.3% of the time when compared with the best baseline. Our code and appendices will be uploaded to https://github.com/ritaranx/NeST.

GDF-10 Induces an Inhibitory Effect on Epithelial-Mesenchymal Transition of Laryngeal Cancer via LPR4.

BACKGROUND: Growth differentiation factor-10 (GDF-10), a member of the TGF-ß superfamily, plays a crucial role in cell proliferation and differentiation. In some tumors, GDF-10 can act as a tumor suppressor to inhibit tumor progression, but its role in posterior squamous cell carcinoma has not been reported yet. METHODS: The aim of this study was to investigate the effect of GDF-10 on the epithelial-mesenchymal transition of laryngeal squamous cell carcinoma, and to provide new ideas for future targets in the treatment of laryngeal squamous carcinoma. RESULTS: The effect of GDF-10 on tumor growth was detected; bioinformatics analysis was performed to predict the downstream targets of GDF-10, and RT-PCR and western blot were performed to detect the expression levels of target genes and proteins, respectively. CONCLUSION: Our findings support that GDF-10 can inhibit the proliferation, migration, and invasion, and promote apoptosis of laryngeal carcinoma AMC-HN-8 cells. GDF-10 inhibits the EMT of laryngeal carcinoma through LRP4 and thus inhibits the progression of laryngeal carcinoma.

R-Mixup: Riemannian Mixup for Biological Networks.

Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-Mixup, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-Mixup leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the swelling effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R-Mixup with five real-world biological network datasets on both regression and classification tasks. Besides, we derive a commonly ignored necessary condition for identifying the SPD matrices of biological networks and empirically study its influence on the model performance. The code implementation can be found in Appendix E.

Human ZBP1 induces cell death-independent inflammatory signaling via RIPK3 and RIPK1.

ZBP1 is an interferon-induced cytosolic nucleic acid sensor that facilitates antiviral responses via RIPK3. Although ZBP1-mediated programmed cell death is widely described, whether and how it promotes inflammatory signaling is unclear. Here, we report a ZBP1-induced inflammatory signaling pathway mediated by K63- and M1-linked ubiquitin chains, which depends on RIPK1 and RIPK3 as scaffolds independently of cell death. In human HT29 cells, ZBP1 associated with RIPK1 and RIPK3 as well as ubiquitin ligases cIAP1 and LUBAC. ZBP1-induced K63- and M1-linked ubiquitination of RIPK1 and ZBP1 to promote TAK1- and IKK-mediated inflammatory signaling and cytokine production. Inhibition of caspase activity suppressed ZBP1-induced cell death but enhanced cytokine production in a RIPK1- and RIPK3 kinase activity-dependent manner. Lastly, we provide evidence that ZBP1 signaling contributes to SARS-CoV-2-induced cytokine production. Taken together, we describe a ZBP1-RIPK3-RIPK1-mediated inflammatory signaling pathway relayed by the scaffolding role of RIPKs and regulated by caspases, which may induce inflammation when ZBP1 is activated below the threshold needed to trigger a cell death response.

Neuroprotection of Bone Marrow-Derived Mesenchymal Stem Cell-Derived Extracellular Vesicle-Enclosed miR-410 Correlates with HDAC4 Knockdown in Hypoxic-Ischemic Brain Damage.

Evidence exists reporting that miR-410 may rescue neurological deficits, neuronal injury, and neuronal apoptosis after experimental hypoxic ischemia. This study aimed to explore the mechanism by which miR-410 transferred by bone marrow-derived mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) may alleviate hypoxic-ischemic brain damage (HIBD) in newborn mice. BMSCs were isolated from total bone marrow cells of femur and tibia of newborn mice, and primary neurons were extracted from the cerebral cortex of newborn mice within 24 h of birth. EVs were extracted from BMSCs transfected with the mimic or inhibitor of miR-410. Primary neurons were subjected to hypoxia and treated with overexpression (oe)-HDAC4, small interfering RNA (siRNA)-ß-catenin, or Wnt pathway inhibitor and/or EV (miR-410 mimic) or EV (miR-410 inhibitor). A neonatal mouse HIBD model was established and treated with EVs. When BMSC-EVs were endocytosed by primary neurons, miR-410 was upregulated, neuronal viability was elevated, and apoptosis was inhibited. miR-410 in BMSC-EVs targeted HDAC4, thus increasing neuronal viability and reducing apoptosis. Conversely, overexpression of HDAC4 activated the Wnt pathway and enhanced the nuclear translocation of ß-catenin. Treatment with miR-410-containing BMSC-EVs improved learning and memory abilities of HIBD mice while attenuating apoptosis by inactivating the Wnt pathway via targeting HDAC4. Taken together, the findings suggest that miR-410 delivered by BMSC-EVs alleviates HIBD by inhibiting HDAC4-dependent Wnt pathway activation.

Synthesis of Metal-Free Chiral Covalent Organic Framework for Visible-Light-Mediated Enantioselective Photooxidation in Water.

Although chiral covalent organic frameworks (CCOFs) presence grows in thermal asymmetric catalysis, their application in equally important asymmetric photocatalysis has yet to begin. Herein, we first report a propargylamine-linked and quaternary ammonium bromide decorated porphyrin-CCOF which can highly promote visible-light-driven enantioselective photooxidation of sulfides to sulfoxides in water and in air. This methodology has also been applied to the synthesis of (R)-modafinil, a wakefulness-promoting medication used for the treatment of excessive sleepiness. This research might open a new way for the application of CCOFs in asymmetric photocatalysis.

Recent research on inherited metabolic diseases in children. / å„¿ç«¥é—ä¼ ä»£è°¢ç— çš„ç ”ç©¶è¿›å±•.

With the improvement in the research level and the diagnosis and treatment technology of inherited metabolic diseases (IMD), the research on pediatric IMD in China has made great progress, but there is still some distance from the international level. Due to the vast territory of China and the uneven distribution of medical resources, the regional characteristics of IMD remain unclear in China, and there are many problems and difficulties in early diagnosis and treatment. Therefore, it is necessary to improve the understanding of pediatric IMD among pediatricians, so as to improve the diagnosis and treatment level, achieve an early identification, diagnosis, and treatment of pediatric IMD, and effectively reduce the fatality and disability rates of children with IMD. This article reviews the research progress of IMD in children in China, and analyzes the features of representative IMDs. Citation:Chinese Journal of Contemporary Pediatrics, 2022, 24(3): 326-331.