Search

A nanoflower-like bimetallic FeP/CoP-1-350 heterostructure electrocatalyst with rich interfaces shows excellent OER electrocatalytic activity.

Intraventricular hemorrhage (IVH) is one of the most fatal types of intracerebral hemorrhage (ICH), especially when the third and the fourth ventricles are involved. The use of external ventricular drainage is limited for evacuation of hemorrhage in the lateral ventricles. Endoscopic surgery can provide visualized evacuation of the hemorrhage in the lateral and third ventricles. However, it is usually challenging to access the fourth ventricle using a routine endoscopic approach.We have reported 3 cases of severe IVH with cast fourth ventricles treated using an endoscopic-assisted trans-lateral ventricular transchoroidal fissure trans-aqueductal approach.The average preoperative Graeb score was 11, and the average IVH volume was 75.12 mL. The IVH evacuation rate was 97.5%-100%. The average Glasgow coma scale score had increased to 12 at discharge from 6.6 at admission. At 3 months after surgery, the average modified Rankin scale score was 3. No cerebrospinal fluid shunt had been required and no surgery-related complication had occurred in any patient.Our results have shown that the endoscopic-assisted trans-lateral ventricular transchoroidal fissure trans-aqueductal approach is a feasible and safe endoscopic option that can achieve one-off complete removal of clots in all 4 ventricles in patients with severe IVH.

A novel N-rimmed PAH molecule containing a dipleiadiene core (

We report the convenient synthesis of a benzobis(imidazolium)-embedded conjugated polyelectrolyte pBBI by a Cu-catalyzed direct C‒H arylation of a cationic benzobis(imidazolium) monomer with a diiodide comonomer. pBBI shows weak fluorescence in solution due to rotation of the repeat units in the conjugated backbone, and enhanced fluorescence when electrostatically interacting with a variety of anions to form aggregates. Specially, pBBI responds to the bisulfite anion with intensified unique deep-blue fluorescence easily discriminated by naked eye.

Guocan Yu,1,* Linhua Wang,2 Yanqin Shen,1 Likui Fang,1 Jun Yang,1 Bo Ye,1 Kan Xu,1 Fangming Zhong1,* 1Zhejiang Tuberculosis Diagnosis and Treatment Center, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Hospital Infection, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guocan Yu; Fangming Zhong, Zhejiang Tuberculosis Diagnosis and Treatment Center, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China, Email dabaitwo@163.com; zhongfangm@126.comPurpose: We evaluated CapitalBio Mycobacterium RT-PCR assay diagnosing tuberculous pericarditis (TBP), performed a head-to-head comparison with Xpert MTB/RIF, and assessed the impact of a parallel test (positive result for either of these two tests).Methods: We reviewed suspected TBP patients with Xpert MTB/RIF, CapitalBio Mycobacterium RT-PCR assay, and Mycobacterium tuberculosis (MTB) culture. We analyzed sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC).Results: Seventy-four patients were included. Overall sensitivity, specificity, PPV, NPV, and AUC of CapitalBio Mycobacterium RT-PCR assay compared with culture were 50%, 91.1%, 64.3%, 85%, and 0.71, respectively. Overall sensitivity, specificity, PPV, NPV, and AUC of Xpert MTB/RIF for TBP were 61.1%, 91.1%, 68.8%, 87.9%, and 0.76. Parallel test values were 72.2%, 91.1%, 72.2%, 91.1%, and 0.82. The diagnostic accuracy of Xpert MTB/RIF was higher than CapitalBio Mycobacterium RT-PCR assay but was not significant (P > 0.05). The parallel test could improve diagnostic accuracy, but it was not significant compared to single tests (P > 0.05).Conclusion: CapitalBio Mycobacterium RT-PCR assay had a moderate diagnostic accuracy, similar to Xpert MTB/RIF. The parallel test maximized diagnostic efficacy, but differences were not significant. CapitalBio Mycobacterium RT-PCR assay and Xpert MTB/RIF for TBP could be an initial option for early diagnosis.Keywords: tuberculosis pericarditis, polymerase chain reaction, Xpert MTB/RIF, CapitalBio, accuracy

SummaryIn the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations.Availability and ImplementationThe Python package SEAGAL can be installed using pip:https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at:https://github.com/linhuawang/SEAGAL.Contactlinhuaw@bcm.edu

A series of cationic π-extended imidazolium salts were synthesized by a sequential Cu-catalyzed arylation/annulation and photocyclization strategy in a simple but efficient way. Among them, a nine-fused-ring compound with a doubly aza[5]helical geometry is by far the largest cationic polycyclic heteroaromatic with a central imidazolium core.

MotivationWith the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity.ResultsHere, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurementL. The graph-based correlation method assigns each cell a localLindex, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson’s r in ruling out confounding regulatory relationships that are not of research interests.Availabilityhttps://github.com/ChaozhongLiu/scGREAT

Sequencing-based spatial transcriptomics (ST) techniques have been groundbreaking in dissecting cell-cell communications within tissues by profiling positional gene expression. However, the most widely used ST technique, Visium Spatial Gene Expression by 10x Genomics (Visium), does not provide single-cell resolution, making it difficult to profile cell type-level information. Many reference-based deconvolution methods have been developed to increase its resolution, but the platform and batch effects between the reference and ST data compromise their accuracy. Here, we propose a new approach,Region-based cellSorting (ReSort), that generates a pseudo-internal-reference to reduce these platform effects. By simulating ST datasets under various scenarios, we demonstrate that ReSort significantly improves the accuracy of six state-of-the-art reference-based deconvolution methods. Moreover, applying ReSort to a mouse breast cancer tumor bearing both epithelial and mesenchymal clones identifies the spatial differences of immune cells between the clones, providing important insights for understanding the relationship between epithelial-mesenchymal transition and immune infiltration in breast cancer.

With the growing availability of Alzheimer’s disease (AD) transcriptomic data, several studies have nominated new therapeutic targets. However, a major challenge is accounting for latent (hidden) factors which affect the discovery of therapeutic targets. Using unsupervised machine learning, we identified a latent factor in brain tissue, and we validated the factor in AD and normal samples, across multiple studies, and different brain tissues. Moreover, significant metabolic differences were observed due to the latent factor. The latent factor was found to reflect cell-type heterogeneity in the brain and after adjusting for it, we were able to identify new biological pathways. The changes observed at both transcriptomic and metabolomic levels support the importance of identifying any latent factors before pursuing downstream analysis to accurately identify biomarkers.

Osteoarthritis (OA) is a common degenerative disease with few treatments. In traditional Chinese medicine (TCM), Radix Angelicae biseratae (RAB) is commonly used to treat OA. Nodakenin (Nod) is one main coumarin active component in RAB and exhibits anti-inflammatory, anti-oxidative, and anti-apoptotic effects. Reactive oxygen species (ROS) produced by mitochondria play a vital role in the pathogenesis of OA. We hypothesized that Nod might ameliorate cartilage degradation and inflammatory responses by regulating the mitochondrial Drp1/ROS/NLRP3 axis. With this, the effects of Nod on a mouse model of knee OA and activated primary chondrocytes were assessed. The results showed that Nod intervention improved bone volume, lowered trabecular separation, and increased trabecular number in the subchondral bone. Nod decreased the Osteoarthritis Research Society International (OARSI) scores and increased collagen II-positive areas in the articular cartilage of the tibial plateau. Compared with OA mice, Nod-treated animals exhibited lower levels of inflammatory factors in the serum and synovitis of the knee joint. In vitro results indicated that Nod suppressed dynamin-related protein 1 (Drp1) phosphorylation and massive ROS production by Drp1-dependent mitochondrial fission in lipopolysaccharide-stimulated chondrocytes. Moreover, Nod inhibited the mRNA levels of inflammatory cytokines (COX 2, IL-1β, and TNF-α), nod-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, and matrix metalloproteinase 13 expression in activated chondrocytes. In conclusion, Nod attenuates cartilage degradation and inflammatory responses in mice with OA by regulating the mitochondrial Drp1/ROS/NLRP3 axis, suggesting its potential for OA therapy.

Motivation Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. Availability and implementation Code and data are available at https://github.com/GauravPandeyLab/ensemble_integration. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Background Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. Results By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. Conclusions MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

A Ni(ii)-catalyzed C–H hydroarylation of diarylacetylenes with imidazolium salts without adding any ligand was developed. It provides an easy and efficient access to (E)-2-(1,2-diarylvinyl)imidazolium salts.

Inflammatory pathways represented by TLR4/NF-κB (Toll-like receptor 4/Nuclear factor-κB) axis signaling are activated in the pathogenesis of endotoxin-induced myocardial dysfunction (EIMD). However, the underlying mechanism by which NF-κB coordinates with other transcriptional coactivators/corepressors to regulate the expression of proinflammatory cytokine genes remains unclear. We established an EIMD-mouse model by intraperitoneal injection of lipopolysaccharides (LPS), and we discovered that NCOA1 (nuclear receptor coactivator 1) assembled with CBP (CREB binding protein) and NF-κB subunits to form a transcriptional complex that specifically bound to promoters of proinflammatory cytokine genes to activate their expression. LPS treatment also inhibited DNMT1 (DNA methyltransferase 1) expression, thereby decreasing DNA methylation of a CpG island located on the promoter of NCOA1 and causing NCOA1 overexpression. Screening small molecules that abolished NCOA1-CBP interaction in a yeast system identified a compound PSSM2126 that effectively blocked the NCOA1-CBP interaction in vitro and in vivo. Administration of PSSM2126 to EIMD mice significantly alleviated the inflammation response and improved cardiac function. Collectively, our results reveal that an NCOA1-dependent transactivation mechanism can regulate proinflammatory cytokine expression, thereby improving our understanding of the activation of NF-κB targets. The promising inhibition of the NCOA1-CBP interaction by PSSM2126 may provide a new therapeutic option for EIMD.