Biology subjects

Kuang, L.

Publications and source records attributed to Kuang, L..

4 recordsLinked to original sources

bioRxiv2024

Global Scientific Trends in Organoids from 2004 to 2023: A Data Bibliometric and Visualized Analysis

To conduct a bibliometric analysis of organoids to describe international research trends and visualize current research directions. This cross-sectional bibliometric analysis examined the development of organoid research from 2004 to 2023. The current study used VOS-viewer to assess and analyze 13,174 documents. Literature data were collected on a specific date (Feb 19, 2024) and downloaded in plain text from Web of Science Core Collection. In this paper, 13,174 organoid papers were retrieved from Web of Science Core Collection. There were only 114 organoid studies in 2004, and from 2015 onward, the number of annual publications on this topic began to proliferate, reaching 10,023 from 2019 to 2023, accounting for as much as 76.1% of the total number of published papers. The United States proudly leads the way in both the volume of articles published and the number of citations garnered, standing tall as the undisputed frontrunner. Among the illustrious institutions, Harvard University and the University of Washington are among the most prolific. Hans Clevers has worked with 121 prolific authors and has the most publications. With the use of organoids in cancer modeling, drug screening, and regenerative medicine, organoid technology has attracted much attention in medicine, and the significant increase in the number of published papers and citations signifies the expanding influence and global collaboration in the field of organoid research. This study contributes to our understanding of current trends and potential future advances in the field of organoid research by identifying five distinct clusters in the field.

cancer biology↗

bioRxiv2023

The Complexity of Functional Connectivity Profiles of the Subgenual Anterior Cingulate Cortex and Dorsal Lateral Prefrontal Cortex in Major Depressive Disorder: a DIRECT Consortium Study

BackgroundThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD), and its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. MethodsLeveraging a large multi-site cross-sectional sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC-FC. Then, we explored the potential impact of such group-level abnormality profiles on the TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to localize TMS targets individually. ResultsWe found an enhanced sgACC-DLPFC FC in MDD patients compared to healthy controls (HC). Such group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. In two independent clinical samples, we showed that the magnitude of TMS targets case-control differences in sgACC FC was related to clinical improvement. The MDD big data-guided individualized TMS targeting algorithm may generate individualized TMS targets that are clinically superior to group-level targets. InterpretationWe reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. FundingMinistry of Science and Technology of the Peoples Republic of China, National Natural Science Foundation of China, and Chinese Academy of Sciences

neuroscience↗

bioRxiv2022

cir-DNA sequencing revealed the landscape of extrachromosomal circular DNA in articular cartilage and the potential roles in osteoarthritis

ObjectiveExtrachromosomal circular DNA (eccDNA) has been shown to be involved in several physiological and pathological processes including immunity, inflammation, aging and tumor. However, the expression of eccDNA in cartilage has been not reported until now. In this study, we aimed to investigate the landscape of eccDNA in articular cartilage and analyze the potential roles in Osteoarthritis (OA). MethodsThe samples of articular cartilage were obtained from total knee arthroplasty (TKA) donors with OA. The mtDNAs (mitochondrial DNA) and the linear DNAs from chondrocytes of articular cartilage were removed. Then the eccDNAs were enriched for cir-DNA sequencing. After quality control evaluation, we systematically revealed the identified eccDNA data including size distribution, the size range and sequence pattern. Moreover, we explored and disscussed the potential roles of eccDNA in OA via motif analysis and GO/KEGG pathways analysis. ResultsThe chondrocytes from OA cartilage contained an abundance of eccDNAs, which was termed as OC-eccDNAs (OA Cartilage-derived eccDNA). The characteristics of OC-eccDNAs were tissue specific, including the distribution, the size range and sequence pattern. Moreover, the functional analysis indicated that eccDNA may be involved in the homeostasis maintenance of chondrocytes and participated in the process of OA. ConclutionsOur data firstly showed the landscape of eccDNA in articular cartilage, and preliminarily indicated the potential roles of eccDNA in OA.

molecular biology↗

bioRxiv2018

Reduced but not Enhanced Default Mode Network Functional Connectivity in Major Depressive Disorder: Evidence from 25 Cohorts in the REST-meta-MDD Project

Major Depressive Disorder (MDD) is common and disabling, but its neural pathophysiology remains unclear. Functional brain network studies in MDD have largely had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. The 25 research groups in China composing the REST-meta-MDD Project contributed R-fMRI data of 1,300 patients with MDD and 1,128 normal controls (NCs). The data were preprocessed locally with a standardized protocol prior to aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to show increased FC in MDD. We found decreased instead of increased DMN FC when comparing 848 MDDs with 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naive MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDDs. Exploratory analyses also revealed alterations of local intrinsic activity in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates treatment response. This manuscript announces the publicly available resting-state fMRI indices of the REST-meta-MDD consortium shared via the R-fMRI Maps Project.\n\nSIGNIFICANCE STATEMENTFunctional connectivity within the default mode network in major depressive disorder patients has been frequently reported abnormal but with contradicting directions in previous small sample size studies. By creating the REST-meta-MDD consortium containing neuroimaging data of 1,300 depressed patients and 1,128 normal controls from 25 research groups in China, we found decreased default mode network functional connectivity in depressed patients, driven by patients with recurrent depression, and associated with current medication treatment but not with disease duration. These findings suggest that default mode network functional connectivity remains a prime target for understanding the pathophysiology of depression, with particular relevance to revealing mechanisms of effective treatments.

neuroscience↗