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  • 1
    In: British Journal of Anaesthesia, Elsevier BV, Vol. 120, No. 1 ( 2018-01), p. 146-155
    Type of Medium: Online Resource
    ISSN: 0007-0912
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Aging Clinical and Experimental Research Vol. 35, No. 3 ( 2023-01-04), p. 639-647
    In: Aging Clinical and Experimental Research, Springer Science and Business Media LLC, Vol. 35, No. 3 ( 2023-01-04), p. 639-647
    Abstract: Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688–0.768) with the sensitivity of 66.2% (95% CI 58.2–73.6) and specificity of 66.8% (95% CI 64.6–68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545–0.737), and sensitivity and specificity were 34.2% (95% CI 19.6–51.4) and 88.8% (95% CI 85.6–91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681–0.844) with the sensitivity of 63.2% (95% CI 46–78.2) and specificity of 80.5% (95% CI 76.6–84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
    Type of Medium: Online Resource
    ISSN: 1720-8319
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
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  • 3
    In: British Journal of Surgery, Oxford University Press (OUP), Vol. 106, No. 2 ( 2019-01-08), p. e73-e80
    Abstract: The Clavien–Dindo classification is perhaps the most widely used approach for reporting postoperative complications in clinical trials. This system classifies complication severity by the treatment provided. However, it is unclear whether the Clavien–Dindo system can be used internationally in studies across differing healthcare systems in high- (HICs) and low- and middle-income countries (LMICs). Methods This was a secondary analysis of the International Surgical Outcomes Study (ISOS), a prospective observational cohort study of elective surgery in adults. Data collection occurred over a 7-day period. Severity of complications was graded using Clavien–Dindo and the simpler ISOS grading (mild, moderate or severe, based on guided investigator judgement). Severity grading was compared using the intraclass correlation coefficient (ICC). Data are presented as frequencies and ICC values (with 95 per cent c.i.). The analysis was stratified by income status of the country, comparing HICs with LMICs. Results A total of 44 814 patients were recruited from 474 hospitals in 27 countries (19 HICs and 8 LMICs). Some 7508 patients (16·8 per cent) experienced at least one postoperative complication, equivalent to 11 664 complications in total. Using the ISOS classification, 5504 of 11 664 complications (47·2 per cent) were graded as mild, 4244 (36·4 per cent) as moderate and 1916 (16·4 per cent) as severe. Using Clavien–Dindo, 6781 of 11 664 complications (58·1 per cent) were graded as I or II, 1740 (14·9 per cent) as III, 2408 (20·6 per cent) as IV and 735 (6·3 per cent) as V. Agreement between classification systems was poor overall (ICC 0·41, 95 per cent c.i. 0·20 to 0·55), and in LMICs (ICC 0·23, 0·05 to 0·38) and HICs (ICC 0·46, 0·25 to 0·59). Conclusion Caution is recommended when using a treatment approach to grade complications in global surgery studies, as this may introduce bias unintentionally.
    Type of Medium: Online Resource
    ISSN: 0007-1323 , 1365-2168
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
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  • 4
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2023-09-14)
    Abstract: Axolotl ( Ambystoma mexicanum ) is an excellent model for investigating regeneration, the interaction between regenerative and developmental processes, comparative genomics, and evolution. The brain, which serves as the material basis of consciousness, learning, memory, and behavior, is the most complex and advanced organ in axolotl. The modulation of transcription factors is a crucial aspect in determining the function of diverse regions within the brain. There is, however, no comprehensive understanding of the gene regulatory network of axolotl brain regions. Here, we utilized single-cell ATAC sequencing to generate the chromatin accessibility landscapes of 81,199 cells from the olfactory bulb, telencephalon, diencephalon and mesencephalon, hypothalamus and pituitary, and the rhombencephalon. Based on these data, we identified key transcription factors specific to distinct cell types and compared cell type functions across brain regions. Our results provide a foundation for comprehensive analysis of gene regulatory programs, which are valuable for future studies of axolotl brain development, regeneration, and evolution, as well as on the mechanisms underlying cell-type diversity in vertebrate brains.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
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  • 5
    In: Ecological Indicators, Elsevier BV, Vol. 144 ( 2022-11), p. 109441-
    Type of Medium: Online Resource
    ISSN: 1470-160X
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 6
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 377, No. 6610 ( 2022-09-02)
    Abstract: Brain regeneration requires the coordination of complex responses in a time- and region-specific manner. Identifying the cell types and molecules involved in this process would advance our understanding of brain regeneration and provide potential targets for regenerative medicine research. However, progress in this field has been hampered by the limited regeneration capacity of the mammalian brain and an incomplete mechanistic understanding of the regeneration process at both the cellular and molecular levels. Axolotls ( Ambystoma mexicanum ) can regenerate damaged appendages and multiple internal organs, including the brain. Therefore, axolotls may serve as a model for studying brain regeneration. RATIONALE If we are to understand the mechanism of brain regeneration, we need research tools that can achieve large-scale data acquisition and analyses to simultaneously decode complex cellular and molecular responses. It also seemed to us that a comparison between brain regeneration and developmental processes would help to provide new insights into the nature of brain regeneration. Accordingly, we removed a small portion of the lateral pallium region of the axolotl left telencephalon and collected tissue samples at multiple stages during regeneration. In parallel, we collected tissue samples of the axolotl telencephalon at multiple developmental stages. We then used high-definition and large-field Stereo-seq (spatial enhanced resolution omics sequencing) technology to generate spatial transcriptomic data from sections that covered both hemispheres of the axolotl telencephalon at single-cell resolution. Analyses of cell type annotation, cell spatial organization, gene activity dynamics, and cell state transition were performed for a mechanistic investigation of injury-induced regeneration compared to these cell attributes during development. RESULTS With the use of Stereo-seq, we generated a group of spatial transcriptomic data of telencephalon sections that covered six developmental and seven injury-induced regenerative stages. The data at single-cell resolution enabled us to identify 33 cell types present during development and 28 cell types involved in regeneration, including different types of excitatory and inhibitory neurons, and several ependymoglial cell subtypes. For development, our data revealed a primitive type of ependymoglial cells that may give rise to three subgroups of adult ependymoglial cells localized in separate areas of the ventricular zone, with different molecular features and potentially different functions. For regeneration, we discovered a subpopulation of ependymoglial cells that may originate from local resident ependymoglial cells activated by injury. This population of progenitor cells may then proliferate to cover the wound area and subsequently replenish lost neurons through a state transition to intermediate progenitors, immature neurons, and eventually mature neurons. When comparing cellular and molecular dynamics of the axolotl telencephalon between development and regeneration, we found that injury-induced ependymoglial cells were similar to developmental-specific ependymoglial cells in terms of their transcriptome state. We also observed that regeneration of the axolotl telencephalon exhibited neurogenesis patterns similar to those seen in development in molecular cascades and the potential cell lineage transition, which suggests that brain regeneration partially recapitulates the development process. CONCLUSION Our spatial transcriptomic data highlight the cellular and molecular features of the axolotl telencephalon during development and injury-induced regeneration. Further characterization of the activation and functional regulation of ependymoglial cells may yield insights for improving the regenerative capability of mammalian brains. Our single-cell spatial transcriptome of the axolotl telencephalon, a tetrapod vertebrate, also provides data useful for further research in developmental, regenerative, and evolutionary brain biology. All data are accessible in an interactive database ( https://db.cngb.org/stomics/artista ). Development and regeneration of axolotl telencephalon. The spatially resolved single-cell transcriptome of the adult axolotl telencephalon as determined by Stereo-seq analyses (left). Upon brain injury in the highlighted lateral pallium region of the left hemisphere, a neural progenitor subpopulation at the wound site was rapidly induced and subsequently replenished lost neurons (bottom right) through a process that partially resembles neurogenesis during development (top right). CREDIT: YUNZHI YANG, BGI
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    RVK:
    RVK:
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2022
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  • 7
    In: Journal of Controlled Release, Elsevier BV, Vol. 332 ( 2021-04), p. 225-232
    Type of Medium: Online Resource
    ISSN: 0168-3659
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 1482453-X
    SSG: 15,3
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