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  1. Book ; Online ; E-Book: Increase your brainability

    Alessi, Charles / Chambers, Larry W. / Gray, J. A. Muir

    and reduce your risk of dementia

    2021  

    Abstract: Based on research from the Optimal Ageing Programme and full of practical, evidence-based advice on managing the major risk factors underpinning dementia, this book will inspire readers to adopt simple but effective lifestyle changes that anyone can make ...

    Author's details Charles Alessi, Larry W. Chambers, Muir Gray
    Abstract Based on research from the Optimal Ageing Programme and full of practical, evidence-based advice on managing the major risk factors underpinning dementia, this book will inspire readers to adopt simple but effective lifestyle changes that anyone can make and to take positive action to increase their brainability and live better for longer.
    Keywords Dementia/Prevention
    Subject code 616.83
    Language English
    Size 1 online resource (155 pages)
    Publisher Oxford University Press
    Publishing place Oxford, England
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 0-19-260417-1 ; 0-19-189251-3 ; 0-19-260416-3 ; 0-19-886034-X ; 978-0-19-260417-0 ; 978-0-19-189251-6 ; 978-0-19-260416-3 ; 978-0-19-886034-1
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Discovery of Carbohydrate Sulfotransferase Inhibitors from a Kinase-Directed Library We thank Sharon Long and Dave Keating for providing both the NodH sulfotransferase and APS Kinase during our preliminary experiments and Jack Kirsch for numerous helpful conversations. J.I.A. and K.G.B were supported by NIH Molecular Biophysics Training Grant (No. T32GM0895). This research was funded by grants to C.R.B. from the Pew Scholars Program, the W. M. Keck Foundation and the American Cancer Society (Grant No. RPG9700501BE).

    Armstrong / Portley / Chang / Nierengarten / Cook / Bowman / Bishop / Gray / Shokat / Schultz / Bertozzi

    Angewandte Chemie (International ed. in English)

    2000  Volume 39, Issue 7, Page(s) 1303–1306

    Language English
    Publishing date 2000-04
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2011836-3
    ISSN 1521-3773 ; 1433-7851
    ISSN (online) 1521-3773
    ISSN 1433-7851
    DOI 10.1002/(sici)1521-3773(20000403)39:7<1303::aid-anie1303>3.0.co;2-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia Risk.

    Eberhard, Braden W / Gray, Kathryn J / Bates, David W / Kovacheva, Vesela P

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: Objective: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, ... ...

    Abstract Objective: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, this assumption is rarely true for most diseases, as the underlying factors have complex, non-linear, and time-varying relationships. This concern is especially relevant for pregnancy, where the risk for pregnancy-related complications, such as preeclampsia, varies across gestation. Recently, deep learning survival models have shown promise in addressing the limitations of classical models, as the novel models allow for non-proportional risk handling, capturing nonlinear relationships, and navigating complex temporal dynamics.
    Methods: We present a methodology to model the temporal risk of preeclampsia during pregnancy and investigate the associated clinical risk factors. We utilized a retrospective dataset including 66,425 pregnant individuals who delivered in two tertiary care centers from 2015-2023. We modeled the preeclampsia risk by modifying DeepHit, a deep survival model, which leverages neural network architecture to capture time-varying relationships between covariates in pregnancy. We applied time series k-means clustering to DeepHit's normalized output and investigated interpretability using Shapley values.
    Results: We demonstrate that DeepHit can effectively handle high-dimensional data and evolving risk hazards over time with performance similar to the Cox Proportional Hazards model, achieving an area under the curve (AUC) of 0.78 for both models. The deep survival model outperformed traditional methodology by identifying time-varied risk trajectories for preeclampsia, providing insights for early and individualized intervention. K-means clustering resulted in patients delineating into low-risk, early-onset, and late-onset preeclampsia groups- notably, each of those has distinct risk factors.
    Conclusion: This work demonstrates a novel application of deep survival analysis in time-varying prediction of preeclampsia risk. Our results highlight the advantage of deep survival models compared to Cox Proportional Hazards models in providing personalized risk trajectory and demonstrating the potential of deep survival models to generate interpretable and meaningful clinical applications in medicine.
    Language English
    Publishing date 2024-01-19
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.01.18.24301456
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Food and agribusiness in 2030

    Gray, Allan W. / Rodrigues, Jonny Mateus / Kalaki, Rafael Bordonal / Neves, Marcos Fava

    a roadmap

    2020  

    Author's details Marcos Fava Neves (coordinator) ; Allan W. Gray, Falvio Runkhe Valerio, Leticia Franco Martinez, Jonny Mateus Rodrigues, Rafael Bordonal Kalaki, Vitor Nardini Marques, Vinícius Cambaúva
    Language English
    Size 1 Online-Ressource (124 Seiten)
    Publisher Wageningen Academic Publishers
    Publishing place Wageningen
    Publishing country Netherlands
    Document type Book ; Online
    HBZ-ID HT020668850
    ISBN 978-90-8686-907-7 ; 9789086863549 ; 90-8686-907-6 ; 908686354X
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  5. Article: Proceedings of the Oregon State Dental Society.

    Cardwell, J R / Gray, G W

    The Dental register

    2021  Volume 30, Issue 1, Page(s) 35–38

    Language English
    Publishing date 2021-03-10
    Publishing country United States
    Document type Journal Article
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Factors associated with conversion from day-case to in-patient elective inguinal hernia repair surgery across England: an observational study using administrative data.

    Joyner, J / Ayyaz, F M / Cheetham, M / Briggs, T W R / Gray, W K

    Hernia : the journal of hernias and abdominal wall surgery

    2024  Volume 28, Issue 2, Page(s) 555–565

    Abstract: Purpose: Elective primary inguinal hernia repair surgery is increasingly being conducted as a day-case procedure. However, some patients planned for day-case surgery have to stay in hospital for at least one night. The aim of this study was to identify ... ...

    Abstract Purpose: Elective primary inguinal hernia repair surgery is increasingly being conducted as a day-case procedure. However, some patients planned for day-case surgery have to stay in hospital for at least one night. The aim of this study was to identify the factors associated with conversion from day-case to in-patient management for elective inguinal hernia repair surgery.
    Methods: This was an exploratory retrospective analysis of observational data from the Hospital Episode Statistics dataset for England. All patients aged ≥ 17 years undergoing a first elective inguinal hernia repair between 1st April 2014 and 31st March 2022 that was planned as day-case surgery were identified. The exposure of interest was discharged on the day of admission (day-case) or requiring overnight stay. The primary outcome of interest was 30-day emergency readmission with an overnight stay. For reporting, providers were aggregated to an Integrated Care Board (ICB) level.
    Results: A total of 351,528 planned day-case elective primary inguinal hernia repairs were identified over the eight-year study period. Of these, 45,305 (12.9%) stayed in hospital for at least one night and were classed as day-case to in-patient stay conversions. Patients who converted to in-patient stay were older, had more comorbidities, and were more likely to have bilateral surgery and be operated on by a low-annual volume surgeon. Post-procedural complications were strongly associated with conversion. Across the 42 ICBs in England, model-adjusted conversion rates varied from 3.3% to 21.3%.
    Conclusions: There was considerable variation in conversion to in-patient stay rates for inguinal hernia repair across ICBs in England. Our findings should help surgical teams to better identify patients suitable for day-case inguinal hernia repair and plan discharge services more effectively. This should help to reduce the variation in conversion rates.
    MeSH term(s) Humans ; Hernia, Inguinal/surgery ; Retrospective Studies ; Herniorrhaphy/methods ; Elective Surgical Procedures ; England
    Language English
    Publishing date 2024-02-12
    Publishing country France
    Document type Observational Study ; Journal Article
    ZDB-ID 1388125-5
    ISSN 1248-9204 ; 1265-4906
    ISSN (online) 1248-9204
    ISSN 1265-4906
    DOI 10.1007/s10029-023-02949-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Jg. 1995 - 2021: Lesesall (2.OG)
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  7. Article ; Online: Structure-based neural network protein-carbohydrate interaction predictions at the residue level.

    Canner, Samuel W / Shanker, Sudhanshu / Gray, Jeffrey J

    Frontiers in bioinformatics

    2023  Volume 3, Page(s) 1186531

    Abstract: Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few ... ...

    Abstract Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.
    Language English
    Publishing date 2023-06-20
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2023.1186531
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  8. Article: Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level.

    Canner, Samuel W / Shanker, Sudhanshu / Gray, Jeffrey J

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few ... ...

    Abstract Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.
    Language English
    Publishing date 2023-03-15
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.03.14.531382
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: IgLM: Infilling language modeling for antibody sequence design.

    Shuai, Richard W / Ruffolo, Jeffrey A / Gray, Jeffrey J

    Cell systems

    2023  Volume 14, Issue 11, Page(s) 979–989.e4

    Abstract: Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, ... ...

    Abstract Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries. Compared with prior methods that leverage unidirectional context for sequence generation, IgLM formulates antibody design based on text-infilling in natural language, allowing it to re-design variable-length spans within antibody sequences using bidirectional context. We trained IgLM on 558 million (M) antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species of origin. We demonstrate that IgLM can generate full-length antibody sequences from a variety of species and its infilling formulation allows it to generate infilled complementarity-determining region (CDR) loop libraries with improved in silico developability profiles. A record of this paper's transparent peer review process is included in the supplemental information.
    MeSH term(s) Peptide Library ; Amino Acid Sequence ; Complementarity Determining Regions/genetics ; Antibodies, Monoclonal
    Chemical Substances Peptide Library ; Complementarity Determining Regions ; Antibodies, Monoclonal
    Language English
    Publishing date 2023-10-30
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2854138-8
    ISSN 2405-4720 ; 2405-4712
    ISSN (online) 2405-4720
    ISSN 2405-4712
    DOI 10.1016/j.cels.2023.10.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Hybrid Thrombectomy and Central Extracorporeal Membrane Oxygenation for Massive Pulmonary Embolism in a Child.

    Hampton Gray, W / Sorabella, Robert A / Law, Mark / Padilla, Luz A / Byrnes, Jonathan W / Dabal, Robert J / Clark, Matthew G

    World journal for pediatric & congenital heart surgery

    2024  , Page(s) 21501351231221430

    Abstract: We describe a hybrid thrombectomy and central extracorporeal membrane oxygenation for a child in cardiogenic shock due to a massive pulmonary embolism. ...

    Abstract We describe a hybrid thrombectomy and central extracorporeal membrane oxygenation for a child in cardiogenic shock due to a massive pulmonary embolism.
    Language English
    Publishing date 2024-01-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2550261-X
    ISSN 2150-136X ; 2150-1351
    ISSN (online) 2150-136X
    ISSN 2150-1351
    DOI 10.1177/21501351231221430
    Database MEDical Literature Analysis and Retrieval System OnLINE

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