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26 results on '"Calvert, P"'

1. Recommendations to promote equity, diversity and inclusion in decentralized clinical trials

Author

Aiyegbusi, Olalekan Lee, Cruz Rivera, Samantha, Kamudoni, Paul, Anderson, Nicola, Collis, Philip, Denniston, Alastair K., Harding, Rosie, Hughes, Sarah E., Khunti, Kamlesh, Kotecha, Dipak, Krumholz, Harlan, Liu, Xiaoxuan, McMullan, Christel, Molony-Oates, Barbara, Monteiro, Joao, Myles, Puja, Rantell, Khadija Rerhou, Soltys, Katherine, Verdi, Ravinder, Wilson, Roger, and Calvert, Melanie J.

Published
2024
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2. Recommendations to address respondent burden associated with patient-reported outcome assessment

Author

Aiyegbusi, Olalekan Lee, Cruz Rivera, Samantha, Roydhouse, Jessica, Kamudoni, Paul, Alder, Yvonne, Anderson, Nicola, Baldwin, Robert Mitchell, Bhatnagar, Vishal, Black, Jennifer, Bottomley, Andrew, Brundage, Michael, Cella, David, Collis, Philip, Davies, Elin-Haf, Denniston, Alastair K., Efficace, Fabio, Gardner, Adrian, Gnanasakthy, Ari, Golub, Robert M., Hughes, Sarah E., Jeyes, Flic, Kern, Scottie, King-Kallimanis, Bellinda L., Martin, Antony, McMullan, Christel, Mercieca-Bebber, Rebecca, Monteiro, Joao, Peipert, John Devin, Quijano-Campos, Juan Carlos, Quinten, Chantal, Rantell, Khadija Rerhou, Regnault, Antoine, Sasseville, Maxime, Schougaard, Liv Marit Valen, Sherafat-Kazemzadeh, Roya, Snyder, Claire, Stover, Angela M., Verdi, Rav, Wilson, Roger, and Calvert, Melanie J.

Published
2024
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7. The value of standards for health datasets in artificial intelligence-based applications

Author

Arora, Anmol, Alderman, Joseph E., Palmer, Joanne, Ganapathi, Shaswath, Laws, Elinor, McCradden, Melissa D., Oakden-Rayner, Lauren, Pfohl, Stephen R., Ghassemi, Marzyeh, McKay, Francis, Treanor, Darren, Rostamzadeh, Negar, Mateen, Bilal, Gath, Jacqui, Adebajo, Adewole O., Kuku, Stephanie, Matin, Rubeta, Heller, Katherine, Sapey, Elizabeth, Sebire, Neil J., Cole-Lewis, Heather, Calvert, Melanie, Denniston, Alastair, and Liu, Xiaoxuan

Published
2023
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8. Considerations for patient and public involvement and engagement in health research

Author

Aiyegbusi, Olalekan Lee, McMullan, Christel, Hughes, Sarah E., Turner, Grace M., Subramanian, Anuradhaa, Hotham, Richard, Davies, Elin Haf, Frost, Chris, Alder, Yvonne, Agyen, Lisa, Buckland, Lewis, Camaradou, Jennifer, Chong, Amy, Jeyes, Felicity, Kumar, Sumita, Matthews, Karen L., Moore, Patricia, Ormerod, Jane, Price, Gary, Saint-Cricq, Michael, Stanton, David, Walker, Anita, Haroon, Shamil, Denniston, Alastair K., and Calvert, Melanie J.

Published
2023
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9. Transcriptional analysis of cystic fibrosis airways at single-cell resolution reveals altered epithelial cell states and composition

Author

Carraro, Gianni, Langerman, Justin, Sabri, Shan, Lorenzana, Zareeb, Purkayastha, Arunima, Zhang, Guangzhu, Konda, Bindu, Aros, Cody J, Calvert, Ben A, Szymaniak, Aleks, Wilson, Emily, Mulligan, Michael, Bhatt, Priyanka, Lu, Junjie, Vijayaraj, Preethi, Yao, Changfu, Shia, David W, Lund, Andrew J, Israely, Edo, Rickabaugh, Tammy M, Ernst, Jason, Mense, Martin, Randell, Scott H, Vladar, Eszter K, Ryan, Amy L, Plath, Kathrin, Mahoney, John E, Stripp, Barry R, and Gomperts, Brigitte N

Subjects
Medical Physiology, Biomedical and Clinical Sciences, Rare Diseases, Orphan Drug, Genetics, Cystic Fibrosis, Lung, 2.1 Biological and endogenous factors, Respiratory, Congenital, Good Health and Well Being, Cell Differentiation, Cilia, Cystic Fibrosis Transmembrane Conductance Regulator, Epithelial Cells, Gene Expression Profiling, Humans, Respiratory Mucosa, Single-Cell Analysis, Transcriptome, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Cystic fibrosis (CF) is a lethal autosomal recessive disorder that afflicts more than 70,000 people. People with CF experience multi-organ dysfunction resulting from aberrant electrolyte transport across polarized epithelia due to mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. CF-related lung disease is by far the most important determinant of morbidity and mortality. Here we report results from a multi-institute consortium in which single-cell transcriptomics were applied to define disease-related changes by comparing the proximal airway of CF donors (n = 19) undergoing transplantation for end-stage lung disease with that of previously healthy lung donors (n = 19). Disease-dependent differences observed include an overabundance of epithelial cells transitioning to specialized ciliated and secretory cell subsets coupled with an unexpected decrease in cycling basal cells. Our study yields a molecular atlas of the proximal airway epithelium that will provide insights for the development of new targeted therapies for CF airway disease.

Published
2021

10. Patient and public involvement within epidemiological studies of long COVID in the UK

Author

Routen, Ash, O’Mahoney, Lauren, Aiyegbusi, Olalekan Lee, Alder, Yvonne, Banerjee, Amitava, Buckland, Lewis, Brightling, Chris, Calvert, Melanie, Camaradou, Jenny, Chaturvedi, Nishi, Chong, Amy, Dalrymple, Emma, Eggo, Rosalind M., Elliott, Paul, Evans, Rachael A., Gibson, Andy, Haroon, Shamil, Herrett, Emily, Houchen-Wolloff, Linzy, Hughes, Sarah E., Jeyes, Flic, Matthews, Karen, McMullan, Christel, Morley, Jessica, Shafran, Roz, Smith, Nikki, Stanton, David, Stephenson, Terence, Sterne, Jonathan, Turner, Grace M., Ward, Helen, and Khunti, Kamlesh

Published
2023
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12. Symptoms and risk factors for long COVID in non-hospitalized adults

Author

Subramanian, Anuradhaa, Nirantharakumar, Krishnarajah, Hughes, Sarah, Myles, Puja, Williams, Tim, Gokhale, Krishna M., Taverner, Tom, Chandan, Joht Singh, Brown, Kirsty, Simms-Williams, Nikita, Shah, Anoop D., Singh, Megha, Kidy, Farah, Okoth, Kelvin, Hotham, Richard, Bashir, Nasir, Cockburn, Neil, Lee, Siang Ing, Turner, Grace M., Gkoutos, Georgios V., Aiyegbusi, Olalekan Lee, McMullan, Christel, Denniston, Alastair K., Sapey, Elizabeth, Lord, Janet M., Wraith, David C., Leggett, Edward, Iles, Clare, Marshall, Tom, Price, Malcolm J., Marwaha, Steven, Davies, Elin Haf, Jackson, Louise J., Matthews, Karen L., Camaradou, Jenny, Calvert, Melanie, and Haroon, Shamil

Published
2022
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13. Patient reported outcome assessment must be inclusive and equitable

Author

Calvert, Melanie J., Cruz Rivera, Samantha, Retzer, Ameeta, Hughes, Sarah E., Campbell, Lisa, Molony-Oates, Barbara, Aiyegbusi, Olalekan Lee, Stover, Angela M., Wilson, Roger, McMullan, Christel, Anderson, Nicola E., Turner, Grace M., Davies, Elin Haf, Verdi, Rav, Velikova, Galina, Kamudoni, Paul, Muslim, Syed, Gheorghe, Adrian, O’Connor, Daniel, Liu, Xiaoxuan, Wu, Albert W., and Denniston, Alastair K.

Published
2022
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14. SARS-CoV-2 infection and COVID-19 vaccination rates in pregnant women in Scotland

Author

Stock, Sarah J., Carruthers, Jade, Calvert, Clara, Denny, Cheryl, Donaghy, Jack, Goulding, Anna, Hopcroft, Lisa E. M., Hopkins, Leanne, McLaughlin, Terry, Pan, Jiafeng, Shi, Ting, Taylor, Bob, Agrawal, Utkarsh, Auyeung, Bonnie, Katikireddi, Srinivasa Vittal, McCowan, Colin, Murray, Josie, Simpson, Colin R., Robertson, Chris, Vasileiou, Eleftheria, Sheikh, Aziz, and Wood, Rachael

Published
2022
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15. Understanding and tracking the impact of long COVID in the United Kingdom

Author

Routen, Ash, O’Mahoney, Lauren, Ayoubkhani, Daniel, Banerjee, Amitava, Brightling, Chris, Calvert, Melanie, Chaturvedi, Nishi, Diamond, Ian, Eggo, Rosalind, Elliott, Paul, Evans, Rachael A., Haroon, Shamil, Herret, Emily, O’Hara, Margaret E., Shafran, Roz, Stanborough, Julie, Stephenson, Terence, Sterne, Jonathan, Ward, Helen, and Khunti, Kamlesh

Published
2022
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17. Tackling bias in AI health datasets through the STANDING Together initiative

Author

Ganapathi, Shaswath, Palmer, Jo, Alderman, Joseph E., Calvert, Melanie, Espinoza, Cyrus, Gath, Jacqui, Ghassemi, Marzyeh, Heller, Katherine, Mckay, Francis, Karthikesalingam, Alan, Kuku, Stephanie, Mackintosh, Maxine, Manohar, Sinduja, Mateen, Bilal A., Matin, Rubeta, McCradden, Melissa, Oakden-Rayner, Lauren, Ordish, Johan, Pearson, Russell, Pfohl, Stephen R., Rostamzadeh, Negar, Sapey, Elizabeth, Sebire, Neil, Sounderajah, Viknesh, Summers, Charlotte, Treanor, Darren, Denniston, Alastair K., and Liu, Xiaoxuan

Published
2022
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19. Author Correction: SARS-CoV-2 infection and COVID-19 vaccination rates in pregnant women in Scotland

Author

Stock, Sarah J., Carruthers, Jade, Calvert, Clara, Denny, Cheryl, Donaghy, Jack, Goulding, Anna, Hopcroft, Lisa E. M., Hopkins, Leanne, McLaughlin, Terry, Pan, Jiafeng, Shi, Ting, Taylor, Bob, Agrawal, Utkarsh, Auyeung, Bonnie, Katikireddi, Srinivasa Vittal, McCowan, Colin, Murray, Josie, Simpson, Colin R., Robertson, Chris, Vasileiou, Eleftheria, Sheikh, Aziz, and Wood, Rachael

Published
2022
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22. Publisher Correction: Neuromodulation of lumbosacral spinal networks enables independent stepping after complete paraplegia

Author

Gill, Megan L., Grahn, Peter J., Calvert, Jonathan S., Linde, Margaux B., Lavrov, Igor A., Strommen, Jeffrey A., Beck, Lisa A., Sayenko, Dimitry G., Van Straaten, Meegan G., Drubach, Dina I., Veith, Daniel D., Thoreson, Andrew R., Lopez, Cesar, Gerasimenko, Yury P., Edgerton, V. Reggie, Lee, Kendall H., and Zhao, Kristin D.

Published
2018
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23. Understanding and tracking the impact of long COVID in the United Kingdom

Author

Routen, Ash, O’Mahoney, Lauren, Ayoubkhani, Daniel, Banerjee, Amitava, Brightling, Chris, Calvert, Melanie, Chaturvedi, Nishi, Diamond, Ian, Eggo, Rosalind, Elliott, Paul, Evans, Rachael A., Haroon, Shamil, Herret, Emily, O’Hara, Margaret E., Shafran, Roz, Stanborough, Julie, Stephenson, Terence, Sterne, Jonathan, Ward, Helen, and Khunti, Kamlesh

Published
2021
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24. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Author

Liu, Xiaoxuan, Cruz Rivera, Samantha, Moher, David, Calvert, Melanie J., and Denniston, Alastair K.

Abstract

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

Published
2020
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25. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Author

Cruz Rivera, Samantha, Liu, Xiaoxuan, Chan, An-Wen, Denniston, Alastair K., and Calvert, Melanie J.

Abstract

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

Published
2020
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26. Alternatively spliced mdm2 transcripts with loss of p53 binding domain sequences: Transforming ability and frequent detection in human cancer

Author

Sigalas, Iakovos, Calvert, A. Hilary, Anderson, John J., Neal, David E., and Lunec, John

Abstract

The mdm2 oncogene encodes a 90–kilodalton nuclear phosphoprotein that binds and inactivates the p53 tumor suppressor protein. Here we report the observation of five alternatively spliced mdm2 gene transcripts in a range of human cancers and their absence in normal tissues. Transfection of NIH 3T3 cells with each of these forms gave foci of morphologically transformed cells. A higher frequency of splice variants lacking p53 binding domain sequences was found in late–stage and high–grade ovarian and bladder carcinomas. Four of the splice variants show loss of p53 binding, consistent with partial deletion of sequences encoding the p53 binding domain, but retain carboxy–terminal zinc–finger domains. These observations suggest a reassessment of the transforming mechanisms of mdm2 and its relation to p53.

Published
1996
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