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Clustering of adherence to personalised dietary recommendations and changes in healthy eating index within the Food4Me study - CORRIGENDUM.

Public health nutrition

Livingstone KM, Celis-Morales C, Lara J, Woolhead C, O'Donovan CB, Forster H, Marsaux CF, Macready AL, Fallaize R, Navas-Carretero S, San-Cristobal R, Kolossa S, Tsirigoti L, Lambrinou CP, Moschonis G, Surwiłło A, Drevon CA, Manios Y, Traczyk I, Gibney ER, Brennan L, Walsh MC, Lovegrove JA, Martinez JA, Saris WH, Daniel H, Gibney M, Mathers JC.
PMID: 31187728
Public Health Nutr. 2019 Aug;22(11):2141-2146. doi: 10.1017/S1368980019001502.

No abstract available.

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Livingstone KM, Celis-Morales C, Lara J, et al. Clustering of adherence to personalised dietary recommendations and changes in healthy eating index within the Food4Me study - CORRIGENDUM. Public Health Nutr. 2019;22(11):2141-2146doi: 10.1017/S1368980019001502.
Livingstone, K. M., Celis-Morales, C., Lara, J., Woolhead, C., O'Donovan, C. B., Forster, H., Marsaux, C. F., Macready, A. L., Fallaize, R., Navas-Carretero, S., San-Cristobal, R., Kolossa, S., Tsirigoti, L., Lambrinou, C. P., Moschonis, G., Surwiłło, A., Drevon, C. A., Manios, Y., Traczyk, I., Gibney, E. R., Brennan, L., Walsh, M. C., Lovegrove, J. A., Martinez, J. A., Saris, W. H., Daniel, H., Gibney, M., & Mathers, J. C. (2019). Clustering of adherence to personalised dietary recommendations and changes in healthy eating index within the Food4Me study - CORRIGENDUM. Public health nutrition, 22(11), 2141-2146. https://doi.org/10.1017/S1368980019001502
Livingstone, Katherine M, et al. "Clustering of adherence to personalised dietary recommendations and changes in healthy eating index within the Food4Me study - CORRIGENDUM." Public health nutrition vol. 22,11 (2019): 2141-2146. doi: https://doi.org/10.1017/S1368980019001502
Livingstone KM, Celis-Morales C, Lara J, Woolhead C, O'Donovan CB, Forster H, Marsaux CF, Macready AL, Fallaize R, Navas-Carretero S, San-Cristobal R, Kolossa S, Tsirigoti L, Lambrinou CP, Moschonis G, Surwiłło A, Drevon CA, Manios Y, Traczyk I, Gibney ER, Brennan L, Walsh MC, Lovegrove JA, Martinez JA, Saris WH, Daniel H, Gibney M, Mathers JC. Clustering of adherence to personalised dietary recommendations and changes in healthy eating index within the Food4Me study - CORRIGENDUM. Public Health Nutr. 2019 Aug;22(11):2141-2146. doi: 10.1017/S1368980019001502. PMID: 31187728.
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GENetic and clinical predictors of treatment response in depression: the GenPod randomised trial protocol.

Trials

Thomas L, Mulligan J, Mason V, Tallon D, Wiles N, Cowen P, Nutt D, O'Donovan M, Sharp D, Peters T, Lewis G.
PMID: 18498636
Trials. 2008 May 22;9:29. doi: 10.1186/1745-6215-9-29.

BACKGROUND: The most effective pharmacological treatments for depression inhibit the transporters that reuptake serotonin (Selective Serotonin Reuptake Inhibitors - SSRIs) and noradrenaline (Noradrenaline Reuptake Inhibitors - NaRIs) into the presynaptic terminal. There is evidence to suggest that noradrenaline and...

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Thomas L, Mulligan J, Mason V, et al. GENetic and clinical predictors of treatment response in depression: the GenPod randomised trial protocol. Trials. 2008;9:29doi: 10.1186/1745-6215-9-29.
Thomas, L., Mulligan, J., Mason, V., Tallon, D., Wiles, N., Cowen, P., Nutt, D., O'Donovan, M., Sharp, D., Peters, T., & Lewis, G. (2008). GENetic and clinical predictors of treatment response in depression: the GenPod randomised trial protocol. Trials, 929. https://doi.org/10.1186/1745-6215-9-29
Thomas, Laura, et al. "GENetic and clinical predictors of treatment response in depression: the GenPod randomised trial protocol." Trials vol. 9 (2008): 29. doi: https://doi.org/10.1186/1745-6215-9-29
Thomas L, Mulligan J, Mason V, Tallon D, Wiles N, Cowen P, Nutt D, O'Donovan M, Sharp D, Peters T, Lewis G. GENetic and clinical predictors of treatment response in depression: the GenPod randomised trial protocol. Trials. 2008 May 22;9:29. doi: 10.1186/1745-6215-9-29. PMID: 18498636; PMCID: PMC2426669.
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The active kinome: The modern view of how active protein kinase networks fit in biological research.

Current opinion in pharmacology

Alganem K, Hamoud AR, Creeden JF, Henkel ND, Imami AS, Joyce AW, Ryan V WG, Rethman JB, Shukla R, O'Donovan SM, Meller J, McCullumsmith R.
PMID: 34968947
Curr Opin Pharmacol. 2021 Dec 27;62:117-129. doi: 10.1016/j.coph.2021.11.007. Epub 2021 Dec 27.

Biological regulatory networks are dynamic, intertwined, and complex systems making them challenging to study. While quantitative measurements of transcripts and proteins are key to investigate the state of a biological system, they do not inform the "active" state of...

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Alganem K, Hamoud AR, Creeden JF, et al. The active kinome: The modern view of how active protein kinase networks fit in biological research. Curr Opin Pharmacol. 2021;62:117-129doi: 10.1016/j.coph.2021.11.007.
Alganem, K., Hamoud, A. R., Creeden, J. F., Henkel, N. D., Imami, A. S., Joyce, A. W., Ryan V, W. G., Rethman, J. B., Shukla, R., O'Donovan, S. M., Meller, J., & McCullumsmith, R. (2021). The active kinome: The modern view of how active protein kinase networks fit in biological research. Current opinion in pharmacology, 62117-129. https://doi.org/10.1016/j.coph.2021.11.007
Alganem, Khaled, et al. "The active kinome: The modern view of how active protein kinase networks fit in biological research." Current opinion in pharmacology vol. 62 (2021): 117-129. doi: https://doi.org/10.1016/j.coph.2021.11.007
Alganem K, Hamoud AR, Creeden JF, Henkel ND, Imami AS, Joyce AW, Ryan V WG, Rethman JB, Shukla R, O'Donovan SM, Meller J, McCullumsmith R. The active kinome: The modern view of how active protein kinase networks fit in biological research. Curr Opin Pharmacol. 2021 Dec 27;62:117-129. doi: 10.1016/j.coph.2021.11.007. Epub 2021 Dec 27. PMID: 34968947.
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A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders.

Journal of affective disorders

Parker G, Spoelma MJ, Tavella G, Alda M, Dunner DL, O'Donovan C, Rybakowski JK, Bayes A, Sharma V, Boyce P, Manicavasagar V.
PMID: 34952109
J Affect Disord. 2021 Dec 22;299:513-516. doi: 10.1016/j.jad.2021.12.070. Epub 2021 Dec 22.

BACKGROUND: While there are several accepted screening measures for identifying those with a bipolar disorder, variations in overall classification rates argue for the pursuit of a more discriminating measure. Extant measures, as well as the DSM-5, rate each diagnostic...

Cite
Parker G, Spoelma MJ, Tavella G, et al. A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders. J Affect Disord. 2021;299:513-516doi: 10.1016/j.jad.2021.12.070.
Parker, G., Spoelma, M. J., Tavella, G., Alda, M., Dunner, D. L., O'Donovan, C., Rybakowski, J. K., Bayes, A., Sharma, V., Boyce, P., & Manicavasagar, V. (2021). A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders. Journal of affective disorders, 299513-516. https://doi.org/10.1016/j.jad.2021.12.070
Parker, Gordon, et al. "A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders." Journal of affective disorders vol. 299 (2021): 513-516. doi: https://doi.org/10.1016/j.jad.2021.12.070
Parker G, Spoelma MJ, Tavella G, Alda M, Dunner DL, O'Donovan C, Rybakowski JK, Bayes A, Sharma V, Boyce P, Manicavasagar V. A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders. J Affect Disord. 2021 Dec 22;299:513-516. doi: 10.1016/j.jad.2021.12.070. Epub 2021 Dec 22. PMID: 34952109.
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Global Brain Flexibility During Working Memory Is Reduced in a High-Genetic-Risk Group for Schizophrenia.

Biological psychiatry. Cognitive neuroscience and neuroimaging

Dimitriadis SI, Lancaster TM, Perry G, Tansey KE, Jones DK, Singh KD, Zammit S, Smith GD, Hall J, O'Donovan MC, Owen MJ, Linden DE.
PMID: 33524599
Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Dec;6(12):1176-1184. doi: 10.1016/j.bpsc.2021.01.007. Epub 2021 Jan 29.

BACKGROUND: Altered functional brain connectivity has been proposed as an intermediate phenotype between genetic risk loci and clinical expression of schizophrenia. Genetic high-risk groups of healthy subjects are particularly suited for the investigation of this proposition because they can...

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Dimitriadis SI, Lancaster TM, Perry G, et al. Global Brain Flexibility During Working Memory Is Reduced in a High-Genetic-Risk Group for Schizophrenia. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6(12):1176-1184doi: 10.1016/j.bpsc.2021.01.007.
Dimitriadis, S. I., Lancaster, T. M., Perry, G., Tansey, K. E., Jones, D. K., Singh, K. D., Zammit, S., Smith, G. D., Hall, J., O'Donovan, M. C., Owen, M. J., & Linden, D. E. (2021). Global Brain Flexibility During Working Memory Is Reduced in a High-Genetic-Risk Group for Schizophrenia. Biological psychiatry. Cognitive neuroscience and neuroimaging, 6(12), 1176-1184. https://doi.org/10.1016/j.bpsc.2021.01.007
Dimitriadis, Stavros I, et al. "Global Brain Flexibility During Working Memory Is Reduced in a High-Genetic-Risk Group for Schizophrenia." Biological psychiatry. Cognitive neuroscience and neuroimaging vol. 6,12 (2021): 1176-1184. doi: https://doi.org/10.1016/j.bpsc.2021.01.007
Dimitriadis SI, Lancaster TM, Perry G, Tansey KE, Jones DK, Singh KD, Zammit S, Smith GD, Hall J, O'Donovan MC, Owen MJ, Linden DE. Global Brain Flexibility During Working Memory Is Reduced in a High-Genetic-Risk Group for Schizophrenia. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Dec;6(12):1176-1184. doi: 10.1016/j.bpsc.2021.01.007. Epub 2021 Jan 29. PMID: 33524599.
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Early identification of frailty: Developing an international delphi consensus on pre-frailty.

Archives of gerontology and geriatrics

Sezgin D, O'Donovan M, Woo J, Bandeen-Roche K, Liotta G, Fairhall N, Rodríguez-Laso A, Apóstolo J, Clarnette R, Holland C, Roller-Wirnsberger R, Illario M, Mañas LR, Vollenbroek-Hutten M, Doğu BB, Balci C, Pernas FO, Paul C, Ahern E, Romero-Ortuno R, Molloy W, Cooney MT, O'Shea D, Cooke J, Lang D, Hendry A, Kennelly S, Rockwood K, Clegg A, Liew A, O'Caoimh R.
PMID: 34896797
Arch Gerontol Geriatr. 2021 Nov 20;99:104586. doi: 10.1016/j.archger.2021.104586. Epub 2021 Nov 20.

BACKGROUND: Frailty is associated with a prodromal stage called pre-frailty, a potentially reversible and highly prevalent intermediate state before frailty becomes established. Despite being widely-used in the literature and increasingly in clinical practice, it is poorly understood.OBJECTIVE: To establish...

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Sezgin D, O'Donovan M, Woo J, et al. Early identification of frailty: Developing an international delphi consensus on pre-frailty. Arch Gerontol Geriatr. 2021;99:104586doi: 10.1016/j.archger.2021.104586.
Sezgin, D., O'Donovan, M., Woo, J., Bandeen-Roche, K., Liotta, G., Fairhall, N., Rodríguez-Laso, A., Apóstolo, J., Clarnette, R., Holland, C., Roller-Wirnsberger, R., Illario, M., Mañas, L. R., Vollenbroek-Hutten, M., Doğu, B. B., Balci, C., Pernas, F. O., Paul, C., Ahern, E., Romero-Ortuno, R., Molloy, W., Cooney, M. T., O'Shea, D., Cooke, J., Lang, D., Hendry, A., Kennelly, S., Rockwood, K., Clegg, A., Liew, A., & O'Caoimh, R. (2021). Early identification of frailty: Developing an international delphi consensus on pre-frailty. Archives of gerontology and geriatrics, 99104586. https://doi.org/10.1016/j.archger.2021.104586
Sezgin, Duygu, et al. "Early identification of frailty: Developing an international delphi consensus on pre-frailty." Archives of gerontology and geriatrics vol. 99 (2021): 104586. doi: https://doi.org/10.1016/j.archger.2021.104586
Sezgin D, O'Donovan M, Woo J, Bandeen-Roche K, Liotta G, Fairhall N, Rodríguez-Laso A, Apóstolo J, Clarnette R, Holland C, Roller-Wirnsberger R, Illario M, Mañas LR, Vollenbroek-Hutten M, Doğu BB, Balci C, Pernas FO, Paul C, Ahern E, Romero-Ortuno R, Molloy W, Cooney MT, O'Shea D, Cooke J, Lang D, Hendry A, Kennelly S, Rockwood K, Clegg A, Liew A, O'Caoimh R. Early identification of frailty: Developing an international delphi consensus on pre-frailty. Arch Gerontol Geriatr. 2021 Nov 20;99:104586. doi: 10.1016/j.archger.2021.104586. Epub 2021 Nov 20. PMID: 34896797.
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Early identification of frailty: Developing an international delphi consensus on pre-frailty.

Archives of gerontology and geriatrics

Sezgin D, O'Donovan M, Woo J, Bandeen-Roche K, Liotta G, Fairhall N, Rodríguez-Laso A, Apóstolo J, Clarnette R, Holland C, Roller-Wirnsberger R, Illario M, Mañas LR, Vollenbroek-Hutten M, Doğu BB, Balci C, Pernas FO, Paul C, Ahern E, Romero-Ortuno R, Molloy W, Cooney MT, O'Shea D, Cooke J, Lang D, Hendry A, Kennelly S, Rockwood K, Clegg A, Liew A, O'Caoimh R.
PMID: 34896797
Arch Gerontol Geriatr. 2021 Nov 20;99:104586. doi: 10.1016/j.archger.2021.104586. Epub 2021 Nov 20.

BACKGROUND: Frailty is associated with a prodromal stage called pre-frailty, a potentially reversible and highly prevalent intermediate state before frailty becomes established. Despite being widely-used in the literature and increasingly in clinical practice, it is poorly understood.OBJECTIVE: To establish...

Cite
Sezgin D, O'Donovan M, Woo J, et al. Early identification of frailty: Developing an international delphi consensus on pre-frailty. Arch Gerontol Geriatr. 2021;99:104586doi: 10.1016/j.archger.2021.104586.
Sezgin, D., O'Donovan, M., Woo, J., Bandeen-Roche, K., Liotta, G., Fairhall, N., Rodríguez-Laso, A., Apóstolo, J., Clarnette, R., Holland, C., Roller-Wirnsberger, R., Illario, M., Mañas, L. R., Vollenbroek-Hutten, M., Doğu, B. B., Balci, C., Pernas, F. O., Paul, C., Ahern, E., Romero-Ortuno, R., Molloy, W., Cooney, M. T., O'Shea, D., Cooke, J., Lang, D., Hendry, A., Kennelly, S., Rockwood, K., Clegg, A., Liew, A., & O'Caoimh, R. (2021). Early identification of frailty: Developing an international delphi consensus on pre-frailty. Archives of gerontology and geriatrics, 99104586. https://doi.org/10.1016/j.archger.2021.104586
Sezgin, Duygu, et al. "Early identification of frailty: Developing an international delphi consensus on pre-frailty." Archives of gerontology and geriatrics vol. 99 (2021): 104586. doi: https://doi.org/10.1016/j.archger.2021.104586
Sezgin D, O'Donovan M, Woo J, Bandeen-Roche K, Liotta G, Fairhall N, Rodríguez-Laso A, Apóstolo J, Clarnette R, Holland C, Roller-Wirnsberger R, Illario M, Mañas LR, Vollenbroek-Hutten M, Doğu BB, Balci C, Pernas FO, Paul C, Ahern E, Romero-Ortuno R, Molloy W, Cooney MT, O'Shea D, Cooke J, Lang D, Hendry A, Kennelly S, Rockwood K, Clegg A, Liew A, O'Caoimh R. Early identification of frailty: Developing an international delphi consensus on pre-frailty. Arch Gerontol Geriatr. 2021 Nov 20;99:104586. doi: 10.1016/j.archger.2021.104586. Epub 2021 Nov 20. PMID: 34896797.
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A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders.

Journal of affective disorders

Parker G, Spoelma MJ, Tavella G, Alda M, Dunner DL, O'Donovan C, Rybakowski JK, Bayes A, Sharma V, Boyce P, Manicavasagar V.
PMID: 34952109
J Affect Disord. 2021 Dec 22;299:513-516. doi: 10.1016/j.jad.2021.12.070. Epub 2021 Dec 22.

BACKGROUND: While there are several accepted screening measures for identifying those with a bipolar disorder, variations in overall classification rates argue for the pursuit of a more discriminating measure. Extant measures, as well as the DSM-5, rate each diagnostic...

Cite
Parker G, Spoelma MJ, Tavella G, et al. A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders. J Affect Disord. 2021;299:513-516doi: 10.1016/j.jad.2021.12.070.
Parker, G., Spoelma, M. J., Tavella, G., Alda, M., Dunner, D. L., O'Donovan, C., Rybakowski, J. K., Bayes, A., Sharma, V., Boyce, P., & Manicavasagar, V. (2021). A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders. Journal of affective disorders, 299513-516. https://doi.org/10.1016/j.jad.2021.12.070
Parker, Gordon, et al. "A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders." Journal of affective disorders vol. 299 (2021): 513-516. doi: https://doi.org/10.1016/j.jad.2021.12.070
Parker G, Spoelma MJ, Tavella G, Alda M, Dunner DL, O'Donovan C, Rybakowski JK, Bayes A, Sharma V, Boyce P, Manicavasagar V. A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders. J Affect Disord. 2021 Dec 22;299:513-516. doi: 10.1016/j.jad.2021.12.070. Epub 2021 Dec 22. PMID: 34952109.
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A new therapeutic communication model "TAGEET" to help nurses engage therapeutically with patients suspected of or confirmed with COVID-19.

Journal of clinical nursing

McCarthy B, O'Donovan M, Trace A.
PMID: 33350012
J Clin Nurs. 2021 Apr;30(7):1184-1191. doi: 10.1111/jocn.15609. Epub 2021 Jan 06.

AIM: To design and present a new communication model "TAGEET" to help nurses engage therapeutically with patients suspected of or confirmed with COVID-19.BACKGROUND: COVID-19 is a highly contagious disease that brings with it fear and anxiety for all involved...

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McCarthy B, O'Donovan M, Trace A. A new therapeutic communication model "TAGEET" to help nurses engage therapeutically with patients suspected of or confirmed with COVID-19. J Clin Nurs. 2021;30(7):1184-1191doi: 10.1111/jocn.15609.
McCarthy, B., O'Donovan, M., & Trace, A. (2021). A new therapeutic communication model "TAGEET" to help nurses engage therapeutically with patients suspected of or confirmed with COVID-19. Journal of clinical nursing, 30(7), 1184-1191. https://doi.org/10.1111/jocn.15609
McCarthy, Bridie, et al. "A new therapeutic communication model "TAGEET" to help nurses engage therapeutically with patients suspected of or confirmed with COVID-19." Journal of clinical nursing vol. 30,7 (2021): 1184-1191. doi: https://doi.org/10.1111/jocn.15609
McCarthy B, O'Donovan M, Trace A. A new therapeutic communication model "TAGEET" to help nurses engage therapeutically with patients suspected of or confirmed with COVID-19. J Clin Nurs. 2021 Apr;30(7):1184-1191. doi: 10.1111/jocn.15609. Epub 2021 Jan 06. PMID: 33350012.
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Activation of acid-sensing ion channels by carbon dioxide regulates amygdala synaptic protein degradation in memory reconsolidation.

Molecular brain

Lin B, Alganem K, O'Donovan SM, Jin Z, Naghavi F, Miller OA, Ortyl TC, Ruan YC, McCullumsmith RE, Du J.
PMID: 33962650
Mol Brain. 2021 May 07;14(1):78. doi: 10.1186/s13041-021-00786-7.

Reconsolidation has been considered a process in which a consolidated memory is turned into a labile stage. Within the reconsolidation window, the labile memory can be either erased or strengthened. Manipulating acid-sensing ion channels (ASICs) in the amygdala via...

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Lin B, Alganem K, O'Donovan SM, et al. Activation of acid-sensing ion channels by carbon dioxide regulates amygdala synaptic protein degradation in memory reconsolidation. Mol Brain. 2021;14(1):78doi: 10.1186/s13041-021-00786-7.
Lin, B., Alganem, K., O'Donovan, S. M., Jin, Z., Naghavi, F., Miller, O. A., Ortyl, T. C., Ruan, Y. C., McCullumsmith, R. E., & Du, J. (2021). Activation of acid-sensing ion channels by carbon dioxide regulates amygdala synaptic protein degradation in memory reconsolidation. Molecular brain, 14(1), 78. https://doi.org/10.1186/s13041-021-00786-7
Lin, Boren, et al. "Activation of acid-sensing ion channels by carbon dioxide regulates amygdala synaptic protein degradation in memory reconsolidation." Molecular brain vol. 14,1 (2021): 78. doi: https://doi.org/10.1186/s13041-021-00786-7
Lin B, Alganem K, O'Donovan SM, Jin Z, Naghavi F, Miller OA, Ortyl TC, Ruan YC, McCullumsmith RE, Du J. Activation of acid-sensing ion channels by carbon dioxide regulates amygdala synaptic protein degradation in memory reconsolidation. Mol Brain. 2021 May 07;14(1):78. doi: 10.1186/s13041-021-00786-7. PMID: 33962650; PMCID: PMC8106190.
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Editorial overview: Neuroscience: Advances in the field.

Current opinion in pharmacology

O'Donovan SM, McCullumsmith R, Shukla R.
PMID: 34942403
Curr Opin Pharmacol. 2021 Dec 20;62:82-84. doi: 10.1016/j.coph.2021.11.004. Epub 2021 Dec 20.

No abstract available.

Cite
O'Donovan SM, McCullumsmith R, Shukla R. Editorial overview: Neuroscience: Advances in the field. Curr Opin Pharmacol. 2021;62:82-84doi: 10.1016/j.coph.2021.11.004.
O'Donovan, S. M., McCullumsmith, R., & Shukla, R. (2021). Editorial overview: Neuroscience: Advances in the field. Current opinion in pharmacology, 6282-84. https://doi.org/10.1016/j.coph.2021.11.004
O'Donovan, Sinead M, et al. "Editorial overview: Neuroscience: Advances in the field." Current opinion in pharmacology vol. 62 (2021): 82-84. doi: https://doi.org/10.1016/j.coph.2021.11.004
O'Donovan SM, McCullumsmith R, Shukla R. Editorial overview: Neuroscience: Advances in the field. Curr Opin Pharmacol. 2021 Dec 20;62:82-84. doi: 10.1016/j.coph.2021.11.004. Epub 2021 Dec 20. PMID: 34942403.
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Editorial overview: Neuroscience: Advances in the field.

Current opinion in pharmacology

O'Donovan SM, McCullumsmith R, Shukla R.
PMID: 34942403
Curr Opin Pharmacol. 2021 Dec 20;62:82-84. doi: 10.1016/j.coph.2021.11.004. Epub 2021 Dec 20.

No abstract available.

Cite
O'Donovan SM, McCullumsmith R, Shukla R. Editorial overview: Neuroscience: Advances in the field. Curr Opin Pharmacol. 2021;62:82-84doi: 10.1016/j.coph.2021.11.004.
O'Donovan, S. M., McCullumsmith, R., & Shukla, R. (2021). Editorial overview: Neuroscience: Advances in the field. Current opinion in pharmacology, 6282-84. https://doi.org/10.1016/j.coph.2021.11.004
O'Donovan, Sinead M, et al. "Editorial overview: Neuroscience: Advances in the field." Current opinion in pharmacology vol. 62 (2021): 82-84. doi: https://doi.org/10.1016/j.coph.2021.11.004
O'Donovan SM, McCullumsmith R, Shukla R. Editorial overview: Neuroscience: Advances in the field. Curr Opin Pharmacol. 2021 Dec 20;62:82-84. doi: 10.1016/j.coph.2021.11.004. Epub 2021 Dec 20. PMID: 34942403.
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Showing 25 to 36 of 263 entries