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4 Evaluating Plasma GFAP for the Detection of Alzheimer’s Disease Dementia
- Madeline Ally, Henrik Zetterberg, Kaj Blennow, Nicholas J. Ashton, Thomas K. Karikari, Hugo Aparicio, Michael A. Sugarman, Brandon Frank, Yorghos Tripodis, Ann C. McKee, Thor D. Stein, Brett Martin, Joseph N. Palmisano, Eric G. Steinberg, Irene Simkina, Lindsay Farrer, Gyungah Jun, Katherine W. Turk, Andrew E. Budson, Maureen K. O’Connor, Rhoda Au, Wei Qiao Qiu, Lee E. Goldstein, Ronald Killiany, Neil W. Kowall, Robert A. Stern, Jesse Mez, Michael L. Alosco
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 408-409
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Objective:
Blood-based biomarkers represent a scalable and accessible approach for the detection and monitoring of Alzheimer’s disease (AD). Plasma phosphorylated tau (p-tau) and neurofilament light (NfL) are validated biomarkers for the detection of tau and neurodegenerative brain changes in AD, respectively. There is now emphasis to expand beyond these markers to detect and provide insight into the pathophysiological processes of AD. To this end, a reactive astrocytic marker, namely plasma glial fibrillary acidic protein (GFAP), has been of interest. Yet, little is known about the relationship between plasma GFAP and AD. Here, we examined the association between plasma GFAP, diagnostic status, and neuropsychological test performance. Diagnostic accuracy of plasma GFAP was compared with plasma measures of p-tau181 and NfL.
Participants and Methods:This sample included 567 participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC) Longitudinal Clinical Core Registry, including individuals with normal cognition (n=234), mild cognitive impairment (MCI) (n=180), and AD dementia (n=153). The sample included all participants who had a blood draw. Participants completed a comprehensive neuropsychological battery (sample sizes across tests varied due to missingness). Diagnoses were adjudicated during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using the Simoa platform. Binary logistic regression analyses tested the association between GFAP levels and diagnostic status (i.e., cognitively impaired due to AD versus unimpaired), controlling for age, sex, race, education, and APOE e4 status. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate diagnostic groups compared with plasma p-tau181 and NfL. Linear regression models tested the association between plasma GFAP and neuropsychological test performance, accounting for the above covariates.
Results:The mean (SD) age of the sample was 74.34 (7.54), 319 (56.3%) were female, 75 (13.2%) were Black, and 223 (39.3%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having cognitive impairment (GFAP z-score transformed: OR=2.233, 95% CI [1.609, 3.099], p<0.001; non-z-transformed: OR=1.004, 95% CI [1.002, 1.006], p<0.001). ROC analyses, comprising of GFAP and the above covariates, showed plasma GFAP discriminated the cognitively impaired from unimpaired (AUC=0.75) and was similar, but slightly superior, to plasma p-tau181 (AUC=0.74) and plasma NfL (AUC=0.74). A joint panel of the plasma markers had greatest discrimination accuracy (AUC=0.76). Linear regression analyses showed that higher GFAP levels were associated with worse performance on neuropsychological tests assessing global cognition, attention, executive functioning, episodic memory, and language abilities (ps<0.001) as well as higher CDR Sum of Boxes (p<0.001).
Conclusions:Higher plasma GFAP levels differentiated participants with cognitive impairment from those with normal cognition and were associated with worse performance on all neuropsychological tests assessed. GFAP had similar accuracy in detecting those with cognitive impairment compared with p-tau181 and NfL, however, a panel of all three biomarkers was optimal. These results support the utility of plasma GFAP in AD detection and suggest the pathological processes it represents might play an integral role in the pathogenesis of AD.
5 Antemortem Plasma GFAP Predicts Alzheimer’s Disease Neuropathological Changes
- Madeline Ally, Henrik Zetterberg, Kaj Blennow, Nicholas J. Ashton, Thomas K. Karikari, Hugo Aparicio, Michael A. Sugarman, Brandon Frank, Yorghos Tripodis, Brett Martin, Joseph N. Palmisano, Eric G. Steinberg, Irene Simkina, Lindsay Farrer, Gyungah Jun, Katherine W. Turk, Andrew E. Budson, Maureen K. O’Connor, Rhoda Au, Wei Qiao Qiu, Lee E. Goldstein, Ronald Killiany, Neil W. Kowall, Robert A. Stern, Jesse Mez, Bertran R. Huber, Ann C. McKee, Thor D. Stein, Michael L. Alosco
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 409-410
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Objective:
Blood-based biomarkers offer a more feasible alternative to Alzheimer’s disease (AD) detection, management, and study of disease mechanisms than current in vivo measures. Given their novelty, these plasma biomarkers must be assessed against postmortem neuropathological outcomes for validation. Research has shown utility in plasma markers of the proposed AT(N) framework, however recent studies have stressed the importance of expanding this framework to include other pathways. There is promising data supporting the usefulness of plasma glial fibrillary acidic protein (GFAP) in AD, but GFAP-to-autopsy studies are limited. Here, we tested the association between plasma GFAP and AD-related neuropathological outcomes in participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC).
Participants and Methods:This sample included 45 participants from the BU ADRC who had a plasma sample within 5 years of death and donated their brain for neuropathological examination. Most recent plasma samples were analyzed using the Simoa platform. Neuropathological examinations followed the National Alzheimer’s Coordinating Center procedures and diagnostic criteria. The NIA-Reagan Institute criteria were used for the neuropathological diagnosis of AD. Measures of GFAP were log-transformed. Binary logistic regression analyses tested the association between GFAP and autopsy-confirmed AD status, as well as with semi-quantitative ratings of regional atrophy (none/mild versus moderate/severe) using binary logistic regression. Ordinal logistic regression analyses tested the association between plasma GFAP and Braak stage and CERAD neuritic plaque score. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate autopsy-confirmed AD status. All analyses controlled for sex, age at death, years between last blood draw and death, and APOE e4 status.
Results:Of the 45 brain donors, 29 (64.4%) had autopsy-confirmed AD. The mean (SD) age of the sample at the time of blood draw was 80.76 (8.58) and there were 2.80 (1.16) years between the last blood draw and death. The sample included 20 (44.4%) females, 41 (91.1%) were White, and 20 (44.4%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having autopsy-confirmed AD (OR=14.12, 95% CI [2.00, 99.88], p=0.008). ROC analysis showed plasma GFAP accurately discriminated those with and without autopsy-confirmed AD on its own (AUC=0.75) and strengthened as the above covariates were added to the model (AUC=0.81). Increases in GFAP levels corresponded to increases in Braak stage (OR=2.39, 95% CI [0.71-4.07], p=0.005), but not CERAD ratings (OR=1.24, 95% CI [0.004, 2.49], p=0.051). Higher GFAP levels were associated with greater temporal lobe atrophy (OR=10.27, 95% CI [1.53,69.15], p=0.017), but this was not observed with any other regions.
Conclusions:The current results show that antemortem plasma GFAP is associated with non-specific AD neuropathological changes at autopsy. Plasma GFAP could be a useful and practical biomarker for assisting in the detection of AD-related changes, as well as for study of disease mechanisms.
Data-driven model for divertor plasma detachment prediction
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- Ben Zhu, Menglong Zhao, Harsh Bhatia, Xue-qiao Xu, Peer-Timo Bremer, William Meyer, Nami Li, Thomas Rognlien
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- Journal:
- Journal of Plasma Physics / Volume 88 / Issue 5 / October 2022
- Published online by Cambridge University Press:
- 21 October 2022, 895880504
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We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.
Delivering person-centred palliative care in long-term care settings: is humanism a quality of health-care employees or their organisations?
- Paulette V. Hunter, Lynn McCleary, Thomas Qiao, Tamara Sussman, Lorraine Venturato, Genevieve Thompson, Abigail Wickson-Griffiths, Sharon Kaasalainen
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- Journal:
- Ageing & Society / Volume 44 / Issue 4 / April 2024
- Published online by Cambridge University Press:
- 09 June 2022, pp. 898-915
- Print publication:
- April 2024
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Reflecting on sustained calls for patient-centredness and culture change in long-term care, we evaluated the relative importance of personal and organisational predictors of palliative care, hypothesising the former as weaker predictors than the latter. Health-care employees (N = 184) from four Canadian long-term care homes completed a survey of person-centred care, self-efficacy, employee wellbeing and occupational characteristics. Using backward stepwise regression models, we examined the relative contributions of these variables to person-centred palliative care. Specifically, blocks of variables representing personal, organisational and occupational characteristics; palliative care self-efficacy; and employee wellbeing were simultaneously regressed on variables representing aspects of person-centred care. The change in R2 associated with the removal of each block was examined to determine each block's overall contribution to the model. We found that occupational characteristics (involvement in care planning), employee wellbeing (compassion satisfaction) and self-efficacy were reliably associated with person-centred palliative care (p < 0.05). Facility size was not associated, and facility profit status was less consistently associated. Demographic characteristics (gender, work experience, education level) and some aspects of employee wellbeing (burnout, secondary trauma) were also not reliably associated. Overall, these results raise the possibility that humanistic care is less related to intrinsic characteristics of employees, and more related to workplace factors, or to personal qualities that can be cultivated in the workplace, including meaningful role engagement, compassion and self-efficacy.
Diagnosing Zygosity in Giant Panda Twins Using Short Tandem Repeats
- Maiju Qiao, Yingmin Zhou, Thomas Connor, Rengui Li, Dan Tang, Hemin Zhang, Jianghong Ran
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- Journal:
- Twin Research and Human Genetics / Volume 21 / Issue 6 / December 2018
- Published online by Cambridge University Press:
- 30 October 2018, pp. 527-532
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The giant panda, native to mountains of south-west China, is one of the world's rarest bear species and is subject to considerable conservation effort. In captivity, the proportion of twins accounts for 54% of the total number of births. To date, little is known about zygosity in panda populations — specifically, the proportion of monozygotic and dizygotic twins. In this study, we used 10 microsatellite markers for reliable zygosity testing, and the probability of monozygotic twins was 99.963% when all 10 markers were concordant. Out of 43 studied twin pairs, no MZ twins were found, indicating that there may be no identical panda twins (or the incidence is very low). We speculate that the fertilized eggs of giant pandas do not have the capability to split into two identical embryos, or that this ability is very poor, which is likely due to delayed implantation that is common in bear species. The results of this study deepen our understanding of giant panda breeding, yield insight into panda twins’ likely mechanism of formation, and reduce the uncertainty of individual identity in wild population surveys.
Contributors
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- By Magdalena Anitescu, Charles E. Argoff, Arash Asher, Nyla Azam, Nomen Azeem, Sachin K. Bansal, Jose E. Barreto, Rodrigo A Benavides, Niteesh Bharara, Justin B. Boge, Robert B. Bolash, Thomas K. Bond, Christopher Centeno, Zachariah W. Chambers, Jonathan Chang, Grace Chen, Hamilton Chen, Jeffry Chen, Jianguo Cheng, Natalia Covarrubias, Claire J. Creutzfeldt, Gulshan Doulatram, Amirpasha Ehsan, Ike Eriator, Jeff Ericksen, Mark Etscheidt, Frank J. E. Falco, Jack Fu, Timothy Furnish, Annemarie E. Gallagher, Kingsuk Ganguly, Eugene Garvin, Cliff Gevirtz, Scott E. Glaser, Brandon J. Goff, Harry J. Gould, Christine Greco, Jay S. Grider, Maged Guirguis, Qiao Guo, Justin Hata, John Hau, Garett J. Helber, Eric R. Helm, Lori Hill Marshall, Dean Hommer, Jeffrey Hopcian, Eric S. Hsu, Jakun Ing, Tracy P. Jackson, Gaurav Jain, Chrystina Jeter, Alan David Kaye, James Kelly, Soorena Khojasteh, Ankur Khosla, Daniel Krashin, Monika A. Krzyzek, Prasad Lakshminarasimhiah, Steven Michael Lampert, Garrett LaSalle, Quan D. Le, Ankit Maheshwari, Edward R. Mariano, Joaquin Maury, John P. McCallin, John Michels, Natalia Murinova, Narendren Narayanasamy, Rebekah L. Nilson, Elliot Palmer, Vikram B. Patel, Devin Peck, Donald B. Penzien, Danielle Perret Karimi, Tilak Raj, Michael R. Rasmussen, Mohit Rastogi, Rahul Rastogi, Nashaat N. Rizk, Rinoo V. Shah, Paul A. Sloan, Julian Sosner, A. Raj Swain, Minyi Tan, Natacha Telusca, Santhosh A. Thomas, Andrea Trescot, Michael Truong, Jason Tucker, Richard D. Urman, Brandon A. Van Noord, Nihir Waghela, Irene Wu, Jiang Wu, Jijun Xu, Jinghui Xie, William Yancey
- Edited by Alan David Kaye, Louisiana State University, Rinoo V. Shah
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- Case Studies in Pain Management
- Published online:
- 05 October 2014
- Print publication:
- 16 October 2014, pp xi-xv
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