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Record W7117233270 · doi:10.1002/alz70857_103480

Astrocyte reactivity and neurodegeneration biomarkers in critical care patients and strategies for intervention

2025· article· en· W7117233270 on OpenAlexaboutno aff
Débora Guerini de Souza, Wyllians Vendramini Borelli, Fabiano Márcio Nagel, Marco Antônio De Bastiani, Christian Limberger, Isabela Scur Carrard, Pedro Rodrigues Vidor, João Pedro Uglione da Ros, Lara Angi Souza, Monica Ochoa Nagel, Francieli Rohden, Eduardo R. Zimmer, Jaderson Costa da Costa, Diogo O. Souza

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAstrocyteNeurodegenerationIntervention (counseling)Reactivity (psychology)Biomarker

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals discharged from intensive care units (ICU) after recovery from severe disease are at high risk of developing neurodegeneration and long-term cognitive impairment. Although common, this condition is poorly understood. Astrocyte reactivity, a heterogeneous response of astrocytes, is a common characteristic of neurodegenerative diseases. Here, we aimed to evaluate the impact of critical diseases on astrocyte reactivity, neurodegeneration, and cognition. METHODS: Individuals from Hospital de Clínicas de Porto Alegre ICU, Brazil, above 40 years old were recruited. The Simplified Acute Physiology Score III (SAPS III) was used to estimate the mortality prediction of ICU patients. GFAP and NfL levels in plasma were measured with SIMOA to evaluate astrocyte reactivity and neuronal damage, respectively. Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MOCA) were used for the cognitive screening. Statistical analyses were conducted with ANCOVA and generalized linear models, accounting for age and sex, with a significant threshold of p <0.05. RESULTS: We included 44 healthy controls and 53 individuals admitted to ICU due to critical diseases (demographics are shown in Table 1). The critical disease group presented higher levels of GFAP (Figure 1a, F 15.094, p <0.001) and NfL (Figure 1b, F 12.32, p <0.001) compared with healthy controls. GFAP levels were positively associated with SAPS III score (Figure 1c, β=0.0351, 95%CI 0.011, 0.0588, p = 0.004), while NfL levels were not (Figure 1d, β=0.00347, 95%CI -0.027, 0.0339, p = 0.824). MMSE (Figure 1e, F=5.371, p = 0.029) and MOCA (Figure 1f, F=11.75, p = 0.002) scores showed a significant difference between groups; however, both tests did not associate with GFAP and NfL levels. CONCLUSIONS: Critical care diseases influence astrocyte reactivity and neurodegeneration, suggesting that severe systemic diseases may be associated with neuropathological processes. Besides, astrocyte reactivity was associated with mortality prediction scores. These results emphasize the need for further research into GFAP and NfL as biomarkers to better understand and manage neurodegeneration in critical care settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.329
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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