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Record W3112483701 · doi:10.21203/rs.3.rs-121836/v2

Linking Neuroinflammation and Extracellular Free Water in HIV Infection: A Longitudinal Study

2020· preprint· en· W3112483701 on OpenAlexaff
Md Nasir Uddin, Abrar Faiyaz, Lu Wang, Yuchuan Zhuang, Kyle D. Murray, Maxime Descoteaux, Madalina E. Tivarus, Miriam T. Weber, Jianhui Zhong, Xing Qiu, Giovanni Schifitto

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsUniversité de Sherbrooke
FundersNational Institutes of Health
KeywordsNeuroinflammationHuman immunodeficiency virus (HIV)ExtracellularMedicineNeuroscienceImmunologyBiologyCell biologyInflammation

Abstract

fetched live from OpenAlex

Abstract Initiation of combination antiretroviral therapy (cART) reduces inflammation in HIV-infected (HIV+) individuals. Recent studies demonstrated that diffusion MRI based extracellular free water (FW) modeling can be sensitive to neuroinflammation. Here, we investigate the FW in HIV-infection, its temporal evolution, and its association with blood markers, and cognitive scores. Using 96 age-matched participants, we found that FW was significantly elevated in grey and white matter in cART-naïve HIV+ compared to healthy controls (HIV-) at baseline. Similarly, at baseline, HIV+ participants had increased neurofilament light chain (NfL) values that correlated with FW and CD4 counts. FW in grey and white matter, as well as NfL decreased in the HIV+ after 12 weeks of cART treatment. No significant FW differences were noted between the HIV+ and HIV- cohorts at 1 and 2-year follow-up. Results suggest that FW elevation in cART-naïve HIV+ participants is likely due to neuroinflammation. The correlation between FW and NfL and the improvement in both FW and NfL after 12 weeks of cART treatment further reinforces this conclusion. The longer follow-up at 1 and 2 years suggests that cART helped control neuroinflammation as inferred by FW. Therefore, FW could be used as a biomarker to monitor HIV-associated neuroinflammation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.129
GPT teacher head0.389
Teacher spread0.261 · 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 teacher head, not a consensus.

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
Published2020
Admission routes1
Has abstractyes

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