alzhub.org
Get help now — free helplines

You can call any of these for free. You do not need a diagnosis, an appointment, or a crisis to call — questions and plain exhaustion are reasons enough.

  • Alzheimer's Association 24/7 Helpline 800-272-3900 24 hours a day, 365 days a year Free, confidential support and information for people with dementia, caregivers, families and the public. Master's-level clinicians. Also Spanish.
  • 988 Suicide & Crisis Lifeline 988 24/7 Call or text 988, or chat at 988lifeline.org. For anyone in emotional distress or a mental-health crisis, including caregivers.
  • Alzheimer's Foundation of America (AFA) Helpline 866-232-8484 9:00 am - 9:00 pm ET, seven days a week Licensed social workers providing support, info and referrals. Also text message and web chat. Text: 646-586-5283

In a medical emergency, call 911. More numbers, and what each line does, on the help page.

Home › Research › Biomarkers

Using neuronal extracellular vesicles and machine learning to predict cognitive deficits in HIV

Pulliam L, Liston M, Sun B, Narvid J.. Journal of neurovirology, 2020-07-17

This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

✓ Peer-reviewed alzhub usefulness score 38 out of 100. Biomarkers

Topic: Brain proteins, microglia and lab models

Our objective was to predict HIV-associated neurocognitive disorder (HAND) in HIV-infected people using plasma neuronal extracellular vesicle (nEV) proteins, clinical data, and machine learning. We obtained 60 plasma samples from 38 women and 22 men, all with HIV infection and 40 with HAND. All underwent neuropsychological testing. nEVs were isolated by immunoadsorption with neuron-specific L1CAM antibody. High-mobility group box 1 (HMGB1), neurofilament light (NFL), and phosphorylated tau-181 (p-T181-tau) proteins were quantified by ELISA. Three different computational algorithms were performed to predict cognitive impairment using clinical data and nEV proteins. Of the 3 different algorithms, support vector machines performed the best. Applying 4 different models of clinical data with 3 nEV proteins, we showed that selected clinical data and HMGB1 plus NFL best predicted cognitive impairment with an area under the curve value of 0.82. The most important features included CD4 count, HMGB1, and NFL. Previous published data showed nEV p-T181-tau was elevated in Alzheimer's disease (AD), and in this study, p-T181-tau had no importance in assessing HAND but may actually differentiate it from AD. Machine learning can access data without programming bias. Identifying a few nEV proteins plus key clinical variables can better predict neuronal damage. This approach may differentiate other neurodegenerative diseases and determine recovery after therapies are identified.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Pulliam, L., Liston, M., Sun, B., & Narvid, J. (2020). Using neuronal extracellular vesicles and machine learning to predict cognitive deficits in HIV. Journal of neurovirology. https://doi.org/10.1007/s13365-020-00877-6

Vancouver

Pulliam L, Liston M, Sun B, Narvid J. Using neuronal extracellular vesicles and machine learning to predict cognitive deficits in HIV. Journal of neurovirology. 2020. doi:10.1007/s13365-020-00877-6. PMID: 32681213.

BibTeX

@article{pulliam2020-32681213,
  author  = {Pulliam, L. and Liston, M. and Sun, B. and Narvid, J.},
  title   = {{Using neuronal extracellular vesicles and machine learning to predict cognitive deficits in HIV}},
  journal = {Journal of neurovirology},
  year    = {2020},
  doi     = {10.1007/s13365-020-00877-6},
  pmid    = {32681213},
  url     = {https://doi.org/10.1007/s13365-020-00877-6}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

Key findings

New to this subject? Start with our plain-English guide: Blood tests & biomarkers →

Study details

How this study scored 38/100
How this study's score is builtJournal tier 7 of 10; Journal's citation record left out, no data; Study design 0 of 12; Strength of evidence 1 of 5; Peer-review validation 10 of 10; How recent 2 of 5; Sample size 0 of 4; Effect reported 0 of 3; Studied in people 0 of 3; Randomised / blinded 0 of 2; Times cited left out, no data; Citations per year left out, no data; Impact for its field and year left out, no data; Cited by studies here left out, no data; Still being cited left out, no data; Senior author's track record left out, no data; Institution's track record left out, no data; Multi-centre collaboration left out, no data; Indexed in PubMed 2 of 2; Free to read 0 of 2. 22 of the 58 points it could be judged on, which is 38 of 100. Total 38 of 100.22 of the 58 points we could judge = 38 of 100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 22 of the 58 points we could judge it on, which is 38 out of 100.

ComponentPointsWhy
Journal tier 7 / 10 Tier 3 — peer-reviewed journal (Journal of neurovirology)
Journal's citation record — / 4 No journal record from OpenAlex — left out of this score
Study design 0 / 12 No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence 1 / 5 Rated “low” in our summary
Peer-review validation 10 / 10 Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent 2 / 5 2020 — within 10 year(s) of the newest study here
Sample size 0 / 4 Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported 0 / 3 No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people 0 / 3 Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded 0 / 2 No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited — / 7 No citation count (not found in OpenAlex) — left out of this score
Citations per year — / 5 No citation count or no year — left out of this score
Impact for its field and year — / 6 No field-weighted citation impact (OpenAlex has none yet) — left out of this score
Cited by studies here — / 6 Not yet checked against this library's citation graph — left out of this score
Still being cited — / 4 No yearly citation counts — left out of this score
Senior author's track record — / 4 Senior author's record not available — left out of this score
Institution's track record — / 3 No institution listed in OpenAlex — left out of this score
Multi-centre collaboration — / 3 Institutions not listed — left out of this score
Indexed in PubMed 2 / 2 Indexed in PubMed (PMID 32681213)
Free to read 0 / 2 Behind a paywall — scores zero
Points 22 / 58 Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 22 of the 58 points we could judge it on is 38 out of 100.
Total 38 / 100 —

What each of these 20 components measures, and what this score is not →

Read the original

Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.

Related studies in Biomarkers

Also in Biomarkers

204 studies in the library are filed under Biomarkers with no topic tag they share with another; these are this study's neighbours in a fixed tour of them. The Biomarkers hub.

Similar studies

More on Biomarkers

See all studies in Biomarkers →