MétaCan
Menu
← Back to cohort

Correlation analysis between serum 8 ⁃ hydroxy deoxyguanosine and malondialdehyde levels and cognitive dysfunction in patients with Parkinson's disease

2024· article· en· W6910280065 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMalondialdehydeMontreal Cognitive AssessmentUnivariate analysisCognitionLogistic regressionDiseaseCorrelationReceiver operating characteristic

Abstract

fetched live from OpenAlex

Objective To explore the relationship between the levels of serum 8 ‑ hydroxy deoxyguanosine (8 ‑ OHdG) and malondialdehyde (MDA) and cognitive dysfunction in patients with Parkinson's disease (PD). Methods From February 2021 to February 2022, 126 patients with PD in The Affiliated Hospital of Xuzhou Medical University were divided into normal cognitive function group (PDN group, n = 41), mild cognitive impairment group (PD‑MCI group, n = 47) and Parkinson's disease dementia group (PDD group, n = 38), and 50 healthy subjects were selected as control group. Hoehn‑Yahr staging was used to evaluate the severity of patients with PD during the "close" period of anti‑PD drugs, and the motor function of patients with PD was evaluated by the Unified Parkinson's Disease Rating Scale Ⅲ (UPDRS Ⅲ). Montreal Cognitive Assessment (MoCA) was used to evaluate the severity of cognitive dysfunction at rest and in the "on" period of anti‑ PD drugs, and the serum 8 ‑ OHdG and MDA of PD patients and controls were collected. Pearson and partial correlation analyses were used to analyze the correlation between the levels of serum 8‑OHdG and MDA and MoCA score in PD patients. Univariate and multivariate Logistic regression analyses were used to analyze the influencing factors of cognitive dysfunction in PD patients. The efficacy analysis of serum 8‑OHdG and MDA levels in predicting the risk of cognitive dysfunction in PD patients was carried out by using receiver operating characteristic curve (ROC curve). Results The results of correlation analysis showed that there was a negative correlation between the MoCA score and the duration (r = ‑ 0.241, P = 0.007), Hoehn‑Yahr staging (r = ‑ 0.333, P = 0.007), 8‑OHdG (r = ‑ 0.310, P = 0.000) and MDA (r = ‑ 0.291, P = 0.004) in PD patients. The results of Logistic regression analysis showed that the elevated levels of 8‑OHdG (OR = 1.335, 95%CI: 1.137-1.568; P = 0.000) and MDA (OR = 2.928, 95%CI: 1.676-5.115; P = 0.000) were risk factors for cognitive dysfunction in PD patients. The results of ROC curve showed the areas under the curve of 8‑OHdG, MDA and their combination in predicting cognitive dysfunction in PD patients were 0.831 (95%CI: 0.761-0.902, P = 0.000), 0.846 (95%CI: 0.775-0.916, P = 0.000) and 0.922 (95%CI: 0.878-0.966, P = 0.000), respectively. Conclusions The detection of 8‑OHdG and MDA in peripheral blood is expected to be a serum marker to evaluate the severity of cognitive dysfunction in patients with PD, and to predict cognitive dysfunction in patients with PD.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.450
Teacher spread0.358 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicParkinson's Disease Mechanisms and Treatments→French-language works237,207→