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Record W4368408169 · doi:10.1186/s41983-023-00662-2

Role of multimodal advanced biomarkers as potential predictors of cognitive and psychiatric aspects of Parkinson's disease

2023· article· en· W4368408169 on OpenAlexaboutno aff
Marwa Y. Badr, Reham A. Amer, Mona Ahmed Kotait, Sara M. Shoeib, Alaa Mohamed Reda

Bibliographic record

VenueThe Egyptian Journal of Neurology Psychiatry and Neurosurgery · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDementiaNeuropsychologyParkinson's diseaseDepression (economics)BiomarkerBeck Depression InventoryMedicineAudiologyInternal medicineCognitionFractional anisotropyMajor depressive disorderPsychologyDiffusion MRIPsychiatryDiseaseMagnetic resonance imagingRadiologyAnxiety

Abstract

fetched live from OpenAlex

Abstract Background The value of biomarker research in Parkinson's disease (PD) exists in the early detection and accurate diagnosis of non-motor neuropsychiatric symptoms with implications for future treatment strategies. The aim of the this work was to assess and predict risk for possible cognitive, psychiatric abnormalities in patients with early stage idiopathic PD using a combination of advanced diagnostic biomarkers for early recognition and intervention. Methods This cross-sectional case–control study was conducted on 58 eligible idiopathic PD-patients, and 45 age/sex-matched healthy controls. All participants were subjected to neuro-psychiatric-, radiological-, audiological-, and laboratory-evaluations. Cognitive assessment was performed using Montreal Cognitive Assessment, Mattis Dementia, and Parkinson’s Disease-Cognitive scales. Depression was evaluated by Hamilton Depression and Beck Depression Inventory-II rating scales. Radiologically, volumetric-MRI, diffusion tensor imaging (DTI), and susceptibility weighted imaging were done. Audiologically, P300 and cortical auditory evoked potentials were elicited. Laboratory investigations included 24 h-urinary 5-HIAA and serum levels of IL6, BDNF, 5-HT, and aberrant cimiRNA 132-3p expression profile. Results Neuropsychological scales revealed mild depression and mild cognitive impairment, with significant differences in PD group. Volumetric-MRI highlighted that PD-patients had a significant bilateral decrease in the mean cortical thickness and thickness/volume of many brain areas. DTI showed a reduction in fractional isotropy and a significant bilateral increase in mean diffusivity through many areas in PD-patients. Patients also had either absent or diminished amplitude of P300,P1, diminished amplitude of N1,P2,N2 and delayed latency of all previous waves. There was a significant reduction of 24 h-urinary 5-HIAA and serum BDNF, with significant elevation of serum IL6, as well as non-significant reduction of serum 5-HT and microRNA-132-3p(2-ΔCt) in PD-patients. Conclusions Early stage PD-patients had subtle cognitive impairment and depression as detected by psychometric scales and correlated significantly with the various biomarkers, including advanced neuro-imaging, evoked potential studies, and laboratory markers. The key message of this work include evaluating the high prevalence of cognitive and psychiatric impairment in early idiopathic PD has encouraged research and workup for precision medicine. Proper integration of advanced multimodal biomarkers in this study has led to predict the risk of early mild cognitive and psychiaric affection. This will optimize the health strategies for early proper management to improve quality of life.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.242
Teacher spread0.234 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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