Bibliographic record
Abstract
Today, upwards of 10 million people—approximately 9 500 of whom reside in New Zealand—are living with Parkinson’s disease (PD). Yet, the means of diagnosing PD remain somewhat similar to those available to James Parkinson in 1817. Recently, however, there has been an increasing interest in the role of biomarkers in PD; these, in turn, are hoped to provide the necessary means by which PD can be diagnosed earlier, treated better and—ultimately—altogether prevented and/or cured. Given the multifaceted nature of the aetiology underlying PD, a “multi-system” approach to biomarkers is more likely to yield fruitful results. Thus, the overarching aim of this study was to explore several biomarkers (within two realms—biological and behavioural) that may be used at different time-points as the disease progresses. In the biological markers trials, biofluid samples (i.e., cerebrospinal fluid ‘CSF’ and plasma) were obtained from 11 patients with PD. Analyses of these samples did not detect any blackcurrant anthocyanins either before or after oral supplementation with blackcurrant concentrate for four weeks. Consumption of blackcurrant concentrate, however, significantly increased the CSF concentration of cyclic glycine-proline. This led to the hypothesis of an indirect mechanism underlying the putative benefit of berry-fruit consumption on the risk of developing PD—perhaps through modulating the peripheral resistance to insulinlike growth factor-1 otherwise observed in patients with PD. CSF concentrations of the aminoterminal fragment of C-type natriuretic peptide were significantly lower in PD patients than the reported range from a group of pre-operative orthopaedic patients. Finally, the obtained samples were utilised to characterise the profile of exosomes present in the CSF and plasma of PD patients. The three patients with the highest plasma exosome concentrations also had the lowest scores on the Montreal Cognitive Assessment. The behavioural markers study investigated biomarkers in patients with established PD—a stage when cognition may become involved. The emphasis was to obtain an in-depth evaluation of novel eye movement-performance associations. In general, no remarkable differences in eye movement parameters were noted among the three study groups (n = 16 per group): PD with normal cognition (PDN), PD with mild cognitive impairment (PD-MCI) and matched controls (NC) in natural and laboratory-based neuropsychological tasks. This indicates a relatively preserved organisation of neuropsychological task performance as evident from eye movements among the participants. In addition, some insights into human behaviour on several tasks were gained. In the animal naming task, participants from all three groups tended to fixate on the animal’s head in order to name it. Participants also fixated on the distal ends of lines when attempting the Judgement of Line Orientation task. PD-MCI participants were found to make significantly more vertical saccades when searching the Where’s Wally?™ Maze task in comparison with NC and PD-N participants. On the Symbol Digit Modalities Test, PD-MCI participants scored significantly lower than NC and PDN participants. Finally, task organisation of the tea-making task was mostly consistent among the study participants; PD participants (of both groups) executed the task significantly slower than NC participants. Given the relatively small sample sizes, an exploratory approach was generally taken. To gain confidence in the results of individual findings, further research ought to be carried out in order to exclude the possibility of sampling variability accounting for the reported observations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".