ROLE OF NEUROSPECIFIC PROTEINS IN THE DEVELOPMENT OF COGNITIVE DYSFUNCTION IN PATIENTS WITH TYPE 1 DIABETES
Bibliographic record
Abstract
Type 1 (type 1 DM) diabetes mellitus is one of the common chronic metabolic diseases, which currently is a significant problem due to disability at a young age and reduce life expectancy. Despite the fact that type 1 diabetes accounts for only 10% of all patients with diabetes, it occurs particularly hard, with a tendency to progression. One of the targets of type 1 diabetes is the central nervous system with the further formation of cognitive dysfunction in young age leads to diminished quality of life. Cognitive deficits may be the result not only of structural lesions of the brain, but it may be due to the development of metabolic disorders. In the case of timely diagnosis and treatment of cognitive impairment associated with metabolic changes that can partially or completely regress. The aim of this study was to identify biomarkers of the brain damage in young patients with type 1 diabetes. The study involved 58 patients with type 1 diabetes, the control group comprised 29 healthy controls. The complex included a neuropsychological examination which was used for testing the Montreal scale (MoCA test) rapid screening of cognitive impairment, assessment of quality of life using a common questionnaire Medical Outcomes Study Short Form (MOS SF-36) and the specific audit – dependent quality of life (ADDQoL). To evaluateearly markersin the developmentof cognitive dysfunctionwere identifiedneurospecific proteins – S100 protein and glial fibrillary acidic protein (GFAP), myelin basic protein (MBP). Found an increased level of neurospecific protein that was correlated with parameters of carbohydrate metabolism, poor quality of life and severe cognitive deficiency (MoCA test lower than 26 points).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".