Glucose Metabolism Measured by Positron Emission Tomography is Reduced in Patients with White Matter Presumably Ischemic Lesions
Bibliographic record
Abstract
BACKGROUND: The severity and progression of white matter ischemic lesion (WMIL) are closely linked to vascular dementia. The function of neural tissue is closely linked to glucose consumption as the most important energy-supplying metabolic process. At present, [18]fluorine-fluorodeoxy glucose ([18]FDG) positron emission tomography (PET) can provide regional and 3-dimensional quantification of glucose metabolism in the human brain. Although MMSE and MoCA are commonly used screens in cognitive impairment, no research team has yet validated their performance in WMIL. The purpose of our study was to compare MMSE and MoCA in screening for cognitive impairment and to explore the correlations between CMRglu values and executive function. MATERIAL AND METHODS: All the participants underwent comprehensive clinical, MoCA, MMSE, MRI, and PET examinations. Patients in the WMIL group were subdivided into 3 severity subgroups according to the Fazekas scale. RESULTS: The MoCA scores were lower in the WMIL group. Our research indicates that MoCA is a more sensitive screening tool than the commonly used MMSE in detecting cognitive impairment in patients with WMIL. CMRglu values of gray matter were decreased in the WMIL group. Reductions of CMRglu in parietal lobe, frontal lobe, and white matter centrum semiovale were observed to different degrees in the WMIL groups according to the modified Fazekas scale. A significant negative correlation was found between executive function and CMRglu in the frontal lobe. CONCLUSIONS: MoCA appears to be a more sensitive screening tool than the commonly used MMSE in detecting cognitive impairment in patients with WMIL. CMRglu can potentially be used as a biomarker for predicting the severity of WMIL.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".