Application Study of Brain Structure and Functional Magnetic Resonance Imaging in Patients with Systemic Lupus Erythematosus and Cognitive Dysfunction.
Bibliographic record
Abstract
Objective: This study was aimed to investigate the application value of brain magnetic resonance imaging (MRI) technique, including arterial spin labeling (ASL) and diffusion tensor imaging (DTI) in patients with systemic lupus erythematosus (SLE) and cognitive dysfunction (CDF). Methods: A total of 50 patients with SLE admitted to the hospital from September 2020 to December 2022 were selected and divided into the group with CDF (n = 21) and the group without CDF (n = 29) according to the score of Montreal Cognitive Assessment Scale (MoCA). Additionally, 10 healthy individuals who underwent physical examinations during the same period were recruited as controls. After the conventional MRI, DTI and ASL data of all subjects were collected, statistical parametric mapping software combined with voxel morphology is applied for gray matter volume, white matter and gray matter cerebral blood flow (CBF) analysis among different groups. Results: There is a statistically significant difference in conventional MRI findings between the SLE group and the control group (P < .05). However, There was no significant difference in white matter fractional anisotropy (FA) values between the two groups (P > .05). The apparent diffusion coefficients (ADC) of the right precuneus and the right Brodmann's area 21 and 6 in SLE patients with CDF were significantly higher than SLE patients without CDF (P < .05). In comparison to the non-CDF group, the CDF group exhibited reduced gray matter volume, primarily in the anterior cingulate gyrus, left frontal lobe, and right insula (P < .05). Meanwhile, the white matter and gray matter cerebral blood flow (CBF) of SLE patients with CDF were significantly lower than those without CDF. (P < .05). Correlation analysis showed that the MoCA score was positively associated with the volume of gray matter in the right insula, bilateral frontal lobe, left temporal lobe, and cingulate gyrus (P < .05). Additionally, MoCA score was also found to be positively associated with the CBF of white matter and gray matter (P < .05). Conclusions: Alterations in gray matter volume and CBF in SLE patients are closely associated with combined CDF and can be observed by DTI and ASL techniques.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".