Neuroprotective Effect of Monosialotetrahexosylganglioside (GM1) on Patients with Parkinson’s Disease Anesthetized by Ketamine under Denoising Algorithm-Based Ultrasound Image Diagnosis
Bibliographic record
Abstract
This study was to investigate the value of ultrasound technology based on the bilateral filtering noise elimination algorithm in evaluating the neuroprotective effect of monosialoganglioside in ketamine-anesthetized Parkinson’s disease patients. The research subjects were 75 patients with Parkinson’s disease admitted to the hospital. The patients were randomly divided into three groups according to different treatment methods: A (GM1 + ketamine anesthesia group), B (conventional treatment + ketamine anesthesia group), and C (GM1 + nonketamine anesthesia group), with 25 patients in each group. Twenty-five healthy people with similar general data in the three groups (groups A, B, and C) were also selected as the control group (group D). All patients underwent ultrasonography, and ultrasound images were processed using the bilateral filter noise elimination. Structural similarity (SSIM), mean absolute error (MAE), and peak signal to noise ratio (PSNR) were used to evaluate the treatment effect. Plasma phospholipids, the third part of the PD unified score scale, Montreal cognitive assessment scale, and other indicators were analyzed and compared among the four groups. The bilateral filtering image noise was effectively suppressed, and the edge details were kept well. Some of the weak edges and texture information in the image were eliminated, the visual effect was ideal, and the accuracy of the edges of the picture remained good. The serotonin lipid level in group A was greatly lower than the serum phospholipid level in group B after GM1 treatment (6.55 VS 7.84, P < 0.05 ). Compared with that before treatment, the serotonin lipid level of group A patients decreased after the treatment, and the difference was considerable (7.46 VS 6.55, P < 0.05 ). In short, GM1 had a protective effect on the nerves of patients with Parkinson’s disease anesthetized by ketamine.
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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 | 0.000 |
| 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 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".