ASSOCIATION OF BLOOD COMPOSITION OF HORMONES, CYTOKINES AND LEUKOCYTES WITH LASER TREATMENT OUTCOMES OF KELOID SCARS
Bibliographic record
Abstract
One of the topical aspects of the problem of keloid treatment is the lack information about biomarkers that can predict the success of the treatment of this pathology. The aim of the work is to identify the association of changed levels of hormones, cytokines and the number of leukocytes in the blood with the laser treatment result of keloid scars. Materials and methods. Hormonal, cytokine and leukocyte blood composition was studied in 45 women: 30 with keloid scars, 15 with normotrophic scars (control). Blood was taken before treatment on the 5th–7th day of the menstrual cycle. Keloids were treated with multiple laser perforations of the scar tissue. Clinical characteristics of keloid scars were assessed using the Vancouver scale before treatment and after 3 months. According to the result of treatment, two subgroups were formed: with positive dynamics of keloids and with no result of treatment. Data analysis was carried out using non-parametric statistics, the level of statistical significance was p<0.05. Results and discussion. In all patients with keloids, the blood level of cortisol is reduced, and tumor necrosis factor-alpha is increased, which promotes to the proliferation of fibroblasts with inhibition of apoptosis. In addition, in the subgroup with a positive result of treatment, the number of segmented neutrophils and the level of anti-inflammatory interleukin-10 were increased. Discriminant analysis confirmed the high informational significance of these indicators. In patients with no result of keloid treatment, the level of a growth hormone is sevenfold increased, the concentration of luteinizing hormone, prolactin, interleukin-10 are reduced. That can lead to a weakening of anti-inflammatory effects and stimulation of fibrosis. Discriminant analysis highlighted the high informational significance of cortisol, growth hormone and tumor necrosis factor-alpha. Regression analysis established the association of detected changes in blood composition with clinical parameters of keloids in each subgroup. Conclusion. The revealed differences in the blood composition in patients with keloid scars are associated with a positive or negative result of laser treatment of keloids.
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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".