Synbiotic Adjuvant Therapy in Atopic Dermatitis: Our Experience
Bibliographic record
Abstract
Background: Atopic dermatitis is a disease with a lot of clinical interest because it is the point of attachment between allergic diseases and autoimmune diseases. Probiotics and synbiotics favor the expression of anti-inflammatory Th1 cytokines which produces therapeutic benefits in patients with atopic dermatitis as revealed by recent meta-analysis.Material and Method: Six patients (3 women and 3 men) suffering from moderate atopic dermatitis aged between 16 and 28 years were treated with Bifidobacterium lactis BS01, Lactobacillus rhamnosus LR05 and prebiotic fructo -oligosaccharides (2x109CFU) once daily in a period of four months added to their previously scheduled topical treatment. SCORAD index and atopic dermatitis quality of life test (QoLIAD) prior to treatment and four months after treatment were analyzed.Results: After 4 months of treatment we objectified clinical improvement by reducing the SCORAD index (average of 6 points) and better results in QoLIAD test in 5 of the 6 patients. Previously scheduled medical treatment remained unchanged and no side effect was observed in any of the patients treated.Discussion: Most of our patients treated obtained clinical improvement and in quality of life without adverse effects, this fact support the results of recent papers concluding that the use of probiotics in diary clinical practice is a safe coadyuvant and possibly effective in the treatment of atopic dermatitis.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".