Improvement in Expanded Disability Status Scale (EDSS) and anti-inflammatory parameters in patients with multiple sclerosis following oral consumption of N-163 strain of <i>Aureobasidium pullulans</i> produced beta glucan in a pilot clinical study
Bibliographic record
Abstract
Abstract Introduction Multiple Sclerosis (MS) is a debilitating neurodegenerative disease in which demyelination due to auto-inflammation is considered to be the underlying pathogenesis, though the exact etiology is not known. Most of the management strategies involve medications that are anti-inflammatory or immune-suppressive, which do have associated side effects. In this study we have evaluated in MS patients, the clinical effects of a novel beta-glucan which has a track record of anti-inflammatory, immune-modulating potentials in earlier clinical and pre-clinical studies. Method The study involved 12 MS patients who consumed two sachets of N-163 strain of Aureobasidium pullulans produced B-Glucan, daily for 60 days along with routine medication. Results The Expanded Disability Status Scale (EDSS) improved by 0.5 in two patients and by 1 in one patient post-intervention, worsened in 1 patient, remaining stable in the rest. Decrease in IL-6, improvement in CD4+ve, CD19+ve, CD3+ve, and CD8+ ve cell count, increase in Lymphocyte to C-reactive protein ratio (LCR), Leukocyte to CRP ratio (LeCR) and a decrease in Neutrophil to Lymphocyte ratio (NLR) were observed. Conclusion This study having proven the safety of N-163 strain of A . pullulans produced B-Glucan food supplement and the efficacy by improvement in the EDSS score, besides beneficial modulation of inflammation and immune parameters of relevance in MS patients in a short duration of 60 days, has significant potential as a disease modifying adjuvant in MS. Immunological parameters like NLR, LCR, LeCR correlating with clinical improvement, in line with earlier reports using the same beta-glucans, gain further significance for their potentials as biomarkers in MS.
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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) | 0.001 | 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".