Levels of peripheral T helper17 cells and serum T helper17-related cytokines in patients with multiple sclerosis: a Meta-analysis
Bibliographic record
Abstract
Objective To test whether T helper 17 (Th17) cells was involved in the pathogenesis of multiple sclerosis (MS) by conducting a meta-analysis of the researches documenting the levels of Th17 cells and Th17-related cytokines [interleukin (IL)-17, IL-23] in patients with MS. Methods We identified articles reporting proportion of Th17 cells and the serum levels of IL-17, IL-23 in MS patients by searching CNKI, Wanfang med online, Embase, PubMed, Cochrane, WebofKnowledge, Food and Drug Administration (FDA). gov, and ClinicalTrials.gov. We registered this study at the International Prospective Register of Systematic Reviews (PROSPERO) (number CRD42017059113). The evidence level of selected studies was identified by guidelines from the Oxford Center for Evidence-Based Medicine 2011. We employed the Newcastle-Ottawa Quality Assessment Scale (Case Control Studies) to perform the included resear. Stata 12.0 was adopted for processing Meta-analysis of the data by using a random effects model. Results A total 16 researches were included in our Meta-analysis from 587 identified studies. The proportion of Th17 cells [1.89%(1.10%, 2.69%),P<0.01] and the levels of serum IL-17 [2.77(1.77, 3.77) pg/ml, P<0.01] and IL-23 [2.96(1.51, 4.40) pg/ml, P<0.01] increased dramatically in MS individuals compared with control subjects. Conclusion The results of this Meta-analysis has demonstrated that MS patients have a higher proportion of Th17 cells in higher levels of serum IL-17 and IL-23 compared to control subjects, which has demonstrated that Th17 cells may play an vital role in the pathogenesis process of MS patients. Key words: Multiple sclerosis; T helper 17 cells; Iinterleukin-17; Iinterleukin-23
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.078 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".