Bibliographic record
Abstract
Aims: Drug-resistant Mycobacterium tuberculosis is very serious threat to public health in clinical, which has become a difficult problem in the world. The study is focus on the effect of Taraxacum mongolicum extract on a rat model induced by multi-drug resistant Mycobacterium tuberculosis (MDR-TB). The bacteria were isolated from sputum samples among patients with pulmonary tuberculosis so as to select of the appropriate Chinese herbal medicine in treating tuberculosis. Methods: The immunologic response is predominantly cellular immunity after Mycobacterium tuberculosis infects human body, which expresses IFN-c, INF-a to kill intracellular parasitic bacteria. In the immune response, Th2 cell mainly takes part in humoral immunity, which can kill extracellular bacteria and parasites from the expression of IL-4, IL-10 and other cytokines. In this study, a total of 45 adult, male Kunming rats were infected with MDR-TB to establish animal model, which randomly split into Taraxacum mongolicum extract group and the model group, and the normal group as control. The rats were killed after 4 weeks, and then PBMCs were separated by density gradient centrifugation. The levels of IFN-c, IL-12, IL-4 and IL-10 in serum were examined using ELISA assay. The contents of mRNA in cytokines were detected using RT-PCR method. The contents of CD3 + , CD4 + and CD8 + T cells in PBMCs were determined by FCM. Results: The level of rat IL-4 in the Taraxacum mongolicum extract group by ELISA assay were 6.24 AE 1.31 pg/mL, IL-10 was 12.22 AE 3.12 pg/mL, IL-12 was 2.87 AE 0.85 pg/mL and IFN-c was 2.51 AE 0.65 pg/mL. The differences were significant, which compared with the model group (P < 0.05). Gray ratios of IFN-c, IL-12, IL-4, IL-10 and Granulysin mRNA contents in rats PBMCs were 0.30 AE 0.05, 0.79 AE 0.08, 0.51 AE 0.05, 0.72 AE 0.07 and 0.29 AE 0.06. The results demonstrated significant decreases in IL-12 and IFN-c, significant increases in IL-4 and IL-10 (P < 0.01 or P < 0.05). The contents of CD3 + T cell and CD4 + T cell in PBMCs of Taraxacum mongolicum group were apparently decreased (P < 0.01), CD8 + T cell content was increased (P < 0.05), and the ratio of CD4 + / CD8 + was also decreased (P < 0.01). Conclusions: Taraxacum mongolicum extract can enhance cellular immune response through up-regulating genetic transcription levels in rats and increased CD8 + T cell proportion, accordingly it provide the approach on healing tuberculosis with traditional Chinese medicine extract.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.658 | 0.498 |
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".