Dynamic Changes of Protein Expression Peripheral Blood Mononuclear Cell of Systemic Lupus Erythematosus with Internal Heat Due to Yin Dificiency
Bibliographic record
Abstract
Objective To investigate the dynamic changes of protein expression of peripheral blood mononuclear cell(PBMC) of systemic lupus erythematosus(SLE) patients with internal heat due to Yin deficiency(IHDYD) according to three different stages: before cure,6 weeks and 12 weeks treating with nourishing Yin clearing heat formula(NYCHF). Methods Surface-enhanced laser desorption/ionization (SELDI) method was used to protein build expression of PBMC of 15 female IHDYD SLE patients with the score between 5 to 25 according to SLEDAI system of the United States and Canada SLE center,and ZUCI-PDAS software package was used to analyze protein expressing difference,adopt the gene data base and the ExPASy to analyze the character of protein mark. Results After 12 weeks,twenty protein markers had significant differences. Discrimination mode with four markers(4787,2724,4804,4831) could discern samples of prior treatment or after 12 weeks' treatment with a exactitude rate as 100% and 90%. Conclusion Differences of protein expression were found before,during and after the treatment. NYCHF may cure IHDYD SLE patients by regulating protein expression of PBMC. Further study may find treatment-related protein.
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 | 0.000 | 0.000 |
| 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".