Using Subjective Adjectives in Opinion Retrieval from Blogs.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evaluation and screening of vaccines against tuberculosis depends on development of proper cost effective disease models along with identification of different immune markers that can be used as surrogate endpoints of protection in preclinical and clinical studies. The objective of the present study was therefore evaluation of subcutaneous model of M.tuberculosis infection along with investigation of different immune biomarkers of tuberculosis infection in BALB/c mice. Groups of mice were infected subcutaneously with two different doses : high (2×10(6) CFU) and low doses (2×10(2) CFU) of M.tuberculosis and immune markers including humoral and cellular markers were evaluated 30 days post M.tuberculosis infections. Based on results, we found that high dose of subcutaneous infection produced chronic disease with significant (p<0.001) production of immune markers of infection like IFNγ, heat shock antigens (65, 71) and antibody titres against panel of M.tuberculosis antigens (ESAT-6, CFP-10, Ag85B, 45kDa, GroES, Hsp-16) all of which correlated with high bacterial burden in lungs and spleen. To conclude high dose of subcutaneous infection produces chronic TB infection in mice and can be used as convenient alternative to aerosol models in resource limited settings. Moreover assessment of immune markers namely mycobacterial antigens and antibodies can provide us valuable insights on modulation of immune response post infection. However further investigations along with optimization of study protocols are needed to justify the outcome of present study and establish such markers as surrogate endpoints of vaccine protection in preclinical and clinical studies in future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it