PATHOLOGICAL IMMUNITY CHANGES IN COMORBID PATIENTS WITH EXACERBATION OF BRONCHIAL ASTHMA
Bibliographic record
Abstract
The aim of the study – to investigate immunological changes in patients with exacerbation of bronchial asthma with different severity and controllability depending on the presence of comorbid pathology. Materials and methods of research. The work is based on the analysis of data from a comprehensive clinical and laboratory examination of 190 patients with exacerbation of asthma. The relative content of T–lymphocytes and their main subpopulations was determined on a flow cytofluorimeter FACS Calibur (Canada) by phenotyping lymphocytes with monoclonal antibodies (BECKMAN COULTER, USA) to surface membrane differentiation antigens (CD): CD3+19– (pan T-cells), CD4+–8+– (T−helpers/inducers), CD4–8+ (cytotoxic T lymphocytes), CD3–16+ (natural killers). To identify the imbalance of immunoregulatory subpopulations of T cells, the immunoregulatory index was calculated – the ratio of the number of CD4+8– Lf and CD4-8+ Lf. To calculate the absolute content in the peripheral blood of individual populations of Lf, leukogram indicators determined on the hematological analyzer ABH-miscros60, France were used. The proliferative activity of lymphocytes was evaluated in cultures of whole blood in the reaction of blast transformation of these cells with morphological accounting of the results. Statistical processing of the obtained data was carried out using licensed software products included in the Microsoft Office Professional 2007 package, license Russian Academic OPEN No Level № 43437596 in Excel. Conclusions. In moderate asthma, the frequency of pathological changes in the T-system of immunity did not depend on the presence of comorbid pathology, as a rule. In severe asthma, an increase in the content of pan–T cells occurred 1.2 times more often than in moderate asthma, but this was not observed in people with concomitant pathology. In this group, the frequency of increasing the content of T–helpers and decreasing the content of cytotoxic T – cells in patients with severe asthma without concomitant diseases was 1.3 times more frequent than in patients with comorbid pathology. No pathological changes in the T–system were detected in patients with controlled asthma. In partially controlled asthma, these changes were registered 1.5 times more often than in controlled asthma, and in its uncontrolled course, the prevalence of pathological changes in the T–system of immunity was much higher than in the two previous groups. In patients with concomitant diseases, these changes, as a rule, had a higher frequency than in patients without comorbid pathology.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".