Effects of three different types of exercise on blood leukocyte count during and following exercise
Bibliographic record
Abstract
CONTEXT: High-intensity exercise causes tissue damage, production of stress hormones, and alterations in the function and quantity of various immune cells. Many clinical-physical stressors such as surgery, trauma, burns and sepsis induce a pattern of hormonal and immunological response similar to that of exercise. It has thus been suggested that heavy exercise might be used to cause graded and well-defined amounts of muscle trauma, thereby serving as an experimental model for inflammation and sepsis. OBJECTIVE: In order to explore whether some form of strenuous exercise might provide an useful model for the inflammatory process, we studied the effects of three different exercise protocols on blood leukocyte count during and following exercise. DESIGN: Four different experimental conditions, using a randomized-block design. SETTING: Defence and Civil Institute of Environmental Medicine, North York, Ontario, Canada. PARTICIPANTS: Eight healthy and moderately fit males. PROCEDURES: Participants were each assigned to four experimental conditions. Subjects performed 5 minutes of cycle-ergometry exercise at 90%, 2 hours of cycle-ergometry exercise at 60%, a standard circuit of resistance exercises with 3 sets of 10 repetitions at 60 to 70% of one-repetition maximum (1-RM) force at each of 5 different stations; or they remained seated for 5 hours. DIAGNOSTIC TEST USED: Flow cytometric analysis. MAIN MEASUREMENTS: Blood samples were analyzed for total leukocyte counts, total T cells, T helper/inducer cells, T suppressor/cytotoxic cells, B cells, cytolytic T cells, and natural killer cells. RESULTS: The peak aerobic and prolonged submaximal exercise induced similar alterations in cell counts. These changes were generally larger than those produced by the resistance exercise, although both resistance and peak aerobic exercise resulted in a significantly longer-lasting decrease in the CD4+/CD8+ ratio than the submaximal exercise bout did. CONCLUSION: The data suggest that, of the three exercise patterns tested, prolonged aerobic exercise induced the largest and most readily measured patterns of immune response. Nevertheless, the changes provided only a partial model for the clinical inflammatory process.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".