Effects of experimental cervical spinal cord injury on peripheral adaptive immunity
Bibliographic record
Abstract
Adaptive immunity is critical for controlling infections, which are a leading cause of morbidity and mortality in patients with spinal cord injury (SCI). In rats and mice, compromised peripheral adaptive immune responses, as shown by splenic atrophy and lowered frequencies of peripheral lymphocytes, were shown to result from high-level thoracic SCI. However, whether cervical SCI, which is the most common level of SCI in humans, impairs adaptive immunity remains largely unknown. In the present study, we induced cervical SCI in rats at the C7/T1 level by clip compression and looked at changes in peripheral adaptive immunity at 2-, 10- and 20-weeks post-injury. Specifically, we quantified changes in the frequencies of T- and B- lymphocytes in the blood and the mandibular and deep cervical lymph nodes, which drain the cervical spinal cord. We also assessed changes in serum IgG and IgM immunoglobulin levels, as well as spleen size. We found a significant decline in circulating T- and B- cell frequencies at 10 weeks post-SCI, which returned to normal at 20 weeks after injury. We found no effect of cervical SCI on T- and B- cell frequencies in the draining lymph nodes. Moreover, cervical SCI had no effect on net spleen size, although injured rats had a higher spleen/body weight ratio than sham controls at all time points of the study. Lastly, IgG and IgM immunoglobulin declined at 2 weeks, followed by a significant increase in IgM levels at 10 weeks of injury. These data indicate that cervical SCI causes a significant imbalance in circulating lymphocytes and immunoglobulin levels at 2 and 10 weeks. As we discuss in this article, these findings are largely in line with clinical observations, and we anticipate that this study will fuel more research on the effect of adaptive immunity on SCI recovery.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".