Predictive <i>N</i> -Glycan Signatures of Severe Traumatic Brain Injury in Biofluids Using LC–MS/MS
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Traumatic brain injury (TBI) poses a significant global health issue, frequently resulting in persistent and even lifelong cognitive and neurological impairments. Despite remarkable advances in biomarker discovery, significant challenges remain in the accurate diagnosis and prognosis of TBI. Glycosylation, an important post-translational modification of proteins and other biomolecules, plays an essential role in neuronal function and neuroinflammation. However, its contribution to the pathogenesis of TBI has been insufficiently investigated. This study examines changes in N -glycosylation patterns in serum and cerebrospinal fluid (CSF) from individuals with severe traumatic brain injury (sTBI) at various time points postinjury. Employing advanced glycomics methodologies and liquid chromatography–tandem mass spectrometry (LC–MS/MS), we identified 102 N -glycans in serum and 86 N -glycans in CSF, revealing substantial alterations in N -glycan expression, including differential expression of fucosylated and sialylated structures. Elevated fucosylation was observed in serum, whereas decreased fucosylation was found in CSF. Altered sialylation patterns were noted, suggesting glycosylation alterations in neuroinflammatory processes and possible neurodegeneration. Furthermore, our study examined N -glycans with isomeric properties. We identified several isomers that demonstrated potential as a biomarker panel reflective of TBI progression. Overall, these studies offer new insights into systemic and central nervous system-specific glycomic responses following sTBI and emphasize the potential of glycan-based biomarkers for monitoring specific changes as TBI progresses, which could be a possible target for enhanced TBI therapy and enhanced prognosis.
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 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 | 0.000 | 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) | 0.000 | 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 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".