Using flow-cytometry in measuring platelet activity in type 2 diabetes and predicting macrovascular complications.
Bibliographic record
Abstract
Platelets are hyperactive in patients with type2 diabetes (T2DM), they adhere to vascular endothelium and play a key role in macrovascular complications. Platelets activity can be measured by flow-cytometry (cluster of differentiation (CD) 41, CD 42, CD 62, CD 63), which allows detection of surface antigens in a sensitive and specific manner. This study aimed to describe platelets activity in T2DM in association with cardiovascular and cerebrovascular complications in relation to duration of diabetes (DM). This was a case-control study with 130 participants (65 diabetic cases and 65 normal controls). All cases were subjected to history and clinical examination, base-line laboratory investigations and surface expression of platelets receptors e.g. CD 41% and mean fluorescent intensity (MFI), CD 42% and MFI, CD 62% and MFI, CD 63% and MFI were determined by flow-cytometry. There was a statistically significant higher expression of CD 62%, CD 62 MFI, CD 63% and CD 63 MFI (p < 0.001 for all) in diabetic cases compared to controls. There were significantly higher CD 62 %, CD 62 MFI, CD 63% and CD 63 MFI in cases with cardiovascular complications (p=0.001, p < 0.001, p=0.05 and p=0.007, respectively) and in cases with cerebrovascular complications compared to cases without complications (p=0.05, p=0.008, p=0.035, p=0.017, respectively). A significant positive correlation was found between glycated hemoglobin, body mass index and CD 62 %, CD 62 MFI and CD 63%. Using the receiver operating characteristic curve showed that CD 62 %, CD 62 MFI, CD 63 % and CD 63 MFI have a diagnostic ability to early predict DM (area under the curve (AUC)=0.998) as well as cardiovascular (AUC=0.855) and cerebrovascular (AUC=0.765) complications. In conclusion CD 62%, CD 62 MFI, CD 63% and CD 63 MFI markers have a diagnostic ability for early prediction of cardiovascular and cerebrovascular complications among diabetic patients.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".