The Isolation and Characterization of Low- and Normal- Density Neutrophils from Whole Blood
Bibliographic record
Abstract
Emerging research shows that the circulating neutrophil population in humans consists of diverse subtypes and should not be studied as a single population, as has been done historically. In particular, low-density and normal-density neutrophils (LDNs, NDNs) have been shown to have functionally and metabolically distinct profiles, a factor that must be considered when publishing neutrophil research. Here, we present a modified method for the untouched isolation and separation of LDNs and NDNs from whole blood. The density gradient medium (1.135 g/mL) is combined at 9:10 with 10x PBS. Specific density gradients of 55%, 70%, and 81% are subsequently made by combining the 100% density gradient medium with 1x phosphate-buffered saline (PBS). Neutrophils isolated from 12 mL of peripheral whole blood obtained from consented donors using a negative selection-based magnetic isolation kit are resuspended in the 55% fraction. A volume of 3 mL of the 81% and 70% fractions is layered into a 15 mL tube, followed by the 55% fraction containing total neutrophils. The density gradients are then centrifuged at 720 x g for 30 min. Two distinct bands are obtained at the 55%/70% interface (LDNs) and 70%/81% interface (NDNs). The cells are carefully pipetted into separate tubes and washed using PBS. The purity of the isolated fractions is determined using flow cytometry. Both LDNs and NDNs were defined as CD14lo CD15+ SSChi by flow cytometry. Isolation purity was calculated at ≥93% of viable cells for both types. This method provides a reliable and efficient approach for separating LDN and NDNs from peripheral blood, ensuring high purity and viability of the isolated cells. Enhancing the precision of neutrophil isolation facilitates more accurate downstream analyses of these distinct neutrophil subpopulations. These are critical for advancing our understanding of neutrophil heterogeneity and its implications in various physiological and pathological contexts.
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".