Bibliographic record
Abstract
Maintenance of a stable hydrogen ion concentration ([H +]) is important in cells that have high metabolic rates, since they have elevated rates of hydrogen ion generation. One of such model systems are white blood cells, specifically polymorphonuclear leukocytes. These cells represent the very first line of protection against infections. In neutrophils during pathogen-induced oxygen consumption the cytosol concurrently experiences massive H+ generation. Failure to maintain a stable cytosolic pH would result in a decreased oxidative burst, inhibition of cellular migration and lower rates of phagocytosis. These protons can be eliminated either by consumption by buffering components and dismutation reactions or by extrusion across the plasma membrane. Efflux of protons can, in principle, be mediated through a H+ -conductive pathway that is expressed in neutrophils at high levels. The H+ conductance was postulated to be activated primarily by large depolarizations of the plasma membrane. I devised a new method to precisely measure the extent of depolarization of the plasma membrane resulting from neutrophil activation. Using soluble fluorescent probes and spectrofluorimetry, I managed to obtain reproducible measurements of membrane potential (E m = +58 ± 6 mV) in activated human neutrophils. The results suggest that H+-conductance contributes to the acid-base homeostasis during leukocyte activation. The pH regulation in leukocytes is critical also in the phagocytic vacuole. Using improved methods I have confirmed that phagosomal pH in neutrophils is neutral. I have established that the insertion of V-ATPases into the phagosomal membrane was reduced, and that passive proton (equivalent) permeability of the phagosomal membrane increased when the oxidase is active. These observations suggest that neutrophils rely mostly on bacteriostatic activity of reactive oxygen species rather than acidic hydrolazes. Finally, I have developed a novel method targeting a pH-sensitive fluorescent protein specifically to the peroxisomal lumen. This allowed me in detail re-examine the luminal pH of peroxisomes. Based on this non-invasive approach I have identified that peroxisomal pH was 6.9, resembling that of the cytosol. Further examination showed, that peroxisomes do not regulate their pH independently. Their large H+ (equivalents) permeability connects them with the buffer reservoir of the cytoplasm and with the homeostatic mechanisms that control cytosolic pH.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".