Entire project-raw mass spectrometry data of the positive protein spots (band 1-4).
Bibliographic record
Abstract
Background: Mimiviruses or giant viruses that infect amoebas have the ability to retain the Gram stain, which is usually used to colour bacteria. There is some evidence suggesting that Mimiviruses can also infect human cells. Guided by these premises, we performed a routine Gram stain on a variety of human specimens to see if we could detect the same Gram positive blue granules that identify Mimiviruses in the amoebas. Methods: We analysed 24 different human specimens (liver, brain, kidney, lymph node and ovary) using Gram stain histochemistry, electron microscopy immunogold, high resolution mass spectrometry and protein identification. Results: We detected in the human cells Gram positive granules that were distinct from bacteria. The fine blue granules displayed the same pattern of the Gram positive granules that diagnose Mimiviruses in the cytoplasm of the amoebas. Electron microscopy confirmed the presence of human Mimiviruses-like structures and mass spectrometry identified histone H4 peptides, which had the same footprints as giant viruses. However, some differences were noted: the Mimivirus-like structures identified in the human cells were ubiquitous and manifested a distinct mammalian retroviral antigenicity. Conclusions: Our main hypotheses are that the structures could be either giant viruses having a retroviral antigenicity or ancestral cellular components having a viral origin. However, other possible alternatives have been proposed to explain the nature and function of the newly identified structures.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.063 |
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".