DNA damage induced by parasitic infections in humans and animals
Bibliographic record
Abstract
Pathologies caused by parasitic infections, including protozoans and helminths remain a burden for healthcare in many countries. The DNA damage is produced by numerous parasites, both protozoans and helminths. However, the exact number of cancer-causing parasites and their role in neoplasma formation is still undetermined. The progression and dynamics of parasitic infections are significantly influenced by endogenously induced increase in oxidative stress (OS). Increased ROS production undermines antioxidant defense mechanisms by disrupting the balance between prooxidants and antioxidants, causing structural damage to important biomolecules, including host DNA. The generation of DNA damage possibly leads to the progression of carcinogenesis. However, direct DNA damage by parasites, eggs and factors released by parasites is also possible, and it leads to genomic instability that is a hallmark of most human and animal cancers. Understanding the way parasites induce DNA damage in the hosts may be helpful in the control of parasitic infections and the prevention of parasite-induced malignancies, ultimately benefiting the health of humans and animals. This review article offers an updated overview of parasitic infection-induced DNA damage mechanisms.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".