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Record W4409260875 · doi:10.1016/j.cimid.2025.102337

DNA damage induced by parasitic infections in humans and animals

2025· review· en· W4409260875 on OpenAlexfundno aff
Milan Rajković, Gunita Deksne, Lada Živković, Elīna Ļeonova, Biljana Spremo‐Potparević, Nikolajs Sjakste

Bibliographic record

VenueComparative Immunology Microbiology and Infectious Diseases · 2025
Typereview
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsnot available
FundersFaculty of Veterinary Medicine, University of CalgaryMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsBiologyDNADNA damageZoologyGenetics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.351
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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