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Record W3193426500 · doi:10.1016/j.biopha.2021.112040

Effects of chemotherapeutic agents on male germ cells and possible ameliorating impact of antioxidants

2021· review· en· W3193426500 on OpenAlexaff
Soudeh Ghafouri‐Fard, Hamed Shoorei, Atefe Abak, Mohammad Seify, Mahdi Mohaqiq, Fatemeh Keshmir, Mohammad Taheri, Seyed Abdulmajid Ayatollahi

Bibliographic record

VenueBiomedicine & Pharmacotherapy · 2021
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsCentennial College
FundersShahid Beheshti University of Medical Sciences
KeywordsCisplatinCyclophosphamidePaclitaxelPharmacologyDoxorubicinChemotherapyIn vivoDrugSpermatogenesisMedicineBiologyInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

Treatment of cancer in young adults is associated with several side effects, particularly in the reproductive system. Detrimental effects of chemotherapy on the germ cells depend on many factors including primary semen parameters, the way of drug administration, the kind and dose of chemotherapeutic regimens, and the phase of spermatogenesis during the time of drug administration. Lack of appropriate fertility preservation treatments particularly in the affected children necessitates the introduction of methods to amend the harmful effects of chemotherapeutic agents on male germ cells. Several studies have assessed the toxic effects of chemotherapeutic agents in rodent models and tested a number of antioxidants to evaluate their possible impact on the preservation of sperm cells. In the present manuscript, we describe the effects of the mostly investigated chemotherapeutic drugs in this regard i.e., cisplatin, doxorubicin, paclitaxel, 5-fluorouracil, and cyclophosphamide. As several in vivo and in vitro studies have shown the impact of antioxidants on chemotherapy-induced damage of sperms, we also describe the protective effects of antioxidants in this regard.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.413
Teacher spread0.369 · 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 designBench or experimental
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

Citations64
Published2021
Admission routes1
Has abstractyes

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