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Record W4416684772 · doi:10.1111/ppl.70649

Role of Polyamines in Mitigating Salinity Stress in Ornamental and Food Crops

2025· article· en· W4416684772 on OpenAlexaff
Soheyla Mohammadi Alagoz, Behnam Asgari Lajayer, Zahra Azimzadeh, Younes Kheirizadeh Arough, B. Dell

Bibliographic record

VenuePhysiologia Plantarum · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSalinitySpermidinePutrescineSoil salinityAbiotic stressOrnamental plantSpermine

Abstract

fetched live from OpenAlex

Soil and irrigation salinity continue to have a major impact on the world's agriculture and horticulture, and loss of plant production is likely to worsen with global warming and climate change. Efforts to mitigate salinity stress and breed better salt-tolerant plants rely on our knowledge of plant response to abiotic stress at the physiological and molecular levels. Salinity usually leads to the accumulation of free and conjugated polyamines (PAs) in plant tissues. Putrescine (Put), and its derivatives spermine (Spm) and spermidine (Spd), perform critical functions by activating biochemical, physiological and molecular defense systems, thus reducing damage caused by salinity stress. Promoting endogenous levels of PAs can improve the salt tolerance of plants. Furthermore, the application of exogenous PAs has been shown to effectively mitigate salt stress across a range of commercially important plant species. This review critically examines the biosynthesis of PAs and their associated physiological, phytochemical, and molecular responses in plants under saline conditions. In addition, it evaluates the potential of PAs as a strategic tool for enhancing salinity tolerance. The review also highlights key gaps in current knowledge and proposes directions for future research to optimize the use of PAs in salinity stress management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.239
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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