Role of Neurotransmitters (Biomediators) in Plant Responses to Stress
Bibliographic record
Abstract
Plants possess a complex signaling system that enables them to sense and adapt to various environmental stressors, including abiotic factors like extreme temperatures, drought, salinity, and toxic heavy metals. While the roles of hormones and signaling molecules in plant stress responses are well established, the involvement of neurotransmitters-traditionally linked to animal nervous systems-in plant stress physiology is a relatively underexplored area. Recent findings indicate that neurotransmitters such as gamma-aminobutyric acid, glutamate, serotonin, and dopamine play crucial roles in several physiological processes within plants. They regulate ion channels, adjust stomatal movements, modulate the production of reactive oxygen species, and influence gene expression. Evidence suggests that these neurotransmitters enhance antioxidant defense mechanisms and regulate stress-responsive pathways vital for plant stress tolerance. Additionally, under stressful conditions, neurotransmitters have been shown to impact plant growth, development, and reproductive activities. This review aims to illuminate the emerging understanding of neurotransmitters as key biomediators in plant responses to abiotic stress.
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) | 0.003 | 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".