Hyperhydricity Syndrome in In Vitro Plants: Mechanisms, Physiology, and Control
Bibliographic record
Abstract
Understanding the physiological characteristics of hyperhydric plantlets is ultimately necessary since hyperhydricity results in financial loss for in vitro plants from a commercial perspective. Although many studies report the possible causes and symptoms of hyperhydricity, knowledge of it remains limited. This review aims to provide an integrated overview of this phenomenon and outline the perspectives for its prevention. First, we summarize the factors of in vitro hyperhydricity, including gelling agents, growth regulators, vessel ventilation and gas exchange, light, and osmotic conditions. Second, we describe physiological and internal changes commonly observed in hyperhydric plants, such as ROS/ethylene imbalance, altered antioxidant capacity, defects in the cell wall, and lignification. Third, we outline ultrastructural characteristics and accumulate HPLC findings to recognize the metabolite profiles of hyperhydric plantlets. Fourth, we introduce emerging AI-assisted MLM (machine learning model) approaches to detect and optimize the culture parameters to prevent hyperhydricity. Finally, we evaluate the strategies for the protection of the culture from hyperhydric conditions. This structured overview intends to reduce hyperhydricity in commercial and research settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".