Essential Oils and Latices as Novel Antiviral Agent Against Potato Leaf Roll Virus and Analysis of Their Phytochemical Constituents Responsible for Antiviral Activity
Bibliographic record
Abstract
The present work was aimed to limit the practice of using pesticides in the managing program of the potato leaf roll disease on potato plants as the misuse of some pesticides had caused severe problems. The study was focused to find compounds nontoxic and safe for the biome. The antiviral activity of essential oils and latices tapped from different plants was investigated against potato leaf roll virus (PLRV). Essential oils from Eucalyptus citriodora leaves, Clove buds, and Fennel seeds were extracted. Latex of Aloe vera, Calotropis procera and Ficus elastic were collected. Essential oil and latex spray solutions were prepared at 5 and 10% concentration. All the sprayings were done at 7 days interval. Inhibition of viral replication was monitored by amplification of PLRV capsid protein gene and mRNA expression using RT-PCR technique. Results depicted inhibitory potential of all the tested essential oils and latices against PLRV infestation and also the effect was found concentration dependent. Our data showed that Aloe vera latex and clove essential oil caused maximum inhibition viral mRNA at 10% concentration. Qualitatively analyzed Tannin, Phlobactanins, Saponin, Flavonoids, Sterols and Terpens from essential oils and latices gave positive results in most of the oils and lesser content in plant latices. In the GC-MS analysis, high contents of some bioactive phytochemical compounds identified were Eugenol in Clove buds, Eucalyptol, D-Limonene and L-Limonene both in Eucalyptus leaves and Fennel seed oils and Fenchone in Fennel seeds. As this research work illustrates, there is innumerable potential of plants essential oils/latex waiting to be evaluated and exploited against genetically and functionally diverse plant viruses.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".