The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.
Smart reprograming of plants against salinity stress using modern biotechnological tools
Open accessAbstract
Climate change gives rise to numerous environmental stresses, including soil salinity. Salinity/salt stress is the second biggest abiotic factor affecting agricultural productivity worldwide by damaging numerous physiological, biochemical, and molecular processes. In particular, salinity affects plant growth, development, and productivity. Salinity responses include modulation of ion homeostasis, antioxidant defense system induction, and biosynthesis of numerous phytohormones and osmoprotectants to protect plants from osmotic stress by decreasing ion toxicity and augmented reactive oxygen species scavenging. As most crop plants are sensitive to salinity, improving salt tolerance is crucial in sustaining global agricultural productivity. In response to salinity, plants trigger stress-related genes, proteins, and the accumulation of metabolites to cope with the adverse consequence of salinity. Therefore, this review presents an overview of salinity stress in crop plants. We highlight advances in modern biotechnological tools, such as omics (genomics, transcriptomics, proteomics, and metabolomics) approaches and different genome editing tools (ZFN, TALEN, and CRISPR/Cas system) for improving salinity tolerance in plants and accomplish the goal of "zero hunger," a worldwide sustainable development goal proposed by the FAO.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mechanisms of Salinity Tolerance
Munns, R., & Tester, M. (2008) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.59.032607.092911 · Cited by 13,792
Cpf1 Is a Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas System
Zetsche, B., Gootenberg, J. S., Abudayyeh, O. O., Slaymaker, I. M., Makarova, K. S., Essletzbichler, P., Volz, S. E., Joung, J., van der Oost, J., Regev, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.038 · Cited by 4,761
The map-based sequence of the rice genome
International Rice Genome Sequencing Project, & Sasaki, T. (2005) · Nature · doi:10.1038/nature03895 · Cited by 3,797
Mitochondrial TCA cycle metabolites control physiology and disease
Martínez-Reyes, I., & Chandel, N. S. (2020) · Nature Communications · doi:10.1038/s41467-019-13668-3 · Cited by 2,813
Wang, Y., Cheng, X., Shan, Q., Zhang, Y., Liu, J., Gao, C., & Qiu, J.-L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2969 · Cited by 2,072
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de los Campos, G., Burgueño, J., González-Camacho, J. M., Pérez-Elizalde, S., Beyene, Y., et al. (2017) · Trends in Plant Science · doi:10.1016/j.tplants.2017.08.011 · Cited by 1,839
Self-citationCermak, T., Doyle, E. L., Christian, M., Wang, L., Zhang, Y., Schmidt, C., Baller, J. A., Somia, N. V., Bogdanove, A. J., & Voytas, D. F. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr218 · Cited by 1,787
Impact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome: A Review
Raza, A., Razzaq, A., Mehmood, S. S., Zou, X., Zhang, X., Lv, Y., & Xu, J. (2019) · Plants · doi:10.3390/plants8020034 · Cited by 1,762 · cited 1×
“The food for the extra mouths will have to come from the marginal areas; hence, strong efforts and practically effective strategies are needed to enhance crop productivity, especially in the marginal areas in the face of ever-changing climate and various other biotic and abiotic stresses [2].”
Self-citationOne thousand plant transcriptomes and the phylogenomics of green plants
Initiative, O. T. P. T. (2019) · Nature · doi:10.1038/s41586-019-1693-2 · Cited by 1,734
Elucidating the molecular mechanisms mediating plant salt‐stress responses
Yang, Y., & Guo, Y. (2018) · New Phytologist · doi:10.1111/nph.14920 · Cited by 1,631
Cited by
Muhammad, M., Waheed, A., Wahab, A., Majeed, M., Nazim, M., Liu, Y.-H., Li, L., & Li, W.-J. (2024) · Plant Stress · doi:10.1016/j.stress.2023.100319 · Cited by 294
Yaqoob, H., Tariq, A., Bhat, B. A., Bhat, K. A., Nehvi, I. B., Raza, A., Djalovic, I., Prasad, P. V., & Mir, R. A. (2023) · GM Crops & Food · doi:10.1080/21645698.2022.2146952 · Cited by 64
Self-citationIntegrated approaches for increasing plant yield under salt stress
Ahmad, I., Zhu, G., Zhou, G., Younas, M. U., Suliman, M. S. E., Liu, J., Zhu, Y. M., & Salih, E. G. I. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1215343 · Cited by 48
“To cope with the negative effects of salt stress’s, plants use various responses, such as regulating gene expression and stimulating hormones (Raza et al., 2022; Feng et al., 2023).”
BackgroundTariq, A., Mushtaq, M., Yaqoob, H., Bhat, B. A., Zargar, S. M., Raza, A., Ali, S., Charagh, S., Mubarik, M. S., Zaman, Q. U., et al. (2023) · GM Crops & Food · doi:10.1080/21645698.2023.2219111 · Cited by 45
Self-citation
Metadata
- Authors
- Raza, A., Tabassum, J., Fakhar, A. Z., Sharif, R., Chen, H., Zhang, C., Ju, L., Fotopoulos, V., Siddique, K. H. M., Singh, R. K., Zhuang, W., & Varshney, R. K.
- Venue
- Critical Reviews in Biotechnology
- Publisher
- Informa UK Limited
- Published
- 2022-08-15
- Volume
- 43
- Issue
- 7
- Pages
- 1035-1062
- ISSN
- 0738-8551 (ISSN-L)
- 1549-7801
- OpenAlex
- W4291535903
- Cited by
- 257
- References
- 180
- FWCI
- 27.65
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Smart reprograming of plants against salinity stress using modern biotechnological tools. https://app.cochat.ai/papers/10.1080/07388551.2022.2093695
- The paper
- Raza, A., Tabassum, J., Fakhar, A. Z., Sharif, R., Chen, H., Zhang, C., Ju, L., Fotopoulos, V., Siddique, K. H. M., Singh, R. K., Zhuang, W., & Varshney, R. K. (2023). Smart reprograming of plants against salinity stress using modern biotechnological tools. Critical Reviews in Biotechnology. https://doi.org/10.1080/07388551.2022.2093695
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
