Plants are continuously challenged by diverse abiotic stresses, which compromise growth, photosynthesis, and nutrient homeostasis. This review aims to elucidate the roles of antioxidant systems and mineral nutrients in stress adaptation, and to highlight the potential of multi-omics approaches to enhance crop resilience. A comprehensive synthesis of current research on enzymatic and non-enzymatic antioxidant mechanisms, nutrient interactions, and stress physiology was performed. Multi-omics datasets—including genomics, transcriptomics, proteomics, metabolomics, ionomics, and miRNomics were analyzed to assess nutrient acquisition, redistribution, and signaling under stress. Genotype-specific responses, stress memory, and ROS–Ca2⁺–hormone cross-talk were emphasized. High-throughput phenotyping and genome-editing strategies were also considered. Evidence shows that plants employ integrated antioxidant systems to maintain redox balance and mitigate reactive oxygen species (ROS)-induced damage. Mineral nutrients act as enzymatic cofactors, regulate antioxidant activity, and modulate osmotic adjustment and signaling pathways. In addition, interactions between essential and toxic metals involve both competitive and protective mechanisms that influence metal uptake, transport, and detoxification. Multi-omics studies highlight genotype- and stress-history-dependent responses and reveal complex ROS–Ca2⁺–hormone signaling networks. The integration of antioxidant defenses, nutrient homeostasis, and signaling networks is critical for plant resilience under abiotic stress. Multi-omics and advanced phenotyping provide actionable insights for developing nutrient-efficient, stress-tolerant crops. Coordinating redox and nutrient signaling pathways represents a promising strategy to translate molecular basis into agronomic solutions for sustaining productivity under climate change. • Integrated networks mediate plant responses to abiotic stress. • ROS act as adaptive signals but, in excess, damage lipids, proteins, and DNA. • Enzymatic and non-enzymatic networks protect plants from stress-induced damage. • Nutrients sustain key functions during adverse conditions. • Metal toxicity disrupts ion balance, photosynthesis, and increases oxidative stress. • Multi-omics reveal how mineral nutrition is regulated under stress.
Jatropha vernicosa Brandegee, Euphorbiaceae, is an endemic plant used for its medicinal properties in Baja California Sur, Mexico. The sap is used for its wound-repairing effect by the native inhabitants. However, there are no scientific reports about its phytochemicals, cytotoxicity, and wound-healing properties of this important plant in México. The objective of this study is to evaluate J. vernicosa sap properties and wound-healing potential with three experiments: in vitro, in vivo, and ex vivo bioassays. Chemical, phytochemical, antioxidant potential, and cytotoxicity were analyzed in lyophilized J. vernicosa sap. Wound closure was analyzed using Detroit 548 cultured cells over time, and the wound-healing properties of J. vernicosa ointment were evaluated in vivo on Balb/c mice for 6 days. After 6 days, spleen leukocytes were isolated to analyze immunological parameters. Lyophilized J. vernicosa sap is rich in polyphenols such as flavonoids, tannins, and saponins, and has antioxidant properties. Its chemical composition is mainly composed of carbohydrates. Jatropha vernicosa sap is safe for cells at concentrations below 600 µg/ml. Interestingly, the cell culture experiment concluded that J. vernicosa sap extract induced a significantly higher rate of wound closure compared to the untreated control and the doxorubicin-treated group, particularly at 24 and 48 h post-scratch. The in vivo experiment showed that the wound area was smaller using J. vernicosa sap ointments compared with other treatments. Finally, immunological parameters after the ex vivo experiment show an anti-inflammatory or immunosuppressive effect. These results suggest that lyophilized J. vernicosa sap has biotechnological potential and wound-healing properties, and further studies are recommended to deepen the understanding of its biological activities and explore its possible applications.
In this work, cisplatin was encapsulated within both pristine and doped zigzag (14,0) SWBPNTs. Adsorption energy calculations were performed to determine the most stable system. Doping takes place when one carbon or titanium atom replaces one boron or phosphorus atom. The structural and electronic properties were investigated using periodic density functional theory (DFT) according to the PWscf code of the Quantum ESPRESSO package. The electron–ion interactions were treated with PAW pseudopotentials, while long-range van der Waals (vdW) interactions were incorporated through the Grimme DFT-D3 dispersion correction. The systems were separated by a vacuum space of 15 Å along the direction perpendicular to z to avoid spurious interactions between adjacent nanotubes. The adsorption energies are: Pristine SWBPNT (-0.92 eV), Carbon-doped SWBPNT (B position) (-0.66 eV), Carbon-doped SWBPNT (P position) (-0.35 eV), Titanium-doped SWBPNT (B position) (-3.14 eV), and Titanium-doped SWBPNT (P position) (-3.86 eV). The pristine and carbon-doped nanotubes are considered more favorable, provided that their adsorption energies are sufficient to ensure interaction without being so strong as to hinder desorption.
We present pyDielectriX, an open-source Python package for modeling and analysis of frequency-dependent dielectric response data, particularly those obtained from broadband dielectric spectroscopy of materials. The package contains a comprehensive library of dielectric models—Debye, Cole–Cole, Cole–Davidson, Havriliak–Negami, and fractional cap–resistor frameworks—organized into modular Python classes. pyDielectriX offers curve-fitting routines based on nonlinear least-squares optimization using the Levenberg–Marquardt algorithm. Furthermore, the package implements Bayesian optimization to improve parameter initialization and mitigate convergence to local minima arising from poor initial parameter selection. To assist users in model discrimination, pyDielectriX incorporates a probabilistic selection strategy based on fuzzy Bayesian networks, enabling objective comparison among competing dielectric models. Additionally, the Bayesian information criterion is included as a complementary deterministic metric to evaluate model parsimony and penalize overparameterization. A graphical user interface is provided to enhance accessibility and adoption by the dielectric spectroscopy community. The package supports dual-domain analysis within the relative permittivity and electric modulus formalisms. Benchmarking using experimental datasets from different sources demonstrates that pyDielectriX enables accurate parameter estimation, improves reproducibility, and facilitates physically interpretable analysis of complex dielectric spectra. The source code and documentation are freely available on GitHub, promoting transparency, reproducibility, and community-driven development.
