Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

Abstract

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure−activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.

Trichostatin C Synergistically Interacts with DNMT Inhibitor to Induce Antineoplastic Effect via Inhibition of Axl in Bladder and Lung Cancer Cells

“…The MTT assay was performed to assess cell viability, following established protocols [ 29 ]. Briefly, cells were seeded into 96-well plates (Corning Inc., Corning, NY, USA) one day prior to drug treatment.…”

“…The absorbance of each well was measured at 544 nm (test wavelength) and 690 nm (background) using the MultiskanTM FC Microplate Photometer (Thermo Fisher Scientific, Waltham, MA, USA). The IC 50 values, the concentration of the cytotoxic agent that led to a decrease of 50% in the recorded signal, were calculated by using GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA, USA) [ 29 ].…”

Investigating Genetic Diversity and Population Structure in Rice Breeding from Association Mapping of 116 Accessions Using 64 Polymorphic SSR Markers

“…The ideal value of K was determined using the Delta K statistic and L(K), as described by Evanno et al [33], and analyzed using a structure harvester [34]. GenAlex 6.5 was utilized to compute various genetic parameters, including the number of observable alleles (Na), and molecular variance (AMOVA) [35][36][37].…”

Investigating Genetic Diversity and Population Structure in Rice Breeding from Association Mapping of 116 Accessions Using 64 Polymorphic SSR Markers

“…The ideal value of K was determined using the Delta K statistic and L(K) as described by Evanno et al [34]and analyzed using structure harvester [35]. GenAlex 6.5 was utilized to compute various genetic parameters, including the number of observable alleles (Na), number of effective alleles (Ne), Shannon's information index (I), and molecular variance (AMOVA) [36][37][38].…”