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Cancer is a very heterogeneous disease, and uncontrolled cell division is the main characteristic of cancer. Cancerous cells need a high nutrition intake to enable aberrant growth and survival. To do so, cancer cells modify metabolic pathways to produce energy and anabolic precursors and preserve redox balance. Due to the importance of metabolic pathways in tumor growth and malignant transformation, metabolic pathways have also been given promising perspectives for cancer treatment, providing more effective treatment strategies, and target-specific with minimum side effects. Metabolism-based therapeutic nanomaterials for targeted cancer treatment are a promising option. Numerous types of nanoparticles (NPs) are employed in the research and analysis of various cancer therapies. The current review focuses on cutting-edge strategies and current cancer therapy methods based on nanomaterials that target various cancer metabolisms. Additionally, it highlighted the primacy of NPs-based cancer therapies over traditional ones, the challenges, and the future potential. [ABSTRACT FROM AUTHOR]

Early-senescence mutants are genetic materials important for the researches on the molecular mechanism of cell apop-tosis in plants. An early-senescence mutant named esl8 (early senescence leaf 8) has been screened from the mutagenesis library of rice variety Kefujing 7. Compared with the wild-type control, the leaves of the esl8 mutant displays a severe of early-senescence phenotype at heading stage in rice, and its agronomic traits, including plant height, tiller number, grain length, grains per panicle, and seed setting rate, were obviously impaired except for the 1000-grain weight. In leaves of the esl8 mutant, the chlorophyll content was abnormally decreased, and the programmed cell death accompanied by excessive accumulation of the reactive oxygen species and the malondialdehyde were detected by histochemical staining. Genetic analysis indicated that the early-senescence phenotype of the esl8 mutant was controlled by a recessive nuclear gene. Based on the strategy of map-based cloning, the esl8 gene was finely mapped into a 359-kb region flanking by two molecular markers (FM12-14 and FM12-15) on chromosome 12. The prediction and verification of candidate genes by PCR sequencing confirmed that esl8 was a new variational allele of the OsSL/ELLl gene. The sequence of mutation esl8 occurred in the conserved region of the corresponding wild-type gene. Our results provide a theoretical basis for further study on the biofunction and molecular mechanism of the protein encoding by ESL8 in early-senescence process. [ABSTRACT FROM AUTHOR]

The objective of the present study is to develop a method for rapid determination of the content of stevioside (ST) and rebaudioside A (RA) in Stevia Rebaudiana leaves. One hundred and five samples of stevia from different areas containing ST of 0.27%-1.40% and RA of 0.61%-3.98% were used. The 105 groups' NIRS diagram was processed by different methods including subtracting a straight line (SLS), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and so on, and then all data were analyzed by partial least square (PLS). The study showed that SLS can be used to extracted spectra information thoroughly to analyze the contents of ST, the correlation coefficients of calibration (Re), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.986, 0.341, 1.00 and 2.8, respectively. The correlation coefficients of RA was 0.967, RMSEC was 1.50, RMSEP was 1.98 and RPD was 4.17. The results indicated that near infrared spectroscopy (NIRS) technique offers effective quantitative capability for ST and RA in Stevia Rebaudiana leaves. Then the model of stevia dried leaves was used to compare with the stevia powder near infrared model whose correlation coefficients of ST was 0.986, RMSEC was 0.32, RMSEP was 0.601 and RPD was 2.86 and the correlation coefficients of RA was 0.968, RMSEC was 1.50, RMSEP was 1.48 and RPD was 4.2. The result showed that there was no significant difference between the model of dried leaves and that of the powders. However, the dried leaves NIR model reduces the unnecessary the steps of drying and grinding in the actual detection process, saving the time and reducing the workload.