METHODS: Characterization of the synthesized AuNPs was done by different techniques such as ultraviolet-visible spectrum absorption, X-ray diffraction, dynamic light scattering, Fourier transform infrared spectroscopy, transmission electron microscopy, and energy-dispersive X-ray analysis.
RESULTS: All the results showed the successful green synthesis of AuNPs from Sx, which induced apoptosis of C666-1 cell line (NPC cell line). There was a decline in both cell viability and colony formation in C666-1 cells upon treatment with Sx-AuNPs. The cell death was proved to be caused by autophagy and mitochondrial-dependent apoptotic pathway.
CONCLUSION: Thus, due to their anticancer potential, these nanoparticles coupled with Sx can be used for in vivo applications and clinical research in future.
PURPOSE: To examine the accuracy of AI-based radiomics in diagnosis, prognosis assessment and predicting the diagnostic value of radiomics for pelvic LN metastasis in cervical cancer patients.
MATERIAL AND METHODS: The study included 118 female patients with 660 LNs and 118 merged LNs. Four imaging histology models-decision tree, random forest, logistic regression, and support vector machine (SVM)-were created in this study. The imaging histology features were extracted from both the independent and merged LN groups. The AUC values for the test sets and the training sets of the four imaging histology models were compared for the independent LN group and the merged LN group. The DeLong test was used to compare the models.
RESULT: The imaging histology prediction model developed in the merged LN group outperformed the independent LN group in terms of test set AUC (0.668 vs. 0.535 for decision tree, 0.841 vs. 0.627 for logistic regression, 0.785 vs. 0.637 for random forest, 0.85 vs. 0.648 for SVM) and accuracy (0.754 vs. 0.676 for decision tree, 0.780 vs. 0.671 for random forest, 0.848 vs. 0.685 for logistic regression, 0.822 vs. 0.657 for SVM).
CONCLUSION: The constructed SVM imaging histology model for the merged LN group might be advantageous in predicting pelvic LN metastasis in cervical cancer.
METHODS: In this work, we performed a systematic review and meta-analysis to precisely examine the association between circulating levels of leptin and adiponectin and CRC risk. A systematic literature search was performed in PubMed/MEDLINE, Scopus, Web of Science, and EMBASE databases from inception until October 2020. The pooled effect size was then estimated by calculating the odds ratio (OR).
RESULTS: A total of 23 records (comprising 26 studies) were included in the meta-analysis. The overall analysis found that circulating levels of leptin and adiponectin were not significantly associated with CRC risk (P > 0.05). Interestingly, subgroup analysis revealed that a higher level of adiponectin was significantly associated with an increased CRC risk among overweight individuals (OR = 1.16; 95 % CI: 1.02, 1.32), and a decreased CRC risk among normal weight individuals (OR = 0.76; 95 % CI: 0.62, 0.92). Besides, a higher level of adiponectin was also significantly associated with a decreased risk of CRC in men (OR = 0.76; 95 % CI: 0.59, 0.98).
CONCLUSIONS: In conclusion, circulating leptin level was not associated with CRC risk, but that of adiponectin was associated with CRC risk only in specific subgroups.