Liver cancer remains a leading cause of death, despite advances in anti-cancer therapies. To develop novel drugs, natural products are being considered as a good source for exploration. In this study, a natural product isolated from a soft coral was applied to evaluate its anti-cancer activities in hepatocellular carcinoma SK-HEP-1 cells. Sinularin was determined to have half-maximal inhibitory concentration (IC50) values of ~10 μM after 24, 48, and 72 h. The TUNEL assay and annexin V/PI staining results showed that sinularin induced DNA fragmentation and apoptosis, respectively. An investigation at the molecular level demonstrated that the expression levels of cleaved caspases 3/9 were significantly elevated at 10 μM sinularin. Mitochondrial and intracellular reactive oxygen species (ROS) levels were significantly increased following sinularin treatment, which also affected the mitochondrial membrane potential. In addition, it significantly lowered the mitochondrial respiration parameters and extracellular acidification rates at 10 μM. Further investigation showed that sinularin significantly attenuated wound healing, cell migration, and potential colony formation at 10 μM. Fluorescence microscopic observations showed that the distribution of F-actin filaments was significantly altered at 10 μM sinularin. Supported by Western blot analyses, the expression levels of AKT, p-ERK (extracellular-signal-related kinase), vimentin and VEGF were significantly down-regulated, whereas p-p38, pJNK and E-cadherin were significantly increased. Overall, at the IC50 concentration, sinularin was able to significantly affect SK-HEP-1 cells.

Herein, we report the development of a novel enzymeless electrochemical biosensor for highly specific detection of creatinine utilizing zwitterion-functionalized cuprous oxide nanoparticles (Cu2O NPs). We utilized a simple yet effective alternative to traditionally used cover layers based on the surface engineering of Cu2O NPs with N-hexadecyl-N,N dimethyl-3-ammonio-1-propanesulfonate zwitterion. This surface modification generates a pseudo-proton-exchange membrane which electrostatically hinders interfering agents from reaching the electrode surface, thus resulting in highly specific creatinine detection without loss in sensitivity. To fabricate the enzymeless biosensor, single-crystalline Cu2O NPs were synthesized via a sulfonate ion-directed seed aging protocol and were simply drop-cast onto screen-printed carbon electrodes. The shape directional effect of sulfonate ions to induce truncation in the final morphologies of the synthesized Cu2O NPs is also reported for the first time. The creatinine biosensor demonstrated fast response time (<50 s), good reproducibility (RSD = 2.8%, n = 10), and high specificity against interferents like ascorbic acid, acetic acid, glucose, urea, and uric acid. A linear response to creatinine concentration from 10 to 200 μM (R 2 = 0.9876 and LOD = 5.0 μM) was observed, which covers the entire range of physiological creatinine in human serum. Moreover, robust storage stability with a negligible decrease in signal strength over an extended storage period of 6 months was achieved, thus highlighting the practical feasibility for point-of-care testing of creatinine.

The prevalence of type 2 diabetes (T2D) has been increasing drastically in recent decades. In the same time, it has been noted that dementia is related to T2D. In the past, traditional multiple linear regression (MLR) is the most commonly used method in analyzing these kinds of relationships. However, machine learning methods (Mach-L) have been emerged recently. These methods could capture non-linear relationships better than the MLR. In the present study, we enrolled old T2D and used four different Mach-L methods to analyze the relationships between risk factors and cognitive function. Our goals were first, to compare the accuracy between MLR and Mach-L in predicting cognitive function and second, to rank importance of the risks for impaired cognitive function in T2D. There were 197 old T2D enrolled (98 men and 99 women). Demographic and biochemistry data were used as independent variables and the cognitive function assessment (CFA) score was measured by Montreal Cognitive Assessment which was regarded as independent variable. In addition to traditional MLR, random forest (RF), stochastic gradient boosting (SGB), Naïve Byer’s classifier (NB) and eXtreme gradient boosting (XGBoost) were also applied. Our results showed that all the RF, SGB, NB and XGBoost outperformed than the MLR. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as the important factors from the more to the less important. In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than the MLR and in predicting CFA score. By these methods, the importance ranks of the risk factors are education level, age, frailty score, fasting plasma glucose and body mass index accordingly in a Chinese T2D cohort.

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