Methyl protodioscin reduces c-Myc to ameliorate diabetes mellitus erectile dysfunction via downregulation of AKAP12.

BACKGROUND: Diabetes mellitus erectile dysfunction (DMED) is one of common complications of diabetes. We aimed to investigate the potential efficacy of methyl protodioscin (MPD) in DMED and explored the underlying mechanism. METHODS: Diabetic mice were induced by streptozotocin, while vascular endothelial cells (VECs) and vascular smooth muscle cells (VSMCs) were stimulated with high glucose. MPD was administrated in vitro and in vivo to verify its efficacy on DMED. The interaction of c-Myc and AKAP12 was determined by luciferase reporter assay and chromatin immunoprecipitation assay. RESULTS: c-Myc and AKAP12 were upregulated in penile tissues in DMED mice. In high glucose-stimulated VSMCs or VECs, MPD intervention enhanced cell viability, inhibited apoptosis, decreased c-Myc and AKAP12, as well as elevated p-eNOS Ser1177. MPD-induced apoptosis inhibition, AKAP12 reduction and p-eNOSSer1177 elevation were reversed by AKAP12 overexpression. c-Myc functioned as a positive regulator of AKAP12. Overexpression of c-Myc reversed the effects induced by MPD in vitro, which was neutralized by AKAP12 silencing. MPD ameliorated erectile function in diabetic mice via inhibiting AKAP12. CONCLUSIONS: MPD improved erectile dysfunction in streptozotocin-caused diabetic mice by regulating c-Myc/AKAP12 pathway, indicating that MPD could be developed as a promising natural agent for the treatment of DMED.

A Supervised ML Applied Classification Model for Brain Tumors MRI.

Brain Tumor originates from abnormal cells, which is developed uncontrollably. Magnetic resonance imaging (MRI) is developed to generate high-quality images and provide extensive medical research information. The machine learning algorithms can improve the diagnostic value of MRI to obtain automation and accurate classification of MRI. In this research, we propose a supervised machine learning applied training and testing model to classify and analyze the features of brain tumors MRI in the performance of accuracy, precision, sensitivity and F1 score. The result presents that more than 95% accuracy is obtained in this model. It can be used to classify features more accurate than other existing methods.

Design of refractive index sensors based on the wavelength-selective resonant coupling phenomenon in dual-core photonic crystal fibers.

Design strategies for high-sensitivity refractive index sensors based on the principle of wavelength-selective resonant coupling in dual-core photonic crystal fibers are presented. Phase matching at a single wavelength can be achieved between an analyte-filled microstructured core and a small core with a down-doped rod or one small air hole in the center, thus enabling selectively directional resonant-coupling between the two cores. The transmission spectra of the output light presents a notch at the index-matched wavelength, yielding a resonant wavelength depending on the refractive index of the analyte. Numerical simulations demonstrate that both of the two proposed sensors can be used for highly sensitive detection of low-index analyte. In particular, the configuration realized by introducing the fiber with a small air hole in one core can be used to the detection of the analyte index around 1.33 and the sensitivity reach to 1.2×10(4) nm per refractive index unit (RIU). In addition, the detection limit is as low as 2.5×10(-7) RIU at n(a)=1.33.

Microstructured-core photonic-crystal fiber for ultra-sensitive refractive index sensing.

We propose a novel photonic crystal fiber refractive index sensor which is based on the selectively resonant coupling between a conventional solid core and a microstructured core. The introduced microstructured core is realized by filling the air-holes in the core with low index analyte. We show that a detection limit (DL) of 2.02×10⁻6 refractive index unit (RIU) and a sensitivity of 8500 nm/RIU can be achieved for analyte with refractive index of 1.33.