Progress in Photocurable 3D Printing of Photosensitive Polyurethane: A Review.

In recent years, as a class of advanced additive manufacturing (AM) technology, photocurable 3D printing has gained increasing attention. Based on its outstanding printing efficiency and molding accuracy, it is employed in various fields, such as industrial manufacturing, biomedical, soft robotics, electronic sensors. Photocurable 3D printing is a molding technology based on the principle of area-selective curing of photopolymerization reaction. At present, the main printing material suitable for this technology is the photosensitive resin, a composite mixture consisting of a photosensitive prepolymer, reactive monomer, photoinitiator, and other additives. As the technique research deepens and its application gets more developed, the design of printing materials suitable for different applications is becoming the hotspot. Specifically, these materials not only can be photocured but also have excellent properties, such as elasticity, tear resistance, fatigue resistance. Photosensitive polyurethanes can endow photocured resin with desirable performance due to their unique molecular structure including the inherent alternating soft and hard segments, and microphase separation. For this reason, this review summarizes and comments on the research and application progress of photocurable 3D printing of photosensitive polyurethanes, analyzing the advantages and shortcomings of this technology, also offering an outlook on this rapid development direction.

Myocardial protective effect of sacubitril-valsartan on rats with acute myocardial infarction.

OBJECTIVE: The purpose of this study was to explore the effect of sacubitril-valsartan on rats with acute myocardial infarction. METHODS: Sprague-dawley rats were randomly divided into six groups. Rats in Group A and B were threaded without deligation and treated with valsartan (34 mg/kg) or sacubitril-valsartan (68 mg/kg) after operation. Rats in Group C and D were given the two drugs (34 mg/kg, 68 mg/kg) after ligation of the left anterior descending branch for 40 minutes. Rats in Group E and F were restored the blood of the coronary artery after ligation, and given the two drugs (34 mg/kg, 68 mg/kg) at the same time. N-terminal pro-brain natriuretic peptide, high sensitivity troponin T, aldosterone and Cyclic guanosine monophosphate were measured and Color Doppler echocardiography was performed. Six weeks later, the rats were killed, the hearts were weighed and stained with Masson staining. RESULTS: Compared with Group A and B, the levels of N-terminal pro-brain natriuretic peptide, high sensitivity troponin T, aldosterone and Cyclic guanosine monophosphate in other groups were significantly increased (p < 0.05). Before treatment, the left ventricular end diastolic diameter and left ventricular end systolic diameter were similar in each group. After treatment, the levels of left ventricular end diastolic diameter and left ventricular end systolic diameter, and collagen fiber range stained with blue in other groups were significantly increased in comparison with Group A and B (p < 0.05). In addition, the left ventricular volume and collagen fiber range stained with blue were notably decreased, the levels of ejection fraction (EF) were increased in sacubitril-valsartan groups in comparison with valsartan groups (p < 0.05). CONCLUSION: Early application of sacubitril-valsartan has a protective effect on rats with acute myocardial infarction.

Angiotensin receptor-neprilysin inhibitor attenuates ischemia-hypoxia-induced myocardial injury via inhibition of autophagy.

OBJECTIVES: Angiotensin receptor-neprilysin inhibitor (ARNI) improves cardiac function and protects from an ischemic myocardium. However, the role and mechanism of ARNI on autophagy in cardiac ischemic injury are poorly understood. Here, we investigated the protective effect and underlying mechanisms of ARNI on autophagy in H9c2 cardiomyocytes induced through ischemia and hypoxia (IH) treatment. METHODS: The cytotoxicity of IH injury on H9C2 cells with and without ARNI were evaluated using cell counting kit-8 (CCK-8) and lactate dehydrogenase (LDH) release assays. The effect of ARNI on apoptosis was detected using flow cytometry. The expression of autophagic proteins (LC3-II, Beclin 1, and p62) was detected using western blot. RESULTS: The viability of H9c2 cells was significantly decreased at different IH-treated time points; ARNI pretreatment increased cell viability and inhibited IH injury in a dose-dependent manner. H9c2 cells treated with IH (6 h) significantly increased LDH release, while ARNI dose-dependently improved LDH release, with 20 µmol/L ARNI having the most significant effect. ARNI also ameliorated IH-induced apoptosis. IH treatment increased the protein expression of LC3-II and Beclin 1 and decreased the expression of p62, which were reversed by ARNI pretreatment. Furthermore, autophagy was further increased after pretreatment with rapamycin in IH-induced H9c2 cells, which abrogated the protective effect of ARNI. CONCLUSIONS: Our study shows that ARNI has a protective effect on IH-induced cardiomyocyte injury, which may be related to the inhibition of autophagy.

Critical assessment of high-throughput standalone methods for secondary structure prediction.

Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the ß- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.