Total phenolics, total flavonoids, and antioxidant capacity of 11 cultivars of fresh plums were determined using spectrophotometric methods. Identification and quantification of individual polyphenolics were performed using reversed-phase high-performance liquid chromatography equipped with a diode array detector. The total phenolic contents of various cultivars widely varied from 125.0 to 372.6 mg/100 g expressed as gallic acid equivalents. The level of total flavonoids in fresh plums ranged between 64.8 and 257.5 mg/100 g expressed as catechin equivalents. Antioxidant capacity, expressed as vitamin C equivalent antioxidant capacity (VCEAC), ranged from 204.9 to 567.0 mg/100 g with an average of 290.9 mg/100 g of fresh weight. Cv. Beltsville Elite B70197 showed the highest amounts of total phenolics and total flavonoids and the highest VCEAC. A positive relationship (correlation coefficient r (2)() = 0.977) was presented between total phenolics and VCEAC, suggesting polyphenolics would play an important role in free radical scavenging. The level of IC(50) value of superoxide radical anion scavenging activity of the plum cultivars ranged from 13.4 to 45.7 mg of VCEAC/100 g. Neochlorogenic acid was the predominant polyphenolic among fresh plums tested. Flavonols found in plum were commonly quercetin derivatives. Rutin was the most predominant flavonol in plums. Various anthocyanins containing cyanidin aglycon and peonidin aglycon were commonly found in all plums except for cv. Mirabellier and NY 101.

Black tea, green tea, red wine, and cocoa are high in phenolic phytochemicals, among which theaflavin, epigallocatechin gallate, resveratrol, and procyanidin, respectively, have been extensively investigated due to their possible role as chemopreventive agents based on their antioxidant capacities. The present study compared the phenolic and flavonoid contents and total antioxidant capacities of cocoa, black tea, green tea, and red wine. Cocoa contained much higher levels of total phenolics (611 mg of gallic acid equivalents, GAE) and flavonoids (564 mg of epicatechin equivalents, ECE) per serving than black tea (124 mg of GAE and 34 mg of ECE, respectively), green tea (165 mg of GAE and 47 mg of ECE), and red wine (340 mg of GAE and 163 mg of ECE). Total antioxidant activities were measured using the 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays and are expressed as vitamin C equivalent antioxidant capacities (VCEACs). Cocoa exhibited the highest antioxidant activity among the samples in ABTS and DPPH assays, with VCEACs of 1128 and 836 mg/serving, respectively. The relative total antioxidant capacities of the samples in both assays were as follows in decreasing order: cocoa > red wine > green tea > black tea. The total antioxidant capacities from ABTS and DPPH assays were highly correlated with phenolic content (r2 = 0.981 and 0.967, respectively) and flavonoid content (r2 = 0.949 and 0.915). These results suggest that cocoa is more beneficial to health than teas and red wine in terms of its higher antioxidant capacity.

The contribution of each phytochemical to the total antioxidant capacity of apples was determined. Major phenolic phytochemicals of six apple cultivars were identified and quantified, and their contributions to total antioxidant activity of apples were determined using a 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical scavenging assay and expressed as vitamin C equivalent antioxidant capacity (VCEAC). Average concentrations of major phenolics and vitamin C in six apple cultivars were as follows (mg/100 g of fresh weight of apples): quercetin glycosides, 13.20; procyanidin B(2), 9.35; chlorogenic acid, 9.02; epicatechin, 8.65; phloretin glycosides, 5.59; vitamin C, 12.80. A highly linear relationship (r (2) > 0.97) was attained between concentrations and total antioxidant capacity of phenolics and vitamin C. Relative VCEAC values of these compounds were in the order quercetin (3.06) > epicatechin (2.67) > procyanidin B(2) (2.36) > phloretin (1.63) > vitamin C (1.00) > chlorogenic acid (0.97). Therefore, the estimated contribution of major phenolics and vitamin C to the total antioxidant capacity of 100 g of fresh apples is as follows: quercetin (40.39 VCEAC) > epicatechin (23.10) > procyanidin B(2) (22.07) > vitamin C (12.80) > phloretin (9.11) > chlorogenic acid (8.75). These results indicate that flavonoids such as quercetin, epicatechin, and procyanidin B(2) rather than vitamin C contribute significantly to the total antioxidant activity of apples.

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The identification of phenolics from various cultivars of fresh sweet and sour cherries and their protective effects on neuronal cells were comparatively evaluated in this study. Phenolics in cherries of four sweet and four sour cultivars were extracted and analyzed for total phenolics, total anthocyanins, and their antineurodegenerative activities. Total phenolics in sweet and sour cherries per 100 g ranged from 92.1 to 146.8 and from 146.1 to 312.4 mg gallic acid equivalents, respectively. Total anthocyanins of sweet and sour cherries ranged from 30.2 to 76.6 and from 49.1 to 109.2 mg cyanidin 3-glucoside equivalents, respectively. High-performance liquid chromatography (HPLC) analysis revealed that anthocyanins such as cyanidin and peonidin derivatives were prevalent phenolics. Hydroxycinnamic acids consisted of neochlorogenic acid, chlorogenic acid, and p-coumaric acid derivatives. Glycosides of quercetin, kaempferol, and isorhamnetin were also found. Generally, sour cherries had higher concentrations of total phenolics than sweet cherries, due to a higher concentration of anthocyanins and hydroxycinnamic acids. A positive linear correlation (r2 = 0.985) was revealed between the total anthocyanins measured by summation of individual peaks from HPLC analysis and the total anthocyanins measured by the pH differential method, indicating that there was in a close agreement with two quantifying methods for measuring anthocyanin contents. Cherry phenolics protected neuronal cells (PC 12) from cell-damaging oxidative stress in a dose-dependent manner mainly due to anthocyanins. Overall results showed that cherries are rich in phenolics, especially in anthocyanins, with a strong antineurodegenerative activity and that they can serve as a good source of biofunctional phytochemicals in our diet.

In order to confirm reasons that deteriorate cathode performances, Ni-rich Li[Ni0.7Mn0.3]O2 is modified by lithium isopropoxide to artificially provide lithium excess environment by forming Li2O on the surface of active materials. X-ray diffraction patterns indicate that the lithium oxide coating does not affect structural change comparing to the bare material. Scanning electron microscopy and transmission electron microscopy data show the presence of coating layers on the surface of Li[Ni0.7Mn0.3]O2. Electrochemical tests demonstrate that the Li2O-coated Li[Ni0.7Mn0.3]O2 exhibits a greater irreversible capacity with a small capacity because of the presence of insulating layers composed of lithium compounds on the active materials since these layers delay facile Li+ diffusion. Also, the Li2O layer forms byproducts such as Li2CO3, LiOH, and LiF, as are proved by X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry. The presence of residual lithium tends to bond with hydrocarbons induced from decomposition of electrolytic salt during electrochemical reactions. And the reaction, accelerated by the decomposition of electrolytic salt that produces the byproducts, causes the formation of passive layers on the surface of active material. As a result, the new layers consequently impede diffusion of lithium ions that deteriorate electrochemical properties.

Because the p300/CBP-mediated hyperacetylation of RelA (p65) is critical for nuclear factor-KB (NF-KB) activation, the attenuation of p65 acetylation is a potential molecular target for the prevention of chronic inflammation. During our ongoing screening study to identify natural compounds with histone acetyltransferase inhibitor (HATi) activity, we identified epigallocatechin-3-gallate (EGCG) as a novel HATi with global specificity for the majority of HAT enzymes but with no activity toward epigenetic enzymes including HDAC, SIRT1, and HMTase. At a dose of 100 Mmol/L, EGCG abrogates p300-induced p65 acetylation in vitro and in vivo, increases the level of cytosolic IKBA, and suppresses tumor necrosis factor A (TNFA)-induced NF-KB activation. We also showed that EGCG prevents TNFA-induced p65 translocation to the nucleus, confirming that hyperacetylation is critical for NF-KB translocation as well as activity. Furthermore, EGCG treatment inhibited the acetylation of p65 and the expression of NF-KB target genes in response to diverse stimuli. Finally, EGCG reduced the binding of p300 to the promoter region of interleukin-6 gene with an increased recruitment of HDAC3, which highlights the importance of the balance between HATs and histone deacetylases in the NF-KB-mediated inflammatory signaling pathway. Importantly, EGCG at 50 Mmol/L dose completely blocks EBV infection-induced cytokine expression and subsequently the EBV-induced B lymphocyte transformation. These results show the crucial role of acetylation in the development of inflammatory-related diseases. [Cancer Res 2009;69(2):583-92]

Background Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. Objective We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. Methods A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Results Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). Conclusions FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.