Antitumor Perspectives of Oleuropein and Its Metabolite Hydroxytyrosol: Recent Updates

Olive fruit is a significant and promising source of potential bioactive compounds such as oleuropein and hydroxytyrosol. Oleuropein is the ester of elenolic acid and 3,4‐dihydroxyphenyl ethanol (HT). It is the main glycoside in olives, the degradation of which results in the formation of hydroxytyrosol in olive oil. Both plays a significant role in the reduction of coronary heart diseases and a certain type of cancers. Both olive oil phenols have an effective role counter to cell proliferation, cell growth, migration, invasion, and angiogenesis. They down regulate the expression of BCL‐2 and COX‐2 proteins, and reduced DNA damage. Hydroxytyrosol and oleuropein inhibited the multiple stages in colon carcinogenesis; initiation, promotion, and metastasis. They also provide protection against various human cancers including colorectal, skin, breast, thyroid, digestive, lung, brain, blood, and cervical. This review article discusses the anticancer perspectives and mechanisms of oleuropein and hydroxytyrosol in cell cultures and animal and human studies.     

Near-infrared spectroscopy coupled chemometric algorithms for prediction of antioxidant activity of black goji berries (<Emphasis Type="Italic">Lycium ruthenicum</Emphasis> Murr.)

In this study, near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression and various efficient variable selection algorithms, synergy interval-PLS (Si-PLS), backward interval PLS (Bi-PLS) and genetic algorithm-PLS (GA-PLS) were applied comparatively for the prediction of antioxidant activity in black wolfberry (BW). The eight assays were used for quantification of antioxidant content. The developed models were assessed using correlation coefficients (R²) of the calibration (Cal.) and prediction (Pre.); root mean square error of prediction, RMSEP; standard Error of Cross-Validation, RMSECV and residual predictive deviation, RPD. The performance of the built model greatly improved by the application of Si-PLS, Bi-PLS and GA-PLS compared with full spectrum PLS. The R² values determined for calibration and prediction set ranged from 0.8479 to 0.9696 and 0.8401 to 0.9638, respectively. These findings revealed that NIR spectroscopy combined with chemometric algorithms can be used for quantification of antioxidant activity in BW samples.     

Identification and molecular mechanisms of novel antioxidative peptides from fermented camel milk (Kachchi breed,India) with anti-inflammatory activity in raw macrophages cell lines

The investigation was aimed at assessing anti-inflammatory and antioxidative activities along with the release of peptides with antioxidative properties during the fermentation of camel milk by Lacticaseibacillus casei (NK9). Reverse-phase high-performance liquid chromatography (RP-HPLC) was used to separate the bioactive peptides of 3 and 10 kDa (permeates and retentates). Reverse-phase liquid chromatography–mass spectrometry (RPLC/MS) was used to identify and characterise the pure bioactive peptides, and the effect of fermented camel milk on inflammation produced by lipopolysaccharide (LPS)/endotoxin in RAW 264.7 (Ralph and William's cell line) was also examined. Furthermore, docking revealed that peptides (LLNEK and IYTFPQPQSL) were predicted to inhibit myeloperoxidase (nMPO) activity by engaging with different residues in and around the human myeloperoxidase (hMPO) active site.     

Enc-Unet: A novel method for Glioma segmentation

The diagnosis' treatment planning, follow-up and prognostication of Gliomas is significantly enhanced on Magnetic Resonance Imaging. In the present research, deep learning-based variant of convolutional neural network methodology is proposed for glioma segmentation where pretrained autoencoder acts as backbone to the 3D-Unet which performs the segmentation task as well as image restoration. Further, Unet accepts input as the combination of three non-native MR images (T2, T1CE, and FLAIR) to extract maximum and superior features for segmenting tumor regions. Further, weighted dice loss employed, focusses on segregating tumor region into three regions of interest namely whole tumor with oedema (WT), enhancing tumor (ET), and tumor core (TC). The optimizer preferred in the proposed methodology is Adam and the learning rate is initially set to $1e-4$, progressively reduced by a cosine decay after 50 epochs. The learning parameters are reduced to a larger extent (up to 9.8 M as compared to 27 M). The experimental results show that the proposed model achieved Dice similarity coefficients: 0.77, 0.92, and 0.84; sensitivity: 0.90, 0.95, and 0.89; specificity: 0.97, 0.99, and 0.99; Hausdorff95: 5.74, 4.89, and 6.00, in the three regions including ET, WT, TC. This proposed Glioma segmentation method is efficient for segregation of tumors.     

Variable chemical process and radiative nonlinear impact on magnetohydrodynamics Cross nanofluid: An approach toward controlling global warming

Solar energy is the basic source of renewable energy, and it is being used for controlling global pollution/warming. As the Cross nanofluid is very useful for cooling solar devices, in this paper analysis of the global warming effect is investigated by incorporating the nonlinear thermal radiation over the exponentially extendable surface because it plays a major role related to solar energy absorption of nanofluid. Furthermore, the mathematical modeling of Cross nanofluid involving magnetic effect and diffusion is discussed by using the fact of chemical reaction. Chemical reaction finds astonishing applications in pollution studies, chemical processing equipment, and polymer production. As a result of this study, it is noticed that more magnetized conducting fluid controls the motion of fluids for both cases of shear thinning and shear thickening. Brownian motion parameter Nb affects the rate of the random motion of nanoparticles. Increased Nb temperature also increases due to these random movements of nanoparticles. That is the reason why pollutant nanoparticles spread in air as a result of global warming increase.     

Membrane Rigidity-Tunable Fusogenic Nanosensor for High Throughput Detection of Fusion-Competent Influenza A Virus

The emergence of fatal viruses that pose continuous threats to global health has fueled the intense effort to develop direct, accurate, and high-throughput virus detection platforms. Current diagnostic methods, including qPCR and rapid antigen tests, indicate how much of the virus is present, whether small fragments or whole viruses. However, these methods do not indicate the probability of the virus to be active, capable of interacting with host cells and initiating the infection cycle. Herein, a sialic acid-presenting fusogenic liposome (sLipo–Chol) nanosensor with purposefully modulated membrane rigidity to rapidly detect the fusion-competent influenza A virus (IAV) is developed. This nanosensor possesses virus-specific features, including hemagglutinin (HA) binding and HA-mediated membrane fusion. It is explored how the fusogenic capability of sLipo–Chol with different membrane rigidities impacts their sensing performance by integrating Förster resonance energy transfer (FRET) pairs into the bilayers. The addition of an intact virus led to instant FRET signal changes, thus enabling the direct detection of diverse IAV subtypes—even in avian fecal samples—within an hour at room temperature. Therefore, the sensing approach, with an understanding of the cellular pathogenesis of influenza viruses, will aid in developing bioinspired nanomaterials for evolution into nanosystems to detect infection-competent viruses.     

Special issue on towards advancements in machine learning for exploiting large-scale and heterogeneous repositories

Neural Computing and Applications -     

Energy optimization in a smart community grid system using genetic algorithm

A smart community grid is an electrical network, which connects several producers, consumers, and prosumers to share energy in an intelligent and secure way. The main challenges of smart community grid are demand response, demand bidding, dynamic electricity tariffs, demand-side management, and prosumers handling. The current state-of-the-art smart grid decision making is focused on consumers and producers behavior while the aim of this research is to achieve prosumer's different goals in an optimized and intelligent way. A genetic algorithm (GA)-based solution is proposed to share energy in an optimized way without affecting the prosumers' preferences. Six prosumers smart community grids data sets are used to validate the performance of the proposed system. The results show that the proposed method significantly improves the loss of energy sharing without compromising the user's preferences.     

Effect of type of polymerization catalyst system on the degradation and stabilization of polyethylenes in the melt state—Part 4: Comparative antioxidant effectiveness on organoleptic extractables

Several polyethylene resins using Ziegler, metallocene, and Phillips catalyst technologies were examined to obtain more detailed information about the effect of different polymerization catalyst systems on the production of extractable thermo-oxidative degradation products formed during melt processing cycles. This produces volatile organoleptic components (VOCs and extractable) such as hydrocarbons, alcohols, aldehydes, ketones, and carboxylic acids. Although some of the oxidation products are in-chain bound, many are produced as free, easily extractable entities or volatile components. The purpose of this study is to identify the nature of the products by gas chromatography–mass spectrometry (GC–MS) and FTIR analysis. The identity of the VOCs formed is necessary to modify the product's quality or establish which are toxic and/or leachable with food products. The results show that the evolution of carbonyl products, nature, and quantity is influenced significantly by the polymer type and catalyst used. Over 300 organoleptics low molar mass degradation products, such as alkane, alkene, carbonyl, and alcohol functionalities were detected by GC–MS analysis coupled with FTIR analysis on hexane extractables. Certain stabilizers can control the generation of certain functionalities and inhibit others. Of importance was the discovery of the relationship between additive activity and structure and inhibition of the formation of specific types of oxidation functionalities to a particular catalyst system.