Phenolic phytochemicals are a broad class of nutraceuticals found in plants which have been extensively researched by scientists for their health-promoting potential. One such a compound which has been comprehensively used is eugenol (4-allyl-2-methoxyphenol), which is the active component of Syzigium aromaticum (cloves). Aromatic plants like nutmeg, basil, cinnamon and bay leaves also contain eugenol. Eugenol has a wide range of applications like perfumeries, flavorings, essential oils and in medicine as a local antiseptic and anesthetic. Increasing volumes of literature showed eugenol possesses antioxidant, antimutagenic, antigenotoxic, anti-inflammatory and anticancer properties. Molecular mechanism of eugenol-induced apoptosis in melanoma, skin tumors, osteosarcoma, leukemia, gastric and mast cells has been well documented. This review article will highlight the antiproliferative activity and molecular mechanism of the eugenol induced apoptosis against the cancer cells and animal models.

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain–computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

Scaffolds supplemented with naturally derived materials seem to be a good choice in bone tissue engineering. This study aims to develop polyurethane (PU) nanofibers added with ylang ylang (YY) and zinc nitrate (ZnNO₃) using the electrospinning method. Field emission scanning electron microscopy (FESEM) images showed that the diameter of the PU nanofibers (869 ± 122 nm) was reduced with the addition of YY and ZnNO₃ (PU/YY—467 ± 132 nm and PU/YY/ZnNO₃—290 ± 163 nm). Fourier transform infrared (FTIR), a thermal gravimetric analysis (TGA) and an X-ray diffraction (XRD) analysis confirmed the interactions between PU with YY and ZnNO₃. In addition, a thermal gravimetric analysis (TGA) study revealed the improved thermal stability for PU/YY and a slight reduction in the thermal stability for PU/YY/ZnNO₃. A tensile test indicated that the addition of YY and ZnNO3 (PU/YY—12.32 MPa and PU/YY/ZnNO₃—14.90 MPa) improved the mechanical properties of the pristine PU (6.83 MPa). The electrospun PU/YY (524 nm) and PU/YY/ZnNO₃ (284 nm) showed a reduced surface roughness when compared with the pristine PU (776 nm) as depicted in the atomic force microscopy (AFM) analysis. The addition of YY and ZnNO₃ improved the anticoagulant and biocompatibility nature of the pristine PU. Furthermore, the bone mineralization study depicted the improved calcium deposition in the fabricated composites (PU/YY—7.919% and PU/YY/ZnNO₃—10.150%) compared to the pristine PU (5.323%). Hence, the developed composites with desirable physico-chemical properties, biocompatibility and calcium deposition can serve as plausible candidates for bone tissue engineering.

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

The growth of retailer lately very significant especially in food product, selling of food product is not only in conventional market but moreover now is in such mini market, mall, shopping complex, etc. Food traceability is required in supply of food product to make sure items are safe to consume and not in expired date. This paper present on food traceability in supply chain based on Electronic Product Code Information Services (EPCIS), the use of EPCIS standard is refer to Global Standard-1 (GS1) for logistic and supply chain based on Radio Frequency Identification (RFID) Technology. RFID is used for items tagging on food product instead of barcode that currently widely used the advantages of RFID tag compare than barcode make the system is more applicable to used in food traceability. In this case one of food product take into example in supplying which is banana that the process started from farmer until reach to retail house are monitored and recorded by the system. End process of this system is to give services to the consumer of customer based on EPCIS database collected in all the way of process.

Objective: Genetic polymorphisms in ACE and ACE2 genes are involved in the RAS regulation of blood pressure and their activity may confer susceptibility to hypertension. In addition, they may play a role in SARS-CoV-2 pathogenesis and the severity of COVID-19. This study aims to determine the effect of genetic variations in the ACE (rs4331) and ACE2 (rs2074192) genes with hypertension comorbidity on the severity of COVID-19 in the Indonesian population. Result: 186 patients were enrolled and assigned into the COVID-19 group (n = 95) and non-COVID-19 group (n = 91) in this cross-sectional study. GG genotype frequency was dominant in ACE gene, but there were no significant differences between the groups (p = 0.163). The two groups had a significant difference (p = 0.000) for the CC genotype frequency (0,37 vs. 0.01) in the ACE2 gene. The proportion of women with COVID-19 is higher (51%), but men with hypertension had more severe symptoms (44%). Men with hypertension comorbidity, GG (ACE), and TT (ACE2) genotypes tended to have moderate-to-severe symptoms (25%). Similarly, women with hypertension as well as GG and CT genotypes tended to have moderate-to-severe symptoms (21%). We conclude that hypertension and mutations in the ACE (rs4331) and ACE2 (rs2074192) genes affect the severity of COVID-19.