Internal Standard-Amplitude Modulated Multiplexed Flow Analysis

A new concept of flow analysis, internal standard-amplitude modulated multiplexed flow analysis, is proposed. A proof of concept was verified by applying it to the determination of ferrous ion (Fe2+) by 1,10-phenanthroline (o-Phen) spectrophotometry. The flow rates of sample solutions containing Methylene Blue (MB) as an internal standard substance were sinusoidally varied at different frequencies. The solutions were merged with a color reagent (o-Phen) solution, while the total flow rate was held constant. Downstream, analytical signals were monitored at the maximum absorption wavelengths of Fe2+-o-Phen complex and of MB (510 and 644 nm, respectively). The signals were respectively analyzed by fast Fourier transform. The concentrations of the analytes in respective samples were simultaneously determined from the amplitudes of the corresponding wave components. The precision, linearity of the calibration curve, limit of detection and robustness against deliberate fluctuation in flow rate were greatly improved by introducing the internal standard method. Good recoveries of around 100% were obtained for Fe2+ spiked into real water samples

Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space.

Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils from Lindera triloba and Cinnamomum sieboldii against Staphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts

Prediction of antibacterial interaction between essential oils via graph embedding approach

Essential oils contain a variety of volatile metabolites, and are expected to be utilized in wide fields such as antimicrobials, insect repellents and herbicides. However, it is difficult to foresee the effect of mixing the oils because hundreds of compounds can be involved in synergistic and antagonistic interactions. For efficient formula optimization, we have developed and evaluated a machine learning method to classify antibacterial interactions between the oils. Cross-validation showed that graph embedding improved areas under the ROC curves for synergistic-versus-rest classification. Furthermore, antibacterial assay against Staphylococcus aureus revealed that oregano–ajowan, lemongrass–hiba, cinnamon–lemongrass and ajowan–ginger combinations exhibited synergistic interaction as predicted. These results indicate that graph embedding approach is useful for predicting synergistic interaction between antibacterial essential oils

In vitro and in silico prediction of antibacterial interaction between essential oils via graph embedding approach

Abstract Essential oils contain a variety of volatile metabolites, and are expected to be utilized in wide fields such as antimicrobials, insect repellents and herbicides. However, it is difficult to foresee the effect of oil combinations because hundreds of compounds can be involved in synergistic and antagonistic interactions. In this research, it was developed and evaluated a machine learning method to classify types of (synergistic/antagonistic/no) antibacterial interaction between essential oils. Graph embedding was employed to capture structural features of the interaction network from literature data, and was found to improve in silico predicting performances to classify synergistic interactions. Furthermore, in vitro antibacterial assay against a standard strain of Staphylococcus aureus revealed that four essential oil pairs (Origanum compactum—Trachyspermum ammi, Cymbopogon citratus—Thujopsis dolabrata, Cinnamomum verum—Cymbopogon citratus and Trachyspermum ammi—Zingiber officinale) exhibited synergistic interaction as predicted. These results indicate that graph embedding approach can efficiently find synergistic interactions between antibacterial essential oils

Home‐based cardiac rehabilitation using information and communication technology for heart failure patients with frailty

Abstract Aims Cardiac rehabilitation (CR) is an evidence‐based, secondary preventive strategy that improves mortality and morbidity rates in patients with heart failure (HF). However, the implementation and continuation of CR remains unsatisfactory, particularly for outpatients with physical frailty. This study investigated the efficacy and safety of a comprehensive home‐based cardiac rehabilitation (HBCR) programme that combines patient education, exercise guidance, and nutritional guidance using information and communication technology (ICT). Methods and results This study was a single‐centre, open‐label, randomized, controlled trial. Between April 2020 and November 2020, 30 outpatients with chronic HF (New York Heart Association II–III) and physical frailty were enrolled. The control group (n = 15) continued with standard care, while the HBCR group (n = 15) also received comprehensive, individualized CR, including ICT‐based exercise and nutrition guidance using ICT via a Fitbit® device for 3 months. The CR team communicated with each patient in HBCR group once a week via the application messaging tool and planned the training frequency and intensity of training individually for the next week according to each patient's symptoms and recorded pulse data during exercise. Dietitians conducted a nutritional assessment and then provided individual nutritional advice using the picture‐posting function of the application. The primary outcome was the change in the 6 min walking distance (6MWD). The participants' mean age was 63.7 ± 10.1 years, 53% were male, and 87% had non‐ischaemic heart disease. The observed change in the 6MWD was significantly greater in the HBCR group (52.1 ± 43.9 m vs. −4.3 ± 38.8 m; P < 0.001) at a 73% of adherence rate. There was no significant change in adverse events in either group. Conclusions Our comprehensive HBCR programme using ICT for HF patients with physical frailty improved exercise tolerance and improved lower extremity muscle strength in our sample, suggesting management with individualized ICT‐based programmes as a safe and effective approach. Considering the increasing number of HF patients with frailty worldwide, our approach provides an efficient method to keep patients engaged in physical activity in their daily life