Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the ‘Drugs.Com’ containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.     

Genetically Encoded Crosslinking Enables Identification of Multivalent Ubiquitin-Deubiquitylating Enzyme Interactions

Ubiquitin (Ub) proteoforms control nearly every aspect of eukaryotic cell biology through their diversity. Inspired by the widely used Ub C-terminal electrophiles (Ub−E), here we report the identification of multivalent binding of Ub with deubiquitylating enzymes (Dubs) using genetic code expansion (GCE) and crosslinking mass spectrometry. While the Ub−Es only gather structural information with the S1 Dub sites, we demonstrate that GCE of Ub with p-benzoyl-L-phenylalanine enables identification of interaction modes beyond the S1 site with a panel of Dubs of both eukaryotic and prokaryotic origin. Collectively, this represents the next generation of Ub-based affinity probes with a unique ability to unravel Ub interaction landscapes beyond what is afforded by cysteine-based chemistries.     

Charge-Driven Self-Assembled Microspheres Hydrogel Scaffolds for Combined Drug Delivery and Photothermal Therapy of Diabetic Wounds

The treatment of diabetic wound remains a big clinical challenge. Hydrogel that can provide physical barrier and humidity displays amazing potentials for managing the diabetic wounds healing. Herein, a new charge-driven self-assembled microsphere hydrogel scaffold (SMHS) is reported based on an electric charge interaction, by combining use of black phosphorus (BP)-contained chitosan methacryloyl (CS) microspheres with positive charge and basic fibroblast growth factor-contained hyaluronic acid methacryloyl (HA) microspheres with negative charge. The weak charge attraction among microspheres gives the SMHS the injectable characteristic. Due to the existence of BP, near-infrared (NIR) irradiation has obvious effects on the degradation and drug release behaviors of SMHS. Significantly, SMHS that combines the short-term physical (photothermal) intervention and long-term chemical (drug release) intervention may be promising in spatio-temporal regulation of regenerative microenvironment. SMHS with NIR irradiation (SMHS+NIR) can promote cell proliferation, cell migration, angiogenesis and macrophage polarization. Moreover, in diabetic rat skin wounds, SMHS+NIR significantly accelerates the wound healing process by simultaneously inhibiting the inflammatory response, promoting angiogenesis and tissues remodeling. The outcome of this research not only provides a biomaterial for diabetic wounds healing, but also demonstrates a new strategy for designing novel hydrogel-based biomaterials which have the free editing and combination functions.     

The critical mass for the unconfined vapour cloud explosion of compressed and liquid hydrogen

The (unconfined) vapour cloud explosion (VCE) is a dramatic phenomenon that generates a severe pressure wave with a high potential to damage assets and produce injuries in the far field. This definition applies also to hydrogen. Nevertheless, no clear tools and methodology have been so far developed and tested for this highly reactive gas, and even advanced numerical simulations lack validation and suffer from large uncertainties. In this view, the comprehension of the physic which subtends this dramatic phenomenon for the specific case of hydrogen is still a central issue. This paper revises some of the most adopted theories on VCE based on classical acoustic theory and models for pressure wave propagation and provides a consequence-based, threshold (minimum) value for the critical mass of hydrogen $m_f^{\text{crit}}\left(4.0\mathrm{kg}\right)$ which is needed—at a stoichiometric concentration in air—for a vapour cloud to behave as a VCE. To this regard, any non-stoichiometric hydrogen concentration in air or lower amount of hydrogen would decrease either the flame Mach number $M_{\mathrm{f}}$ or the total energy, thus resulting in negligible overpressure. In this sense, the effects of buoyancy, diffusivity, and weather conditions on the dispersion of hydrogen should be taken into account. The results are valid either for compressed or cryogenic liquid tanks and can be adopted for the sake of distinction between hydrogen flash fire and VCE; for the hazard analysis of hydrogen production and storage; and more in general for the risk assessment of hydrogen systems.     

Performance assessment of drop tube reactor for biomass fast pyrolysis using process simulator

Biomass pyrolysis process from a drop tube reactor was modelled in a plug flow reactor using Aspen Plus process simulation software. A kinetic mechanism for pyrolysis was developed considering the recent improvements and updated kinetic schemes to account for different content of cellulose, hemicellulose, and lignin. In this regard, oak, beechwood, rice straw, and cassava stalk biomasses were analyzed. The main phenomena governing the pyrolysis process are identified in terms of the characteristic times. Pyrolysis process was found to be reaction rate controlled. Effects of pyrolysis temperature on bio-oil, gases, and char yields were evaluated. At optimum pyrolysis conditions (i.e., 500°C), a bio-oil yield of 67.3, 64, 43, and 52 wt.% were obtained from oak, beechwood, rice straw, and cassava stalk, respectively. Oak and beechwood were found to give high yields of bio-oil, while rice straw produced high gas and char yields compared to other biomasses. Although temperature is the main factor that plays a key role in the distribution of pyrolysis products, the composition of cellulose, hemicellulose, and lignin in the feedstock also determines the yield behaviour and composition of products. With the rise in pyrolysis temperature, further decomposition of intermediate components was initiated favouring the formation of lighter fractions. Comparably, species belonging to the aldehyde chemical family had the highest share of bio-oil components in all the investigated feedstocks. Overall, the present study shows a good agreement with the experimental study reported in the literature, confirming its validity as a predictive tool for the biomass pyrolysis process.     

Solution-processed ZnO energy harvester devices based on flexible substrates

Microsystem Technologies - The smart healthcare devices connected to the internet of things (IoT) for medical services can acquire and process physiological data of risk patients, real-time...