Are Phosphatidylcholine and Lysophosphatidylcholine Body Levels Potentially Reliable Biomarkers in Obesity? A Review of Human Studies

Phosphatidylcholines (PCs) are the major components of biological membranes in animals and are a class of phospholipids that incorporate choline as a headgroup. Lysophosphatidylcholines (LPCs) are a class of lipid biomolecules derived from the cleavage of PCs, and are the main components of oxidized low-density lipoproteins (oxLDLs) that are involved in the pathogenesis of atherosclerosis. Since obesity is associated with a state of chronic low-grade inflammation, one can anticipate that the lipidomic profile changes in this context and both PCs and LPCs are gaining attention as hypothetically reliable biomarkers of obesity. Thus, a literature search is performed on PubMed, Latin American and Caribbean Health Science Literature (LILACS), and Excerpta Medica DataBASE (Embase) to obtain the findings of population studies to clarify this hypothesis. The search strategy resulted in a total of 2403 reports and 21 studies were included according to the eligibility criteria. Controversial data on the associations of PCs and LPCs with body mass index (BMI) and body fat parameters have been identified. There is an inverse relationship between BMI and most species of PCs, and a majority of studies exhibited negative associations between BMI and LPCs. Other findings regarding the differences between PCs and LPCs in obesity are presented, and the associated uncertainties are discussed in detail.     

A Janus Molecule for Screen-Printable Conductive Carbon Ink for Composites with Superior Stretchability

Inspired by decades of research in the compatibilization of fillers into elastomeric composites for high-performance materials, a novel polyurethane-based stretchable carbon ink is created by taking advantage of a Janus molecule, 2-(2,5-dimethyl-1H-pyrrol-1-yl)propane-1,3-diol (serinol pyrrole, SP). SP is used to functionalize the carbon and comonomer in the polymer phase. The use of SPs in both the organic and inorganic phases results in an improved interaction between the two phases. When printed, the functionalized material has a factor 1.5 lower resistance-strain dependence when compared to its unfunctionalized analogue. This behavior is superior to commercially available carbon inks. To demonstrate the suitability of ink in an industrial application, an all-printed, elastomer-based force sensor is fabricated. This “pyrrole methodology” is scalable and broadly applicable, laying the foundation for the realization of printed functionalities with improved electromechanical performance.     

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.     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

Estimation of Desired Motion Intention and compliance control for upper limb assist exoskeleton

International Journal of Control, Automation and Systems - In this paper, we have addressed two issues for upper limb assist exoskeleton. 1) Estimation of Desired Motion Intention (DMI); 2) Robust...     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Physico-chemical analysis of tannery solid waste and structural characterization of its isolated humic acids after composting

In Marrakech, solid by-products from tanneries are highly polluting, generating large amounts of nitrogenous and organic matter. In the present study composting is tested as a cost-effective method for waste management to overcome many of the environmental hazards and produce a stable, rich material for soil fertilization. Two composting trials were conducted after neutralization by ammonia or lime. The aim of the neutralization was to avoid the antimicrobial effects of the acidity in the tannery waste, thus ensuring correct composting. Different techniques such as elemental analysis and ¹³C NMR spectroscopy were applied to analyse humic acids isolated from raw and composted materials, and to monitor the process of tannery waste composting, and the stability and maturity of the final product according to the means of neutralization. Comparison of data showed similar behaviour in both trials, but the composting process appeared to be more complete following neutralization with lime. The C, H and N content decreased, while the O increased. The FTIR and ¹³C NMR spectra show the decrease of aliphatic compounds demonstrated by the reduction of absorbance around 2922 cm⁻¹ and of the resonance in the C-alkyl area around 0–55 ppm. The humic acids newly formed during composting were richer in the O–N alkyl and oxidized aromatic structures that increased almost twofold on composting after neutralization with lime. The first principal component axis PC1 (54%) separated C-aliphatic, C-carboxylic and other less stable and less polycondensed compounds such as polyphenols from the more polycondensed O–N alkyl and oxidized C-aromatic compounds.     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.