Plasma Amino Acids Metabolomics' Important in Glucose Management in Type 2 Diabetes

The perturbation in plasma free amino acid metabolome has been observed previously in diabetes mellitus, and is associated with insulin resistance as well as the onset of cardiovascular disease in this population. In this study, we investigated, for the first time, changes in the amino acid profile in a group of people with and without type 2 diabetes (T2D) with normal BMI, from Jordan, who were only managed on metformin. Twenty one amino acids were evaluated in plasma samples from 124 people with T2D and 67 healthy controls, matched for age, gender and BMI, using amino acids analyser. Total amino acids, essential amino acids, non-essential amino acids and semi-essential amino acids were similar in T2D compared to healthy controls. Plasma concentrations of four essential amino acids were increased in the presence of T2D (Leucine, p < 0.01, Lysine, p < 0.001, Phenylalanine, p < 0.01, Tryptophan, p < 0.05). On the other hand, in relation to non-essential amino acids, Alanine and Serine were reduced in T2D (p < 0.01, p < 0.001, respectively), whereas Aspartate and Glutamate were increased in T2D compared to healthy controls (p < 0.001, p < 0.01, respectively). A semi-essential amino acid, Cystine, was also increased in T2D compared to healthy controls (p < 0.01). Citrulline, a metabolic indicator amino acid, demonstrated lower plasma concentration in T2D compared to healthy controls (p < 0.01). These amino acids were also correlated with fasting blood glucose and HbA1c (p < 0.05). Glutamate, glycine and arginine were correlated with the duration of metformin treatment (p < 0.05). No amino acid was correlated with lipid profiles. Disturbances in the metabolism of these amino acids are closely implicated in the pathogenesis of T2D and associated cardiovascular disease. Therefore, these perturbed amino acids could be explored as therapeutic targets to improve T2D management and prevent associated cardiovascular complications

Digital Forensics Classification Based on a Hybrid Neural Network and the Salp Swarm Algorithm

In recent times, cybercrime has increased significantly and dramatically. This made the need for Digital Forensics (DF) urgent. The main objective of DF is to keep proof in its original state by identifying, collecting, analyzing, and evaluating digital data to rebuild past acts. The proof of cybercrime can be found inside a computer’s system files. This paper investigates the viability of Multilayer perceptron (MLP) in DF application. The proposed method relies on analyzing the file system in a computer to determine if it is tampered by a specific computer program. A dataset describes a set of features of file system activities in a given period. These data are used to train the MLP and build a training model for classification purposes. Identifying the optimal set of MLP parameters (weights and biases) is a challenging matter in training MLPs. Using traditional training algorithms causes stagnation in local minima and slow convergence. This paper proposes a Salp Swarm Algorithm (SSA) as a trainer for MLP using an optimized set of MLP parameters. SSA has proved its applicability in different applications and obtained promising optimization results. This motivated us to apply SSA in the context of DF to train MLP as it was never used for this purpose before. The results are validated by comparisons with other meta-heuristic algorithms. The SSAMLP-DF is the best algorithm because it achieves the highest accuracy results, minimum error rate, and best convergence scale

The Use of Stemming in the Arabic Text and Its Impact on the Accuracy of Classification

The ongoing growth in the vast amount of digital documents and other data in the Arabic language available online has increased the need for classification methods that can deal with the complex nature of such data. The classification of Arabic plays a large and important role in many modern applications and interferes with other sciences, which start from search engines and do not end with the Internet of Things. However, addressing the Arab classification errors with high performance is largely insufficient to deal with the huge quantities to reveal the classification of Arab documents; while some work was tackled out on the classification of the Arabic text, most of the research has focused on English text. The methods proposed for English are not suitable for Arabic as the morphology of the two languages differs substantially. Moreover, morphologically, the preprocessing of Arabic text is a particularly challenging task. In this study, three commonly used classification algorithms, namely, the K-nearest neighbor, Naïve Bayes, and decision tree, were implemented for Arabic text in order to assess their effectiveness with and without the use of a light stemmer in the preprocessing phase. In the experiment, a dataset from Agency France Persse (AFP) Arabic Newswire 2001 consisting of four categories and 800 files was classified using the three classifiers. The result showed that the decision tree with light stemmer had the best accuracy rate for classification algorithm with 93%

A New Augmented Reality System for Calculating Social Distancing between Children at School

Social distancing is one of the most important ways to prevent many diseases, especially the respiratory system, where the latest internationally spread is coronavirus disease, and it will not be the last. The spreading of this pandemic has become a major threat to human life, especially to the elderly and people suffering from chronic diseases. During the Corona pandemic, medical authorities were keen to control the spread through social distancing and monitoring it in markets, universities, and schools. This monitoring was mostly used to estimate the distance with the naked eye and interfere with estimating the distance on the observer only. In this study, a computer application was designed to monitor social distancing in closed areas, especially in schools and kindergartens, using a fast, effective and unobtrusive technique for children. In addition to this system, we use augmented reality to help to determine the location of violation of social distancing. This system was tested, and the results were accurate exceeding 98.5%

New Treatment for Type 2 Diabetes Mellitus Using a Novel Bipyrazole Compound.

2',3,3,5'-Tetramethyl-4'-nitro-2'H-1,3'-bipyrazole (TMNB) is a novel bipyrazole compound with unknown therapeutic potential in diabetes mellitus. This study aims to investigate the anti-diabetic effects of TMNB in a high-fat diet and streptozotocin-(HFD/STZ)-induced rat model of type 2 diabetes mellitus (T2D). Rats were fed HFD, followed by a single low dose of STZ (40 mg/kg). HFD/STZ diabetic rats were treated orally with TMNB (10 mg/kg) or (200 mg/kg) metformin for 10 days before terminating the experiment and collecting plasma, soleus muscle, adipose tissue, and liver for further downstream analysis. TMNB reduced the elevated levels of serum glucose in diabetic rats compared to the vehicle control group (p < 0.001). TMNB abrogated the increase in serum insulin in the treated diabetic group compared to the vehicle control rats (p < 0.001). The homeostasis model assessment of insulin resistance (HOMA-IR) was decreased in the diabetic rats treated with TMNB compared to the vehicle controls. The skeletal muscle and adipose tissue protein contents of GLUT4 and AMPK were upregulated following treatment with TMNB (p < 0.001, < 0.01, respectively). TMNB was able to upregulate GLUT2 and AMPK protein expression in liver (p < 0.001, < 0.001, respectively). LDL, triglyceride, and cholesterol were reduced in diabetic rats treated with TMNB compared to the vehicle controls (p < 0.001, 0.01, respectively). TMNB reduced MDA and IL-6 levels (p < 0.001), and increased GSH level (p < 0.05) in diabetic rats compared to the vehicle controls. Conclusion: TMNB ameliorates insulin resistance, oxidative stress, and inflammation in a T2D model. TMNB could represent a promising therapeutic agent to treat T2D

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

d- and l-amino acids in Antarctic lakes: assessment of a very sensitive HPLC-MS method

Amino acids represent a fraction of organic matter in marine and freshwater ecosystems, and a source of carbon, nitrogen and energy. l-Amino acids are the most common enantiomers in nature because these chiral forms are used during the biosynthesis of proteins and peptide. To the contrary, the occurrence of d-amino acids is usually linked to the presence of bacteria. We investigated the distribution of l- and d-amino acids in the lacustrine environment of Terra Nova Bay, Antarctica, in order to define their natural composition in this area and to individuate a possible relationship with primary production. A simultaneous chromatographic separation of 40 l- and d-amino acids was performed using a chiral stationary phase based on teicoplainin aglycone (chirobiotic tag). The chromatographic separation was coupled to two different mass spectrometers-an LTQ-Orbitrap XL (Thermo Fisher Scientific) and an API 4000 (ABSciex)-in order to investigate their quantitative performance. High-performance liquid chromatography coupled with mass spectrometry methods were evaluated through the estimation of their linear ranges, repeatability, accuracy and detection and quantification limits. The high-resolution mass spectrometer LTQ-Orbitrap XL presented detection limits between 0.4 and 7 mu g l (-1), while the triple quadrupole mass spectrometer API 4000 achieved the best detection limits reported in the literature for the quantification of amino acids (between 4 and 200 ng l (-1)). The most sensitive method, HPLC-API 4000, was applied to lake water samples

A novel virtual sample generation method to overcome the small sample size problem in computer aided medical diagnosing

© 2019 by the authors. Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%-95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples