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Background: Hypercoagulation is a hallmark in coronavirus disease (COVID-19). The activity of the enzyme methylenetetrahydrofolate reductase (MTHFR) determines homocysteine levels, and polymorphisms in the enzyme’s gene can influence the enzyme activity with a consequence of hypercoagulability in patients with COVID-19. Objectives: To investigate the association of two single nucleotide polymorphisms (SNPs) in the MTHFR gene with hypercoagulability status in COVID-19. Materials and Methods: This is a retrospective cross-sectional study, which included 90 patients diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with variable severity. Patients were classified according to D-dimer level at admission into two groups: with and without hypercoagulability. Nucleic DNA was extracted from leukocytes and gene fragments corresponding to C677T and A1298C MTHFR gene were amplified and genotyped using allele specific polymerase chain reaction. Results: Hypercoagulation was reported in 42.22% of the patients. The mutant homozygous genotype (TT) was more frequent among hyper - than normocoagulable patients (13.6% vs. 1.92%) with a significant difference (odds ratio [OR] = 9.28, 95% confidence interval [CI] = 1.02–84.78, P = 0.048). Furthermore, T allele was more common among hyper- than normocoagulable patients (28.95% vs. 13.46%) with a significant difference (OR = 2.62, 95% CI = 1.24–5.5, P = 0.012). In contrast, the SNP A1298C had no significant impact. Conclusions: The TT genotype and T allele of C677T polymorphism but not A1298C in cMTHFE gene could be considered a risk factor for the hypercoagulable status in COVID-19.

In recent years, deep learning has had enormous success in speech recognition and natural language processing. In other languages, recent progress in speech recognition has been quite promising, but the Kurdish language has not seen comparable development. There are extremely few research papers on Kurdish speech recognition. In this paper, investigated Gated Recurrent Units (GRUs) which is one of the popular RNN models to recognize individual Kurdish words, and propose a very simplified deep-learning architecture to get more efficient and high accuracy model. The proposed model consists of a combination of CNN and GRU layers. The Kurdish Sorani Speech KSS dataset was created for the speech recognition system, as its 18799 sound files for 500 formal Kurdish words. Finally, the model proposed was trained with collected data and yielded over %96 accuracy. The combination of CNN an RNN (GURs) for speech recognition achieved superior performance compared to the other feed-forward deep neural network models and other statistical methods.

Background: Dorema aucheri gum (DAG) is a bitter flavonoid gum widely used for numerous medicinal purposes including wound recovery. The present work investigates the acute toxicity and wound‐healing effects of DAG in excisional skin injury in rats. Materials and methods: Sprague Dawley rats (24) were clustered into four groups, each rat had a full‐thickness excisional dorsal neck injury (2.00 cm) and addressed with 0.2 mL of the following treatments for 15 days: Group A (vehicle), rats addressed with normal saline; Group B, rats received intrasite gel; C and D, rats addressed with 250 and 500 mg/kg of DAG, respectively. Results: The results revealed the absence of any toxic signs in rats who received oral dosages of 2 and 5 g/kg of DAG. Wound healing was significantly accelerated following DAG treatments indicated by smaller open areas and higher wound contraction percentages compared to vehicle rats. Histological evaluation revealed higher fibroblast formation, collagen deposition, and noticeably lower inflammatory cell infiltration in granulated skin tissues of DAG‐addressed rats compared to vehicle rats. DAG treatment caused significant modulation of immunohistochemical proteins (decreased Bax and increased HSP 70) and inflammatory mediators (reduced TNF‐α, IL‐6, and magnified IL‐10), which were significantly varied compared to vehicle rats. Moreover, topical DAG treatment led to significant upregulation of the hydroxyproline (HDX) (collagen) and antioxidant content. At the same time, decreased the lipid peroxidation (MDA) levels in healed tissues obtained from DAG‐treated rats. Conclusion: The present wound contraction by DAG might be linked with the modulatory effect of its phytochemicals (polysaccharides, flavonoids, and phenolic) on the cellular mechanisms, which justify their folkloric use and provokes further investigation as therapeutic drug additives for wound contraction. [ABSTRACT FROM AUTHOR]

With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as business, politics, applications, and services. Twitter Application Programming Interface (Twitter-API), which is an interface between developers and Twitter, allows them to search for tweets based on the desired keyword using some secret keys and tokens. In this work, Twitter-API used to download the most recent tweets about four keywords, namely, (Trump, Bitcoin, IoT, and Toyota) with a different number of tweets. “Vader” that is a lexicon rule-based method used to categorize downloaded tweets into “Positive” and “Negative” based on their polarity, then the tweets were protected in Mongo database for the next processes. After pre-processing, the hold-out technique was used to split each dataset to 80% as “training-set” and rest 20% “testing-set.” After that, a deep learning-based Document to Vector model was used for feature extraction. To perform the classification task, Radial Bias Function kernel-based support vector machine (SVM) has been used. The accuracy of (RBF-SVM) mainly depends on the value of hyperplane “Soft Margin” penalty “C” and γ “gamma” parameters. The main goal of this work is to select best values for those parameters in order to improve the accuracy of RBF-SVM classifier. The objective of this study is to show the impacts of using four meta-heuristic optimizer algorithms, namely, particle swarm optimizer (PSO), modified PSO (MPSO), grey wolf optimizer (GWO), and hybrid of PSO-GWO in improving SVM classification accuracy by selecting the best values for those parameters. To the best of our knowledge, hybrid PSO-GWO has never been used in SVM optimization. The results show that these optimizers have a significant impact on increasing SVM accuracy. The best accuracy of the model with traditional SVM was 87.885%. After optimization, the highest accuracy obtained with GWO is 91.053% while PSO, hybrid PSO-GWO, and MPSO best accuracies are 90.736%, 90.657%, and 90.557%, respectively.

Gaining high yield of potato is very crucial for farmer and quality also has a great impact on their production. Beside the quality and yield using the amount of irrigation also important to reduce water lose. The tuber yield and quality of potato (Solanum tuberosum L.) is impacted by irrigated water. This study was carried out in two respectively season (2017 and 2018) in Halabja province of Kurdistan regional of Iraq to evaluate the impact of irrigation interval on quality and tuber yield in potato cultivars. In this study there are four irrigation intervals (3,5,7 and 9 days) used by furrow irrigation method which subjected in complete randomized design (CRD) with three replications. The potato tuber production in 3 day interval showed highest percentage also protein and starch content was recorded highest in both year of cultivation, but the abscisic acid was affected by more irrigation which in 3 day interval irrigation the rate was less than other. Also in 5 day interval irrigation the rate was acceptable because there is no significant difference in terms of tuber yield, protein and starch content if compare with 3 day interval irrigation in particular for those area faced restricted in using water irrigation.