Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%

A survey of voice pathology surveillance systems based on internet of things and machine learning algorithms

The incorporation of the cloud technology with the Internet of Things (IoT) is significant in order to obtain better performance for a seamless, continuous, and ubiquitous framework. IoT has many applications in the healthcare sector, one of these applications is voice pathology monitoring. Unfortunately, voice pathology has not gained much attention, where there is an urgent need in this area due to the shortage of research and diagnosis of lethal diseases. Most of the researchers are focusing on the voice pathology and their finding is only to differentiating either the voice is normal (healthy) or pathological voice, where there is a lack of the current studies for detecting a certain disease such as laryngeal cancer. In this paper, we present an extensive review of the state-of-the-art techniques and studies of IoT frameworks and machine learning algorithms used in the healthcare in general and in the voice pathology surveillance systems in particular. Furthermore, this paper also presents applications, challenges and key issues of both IoT and machine learning algorithms in the healthcare. Finally, this paper highlights some open issues of IoT in healthcare that warrant further research and investigation in order to present an easy, comfortable and effective diagnosis and treatment of disease for both patients and doctors

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Ultrashort Self-Assembling Peptide Hydrogel for the Treatment of Fungal Infections

The threat of antimicrobial resistance to society is compounded by a relative lack of new clinically effective licensed therapies reaching patients over the past three decades. This has been particularly problematic within antifungal drug development, leading to a rise in fungal infection rates and associated mortality. This paper highlights the potential of an ultrashort peptide, (naphthalene-2-ly)-acetyl-diphenylalanine-dilysine-OH (NapFFKK-OH), encompassing hydrogel-forming and antifungal properties within a single peptide motif, thus overcoming formulation (e.g., solubility, drug loading) issues associated with many currently employed highly hydrophobic antifungals. A range of fungal susceptibility (colony counts) and cell cytotoxicity (MTS cell viability, LIVE/DEAD staining&reg; with fluorescent microscopy, haemolysis) assays were employed. Scanning electron microscopy confirmed the nanofibrous architecture of our self-assembling peptide, existing as a hydrogel at concentrations of 1% w/v and above. Broad-spectrum activity was demonstrated against a range of fungi clinically relevant to infection (Aspergillus niger, Candida glabrata, Candida albicans, Candida parapsilosis and Candida dubliniensis) with greater than 4 log10 CFU/mL reduction at concentrations of 0.5% w/v and above. We hypothesise antifungal activity is due to targeting of anionic components present within fungal cell membranes resulting in membrane disruption and cell lysis. NapFFKK-OH demonstrated reduced toxicity against mammalian cells (NCTC 929, ARPE-19) suggesting increased selectivity for fungal cells. However, further studies relating to safety for systemic administration is required, given the challenges toxicity has presented in the wider context of antimicrobial peptide drug development. Overall this study highlights the promise of NapFFKK-OH hydrogels, particularly as a topical formulation for the treatment of fungal infections relating to the skin and eyes, or as a hydrogel coating for the prevention of biomaterial related infection

Arterial Thrombosis in an Asymptomatic COVID-19 Complicated by Malignant Middle Cerebral Artery Syndrome: A Case Report and Literature Review

Ali A Alzahrani,1 Hissah Al Abdulsalam,2 Hussein Al-Sakkaf,3 Ayat Yousef,3 Fahad B Albadr3 1College of Medicine, King Saud University, Riyadh, Saudi Arabia; 2Division of Neurosurgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 3Department of Radiology, College of Medicine, King Saud University, Riyadh, Saudi ArabiaCorrespondence: Ali A AlzahraniCollege of Medicine, King Saud University, P.O. Box 13265, Riyadh, 8101, Saudi ArabiaTel +966508622244Email [email protected]: Coronavirus disease 2019 (COVID-19) is a severe infectious respiratory disease caused by the novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies in the literature highlight the association between COVID-19 and stroke. We report a case of acute ischemic stroke in a COVID-19 patient without displaying symptoms of active COVID-19 infection or risk factors for stroke with further review of the literature. The patient&rsquo;s recovery was complicated by hemorrhagic stroke, which resulted in death. Acute ischemic strokes are one of the challenging complications of COVID-19 infection. Initial rapid assessment and management are crucial in optimizing the outcomes on these patients. Nevertheless, wearing appropriate PPE should be instituted while providing adequate care.Keywords: ischemic stroke, COVID-19, SARS-CoV-2, carotid artery thrombosis, infection, case repor

The Synergistic Protective Effect of γ-Oryzanol (OZ) and N-Acetylcysteine (NAC) against Experimentally Induced NAFLD in Rats Entails Hypoglycemic, Antioxidant, and PPARα Stimulatory Effects

This study estimated that the combined effect of γ-Oryzanol and N-acetylcysteine (NAC) against high-fat diet (HFD)-induced non-alcoholic fatty liver disease (NAFLD) in rats also estimated some of their mechanisms of action. Adult male rats were divided into seven groups (n = 8 each) as control, control + NAC, control + γ-Oryzanol, HFD, HFD + NAC, HFD + γ-Oryzanol, and HFD + NAC + γ-Oryzanol. NAC was administered orally at a final concentration of 200 mg/kg, whereas γ-Oryzanol was added to diets at a concentration of 0.16. All treatments were conducted for 17 weeks and daily. Both NAC and γ-Oryzanol were able to reduce final body weights, fat weights, fasting glucose, fasting insulin, serum, and serum levels of liver function enzymes as well as the inflammatory markers such as tumor necrosis factor-α (TNF-α), interleukine-6 (IL-6), and leptin in HFD-fed rats. They also improved hepatic structure and glucose tolerance, increased adiponectin levels, and reduced serum and hepatic levels of triglycerides (TGs) and cholesterol (CHOL) in these rats. These effects were concomitant with a reduction in the hepatic levels of lipid peroxides (MDA) and serum levels of LDL-C, but also with an increment in the hepatic levels of superoxide dismutase (SOD) and glutathione (GSH). Interestingly, only treatment with γ-Oryzanol stimulated the mRNA levels of proliferator-activated receptor alpha (PPARα) and carnitine palmitoyltransferase 1 (CPT1) in the liver and white adipose tissue (WAT) of rats. Of note, the combination therapy of both drugs resulted in maximum effects and restored almost normal liver structure and basal levels of all the above-mentioned metabolic parameters. In conclusion, a combination therapy of γ-Oryzanol and NAC is an effective therapy to treat NAFLD, which can act via several mechanisms on the liver and adipose tissue

Fenugreek Seed Galactomannan Aqueous and Extract Protects against Diabetic Nephropathy and Liver Damage by Targeting NF-&kappa;B and Keap1/Nrf2 Axis

This investigation was conducted to test the potential of the galactomannan (F-GAL) and aqueous extract (FS-AE) of the Fenugreek seed aqueous to prevent liver and kidney damage extracts in streptozotocin (STZ)-induced T1DM in rats. Non-diabetic and diabetic rats received the normal saline as a vehicle or were treated with FS-EA or F-GAL at a final concentration of 500 mg/kg/each. Treatments with both drugs reduced fasting hyperglycemia and improved serum and hepatic lipid profiles in the control and diabetic rats. Additionally, F-GAL and FS-AE attenuated the associated reduction in the mass and structure of the islets of Langerhans in diabetic rats and improved the structure of the kidneys and livers. In association, they also reduced the generation of reactive oxygen species (ROS), lipid peroxides, factor (TNF-&alpha;), interleukin-6 (IL-6), and nuclear levels of NF-&kappa;B p65, and improved serum levels of ALT, AST, albumin, and creatinine. However, both treatments increased hepatic and renal superoxide dismutase (SOD) in the livers and kidneys of both the control and diabetic-treated rats, which coincided with a significant increase in transcription, translation, and nuclear localization of Nrf2. In conclusion, FS-AE and F-GAL are effective therapeutic options that may afford a possible treatment for T1DM by attenuating pancreatic damage, hyperglycemia, hyperlipidemia, and hepatic and renal damage

Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

<div><p>Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.</p></div

Esculeogenin A, a Glycan from Tomato, Alleviates Nonalcoholic Fatty Liver Disease in Rats through Hypolipidemic, Antioxidant, and Anti-Inflammatory Effects

This study examined the preventative effects of esculeogenin A (ESGA), a newly discovered glycan from tomato, on liver damage and hepatic steatosis in high-fat-diet (HFD)-fed male rats. The animals were divided into six groups (each of eight rats): a control group fed a normal diet, control + ESGA (200 mg/kg), HFD, and HFD + ESAG in 3 doses (50, 100, and 200 mg/kg). Feeding and treatments were conducted for 12 weeks. Treatment with ESGA did not affect gains in the body or fat weight nor increases in fasting glucose, insulin, and HOMA-IR or serum levels of free fatty acids (FFAs), tumor-necrosis factor-α, and interleukin-6 (IL-6). On the contrary, it significantly reduced the serum levels of gamma-glutamyl transpeptidase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total triglycerides (TGs), cholesterol (CHOL), and low-density lipoprotein cholesterol (LDL-c) in the HFD-fed rats. In addition, it improved the liver structure, attenuating the increase in fat vacuoles; reduced levels of TGs and CHOL, and the mRNA levels of SREBP1 and acetyl CoA carboxylase (ACC); and upregulated the mRNA levels of proliferator-activated receptor α (PPARα) and carnitine palmitoyltransferase I (CPT I) in HFD-fed rats. These effects were concomitant with increases in the mRNA, cytoplasmic, and nuclear levels of nuclear factor erythroid 2-related factor 2 (Nrf2), glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and heme oxygenase-1 (HO); a reduction in the nuclear activity of nuclear factor-kappa beta (NF-κB); and inhibition of the activity of nuclear factor kappa B kinase subunit beta (IKKβ). All of these effects were dose-dependent effects in which a normal liver structure and normal levels of all measured parameters were seen in HFD + ESGA (200 mg/kg)-treated rats. In conclusion, ESGA prevents NAFLD in HFD-fed rats by attenuating hyperlipidemia, hepatic steatosis, oxidative stress, and inflammation by acting locally on Nrf2, NF-κB, SREBP1, and PPARα transcription factors