Knowledge & Awareness about COVID-19 and the Practice of Respiratory Hygiene and Other Preventive Measures among Patients with Diabetes Mellitus in Pakistan

COVID-19 is a global pandemic that has emerged and it is rapidly spreading throughout the world and subsequently causing great damage to the global economy and health-care. Patients with diabetes or other comorbidities are at a greater risk of developing severe illness. Knowledge and awareness are key elements to stimulate practice of preventive measures. The present study evaluated the level of knowledge and awareness about COVID-19 among individuals with diabetes and their compliance with the preventive measures against it. A total of 242 individuals who were diagnosed with diabetes mellitus and were 18 years or older participated in the study. The data was collected using an interview based questionnaire. Data was analyzed using Statistical Package for Social Sciences(SPSS) version 24. The mean age ± SD of the study population was 50.78 ± 11.24 years. In this study, 215 (88.8%) participants were aware that COVID-19 is caused by a virus and the disease is spread through droplets after sneezing or touching and shaking hands with an infected person (78.5%). However, only half the study populace regularly monitored their glucose level and complied with the “sick day rules” that is applicable during this pandemic. The present study indicates that despite the adequate knowledge and awareness about the coronavirus disease, the study participants were non-compliant with the practice of preventive and precautionary measures against the pandemic

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

Medicinal potential of antimicrobial peptides from two plants against <i>Bacillus cereus</i> and <i>Staphylococcus aureus</i>

Bacillus cereus and Staphylococcus aureus are the most important bacteria that cause nosocomial infection and are resistant to antibiotics. Crude proteins from Cassia fistula and Ricinus communis were isolated to study their medicinal potential against Bacillus cereus, and Staphylococcus aureus. Extraction of the crude proteins from plants was done by phosphate buffer saline (PBS) and Tris NaCl buffer by using the roots and seeds of both plants. Antimicrobial activity was checked against bacterial strains by using agar disc diffusion and agar well diffusion methods. Zones of inhibitions were measured. On well diffusion method, PBS buffer protein extract of C. fistula roots showed a maximum zone of inhibition of 25 mm against B. cereus. Tris NaCl buffer extracts of C. fistula roots and seeds showed zones of inhibition of 12mm and 5mm respectively against S. aureus while Ricinus communis roots showed a zone of 12mm against B. cereus. Because the protein of the plants showed good antimicrobial activity, we can use these plants against various diseases caused by Bacillus cereus and Staphylococcus aureus

Digoxin use in atrial fibrillation; Insights from National Ambulatory Medical Care Survey

OBJECTIVE: To evaluate the characteristics and trends of digoxin use during outpatient visits with atrial fibrillation in the US from 2006 to 2015. METHODS: We conducted a retrospective analysis of adult (age ≥18) patient visits to office-based physicians from National Ambulatory Medical Care Survey (NAMCS) database between 2006-2015. The International Classification of Diseases, Ninth Revision, Clinical Modification codes were used to identify patients with atrial fibrillation. Visits in which digoxin was listed as a medication were analyzed with descriptive statistics. Multivariable logistic regression analysis was used to identify the predictors of digoxin usage. RESULTS: Of a weighted sample of 108,113,894 patient visits, 17,617,853 (16.3%) visits included use of digoxin. Patients who used digoxin had a mean age of 75 ± 0.7 years and were predominantly Caucasian (92.56%). Among the patients who used digoxin, 24% had a diagnosis of heart failure. Multivariate analysis showed that the increased likelihood of digoxin utilization was associated with female sex (adjusted odds ratio [aOR] 1.34, 95% CI 1.05-1.71, p = .019), heart failure (aOR 1.51, 95% CI 1.05-1.17, p = .025), and usage of ³5 medications (aOR 5.32, 95% CI 3.67-7.71, p = \u3c0.001). Among the visits with atrial fibrillation, the percentage of visits with digoxin usage decreased from 23% in 2006 to 9% in 2013 and then again increased to 14% in 2015(P-trend \u3c0.001). CONCLUSION: This is the first study to examine the use of digoxin in atrial fibrillation patients in a large outpatient setting. During 2006-2015, the percentage of digoxin prescriptions in atrial fibrillation patients has declined. Predictors of digoxin use in atrial fibrillation patients are female sex, congestive heart failure, and higher number of concurrent medications

Biosynthesis and characterization of silver nanoparticles using strawberry seed extract and evaluation of their antibacterial and antioxidant activities

Synthesis of nanomaterials is an emerging field due to their fascinating properties for applications in different field and green synthesis offers various advantages versus physical and chemical methods. Herein, green protocol has been adopted for the synthesis of silver nanoparticles (Ag NPs) using seeds extract of strawberry. The Ag NPs were characterized using advanced techniques comprising UV/Vis, XRD, FTIR, SEM, DLS and EDX. The λmax for the Ag NPs was recorded at 405 nm. The functional groups present in the extract and involved in Ag ions reduction were determined using FTIR analysis. The SEM-EDX analysis confirmed the mono-dispersive nature of Ag NPs along with confirmation of elemental composition. The nanoparticles size distribution was recorded in 50-70 nm range. Bio-fabricated Ag NPs were appraised for antioxidant activity (DPPH with % inhibition 56.61 and ABTS with % inhibition 77.81) and antimicrobial activity, i.e., Escherichia coli, Salmonella typhimurium, Shigella sonnei, Halomonas halophile, Staphylococcus aureus and Bacillus subtilis. It is concluded that these synthesized NPs could probably be applied as potent antibacterial and antioxidant materials

Wearables-Assisted Smart Health Monitoring for Sleep Quality Prediction Using Optimal Deep Learning

Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the class of wearables worn on the wrist to compute the sleep parameters by storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have been employed for predicting sleep quality depending upon the wearables data from the period of being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next, data pre-processing is performed to transform the data into a uniform format. For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQP-ODL method are examined under different measures. An extensive comparison study shows the significant performance of the WSHMSQP-ODL model over other models

Optimal and Fully Connected Deep Neural Networks Based Classification Model for Unmanned Aerial Vehicle Using Hyperspectral Remote Sensing Images

Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution aerial images. The UAV based aerial image collection is highly preferred due to its inexpensive and effective nature. However, automatic classification of aerial images poses a major challenging issue in the design of UAV, which could be handled by the deep learning (DL) models. This study designs a novel UAV assisted DL based image classification model (UAVDL-ICM) for Industry 4.0 environment. The proposed UAVDL-ICM technique involves an ensemble of voting based three DL models, namely Residual network (ResNet), Inception with ResNetv2, and Densely Connected Networks (DenseNet). Also, the hyperparameter tuning of these DL models takes place using a genetic programming (GP) approach. Finally, Oppositional Water Wave Optimization (OWWO) with Fully Connected Deep Neural networks (FCDNN) is employed for the classification of aerial images. A wide range of simulations takes place and the results are examined in terms of different parameters. A detailed comparative study highlighted the betterment of the UAVDL-ICM technique compared to other recent approaches

Future Challenges of Particulate Matters (PMs) Monitoring by Computing Associations Among Extracted Multimodal Features Applying Bayesian Network Approach

The particulate matter (PM) is emitted from diverse sources and affects the human health very badly. In the past, researchers applied different automated computational tools in the predication of PM. Accurate prediction of PM requires more relevant features and feature importance. In this research, we first extracted the multimodal features from time domain standard deviation average (SDAPM), standard deviation of standard deviation (SDSD), standard deviation of particulate matter (SDPM), root-mean square of standard deviation (RMSSD), and nonlinear dynamical measure wavelet entropy (WE) – Shannon, norm, threshold, multiscale entropy based on KD tree (MSEKD), and multiscale approximate entropy (MAEnt). We then applied the intelligent-based Bayesian inference approach to compute the strength of relationship among multimodal features. We also computed total incoming and outgoing forces between the features (nodes). The results reveal that there was a very highly significant correlation (p-value <0.05) between the selected nodes. The highest total force was yielded by WE-norm followed by SDAPM and SDPM. The association will further help to investigate that which extracted features are more positively or negatively correlated and associated with each other. The results revealed that the proposed methodology can further provide deeper insights into computing the association among the features