Statistical and cross-sectional analysis of factors effecting the prevalence of pressure ulcer in hospital admitted stroke patients.

Every year, millions of people around the world are disabled by stroke, it is well recognized that complications aftera stroke extend hospital stays and pressure ulcers, a stroke consequence, which can be prevented by educating the caregiver. The primary focus of this research is not only to investigate the prevalence of pressure ulcers (PU) among stroke patients, but this study also introduced a variety of factors which influence the formation of PU, such as restricted mobility, gender, duration of stroke, hypertension, diabetes, hygiene, type of mattress, malnutrition, awareness, etc. In addition, this research provides a comparative and statistical analysis, a cause of the catastrophic disabilities influenced by a variety of factors. Moreover, the proposed research also provides a room for the pertinent treatment of stroke patient to curtail the formation of pressure ulcer. In this research, a total of 120 stroke patients were initially included to monitor the frequency of pressure ulcers at incipient stage. Out of the total patients, the number of patients with ischemic stroke were 78.5 % while 8.3 % were of haemorrhagic type. In the results, the demographic characteristics and the factors which influence the formation of PU of the patients were examined with their cross-sectional impact on each other through comparative and statistical analysis. It was discovered that among all the stroke patients, 8.3 % were found with a PUs and the most frequent localization was sacrum and no new PU was observed for the participants under the observation.

Robustness of Deep Learning models in electrocardiogram noise detection and classification.

BACKGROUND AND OBJECTIVE: Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of ECG signal analysers. Traditional blind filtering methods use predefined noise frequency and filter order, but they alter ECG biomarkers. Several Deep Learning-based ECG noise detection and classification methods exist, but no study compares recurrent neural network (RNN) and convolutional neural network (CNN) architectures and their complexity. METHODS: This paper introduces a knowledge-based ECG filtering system using Deep Learning to classify ECG noise types and compare popular computer vision model architectures in a practical Internet of Medical Things (IoMT) framework. Experimental results demonstrate that the CNN-based ECG noise classifier outperforms the RNN-based model in terms of performance and training time. RESULTS: The study shows that AlexNet, visual geometry group (VGG), and residual network (ResNet) achieved over 70% accuracy, specificity, sensitivity, and F1 score across six datasets. VGG and ResNet performances were comparable, but VGG was more complex than ResNet, with only a 4.57% less F1 score. CONCLUSIONS: This paper introduces a Deep Learning (DL) based ECG noise classifier for a knowledge-driven ECG filtering system, offering selective filtering to reduce signal distortion. Evaluation of various CNN and RNN-based models reveals VGG and Resnet outperform. Further, the VGG model is superior in terms of performance. But Resnet performs comparably to VGG with less model complexity.

Study of Kinetics and the Working Mechanism of Silica-Coated Amino-Functionalized CoFe2O4 Ferrite Nanoparticles to Treat Wastewater for Heavy Metals.

This study used a simple coprecipitation method to produce pristine, silica-coated, and amino-functionalized CoFe2O4 nanoadsorbents. Amino-functionalization was done to increase the active surface area and metal ion removal efficiency. Both pristine and functionalized adsorbents were employed to recover Pb(II), Zn(II), and Cu(II) ions from wastewater. The adsorption tests were performed by varying the initial concentration of metal ions and contact time at a fixed pH of 6.5. Atomic adsorption spectroscopy was utilized to detect the proportion of metals removed from water. Additionally, the pseudo-first-order, pseudo-second-order, Freundlich, and Langmuir models were employed to compute the kinetic and isothermic data from metal ion adsorption onto the adsorbents. The amino-functionalized adsorbent showed adsorption capacities of 277.008, 254.453, and 258.398 mg/g for Cu(II), Pb(II), and Zn(II) ions, respectively. According to the adsorption results, the Langmuir isotherm and the pseudo-second-order model best suit the data. The best fitting of the pseudo-second-order model with the data indicates that coordinative interactions between amino groups and metal ions are responsible for chemisorption. The metal ions bind with -NH2 groups on the adsorbent surface through chelate bonds. Chelate bonds are extremely strong and stable, indicating the effectiveness of the CoFe2O4@SiO2-NH2 adsorbent in adsorbing heavy-metal ions. The tested adsorbent exhibited good performance, batter stability, and good reusable values around 77, 81, and 76% for Cu(II), Pb(II), and Zn(II) ions, respectively, after five adsorption cycles.

Nanofiltration PVP Membrane with Gradient Cross-Linking for Efficient Polycyclic Aromatic Hydrocarbons Removal.

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants that may contaminate various water sources and pose serious dangers to human health and the environment. Due to their capacity for size-based separation, nanofiltration membranes have become efficient instruments for PAH removal. However, issues such as membrane fouling and ineffective rejection still exist. To improve PAH rejection while reducing fouling problems, this work created a new gradient cross-linking poly(vinylpyrrolidone) (PVP) nanofiltration membrane. The gradient cross-linking technique enhanced the rejection performance and antifouling characteristics of the membrane. The results demonstrated that the highest membrane flow was achieved at a 0.15% SDS-PVP membrane. There is a trade-off between membrane flux and salt rejection since salt rejection increases with SDS owing to the growth of big pores. The membrane flux was reduced for the 0.25% SDS-PVP membrane owing to poor SDS dispersion. The prepared membrane showed enhanced removal efficiencies for the removal of the PAH compounds. The PVP membrane has the potential to be used in several water treatment applications, improving water quality, and preserving the environment.

Statistically Analyzed Heavy Metal Removal Efficiency of Silica-Coated Cu0.50Mg0.50Fe2O4 Magnetic Adsorbent for Wastewater Treatment.

Even low concentrations of pollutants in water, particularly heavy metals, can significantly affect the ecosystem and human health. Adsorption has been determined to be one of the most effective techniques of removing pollution from wastewater among the various strategies. To remove heavy metals such as Zn2+ and Pb2+, we prepared a silica-coated CuMgFe2O4 magnetic adsorbent using sol-gel method and tested it for wastewater treatment. X-ray diffraction investigation validated the creation of cubic spinel structure, while morphological analysis showed that silica coating reduces the particle size but boosts the surface roughness of the nanoparticles and also reduces the agglomeration between particles. UV-visible spectroscopy indicates a rise in bandgap and magnetic characteristics analysis indicates low values of magnetization due to silica coating. The kinetic and isotherm parameters for heavy metal ions adsorption onto silica-coated Cu0.50Mg0.50Fe2O4 nanoparticles are calculated by applying pseudo-first-order, pseudo-second-order, Langmuir and Freundlich models. Adsorption kinetics revealed that the pseudo-second-order and Langmuir models are the best fit to explain adsorption kinetics. Synthesized adsorbent revealed 92% and 97% removal efficiencies for Zn2+ and Pb2+ ions, respectively.