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.

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.

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.

Nonlinear Features from Multi-Modal Signals for Continuous Stress Monitoring.

Continuous monitoring of stress in individuals during their daily activities has become an inevitable need in present times. Unattended stress is a silent killer and may lead to fatal physical and mental disorders if left unidentified. Stress identification based on individual judgement often leads to under-diagnosis and delayed treatment possibilities. EEG-based stress monitoring is quite popular in this context, but impractical to use for continuous remote monitoring.Continuous remote monitoring of stress using signals acquired from everyday wearables like smart watches is the best alternative here. Non-EEG data such as heart rate and ectodermal activity can also act as indicators of physiological stress. In this work, we have explored the possibility of using nonlinear features from non-EEG data such as (a) heart rate, (b) ectodermal activity, (c) body temperature (d) SpO2 and (e) acceleration in detecting four different types of neurological states; namely (1) Relaxed state, (2) State of Physical stress, (3) State of Cognitive stress and (4) State of Emotional stress. Physiological data of 20 healthy adults have been used from the noneeg database of PhysioNet.Results: We used two machine learning models; a linear logistic regression and a nonlinear random forest to detect (a) stress from relaxed state and (4) the four different neurological states. We trained the models using linear and nonlinear features separately. For the 2-class and 4-class problems, using nonlinear features increased the accuracy of the models. Moreover, it is also proved in this study that by using nonlinear features, we can avoid the use of complex machine learning models.

Impact of synthetic noise signature and physiologic ECG signal on designing ML-based ECG noise detection framework.

Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are collected from traditional ECG machines or wearable ECG sensors. However, the risk of using an automated system for ECG analysis when noise is present can lead to incorrect diagnosis or treatment decisions. A noise detector is crucial to minimise the risk of incorrect diagnosis. Machine learning (ML) models are used in ECG noise detection before clinical decision-making systems to mitigate false alarms. However, it is essential to prove the generalisation capability of the ML model in different situations. ML models performance is 50% lesser when the model is trained with synthetic and tested with physiologic ECG datasets compared to trained and tested with physiologic ECG datasets. This suggests that the ML model must be trained with physiologic ECG datasets rather than synthetic ones or add more various types of noise in synthetic ECG datasets that can mimic physiologic ECG.Clinical relevance- ML model trained with synthetic noisy ECG can increase the 50% misclassification rate in ECG noise detection compared to training with physiologic ECG datasets. The wrong classification of noise-free and noisy ECG will lead to misdiagnosis regarding the patient's condition, which could be a cause of death.

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.

Dual-polarized 8-port sub 6 GHz 5G MIMO diamond-ring slot antenna for smart phone and portable wireless applications.

This manuscript presents high performance dual polarized eight-element multiple input multiple output (MIMO) fifth generation (5G) smartphone antenna. The design consists of four dual-polarized microstrip diamond-ring slot antennas, positioned at corners of printed circuit board (PCB). Cheap Fr-4 dielectric with permittivity 4.3 and thickness of 1.6mm is used as substrate with overall dimension of 150 × 75 × 1.6 mm3. In mobile system due to limited space mutual coupling between nearby antenna elements is an issue that distort MIMO antenna performance. Defected ground structure is used to control coupling. The defected ground structure has advantages like ease of fabrication, compact size and high efficiency as compare to other techniques. Less than 30dB coupling is achieved for adjacent elements. The -10 dB impedance bandwidth of 700 MHz is achieved for all radiating elements ranging from 3.3 GHz to 4.1 GHz. The value is about 900MHz for -6dB. The proposed antenna offers good results in terms of fundamental antenna parameters like reflection coefficient, transmission coefficient, maximum gain, total efficiency. The antenna achieved average gain more than 3.8dBi and average radiation efficiency more than 80% for single dual polarized element. The antenna provides sufficient radiation coverage in all sides. The MIMO antenna characteristics like diversity gain (DG), envelope correlation coefficient (ECC), total active reflection coefficient (TARC) and channel capacity are calculated and found according to standards. Furthermore, effect of user on antenna performance in data-mode and talk-mode are studied. Proposed design is fabricated and tested in real time. The measured results shows that proposed design can be used in future smartphones applications. The design is compared with some of the existing work and found to be the best one in many parameters and can be used for commercial use.

Dual S-Scheme Heterojunction CdS/TiO2/g-C3N4 Photocatalyst for Hydrogen Production and Dye Degradation Applications.

This study investigated a ternary CdS/TiO2/g-C3N4 heterojunction for degrading synthetic dyes and hydrogen production from aqueous media through visible light-initiated photocatalytic reactions. CdS, TiO2, and g-C3N4 were combined in different mass ratios through a simple hydrothermal method to create CdS/TiO2/g-C3N4 composite photocatalysts. The prepared heterojunction catalysts were investigated by using FTIR, XRD, EDX, SEM, and UV-visible spectroscopy analysis for their crystal structures, functional groups, elemental composition, microtopography, and optical properties. The rhodamine B dye was then degraded by using fully characterized photocatalysts. The maximum dye degradation efficiency of 99.4% was noted in these experiments. The evolution rate of hydrogen from the aqueous solution with the CdS/TiO2/g-C3N4 photocatalyst remained 2910 µmol·h-1·g-1, which is considerably higher than those of g-C3N4, CdS, CdS/g-C3N4, and g-C3N4/TiO2-catalyzed reactions. This study also proposes a photocatalytic activity mechanism for the tested ternary CdS/TiO2/g-C3N4 heterojunctions.

Ensemble learning approach for advanced metering infrastructure in future smart grids.

Typically, load forecasting models are trained in an offline setting and then used to generate predictions in an online setting. However, this approach, known as batch learning, is limited in its ability to integrate new load information that becomes available in real-time. On the other hand, online learning methods enable load forecasting models to adapt efficiently to new incoming data. Electricity Load and Price Forecasting (ELPF) is critical to maintaining energy grid stability in smart grids. Existing forecasting methods cannot handle the available large amount of data, which are limited by different issues like non-linearity, un-adjusted high variance and high dimensions. A compact and improved algorithm is needed to synchronize with the diverse procedure in ELPF. Our model ELPF framework comprises high/low consumer data separation, handling missing and unstandardized data and preprocessing method, which includes selecting relevant features and removing redundant features. Finally, it implements the ELPF using an improved method Residual Network (ResNet-152) and the machine-improved Support Vector Machine (SVM) based forecasting engine to forecast the ELP accurately. We proposed two main distinct mechanisms, regularization, base learner selection and hyperparameter tuning, to improve the performance of the existing version of ResNet-152 and SVM. Furthermore, it reduces the time complexity and the overfitting model issue to handle more complex consumer data. Furthermore, numerous structures of ResNet-152 and SVM are also explored to improve the regularization function, base learners and compatible selection of the parameter values with respect to fitting capabilities for the final forecasting. Simulated results from the real-world load and price data confirm that the proposed method outperforms 8% of the existing schemes in performance measures and can also be used in industry-based applications.

Green Synthesis of ZnO and Black TiO2 Materials and Their Application in Photodegradation of Organic Pollutants.

ZnO and black TiO2 have been selected as the most efficient materials for organic pollution abatement due to their increased efficiency when compared to other materials. However, the concept of green chemistry makes it desirable to design green synthesis approaches for their production. In this study, black TiO2 was synthesized using an environmentally safe synthetic technique with glycerol as a reductant. ZnO was prepared by using ionic-liquid-based microwave-assisted extracts of Polygonum minus. To investigate the materials' potential to photodegrade organic pollutants, methylene blue (MB) and phenol were chosen as model organic pollutants. Both materials were found to exhibit spherical morphologies and a mesoporous structure and were efficient absorbers of visible light. ZnO exhibited electron-hole pair recombination lower than that of black TiO2. Black TiO2 was discovered to be an anatase phase, whereas ZnO was found to have a hexagonal wurtzite structure. In contrast to black TiO2, which had a surface area of 239.99 m2/g and a particle size of 28 nm, ZnO had a surface area of 353.11 m2/g and a particle size of 32 nm. With a degradation time of 60 min, ZnO was able to eliminate 97.50% of the 40 mg/L MB. Black TiO2, on the other hand, could reduce 90.0% of the same amount of MB in 60 min. When tested for phenol degradation, ZnO and black TiO2 activities were reduced by nearly 15 and 25%, respectively. A detailed examination of both ZnO and black TiO2 materials revealed that ZnO has more potential and versatility for the degradation of organic pollutants under visible light irradiation.

Nano-Enabled Antivirals for Overcoming Antibody Escaped Mutations Based SARS-CoV-2 Waves.

This review discusses receptor-binding domain (RBD) mutations related to the emergence of various SARS-CoV-2 variants, which have been highlighted as a major cause of repetitive clinical waves of COVID-19. Our perusal of the literature reveals that most variants were able to escape neutralizing antibodies developed after immunization or natural exposure, pointing to the need for a sustainable technological solution to overcome this crisis. This review, therefore, focuses on nanotechnology and the development of antiviral nanomaterials with physical antagonistic features of viral replication checkpoints as such a solution. Our detailed discussion of SARS-CoV-2 replication and pathogenesis highlights four distinct checkpoints, the S protein (ACE2 receptor coupling), the RBD motif (ACE2 receptor coupling), ACE2 coupling, and the S protein cleavage site, as targets for the development of nano-enabled solutions that, for example, prevent viral attachment and fusion with the host cell by either blocking viral RBD/spike proteins or cellular ACE2 receptors. As proof of this concept, we highlight applications of several nanomaterials, such as metal and metal oxide nanoparticles, carbon-based nanoparticles, carbon nanotubes, fullerene, carbon dots, quantum dots, polymeric nanoparticles, lipid-based, polymer-based, lipid-polymer hybrid-based, surface-modified nanoparticles that have already been employed to control viral infections. These nanoparticles were developed to inhibit receptor-mediated host-virus attachments and cell fusion, the uncoating of the virus, viral gene expression, protein synthesis, the assembly of progeny viral particles, and the release of the virion. Moreover, nanomaterials have been used as antiviral drug carriers and vaccines, and nano-enabled sensors have already been shown to enable fast, sensitive, and label-free real-time diagnosis of viral infections. Nano-biosensors could, therefore, also be useful in the remote testing and tracking of patients, while nanocarriers probed with target tissue could facilitate the targeted delivery of antiviral drugs to infected cells, tissues, organs, or systems while avoiding unwanted exposure of non-target tissues. Antiviral nanoparticles can also be applied to sanitizers, clothing, facemasks, and other personal protective equipment to minimize horizontal spread. We believe that the nanotechnology-enabled solutions described in this review will enable us to control repeated SAR-CoV-2 waves caused by antibody escape mutations.

Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG.

The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly. However, the impact of various wavelengths of the PPG signal has not been investigated for estimating SA. In this study, we explore the feasibility of using various statistical and nonlinear features derived from peak-to-peak (AC) values of PPG signals of different wavelengths (green, blue, infrared and red) to estimate stress-induced changes in SA and compare their performances. The impact of two physical stressors: and Hand Grip are studied on 32 healthy individuals. Linear (Mean, s.d.) and nonlinear (Katz, Petrosian, Higuchi, SampEn, TotalSampEn) features are extracted from the PPG signal's AC amplitudes to identify the onset, continuation and recovery phases of those stressors. The results show that the nonlinear features are the most promising in detecting stress-induced sympathetic activity. TotalSampEn feature was capable of detecting stress-induced changes in SA for all wavelengths, whereas other features (Petrosian, AvgSampEn) are significant (AUC ≥ 0.8) only for IR and Red wavelengths. The outcomes of this study can be used to make device design decisions as well as develop stress detection algorithms.

Assessing spatiotemporal trends of total and extreme precipitation in a subtropical highland region: A climate perspective.

This study used a dataset of 30 years (1990-2020) of daily observations from 24 meteorological stations in the northern highlands of Pakistan to assess trends in extreme precipitation indices. The RClimDex model was used to analyze the indices, and the Modified Mann-Kendal test and the Theil-Sen slope estimator were applied to determine trends and slopes, respectively. The results showed a significant decrease in total annual precipitation amount (PRCPTOT) with varying rates of negative trend from -4.44 mm/year to -19.63 mm/year. The total winter and monsoon precipitation amounts were also decreased during the past three decades. The intensity-based precipitation indices (RX1Day, RX5Day, R95p, R99p, and SDII) showed a significant decrease in extreme intensity events over time, while the count of consecutive dry days (CDD) and consecutive wet days (CWD) indicated a significant decrease in duration at multiple stations. The annual counts of days with precipitation more than or equal to 10 mm (R10), 20 mm (R20), and 25 mm (R25) exhibited a significant decrease in frequency of extreme precipitation events, with the decrease more pronounced in the northern parts of the study domain. The findings of this study indicate a significant decline in the intensity, frequency, and extent of precipitation extremes across the northern highlands of Pakistan over the past 30 years.

Characteristics Analysis of Plasticized Polyvinyl Chloride Gel-Based Microlens at Different Temperatures.

Temperature plays a crucial role in the preparation of polyvinyl chloride (PVC) gels for optical applications. Incorrect temperature selection can lead to various issues such as poor surface roughness, inadequate light transmission, and insufficient solution for optical devices. To address this challenge, this study focuses on the preparation of PVC gel samples by combining PVC powder (n = 3000), eco-friendly dibutyl adipate, and tetrahydrofuran at different stirring temperatures ranging from 40 to 70 °C. The PVC gel preparation process is categorized into four groups (T40, T50, T60, and T70) based on the mixing temperatures, employing a controlled test method with specific temperature conditions. The prepared PVC gel samples are then subjected to analysis to evaluate various properties including surface morphology, tensile strength, light transmittance, and electrical response time. Among the samples, the PVC gel prepared at 60 °C (referred to as T60) exhibits excellent optical properties, with a transmittance of 91.2% and a tensile strength of 2.07 MPa. These results indicate that 60 °C is an optimal reaction temperature. Notably, the PVC gel microlenses produced at this temperature achieve their maximum focal length (ranging from -8 to -20 mm) within approximately 60 s, and they recover their initial state within around 80 s after the power is switched off. This focal length achievement is twice as fast as reported in previous studies on microlenses. It is observed that the reaction temperature significantly influences the solubility of the resin-based raw materials and the homogeneity of the gel. Consequently, these findings open up possibilities for utilizing PVC gel microlenses in novel commercial optics applications, thanks to their desirable properties.

Electrophysiological In Vitro Study of Long-Range Signal Transmission by Astrocytic Networks.

Astrocytes are diverse brain cells that form large networks communicating via gap junctions and chemical transmitters. Despite recent advances, the functions of astrocytic networks in information processing in the brain are not fully understood. In culture, brain slices, and in vivo, astrocytes, and neurons grow in tight association, making it challenging to establish whether signals that spread within astrocytic networks communicate with neuronal groups at distant sites, or whether astrocytes solely respond to their local environments. A multi-electrode array (MEA)-based device called AstroMEA is designed to separate neuronal and astrocytic networks, thus allowing to study the transfer of chemical and/or electrical signals transmitted via astrocytic networks capable of changing neuronal electrical behavior. AstroMEA demonstrates that cortical astrocytic networks can induce a significant upregulation in the firing frequency of neurons in response to a theta-burst charge-balanced biphasic current stimulation (5 pulses of 100 Hz × 10 with 200 ms intervals, 2 s total duration) of a separate neuronal-astrocytic group in the absence of direct neuronal contact. This result corroborates the view of astrocytic networks as a parallel mechanism of signal transmission in the brain that is separate from the neuronal connectome. Translationally, it highlights the importance of astrocytic network protection as a treatment target.

An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff.

The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study's objective is to evaluate COVID-19's psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers.

Production of combustible fuels and carbon nanotubes from plastic wastes using an in-situ catalytic microwave pyrolysis process.

This study performed in-situ microwave pyrolysis of plastic waste into hydrogen, liquid fuel and carbon nanotubes in the presence of Zeolite Socony Mobil ZSM-5 catalyst. In the presented microwave pyrolysis of plastics, activated carbon was used as a heat susceptor. The microwave power of 1 kW was employed to decompose high-density polyethylene (HDPE) and polypropylene (PP) wastes at moderate temperatures of 400-450 °C. The effect of plastic composition, catalyst loading and plastic type on liquid, gas and solid carbon products was quantified. This in-situ CMP reaction resulted in heavy hydrocarbons, hydrogen gas and carbon nanotubes as a solid residue. A relatively better hydrogen yield of 129.6 mmol/g as a green fuel was possible in this process. FTIR and gas chromatography analysis revealed that liquid product consisted of C13+ fraction hydrocarbons, such as alkanes, alkanes, and aromatics. TEM micrographs showed tubular-like structural morphology of the solid residue, which was identified as carbon nanotubes (CNTs) during X-ray diffraction analysis. The outer diameter of CNTs ranged from 30 to 93 nm from HDPE, 25-93 nm from PP and 30-54 nm for HDPE-PP mixure. The presented CMP process took just 2-4 min to completely pyrolyze the plastic feedstock into valuable products, leaving no polymeric residue.

Testing of Sr-Doped ZnO/CNT Photocatalysts for Hydrogen Evolution from Water Splitting under Atmospheric Dielectric Barrier Plasma Exposure.

Nonthermal plasma is a well-recognized environmentally advantageous method for producing green fuels. This work used different photocatalysts, including PZO, SxZO, and SxZCx for hydrogen production using an atmospheric argon coaxial dielectric barrier discharge (DBD)-based light source. The photocatalysts were produced using a sol-gel route. The DBD discharge column was filled with water, methanol, and the catalyst to run the reaction under argon plasma. The DBD reactor was operated with a 10 kV AC source to sustain plasma for water splitting. The light absorption study of the tested catalysts revealed a decrease in the band gap with an increase in the concentration of Sr and carbon nanotubes (CNTs) in the Sr/ZnO/CNTs series. The photocatalyst S25ZC2 demonstrated the lowest photoluminescence (PL) intensity, implying the most quenched recombination of charge carriers. The highest H2 evolution rate of 2760 µmol h-1 g-1 was possible with the S25ZC2 catalyst, and the lowest evolution rate of 56 µmol h-1 g-1 was observed with the PZO catalyst. The photocatalytic activity of S25ZC2 was initially high, which decreased slightly over time due to the deactivation of the photocatalyst. The photocatalytic activity decreased from 2760 to 1670 µmol h-1 g-1 at the end of the process.

Shared and Distinctive Transcriptomic and Proteomic Pathways in Adult and Juvenile Dermatomyositis.

OBJECTIVE: Transcript and protein expression were interrogated to examine gene locus and pathway regulation in the peripheral blood of active adult dermatomyositis (DM) and juvenile DM patients receiving immunosuppressive therapies. METHODS: Expression data from 14 DM and 12 juvenile DM patients were compared to matched healthy controls. Regulatory effects at the transcript and protein level were analyzed by multi-enrichment analysis for assessment of affected pathways within DM and juvenile DM. RESULTS: Expression of 1,124 gene loci were significantly altered at the transcript or protein levels across DM or juvenile DM, with 70 genes shared. A subset of interferon-stimulated genes was elevated, including CXCL10, ISG15, OAS1, CLEC4A, and STAT1. Innate immune markers specific to neutrophil granules and neutrophil extracellular traps were up-regulated in both DM and juvenile DM, including BPI, CTSG, ELANE, LTF, MPO, and MMP8. Pathway analysis revealed up-regulation of PI3K/AKT, ERK, and p38 MAPK signaling, whose central components were broadly up-regulated in DM, while peripheral upstream and downstream components were differentially regulated in both DM and juvenile DM. Up-regulated components shared by DM and juvenile DM included cytokine:receptor pairs LGALS9:HAVCR2, LTF/NAMPT/S100A8/HSPA1A:TLR4, CSF2:CSF2RA, EPO:EPOR, FGF2/FGF8:FGFR, several Bcl-2 components, and numerous glycolytic enzymes. Pathways unique to DM included sirtuin signaling, aryl hydrocarbon receptor signaling, protein ubiquitination, and granzyme B signaling. CONCLUSION: The combination of proteomics and transcript expression by multi-enrichment analysis broadened the identification of up- and down-regulated pathways among active DM and juvenile DM patients. These pathways, particularly those which feed into PI3K/AKT and MAPK signaling and neutrophil degranulation, may be potential therapeutic targets.