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Search Results (29)

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Keywords = non-PDC controller

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12 pages, 1252 KiB  
Article
Impact of Sofosbuvir Plus Daclatasvir Therapy on the Frequency of CD200R+ Dendritic Cells in Chronic Hepatitis C Virus Infection
by Helal F. Hetta, Mohamed A. Mekky, Hani I. Sayed, Ahmed AbdElkader Soliman Mahran, Eman H. Salama, Douaa Sayed, Mariam E. Abdallah, Doaa Safwat Mohamed, Omnia El-Badawy and Mohamed A. El-Mokhtar
Immuno 2025, 5(1), 2; https://doi.org/10.3390/immuno5010002 - 28 Dec 2024
Viewed by 616
Abstract
Dendritic cells (DCs) play a crucial role in controlling viral infections. Little is known about the changes in frequencies of the DC subsets in patients with chronic hepatitis C (CHC), particularly in the era of interferon-free regimens. We aimed to evaluate the impact [...] Read more.
Dendritic cells (DCs) play a crucial role in controlling viral infections. Little is known about the changes in frequencies of the DC subsets in patients with chronic hepatitis C (CHC), particularly in the era of interferon-free regimens. We aimed to evaluate the impact of sofosbuvir/daclatasvir on the frequency of different peripheral DC subsets, the expression of the inhibitory CD200R and its ligand CD200 on DC, and their relation to the treatment outcome. A total of 1000 patients with CHC were enrolled and treated with a fixed oral dose of 400 mg of sofosbuvir and 60 mg of daclatasvir for 12 weeks. A total of 940 patients achieved sustained virologic response (SVR), and only 60 patients were non-responders (NRs). The frequencies of the peripheral plasmacytoid (pDC) and myeloid (mDCs) subsets and their surface expressions of CD200R and CD200 molecules were analyzed using flow cytometry. This analysis included 60 non-responders (NR group), 60 randomly selected sustained virologic responders (SVR group) at baseline, and at the end of treatment, and 60 healthy controls. HCV infection was associated with a down-regulation in the frequency of mDC, compared to healthy controls. In addition, mDC in HCV-infected patients showed lower levels of CD200R. However, neither the pDC frequency nor their CD200R expression was significantly altered. Interestingly, by the end of therapy, the frequencies of circulating mDCs and CD200R+mDC increased significantly in the SVR group and were even comparable to healthy controls. The levels of these cells were not normalized in the NR group. Percentages of mDCs and CD200R+mDC subsets showed good prognostic accuracy for predicting virologic response to therapy. Our results showed that HCV infection was associated with modulation of the mDC frequency and their surface expression of CD200R. Successful daclatasvir and sofosbuvir combined therapy was associated with the normalization of the percentages of mDC and CD200R+mDC. Full article
(This article belongs to the Section Infectious Immunology and Vaccines)
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<p><b>Gating strategy used to identify DC subsets and CD200R expression.</b> PBMCs were stained with anti-lineage cocktail (CD3/14/16/19/20/56), anti-HLA-DR, labeled anti-CD11c, anti-human CD123, and APC anti-CD200R. DC subsets were identified by specific phenotype gating and analyzed for the expression of CD200R (black-filled histograms) against an isotype-matched control (gray-filled histograms).</p>
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<p><b>Changes in the frequency of mDC and pDC subsets and their CD200R expression across different study groups.</b> (<b>A</b>) shows the percentage of myeloid dendritic cells (mDC), while (<b>B</b>) depicts the percentage of plasmacytoid dendritic cells (pDC). (<b>C</b>) presents the percentage of mDC expressing CD200R, and (<b>D</b>) illustrates the percentage of pDC expressing CD200R. <b>SVR</b>: patients who achieved sustained virological response; <b>NR</b>: Non-responders. Group comparisons were carried out using a paired <span class="html-italic">t</span>-test. Columns represent the mean, and error bars indicate the standard deviation.</p>
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<p>Receiver operating characteristic (ROC) curve analysis to determine the performance of the percentages of mDCs (<b>A</b>) and mDCs expressing CD200R (<b>B</b>) in predicting the SVR in DCV plus SOF-treated CHC patients.</p>
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18 pages, 1198 KiB  
Article
Survive and Thrive: Outcomes of Children Enrolled in a Follow-Up Clinic for Small and Sick Newborns in Rural Rwanda
by Alphonse Nshimyiryo, Dale A. Barnhart, Mathieu Nemerimana, Kathryn Beck, Kim Wilson, Christine Mutaganzwa, Olivier Bigirumwami, Evelyne Shema, Alphonsine Uwamahoro, Cécile Itangishaka, Silas Havugarurema, Felix Sayinzoga, Erick Baganizi, Hema Magge and Catherine M. Kirk
Healthcare 2024, 12(23), 2368; https://doi.org/10.3390/healthcare12232368 - 26 Nov 2024
Viewed by 557
Abstract
Introduction: Children born small or sick are at risk of death and poor development, but many lack access to preventative follow-up services. We assessed the impact of Pediatric Development Clinics (PDC), which provide structured follow-up after discharge from hospital neonatal care units, on [...] Read more.
Introduction: Children born small or sick are at risk of death and poor development, but many lack access to preventative follow-up services. We assessed the impact of Pediatric Development Clinics (PDC), which provide structured follow-up after discharge from hospital neonatal care units, on children’s survival, nutrition and development in rural Rwanda. Methods: This quasi-experimental study compared a historic control group to children receiving PDC in Kayonza and Kirehe districts. Study populations in both districts included children born preterm or with birthweight < 2000 g and discharged alive. Kirehe additionally included children with hypoxic ischemic encephalopathy (HIE). Home-based cross-sectional surveys were conducted in Kayonza among children with expected chronological age 11–36 months in 2014 (controls) and 2018 (PDC group) and in Kirehe among children with expected chronological age 17–39 months in 2018 (controls) and 2019 (PDC group). Outcomes were measured using anthropometrics and the Ages and Stages Questionnaires. We used weighted logistic regression to control for confounding and differential non-participation. Results: PDC children (n = 464/812, 57.1%) were significantly more likely to participate in surveys (83.0% vs. 65.5%), have very low birthweight (27.6% vs. 19.0%), and be younger at the survey (26.2 vs. 31.1 months). 6.9% (n = 56) died before the survey. PDC was associated with reduced odds of death (aOR = 0.49, 95% CI: 0.26–0.92) and reduced odds of developmental delay (aOR = 0.48, 95% CI: 0.30–0.77). In Kayonza, PDC was associated with reduced stunting (aOR = 0.52, 95% CI: 0.28–0.98). PDC was not associated with reduced underweight or wasting. Conclusions: PDC was associated with improved survival and development among children born preterm, with low birthweight, or with HIE. Increased access to PDC, scale-up across Rwanda, and implementation of similar services and early intervention in other low-resource settings could support children born small or sick. Full article
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<p>Study period and timeline of household surveys.</p>
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<p>Study participant flow diagram. PDC, pediatric development clinic; LBW (low birth weight) condition, but birthweight &gt; 2050 g; HIE, hypoxic ischemic encephalopathy.</p>
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16 pages, 579 KiB  
Article
Fuzzy Modelling Algorithms and Parallel Distributed Compensation for Coupled Electromechanical Systems
by Christian Reyes, Julio C. Ramos-Fernández, Eduardo S. Espinoza and Rogelio Lozano
Algorithms 2024, 17(9), 391; https://doi.org/10.3390/a17090391 - 3 Sep 2024
Cited by 1 | Viewed by 1015
Abstract
Modelling and controlling an electrical Power Generation System (PGS), which consists of an Internal Combustion Engine (ICE) linked to an electric generator, poses a significant challenge due to various factors. These include the non-linear characteristics of the system’s components, thermal effects, mechanical vibrations, [...] Read more.
Modelling and controlling an electrical Power Generation System (PGS), which consists of an Internal Combustion Engine (ICE) linked to an electric generator, poses a significant challenge due to various factors. These include the non-linear characteristics of the system’s components, thermal effects, mechanical vibrations, electrical noise, and the dynamic and transient impacts of electrical loads. In this study, we introduce a fuzzy modelling identification approach utilizing the Takagi–Sugeno (T–S) structure, wherein model and control parameters are optimized. This methodology circumvents the need for deriving a mathematical model through energy balance considerations involving thermodynamics and the non-linear representation of the electric generator. Initially, a non-linear mathematical model for the electrical power system is obtained through the fuzzy c-means algorithm, which handles both premises and consequents in state space, utilizing input–output experimental data. Subsequently, the Particle Swarm Algorithm (PSO) is employed for optimizing the fuzzy parameter m of the c-means algorithm during the modelling phase. Additionally, in the design of the Parallel Distributed Compensation Controller (PDC), the optimization of parameters pertaining to the poles of the closed-loop response is conducted also by using the PSO method. Ultimately, numerical simulations are conducted, adjusting the power consumption of an inductive load. Full article
(This article belongs to the Special Issue Algorithms for PID Controller 2024)
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<p>Connection diagram of the power generator system.</p>
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<p>Measurements of the excitation input and state variable responses, characterised with groups by clustering centres identified as <span style="color: #FF0000">*</span>.</p>
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<p>Open-loop generated voltage and output of the identified fuzzy model.</p>
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<p>Open-loop generated voltage and output of the identified fuzzy model under an inductive load, where <span class="html-fig-inline" id="algorithms-17-00391-i001"><img alt="Algorithms 17 00391 i001" src="/algorithms/algorithms-17-00391/article_deploy/html/images/algorithms-17-00391-i001.png"/></span>, Input, is the pwm signal sent to the servomotor used to moves the throttle of the combustion engine, and <span class="html-fig-inline" id="algorithms-17-00391-i002"><img alt="Algorithms 17 00391 i002" src="/algorithms/algorithms-17-00391/article_deploy/html/images/algorithms-17-00391-i002.png"/></span>, Load, is the pwm signal sent to the brushless motor used as an inductive load.</p>
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<p>Simulation results of the voltage regulation using the obtained model of the power generator system.</p>
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<p>Comparison between the PDC strategy and a classic PD controller.</p>
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16 pages, 1771 KiB  
Article
Admissibility Analysis and Controller Design Improvement for T-S Fuzzy Descriptor Systems
by Han Yang, Shuanghong Zhang and Fanqi Yu
Symmetry 2024, 16(8), 992; https://doi.org/10.3390/sym16080992 - 5 Aug 2024
Cited by 1 | Viewed by 901
Abstract
In this paper, a stability analysis and the controller improvement of T-S fuzzy Descriptor system are studied. Firstly, by making full use of the related theory of fuzzy affiliation function and combining the design method of fuzzy Lyapunov function with the method of [...] Read more.
In this paper, a stability analysis and the controller improvement of T-S fuzzy Descriptor system are studied. Firstly, by making full use of the related theory of fuzzy affiliation function and combining the design method of fuzzy Lyapunov function with the method of inequality deflation, a stability condition with wider admissibility and less system conservatism is proposed. The advantage of this method is that it is not necessary to ensure that each fuzzy subsystem is progressively stable. We also maximise the boundary of the derivatives of the affiliation function mined. Secondly, a PDC controller and a Non-PDC controller are designed, and the deflation conditions for the linear matrix inequalities of the two controllers are constructed. Finally, some arithmetic simulations and practical examples are given to demonstrate the effectiveness of the method studied in this paper, and the results obtained are less conservative and have larger feasible domains than previous methods. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Neural Networks)
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<p>Controller problem solving process.</p>
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<p>Compare Theorem 3.1 (*) with the feasible domains of [<a href="#B34-symmetry-16-00992" class="html-bibr">34</a>] (∘) and [<a href="#B35-symmetry-16-00992" class="html-bibr">35</a>] (&gt;).</p>
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<p>Compare Theorem 3.2 (&lt;) with [<a href="#B35-symmetry-16-00992" class="html-bibr">35</a>] (∘).</p>
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<p>Inverted pendulum model.</p>
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<p>Fuzzy descriptor system state response with PDC controller.</p>
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<p>Fuzzy descriptor system state response with Non-PDC controller.</p>
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10 pages, 1376 KiB  
Article
Urine Nephrin and Podocalyxin Reflecting Podocyte Damage and Severity of Kidney Disease in Various Glomerular Diseases—A Cross-Sectional Study
by Panagiota Giannou, Harikleia Gakiopoulou, Emelina Stambolliu, Dimitrios Petras, Aglaia Chalkia, Athanasia Kapota, Kostas Palamaris, Emilia Hadziyannis, Konstantinos Thomas, Zoe Alexakou, Margarita Bora, Theodoros Mintzias, Dimitrios Vassilopoulos, Eustratios Patsouris and Melanie Deutsch
J. Clin. Med. 2024, 13(12), 3432; https://doi.org/10.3390/jcm13123432 - 12 Jun 2024
Cited by 1 | Viewed by 1151
Abstract
Background/Objectives: Glomerulopathy is a term used to describe a broad spectrum of renal diseases, characterized by dysfunction of glomerular filtration barrier, especially of podocytes. Several podocyte-associated proteins have been found and proved their usefulness as urine markers of podocyte dysfunction. Two of them [...] Read more.
Background/Objectives: Glomerulopathy is a term used to describe a broad spectrum of renal diseases, characterized by dysfunction of glomerular filtration barrier, especially of podocytes. Several podocyte-associated proteins have been found and proved their usefulness as urine markers of podocyte dysfunction. Two of them are nephrin (NEP) and prodocalyxin (PDC). This study aims to evaluate the association of podocyte damage, as it is demonstrated via the concentrations of urinary proteins, with clinical and histological data from patients with several types of glomerulonephritis. Methods: We measured urine levels of two podocyte-specific markers, NEP and PDC (corrected for urine creatinine levels), in patients with a wide range of glomerulopathies. Serum and urine parameters as well as histological parameters from renal biopsy were recorded. Results: In total, data from 37 patients with glomerulonephritis and 5 healthy controls were analyzed. PDC and NEP concentrations correlated between them and with serum creatinine levels (p = 0.001 and p = 0.013 respectively), and with histological lesions associated with chronicity index of renal cortex, such as severe interstitial fibrosis, severe tubular atrophy and hyalinosis (for PDC/NEP, all p < 0.05). In addition, the PDC and NEP demonstrated statistically significant correlations with interstitial inflammation (p = 0.018/p = 0.028). Regarding electron microscopy evaluation, PDC levels were correlated with distinct characteristics, such as fibrils and global podocyte foot process fusion, whereas the NEP/CR ratio was uniquely significantly associated with podocyte fusion only in non-immune-complex-mediated glomerulonephritis (p = 0.02). Among the other clinical and histological parameters included in our study, a strong correlation between proteinuria >3 g/24 h and diffuse fusion of podocyte foot processes (p = 0.016) was identified. Conclusions: Podocalyxin and nephrin concentrations in urine are markers of podocyte dysfunction, and in our study, they were associated both with serum creatinine and histological chronicity indices. Full article
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<p>Preservation of podocyte foot processes in a patient with low PDC (29 ng/mL) and NEP (32 ng/mL) urine concentrations.</p>
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<p>GBM thickening (arrow) in a patient with high PDC and NEP urine concentrations.</p>
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<p>ROC curves for (<b>A</b>) tubular atrophy, (<b>B</b>) arterial hyalinosis, (<b>C</b>) interstitial inflammation, (<b>D</b>) interstitial fibrosis, and (<b>E</b>) segmental podocyte foot process fusion.</p>
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14 pages, 1957 KiB  
Article
Predictive Modeling of Factors Influencing Adherence to SGLT-2 Inhibitors in Ambulatory Care: Insights from Prescription Claims Data Analysis
by Nadia Khartabil, Candis M. Morello and Etienne Macedo
Pharmacy 2024, 12(2), 72; https://doi.org/10.3390/pharmacy12020072 - 22 Apr 2024
Viewed by 1664
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are novel oral anti-hyperglycemic drugs that demonstrate cardiovascular and metabolic benefits for patients with type 2 diabetes (T2D), heart failure (HF), and chronic kidney disease (CKD). There is limited knowledge of real-world data to predict adherence to SGLT-2i [...] Read more.
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are novel oral anti-hyperglycemic drugs that demonstrate cardiovascular and metabolic benefits for patients with type 2 diabetes (T2D), heart failure (HF), and chronic kidney disease (CKD). There is limited knowledge of real-world data to predict adherence to SGLT-2i in an ambulatory setting. The study aims to predict SGLT-2i adherence in patients with T2D and/or HF and/or CKD by building a prediction model using electronic prescription claims data presented within EPIC datasets. This is a retrospective study of 174 adult patients prescribed SGLT-2i at UC San Diego Health ambulatory pharmacies between 1 January 2020 to 30 April 2021. Adherence was measured by the proportion of days covered (PDC). R packages were used to identify regression and non-linear regression predictive models to predict adherence. Age, gender, race/ethnicity, hemoglobin A1c, and insurance plan were included in the model. Diabetes control based on hemoglobin A1c (HbA1c) and the glomerular filtration rate (GFR) was also evaluated using Welch t-test with a p-value of 0.05. The best predictive model for measuring adherence was the simple decision tree. It had the highest area under the curve (AUC) of 74% and accuracy of 82%. The model accounted for 21 variables with the main node predictors, including glycated hemoglobin, age, gender, and insurance plan payment amount. The adherence rate was inversely proportional to HbA1c and directly proportional to the plan payment amount. As for secondary outcomes, HbA1c values from baseline till 90 days post-treatment duration were consistently higher in the non-compliant group: 7.4% vs. 9.6%, p < 0.001 for the PDC ≥ 0.80 and PDC < 0.80, respectively. Baseline eGFR was 55.18 mL/min/1.73m2 vs. 54.23 mL/min/m2 at 90 days. The mean eGFR at the end of the study (minimum of 90 days of treatment) was statistically different between the groups: 53.1 vs. 59.6 mL/min/1.73 m2, p < 0.001 for the PDC ≥ 0.80 and PDC < 0.80, respectively. Adherence predictive models will help clinicians to tailor regimens based on non-adherence risk scores. Full article
(This article belongs to the Section Pharmacy Practice and Practice-Based Research)
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<p>Conceptual model for contributing factors to medication adherence [<a href="#B11-pharmacy-12-00072" class="html-bibr">11</a>].</p>
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<p>Flow chart illustration of patient cohort for the retrospective study design. <span class="html-italic">n</span> = number of patients, NC = number of insurance claims, Baseline 30-, 60-, 90-days index is defined as grouping of claims provided for an average of 30, 60, 90-days and beyond average duration.</p>
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<p>Receiver Operating Characteristic (ROC) curves comparing the performance of Lasso and CART methods. The diagonal line, representing the performance of a random classifier, serves as a baseline for comparison. The ROC curve for the Lasso method, denoted by the red line, exhibits an accuracy of 76%, while the ROC curve for the CART method, depicted in blue, achieves a higher accuracy of 82%. The ROC curves illustrate the trade-off between the True Positive Rate (sensitivity) and the False Positive Rate, with curves further away from the diagonal indicating superior performance.</p>
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<p>Final Predictive Tree model, CART (Classification and Regression Tree).</p>
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20 pages, 17257 KiB  
Case Report
Orthodontic Treatment of Palatally Impacted Canines in Severe Non-Syndromic Oligodontia with the Use of Mini-Implants: A Case Report
by Marcin Stasiak, Aleksandra Kołodziejska and Bogna Racka-Pilszak
Medicina 2023, 59(11), 2032; https://doi.org/10.3390/medicina59112032 - 17 Nov 2023
Cited by 2 | Viewed by 2866
Abstract
Background: The risk of palatally displaced canines (PDCs) rises in patients with tooth agenesis. The orthodontic extrusion and alignment of PDCs require adequate anchorage to enable tooth movement and control the side effects. There is no paper presenting treatment in the case [...] Read more.
Background: The risk of palatally displaced canines (PDCs) rises in patients with tooth agenesis. The orthodontic extrusion and alignment of PDCs require adequate anchorage to enable tooth movement and control the side effects. There is no paper presenting treatment in the case of severe oligodontia with simultaneous PDCs and the use of mini-implants (MIs) for their orthodontic extrusion. Case presentation: A 15-year-old patient presented with non-syndromic oligodontia and bilateral PDCs. Cone beam computed tomography revealed that both PDCs were in proximity to the upper incisors’ roots. There was no evident external root resorption of the incisors. The “canines first” approach was chosen. MIs were used both as direct and indirect anchorage. First, the extrusive forces of cantilevers were directed both occlusally and distally. Next, the buccal directions of forces were implemented. Finally, fixed appliances were used. PDCs were extruded, aligned, and torqued. Proper alignment and occlusion were achieved to enable further prosthodontic restorations. Conclusions: The use of MIs made it possible to avoid collateral effects, reduce the risk of complications, and treat the patient effectively. MIs provide adequate anchorage in demanding cases. The use of MIs for the extrusion of PDCs made it possible to offer this treatment option to patients with severe oligodontia. The presented protocol was effective and served to circumvent treatment limitations associated with an inadequate amount of dental anchorage and a high risk of root resorption. Full article
(This article belongs to the Special Issue Medicine and Dentistry: New Methods and Clinical Approaches)
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<p>Initial extraoral photos.</p>
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<p>Initial intraoral photos.</p>
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<p>Pretreatment panoramic radiograph.</p>
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<p>Pretreatment lateral cephalogram.</p>
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<p>Cone beam computed tomography cross-sections with palatally impacted maxillary canines. (<b>a</b>) Upper right canine; (<b>b</b>) Proximity of the upper left canine and the upper left central incisor; (<b>c</b>) Upper left canine; (<b>d</b>) Transversal cross-section; (<b>e</b>) Horizontal cross-section; (<b>f</b>) Cone beam computed tomography rendering.</p>
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<p>Treatment process. (<b>a</b>) Surgical exposure; (<b>b</b>) Placement of palatal alveolar mini-implants and installation of cantilevers; (<b>c</b>) Orthodontic extrusion with cantilevers in distal and downward direction; (<b>d</b>) Orthodontic extrusion with power chains; (<b>e</b>) Derotation of canines with power chains; (<b>f</b>) Extraction of upper right deciduous canine and buccal movements of impacted canines; (<b>g</b>) Palatal alveolar mini-implants and cantilevers for buccal tooth movements; (<b>h</b>) Buccal alveolar mini-implant and cantilever for buccal movement of upper left canine; (<b>i</b>) Mini-implant in the palatal suture used as direct anchorage for upper left canine and bend-out for upper right canine; (<b>j</b>) Cantilever for buccal movement of upper left canine; (<b>k</b>) Mini-implant in the palatal suture used as indirect anchorage; (<b>l</b>) Intermaxillary elastics from palatal buttons on upper lateral incisors to the lower arch.</p>
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<p>Panoramic radiograph performed after orthodontic extrusion.</p>
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<p>Panoramic radiograph performed during the finishing phase.</p>
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<p>Final extraoral photos.</p>
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<p>Final intraoral photos.</p>
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<p>Posttreatment panoramic radiograph.</p>
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<p>Posttreatment lateral cephalogram.</p>
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<p>Superimposition of the initial (black color) and final (red color) cephalometric radiographs to monitor skeletal and soft tissue changes. (<b>a</b>) Superimposition of cranial base structures; (<b>b</b>) Maxillary superimposition; (<b>c</b>) Mandibular superimposition.</p>
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<p>Extraoral photos after the initial phase of prosthetic treatment.</p>
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<p>Intraoral photos after the initial phase of prosthetic treatment.</p>
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<p>Changes in the canines’ anatomy due to conducted orthodontic treatment. (<b>a</b>) Upper right canine; (<b>b</b>) Upper left canine; (<b>c</b>) Bone defect of upper left canine—sagittal cross-section; (<b>d</b>) Bone defect of upper left canine—horizontal cross-section.</p>
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14 pages, 4080 KiB  
Article
Contribution of Dynamic Rheology Coupled to FTIR and Raman Spectroscopies to the Real-Time Shaping Ability of a Hyperbranched Polycarbosilane
by Nilesh Dhondoo, Julie Cornette, Sylvie Foucaud, Maggy Colas and Romain Lucas-Roper
Molecules 2023, 28(18), 6476; https://doi.org/10.3390/molecules28186476 - 6 Sep 2023
Viewed by 1252
Abstract
In the field of non-oxide ceramics, the polymer-derived ceramic (PDC) approach appears to be very promising, especially for obtaining easily shaped and homogeneous materials in terms of structure and composition. However, in order to reach a suitable form during the process, it is [...] Read more.
In the field of non-oxide ceramics, the polymer-derived ceramic (PDC) approach appears to be very promising, especially for obtaining easily shaped and homogeneous materials in terms of structure and composition. However, in order to reach a suitable form during the process, it is often necessary to study the rheology of preceramic polymers while they are modified during polymerisation or crosslinking reactions. Given this need in the understanding of the real-time rheology of macromolecules during their synthesis, a rheometer coupled with both an infrared spectrometer and a Raman probe is described as a powerful tool for monitoring in situ synthesised polycarbosilanes. Indeed, this original device allows one to control the viscosity of a hyberbranched polycarbosilane from defined difunctional and tetrafunctional monomers. Meanwhile, it links this evolution to structural modifications in the macromolecular structure (molar masses, dispersity and conformation), based on SEC-MALS analyses, synchronised by the monomer conversion determined by using Raman and infrared spectroscopies, a common denominator of the aforementioned instrumental platform. Full article
(This article belongs to the Special Issue Linking Rheology and Polymer Chemistry)
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<p>Schematic representation of the instrumentation and of the polymerisation reaction leading to the hyperbranched polycarbosilane (<span class="html-italic">hb</span>-PCS).</p>
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<p>IR and Raman spectra for reagents.</p>
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<p>From benzene to BDSB: a question of symmetry.</p>
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<p>Calculated FTIR and Raman intensities obtained by DFT for four molecules: benzene, toluene, xylene and BDSB. The stretching vibration of C=C<sub>ar</sub> was marked in blue colour.</p>
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<p>Raman spectra of the starting mixture (black line) and of the resulting polymer solution (red line) obtained from the rheo–FTIR–Raman setup.</p>
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<p>Coupling results of the rheo–Raman–FTIR in a PP configuration.</p>
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<p>Sampling window from graph plotting the alkene conversion versus time.</p>
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<p>SEC elution pattern plots of the samples (<b>S1</b>–<b>S7</b>) versus time.</p>
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<p>Cumulative molar mass distribution curve.</p>
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<p>Conformation plots of samples <b>S5</b>–<b>S7</b>. The slope values are indicated for each regression.</p>
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<p>Structure/conformation of the samples <b>S1</b>–<b>S7</b> along with their average molar mass, molar-mass dispersity, alkene conversion and viscosity.</p>
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<p>3D representation of alkene conversion plotted against average molar mass and viscosity of the medium.</p>
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<p>Shaping applications of the hyperbranched polymer on a graph plotting the viscosity as a function of the shear rate with [<a href="#B21-molecules-28-06476" class="html-bibr">21</a>].</p>
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19 pages, 3854 KiB  
Article
Phenotypic Profiling of Immune Cells and Their Mediators in Chronic Obstructive Pulmonary Disease
by Meghashree Sampath, Geetanjali Bade, Randeep Guleria, Anant Mohan, Sudip Sen, Devanjan Dey and Anjana Talwar
Biomedicines 2023, 11(8), 2166; https://doi.org/10.3390/biomedicines11082166 - 1 Aug 2023
Viewed by 1163
Abstract
Background: Chronic obstructive pulmonary disease (COPD) is an inflammatory disorder and has been proposed to have an imbalance between pro-inflammatory and anti-inflammatory factors. Methods: This study was conducted on 41 participants {18 COPD patients (smokers, COPD S (n = 9); reformed smokers, [...] Read more.
Background: Chronic obstructive pulmonary disease (COPD) is an inflammatory disorder and has been proposed to have an imbalance between pro-inflammatory and anti-inflammatory factors. Methods: This study was conducted on 41 participants {18 COPD patients (smokers, COPD S (n = 9); reformed smokers, COPD RS (n = 9)) and 23 controls (non-smokers, CNS (n = 14); smokers, CS (n = 9))}. Flow cytometry was used to identify circulatory immune cells and correlated with serum cytokines. Results: On comparison, significantly lower frequency of CD3+ T cells were observed in COPD S as compared to CNS (p < 0.01) and CS (p < 0.01); CD4+ T cells were lower in COPD S (p < 0.05), COPD RS (p < 0.05) and CNS (p < 0.01) as compared to CS. CD8+ T cells were elevated in COPD S as compared to CS (p < 0.05). Lower frequency of cDCs were observed in COPD S as compared to CS (p < 0.05) and COPD RS as compared to CNS (p < 0.01) and CS (p < 0.01). Lower frequency of pDCs were observed in COPD RS as compared to COPD S (p < 0.05), CNS (p < 0.05) and CS (p < 0.01). Lower frequency of Tregs was observed in COPD S as compared to CNS (p < 0.05) and CS (p < 0.05). Conclusions: Characteristic changes observed indicate a significant impact of immune cells in the progression of the disease. Full article
(This article belongs to the Section Immunology and Immunotherapy)
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<p>Representative pseudocolor plots showing sequential gating strategy used to identify T cell subtypes (<b>A</b>) Gating of CD3+ T cells from single cells. (<b>B</b>) T helper cells (CD4+) and cytotoxic T cells (CD8+) were gated from CD3+ T cells in control non-smoker (CNS) and control smoker (CS), (<b>C</b>) COPD smoker (COPD S) and COPD reformed smoker (COPD RS). Violin box plots showing (<b>D</b>) significantly lower frequency of CD3+ T cells in COPD S as compared to CNS and CS. (<b>E</b>) significantly lower frequency of CD4+ T cells in COPD S, COPD RS and CNS as compared to CS (<b>F</b>) significantly higher frequency of CD8+ T cells in COPD S as compared to CS. One asterisk (*) <span class="html-italic">p</span>-value &lt; 0.05; two asterisks (**) <span class="html-italic">p</span>-value &lt; 0.01.</p>
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<p>Representative pseudocolor plots showing sequential gating strategy used to identify classical Dendritic cells (cDCs) (<b>A</b>) shows gating of Lin1-cells from single cells. (<b>B</b>) CD11c+ HLADR+ cells in control non-smoker (CNS) and control smoker (CS), (<b>C</b>) COPD smoker (COPD S) and COPD reformed smoker (COPD RS). Violin box plots showing (<b>D</b>) significantly lower frequency of cDCs in COPD S as compared to CS and COPD RS as compared to CNS and CS. One asterisk (*) <span class="html-italic">p</span>-value &lt; 0.05; two asterisks (**) <span class="html-italic">p</span>-value &lt; 0.01.</p>
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<p>Representative pseudocolor plots showing sequential gating strategy used to identify plasmacytoid Dendritic cells (pDCs) (<b>A</b>) shows gating of Lin1-cells from single cells. (<b>B</b>) CD123+ HLADR+ cells in control non-smoker (CNS) and control smoker (CS), (<b>C</b>) COPD smoker (COPD S) and COPD reformed smoker (COPD RS). Violin box plots showing (<b>D</b>) significantly lower frequency of pDCs in COPD RS as compared to COPD S, CNS and CS. One asterisk (*) <span class="html-italic">p</span>-value &lt; 0.05; two asterisks (**) <span class="html-italic">p</span>-value &lt; 0.01.</p>
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<p>Representative pseudocolor plots and histograms showing sequential gating strategy used to identify T regulatory cells (<b>A</b>) shows gating of CD4+CD25+ T cells from single cells and CD127-CD45RA- cells from CD4+CD25+ cells (<b>B</b>) FoxP3+ cells in control non-smoker (CNS) and control smoker (CS), (<b>C</b>) COPD smoker (COPD S) and COPD reformed smoker (COPD RS). Violin box plots showing (<b>D</b>) significantly lower frequency of Tregs in COPD S as compared to CNS and CS. One asterisk (*) <span class="html-italic">p</span>-value &lt; 0.05.</p>
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<p>Representative pseudocolor plots and histograms showing sequential gating strategy used to identify Monocytes (<b>A</b>) shows gating of single cells from monocytes in FSC × SSC plot. (<b>B</b>) CD14+ cells in control non-smoker (CNS) and control smoker (CS), (<b>C</b>) COPD smoker (COPD S) and COPD reformed smoker (COPD RS). Violin box plots showing (<b>D</b>) significantly lower frequency of monocytes in COPD S as compared to CNS. Two asterisks (**) <span class="html-italic">p</span>-value &lt; 0.01.</p>
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<p>Violin box plots showing (<b>A</b>) elevated levels of MCP-1 in COPD S as compared to COPD RS, (<b>B</b>) lower levels of IL-8 in COPD RS as compared to CS, (<b>C</b>) lower levels of TGFß1 in COPD S as compared to CS, (<b>D</b>,<b>E</b>) IL-2 and IL-4 were comparable between subgroups. COPD S–COPD smoker, COPD RS—COPD reformed smoker, CNS—control non-smoker, CS—control smoker. One asterisk (*) <span class="html-italic">p</span>-value&lt; 0.05; two asterisks (**) <span class="html-italic">p</span>-value&lt; 0.01.</p>
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<p>Correlation of immune cells and serum cytokines in peripheral circulation of COPD patients. (<b>A</b>) Positive correlation of T cytotoxic cells with Basophils. (<b>B</b>) Negative correlation of T cytotoxic cells with monocytes. (<b>C</b>) Positive correlation of classical dendritic cells with plasmacytoid dendritic cells. (<b>D</b>) Negative correlation of plasmacytoid dendritic cells with Tregs. (<b>E</b>) Positive correlation of IL-4 with MCP-1. (<b>F</b>) Positive correlation of IL-2 with MCP-1.</p>
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<p>Schematic diagram depicting putative mechanism involving plausible interaction, leading to imbalance in the subsets of peripheral immune cells along with their soluble mediators in COPD. Though cigarette smoke is considered to be the primary risk factor, the progression of the disease has been attributed to dysfunction in regulatory mechanisms that include weak anti-protease and anti-oxidant activities, and maladaptive immune modulation [<a href="#B49-biomedicines-11-02166" class="html-bibr">49</a>,<a href="#B50-biomedicines-11-02166" class="html-bibr">50</a>]. It is still unclear whether systemic inflammatory markers represent a “spillover” from inflammation in the lungs, or if they are a parallel aberration or linked to a concomitant disease that affects the lungs. The decrease in dendritic cells and monocytes observed in the present study might result in decreased antigen presentation leading to decreased helper T cells. The lower numbers of helper T cells along with plasmacytoid dendritic cells might predispose COPD patients to bacterial/viral infections. However, on the other hand, the decrease in anti-inflammatory Tregs along with serum TGFß1, despite increase in proinflammatory cytotoxic T cells and serum MCP-1 might cause the observed imbalance in the immune response. It is worth noting that there also exists a network of crosstalk between the immune cells and the cytokines leading to the observed pathology in COPD (Created with BioRender.com).</p>
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11 pages, 574 KiB  
Article
Risk Factors for Non-Adherence to Inhaled Corticosteroids in Preschool Children with Asthma
by Louise Mandrup Bach, Sune Rubak, Adam Holm-Weber, Julie Prahl, Mette Hermansen, Kirsten Skamstrup Hansen and Bo Chawes
Children 2023, 10(1), 43; https://doi.org/10.3390/children10010043 - 25 Dec 2022
Cited by 1 | Viewed by 1795
Abstract
Non-adherence to asthma controllers increases morbidity among school-aged children. This study aimed to determine non-adherence risk factors in preschool children with asthma. We investigated 172 children <6 years diagnosed with asthma in 2018 and analyzed baseline characteristics and loss of control events extracted [...] Read more.
Non-adherence to asthma controllers increases morbidity among school-aged children. This study aimed to determine non-adherence risk factors in preschool children with asthma. We investigated 172 children <6 years diagnosed with asthma in 2018 and analyzed baseline characteristics and loss of control events extracted from the medical records for four years following diagnosis. At end of follow-up, 79 children had a prescription of inhaled corticosteroids (ICS) and were included in the analyses. Adherence was assessed in a two-year period through pharmacy claims using percentage of days covered (PDC) analyzed dichotomously with non-adherence defined as PDC < 80% and using adherence ratio (AR) defined as days with medical supply divided by days without. Of the 79 children, 59 (74.7%) were classified as non-adherent. In analyses adjusted for sex, age and exacerbations prior to inclusion, adherence was positively associated with having had a loss of control event requiring a step-up in asthma controller (aAR:2.34 [1.10;4.98], p = 0.03), oral corticosteroids (aAR:2.45 [1.13;5.34], p = 0.026) or redeeming a short-acting b2-agonist prescription (aAR:2.91 [1.26;6.74], p = 0.015). Further, atopic comorbidity was associated with increased adherence (aAR:1.18 [1.01;1.37], p = 0.039), whereas having a first degree relative with asthma was associated with worse adherence (aAR:0.44 [0.23;0.84], p = 0.015). This study found poor adherence to ICS among three quarters of preschool children with asthma. Increasing adherence was associated with atopic comorbidity and loss of control events, whereas lower adherence was associated with atopic predisposition. These findings should be considered to improve adherence in preschool children with asthma. Full article
(This article belongs to the Special Issue Lung Function, Respiratory and Asthma Disease in Children)
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<p>Timeline of the study.</p>
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<p>Flowchart of children included in the study.</p>
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11 pages, 221 KiB  
Article
Healthcare Workers’ Perception of Measures to Reduce the Risk of New Tuberculosis Infections: A Qualitative Study Report
by Debra Madzinga, Takalani Grace Tshitangano, Ndidzulafhi Selina Raliphaswa and Lufuno Razwiedani
Nurs. Rep. 2022, 12(4), 873-883; https://doi.org/10.3390/nursrep12040084 - 17 Nov 2022
Cited by 1 | Viewed by 3211
Abstract
Tuberculosis, which is an infectious airborne disease remained the main leading cause of death in South Africa for three consecutive years from 2016 to 2018. In 2020 alone, the country had an estimated 328,000 people who suffered from TB, with 61,000 dying from [...] Read more.
Tuberculosis, which is an infectious airborne disease remained the main leading cause of death in South Africa for three consecutive years from 2016 to 2018. In 2020 alone, the country had an estimated 328,000 people who suffered from TB, with 61,000 dying from it. Collins Chabane Municipality had 129 and 192 new TB cases recorded in 2017 and 2018, respectively, which is far from reaching the END TB STRATEGY targets. WHO scientific evidence demonstrates that TB control measures are effective in reducing the spread and development of new cases. Though scientific evidence revealed negative attitudes towards the recommended TB control measures in public hospitals of the Vhembe district, a deeper understanding of these attitudes is needed to remedy the situation. This study aimed to describe healthcare workers’ perceptions of TB control measures at Collins Chabane Municipality in South Africa. A qualitative, exploratory case study design was adopted. Multi-stage sampling technique was used to select both the healthcare facilities and the participants. Only 24 healthcare workers trained on tuberculosis management were voluntarily recruited. However, data were saturated at the twelfth (12) participant purposively selected from six healthcare facilities of Collins Chabane Municipality. Data collected through unstructured in-depth individual interviews were analyzed thematically. The proposal for this study was ethically cleared by the University of Venda Ethics Committee (SHS/20/PDC/35/1111). Results indicate that TB administrative, environmental and respiratory control measures are well understood by health workers even though there are challenges with implementation concerning some, such as closing windows during winter, UVGI lights that are non-functional and taking too long to be fixed, no specimen collection during weekends and holidays thereby delaying TB diagnosis and lack of skills concerning how to use respirators and cough etiquette. The Vhembe district TB control programme should intensify infection control training and continue monitoring giving the needed support. Full article
19 pages, 3404 KiB  
Article
Exogenous Melatonin Improves Waterlogging Tolerance in Wheat through Promoting Antioxidant Enzymatic Activity and Carbon Assimilation
by Shangyu Ma, Panpan Gai, Bingjie Geng, Yanyan Wang, Najeeb Ullah, Wenjing Zhang, Haipeng Zhang, Yonghui Fan and Zhenglai Huang
Agronomy 2022, 12(11), 2876; https://doi.org/10.3390/agronomy12112876 - 17 Nov 2022
Cited by 15 | Viewed by 2229
Abstract
In a pot experiment, we explored the regulatory pathways through which melatonin (MT) protects wheat growth and grain yield loss from waterlogging injury. Two wheat cultivars, Yangmai 18 and Yannong 19, were exposed to seven days of soil waterlogging at flowering. Melatonin (100 [...] Read more.
In a pot experiment, we explored the regulatory pathways through which melatonin (MT) protects wheat growth and grain yield loss from waterlogging injury. Two wheat cultivars, Yangmai 18 and Yannong 19, were exposed to seven days of soil waterlogging at flowering. Melatonin (100 μmol·L−1) was sprayed before and after waterlogging to explore its regulation on root growth, photosynthetic characteristics, dry matter accumulation, and grain yield. Soil waterlogging intensified malondialdehyde (MDA) and O2 production rates in wheat tissues, impairing leaf photosynthesis, biomass accumulation, and final grain yield formation. In this study, the roots waterlogged at 7 days after anthesis (DAA) accumulated 20.9%, 76.2%, 17.6%, 28.5%, and 5.6% higher MDA content, O2 production rate, pyruvate decarboxylase (PDC), lactate dehydrogenase (LDH), and alcohol dehydrogenase (ADH) activities, respectively, in Yangmai 18, and 25.7%, 74.8%, 35.8%, 70.8%, and 30.7% higher in Yannong 19, respectively, compared with their respective non-waterlogged controls. Further, Yangmai 18 achieved a maximum net photosynthetic rate (Pn) reduction of 22.1% at 7 DAA, while the maximum Pn reduction of Yannong 19 was 27.4% at 14 DAA, respectively, compared with their respective non-waterlogged plants. Thus, waterlogging decreased total dry matter accumulation, 1000-grain weight (TGW), and total grain yield by 14.0%, 13.8%, and 16.2%, respectively, in Yangmai 18, and 16.0%, 8.1%, and 25.1%, respectively, in Yannong 19. Our study also suggests that exogenously applied melatonin can protect wheat root tissues from waterlogging-induced oxidative injury by upregulating antioxidant enzymes and sustaining leaf photosynthesis. The plants treated with melatonin showed better water status and less oxidative damage, which was conducive to maintaining a higher photosynthetic capacity, thereby improving the waterlogging tolerance of wheat. For example, compared with waterlogged plants, melatonin treatments significantly reduced MDA content, O2 production rate, PDC, LDH, and ADH activities by 7.7%, 25.4%, 2.6%, 32.1%, and 3.2%, respectively, in Yangmai 18, and 6.7%, 17.9%, 4.1%, 22.0%, and 15.3%, respectively, in Yannong 19. MT treatments significantly increased total dry matter accumulation, TGW, and yield by 5.9%, 8.7%, and 14.9%, respectively, in Yangmai 18, and 3.2%, 7.3%, and 26.0%, respectively, in Yannong 19. Full article
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<p>The field under the soil water treatment: the pool (<b>A</b>) and the pots under waterlogging (<b>B</b>).</p>
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<p>Daily precipitation and temperature during the wheat growing season.</p>
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<p>Post-flowering changes in root dry weight (<b>A</b>,<b>B</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions.</p>
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<p>Post-flowering changes in root superoxide dismutase (SOD) and peroxidase (POD) activities (<b>A</b>–<b>D</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions.</p>
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<p>Post-flowering changes in root malondialdehyde (MDA) content and O<sub>2</sub><sup>−</sup> production rate (<b>A</b>–<b>D</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions.</p>
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<p>Post-flowering changes in root enzymes’ activities related to anaerobic respiration (<b>A</b>–<b>F</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions. * Significance at the 0.05 level, ** significance at the 0.01 level, *** significance at the 0.001 level, **** significance at the 0.0001 level.</p>
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<p>Post-flowering changes in flag leaf relative chlorophyll content (SPAD units) (<b>A</b>,<b>B</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions.</p>
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<p>Post-flowering changes in the flag leaf net photosynthetic rate and intercellular CO<sub>2</sub> concentration (<b>A</b>–<b>D</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions.</p>
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<p>Post-flowering changes in flag leaf actual photochemical efficiency (<b>A</b>,<b>B</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions.</p>
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<p>Dry matter accumulation and distribution (<b>A</b>–<b>D</b>) under different treatments, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions.</p>
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<p>Using a bubble chart to analyze the effect of grain number and thousand-grain weight on yield by cultivar and treatment, i.e., CK + QS: leaf spraying with distilled water under non-waterlogged conditions; CK + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under non-waterlogged conditions; WL + QS: leaf spraying with distilled water under waterlogged conditions; WL + MT: leaf spraying with 100 μmol·L<sup>−1</sup> of melatonin under waterlogged conditions. 1 represents Yangmai 18, and 2 represents Yannong 19.</p>
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<p>Correlation between wheat root measurement indexes at 7 DAA and yield under different treatments. Abbreviations: RDW, root dry weight; MDA, malondialdehyde content; SOD, superoxide dismutase activity; POD, peroxidase activity; O<sub>2</sub><sup>−</sup>, superoxide anion production rate; PDC, pyruvate decarboxylase activity; LDH, lactate dehydrogenase activity; ADH, alcohol dehydrogenase activity.</p>
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20 pages, 3942 KiB  
Article
Longitudinal Assessment of Multiple Immunological and Inflammatory Parameters during Successful DAA Therapy in HCV Monoinfected and HIV/HCV Coinfected Subjects
by Paola Zuccalà, Tiziana Latronico, Raffaella Marocco, Stefano Savinelli, Serena Vita, Fabio Mengoni, Tiziana Tieghi, Cosmo Borgo, Blerta Kertusha, Anna Carraro, Gabriella D’Ettorre, Vincenzo Vullo, Claudio Maria Mastroianni, Grazia Maria Liuzzi and Miriam Lichtner
Int. J. Mol. Sci. 2022, 23(19), 11936; https://doi.org/10.3390/ijms231911936 - 8 Oct 2022
Cited by 5 | Viewed by 2161
Abstract
In the direct-acting antiviral (DAA) era, it is important to understand the immunological changes after HCV eradication in HCV monoinfected (mHCV) and in HIV/HCV coinfected (HIV/HCV) patients. In this study, we analyzed sub-populations of monocytes, dendritic cells (DCs), T-lymphocytes and inflammatory biomarkers following [...] Read more.
In the direct-acting antiviral (DAA) era, it is important to understand the immunological changes after HCV eradication in HCV monoinfected (mHCV) and in HIV/HCV coinfected (HIV/HCV) patients. In this study, we analyzed sub-populations of monocytes, dendritic cells (DCs), T-lymphocytes and inflammatory biomarkers following initiation of DAA in 15 mHCV and 16 HIV/HCV patients on effective antiretroviral therapy at baseline and after sustained virological response at 12 weeks (SVR12). Fifteen age- and sex-matched healthy donors (HD) were enrolled as a control group. Activated CD4+ and CD8+ T-lymphocytes, mDCs, pDCs, MDC8 and classical, non-classical and intermediate monocytes were detected using flow cytometry. IP-10, sCD163 and sCD14 were assessed by ELISA while matrix metalloproteinase-2 (MMP-2) was measured by zymography. At baseline, increased levels of IP-10, sCD163 and MMP-2 were found in both HIV/HCV and mHCV patients compared to HD, whereas sCD14 increased only in HIV/HCV patients. After therapy, IP-10, sCD163 and sCD14 decreased, whereas MMP-2 persistently elevated. At baseline, activated CD8+ T-cells were high in HIV/HCV and mHCV patients compared to HD, with a decrease at SVR12 only in HIV/HCV patients. Activated CD4+ T-cells were higher in HIV/HCV patients without modification after DAAs therapy. These results suggest complex interactions between both viruses and the immune system, which are only partially reversed by DAA treatment. Full article
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<p><b>Plasma levels of IP-10, sCD163, sCD14 and MMP-2 in mHCV and HIV/HCV patients, at baseline, compared to healthy controls.</b> Box plots show circulating levels of IP-10 (<b>a</b>,<b>b</b>), sCD163 (<b>c</b>,<b>d</b>), sCD14 (<b>e</b>,<b>f</b>) and MMP-2 (<b>g</b>,<b>h</b>), at baseline, in the total population of mHCV and in HIV/HCV patients (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and in mHCV and HIV/HCV patients with advanced degree of fibrosis (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>). Horizontal bars represent the median values. Kruskal–Wallis ANOVA with Dunn’s post-test was used to assess statistically significant differences between the groups. HD, healthy donors; * = <span class="html-italic">p</span> &lt; 0.05; ** = <span class="html-italic">p</span>&lt; 0.01; *** = <span class="html-italic">p</span>&lt; 0.001; **** = <span class="html-italic">p</span>&lt; 0.0001.</p>
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<p><b>Percentage of immune activation of CD4 + and CD8 + in mHCV and HIV/HCV coinfected patients, at baseline, compared to healthy controls.</b> Box plots represent circulating percentage of CD4+ (<b>a</b>,<b>c</b>) and CD8+ (<b>b</b>,<b>d</b>) activation, at baseline, in the total population of mHCV and HIV/HCV patients (<b>a</b>,<b>b</b>) and in mHCV and in HIV/HCV patients with advanced degree of fibrosis (<b>c</b>,<b>d</b>). Horizontal bars represent the median values. Kruskal–Wallis ANOVA with Dunn’s post-test was used to assess statistically significant differences between the groups. HD, healthy donors; * = <span class="html-italic">p</span> &lt; 0.05; ** = <span class="html-italic">p</span>&lt; 0.01; *** = <span class="html-italic">p</span> &lt; 0.001.</p>
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<p><b>Counts of classical, intermediate and non-classical monocytes in mHCV and HIV/HCV coinfected patients, at baseline, compared to healthy controls.</b> Box plots show counts of circulating classical, intermediate and non-classical monocytes, at baseline, in the total population of mHCV and in HIV/HCV patients (<b>a</b>–<b>c</b>) and in mHCV and in HIV/HCV patients with advanced degree of fibrosis (<b>d</b>–<b>f</b>). Horizontal bars represent the median values. Kruskal–Wallis ANOVA with Dunn’s post-test was used to assess statistical differences between the groups. HD, healthy donors; * = <span class="html-italic">p</span> &lt; 0.05.</p>
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<p><b>Counts of MDC8, pDC and mDC in mHCV and HIV/HCV coinfected patients, at baseline, compared to healthy controls.</b> Box plots show circulating counts of MDC8, pDC and mDC, at baseline, in the total population of mHCV and HIV/HCV patients (<b>a</b>–<b>c</b>) and in mHCV and in HIVHCV patients with advanced degree of fibrosis (<b>d</b>–<b>f</b>). Horizontal bars represent the median values. Kruskal–Wallis ANOVA with Dunn’s post-test was used to assess statistical differences between the groups. HD, healthy donors; * = <span class="html-italic">p</span> &lt; 0.05; ** = <span class="html-italic">p</span>&lt; 0.01.</p>
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<p><b>Plasma levels of IP-10, sCD163, sCD14 and MMP-2 in mHCV patients and HIV/HCV patients, during and after anti-HCV therapy with DAAs, compared to healthy controls.</b> Box plots show circulating levels of IP-10, sCD163, sCD14 and MMP-2 in mHCV patients (<b>a</b>–<b>d</b>) and in HIV/HCV patients (<b>e</b>–<b>h</b>) during and after therapy with DAAs. Horizontal bars represent the median values. Wilcoxon test was performed to assess differences between the baseline and SVR12, while Mann–Whitney test assessed differences between SVR12 and HD. HD, healthy donors; T0, baseline before therapy; SVR12, sustained virologic response 12 weeks after the end of therapy. * = <span class="html-italic">p</span> &lt; 0.05; ** = <span class="html-italic">p</span>&lt; 0.01; *** = <span class="html-italic">p</span>&lt; 0.001; **** = <span class="html-italic">p</span>&lt; 0.0001.</p>
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<p><b>Percentage of immune activation of CD4 + and CD8 + in mHCV patients and HIV/HCV patients during and after anti-HCV therapy with DAAs, compared to healthy controls.</b> Box plots represent percentage of circulating CD4+ and CD8+ lymphocytes in mHCV patients (<b>a</b>,<b>b</b>) and in HIV/HCV coinfected patients (<b>c</b>,<b>d</b>) during and after therapy with DAAs. Horizontal bars represent the median values. ANOVA with Dunn’s post-test was performed to assess differences between patients at baseline and controls, Wilcoxon test was performed to assess differences between the baseline and SVR12 and Mann–Whitney test assessed differences between SVR12 and HD. HD, healthy donors; T0, baseline before therapy; SVR12, sustained virologic response 12 weeks after the end of therapy. * = <span class="html-italic">p</span>&lt;0.05; ** = <span class="html-italic">p</span> &lt; 0.01; *** = <span class="html-italic">p</span> &lt; 0.001.</p>
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<p><b>Counts of classical, intermediate and non-classical monocytes in mHCV patients and HIV/HCV patients, during and after anti-HCV therapy with DAAs, compared to healthy controls (HD).</b> Box plots show counts of circulating classical, intermediate and non-classical monocytes in HCV infected patients (<b>a</b>–<b>c</b>) and in HIV/HCV coinfected patients (<b>d</b>–<b>f</b>) during and after therapy with DAAs. Horizontal bars represent the median values. ANOVA with Dunn’s post-test was performed to assess differences between patients at baseline and control, Wilcoxon test was performed to assess differences between the baseline and SVR12 and Mann–Whitney test assessed differences between SVR12 and HD. HD, healthy donors; T0, baseline before therapy; SVR12, sustained virological response 12 weeks after the end of therapy. * = <span class="html-italic">p</span> &lt; 0.05.</p>
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<p><b>Counts of MDC8, pDC and mDC in mHCV and HIV/HCV patients, during and after anti-HCV therapy with DAAs, compared to healthy controls.</b> Box plots show circulating counts of MDC8, pDC and mDC in mHCV patients (<b>a</b>–<b>c</b>) and in HIV/HCV coinfected patients (<b>d</b>–<b>f</b>) during and after therapy with DAAs. Horizontal bars represent the median values. ANOVA with Dunn’s post-test was performed to assess differences between patients at baseline and control, Wilcoxon test was performed to assess differences between the baseline and SVR12 and Mann–Whitney test assessed differences between SVR12 and HD. HD, healthy donors; T0, baseline before therapy; SVR12, sustained virologic response 12 weeks after the end of therapy. ** = <span class="html-italic">p</span> &lt; 0.01.</p>
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<p><b>Correlation analysis between soluble inflammatory factors, clinical parameters and peripheral blood cell counts.</b> Correlation analysis in the total population of mHCV and HIV/HCV coinfected patients was performed, at baseline, by the Spearman’s rank correlation coefficient (r). Colors express the strength of correlation by r, and asterisks indicate the <span class="html-italic">p</span> value (* = <span class="html-italic">p</span>&lt;0.05; ** = <span class="html-italic">p</span> &lt; 0.01; *** = <span class="html-italic">p</span> &lt; 0.001).</p>
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15 pages, 6384 KiB  
Article
Mesoscale Modeling of Phase Separation Controlled by Hydrosilylation in Polyhydromethylsiloxane (PHMS)-Containing Blends
by Yao Xiong, Chandan K. Choudhury, Vaibhav Palkar, Raleigh Wunderlich, Rajendra K. Bordia and Olga Kuksenok
Nanomaterials 2022, 12(18), 3117; https://doi.org/10.3390/nano12183117 - 8 Sep 2022
Cited by 3 | Viewed by 4212
Abstract
Controlling morphology of polysiloxane blends crosslinked by the hydrosilylation reaction followed by pyrolysis constitutes a robust strategy to fabricate polymer-derived ceramics (PDCs) for a number of applications, from water purification to hydrogen storage. Herein, we introduce a dissipative particle dynamics (DPD) approach that [...] Read more.
Controlling morphology of polysiloxane blends crosslinked by the hydrosilylation reaction followed by pyrolysis constitutes a robust strategy to fabricate polymer-derived ceramics (PDCs) for a number of applications, from water purification to hydrogen storage. Herein, we introduce a dissipative particle dynamics (DPD) approach that captures the phase separation in binary and ternary polymer blends undergoing hydrosilylation. Linear polyhydromethylsiloxane (PHMS) chains are chosen as preceramic precursors and linear vinyl-terminated polydimethylsiloxane (v-PDMS) chains constitute the reactive sacrificial component. Hydrosilylation of carbon–carbon unsaturated double bonds results in the formation of carbon–silicon bonds and is widely utilized in the synthesis of organosilicons. We characterize the dynamics of binary PHMS/v-PDMS blends undergoing hydrosilylation and ternary blends in which a fraction of the reactive sacrificial component (v-PDMS) is replaced with the non-reactive sacrificial component (methyl-terminated PDMS (m-PDMS), polyacrylonitrile (PAN), or poly(methyl methacrylate) (PMMA)). Our results clearly demonstrate that the morphology of the sacrificial domains in the nanostructured polymer network formed can be tailored by tunning the composition, chemical nature, and the degree of polymerization of the sacrificial component. We also show that the addition of a non-reactive sacrificial component introduces facile means to control the self-assembly and morphology of these nanostructured materials by varying the fraction, degree of polymerization, or the chemical nature of this component. Full article
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Graphical abstract
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<p>(<b>a</b>) Types of coarse-grained beads, “★” indicates that the bead is reactive, (<b>b</b>) schematic representation of the <span class="html-italic">v</span>-PDMS and PHMS chains, (<b>c</b>) schematic of the hydrosilylation reaction, (<b>d</b>) initial morphology snapshot (generated with <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mn>78</mn> </mrow> </semantics></math> for all the beads; no reactions occur during the equilibration) for a sample with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>PHMS</mi> </mrow> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>. The interaction parameters between all the types of beads are provided in <a href="#app1-nanomaterials-12-03117" class="html-app">Table S4 of Supplementary Materials</a>.</p>
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<p>(<b>a</b>) Snapshots (I–IV) of the blend with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>. The time steps from the left to the right are <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>5</mn> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>6</mn> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>6</mn> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>1.2</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>7</mn> </msup> </mrow> </semantics></math>. (<b>b</b>) The <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mrow> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mfenced> <mrow> <mi>r</mi> <mo>,</mo> <mi>t</mi> </mrow> </mfenced> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> at various times as listed in the legend. (<b>c</b>) The time evolution of the characteristic length scale, <math display="inline"><semantics> <mrow> <mi>l</mi> <mfenced> <mi>t</mi> </mfenced> </mrow> </semantics></math>, and the number of vinyl groups in the system normalized by the initial number of these groups, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi mathvariant="normal">I</mi> </msub> <mfenced> <mi>t</mi> </mfenced> <mo>/</mo> <msub> <mi>N</mi> <mi mathvariant="normal">I</mi> </msub> <mfenced> <mn>0</mn> </mfenced> </mrow> </semantics></math>. The time instances corresponding to the snapshots in (<b>a</b>) are marked by the open circles. The inset shows evolution at early times.</p>
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<p>Effect of the degree of polymerization of the sacrificial component, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>a</b>) Equilibrium morphology snapshots of the blends with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and various <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math>. The values of <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math> from the left to the right are <math display="inline"><semantics> <mrow> <mn>10</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>60</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>100</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>120</mn> </mrow> </semantics></math>. (<b>b</b>) The <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mrow> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mfenced> <mi>r</mi> </mfenced> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> at various <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math> as listed in the legend. (<b>c</b>) The average equilibrium characteristic length scale <math display="inline"><semantics> <mover accent="true"> <mi>l</mi> <mo>¯</mo> </mover> </semantics></math>. Here and below, <math display="inline"><semantics> <mover accent="true"> <mi>l</mi> <mo>¯</mo> </mover> </semantics></math> is averaged over the data taken within the last five frames (upon reaching an equilibrium) for four independent simulation runs; the error bars correspond to the standard deviation.</p>
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<p>Effects of fraction of sacrificial component, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>a</b>) Snapshots of the blends with <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> and various <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math>. The values of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math> from the left to the right are <math display="inline"><semantics> <mrow> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>0.3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>0.4</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>0.5</mn> </mrow> </semantics></math>. (<b>b</b>) The <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mrow> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mfenced> <mi>r</mi> </mfenced> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> at various <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math> as listed in the legend. (<b>c</b>) The average equilibrium characteristic length scale <math display="inline"><semantics> <mover accent="true"> <mi>l</mi> <mo>¯</mo> </mover> </semantics></math> as a function of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math> (in black) and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math> (in blue).</p>
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<p>Ternary blends containing PHMS (<math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>PHMS</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>PHMS</mi> </mrow> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>), <span class="html-italic">m</span>-PDMS, and <span class="html-italic">v</span>-PDMS. (<b>a</b>) Compositions for cases I–VII. (<b>b</b>) Equilibrium snapshots for cases I–VI. (<b>c</b>) The equilibrium characteristic length scale, <math display="inline"><semantics> <mover accent="true"> <mi>l</mi> <mo>¯</mo> </mover> </semantics></math>, for cases I–VII.</p>
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<p>Ternary systems with <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>PHMS</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>PHMS</mi> </mrow> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>v</mi> <mo>−</mo> <mi>PDMS</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>N</mi> <mi mathvariant="normal">X</mi> </msub> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi mathvariant="normal">X</mi> <mo>=</mo> </mrow> </semantics></math> PAN, PMMA, <span class="html-italic">m</span>-PDMS. (<b>a</b>) Simulation snapshots upon equilibration. All beads are shown in the top row (beads of both sacrificial components are in green); and only beads of the non-reactive component (<math display="inline"><semantics> <mi mathvariant="normal">X</mi> </semantics></math>) are shown in the bottom row. (<b>b</b>) Equilibrium characteristic length scale, <math display="inline"><semantics> <mover accent="true"> <mi>l</mi> <mo>¯</mo> </mover> </semantics></math>.</p>
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26 pages, 7188 KiB  
Article
Robust Fuzzy Control for Uncertain Nonlinear Power Systems
by Tawfik Guesmi, Badr M. Alshammari, Yosra Welhazi, Hsan Hadj Abdallah and Ahmed Toumi
Mathematics 2022, 10(9), 1463; https://doi.org/10.3390/math10091463 - 27 Apr 2022
Cited by 1 | Viewed by 1564
Abstract
This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. This approach is applied for the stabilization of a multimachine power system subject to disturbances. In this case, a state-feedback controller based on parallel [...] Read more.
This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. This approach is applied for the stabilization of a multimachine power system subject to disturbances. In this case, a state-feedback controller based on parallel distributed compensation (PDC) is applied for the stabilization of the fuzzy system, where the design of control laws is based on the Lyapunov function method and the stability conditions are solved using a linear matrix inequalities (LMI)-based framework. Due to the high number of system nonlinearities, two steps are followed to reduce the number of fuzzy rules. Firstly, the power network is subdivided into sub-systems using Thevenin’s theorem. Actually, each sub-system corresponds to a generator which is in series with the Thevenin equivalent as seen from this generator. This means that the number of sub-systems is equal to the number of system generators. Secondly, the significances of the nonlinearities of the sub-systems are ranked based on their limits and range of variation. Then, nonlinearities with non-significant variations are assumed to be uncertainties. The proposed strategy is tested on the Western systems coordinating council (WSCC) integrated with a wind turbine. The disturbances are assumed to be sudden variations in wind power output. The effectiveness of the suggested fuzzy controller is compared with conventional regulators, such as an automatic voltage regulator (AVR) and power system stabilizers (PSS). Full article
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Figure 1

Figure 1
<p>Thevenin equivalent circuit of a sub-system; <span class="html-italic">R<sub>Th</sub></span>, <span class="html-italic">X<sub>Th</sub></span> and <span class="html-italic">V<sub>Th</sub></span> are the Thevenin equivalent circuit parameters.</p>
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<p>Blondel diagram of a synchronous machine.</p>
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<p>Excitation system with PSS and AVR.</p>
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<p>Single line diagram of the WSCC system.</p>
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<p>Flowchart of the implementation of the proposed strategy.</p>
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<p>Block structure of the SMIB system with controllers.</p>
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<p>Block diagram of the closed-loop system.</p>
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<p>Rotor angle variations.</p>
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<p>Speed deviation.</p>
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<p>Internal voltage variations.</p>
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<p>Field voltage variations.</p>
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<p>Variations of PSS outputs.</p>
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<p>Fuzzy control signal.</p>
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