Universal scaling of the diffusivity of dendrimers in a semidilute solution of linear polymers.

The static and dynamic properties of dendrimers in semidilute solutions of linear chains of comparable size are investigated using Brownian dynamics simulations. The radius of gyration and diffusivity of a wide variety of low generation dendrimers and linear chains in solution follow universal scaling laws independent of their topology. Analysis of the shape functions and internal density of dendrimers shows that they are more spherical than linear chains and have a dense core. At intermediate times, dendrimers become subdiffusive, with an exponent higher than that previously reported for nanoparticles in semidilute polymer solutions. The long-time diffusivity of dendrimers does not follow theoretical predictions for nanoparticles. We propose a new scaling law for the long-time diffusion coefficients of dendrimers which accounts for the fact that, unlike nanoparticles, dendrimers shrink with an increase in background solution concentration. Analysis of the properties of a special case of a higher functionality dendrimer shows a transition from polymer-like to nanoparticle-like behaviour.

A vehicular network based intelligent transport system for smart cities using machine learning algorithms.

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%.

Naringin and graphene oxide incorporated Moringa oleifera gum/poly(vinyl) alcohol patch for enhanced wound healing.

Patients and healthcare systems stand to gain much from the use of substances that can accelerate wound healing. In this research work, a polymeric patch was fabricated using polymers like poly (vinyl alcohol) (PVA) and Moringa oleifera gum (MO) incorporated with graphene oxide (GO) and naringin (Nar) (drug). This study determined the impact of using PVA/MO/GO/Nar polymeric patch on wound healing via in vitro and in vivo investigations. Graphene oxide was synthesized by modified Hummer's method. The synthesized sample was characterized using XRD, FT-IR, RAMAN Spectroscopy, FESEM and HRTEM. Antibacterial analysis of the GO on four different bacteria was studied through well diffusion, colony count, growth curve and biofilm assay. Biocompatibility was analysed by haemolysis assay. The morphology, antibacterial activity, haemolysis assay, swelling, degradation, porosity, water vapour transmission rate, drug release, blood pump model, in-vitro scratch assay and MTT assay were analysed for the fabricated polymeric patches under in-vitro condition. The PVA/MO/GO/Nar patch has shown enhanced wound healing in in-vivo wound healing experiments on albino Wistar rats.

Fabrication of poly (lactic-co-glycolic acid)/gelatin electro spun nanofiber patch containing CaCO3/SiO2 nanocomposite and quercetin for accelerated diabetic wound healing.

An open wound or sore on the bottom of the foot caused by diabetes is known as a diabetic foot ulcer. Preventive measures are essential, including consistent foot care and glycemic management. The dangers associated with diabetic foot ulcers can be reduced via early identification and timely treatment. The risk of foot ulcers and limb amputation increases with age and duration of diabetes. Quercetin contains anti-inflammatory and antioxidant properties. Furthermore, the calcium carbonate/silica (CaCO3/SiO2) nanocomposite has a good anti-inflammatory property due to the presence of calcium, which will aid in wound healing. As a result, combining quercetin (plant based anti-inflammatory drug) and CaCO3/SiO2 nanocomposite will boost the wound healing rate. We have synthesized CaCO3/SiO2 nanocomposite in sol-gel method and characterized using XRD, FTIR and TEM. Cell line tests and the MTT assay revealed that the PLGA/gelatin/CaCO3/SiO2/quercetin patch enhanced the proliferation of cells. Its anti-bacterial efficacy against four major bacterial strains often found in wound locations, as well as its water retention, make it an ideal material for diabetic wound healing. In-vivo trials confirms the enhanced diabetic wound healing potential of the patch.

Calcium oxide/silica nanocomposite and L. coromandelica bark incorporated κ-carrageenan/sodium alginate hydrogel for rapid hemostasis.

Hemorrhage stands out as a leading factor contributing to fatalities in trauma cases. Hemorrhage is associated with the process of hemostasis. Hemostasis is the primary stage of wound healing. Hydrogels can aid in hemostasis and minimize the duration of wound healing. Calcium is one of the clotting factors and is a key component for the activation of the coagulation cascade. In this work, we have developed a polymeric hydrogel matrix made up of κ-carrageenan and sodium alginate containing a calcium silica nanocomposite and a natural drug, namely the bark of L. coromandelica. The nanocomposite was characterized using various modalities such as XRD, FTIR, FESEM and HRTEM. The rheological and morphological properties of the pure and composite hydrogels were examined. The antimicrobial activity, hemocompatibility and hemostatic efficacy of the materials were studied using various in vitro assays including bacterial growth curve analysis, colony counting, anti-biofilm assay, hemolysis assay and in vivo clotting studies. The drug incorporated nanocomposite hydrogel exhibited superior activity in animal models.

Social and Demographic Factors Associated With Obese Children in the Age Group of 6-12 Years Attending a Tertiary Care Institute in Central India and the Prevalence of Depression in These Children: An Observational Study.

INTRODUCTION: Childhood obesity in India is on the rise and is rarely raised as a concern. In the central Indian states, focus is largely on undernutrition. Thus, studies related to risk factors for being overweight and obese and the impact of obesity on the psychology of children are lacking. Hence, a hospital-based study with objectives to identify social and demographic factors associated with obesity and the estimation of the prevalence of depression among these children was conducted. METHODS: This observational study was conducted in a tertiary care institute in the state of Chhattisgarh from July 2020 to October 2021. Children with obesity as per the WHO (> 95th percentile for the BMI) in the age group of 6-12 years were included after due consent. A proforma was administered targeting the objectives of the study and the Childhood Depression Rating Scale (CDR-S) and clinical evaluation identified the depressed. RESULTS: Among the 5,019 children screened during the study period, 54 met the inclusion criteria (1.07%). Fifty percent of children were from the upper middle class as per the Kuppuswamy scale. Seventy percent consumed junk food more than three times a week. Twenty-three children (42.6%) engaged in physical activity of > 1 hour and 49(90.7%) had a screen time of > 2 hours. The prevalence of depression among these children stood at 5.6% with the mean age being 11.67 years. Multiple logistic regression showed an inverse association of depression with physical activity. CONCLUSION: Obesity is prevalent in higher socioeconomic groups. Many risk factors like screen time, junk food consumption, and physical activity are modifiable. The prevalence of depression increased with age. Physical activity showed an inverse relation to depression in obese children.

Creation of EGD-Derived Gastric Cancer Organoids to Predict Treatment Responses.

Gastric adenocarcinoma (GAd) is the third leading cause of cancer-related deaths worldwide. Most patients require perioperative chemotherapy, yet methods to accurately predict responses to therapy are lacking. Thus, patients may be unnecessarily exposed to considerable toxicities. Here, we present a novel methodology using patient-derived organoids (PDOs) that rapidly and accurately predicts the chemotherapy efficacy for GAd patients. Methods:Endoscopic GAd biopsies were obtained from 19 patients, shipped overnight, and PDOs were developed within 24 h. Drug sensitivity testing was performed on PDO single-cells with current standard-of-care systemic GAd regimens and cell viability was measured. Whole exome sequencing was used to confirm the consistency of tumor-related gene mutations and copy number alterations between primary tumors, PDOs, and PDO single-cells. Results:Overall, 15 of 19 biopsies (79%) were appropriate for PDO creation and single-cell expansion within 24 h of specimen collection and overnight shipment. With our PDO single-cell technique, PDOs (53%) were successfully developed. Subsequently, two PDO lines were subjected to drug sensitivity testing within 12 days from initial biopsy procurement. Drug sensitivity assays revealed unique treatment response profiles for combination drug regimens in both of the two unique PDOs, which corresponded with the clinical response. Conclusions:The successful creation of PDOs within 24 h of endoscopic biopsy and rapid drug testing within 2 weeks demonstrate the feasibility of our novel approach for future applications in clinical decision making. This proof of concept sets the foundation for future clinical trials using PDOs to predict clinical responses to GAd therapies.

Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures.

Pediatric pneumonia has drawn immense awareness due to the high mortality rates over recent years. The acute respiratory infection caused by bacteria, viruses, or fungi infects the lung region and hinders oxygen transport, making breathing difficult due to inflamed or pus and fluid-filled alveoli. Being non-invasive and painless, chest X-rays are the most common modality for pediatric pneumonia diagnosis. However, the low radiation levels for diagnosis in children make accurate detection challenging. This challenge initiates the need for an unerring computer-aided diagnosis model. Our work proposes Contrast Limited Adaptive Histogram Equalization for image enhancement and a stacking classifier based on the fusion of deep learning-based features for pediatric pneumonia diagnosis. The extracted features from the global average pooling layers of the fine-tuned MobileNet, DenseNet121, DenseNet169, and DenseNet201 are concatenated for the final classification using a stacked ensemble classifier. The stacking classifier uses Support Vector Classifier, Nu-SVC, Logistic Regression, K-Nearest Neighbor, Random Forest Classifier, Gaussian Naïve Bayes, AdaBoost classifier, Bagging Classifier, and Extra-trees Classifier for the first stage, and Nu-SVC as the meta-classifier. The stacking classifier validated using Stratified K-Fold cross-validation achieves an accuracy of 98.62%, precision of 98.99%, recall of 99.53%, F1 score of 99.26%, and an AUC score of 93.17% on the publicly available pediatric pneumonia dataset. We expect this model to greatly help the real-time diagnosis of pediatric pneumonia.

Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images.

Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia. Delayed diagnosis and overpriced tests are the prime reason for the high mortality rate, especially in underdeveloped countries. A time and cost-efficient diagnosis tool: Chest X-rays, was thus accepted as the standard diagnostic test for pediatric pneumonia. However, the lower radiation levels for diagnosis in children make the task much more onerous and time-consuming. The mentioned challenges initiate the need for a computer-aided detection model that is instantaneous and accurate. Our work proposes a stacked ensemble learning of deep learning-based features for pediatric pneumonia classification. The extracted features from the global average pooling layer of the fine-tuned Xception model pretrained on ImageNet weights are sent to the Kernel Principal Component Analysis for dimensionality reduction. The dimensionally reduced features are further trained and validated on the stacking classifier. The stacking classifier consists of two stages; the first stage uses the Random-Forest classifier, K-Nearest Neighbors, Logistic Regression, XGB classifier, Support Vector Classifier (SVC), Nu-SVC, and MLP classifier. The second stage operates on Logistic Regression using the first stage predictions for the final classification with Stratified K-fold cross-validation to prevent overfitting. The model was tested on the publicly available pediatric pneumonia dataset, achieving an accuracy of 98.3%, precision of 99.29%, recall of 98.36%, F1-score of 98.83%, and an AUC score of 98.24%. The performance shows its reliability for real-time deployment in assisting radiologists and physicians.

Stabilization of Eu2+ in Li2B4O7 with the BO3 network through U6+ co-doping and defect engineering.

Owing to the unique 4f-5d transitions and the involvement of 5d electrons, the divalent europium (Eu2+) ion is extensively used as a dopant ion in luminescent materials for phosphor-converted light emitting diodes (pc-LEDs) and other technological applications. Earlier reports in most of the cases have shown that the reduction of Eu3+ to Eu2+ requires very high temperatures and large hydrogen flux. In this study, a co-doping strategy with higher valent U6+ ions was utilized to successfully stabilize Eu2+ ions in the Li2B4O7 (LTB) host with both the BO3 and BO4 network in low H2 flux of only 8%. It is postulated that charge transfer occurs from U to Eu, resulting in the reduction of the charged state of Eu and the reaction probably proceeds via the formation of paramagnetic transient [U5+-Eu3+] species in the co-doped LTB. The same is also believed to be facilitated by the enhanced formation of Li-O type vacancy clusters in co-doped samples and enhanced oxygen vacancies in a reducing atmosphere. We believe this work will pave a new pathway for stabilizing the unusual oxidation state of lanthanides and transition metal ions through co-doping with hexavalent uranium ions.

Risk Factors for Capsular Contracture in Alloplastic Reconstructive and Augmentation Mammaplasty: Analysis of the National Surgical Quality Improvement Program (NSQIP) Database.

BACKGROUND: Capsular contracture is the most common complication of breast augmentation and reconstruction. It occurs in up to 45% of patients and is theorized to occur secondary to an immune reaction. It can lead to pain, dissatisfaction with aesthetic outcomes, and reoperation. The gold standard for management is capsulectomy. Prior similar studies are limited by narrow inclusion criteria, single-surgeon analysis, small sample size, or univariate analysis. The goal of the following study is to prospectively identify possible risk factors for capsular contracture using a national database. METHODS: A retrospective review was conducted utilizing the National Surgical Quality Improvement Program (NSQIP) Database of prospectively collected data of patients undergoing periprosthetic and/or total capsulectomy for capsular contracture from 2013 to 2016. Odds ratios (OR) with 95% confidence intervals (CI) were calculated for variables using a multivariable binary logistic regression model. RESULTS: A total of 6547 patients underwent reconstructive or augmentation mammaplasty with a prosthetic implant, out of which 2543 (39%) underwent capsulectomy. Capsular contracture was more likely in older (OR: 1.10, 95% CI: 1.09-1.10, p<.001), overweight (OR: 1.12, 95% CI: 1.10-1.13, p<.001), and cancer patients (OR: 7.71, 95% CI: 2.22-28.8, p=0.001). Wound infection was associated with capsulectomy (OR: 6.69, 95% CI: 1.74-25.8, p<.001). CONCLUSION: These identified risk factors should be comprehensively addressed with patients during the informed consent process before breast augmentation or reconstruction with implants. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Influence of tumor size in the progression of venous tumor thrombus in renal cell carcinoma: A 7-year single-center experience.

Objective: The objective of the study is to describe the perioperative outcomes, disease-specific, and overall survival status in patients diagnosed with renal cell carcinoma with inferior vena cava (IVC) tumor thrombus. Patients and Methods: We did a retrospective analysis of all patients who underwent radical nephrectomy along with IVC thrombectomy from the year 2013 to 2020. Mayo's classification was used to stratify the level of IVC thrombus. Demographic, perioperative, histopathology data, complications, and survival status were analyzed. Results: Total number of patients included in the study was 39, (Male: Female = 84.6%: 15.4%). Median age of patients was 58 (interquartile range [IQR] 50-63) years. Median size of renal tumor (in cms) was 9.5 (IQR 7.5-12), 8 (IQR 7-11.5), 8.5 (IQR 7-11.75), and 11 (IQR 9.5-11) (P = 0.998) in level 1,2,3, and 4 tumors, respectively. Clear cell variant was seen in 32 patients (82%) with R0 resection in 17 patients. Twelve patients (30.7%) had systemic metastasis on presentation. The overall mean survival time was 66.4 months with 95% confidence interval (CI) (52.4-80.5 months). Mean recurrence-free survival is 76 months with (63-90) CI of 95%. Mean survival in patients who presented with metastasis is 47 months with 95% CI (52.4-80.5). Perioperative mortality rate was 5.12% in this study. Conclusion: The tumor size does not have an influence on the progression of tumor thrombus into IVC. Significant difference in survival was observed between different levels of thrombus with high mortality in level four tumors.

Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis.

The objective of this investigational analysis was to study the influence of process variables on the response during the drilling of LM6/B4C composite materials. Stir casting was employed to produce the LM6/B4C composites. A Vertical Machining Center (VMC) with a dynamometer was used to drill the holes and to record the thrust force. An L27 orthogonal array was used to carry out the experimental work. A grey relational analysis (GRA) was employed to perform optimization in order to attain the lowest Thrust Force (TF), Surface Roughness (SR) and Burr Height (BH). For minimal responses, the optimum levels of the process variables viz. the feed rate (F), spindle speed (S), drill material (D) and reinforcing percentage (R) were determined. The process variables in the drilling of the LM6/B4C composites were indeed optimized, according to confirmational investigations. The predicted Grey Relational Grade was 0.846, whereas the experimental GRG was 0.865, with a 2.2% error-indicating that the optimization process was valid.

Heterogeneous interactions and polymer entropy decide organization and dynamics of chromatin domains.

Chromatin is known to be organized into multiple domains of varying sizes and compaction. While these domains are often imagined as static structures, they are highly dynamic and show cell-to-cell variability. Since processes such as gene regulation and DNA replication occur in the context of these domains, it is important to understand their organization, fluctuation, and dynamics. To simulate chromatin domains, one requires knowledge of interaction strengths among chromatin segments. Here, we derive interaction-strength parameters from experimentally known contact maps and use them to predict chromatin organization and dynamics. Taking two domains on the human chromosome as examples, we investigate its three-dimensional organization, size/shape fluctuations, and dynamics of different segments within a domain, accounting for hydrodynamic effects. Considering different cell types, we quantify changes in interaction strengths and chromatin shape fluctuations in different epigenetic states. Perturbing the interaction strengths systematically, we further investigate how epigenetic-like changes can alter the spatio-temporal nature of the domains. Our results show that heterogeneous weak interactions are crucial in determining the organization of the domains. Computing effective stiffness and relaxation times, we investigate how perturbations in interactions affect the solid- and liquid-like nature of chromatin domains. Quantifying dynamics of chromatin segments within a domain, we show how the competition between polymer entropy and interaction energy influence the timescales of loop formation and maintenance of stable loops.

Face Off: 3D-Printed Masks as a Cost-Effective and Reusable Alternative to N95 Respirators: A Feasibility Study.

BACKGROUND: One of the best methods for protection against respiratory diseases is the use of an N95 mask. Supply shortages have demonstrated a significant need for effective alternatives to N95 masks. Benefits of 3D-printed respirators over N95s include reduced cost and ease of production, widespread availability, reusability/sterilizability, and customizability. 3D-printed mask designs have been downloaded thousands of times; however, there is little to no data on the efficacy of these potential alternatives. METHODS: Three of the most popular 3D-printed respirator designs were modified to allow for the Occupational Safety and Health Administration (OSHA) quantitative fit testing that disperses saline into the ambient air and determines concentrations within the mask during multiple trials. Five volunteers conducted standardized fit tests of these masks, as well as an N95 and a KN95, and the results were compared. RESULTS: One of the 3D-printed respirators, low poly COVID-19 face mask respirator (mask 2), achieved a fit factor greater than 100 in every trial, representing sufficient fit according to OSHA protocols. The N95 mask achieved a sufficient fit in 60% of the trials, and none of the remaining masks provided a suitable fit factor reliably according to the OSHA fit test. Further trials showed no change in fit factor when different 3D-printable plastics are used or when a widely available high efficiency particulate air (HEPA) filter was used. CONCLUSION: 3D-printed respirators provide a possible alternative to N95 masks to protect against respiratory pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Fit testing results demonstrate that certain 3D-printed mask designs may exceed the fit of N95 masks.

Electroosmotic modulated unsteady squeezing flow with temperature-dependent thermal conductivity, electric and magnetic field effects.

Modern lubrication systems are increasingly deploying smart (functional) materials. These respond to various external stimuli including electrical and magnetic fields, acoustics, light etc. Motivated by such developments, in the present article unsteady electro-magnetohydrodynamics squeezing flow and heat transfer in a smart ionic viscous fluid intercalated between parallel plates with zeta potential effects is examined. The proposed mathematical model of problem is formulated as a system of partial differential equations (continuity, momenta and energy). Viscous dissipation and variable thermal conductivity effects are included. Axial electrical distribution is also addressed. The governing equations are converted into ordinary differential equations via similarity transformations and then solved numerically with MATLAB software. The transport phenomena are scrutinized for both when the plates move apart or when they approach each other. Also, the impact of different parameters such squeezing number, variable thermal conductivity parameter, Prandtl number, Hartmann number, Eckert number, zeta potential parameter, electric field parameter and electroosmosis parameter on the axial velocity and fluid temperature are analysed. For varied intensities of applied plate motion, the electro-viscous effects derived from electric double-capacity flow field distortions are thoroughly studied. It has been shown that the results from the current model differ significantly from those achieved by using a standard Poisson-Boltzmann equation model. Axial velocity acceleration is induced with negative squeeze number (plates approaching,S< 0) in comparison to that of positive squeeze number (plates separating,S> 0). Velocity enhances with increasing electroosmosis parameter and zeta potential parameter. With rising values of zeta potential and electroosmosis parameter, there is a decrease in temperatures forUe> 0 for both approaching i.e. squeezing plates (S< 0) and separating (S> 0) cases. The simulations provide novel insights into smart squeezing lubrication with thermal effects and also a solid benchmark for further computational fluid dynamics investigations.

Development of chitosan/poly (vinyl alcohol)/graphene oxide loaded with vanadium doped titanium dioxide patch for visible light driven antibacterial activity and accelerated wound healing application.

Wound healing is a multi-stage process that is dynamic, interactive, and complicated. However, many nanomaterials are employed to expedite wound healing by demonstrating antibacterial activity or boosting cell proliferation. But only one phase is focused during the wound healing process. As a result, there is a need for optimum wound dressing materials that promotes different wound healing cascades with ideal properties. Herein, Graphene Oxide loaded with vanadium (V) doped titanium dioxide (TiO2) blended with chitosan, and polyvinyl alcohol (CS/PVA/GO/TiO2-V) patch was developed for wound healing. XRD, FTIR and FE-SEM analyses were carried out to study the morphology and structural property of the patch. The fabricated patch has a high surface porosity, excellent moisture vapor transfer rate, appropriate swelling behaviour, and oxygen permeability, which results in an excellent moist environment for wound breathing and effective management of wound exudates. The antibacterial test showed significant antibacterial efficacy against wound infections in the presence of light when compared to dark. In-vitro analysis such as hemocompatibility, cytotoxicity, cell adhesion, and scratch assay show the predicted potential wound healing application with high biocompatibility. These results suggest that CS/PVA/GO/TiO2-V patch provides a microenvironment favourable to cells' growth and differentiation and positively modulates full-thickness wounds' healing.