This work was aimed to prepare Fe doped TiO
2
on an ionic liquid template so that nanoparticles (IL-Fe/TiO
2(Ar)
) could be formed to degrade acetaminophen (ACE) using the photocatalytic process. The ...synthesized nanoparticles were characterized using XRD, DRS, FTIR and FESEM–EDX. IL-Fe/TiO
2(Ar)
enhanced ACE photodegradation using the response surface methodology based on central composite design. The results evidenced that the presence of the 1-octadecyl-3-methylimidazolium bromide template led to development of nanocrystals with a uniform, small grain size. Moreover, the band gap energy calculated for IL-Fe/TiO
2(Ar)
was observed to be 1.6 eV. The ANOVA results of the photodegradation process revealed a second term model with an adjusted R
2
equal to 0.996. The results also showed an ACE removal efficiency of 90.35% under optimal conditions (IL-Fe/TiO
2(Ar)
dosage = 0.63 g/L, pH 9 and UV irradiation time = 90.35 min). According to the findings, the pseudo first order model was introduced to study photodegradation kinetics. The findings also yielded insight into the most effective variables on degradation of ACE, which were time, IL-Fe/TiO
2(Ar)
and pH, respectively. Moreover, IL-Fe/TiO
2(Ar)
had high potential for the removal of ACE by photodegradation and proved to be an appropriate method to obtain optimum operating conditions.
The aim of this study was to determine the accuracy of the contrast "bolus only" T1 mapping cardiac magnetic resonance (CMR) technique for measuring myocardial extracellular volume fraction (ECV).
...Myocardial ECV can be measured with T1 mapping before and after contrast agent if the contrast agent distribution between blood/myocardium is at equilibrium. Equilibrium distribution can be achieved with a primed contrast infusion (equilibrium contrast-CMR EQ-CMR) or might be approximated by the dynamic equilibration achieved by delayed post-bolus measurement. This bolus only approach is highly attractive, but currently limited data support its use. We compared the bolus only technique with 2 independent standards: collagen volume fraction (CVF) from myocardial biopsy in aortic stenosis (AS); and the infusion technique in 5 representative conditions.
One hundred forty-seven subjects were studied: healthy volunteers (n = 50); hypertrophic cardiomyopathy (n = 25); severe AS (n = 22); amyloid (n = 20); and chronic myocardial infarction (n = 30). Bolus only (at 15 min) and infusion ECV measurements were performed and compared. In 18 subjects with severe AS the results were compared with histological CVF.
The ECV by both techniques correlated with histological CVF (n = 18, r² = 0.69, p < 0.01 vs. r² = 0.71, p < 0.01, p = 0.42 for comparison). Across health and disease, there was strong correlation between the techniques (r² = 0.97). However, in diseases of high ECV (amyloid, hypertrophic cardiomyopathy late gadolinium enhancement, and infarction), Bland-Altman analysis indicates the bolus only technique has a consistent and increasing offset, giving a higher value for ECVs above 0.4 (mean difference ± limit of agreement for ECV <0.4 = -0.004 ± 0.037 vs. ECV >0.4 = 0.040 ± 0.075, p < 0.001).
Bolus only, T1 mapping-derived ECV measurement is sufficient for ECV measurement across a range of cardiac diseases, and this approach is histologically validated in AS. However, when ECV is >0.4, the bolus only technique consistently measures ECV higher compared with infusion.
The current evolving global pandemic caused by coronavirus disease‐2019 (COVID‐19) has dramatically impacted global health care systems, resulting in governments taking unprecedented measures to ...contain the spread of the infection, with adaptations by health care organizations. Research into understanding the pathophysiology behind this virus, to ascertain best medical management and treatment, has been accelerated to keep up with the rapidly evolving situation. There has been redeployment of medical and nursing staff to the frontlines and redistribution of health care resources. In addition, the cancellation of elective surgery and centralization of services to treat high‐risk surgical cases will all, undeniably, have an impact on current surgical training with possible future implications. We aim to explore the impact COVID‐19 is having on cardiac surgical training in the UK and what future implications this may have.
•CYP2C9 is highly polymorphic and frequently expressing CYP drug metabolizing enzyme.•Genetic polymorphism in CYP2C9 reported in association with wide variety of alter drug response.•Partial DNA ...sequencing was performed to assess genetic composition of CYP2C9 among major ethnicities of Pakistani population.•Significant genetic differentiation was observed across CYP2C9 among different ethnicities of Pakistan.•A novel intronic SNP was identified in currently studied samples.
The genetic polymorphism of cytochrome P450 (CYPs)drug-metabolizing enzymes are well studied in human populations for drug safety and efficacy. CYP2C9 is a highly polymorphic CYP enzyme that oxidizing the indigenous compounds and xenobiotics. The present study was pursued to evaluate the genetic variation across the CYP2C9 gene among major groups of the Pakistani population. The CYP2C9 genomic region holding important warfarin drug-metabolizing SNPs was sequenced from 159 individuals belong from five major ethnic groups of Pakistani population. The population genetic analyses of the high-quality sequences data was performed using Arlequin v3.5, DnaSP v6.12 and Network 5 resources. The data analyses unveiled that genetic variance among samples mainly arose from population-scale differentiation among these ethnic groups with global Fst of 0.78, P-value < 0.0001. The highest pairwise population genetic variation observed between Saraiki and Baloch groups based on different statistical tests. Whereas, uniform genetic composition across CYP2C9 loci was inferred among Punjabi, Pathan and Sindhi groups with minimal genetic differentiation. Several SNPs, including the previously reported warfarin associated variants, i.e. rs2860905, rs1799853 (CYP2C9*2) and rs72558189 (CYP2C9*14) were detected in these population groups with diverse allelic frequencies. Besides, a novel intronic SNP, i.e. not available in dbSNP and Ensemble databases, was identified for a Sindhi individual sample. This novel SNP predicted to influence the CYP2C9 alternative transcript splicing. The pharmacogeneticsassessment of the CYP2C9 genetic variations identified in current study may important to test against the warfarin efficacy for different ethnicity of Pakistani population.
It is critical to select the model that best fits the situation while analyzing the data. Many scholars on classification and regression issues have offered ensemble techniques on tabular data, as ...well as other approaches to classification and regression problems (Like Boosting and Logistic Model tree ensembles). Furthermore, various deep learning algorithms have recently been implemented on tabular data, with the authors claiming that deep models outperform Boosting and Model tree approaches. On a range of datasets including historical geographical data, this study compares the new deep models (TabNet, NODE, and DNF-net) against the boosting model (XGBoost) to see if they should be regarded a preferred choice for tabular data. We look at how much tweaking and computation they require, as well as how well they perform based on the metrics evaluation and statistical significance test. According to our study, XGBoost outperforms these deep models across all datasets, including the datasets used in the journals that presented the deep models. We further show that, when compared to deep models, XGBoost requires considerably less tweaking. In addition, we can also confirm that a combination of deep models with XGBoost outperforms XGBoost alone on almost all datasets.
Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous ...innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.
Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to ...identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models.
Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases.
Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity.
Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy.
Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness.
Keywords: Cardiovascular data, Clinical data., Decision tree, GWLM-NARX, Linear model functions
Chitosan is a biodegradable and biocompatible natural polymer that has been extensively explored in recent decades. The Food and Drug Administration has approved chitosan for wound treatment and ...nutritional use. Furthermore, chitosan has paved the way for advancements in different biomedical applications including as a nanocarrier and tissue-engineering scaffold. Its antibacterial, antioxidant, and haemostatic properties make it an excellent option for wound dressings. Because of its hydrophilic nature, chitosan is an ideal starting material for biocompatible and biodegradable hydrogels. To suit specific application demands, chitosan can be combined with fillers, such as hydroxyapatite, to modify the mechanical characteristics of pH-sensitive hydrogels. Furthermore, the cationic characteristics of chitosan have made it a popular choice for gene delivery and cancer therapy. Thus, the use of chitosan nanoparticles in developing novel drug delivery systems has received special attention. This review aims to provide an overview of chitosan-based nanoparticles, focusing on their versatile properties and different applications in biomedical sciences and engineering.
One of the earliest and most popular (and effective) machine learning methods is decision trees. Different decision tree changes have been suggested and put into practice over time. Selecting the ...model that best fits the situation is essential while examining the data. Numerous classification and regression specialists have suggested ensemble tactics for tabular data as well as diverse methods for solving classification and regression issues. In this study, the raw historical apple crop dataset is transformed into a discrete dataset using the Gini Index and information gain. On the resulting discrete dataset, the decision tree algorithm is used. Information Gain is determined for each attribute, and the attribute with the highest information gain is used as the splitting node, which is then applied recursively. With an accuracy of 84.54%, the decision tree algorithm used predicts the apple yield in Kashmir province. Later, a comparison between the accuracy of decision tree and other algorithms has also been made and it was observed that the decision tree performed better in accuracy and other statistics than all the other implemented algorithms.
Determining real-time machine simulation and functionalities of complex AI Engines is difficult to comprehend and is rarely discussed. We present a technique to analyze the workflow of one such ...engine, the NEAT engine, one of the fundamental and robust training engines in the current machine learning scenario. Computer Vision also presents a great approach towards working in real-time speedy functioning virtual simulators and visual platforms, whereas NEAT is not specialized in the same, but can perform to the best of the extent in the present day Machine Learning aspect. Technologies like Python, PyGame, and CsV were used to facilitate the research. So far, we have tested both the frameworks on real time cases, and it is safe to say that the NEAT module has presented an accurate trajectory, besides greater time complexity. Thus, we not only evaluate the accuracy but the other key factors as well. This study demonstrates that NEAT has the ability to address other difficult issues in the future and can produce excellent outcomes with a relatively small population. Robotics, artificial intelligence for video games, natural language processing, and healthcare are some of the potential future applications for NEAT.
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