To study the use of partial or total potassium bicarbonate (PBC) to replace sodium tripolyphosphate (STPP) on reduced-phosphate silver carp batters, all the batters were composed of silver carp surimi, pork back fat, ice water, spices, sugar, and sodium chloride. Therein, the sample of T1 contained 4 g/kg STPP; T2 contained 1 g/kg PBC, 3 g/kg STPP; T3 contained 2 g/kg PBC, 2 g/kg STPP; T4 contained 3 g/kg PBC, 1 g/kg STPP; T5 contained 4 g/kg PBC, and they were all produced using a bowl chopper. The changes in pH, whiteness, water- and oil-holding capacity, gel and rheological properties, as well as protein conformation were investigated. The pH, cooking yield, water- and oil-holding capacity, texture properties, and the G\' values at 90 °C of the reduced-phosphate silver carp batters with PBC significantly increased (p < 0.05) compared to the sample without PBC. Due to the increasing pH and enhanced ion strength, more β-sheet and β-turns structures were formed. Furthermore, by increasing PBC, the pH significantly increased (p < 0.05) and the cooked silver carp batters became darkened. Meanwhile, more CO2 was generated, which destroyed the gel structure, leading the water- and oil-holding capacity, texture properties, and G\' values at 90 °C to be increased and then decreased. Overall, using PBC partial as a substitute of STPP enables reduced-phosphate silver carp batter to have better gel characteristics and water-holding capacity by increasing its pH and changing its rheology characteristic and protein conformation.

Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system\'s ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images\' lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system\'s performance and can aid in an improved clinical diagnosis process.

The synergism between thermoresponsive and bioadhesive polymers can lead to the optimization of materials with enhanced mechanical and bioadhesive properties. Quality by Design can assure the understanding and control of formulation variables. In this approach, Design of Experiment has been widely utilized as an important strategy. Poly(methyl vinyl ether-alt-maleic anhydride) (PVMMA) is a bioadhesive polymer and Pluronic F127 (PF127) shows thermoresponsiveness. The association of these two polymers has been poorly investigated. The aim of this work was to study the mechanical, bioadhesive and rheological properties of polymer mixtures composed of PVMMA and PF127, in order to select the best conditions and formulations for biomedical applications. Textural properties (hardness, compressibility, adhesiveness, cohesiveness and elasticity), softness index, bioadhesion and rheological characteristics (flow and viscoelasticity) showed that 17.5-20% (w/w) PF127-polymer mixtures displayed improved values of the parameters. However, the rheological interaction parameter showed low synergism, due to the polymers\' characteristics and system organization. The formulations displayed gelation temperatures suitable for administration, with improved bioadhesive properties mainly at 34 °C and suggests the formulations can be used for biomedical applications. DoE constituted an important tool to investigate these systems showing the main effects that significantly influence the binary mixtures.

BACKGROUND: According to the Chinese Health Commission guidelines, coronavirus disease 2019 (COVID-19) severity is classified as mild, moderate, severe, or critical. The mortality rate of COVID-19 is higher among patients with severe and critical diseases; therefore, early identification of COVID-19 prevents disease progression and improves patient survival. Computed tomography (CT) radiomics, as a machine learning method, provides an objective and mathematical evaluation of COVID-19 pneumonia. As CT-based radiomics research has recently focused on COVID-19 diagnosis and severity analysis, this meta-analysis aimed to investigate the predictive power of a CT-based radiomics model in determining COVID-19 severity.
METHODS: This study followed the diagnostic version of PRISMA guidelines. PubMed, Embase databases and the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews were searched to identify relevant articles in the meta-analysis from inception until July 16, 2021. The sensitivity and specificity were analyzed using forest plots. The overall predictive power was calculated using the summary receiver operating characteristic curve. The bias was evaluated using a funnel plot. The quality of the included literature was assessed using the radiomics quality score and quality assessment of diagnostic accuracy studies tool.
RESULTS: The radiomics quality scores ranged from 7 to 16 (achievable score: 2212 8 to 36). The pooled sensitivity and specificity were 0.800 (95% confidence interval [CI] 0.662-0.891) and 0.874 (95% CI 0.773-0.934), respectively. The pooled area under the receiver operating characteristic curve was 0.908. The quality assessment tool showed favorable results.
CONCLUSIONS: This meta-analysis demonstrated that CT-based radiomics models might be helpful for predicting the severity of COVID-19 pneumonia.

The pandemic was announced by the world health organization coronavirus (COVID-19) universal health dilemma. Any scientific appliance which contributes expeditious detection of coronavirus with a huge recognition rate may be excessively fruitful to doctors. In this environment, innovative automation like deep learning, machine learning, image processing and medical image like chest radiography (CXR), computed tomography (CT) has been refined promising solution contrary to COVID-19. Currently, a reverse transcription-polymerase chain reaction (RT-PCR) test has been used to detect the coronavirus. Due to the moratorium period is high on results tested and huge false negative estimates, substitute solutions are desired. Thus, an automated machine learning-based algorithm is proposed for the detection of COVID-19 and the grading of nine different datasets. This research impacts the grant of image processing and machine learning to expeditious and definite coronavirus detection using CXR and CT medical imaging. This results in early detection, diagnosis, and cure for the accomplishment of COVID-19 as early as possible. Firstly, images are preprocessed by normalization to enhance the quality of the image and removing of noise. Secondly, segmentation of images is done by fuzzy c-means clustering. Then various features namely, statistical, textural, histogram of gradients, and discrete wavelet transform are extracted (92) and selected from the feature vector by principle component analysis. Lastly, k-NN, SRC, ANN, and SVM are used to make decisions for normal, pneumonia, COVID-19 positive patients. The performance of the system has been validated by the k (5) fold cross-validation technique. The proposed algorithm achieves 91.70% (k-Nearest Neighbor), 94.40% (Sparse Representation Classifier), 96.16% (Artificial Neural Network), and 99.14% (Support Vector Machine) for COVID detection. The proposed results show feature combination and selection improves the performance in 14.34 s with machine learning and image processing techniques. Among k-NN, SRC, ANN, and SVM classifiers, SVM shows more efficient results that are promising and comparable with the literature. The proposed approach results in an improved recognition rate as compared to the literature review. Therefore, the algorithm proposed shows immense potential to benefit the radiologist for their findings. Also, fruitful in prior virus diagnosis and discriminate pneumonia between COVID-19 and other pandemics.

The impact of ozone-induced oxidation on the gel properties of egg yolk was investigated for the first time in this research. The textural properties, water-holding capacity, cooking loss rate and color of the chicken egg yolk gel (CEYG) were significantly improved after ozonation. The maximum hardness value (976.04 g) was reached at 20 min of ozonation and it was 134.92 g higher than that of the natural group. Additionally, the ozone-treated yolk showed an increase of 58.47% in carbonyl content and a decrease of 44.33% in free sulfhydryl groups. The results of low-field nuclear magnetic resonance indicated that ozone promoted the conversion of free water to non-flowing water in the CEYG. Scanning electron microscopy represented that the moderate ozone treatment resulted in a more regular, continuous and smooth network structure of the CEYG. These results provided a theoretical basis for the application of ozone to improve the performance of heat-induced CEYG.

The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: \"radiomics OR radiomic\" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.

BACKGROUND: Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression.
METHODS: Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria.
RESULTS: Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04).
CONCLUSIONS: Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

In the present study phytosterols (have hypocholesterolemic effect) have been used in processed cheese spread (PCS) to enhance its functionality and its effect on textural and melting properties have been evaluated at three different levels of addition (2%, 3% and 4%). On textural attributes, the firmness of the PCS at 3% and 4% of phytosterols addition were significantly (p<0.05) higher as compared to the control. All the phytosterols incorporated cheese spread samples were significantly higher (p<0.05) in work of shear as compared to the control. As the levels of phytosterols addition were increased in cheese spread from 0 to 4%, a sharp, steady and significant (p<0.05) decrease in work of adhesion was noticed. The RVA™ cheese melt viscosity index of all the phytosterols incorporated cheese spread samples were significantly (p<0.05) lower as compared to the control.