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BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (

In the study we proposed a novel approach is called a Customized Convolutional Neural Network (CCNN) to innovate in the field of art creation, particularly in composite material paintings. This research harnesses the power of image processing technology to analyze and synthesize various artistic elements, thereby facilitating the creation of composite material paintings. The core of the study revolves around the development of a unique algorithm that enables the integration of diverse materials and textures into a cohesive artistic expression. The Customized CNN is trained on a vast dataset of images, encompassing a wide spectrum of textures, colors, and patterns, representative of different materials commonly used in art. The network learns to identify and replicate the aesthetic qualities of these materials, thereby empowering artists to explore new realms of creativity. The algorithm not only recognizes the distinct characteristics of each material but also understands how to blend them effectively, maintaining artistic coherence. The results are evaluated to prove proposed performance. [ABSTRACT FROM AUTHOR]

Introduction: Cognitive impairment in patients with end-stage renal disease (ESRD) is associated with brain structural damage. However, no prior studies have investigated the relationship between brain texture features and the cognitive function in ESRD patients. This study aimed to investigate changes in brain texture features in ESRD patients and their relationships with cognitive function using voxel-based 3D brain texture analysis (TA), and further predict individual cognitive-related brain damage in ESRD patients. Methods: Forty-seven ESRD patients and 45 control subjects underwent wholebrain high-resolution 3D T1-weighted imaging scans and neuropsychological assessments. The voxel-based 3D brain TA was performed to examine intergroup differences in brain texture features. Additionally, within the ESRD group, the relationships of altered texture features with neuropsychological function and clinical indicators were analyzed. Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of brain texture features for cognitive-related brain damage in ESRD patients. Results: Compared to the control group, the ESRD group exhibited altered texture features in several brain regions, including the insula, temporal lobe, striatum, cerebellum, and fusiform gyrus (p < 0.05, Gaussian random-field correction). Some of these altered texture features were associated with scores from the Digit Symbol Substitution Test and the Trail Making Test Parts A (p < 0.05), and showed significant correlations with serum creatinine and calcium levels within the ESRD group (p < 0.05). Notably, ROC curve analysis revealed that the texture features in the right insula and left middle temporal gyrus could accurately predict cognitive-related brain damage in ESRD patients, with the area under the curve values exceeding 0.90. Conclusion: Aberrant brain texture features may be involved in the neuropathological mechanism of cognitive decline, and have high accuracy in predicting cognitive-related brain damage in ESRD patients. TA offers a novel neuroimaging marker to explore the neuropathological mechanisms of cognitive impairment in ESRD patients, and may be a valuable tool to predict cognitive decline. [ABSTRACT FROM AUTHOR]

Innovations in chewable dosage forms provide solutions for swallowing difficulties faced by certain patient populations. Chewable softgel capsules (SGC) offer opportunities to expand the benefits of SGC, excelling in the delivery of lipid‐based formulations by optimizing the mechanical properties of the shell material without compromising industrial‐scale manufacturing. This study employs an original approach, with a target product profile that aims to combine processability and chewability, to develop optimized chewable dosage forms. Through a mixture design of experiments, key factors such as plasticizer content and the addition of acid‐modified thin‐boiling corn starch are explored, focusing on response parameters critical to achieving the desired properties of the capsule shell material. Optimal shell compositions—one starch‐free and another containing 9% w/w starch, with respective gelatin/non‐volatile plasticizer ratios of 0.81 and 0.67 are determined and evaluated by manufacturing chewable SGC at pilot scale using rotary die technology. The successful manufacturing and resulting capsules, which exhibit the intended properties, highlight the efficiency of this approach in striking the right balance between processability, soft texture, and quick disintegration time. [ABSTRACT FROM AUTHOR]

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Objectives: The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods: PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results: The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0.82~0.89). As for PET images, the pooled sensitivity was 0.80 (95% CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82). Conclusion: Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application. [ABSTRACT FROM AUTHOR]

Sweet potato flour (SPF) is used to produce food products worldwide due to good biological activity in the human diet and low cost. In this experiment, sweet potato tubers were processed into flour including purple-SPF, yellow-SPF and orange-SPF and applied in breadsticks. The proximate, physical, phytochemical and antioxidant properties of flour and breadsticks were investigated. The carbohydrate, moisture, ash, fat, protein and fiber contents of SPF ranged from 82.19 - 85.21, 4.12 - 5.78, 1.30 - 2.46, 0.32 - 0.86, 4.07 - 4.74 and 3.58 - 5.65 %, respectively. The water activity (aw) of SPF ranged from 0.2277 - 0.2695. The phytochemical analysis showed that purple-SPF and purple-SPF based breadstick products exhibited the highest total phenolic contents (TPC), total flavonoid contents (TFC) and total anthocyanin contents (TAC), while total carotenoid contents (TCC) was observed in both orange-SPF and breadstick made from orange-SPF. In addition, purple-SPF and breadstick produced from purple-SPF had the highest antioxidant activity against ABTS, FRAP, DPPH and metal chelating activity. The X-ray diffraction patterns of SPF samples showed a C-type crystal structure, with a crystallinity ranging from 19.43 - 31.05 %. The scanning electron micrographs of SPF granules revealed that they were mostly round and spherical, with a size range between 5 - 28 µm. The viscosity characteristics of yellow-SPF had the highest peak viscosity (307.97 ± 9.87 RVU), trough viscosity (200.81 ± 10.07 RVU), breakdown (108.42 ± 3.13 RVU), final viscosity (277.75 ± 8.60 RVU) and setback (76.95 ± 4.03 RVU). The physical quality showed breadstick from yellow-SPF had the highest hardness and fracturability with values of 11,537.67 ± 190.58 g and 11,143.33 ± 161.97 g, respectively. These results indicate that sweet potato flour composite breadstick has high phytochemical and antioxidant potential and may be applied in the food industry. [ABSTRACT FROM AUTHOR]

Fruit texture is a priority trait that guarantees the long‐term economic sustainability of the cranberry industry through value‐added products such as sweetened dried cranberries (SDCs). To develop a standard methodology to measure texture, we conducted a comparative analysis of 22 textural traits using five different methods under both harvest and postharvest conditions in 10 representative cranberry cultivars. A set of textural traits from the 10%‐strain compression and puncture methods were identified that differentiate between cultivars primarily based on hardness/stiffness and elasticity properties. The complementary use of both methodologies allowed for a detailed evaluation by capturing the effect of key texture‐determining factors such as structure, flesh, and skin. Furthermore, the high effectiveness of this approach in different conditions and its ability to capture high phenotypic variation in cultivars highlights its great potential for applicability in various areas of the value chain and research. Therefore, this study provides an informed reference for unifying future efforts to enhance cranberry fruit texture and quality. [ABSTRACT FROM AUTHOR]

Cutting is a pivotal step in the initial processing of fish products, encompassing beheading, trimming, slicing, and dicing. Traditional metal-knife cutting methods are marred by inefficiency, imprecision, and a propensity for bacterial growth, failing to meet market demands for precision and quality. Additionally, the reluctance of workers to perform manual labor in wet conditions exacerbates labor shortages and inefficiency. Adopting innovative cutting technologies, complemented by intelligent controls, is thus imperative. This study reviews the advancements and applications of waterjet and ultrasonic knives in sustainable cutting methods within the fish and food industries. It evaluates their respective merits and demerits, noting that waterjets excel in cutting hard-textured fish. At the same time, ultrasonic knives are adept at handling fish's viscous, elastic, and adhesive properties. The abstract further explores the integration of intelligent technologies in fish cutting, such as machine vision for precise cutting paths, simulation technology for adjusting process parameters, and multi-sensor data fusion for decision-making, which could potentially replace human labor. The study also addresses the current challenges and future directions for these technologies, highlighting the potential of artificial intelligence, machine learning, and deep learning to enhance the autonomy and robustness of fish-cutting equipment. By reducing operational and maintenance costs and integrating advanced technologies, the study envisions a future where fish cutting is more automated, intelligent, and capable of producing high-quality products efficiently to satisfy escalating market demands. This research is a valuable reference for industry professionals and researchers aiming to innovate in fish product processing, thereby enhancing the automation and intelligence of fish-cutting processes. [ABSTRACT FROM AUTHOR]

Soaking and germination are processes used to improve nutritional characteristics in rice especially concerning γ‐aminobutyric acid (GABA) and phenolic compounds. In contrast, during these processes the activation of enzymes may affect the rice starch. Hence, the current investigation aims to understand the impact of soaking and germination processes on starch structure in terms of morphological, texture, physicochemical and gelatinization properties of non‐germinated rice starch (NGRS), soaked rice starch (SRS), and germinated rice starch (GRS). It is observed that soaking and germination cause changes in granule surface and reduce relative crystallinity (27.40–26.08 and 27.40–25.65%) and enthalpy (9.69–8.81 and 9.69–8.37%) compared with NGRS. Unexpectedly, germination affects the gel strength during cooling showing an increase of maximum peak force (0.28 N), demonstrating that the gel of GRS is firmer than NGRS and SRS. The findings are useful to provide bases for utilization of soaked and germinated rice starches in developing products for the food industry. [ABSTRACT FROM AUTHOR]

Peanut skin, a by-product of the peanut processing industry, is typically disposed of as an inexpensive agricultural by-product. However, peanut skin is a rich source of bioactive compounds that can be used to enrich foods that are better for your health and provide more nutrition. Therefore, this research article focuses on the valorisation of peanut skin by developing functional skin and its addition into peanut butter with improved nutritional value and sensory quality. The study explored the effect of pressure-cooking on the development of peanut skin-fortified peanut butter and studied its physicochemical, functional and organoleptic characteristics. The results indicated that adding peanut skin significantly improved the colour attributes (L* = 40.73–44.76, a* = 4.35–5.57 and b* = 7.83–12.43) and phenolic content. Different processing conditions like pressure cooking improved the phenolic contents and anti-oxidant activities of peanut butter. Moreover, rheological properties and textural properties have been increased with the application of moist heat treatment. Functional peanut butter had increased viscosity and hardness but decreased spreadability and oil separation compared to the control. Sensory evaluation showed that the skin-on the product had desirable sensory attributes, including appearance, flavour and overall acceptability. The study concludes that incorporating peanut skin into peanut butter enhanced its functional and nutritional properties without compromising sensory quality. These findings provide valuable insights for the food industry to develop healthier and value-added peanut butter products with different treatment methods. [ABSTRACT FROM AUTHOR]

Introduction: Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in metastatic castration-resistant prostate cancer (MCRPC). We aimed to predict lesion-based treatment response after Lu-177 PSMA treatment using machine learning with texture analysis data obtained from pretreatment Gallium-68 (Ga-68) PSMA positron emission tomography/computed tomography (PET/CT). Methods: Eighty-three progressed, and 91 nonprogressed malignant foci on pretreatment Ga-68 PSMA PET/CT of 9 patients were used for analysis. Malignant foci with at least a 30% increase in Ga-68 PSMA uptake after two cycles of treatment were considered progressed lesions. All other changes in Ga-68 PSMA uptake of the lesions were considered nonprogressed lesions. The classifiers tried to predict progressed lesions. Results: Logistic regression, Naive Bayes, and k-nearest neighbors’ area under the ROC curve (AUC) values in detecting progressed lesions in the training group were 0.956, 0.942, and 0.950, respectively, and their accuracy was 87%, 85%, and 89%, respectively. The AUC values of the classifiers in the testing group were 0.937, 0.954, and 0.867, respectively, and their accuracy was 85%, 88%, and 79%, respectively. Conclusion: Using machine learning with texture analysis data obtained from pretreatment Ga-68 PSMA PET/CT in MCRPC predicted lesion-based treatment response after two cycles of Lu-177 PSMA treatment. [ABSTRACT FROM AUTHOR]

The distribution and amount of intramuscular fat and fibrous tissue can be influenced by biological sex and impact muscle quality in both the functional (force‐generating capacity) and morphological (muscle composition) domains. While ultrasonography (US) has proven effective in assessing age‐ or sex‐related differences in muscle quality, limited information is available on sex differences in children. Quantitative ultrasonographic measurements, such as echo intensity (EI), EI bands (number of pixels across 50‐unit intervals) and texture, may offer a comprehensive framework for identifying sex differences in muscle composition. The aim of our study was to examine the effect of sex on the rectus femoris (RF) muscle quality in children. We used EI (mean and bands) and texture as muscle quality estimates derived from B‐mode US. We hypothesised that RF muscle quality differs significantly between girls and boys. Additionally, we also hypothesised that there is a significant correlation between EI bands and texture. Forty‐four non‐active healthy children were recruited (n = 22 girls, 12.8 ± 1.5 years; and n = 22 boys, 13.5 ± 1.2 years). RF was assessed using EI mean, EI bands, and texture analysis (homogeneity and correlation) using the Gray‐Level Co‐Occurrence Matrix. The results revealed significant (p < 0.05) sex differences in RF EI bands and texture. Boys displayed higher values in the 0–50 EI band and had more homogeneous muscle texture than girls. Conversely, girls displayed greater values in the 51–100 EI band and had less homogenous texture compared to boys (p < 0.05). A positive correlation was observed between the 0–50 EI band and muscle homogeneity. However, the 51–100 EI band correlated negatively with homogeneity (p < 0.05), particularly for girls. In conclusion, our study revealed sex‐specific differences in mean EI, EI bands, and texture of the RF muscle in children. The variations in the correlations between the first and second EI bands and texture reveal different levels of homogeneity in each band. This indicates that distinct muscle tissue constituents, such as intramuscular fat and/or connective tissue, may be reflected in EI bands. Overall, the methods used in this study may be useful for examining muscle quality in healthy children and those with medical conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, a computed tomography (CT)-based radiomics model was developed to improve predictions of survival and recurrence in patients with upper tract urothelial carcinoma (UTUC)—a rare and aggressive cancer. Notably, an accurate prediction of survival and recurrence is essential for deciding on effective therapeutic management scheme(s) in UTUC. The combined radiomics features extracted from CT scans with clinical data significantly improved the accuracy of predicting patient outcomes compared to clinical data alone. The combined clinico-radiomics model also demonstrated a superior predictive performance to the models developed using only clinical or radiomics data, achieving high concordance indices for survival and recurrence prediction. The integrated approach unraveled hidden patterns in CT data indicative of the recurrence risk and enabled precise patient risk stratification to inform treatment planning. It also provided a deeper understanding of the tumor matrix and its evolution, enabling more refined, tailored, and (potentially) more effective treatment plans for UTUC patients. Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with a poor prognosis. The accurate prediction of survival and recurrence in UTUC is crucial for effective risk stratification and guiding therapeutic decisions. Models combining radiomics and clinicopathological data features derived from computed tomographic urograms (CTUs) can be a way to predict survival and recurrence in UTUC. Thus, preoperative CTUs and clinical data were analyzed from 106 UTUC patients who underwent radical nephroureterectomy. Radiomics features were extracted from segmented tumors, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the most relevant features. Multivariable Cox models combining radiomics features and clinical factors were developed to predict the survival and recurrence. Harrell's concordance index (C-index) was applied to evaluate the performance and survival distribution analyses were assessed by a Kaplan–Meier analysis. The significant outcome predictors were identified by multivariable Cox models. The combined model achieved a superior predictive accuracy (C-index: 0.73) and higher recurrence prediction (C-index: 0.84). The Kaplan–Meier analysis showed significant differences in the survival (p < 0.0001) and recurrence (p < 0.002) probabilities for the combined datasets. The CTU-based radiomics models effectively predicted survival and recurrence in the UTUC patients, and enhanced the prognostic performance by combining radiomics features with clinical factors. [ABSTRACT FROM AUTHOR]

Featured Application: Quantitative characterization of intratumor heterogeneity using medical imaging is valuable for guiding theranostic technology in inductive moderate hyperthermia for sarcoma patients. Evaluating intratumor heterogeneity with image texture analysis offers a more sophisticated understanding of sarcoma response to treatment. We examined the effects of inductive moderate hyperthermia (IMH) on sarcoma-45 growth and intratumor heterogeneity across tissue, cellular and molecular levels using magnetic resonance imaging (MRI), ultrasound and histology image analysis. IMH (42 MHz, 20 W) inhibited sarcoma-45 growth kinetics by 34% compared to the untreated control group. T2-weighted MRI brightness was increased by 42%, reflecting more extensive tumor necrosis, while Young's modulus increased by 37% due to more pronounced connective tissue replacement in response to IMH. Whereas calculations of Moran's spatial autocorrelation index revealed distinctions in heterogeneity between tumor core, periphery and capsule regions of interest (ROIs) on MRI, ultrasound and histological examination in the untreated tumor-bearing animals, there was no significant difference between core and periphery after IMH. Exposure to IMH increased overall tumor ROI heterogeneity by 22% on MRI but reduced heterogeneity in the core and periphery on ultrasound and histology images. Ki-67 protein distribution was 25% less heterogeneous on the tumor periphery after IMH. Therefore, this study provides a quantitative characterization of IMH effects on different manifestations of intratumor sarcoma-45 heterogeneity using experimental imaging data. [ABSTRACT FROM AUTHOR]

The incorporation of okara, a by-product of soybean milk production, into gluten-free products such as crispy waffles poses challenges due to the absence of gluten's viscoelastic properties and the high fiber content of okara. This study aimed to evaluate the effects of okara flour on the rheological properties, physical attributes, and sensory qualities of gluten-free waffles. Waffle batters with varying levels of okara flour (10%, 20%, 30%, and 40%) were prepared, and their rheological properties were analyzed using oscillatory shear and creep-recovery tests. Physical properties, proximate composition, cholesterol and glucose adsorption capacities, storage stability, and sensory attributes were also assessed. The results demonstrated that increasing okara flour content improved batter elasticity and viscosity (with complex viscosity reaching up to 10,923 Pa·s for 40% okara flour) but decreased spread ratio by up to 45% and increased moisture content by approximately 2.7%. Higher okara content also led to a 16% decrease in brightness (L*) and increased hardness, reaching 325.26 g/s at 40% substitution. Sensory evaluation revealed that waffles with 30% okara flour were preferred for their texture and overall liking, with a score of 7.43 compared to higher substitution levels. Cholesterol and glucose adsorption capacities were high in okara flour, contributing to potential health benefits. Storage stability tests showed acceptable moisture content, water activity, and microbiological safety over 60 days, though hardness decreased by about 42%. In conclusion, okara flour enhances the nutritional profile of gluten-free waffles, but its impact on texture and flavor requires careful formulation adjustments to optimize consumer acceptance. [ABSTRACT FROM AUTHOR]

Inferior vena cava (IVC) involvement by renal cell carcinoma (RCC) is associated with a higher disease stage and is considered a risk factor for poor prognosis. This study aimed to investigate the role of the apparent diffusion coefficient (ADC) of MRI 3D texture analysis in the differentiation of solid and friable tumour thrombus in patients with RCC. The study involved 27 patients with RCC with tumour thrombus in the renal vein or IVC, surgically treated with nephrectomy and thrombectomy and in whom preoperatively abdominal MRI including the DWI sequence was conducted. For 3D texture analysis, the ADC map was used, and the first-order radiomic features were calculated from the whole volume of the thrombus. All tumour thrombi were histologically classified as solid or friable. The solid and friable thrombus was detected in 51.9 ​% and 48.1 ​% of patients, respectively. No differences in mean values of range, 90th percentile, interquartile range, kurtosis, uniformity and variance were found between groups. Equal sensitivity and specificity (93 ​% and 69 ​%, respectively) of ADC mean, median and entropy in differentiation between solid and friable tumour thrombus, with the highest AUC for entropy (0.808), were observed. Applying the skewness threshold value of 0.09 allowed us to achieve a sensitivity of 86 ​% and a specificity of 92 ​%. In patients with RCC and tumour thrombus in the renal vein or IVC, the 3D texture analysis based on ADC-map allows for precise differentiation of a solid from a friable thrombus. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer, accounting for approximately 90% of liver cancer cases. It currently ranks as the fifth most prevalent cancer worldwide and represents the third leading cause of cancer-related mortality. As a malignant disease with surgical resection and ablative therapy being the sole curative options available, it is disheartening that most HCC patients who undergo liver resection experience relapse within five years. Microvascular invasion (MVI), defined as the presence of micrometastatic HCC emboli within liver vessels, serves as an important histopathological feature and indicative factor for both disease-free survival and overall survival in HCC patients. Therefore, achieving accurate preoperative noninvasive prediction of MVI holds vital significance in selecting appropriate clinical treatments and improving patient prognosis. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. Consequently, extensive research efforts have been directed towards preoperative imaging prediction of MVI to address this problem and the relative research progresses were reviewed in this article to summarize its current limitations and future research prospects. [ABSTRACT FROM AUTHOR]

Objective: In this study we aimed to investigate correlation between computed tomography (CT) histogram analysis of kidney parenchyma and renal functional status. Material and Method: Renal parenchyma CT densities were measured by using a region of interest (ROI) from both cortex and medulla, who had a non-contrast CT of abdomen (120 kV) and serum creatinine level in the same day in a cohort of 225 patients (115 female, 110 male). Serum creatinine levels were within normal limits in 186 and high in 39 of patients. ROI histogram data was analyzed by using Matlab® program which calculated the median, mean, maxiumum, minimum, standard deviation, variance, entropy, size % L, size % U, size % M, kurtosis, skewness and uniformity values. Results: Age and gender were significantly different among the groups (age p <0.001, gender p =0.002). Significant correlations was noted between serum creatinine level and kidney medulla mean (r=-0.22, p <0.001), maximum (r =-0,16, p <0.05), median (r =-0.17, p <0.001), size % L (r =-0.14, p <0.05) and entropy (r =-0.13, p <0.05) values as well as size %U (r =-0.14, p <0.05), size %M (r =0.14, p <0.05) density from kidney cortex. There was significant difference among patients with normal and high creatinine levels in terms of median (p =0.011), mean (p =0.004), size %M (p =0.032), and uniformity (p =0.045) values from kidney medulla as well as entropy (p =0.036) value from cortex. Conclusion: There is a negative correlation between serum creatinine levels and renal parenchyma CT densities particulary from medulla. Decreased renal function should be predicted by calculating the histogram analysis of kidney from cortex or medulla in a non-enhanced abdomen CT. [ABSTRACT FROM AUTHOR]

This study investigated whether the textural features of peritumoral adipose tissue (AT) on [18F]fluoro-2-deoxy-2-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) can predict the pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. We retrospectively enrolled 147 female breast cancer patients who underwent staging FDG PET/CT and completed NAC and underwent curative surgery. We extracted 10 first-order features, 6 gray-level co-occurrence matrix (GLCM) features, and 3 neighborhood gray-level difference matrix (NGLDM) features of peritumoral AT and evaluated the predictive value of those imaging features for pathological complete response (pCR) and PFS. The results of our study demonstrated that GLCM homogeneity showed the highest predictability for pCR among the peritumoral AT imaging features in the receiver operating characteristic curve analysis. In multivariate logistic regression analysis, the mean standardized uptake value (SUV), 50th percentile SUV, 75th percentile SUV, SUV histogram entropy, GLCM entropy, and GLCM homogeneity of the peritumoral AT were independent predictors for pCR. In multivariate survival analysis, SUV histogram entropy and GLCM correlation of peritumoral AT were independent predictors of PFS. Textural features of peritumoral AT on FDG PET/CT could be potential imaging biomarkers for predicting the response to NAC and disease progression in breast cancer patients. [ABSTRACT FROM AUTHOR]

In multiple myeloma (MM) bone marrow infiltration by monoclonal plasma cells can occur in both focal and diffuse manner, making staging and prognosis rather difficult. The aim of our study was to test whether texture analysis of 18 F-2-deoxy-d-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) images can predict survival in MM patients. Forty-six patients underwent 18 F-FDG-PET/CT before treatment. We used an automated contouring program for segmenting the hottest focal lesion (FL) and a lumbar vertebra for assessing diffuse bone marrow involvement (DI). Maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and texture features such as Coefficient of variation (CoV), were obtained from 46 FL and 46 DI. After a mean follow-up of 51 months, 24 patients died of myeloma and were compared to the 22 survivors. At univariate analysis, FL SUVmax (p = 0.0453), FL SUVmean (p = 0.0463), FL CoV (p = 0.0211) and DI SUVmax (p = 0.0538) predicted overall survival (OS). At multivariate analysis only FL CoV and DI SUVmax were retained in the model (p = 0.0154). By Kaplan-Meier method and log-rank testing, patients with FL CoV below the cut-off had significantly better OS than those with FL CoV above the cut-off (p = 0.0003), as well as patients with DI SUVmax below the threshold versus those with DI SUVmax above the threshold (p = 0.0006). Combining FL CoV and DI SUVmax by using their respective cut-off values, a statistically significant difference was found between the resulting four survival curves (p = 0.0001). Indeed, patients with both FL CoV and DI SUVmax below their respective cut-off values showed the best prognosis. Conventional and texture parameters derived from 18F-FDG PET/CT analysis can predict survival in MM patients by assessing the heterogeneity and aggressiveness of both focal and diffuse infiltration. [ABSTRACT FROM AUTHOR]

Objective This study aims to determine which qualitative and quantitative US features are independently associated with malignancy, including those derived from grayscale imaging morphology, shear wave elastography (SWE), and texture analysis. Methods This single-center retrospective study was approved by the institutional research ethics board. Consecutive breast US studies performed between January and December 2020 were included. Images were acquired using a Canon Aplio i800 US unit (Canon Medical Systems, Inc. CA) and i18LX5 wideband linear matrix transducer. Grayscale US features, SWE mean, and median elasticity were obtained. Single representative grayscale images were analyzed using dedicated software (LIFEx, version 6.30). First-order and gray-level co-occurrence matrix second-order texture features were extracted. Multivariate logistic regression was performed to assess for predictors of malignancy (STATA v16.1). Results One hundred forty-seven cases with complete SWE data were selected for analysis (mean age 54.3, range 21-92). The following variables were found to be independently associated with malignancy: age (P  <.001), family history (P  = .013), irregular mass shape (P  = .024), and stiffness on SWE (mean SWE ≥40 kPa; P  <.001). Remaining variables (including texture features) were not found to be independently associated with malignancy (P  >.05). Conclusion US texture analysis features were not associated with malignancy independent of other qualitative and quantitative US characteristics currently utilized in clinical practice. This suggests texture analysis may not be warranted when differentiating benign and malignant breast masses on US. In contrast, irregular mass shape on grayscale imaging and increased stiffness on SWE were found to be independent predictors of malignancy. [ABSTRACT FROM AUTHOR]

Rheumatoid Arthritis (RA) is a chronic autoimmune illness that occurs in the joints, resulting in inflammation, pain, and stiffness. X-ray examination is one of the most common diagnostic procedures for RA, but manual X-ray image analysis has limitations because it is a time-consuming procedure and is prone to errors. A specific algorithm aims to a lay stable and accurate segmenting of carpal bones from hand bone images, which is vitally important for identifying rheumatoid arthritis. The algorithm demonstrates several stages, starting with Carpal bone Region of Interest (CROI) specification, dynamic thresholding, and Gray Level Co-occurrence Matrix (GLCM) application for texture analysis. To get the clear edges of the image, the component is first converted to the greyscale function and thresholding is carried out to separate the hand from the background. The pad region is identified to obtain the contours of it, and the CROI is defined by the bounding box of the largest contour. The threshold value used in the CROI method is given a dynamic feature that can separate the carpal bones from the surrounding tissue. Then the GLCM texture analysis is carried out, calculating the number of pixel neighbors, with the specific intensity and neighbor relations of the pixels. The resulting feature matrix is then employed to extract features such as contrast and energy, which are later used to categorize the images of the affected carpal bone into inflamed and normal. The proposed technique is tested on a rheumatoid arthritis image dataset, and the results show its contribution to diagnosis of the disease. The algorithm efficiently divides carpal bones and extracts the signature parameters that are critical for correct classification of the inflammation in the cartilage images. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM–DKI) and their machine‐learning‐based texture analysis for the detection and assessment of severity in prostate cancer (PCa). Materials and methods: Eighty‐eight patients underwent MRI on a 3 T scanner after giving informed consent. IVIM–DKI data were acquired using 13 b values (0–2000 s/mm2) and analyzed using the IVIM–DKI model with the total variation (TV) method. PCa patients were categorized into two groups: clinically insignificant prostate cancer (CISPCa) (Gleason grade ≤ 6) and clinically significant prostate cancer (CSPCa) (Gleason grade ≥ 7). One‐way analysis‐of‐variance, t test, and receiver operating characteristic analysis was performed to measure the discriminative ability to detect PCa using IVIM–DKI parameters. A chi‐square test was used to select important texture features of apparent diffusion coefficient (ADC) and IVIM–DKI parameters. These selected texture features were used in an artificial neural network for PCa detection. Results: ADC and diffusion coefficient (D) were significantly lower (p < 0.001), and kurtosis (k) was significantly higher (p < 0.001), in PCa as compared with benign prostatic hyperplasia (BPH) and normal peripheral zone (PZ). ADC, D, and k showed high areas under the curves (AUCs) of 0.92, 0.89, and 0.88, respectively, in PCa detection. ADC and D were significantly lower (p < 0.05) as compared with CISPCa versus CSPCa. D for detecting CSPCa was high, with an AUC of 0.63. A negative correlation of ADC and D with GS (ADC, ρ = −0.33; D, ρ = −0.35, p < 0.05) and a positive correlation of k with GS (ρ = 0.22, p < 0.05) were observed. Combined IVIM–DKI texture showed high AUC of 0.83 for classification of PCa, BPH, and normal PZ. Conclusion: D, f, and k computed using the IVIM–DKI model with the TV method were able to differentiate PCa from BPH and normal PZ. Texture features of combined IVIM–DKI parameters showed high accuracy and AUC in PCa detection. [ABSTRACT FROM AUTHOR]

Copyright of China Condiment is the property of China Condiment and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The precise control of tool offset in static shoulder friction stir welding (SSFSW) is vital for enhancing the mechanical properties and overall effectiveness of dissimilar metal joints. The present study investigates the impact of tool offset on the development of microstructural features, intermetallic compound formation, texture analysis, and mechanical characteristics of SSFSWed Al6061 to Ti6Al4V joints. The results demonstrate that a 2.5 mm tool offset results in an average grain size of 2.65 μm, compared to larger grains of about 4.76 μm and 7.31 μm observed at 2.0 mm and 1.5 mm offsets, respectively. The percentage of low-angle grain boundaries also varies, reaching up to 42.2% at a 2.5 mm offset compared to 48.6% at 2.0 mm and 64.1% at 1.5 mm, indicating enhanced homogeneity in the microstructural properties. Moreover, the ultimate tensile strength at the 2.5 mm tool offset reaches 279 MPa, which is 5.7% higher than at the 2.0 mm offset and 13.4% higher than at the 1.5 mm offset. This improvement is attributed to more uniform distribution of Ti particles over the stir zone. Texture analysis further supports these findings, with the development of alpha fibre at 2.5 mm offset enhancing ductility, thereby underscoring the critical role of tool offset in achieving sound dissimilar metal weldments.

It has been established that 7.2% of patients hospitalized with coronavirus disease (COVID-19) exhibit signs of heart disease, with 23% of these patients experiencing heart failure. Currently, there is a lack of data on chest computed tomography (CT) for diagnosing myocarditis associated with COVID-19. The aim. To justify the feasibility and develop classification models for diagnosing myocarditis in COVID-19 patients based on chest CT data processing. Materials and methods. A retrospective analysis of data from 140 COVID-19 patients was conducted. Chest CT scans were analyzed using DRAGONFLY software, with permission from Object Research Systems. The COVID-CT-MD database, which includes CT data from 169 confirmed cases of SARS-CoV-2 infection, was used to build classification models. The regions of interest were fragments of heart CT images. Texture analysis methods were employed to create diagnostic models. Results. It was shown that the average density of the myocardium of a patient with a confirmed diagnosis of SARS-CoV-2 infection according to the Hounsfield scale does not essentially differ from the densitometric indicators of a healthy person. Therefore, the research was focused on finding structural changes in CT images for their use in constructing diagnostic models. The use of different classification algorithms had little effect on classification accuracy, probably due to the informational content of the input data. However, the obtained accuracy of the diagnostic models is acceptable and allows them to be used to support medical decision-making regarding diagnosis and treatment. Conclusions. Using classic methods, myocarditis was diagnosed in 7.1% of patients with severe pneumonia caused by the coronavirus. The global data closely aligns with the results of our clinical studies. The obtained results allowed for assessing structural changes in the myocardium characteristic of the acute form of SARS-CoV-2 infection. The constructed classification models indicate that specific changes in the myocardium during the acute form of SARS-CoV-2 infection can be identified using CT. The highest diagnostic accuracy on test samples reached 74%. The implementation of the developed diagnostic programs based on texture analysis of CT data and artificial intelligence technologies enables the diagnosis of myocarditis and the assessment of long-term treatment efficiency. Creation of these diagnostic programs using artificial intelligence technologies significantly simplifies the work of radiologists and improves the efficiency of myocarditis diagnosis in patients with SARS-CoV-2 infection.

Objective: This study aimed to analyze and compare the quality and flavor of dromedary and bifocal camel yogurt. Methods: Through physicochemical analysis, texture analysis, rheological analysis, SPME-GC-MC, sensory evaluation, etc., the overall differences in quality and flavor of dromedary and bifocal camel yogurt were determined. Results: The viable bacteria count, protein, fat, total solids content, and antioxidant activity of dromedary camel yogurt were lower than those of bifocal camel yogurt （PPPPPP

Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the carotid arteries is generally considered the leading cause of severe cerebrovascular events. In recent years, new reports have reinforced the role of an accurate histopathological analysis of carotid plaques to perform the stratification of affected patients and proceed to the correct prevention of complications. This work proposes applying an unsupervised learning approach to analyze complex whole-slide images (WSIs) of atherosclerotic carotid plaques to allow a simple and fast examination of their most relevant features. All the code developed for the present analysis is freely available. The proposed method offers qualitative and quantitative tools to assist pathologists in examining the complexity of whole-slide images of carotid atherosclerotic plaques more effectively. Nevertheless, future studies using supervised methods should provide evidence of the correspondence between the clusters estimated using the proposed textural-based approach and the regions manually annotated by expert pathologists. [ABSTRACT FROM AUTHOR]

Background: Objective quantitative texture characteristics may be helpful in salivary glandular tumor differential diagnosis. This study uses machine learning (ML) to explore and validate the performance of ultrasound (US) texture features in diagnosing salivary glandular tumors. Material and methods: 122 patients with salivary glandular tumors, including 71 benign and 51 malignant tumors, are enrolled. Representative brightness mode US pictures are selected for further Gray Level Co-occurrence Matrix (GLCM) texture analysis. We use a t-test to test the significance and use the receiver operating characteristic curve method to find the optimal cut-point for these significant features. After splitting 80% of the data into a training set and 20% data into a testing set, we use five machine learning models, k-nearest Neighbors (kNN), Naïve Bayes, Logistic regression, Artificial Neural Networks (ANNs) and supportive vector machine (SVM), to explore and validate the performance of US GLCM texture features in diagnosing salivary glandular tumors. Results: This study includes 49 female and 73 male patients, with a mean age of 53 years old, ranging from 21 to 93. We find that six GLCM texture features (contrast, inverse difference movement, entropy, dissimilarity, inverse difference and difference entropy) are significantly different between benign and malignant tumors (p < 0.05). In ML, the overall accuracy rates are 74.3% (95%CI: 59.8–88.8%), 94.3% (86.6–100%), 72% (54–89%), 84% (69.5–97.3%) and 73.5% (58.7–88.4%) for kNN, Naïve Bayes, Logistic regression, a one-node ANN and SVM, respectively. Conclusions: US texture analysis with ML has potential as an objective and valuable tool to make a differential diagnosis between benign and malignant salivary gland tumors. [ABSTRACT FROM AUTHOR]

Small‐angle X‐ray tensor tomography and the related wide‐angle X‐ray tensor tomography are X‐ray imaging techniques that tomographically reconstruct the anisotropic scattering density of extended samples. In previous studies, these methods have been used to image samples where the scattering density depends slowly on the direction of scattering, typically modeling the directionality, i.e. the texture, with a spherical harmonics expansion up until order ℓ = 8 or lower. This study investigates the performance of several established algorithms from small‐angle X‐ray tensor tomography on samples with a faster variation as a function of scattering direction and compares their expected and achieved performance. The various algorithms are tested using wide‐angle scattering data from an as‐drawn steel wire with known texture to establish the viability of the tensor tomography approach for such samples and to compare the performance of existing algorithms. [ABSTRACT FROM AUTHOR]

The incidence and prevalence of dysphagia worldwide are increasing yearly requiring a change in food texture to avoid malnutrition, dehydration, or sever complications. Riceberry porridges fortified with protein hydrolysate (1.5%), bio‐calcium (589 mg), and thickened with xanthan gum (XG) of varying concentrations (0%, 0.255, 0.50%, 0.75%, 1.0%, and 2.0%) showed suitability for use in enriching diets of these patients. Porridges were examined using specified tests from the International Dysphagia Diet Standardization Initiative (IDDSI) and National Dysphagia Diet (NDD), and coupled with rheological, textural analyses, in vitro swallowing simulator and sensory analysis performed by a trained panel. Porridges with 0%–0.25% and 0.50%–2.0% XG were classified as IDDSI level 3 and 4, respectively, and apparent viscosities of porridges showed samples with XG displayed shear thinning behavior beneficial for patients with dysphagia. Increasing XG concentrations increased the consistency coefficient and decreased the flow behavior index (p <.05) with positive correlation of XG concentration with textural properties including firmness, consistency, cohesiveness, adhesiveness, and stickiness values. The relationship between instrumental measurements, in vitro and in vivo swallowing behavior showed high correlations with regards to XG concentration (r =.995). The findings indicate Riceberry porridges containing XG have significantly improved textural properties over those without XG for patients with dysphagia. [ABSTRACT FROM AUTHOR]

Simple Summary: Oesogastric cancers are often diagnosed at a locally advanced stage, especially in western countries. Thus, their prognosis is highly influenced by correct staging and response rate to preoperative therapy. Radiomics may offer a promising tool for improving the quality of current diagnostics of these tumors. Radiomic predictive models seem to work best when integrated with clinical characteristics. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic characteristics could even increase the effectiveness of these predictive and prognostic models. Background: Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers. Methods: The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms "radiomics", "texture analysis", "oesophageal cancer", "gastroesophageal junction cancer", "oesophagogastric junction cancer", "gastric cancer", "stomach cancer", "staging", and "treatment response" until May 2024. Results: Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature. Conclusions: Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models. [ABSTRACT FROM AUTHOR]

The effects of various combinations of storage and heating conditions on the texture and taste-active compounds in red seabream Pagrus major were investigated. The hardness increased more with high-temperature/short-time heating and storage for 14 days. Hierarchical cluster analysis based on taste-active compounds was affected by storage and heating conditions, particularly by storage. The equivalent umami concentration was higher after 14 days of storage, although the differences were not always significant. Our results suggest that red seabream heated after a short storage period, such as 14 days, may improve the levels of taste-active compounds and umami concentration in heated fish. [ABSTRACT FROM AUTHOR]

Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia. The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants). The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes. These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia. [ABSTRACT FROM AUTHOR]

To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at baseline can predict response. Our IRB-approved study included 203 women (mean age 54 ± 11 years) with metastatic liver disease from breast cancer. All patients underwent contrast abdomen-pelvis CT in the portal venous phase at two points: baseline (pre-treatment) and follow-up (between 3 and 12 months following treatment). Patients were subcategorized into three subgroups based on RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors version 1.1): 66 with SD, 69 with PR, and 68 with PD on follow-up CT. The deidentified baseline and follow-up CT images were exported to the radiomics prototype. The prototype enabled semi-automatic segmentation of the target liver lesions for the extraction of first and high order radiomics. Statistical analyses with logistic regression and random forest classifiers were performed to differentiate SD from PD and PR. There was no significant difference between the radiomics on the baseline and follow-up CT images of patients with SD (area under the curve (AUC): 0.3). Random forest classifier differentiated patients with PR with an AUC of 0.845. The most relevant feature was the large dependence emphasis's high and low pass wavelet filter (derived gray level dependence matrix features). Random forest classifier differentiated PD with an AUC of 0.731, with the most relevant feature being the surface-to-volume ratio. There was no difference in radiomics among the three groups at baseline; therefore, a response could not be predicted. Radiomics of liver metastases with semi-automatic segmentation demonstrate differences between SD from PR and PD. Semiautomatic segmentation and radiomics of metastatic liver disease demonstrate differences in SD from the PR and progressive metastatic on the baseline and follow-up CT. Despite substantial variations in the scanners, acquisition, and reconstruction parameters, radiomics had an AUC of 0.84–0.89 for differentiating stable hepatic metastases from decreasing and increasing metastatic disease. • Radiomics correlates with treatment response. • Semi-automatic segmentation is reliable. • Future role for radiomics. [ABSTRACT FROM AUTHOR]

Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images into common lung infections, lung opacity (LO), COVID-19, and viral pneumonia (VP). The results demonstrate that the fusion of texture and vessel-based features provides an effective ML model for aiding diagnosis. The ML model validation using performance measures, including an accuracy of approximately 91.8% with an RF-based classifier, supports the usefulness of the feature set and classifier model in categorizing the four different pathologies. Furthermore, the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogram-based analysis. This analysis reveals varying natural pixel distributions in CXR images belonging to the normal, COVID-19, LO, and VP groups, motivating the incorporation of additional features such as mean, standard deviation, skewness, and percentile based on the filtered images. Notably, the study achieves a considerable improvement in categorizing COVID-19 from LO, with a true positive rate of 97%, further substantiating the effectiveness of the methodology implemented. Graphic Abstract [ABSTRACT FROM AUTHOR]

Gummy formulations are considered suitable alternatives to traditional oral dosage forms like tablets and capsules due to their merits that include chewability, softness/flexibility, improved drug release, administration without water, appealing organoleptic properties, better patient compliance, easy preparation and usefulness for persons of different ages (e.g. children). Though there is increasing interest in gummy formulations containing drugs, measurable parameters, and specification limits for evaluating their quality are scarce. Quality check forms an essential part of the pharmaceutical development process because drug products must be distributed as consistently stable, safe, and therapeutically effective entities. Consequently, some quality parameters that could contribute to the overall performance of typical gummy formulations were investigated employing six brands of non-medicinal gummies as specimens. Accordingly, key physicochemical and micromechanical characteristics namely adhesiveness (0.009 – 0.028 mJ), adhesive force (0.009 – 0.055 N), chewiness (2.780 – 6.753 N), cohesiveness (0.910 – 0.990), hardness (2.984 – 7.453 N), springiness (0.960 – 1.000), and resilience (0.388 – 0.572), matrix firmness – compression load (2.653 – 6.753 N) and work done (3.288 – 6.829 mJ), rupture (5.315 – 29.016 N), moisture content (< 5%), weight uniformity (< 2.5 g; < 7.5% deviation), and intraoral dissolution pH (≥ 3.5 ≤ 6.8) were quantified to identify measures that may potentially function as specification limits and serve as prospective reference points for evaluating the quality of gummy formulations. Findings from this work contribute to ongoing efforts to standardize the quality control strategies for gummy formulations, particularly those intended for oral drug delivery. [ABSTRACT FROM AUTHOR]

Background: Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. Methods: Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool. Results: Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. Conclusions: Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches. [ABSTRACT FROM AUTHOR]

Linear dichroism is a novel method for texture analysis and is used to measure the order parameter of liquid crystals. It determines the dichroic ratio by analyzing the maximum absorption of textural intensities polarized parallel and perpendicular to the optical axis. To capture liquid crystal textures as a function of temperature, a polarized optical microscope with an attached camera is employed under parallel and crossed polarizer conditions. This analysis adheres to the Beer-Lambert law, relating transmitted and absorbed textural intensities in liquid crystals. Here, we investigate nematic liquid crystals, specifically 4-cyano-4′-pentylbiphenyl and p-n-alkyl benzoic acid (where n = 8), using texture analysis to study their physical characteristics. [ABSTRACT FROM AUTHOR]

Copyright of China Condiment is the property of China Condiment and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nutrient-dense colostrum can be employed as a functional food ingredient. This work aimed to produce novel functional probiotic Cream cottage cheese (FPC) using probiotic (ABT) culture and bovine colostrum powder (BCP) at levels of 1, 2, and 3%. Physicochemical and functional properties (antioxidant activity, fatty acid profile, and antibacterial activity) were analyzed. The outcome revealed that hardness, cohesiveness, and gumminess were increased while springiness and chewiness were decreased for the treated cheeses. In FPC, medium-chain fatty acids were the predominant forms, followed by short- and long-chain fatty acids, polyunsaturated (PUFA), and small amounts of monounsaturated (MUFA). The antioxidant activity of all the cheese samples was significantly (P < 0.05) increased by increasing the quantity of colostrum powder and lengthening storage time. Color parameters were influenced by enrichment with BCP, whether in fresh or stored samples. With the addition of BCP, the growth of lactic acid bacteria and Bifidobacteria was enhanced, whereas that of pathogenic bacteria, mold and yeast, and coliform groups was inhibited. Cheeses fortified with 2% BCP had significantly higher score values than those in the other treatments. Therefore, it could be concluded that cottage cheese fortified with 2% BCP has high nutritional value and health benefits. [ABSTRACT FROM AUTHOR]

Background and aims. The individualization of cosmetic products or personalized dermatology preparations are in great demand at the present time. Methods. 24 emulsifying cream bases were proposed which were prepared by the classical, automatic and semi-automatic methods, respectively, and the physical stability resulted from the three types of homogenization was taken into account. Texture parameters were also studied for the most stable cream bases in the preformulation stage and the t - statistical test was applied. In order to choose the most optimal preservative, the effectiveness of the NipaEster solution 0.1%, Cosgard and Euxyl® PE 9010 was tested on the strains of Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans. Results. 9 cream bases were stable through all the preparation methods used, and preservation was achieved with Euxyl® PE 9010. Following the texture parameters, significant differences were observed for the same formula in the case of choosing a different preparation method. Conclusions. Formulas F1, with methyl glucose sesquistearate as emulsifier, F8, with cetearyl glucosite as emulsifier, and F14, with Ceteareth-20 can be used as cream bases for customized products. [ABSTRACT FROM AUTHOR]

Normalized signal intensity (SI) obtained from magnetic resonance imaging (MRI) has been used to track anterior cruciate ligament (ACL) postoperative remodeling. We aimed to assess the effect of MRI sequence (PD: proton density‐weighted; T2: T2‐weighted; CISS: constructive interference in steady state) on postoperative changes in healing ACLs/grafts. We hypothesized that CISS is better at detecting longitudinal SI and texture changes of the healing ACL/graft compared to the common clinical sequences (PD and T2). MR images of patients who underwent ACL surgery were evaluated and separated into groups based on surgical procedure (Bridge‐Enhanced ACL Repair (BEAR; n = 50) versus ACL reconstruction (ACLR; n = 24)). CISS images showed decreasing SI across all timepoints in both the BEAR and ACLR groups (p < 0.01), PD and T2 images showed decreasing SI in the 6‐to‐12‐ and 12‐to‐24‐month postoperative timeframes in the BEAR group (p < 0.02), and PD images additionally showed decreasing SI between 6‐ and 24‐months postoperation in the ACLR group (p = 0.02). CISS images showed texture changes in both the BEAR and ACLR groups, showing increases in energy and decreases in entropy in the 6‐to‐12‐ and 6‐to‐24‐month postoperative timeframes in the BEAR group (p < $\lt $ 0.04), and increases in energy, decreases in entropy, and increases in homogeneity between 6 and 24 months postoperation in the ACLR group (p < 0.04). PD images showed increases in energy and decreases in entropy between 6‐ and 24‐months postoperation in the ACLR group (p < 0.008). Finally, CISS was estimated to require a smaller sample size than PD and T2 to detect SI differences related to postoperative remodeling. [ABSTRACT FROM AUTHOR]

Copyright of Magyar Radiológia Online is the property of Society of Hungarian Radiologists and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). Materials and methods: Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC. Results: Four features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models. Conclusion: Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases. [ABSTRACT FROM AUTHOR]

Background: The clinical presentation of juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic–clonic seizures alone (GTCA) is similar, and MRI scans are often perceptually normal in both conditions making them challenging to differentiate. Purpose: To develop and validate an MRI‐based radiomics model to accurately diagnose JME and GTCA, as well as to classify prognostic groups. Study Type: Retrospective. Population: 164 patients (127 with JME and 37 with GTCA) patients (age 24.0 ± 9.6; 50% male), divided into training (n = 114) and test (n = 50) sets in a 7:3 ratio with the same proportion of JME and GTCA patients kept in both sets. Field Strength/Sequence: 3T; 3D T1‐weighted spoiled gradient‐echo. Assessment: A total of 17 region‐of‐interest in the brain were identified as having clinical evidence of association with JME and GTCA, from where 1581 radiomics features were extracted for each subject. Forty‐eight machine‐learning combinations of oversampling, feature selection, and classification algorithms were explored to develop an optimal radiomics model. The performance of the best radiomics models for diagnosis and for classification of the favorable outcome group were evaluated in the test set. Statistical Tests: Model performance measured using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Shapley additive explanations (SHAP) analysis to estimate the contribution of each radiomics feature. Results: The AUC (95% confidence interval) of the best radiomics models for diagnosis and for classification of favorable outcome group were 0.767 (0.591–0.943) and 0.717 (0.563–0.871), respectively. SHAP analysis revealed that the first‐order and textural features of the caudate, cerebral white matter, thalamus proper, and putamen had the highest importance in the best radiomics model. Conclusion: The proposed MRI‐based radiomics model demonstrated the potential to diagnose JME and GTCA, as well as to classify prognostic groups. MRI regions associated with JME, such as the basal ganglia, thalamus, and cerebral white matter, appeared to be important for constructing radiomics models. Level of Evidence: 3 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Soft confections can serve as a reservoir for bioactive peptide delivery in both functional food and food supplement applications. In this study, pectin-based soft confections were fortified with proteolytic plant protein hydrolysates and the residual bioactivity of hydrolysates was studied after processing or simulated digestion. Cold press sunflower or hazelnut cakes were used in the manufacture of protein isolates based on an alkali extraction-isoelectric precipitation (AE-IP) method. Trypsin or bromelain were utilized in the proteolysis of the isolates and thus prepared liquid hydrolysates were used in confectionery manufacture. DPP-IV (dipeptidyl peptidase-IV) inhibitory activity (i.e., in vitro antidiabetic activity) and antioxidative activities were measured. In addition, sensory and textural attributes were investigated. In all cases, a significant concentration of hydrolysates were added to the confections (27%), which lead to significant changes in color, texture and sensory acceptance. The peptide profile and size distribution mostly altered such characteristics, while observed bioactivity was significant after processing. Simulated digestion enhanced DPP-IV inhibitory activity up to approx. 40%, whereas antioxidative performance decreased. While the applicability of the current findings is limited by hydrolysate solubility, ingredient interactions, and processing costs, the relevance of degree of hydrolysis (DH%), peptide characteristics and phenolic-peptide interactions on product quality and eventual bioactivity are being discussed. [ABSTRACT FROM AUTHOR]

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