Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential. Bones that have cancer have a different texture than bones in unaffected areas. Diagnosing hematological illnesses relies on correct labeling and categorizing nucleated cells in the bone marrow. However, timely diagnosis and treatment are hampered by pathologists' need to identify specimens, which can be sensitive and time-consuming manually. Humanity's ability to evaluate and identify these more complicated illnesses has significantly been bolstered by the development of artificial intelligence, particularly machine, and deep learning. Conversely, much research and development is needed to enhance cancer cell identification-and lower false alarm rates. We built a deep learning model for morphological analysis to solve this problem. This paper introduces a novel deep convolutional neural network architecture in which hybrid multi-objective and category-based optimization algorithms are used to optimize the hyperparameters adaptively. Using the processed cell pictures as input, the proposed model is then trained with an optimized attention-based multi-scale convolutional neural network to identify the kind of cancer cells in the bone marrow. Extensive experiments are run on publicly available datasets, with the results being measured and evaluated using a wide range of performance indicators. In contrast to deep learning models that have already been trained, the total accuracy of 99.7% was determined to be superior.
- MeSH
- Algorithms MeSH
- Deep Learning * MeSH
- Bone Marrow diagnostic imaging pathology MeSH
- Humans MeSH
- Bone Neoplasms pathology diagnostic imaging diagnosis MeSH
- Neural Networks, Computer * MeSH
- Image Processing, Computer-Assisted methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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 %. CONCLUSIONS: 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.
- MeSH
- Diffusion Magnetic Resonance Imaging methods MeSH
- Adult MeSH
- Carcinoma, Renal Cell * diagnostic imaging pathology complications MeSH
- Middle Aged MeSH
- Humans MeSH
- Kidney Neoplasms * diagnostic imaging pathology complications MeSH
- Prognosis MeSH
- Retrospective Studies MeSH
- Aged MeSH
- Thrombectomy methods MeSH
- Thrombosis * diagnostic imaging pathology MeSH
- Vena Cava, Inferior diagnostic imaging pathology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Edibles are the only source of nutrients and energy for humans. However, ingredients of edibles have undergone many physicochemical changes during preparation and storage. Aging, hydrolysis, oxidation, and rancidity are some of the major changes that not only change the native flavor, texture, and taste of food but also destroy the nutritive value and jeopardize public health. The major reasons for the production of harmful metabolites, chemicals, and toxins are poor processing, inappropriate storage, and microbial spoilage, which are lethal to consumers. In addition, the emergence of new pollutants has intensified the need for advanced and rapid food analysis techniques to detect such toxins. The issue with the detection of toxins in food samples is the nonvolatile nature and absence of detectable chromophores; hence, normal conventional techniques need additional derivatization. Mass spectrometry (MS) offers high sensitivity, selectivity, and capability to handle complex mixtures, making it an ideal analytical technique for the identification and quantification of food toxins. Recent technological advancements, such as high-resolution MS and tandem mass spectrometry (MS/MS), have significantly improved sensitivity, enabling the detection of food toxins at ultralow levels. Moreover, the emergence of ambient ionization techniques has facilitated rapid in situ analysis of samples with lower time and resources. Despite numerous advantages, the widespread adoption of MS in routine food safety monitoring faces certain challenges such as instrument cost, complexity, data analysis, and standardization of methods. Nevertheless, the continuous advancements in MS-technology and its integration with complementary techniques hold promising prospects for revolutionizing food safety monitoring. This review discusses the application of MS in detecting various food toxins including mycotoxins, marine biotoxins, and plant-derived toxins. It also explores the implementation of untargeted approaches, such as metabolomics and proteomics, for the discovery of novel and emerging food toxins, enhancing our understanding of potential hazards in the food supply chain.
- MeSH
- Food Analysis MeSH
- Humans MeSH
- Marine Toxins MeSH
- Mycotoxins * MeSH
- Reference Standards MeSH
- Tandem Mass Spectrometry * methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
BACKGROUND: Tumor consistency is considered to be a critical factor for the surgical removal of meningiomas and its preoperative assessment is intensively studied. A significant drawback in the research of predictive methods is the lack of a clear shared definition of tumor consistency, with most authors resorting to subjective binary classification labeling the samples as "soft" and "hard." This classification is highly observer-dependent and its discrete nature fails to capture the fine nuances in tumor consistency. To compensate for these shortcomings, we examined the utility of texture analysis to provide an objective observer-independent continuous measure of meningioma consistency. METHODS: A total of 169 texturometric measurements were conducted using the Brookfield CT3 Texture Analyzer on meningioma samples from five patients immediately after the removal and on the first, second, and seventh postoperative day. The relationship between measured stiffness and time from sample extraction, subjectively assessed consistency grade and histopathological features (amount of collagen and reticulin fibers, presence of psammoma bodies, predominant microscopic morphology) was analyzed. RESULTS: The stiffness measurements exhibited significantly lower variance within a sample than among samples (p = 0.0225) and significant increase with a higher objectively assessed consistency grade (p = 0.0161, p = 0.0055). A significant negative correlation was found between the measured stiffness and the time from sample extraction (p < 0.01). A significant monotonic relationship was revealed between stiffness values and amount of collagen I and reticulin fibers; there were no statistically significant differences between histological phenotypes in regard to presence of psammoma bodies and predominant microscopic morphology. CONCLUSIONS: We conclude that the values yielded by texture analysis are highly representative of an intrinsic consistency-related quality of the sample despite the influence of intra-sample heterogeneity and that our proposed method can be used to conduct quantitative studies on the role of meningioma consistency.
- MeSH
- Collagen MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Meningeal Neoplasms * surgery pathology MeSH
- Meningioma * diagnostic imaging surgery pathology MeSH
- Reticulin MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Alternativy výrobků živočišného původu hrají stále důležitější roli ve výživě nejen vegetariánů a veganů. Cílem předkládané práce je monitoring kvality 16 vzorků alternativ masa a výrobků z nich prodávaných v malospotřebitelských baleních; do senzorické analýzy byl navíc zahrnut vzorek masného výrobku pro porovnání organoleptických vlastností. V analyzovaných vzorcích bylo zastoupení hlavních živin, a tedy i jejich poměr, velmi variabilní. Byl zjištěn obsah tuku v rozpětí 0,2-22,5 g/100 g, obsah bílkovin 6,7-57,4 g/100 g a obsah sacharidů 1,2-37,8 g/100 g. Kvalita tuku byla též rozdílná, o čemž svědčí i rozpětí podílu nasycených mastných kyselin 7,0-47,0 %. V rámci senzorické analýzy předčily živočišný vzorek 3 rostlinné alternativy masa, u většiny z nich byla však zaznamenána senzorická jakost nižší.
Alternatives to products of animal origin play an increasingly important role in the nutrition of not only vegetarians and vegans. The work is a monitoring of quality performed on 16 samples of meat alternatives, sold in small consumer packages; in addition, a sample of meat product was analysed to compare the organoleptic properties. The ratio of main nutrients in samples was very variable. Fat content was found to be in range 0.2-22.5 g/100 g, protein content 6.7-57.4 g/100 g and saccharides 0-37.8 g/100 g. The quality of fat was also variable, which is evidenced by the range 7.0-47.0% of saturated fatty acids. In the sensory analysis, 3 plant-based meat alternatives outperformed the meat sample, but most of them had a lower sensory quality.
Wilt (Fusarium oxysporum f. sp. lentis; Fol) is one of the major diseases of lentil worldwide. Two hundred and thirty-five isolates of the pathogen collected from 8 states of India showed substantial variations in morphological characters such as colony texture and pattern, pigmentation and growth rate. The isolates were grouped as slow (47 isolates), medium (118 isolates) and fast (70 isolates) growing. The macroconidia and microconidia (3.0-77.5 × 1.3-8.8 μm for macroconidia and 1.8-22.5 × 0.8-8.0 μm for microconidia for length × width) were variable in size and considering the morphological features, the populations were grouped into 12 categories. Seventy representative isolates based on their morphological variability and place of origin were selected for further study. A set of 10 differential genotypes was identified for virulence analysis and based on virulence patterns on these 10 genotypes, 70 Fol isolates were grouped into 7 races. Random amplified polymorphic DNA (RAPD), universal rice primers (URPs), inter simple sequence repeats (ISSR) and sequence-related amplified polymorphism (SRAP) were used for genetic diversity analysis. URPs, ISSR and SRAP markers gave 100% polymorphism while RAPD gave 98.9% polymorphism. The isolates were grouped into seven clusters at genetic similarities ranging from 21 to 80% using unweighted paired group method with arithmetic average analysis. The major clusters include the populations from northern and central regions of India in distinct groups. All these three markers proved suitable for diversity analysis, but their combined use was better to resolve the area specific grouping of the isolates. The sequences of rDNA ITS and TEF-1α genes of the representative isolates were analysed. Phylogenetic analysis of ITS region grouped the isolates into two major clades representing various races. In TEF-1α analysis, the isolates were grouped into two major clades with 28 isolates into one clade and 4 remaining isolates in another clade. The molecular groups partially correspond to the lentil growing regions of the isolates and races of the pathogen.
Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT)-derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression-free survival (PFS) and overall survival (OS) predictions. Baseline FDG-PET scans were available for 1263 patients, 832 patients of these were cell-of-origin (COO)-evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low-, intermediate- and high-risk groups. The random forest model with COO subgroups identified a clearer high-risk population (45% 2-year PFS [95% confidence interval (CI) 40%-52%]; 65% 2-year OS [95% CI 59%-71%]) than the IPI (58% 2-year PFS [95% CI 50%-67%]; 69% 2-year OS [95% CI 62%-77%]). This study confirms that standard clinical risk factors can be combined with PET-derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.
- Publication type
- Journal Article MeSH
Androgenic alopecia (AGA) is a common and chronic condition. It may impact self-esteem, self-image and quality of life. Benefit, tolerability, cosmetic acceptance and patient satisfaction are key to ensure good treatment outcome. Hair loss improvement and hair quality with AC5 (2,4-Diamino-Pyrimidine-N-Oxyde, arginine, 6-O glucose linoleate (SP94), piroctone olamine and Vichy mineralizing water) once daily was assessed in 527 subjects with mild AGA in an open-label, observational, international real-life study. After 3 months, investigators evaluated the impact of AC5 on hair loss, product satisfaction and asked subjects about local tolerance; subjects assessed hair growth and quality and satisfaction. Data from 357 subjects were evaluable for the benefit analysis; 59.9% of subjects were female; the mean age was 33.6±8.7 years. Duration of hair loss was 1.62±2.24 years. 71.3% of women had a Ludwig score of 1 and 40.8% of men had a Hamilton Norwood score of 2. At the end of study, hair loss was reduced in 89.0% of subjects; it was slightly higher in women (92.5%) than in men (83.8%). Subject satisfaction on a scale from 0 (not satisfied at all) to 10 (completely satisfied) was 7.9±1.7. Tolerance was rated good to very good by 98.6% of all subjects. In conclusion, AC5 reduces mild AGA in both men and women with a pleasant texture. AC5 was well tolerated and highly appreciated.
- MeSH
- Alopecia * drug therapy therapy MeSH
- Adult MeSH
- Quality of Life * MeSH
- Humans MeSH
- Young Adult MeSH
- Patient Satisfaction MeSH
- Hair MeSH
- Treatment Outcome MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Observational Study MeSH
Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied.
- MeSH
- Color MeSH
- Honey analysis MeSH
- Microscopy methods MeSH
- Image Processing, Computer-Assisted methods MeSH
- Pollen classification MeSH
- Beekeeping * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
A new method to estimate the selected viscoelastic parameters of foods using damped vibration analysis is presented for the evaluation of fruits and baked products. A flat disk is attached to the flat sample surface using a laser rangefinder that measures the sample thickness in advance, and it is locked by a trigger. Next, the trigger is released to allow the probe to press the sample through the force of gravity. The damped vibration of the probe caused by the deformation of the food is measured by monitoring the displacement of the probe via a linear encoder. The bulk modulus and viscosity are estimated using the fractional Zener model and mass. Young's modulus (E) is estimated independently by determining the maximum velocity of the probe using Hooke's law. Poisson's ratio (ν), and the shear modulus and viscosity are calculated by employing the estimated E and bulk modulus. The bulk modulus, bulk viscosity, shear modulus, shear viscosity, and E of apples were found to be higher than those of bananas. The bulk modulus, bulk viscosity, E, and shear modulus for white bread were lower than those for pound cake, but the ν values were higher, whereas those of sponge cake were intermediate. After drying the baked products for 1 day, most of the parameters of the samples increased, but the value of ν for white bread decreased. The proposed free-falling device estimated the four viscoelastic coefficients, Poisson's ratio, and Young's modulus of the food sample in less than 1 s.
- MeSH
- Elastic Modulus MeSH
- Elasticity * MeSH
- Viscosity MeSH
- Publication type
- Journal Article MeSH
