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The radioactive fission product 90Sr has a sufficient half-life (28.8 years) to be detected long after its appearance in the environment. After its uptake into the soil-edible plant system, it enters the food chain and represents a potential source of contamination that threatens human health. Due to these facts, tracking the distribution of the artificial radionuclide 90Sr in the soil-edible plant system is a subject of intense research. The tracking of the 90Sr radionuclide distribution in the soil profile, as well as in the crops on the long-term experimental fields was carried out using beta radiation spectrometry. The radiochemical analytical method was used to analyze the 90Sr content in cultivated soil and crops. The conducted study focused on the experimental substantiation of the developed model for predicting the behavior of 90Sr in the cultivated soil-crop system. The results of using the applied radioecological model for the transfer of 90Sr from the soil to the above-ground part of crops showed a relatively good agreement with the experimentally determined values of the soil-crop transfer factor, which indicates that the used model can be successfully applied for the prediction of the behavior of 90Sr in the soil-soil solution-crop system.

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BACKGROUND: Previous studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field. We aimed to test several ML algorithms to study the potential of structural cortical features for predicting the MACS and therefore gain a better insight into MwA pathophysiology. METHODS: The data set used in this research consists of 340 MRI features collected from 40 MwA patients. Average MACS score was obtained for each subject. Feature selection for ML models was performed using several approaches, including a correlation test and a wrapper feature selection methodology. Regression was performed with the Support Vector Machine (SVM), Linear Regression, and Radial Basis Function network. RESULTS: SVM achieved a 0.89 coefficient of determination score with a wrapper feature selection. The results suggest a set of cortical features, located mostly in the parietal and temporal lobes, that show changes in MwA patients depending on aura complexity. CONCLUSIONS: The SVM algorithm demonstrated the best potential in average MACS prediction when using a wrapper feature selection methodology. The proposed method achieved promising results in determining MwA complexity, which can provide a basis for future MwA studies and the development of MwA diagnosis and treatment.

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Modern studies focus on the discovery of innovative methods to improve the value of post-treatment magnetic resonance imaging (MRI) in the prediction of pathological responses to preoperative neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). The aim of this study was to assess the potential benefits of combining magnetic resonance tumor regression grade (mrTRG) with T2-weighted volumetry in the prediction of pathological responses to nCRT in LARC. This was a cohort study conducted on patients with histopathologically confirmed LARC in a period from 2020 to 2022. After histopathological verification, all patients underwent initial MRI studies, while the follow-up MRI was performed after nCRT. Tumor characteristics, MRI estimated tumor regression grade (mrTRG) and tumor volumetry were evaluated both initially and at follow-up. All patients were classified into responders and non-responders according to pathological tumor regression grade (pTRG) and mrTRG. A total of 71 patients, mostly male (66.2%) were included in the study. The median tumor volume reduction rate was significantly higher in nCRT-responders compared to non-responders (79.9% vs. 63.3%) (p = 0.003). Based on ROC analysis, optimal cut-off value for tumor volume reduction rate was determined with an area under the curve (AUC) value of 0.724 (p = 0.003). Using the tumor volume reduction rate ≥75% with the addition of response to nCRT according to mrTRG, a new scoring system for prediction of pTRG to preoperative nCRT in LARC was developed. Diagnostic performance of prediction score was tested and the sensitivity, PPV, specificity, and NPV were 81.8%, 56.3%, 71.4%, and 89.7%, respectively. The combination of mrTRG and T2-weighted volumetry increases the MRI-based prediction of pTRG to preoperative nCRT in LARC. The proposed scoring system could aid in distinguishing responders to nCRT, as these patients could benefit from organ-preserving treatment and a "watch and wait" strategy.

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BACKGROUND: Age-related neurodegenerative diseases are constantly increasing with prediction that in 2050 over 60 % of population will suffer from some level of cognitive impairment. A cure for the Alzheimer's disease (AD) does not exist, so early diagnosis is of a great importance. Machine learning techniques can help in early diagnosis with deep medical data processing, disease understanding, intervention analysis and knowledge discovery for achieving better medical decision making. METHODS: In this paper, we analyze the dataset consisting of 90 individuals and 482 input features. We investigate the achieved AD prediction performances using seven classifiers and five feature selection algorithms. We pay special focus on analyzing performance by utilizing only a subset of best ranked attributes to establish the minimum amount of input features that ensure acceptable performance. We also investigate the significance of neuropsychological (NP) and neuroradiological (NR) attributes for the AD diagnosis. RESULTS: The accuracy for the whole set of attributes ranged between 66.22 % and 81.00 %, and the weighted average AUROC was between 76.3 % and 95.0 %. The best results were achieved by the naive Bayes classifier and the Relief feature selection algorithm. Additionally, Support Vector Machines classifier shows the most stable results since it depends the least on the feature selection algorithm which is used. As the main result of this paper, we compare the performance of models trained with automatically selected features to models trained with hand-selected features performed by medical experts (NP and NR features). CONCLUSIONS: The results reveal that unlike the NR attributes, the NP attributes achieve a good performance that is comparable to the full set of attributes, which suggests that they possess a high predictive power for AD diagnosis.

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Introduction: Migraine with aura (MwA) is a neurological condition manifested in moderate to severe headaches associated with transient visual and somatosensory symptoms, as well as higher cortical dysfunctions. Considering that about 5% of the world's population suffers from this condition and manifestation could be abundant and characterized by various symptoms, it is of great importance to focus on finding new and advanced techniques for the detection of different phenotypes, which in turn, can allow better diagnosis, classification, and biomarker validation, resulting in tailored treatments of MwA patients. Methods: This research aimed to test different machine learning techniques to distinguish healthy people from those suffering from MwA, as well as people with simple MwA and those experiencing complex MwA. Magnetic resonance imaging (MRI) post-processed data (cortical thickness, cortical surface area, cortical volume, cortical mean Gaussian curvature, and cortical folding index) was collected from 78 subjects [46 MwA patients (22 simple MwA and 24 complex MwA) and 32 healthy controls] with 340 different features used for the algorithm training. Results: The results show that an algorithm based on post-processed MRI data yields a high classification accuracy (97%) of MwA patients and precise distinction between simple MwA and complex MwA with an accuracy of 98%. Additionally, the sets of features relevant to the classification were identified. The feature importance ranking indicates the thickness of the left temporal pole, right lingual gyrus, and left pars opercularis as the most prominent markers for MwA classification, while the thickness of left pericalcarine gyrus and left pars opercularis are proposed as the two most important features for the simple and complex MwA classification. Discussion: This method shows significant potential in the validation of MwA diagnosis and subtype classification, which can tackle and challenge the current treatments of MwA.

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Background: The role of advanced functional imaging techniques in prediction of pathological risk categories of gastrointestinal stromal tumors (GIST) is still unknown. The purpose of this study was to evaluate classical CT features, CT-perfusion and magnetic-resonance-diffusion-weighted-imaging (MR-DWI)-related parameters in predicting the metastatic risk of gastric GIST. Patients and methods: Sixty-two patients with histologically proven GIST who underwent CT perfusion and MR-DWI using multiple b-values were prospectively included. Morphological CT characteristics and CT-perfusion parameters of tumor were comparatively analyzed in the high-risk (HR) and low-risk (LR) GIST groups. Apparent diffusion coefficient (ADC) and intravoxel-incoherent-motion (IVIM)-related parameters were also analyzed in 45 and 34 patients, respectively. Results: Binary logistic regression analysis revealed that greater tumor diameter (p < 0.001), cystic structure (p < 0.001), irregular margins (p = 0.007), irregular shape (p < 0.001), disrupted mucosa (p < 0.001) and visible EFDV (p < 0.001), as well as less ADC value (p = 0.001) and shorter time-to-peak (p = 0.006), were significant predictors of HR GIST. Multivariate analysis extracted irregular shape (p = 0.006) and enlarged feeding or draining vessels (EFDV) (p = 0.017) as independent predictors of HR GIST (area under curve (AUC) of predicting model 0.869). Conclusion: Although certain classical CT imaging features remain most valuable, some functional imaging parameters may add the diagnostic value in preoperative prediction of HR gastric GIST.

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The objective of the study was to describe cellular and molecular markers of radioprotection by anisomycin, focusing on the changes in rat brain tissue. Two-month-old Wistar rats were exposed to a 60Co radiation source at a dose of 6 Gy, with or without radioprotection with anisomycin (150 mg/kg) administered subcutaneously 30 min before or 3 or 6 h after irradiation. Survivors were analyzed 30 days after treatment. Astroglial and microglial responses were investigated based on the expression of glial markers assessed with immunohistochemistry, and quantitative changes in brain biomolecules were investigated by Raman microspectroscopy. In addition, blood plasma levels of pro-inflammatory (interleukin 6 and tumor necrosis factor α) and anti-inflammatory (interleukin 10) cytokines were assessed. We found that application of anisomycin either before or after irradiation significantly decreased the expression of the microglial marker Iba-1. We also found an increased intensity of Raman spectral bands related to nucleic acids, as well as an increased level of cytokines when anisomycin was applied after irradiation. This suggests that the radioprotective effects of anisomycin are by decreasing Iba-1 expression and stabilizing genetic material by increasing the level of nucleic acids.

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BACKGROUND: The utility of intravoxel incoherent motion (IVIM) related parameters in differentiation of hypovascular liver lesions is still unknown. PURPOSE: The purpose of this study was to evaluate the value of IVIM related parameters in comparison to apparent diffusion coefficient (ADC) for differentiation among intrahepatic mass-forming cholangiocarcinoma (IMC), and hypovascular liver metastases (HLM). METHODS: Seventy-four prospectively enrolled patients (21 IMC, and 53 HLM) underwent 1.5T magnetic resonance examination with IVIM diffusion-weighted imaging using seven b values (0-800 s/mm2). Two independent readers performed quantitative analysis of IVIM-related parameters and ADC. Interobserver reliability was tested using a intraclass correlation coefficient. ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (ƒ) were compared among the lesions using Kruskal-Wallis H test. The diagnostic accuracy of each parameter was assessed by receiver operating characteristic (ROC) curve analysis. RESULTS: The interobserver agreement was good for ADC (0.802), and excellent for D, D*, and ƒ (0.911, 0.927, and 0.942, respectively). ADC, and D values were significantly different among IMC and HLM (both p < 0.05), while there was no significant difference among these lesions for ƒ and D* (p = 0.101, and p = 0.612, respectively). ROC analysis showed higher diagnostic performance of D in comparison to ADC (AUC = 0.879 vs 0.821). CONCLUSION: IVIM-derived parameters in particular D, in addition to ADC, could help in differentiation between most common hypovascular malignant liver lesions, intrahepatic mass-forming cholangiocarcinoma and hypovascular liver metastases.

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Background: Manifestations of typical migraine aura can be numerous. Investigation of its pathophysiological mechanisms can be challenging if a stratification of phenotypes is not performed. In this context, the Migraine Aura Complexity Score (MACS), recently developed, may help. Here we aimed to categorize migraine patients into homogenous groups using MACS and to compare those groups with respect to patients' characteristics and neuroimaging findings. Methods: Participants who have a migraine with aura (MwA) were interviewed after each attack in order to obtain the characteristics of migraine aura. Thereafter, we scored the complexity of their auras by MACS. The MACS was used to categorize patients into three groups: MwA-S (with simple aura), MwA-MC (with moderately complex aura), and MwA-C (with complex aura). The patient characteristics and estimated cortical thickness of regions of interest, which are potentially linked to the symptoms that develop during the aura, were used to compare these groups. Results: In total, 338 MwA attacks were recorded in analyzed groups. Scotoma was the most frequently reported symptom in the groups, followed by somatosensory aura in the MwA-C group and zig-zag lines in the MwA-MC and MwA-S groups. Patients in the MwA-C and MwA-MC groups had a thicker cortex in the left primary visual cortex with respect to MwA-S group. In addition, patients in the MwA-C group had a thicker cortex in several visual and somatosensory cortical regions relative to the MwA-S group. Conclusions: Our results show that the newly developed MACS can be used for the stratification of MwA patients, herewith allowing the better investigation of changes in migraineurs' brains.

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BACKGROUND AND PURPOSE: Various features of the cerebral cortex and white matter have been extensively investigated in migraine with aura (MwA), but the morphological characteristics of subcortical structures have been largely neglected. The aim of this study was to identify possible differences in subcortical structures between MwA patients and healthy subjects (HS), and also to determine the correlations between the characteristics of migraine aura and the volumes of subcortical structures. METHODS: Thirty-two MwA patients and 32 HS matched by sex and age were analyzed in this study. Regional subcortical brain volumes were automatically calculated using the FSL/FMRIB Image Registration and Segmentation Tool software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Glossary). A general linear model analysis was used to investigate differences in the volume of subcortical structures between the MwA patients and HS. A partial correlation test was used to assess correlations between the volume of subcortical structures and characteristics of MwA. RESULTS: The volumes of the right globus pallidus, left globus pallidus, and left putamen were significantly smaller in MwA patients than in HS (mean±SD): 1,427±135 mm³ vs. 1,557±136 mm³ (p<0.001), 1,436±126 mm³ vs. 1,550±139 mm³ (p=0.001), and 4,235±437 mm³ vs. 4,522±412 mm³ (p=0.006), respectively. There were no significant relationships between subcortical structures and clinical parameters. CONCLUSIONS: These findings suggest that both the globus pallidi and left putamen play significant roles in the pathophysiology of the MwA. Future studies should determine the cause-and-effect relationships, since these could not be discriminated in this study due to its cross-sectional design.

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BACKGROUND: The brainstem plays a significant role in migraine pathogenesis, but a relationship between volume alterations of brainstem subregions and migraine aura characteristics has not been sufficiently investigated. The aim of this study is to compare the volume of the brainstem, and its subregions, between patients with a migraine with aura (MwA) and healthy controls (HC), and also to correlate characteristics of MwA and the volume of the brainstem subregions. METHODS: Forty-two MwA and 42 HCs, balanced by sex and age, were selected for this study. Total brainstem volume changes as well as volume changes in the pons, medulla, midbrain and the superior cerebellar peduncles were investigated in MwA relative to HCs. In addition, the relationships between brainstem subregions and aura characteristics (aura duration, the frequency of the aura, occurrence of somatosensory and dysphasic aura, duration of a headache, intensity of headache pain and disease duration) were explored in MwA. RESULTS: MwA patients had a larger brainstem volume relative to HCs (25,941.35 ±â€¯2559.2 mm3 vs. 25,179.32 ±â€¯2019.1 mm3; p = .008), as well as the midbrain and pons (6155.98 ±â€¯565.7 mm3 vs. 5964.22 ±â€¯457.0 mm3, p = .002; 15,105.13 ±â€¯1765.5 mm3 vs. 14,539.89 ±â€¯1408.4 mm3, p = .007, respectively). Total brainstem volume, as well as volumes of brainstem subregions, were not significantly correlated to the MwA characteristics. CONCLUSION: The results of this study reveal that a migraine with aura is associated with a larger volume of the brainstem with a particular involvement of the midbrain and pons.

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OBJECTIVES: Currently, there is no scoring system for assessing the complexity of migraine aura. Our goal was to develop a Migraine Aura Complexity Score that synthesizes the quantity and quality of aura symptoms and to test its applicability in neuroimaging studies. METHODS: Patients with migraine aura were interviewed in order to obtain characteristics of migraine aura. Explorative and confirmatory analyses were used to develop the Migraine Aura Complexity Score. Median values were derived from 10 consecutive migraine auras in each patient. The Migraine Aura Complexity Score was correlated with an average cortical thickness of different brain areas in studied patients. The Surface-based Morphometric Analysis approach was used to estimate cortical thickness. RESULTS: This study included 23 (16 females and seven males) migraineurs with aura. Confirmatory factor analysis suggested the second-order model with three-factor measurement for grading migraine aura. The first factor is linked to higher cortical dysfunction during migraine aura, while the second is associated with the degree of involvement of primary visual and somatosensory cortices; the third linked symptoms of somatosensory aura and hand and head involvement. Positive correlation of Migraine Aura Complexity Score and averaged cortical thickness were found in the left and right hemispheres overall (r = 0.568, p = 0.007; r = 0.617, p = 0.003) and in some of their regions. CONCLUSIONS: This study demonstrates that the Migraine Aura Complexity Score could be a valuable tool for assessing migraine aura. The score could be used in neuroradiological studies in order to achieve a stratification of patients with migraine aura.

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Objective: Previous migraine studies have reported gray matter alterations in various cortical regions with conflicting results. This study aimed to explore a cortical morphometric difference in migraineurs with aura (MA) compared to healthy subjects (HS) and to delineate a possible difference between the cortical morphological features and different aura phenotypes. Materials and Methods: Forty-eight MA and 30 HS that were balanced by sex, age, and educational level were selected for this study. T2-weighted and three-dimensional T1-weighted magnetic resonance imaging (MRI) of the brain were acquired using a 1.5T MRI scanner. Surface-based morphometry from the MRI data was used to identify differences between the MA and HS group, and then between MA subgroups. The MA group was subdivided into migraineurs who experienced only visual aura (MVA) and migraineurs who had visual, somatosensory and dysphasic symptoms (MVA+). Results: The MVA+ group had significantly reduced cortical surface area of the left rostral middle frontal cortex compared with the MVA group (p < 0.001). Migraine patients had significantly reduced volume of the left fusiform gyrus relative to HS (p < 0.001). Also, the sulcal depth increased at the level of the left temporal pole in the MVA+ group relative to the MVA group (p < 0.001). The vertex-by-vertex analysis did not exhibit any significant difference in cortical thickness between MA and HS, and between MVA+ and MVA, when corrected for multiple comparisons. Conclusion: Migraineurs with aura demonstrates different morphometric features from HS in multiple cortical regions. MVA+ have different morphometric features in the left frontal and temporal lobe relative to MVA, which could be a source of distinct symptoms and serve as potential biomarkers of different MA subtypes.

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We aimed to explore whether a migraine with aura (MA) is associated with structural changes in tracts of a white matter and to compare parameters of diffusivity between subgroups in migraineurs. Forty-three MA and 20 healthy subjects (HS), balanced by sex and age, were selected for this study. Analysis of diffusion tensor parameters was used to identify differences between MA patients and HS, and then between MA subgroups. A diffusion tensor probabilistic tractography analysis showed that there is no difference between MA patients and HS. However, using more-liberal uncorrected statistical threshold, we noted a trend in MA patients toward lower diffusivity indices of selected white matter tracts located in the forceps minor and right anterior thalamic radiation (ATR), superior longitudinal fasciculus (temporal part) (SLFT), cingulum-cingulate tract, and left uncinate fasciculus. Migraineurs who experienced somatosensory and dysphasic aura, besides visual symptoms, had tendency toward lower diffusivity indices, relative to migraineurs who experienced only visual symptoms, in the right inferior longitudinal fasciculus, forceps minor, and right superior longitudinal fasciculus (parietal part), SLFT, and cingulum-angular bundle. Aura frequency were negatively correlated with axial diffusivity and mean diffusivity of the right ATR (partial correlation = - 0.474; p = 0.002; partial correlation = - 0.460; p = 0.002), respectively. There were no significant differences between MA patients and HS, neither between MA subgroups. Migraineurs with abundant symptoms during the aura possibly have more myelinated fibers relative to those who experience only visual symptoms. Lower diffusivity indices of the right ATR are linked to more frequent migraine with aura attacks.

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Modern diagnostic tools ever aim to reduce the amount of analyte and the time needed for obtaining the result. Surface-enhanced Raman spectroscopy is a method that could satisfy both of these requirements, provided that for each analyte an adequate substrate is found. Here we demonstrate the ability of gold-sputtered silicon nanowires (SiNW) to bind p-mercaptobenzoic acid in 10-3, 10-4 and 10-5M and adenine in 30 and 100µM concentrations. Based on the normal mode analysis, presented here for the first time, the binding of p-mercaptobenzoic acid is deduced. The intensity enhancement of the 1106cm-1 band is explained by involvement of the CS stretching deformation, and the appearance of the broad 300cm-1 band attributed to SAu stretching mode. Adenine SERS spectra demonstrate the existence of the 7H tautomer since the strongest band observed is at 736cm-1. The adenine binding is likely to occur in several ways, because the number of observed bands in the 1200-1600cm-1 interval exceeds the number of observed bands in the normal Raman spectrum of the free molecule.

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Gaucher disease (GD) is a lysosomal storage disorder, caused by an impaired function of ß-glucocerebrosidase, which results in accumulation of glucocerebroside in cells, and altered membrane ordering. Using electron paramagnetic resonance spin labeling, a statistically significant difference in the order parameter between the peripheral blood mononuclear cell membranes of GD patients and healthy controls was observed. Moreover, the results show that the introduction of the enzyme replacement therapy leads to the restoration of the physiological membrane fluidity. Accordingly, this simple method could serve as a preliminary test for GD diagnosis and therapy efficiency.

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The present paper focuses on the determination of radiological characteristics of cultivated chernozem soil and crops from long-term field experiments, taking into account the importance of distribution and transfer of radionuclides in the soil-plant system, especially in agricultural cropland. The investigation was performed on the experimental fields where maize, winter wheat, and rapeseed were cultivated. Analysis of radioactivity included determination of the gross alpha and beta activity as a screening method, as well as the activities of the following radionuclides: natural ((210)Pb, (235)U, (238)U, (226)Ra, (232)Th, (40)K, (7)Be) and artificial ((90)Sr and (137)Cs). The activities of natural and artificial ((137)Cs) radionuclides were determined by gamma spectrometry, while the artificial radionuclide (90)Sr was determined by a radiochemical analytical method. Based on the obtained results for the specific activity of (40)K, (137)Cs, and (90)Sr, accumulation factors for these radionuclides were calculated in order to estimate transfer of radionuclides from soil to crops. The results of performed analyses showed that there is no increase of radioactivity that could endanger the food production through the grown crops.

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OBJECTIVE: To determine cortical thickness (CTh), cortical surface area (CSA), curvature and sulcal depth (SD) in patients with psychogenic nonepileptic seizures (PNES). METHODS: Freesurfer software was used to identify differences between active and control group in Cth, CSA, curvature, and SD. Neuropsychological tests intending to document possible frontal lobe deficit were applied. RESULTS: We included 37 patients with PNES (age 37.3±13.8; female/male 31/6; age of disease onset 26.1±10.6; age of disease duration 11.1±11.1), and 37 healthy controls (age 38.4; ±12.7; female/male 26/11). No difference in CSA and curvature was detected between groups. Patients with PNES had increased CTh in the left insula, left and right medial-orbitofrontal, and left lateral-orbitofrontal, and decreased CTh in the left and right precentral, right enthorinal, and right lateral-occipital region than healthy controls. SD was increased at the level of the left and right insula, right rostral anterior cingulate, right posterior cingulate, and left cuneus, and reduced at the level of the right and left medial-orbitofrontal sulci in patients with PNES compared to healthy controls. CONCLUSION: Individuals with PNES display a distinct profile of changes in CTh, in association with increase in SD in both insula as compared to controls. Our results may contribute to the understanding of the neurobiological background of PNES. Further research, to include replication of the findings and directed to understand the role of insula is needed.

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