00000nab a2200000 4500 10.1007_s11060-022-04063-y OREX-CRIS 261111s2022 sp o 000 0 eng d m o h cr ||||||||||| 0167-594X 1573-7373 10.1007/s11060-022-04063-y doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Speckter, Herwin, autor aut (orcid)0000-0002-9904-2395 Radulović, Marko, autor aut (orcid)0000-0002-2314-7457 Trivodaliev, Kire V., autor aut (orcid)0000-0002-7350-9583 Joaquin, Johanna, autor aut Hernández, Wenceslao, autor aut Mota, Angel, autor aut (orcid)0000-0002-8878-1651 Bido, José, autor aut Hernandez, Giancarlo, autor aut Rivera, Diones, autor aut Suazo, Luis, autor aut (orcid)0000-0003-0227-6775 Valenzuela, Santiago, autor aut (orcid)0009-0003-4136-2745 Stoeter, Peter, autor aut (orcid)0000-0003-4045-4990 MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery Velicko Vranes; Herwin Speckter; Marko Radulović; Kire V. Trivodaliev; Johanna Joaquin [et al.] s.n. 2022 páginas 281-291 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Journal of Neuro-Oncology 0167-594X Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid, sjr Acceso abierto — GREEN Citas OpenAlex: 20 | Cuartil SJR: Q1 Meningioma Radiomics Radiosurgery Medicine Nuclear medicine Radiology Radiation therapy Journal of Neuro-Oncology 0167-594X vol. 159, n. 2, p. 281-291, (2022) doi:10.1007/s11060-022-04063-y https://doi.org/10.1007/s11060-022-04063-y Texto completo vía DOI Versión publicada https://zenodo.org/record/6939897 Acceso abierto ART 00000nab a2200000 4500 10.1093_mam_ozaf044 OREX-CRIS 261111s2025 sp o 000 0 eng d m o h cr ||||||||||| 1431-9276 1435-8115 10.1093/mam/ozaf044 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Lin, Yifei Sylvia, autor aut (orcid)0000-0003-4404-4593 Li, Xingyu, autor aut (orcid)0000-0002-3494-2552 Milovanović, Jelena, autor aut (orcid)0000-0002-5778-5393 Todorović‐Raković, Nataša, autor aut (orcid)0000-0001-8975-3129 Vujasinović, Tijana, autor aut (orcid)0000-0002-6075-7246 Kanjer, Ksenija, autor aut Radulović, Marko, autor aut (orcid)0000-0002-2314-7457 Enhanced Prognostication of Early Breast Cancer Outcomes Using Deep Learning on Merged Multistain and Multicolor-Depth Tumor Histopathology Velicko Vranes; Yifei Sylvia Lin; Xingyu Li; Jelena Milovanović; Nataša Todorović‐Raković [et al.] s.n. 2025 texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Microscopy and Microanalysis 1431-9276 Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid, sjr Acceso abierto — GREEN Accurate breast cancer prognosis helps clinicians in selecting optimal treatments, potentially improving patient survival. We tested whether combining deep learning with tumor histopathology images could reliably predict cancer spread. Advantages of this study include the use of deep learning, which often outperforms traditional methods, and the analysis of tumor histopathology images that offer higher resolution than MRI or CT. We also optimized tumor immunostaining by separately staining slides with AE1/AE3 pan-cytokeratin and hematoxylin and eosin (H&E), and evaluated different image color-depth representations (color, grayscale, and binary) for their prognostic utility. The results indicate that grayscale images outperformed both color and binary formats. Grayscale pan-CK-stained images achieved 94.4% accuracy [area under the curve (AUC) = 0.982], while grayscale H&E-stained images reached 85.7% accuracy (AUC = 0.992) on the test set. Notably, training the ResNet-50 model with experimentally augmented data comprising six distinct datasets differing in staining type and color depth, totaling 2,646 images, further enhanced performance, to 100% accuracy (AUC of 1.0). Importantly, our pipeline ensured no contamination between the development and test sets. Deep learning applied to tumor histopathology images of early-stage breast cancer patients using two stains and varying color depths achieved exceptional prognostic accuracy and robust generalization. Citas OpenAlex: 0 | Cuartil SJR: Q3 Histopathology Breast cancer Medicine Cancer Artificial intelligence Oncology Pathology Internal medicine Computer science Microscopy and Microanalysis 1431-9276 vol. 31, n. 3, (2025) doi:10.1093/mam/ozaf044 https://doi.org/10.1093/mam/ozaf044 Texto completo vía DOI Versión publicada https://doi.org/10.1093/mam/ozaf044 Acceso abierto ART 00000naa a2200000 4500 10.1109_cscs.2019.00092 OREX-CRIS 261111s2019 sp 000 0 eng d m o h cr ||||||||||| 10.1109/cscs.2019.00092 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Milošević, Nebojša T., autor aut (orcid)0000-0002-0589-1848 Stojić, Damjan, autor aut (orcid)0000-0001-8062-8022 The Binary Images of Aspiny Neurons from the Human Neostriatum: Cluster Classification Using Parameters of Monofractal Analysis Velicko Vranes; Nebojša T. Milošević; Damjan Stojić s.n. 2019 páginas 501-506 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier 2019 22nd International Conference on Control Systems and Computer Science (CSCS) Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid Acceso restringido The striatum (i.e. neostriatum) is one of the principal components of the basal ganglia. It is complex structure which consists of the three nuclei. Human striatum neurons firstly can be visually classified into two types (spiny and aspiny), but further classification recognizes two subgroups of spiny, and three subgroups of aspiny neurons. The original goal of this study is to confirm or improve the existing division of striatal neurons using two main techniques of cluster analysis. A total of 175 two dimensional images of aspiny neurons have been captured by the light microscope, and recorded with accompanying digital camera. Specialized public software Image J has been used for both image reconstruction and measurement. Each binary image of the neuron have been quantified with apparent parameters of monofractal analysis. Hierarchical cluster analysis and k-cluster method classified two existing groups of aspiny cells into three classes. Moreover, the morphometric difference in all groups between two functional different nuclei were reported. To the best of our knowledge, the presence of neuronal types in the adult human neostriatum has thus far been established and described mainly with Euclidean parameters. Thus, the present study, quantifies cells with monofractal parameters only. In the present study, only two types of aspiny neurons were used. But two techniques of cluster analysis found three groups of neurons. This results need to be verified with same classification technique, but using different computational parameters. Finally, the present study offer vague conclusion regarding difference in cell types between two cores of dorsal lamina. Citas OpenAlex: 1 Artificial intelligence Medium spiny neuron Pattern recognition (psychology) Hierarchical clustering Computer science Computational biology Cytoarchitecture Soma Striatum Basal ganglia 2019 22nd International Conference on Control Systems and Computer Science (CSCS) vol. 9, p. 501-506, (2019) doi:10.1109/cscs.2019.00092 https://doi.org/10.1109/cscs.2019.00092 Texto completo vía DOI Versión publicada ART 00000nab a2200000 4500 10.11648_j.ejb.20190701.13 OREX-CRIS 261111s2019 sp o 000 0 eng d m o h cr ||||||||||| 2329-1737 2329-1745 10.11648/j.ejb.20190701.13 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Krstonošić, Bojana, autor aut Milošević, Nebojša T., autor aut (orcid)0000-0002-0589-1848 Two Dimensional Grayscale Images of the Aspiny Neurons from the Human Neostriatum: Monofractal and Gray Level Co-occurrence Matrix Analysis Velicko Vranes; Bojana Krstonošić; Nebojša T. Milošević s.n. 2019 páginas 15-15 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier European Journal of Biophysics 2329-1737 Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid Acceso abierto — DIAMOND The striatum (neostriatum) is one of the principal constituents of the basal nuclei. It is a complex structure which consists of a dorsal and the ventral components. According to the spine distribution and their density, neurons of the human striatum can be classified into two main types: spiny and aspiny cells. Further classification recognizes two groups of spiny, and three groups of aspiny neurons. The goal of this study was to analyze different morphometric properties of the digital images of the group IV and group V aspiny neurons, from the dorsal striatum of both cerebral hemispheres. In this study, a total of 175 two-dimensional images of aspiny neurons were analyzed. Image reconstruction and measurement was performed with the specialized, public software Image J. Four parameters of standard fractal analysis were quantified from these binary images. In addition, five textural parameters were obtained by analyzing grayscale images of the entire neuron. Results of both analyses show that six of nine parameters differed between the group IV and group V aspiny neurons. Moreover, in both groups of neurons, one parameter of the fractal and three of the texture analyses differed between the putamen and the caudate nucleus neurons. Thus, this study corroborates previous classification of aspiny neurons. Although they belong to the same aspiny group, different type of cells can qualify nerve signals in their own way. Therefore, this study supports the hypothesis that neuronal morphology differences can reflect their functional diversity and their role in communication. Citas OpenAlex: 2 Biology Medium spiny neuron Striatum Putamen Basal ganglia Caudate nucleus Neuroscience Computational biology Central nervous system European Journal of Biophysics 2329-1737 vol. 7, n. 1, p. 15-15, (2019) doi:10.11648/j.ejb.20190701.13 https://doi.org/10.11648/j.ejb.20190701.13 Texto completo vía DOI Versión publicada http://article.sciencepublishinggroup.com/pdf/10.11648.j.ejb.20190701.13.pdf Acceso abierto ART 00000nab a2200000 4500 10.3390_cancers11101615 OREX-CRIS 261111s2019 sp o 000 0 eng d m o h cr ||||||||||| 2072-6694 10.3390/cancers11101615 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Rajković, Nemanja, autor aut (orcid)0000-0002-3845-3780 Li, Xingyu, autor aut (orcid)0000-0002-3494-2552 Plataniotis, Konstantinos N., autor aut (orcid)0000-0003-3647-5473 Todorović‐Raković, Nataša, autor aut (orcid)0000-0001-8975-3129 Milovanović, Jelena, autor aut (orcid)0000-0002-5778-5393 Kanjer, Ksenija, autor aut Radulović, Marko, autor aut (orcid)0000-0002-2314-7457 Milošević, Nebojša T., autor aut (orcid)0000-0002-0589-1848 Size and Shape Filtering of Malignant Cell Clusters within Breast Tumors Identifies Scattered Individual Epithelial Cells as the Most Valuable Histomorphological Clue in the Prognosis of Distant Metastasis Risk Velicko Vranes; Nemanja Rajković; Xingyu Li; Konstantinos N. Plataniotis; Nataša Todorović‐Raković [et al.] s.n. 2019 páginas 1615-1615 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Cancers 2072-6694 Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid, sjr Acceso abierto — GOLD Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy. Citas OpenAlex: 18 | Cuartil SJR: Q1 Breast cancer Stroma Cytokeratin Metastasis Medicine Receiver operating characteristic Pathology Oncology Internal medicine Cancer Cancers 2072-6694 vol. 11, n. 10, p. 1615-1615, (2019) doi:10.3390/cancers11101615 https://doi.org/10.3390/cancers11101615 Texto completo vía DOI Versión publicada https://www.mdpi.com/2072-6694/11/10/1615/pdf?version=1571750234 Acceso abierto ART 00000nab a2200000 4500 10.3390_fractalfract10060357 OREX-CRIS 261111s2026 sp o 000 0 eng d m o h cr ||||||||||| 2504-3110 10.3390/fractalfract10060357 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Speckter, Herwin, autor aut (orcid)0000-0002-9904-2395 Radulović, Marko, autor aut (orcid)0000-0002-2314-7457 Gonzalez, Ivan, autor aut (orcid)0009-0008-6043-6718 Hernandez, Giancarlo, autor aut Bido, Jose, autor aut Rivera, Diones, autor aut Suazo, Luis, autor aut (orcid)0000-0003-0227-6775 Valenzuela, Santiago, autor aut (orcid)0009-0003-4136-2745 Peralta, Ismael, autor aut (orcid)0000-0002-1162-1318 Paulino, Jeffrey, autor aut Bernard, Teuddis, autor aut Ramirez, Issael, autor aut (orcid)0000-0002-1266-9460 Stoeter, Peter, autor aut (orcid)0000-0003-4045-4990 Individualized Prediction of Meningioma Response to Gamma Knife Radiosurgery Using Nested Consensus Machine Learning with 3D Fractal, Lacunarity and Radiomic Features from MRI Velicko Vranes; Herwin Speckter; Marko Radulović; Ivan Gonzalez; Giancarlo Hernandez [et al.] s.n. 2026 páginas 357-357 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Fractal and Fractional 2504-3110 Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid, sjr Acceso abierto — GOLD Background: This study aimed to develop a fully nested, information leakage-free machine-learning workflow to predict the volumetric response of meningioma to Gamma Knife radiosurgery (GKRS) from pre-treatment MRI and to compare the predictive value of radiomic, fractal, lacunarity and clinical/radiosurgical features. GKRS is widely used for treating meningiomas because of its high precision and efficacy. Variability in tumor volumetric response highlights the need for reliable predictors of treatment outcome. Methods: This retrospective cohort study included 204 patients treated with GKRS for grade I meningioma. Radiomic, fractal and lacunarity features were extracted from pre-treatment CE-T1w 3-Tesla MRIs. Feature signatures were generated using a machine-learning workflow incorporating five feature selectors based on a consensus principle to reduce spurious feature selection, followed by five classifiers to predict binary outcome. Results: The models demonstrated consistent predictive performance in the test folds, with AUC values from 0.77 to 0.84. Supplementing radiomic features with clinical, fractal or lacunarity features did not improve predictive performance. Conclusions: Radiomic features showed the strongest predictive value for meningioma volumetric response to GKRS. Darker intratumoral intensity values were associated with a favorable volumetric response, possibly reflecting biologically less active tumor regions. The supplied code enables individual-level prediction for newly encountered patients. Citas OpenAlex: 0 | Cuartil SJR: Q2 Lacunarity Feature (linguistics) Workflow Radiosurgery Predictive value Radiomics Meningioma Pattern recognition (psychology) Fractal and Fractional 2504-3110 vol. 10, n. 6, p. 357-357, (2026) doi:10.3390/fractalfract10060357 https://doi.org/10.3390/fractalfract10060357 Texto completo vía DOI Versión publicada https://doi.org/10.3390/fractalfract10060357 Acceso abierto ART 00000nab a2200000 4500 10.3390_ijms21124434 OREX-CRIS 261111s2020 sp o 000 0 eng d m o h cr ||||||||||| 1422-0067 1661-6596 10.3390/ijms21124434 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Vujasinović, Tijana, autor aut (orcid)0000-0002-6075-7246 Rajković, Nemanja, autor aut (orcid)0000-0002-3845-3780 Kanjer, Ksenija, autor aut Milošević, Nebojša T., autor aut (orcid)0000-0002-0589-1848 Radulović, Marko, autor aut (orcid)0000-0002-2314-7457 Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections Velicko Vranes; Tijana Vujasinović; Nemanja Rajković; Ksenija Kanjer; Nebojša T. Milošević [et al.] s.n. 2020 páginas 4434-4434 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier International Journal of Molecular Sciences 1422-0067 Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid, sjr Acceso abierto — GOLD Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0-255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0-130, 130-160, 160-180, 180-200, 200-220, 220-240, and 240-255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities. Citas OpenAlex: 8 | Cuartil SJR: Q1 Immunostaining Cytokeratin Pathology Context (archaeology) Breast cancer Immunohistochemistry Medicine Biology Internal medicine Oncology International Journal of Molecular Sciences 1422-0067 vol. 21, n. 12, p. 4434-4434, (2020) doi:10.3390/ijms21124434 https://doi.org/10.3390/ijms21124434 Texto completo vía DOI Versión publicada https://www.mdpi.com/1422-0067/21/12/4434/pdf?version=1592827147 Acceso abierto ART 00000nab a2200000 4500 10.3390_jcm14092896 OREX-CRIS 261111s2025 sp o 000 0 eng d m o h cr ||||||||||| 2077-0383 10.3390/jcm14092896 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Speckter, Herwin, autor aut (orcid)0000-0002-9904-2395 Radulović, Marko, autor aut (orcid)0000-0002-2314-7457 Lazo, Erwin, autor aut (orcid)0000-0003-4714-2340 Hernandez, Giancarlo, autor aut Bido, José, autor aut Rivera, Diones, autor aut Suazo, Luis, autor aut (orcid)0000-0003-0227-6775 Valenzuela, Santiago, autor aut (orcid)0009-0003-4136-2745 Stoeter, Peter, autor aut (orcid)0000-0003-4045-4990 Prediction of Pituitary Adenoma’s Volumetric Response to Gamma Knife Radiosurgery Using Machine Learning-Supported MRI Radiomics Velicko Vranes; Herwin Speckter; Marko Radulović; Erwin Lazo; Giancarlo Hernandez [et al.] s.n. 2025 páginas 2896-2896 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Journal of Clinical Medicine 2077-0383 Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid, sjr Acceso abierto — GOLD Background/Objectives: Gamma knife radiosurgery (GKRS) is widely performed as an adjuvant management of patients with residual or recurrent pituitary adenoma (PA). However, the variability in the tumor volume response to GKRS emphasizes the need for reliable predictors of treatment outcomes. The application of radiomics, an analytical approach for quantitative imaging, remains unexplored in predicting treatment responses for PAs. This study aimed to pioneer the use of radiomic MRI analysis to predict the volumetric response of PA to GKRS. Methods: This retrospective observational cohort study involved 81 patients who underwent GKRS for PA. Pre-treatment 3-Tesla MRI scans were used to extract radiomic features capturing the intensity, shape, and texture of the tumors. Radiomic signatures were generated using the least absolute shrinkage and selection operator (LASSO) for feature selection, in conjunction with several classifiers: random forest, naïve Bayes, kNN, logistic regression, neural network, and SVM. Results: The models demonstrated predictive performance in the test folds, with AUC values ranging from 0.759 to 0.928 and R2 values between 0.272 and 0.665. Single-sequence T1w, dual-sequence T1w + CE-T1w, and multi-modality including clinicopathological (CP) parameters (CP + T1w + CE-T1w) achieved rather similar prognostic performance in the test folds, with respective AUCs of 0.928, 0.899, and 0.909. All these radiomics models significantly outperformed a benchmark model involving only CP features (AUC = 0.846). Conclusions: This study represents a radiomic analysis focused on predicting the volume response of PAs to GKRS to facilitate treatment individualization. The developed MRI-based radiomics models exhibited superior classification performance compared with the benchmark model composed solely of standard clinicopathological parameters. Citas OpenAlex: 5 | Cuartil SJR: Q2 Medicine Pituitary adenoma Radiomics Logistic regression Receiver operating characteristic Radiosurgery Lasso (programming language) Radiology Nuclear medicine Artificial intelligence Journal of Clinical Medicine 2077-0383 vol. 14, n. 9, p. 2896-2896, (2025) doi:10.3390/jcm14092896 https://doi.org/10.3390/jcm14092896 Texto completo vía DOI Versión publicada https://www.mdpi.com/2077-0383/14/9/2896/pdf?version=1745388142 Acceso abierto ART 00000nab a2200000 4500 10.4064_bc124-9 OREX-CRIS 261111s2021 sp 000 0 eng d m o h cr ||||||||||| 0137-6934 1730-6299 10.4064/bc124-9 doi OREX-CRIS spa rda OREX-CRIS eng Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Milošević, Nebojša T., autor aut (orcid)0000-0002-0589-1848 Digital images of multipolar neurons from the human dentate nucleus: topologic and morphometric analysis accompanied with the classification by cluster analysis Velicko Vranes; Nebojša T. Milošević s.n. 2021 páginas 95-109 recurso en línea texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Banach Center Publications 0137-6934 Registro importado desde CRIS OREX Digital. Fuentes: openalex, orcid Acceso restringido The present study shows morphometric analyses of the 2D digital image of a neuron from the human dentate nucleus. Based on the minimum number of parameters that describe three image properties, this study improves the existing anatomical classification Citas OpenAlex: 0 Dentate nucleus Nucleus Digital image analysis Neuroscience Pattern recognition (psychology) Cluster (spacecraft) Digital image Artificial intelligence Image (mathematics) Biology Banach Center Publications 0137-6934 vol. 124, p. 95-109, (2021) doi:10.4064/bc124-9 https://doi.org/10.4064/bc124-9 Texto completo vía DOI Versión publicada ART 00000nam a2200000 4500 cris-14c65aa1 OREX-CRIS 261111s2019 sp 000 0 spa d m o h cr ||||||||||| OREX-CRIS spa rda OREX-CRIS spa Marte Martines, Katherine, autor aut Duran, Yocaira, autor aut Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Eficiencia del desarrollo de las competencias específicas de los contenidos procedimentales y conceptuales de las operaciones básicas de la aritmética en tercero de secundaria del Liceo María Teresa Quidiello en el periodo escolar 2018-2019 Marte Martines, Katherine; Duran, Yocaira; Vranes, Velicko Instituto Tecnológico de Santo Domingo (INTEC) 2019 texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Registro importado desde CRIS OREX Digital. Fuentes: koha Acceso restringido La presente investigación acción tiene un enfoque cualitativo, con miras a aportar estrategias y metodologías que provoquen un cambio en el comportamiento y accionar del docente y de los estudiantes de tercero de secundaria del Liceo María Teresa Quidiello, referente a los contenidos de aritmética básica. Para lograr mejorar y enriquecer las competencias específicas de los mediadores aritméticos en los estudiantes de tercero de secundaria, se han planteado estrategias como, el aprendizaje colaborativo, la atención a la diversidad de aprendizaje, la creatividad e innovación y la contextualización de los contenidos, sustentadas en teoría y talleres prácticos que sostienen que estas estrategias pueden favorecer el proceso de enseñanza aprendizaje de los estudiantes. Esta investigación se caracterizará por ser participativa, donde los estudiantes de tercero del Liceo María Teresa Quidiello, trabajarán para mejorar sus propias prácticas; conjuntamente se abordará un enfoque colaborativo en el proceso de enseñanza aprendizaje. Además, se seguirá una espiral introspectiva es decir una espiral de ciclos de planificación, acción, observación y reflexión. Aritmética Educación inclusiva Estrategias de aprendizaje Investigación cualitativa BK 00000nam a2200000 4500 cris-586c2b0a OREX-CRIS 261111s2015 sp 000 0 spa d m o h cr ||||||||||| OREX-CRIS spa rda OREX-CRIS spa Vranes, Velicko, autor aut (orcid)0000-0001-8907-7781 Fundamentos de biofísica Vranes, Velicko Instituto Tecnológico de Santo Domingo 2015 texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Registro importado desde CRIS OREX Digital. Fuentes: koha Acceso restringido Biofísica BK