Identification and Biological Characterization of the Pyrazolo[3,4-d]pyrimidine Derivative SI388 Active as Src Inhibitor

Src is a non-receptor tyrosine kinase (TK) whose involvement in cancer, including glioblastoma (GBM), has been extensively demonstrated. In this context, we started from our in-house library of pyrazolo[3,4-d]pyrimidines that are active as Src and/or Bcr-Abl TK inhibitors and performed a lead optimization study to discover a new generation derivative that is suitable for Src kinase targeting. We synthesized a library of 19 compounds, 2a-s. Among these, compound 2a (SI388) was identified as the most potent Src inhibitor. Based on the cell-free results, we investigated the effect of SI388 in 2D and 3D GBM cellular models. Interestingly, SI388 significantly inhibits Src kinase, and therefore affects cell viability, tumorigenicity and enhances cancer cell sensitivity to ionizing radiation

Overview of radiomics in breast cancer diagnosis and prognostication.

Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication

Anxiety and depression in Charcot-Marie-Tooth disease: data from the Italian CMT national registry

Background There is little information about neuropsychiatric comorbidities in Charcot-Marie-Tooth disease (CMT). We assessed frequency of anxiety, depression, and general distress in CMT.Methods We administered online the Hospital Anxiety-Depression Scale (HADS) to CMT patients of the Italian registry and controls. HADS-A and HADS-D scores >= 11 defined the presence of anxiety/depression and HADS total score (HADS-T) >= 22 of general distress. We analysed correlation with disease severity and clinical characteristics, use of anxiolytics/antidepressants and analgesic/anti-inflammatory drugs.Results We collected data from 252 CMT patients (137 females) and 56 controls. CMT patient scores for anxiety (mean +/- standard deviation, 6.7 +/- 4.8), depression (4.5 +/- 4.0), and general distress (11.5 +/- 8.1) did not differ from controls and the Italian population. However, compared to controls, the percentages of subjects with depression (10% vs 2%) and general distress (14% vs 4%) were significantly higher in CMT patients. We found no association between HADS scores and disease duration or CMT type. Patients with general distress showed more severe disease and higher rate of positive sensory symptoms. Depressed patients also had more severe disease. Nineteen percent of CMT patients took antidepressants/anxiolytics (12% daily) and 70% analgesic/anti-inflammatory drugs. Patients with anxiety, depression, and distress reported higher consumption of anxiolytics/antidepressants. About 50% of patients with depression and/or general distress did not receive any specific pharmacological treatment.Conclusions An appreciable proportion of CMT patients shows general distress and depression. Both correlated with disease severity and consumption of antidepressants/anxiolytics, suggesting that the disease itself is contributing to general distress and depression

Frequency, entity and determinants of fatigue in Charcot-Marie-Tooth disease

Fatigue, a disabling symptom in many neuromuscular disorders, has been reported also in Charcot-Marie-Tooth disease (CMT). The presence of fatigue and its correlations in CMT was investigated

Mathematical tools for images

Digital image processing is a vast field of applied mathematics that covers those processes whose inputs and outputs are images and those that extract attributes and patterns from images. In my thesis two different subcategories of digital image processing are investigated: pattern recognition and feature extraction, in particular the recognition of algebraic curves in images and edge detection techniques, and image compression, with particular attention to map-aided techniques. Patter recognition is the study of semi-automated and automated methods for the recognition of pattern and regularities in data. In the first part of my thesis, I present a novel method for the recognition of curvilinear profiles in digital images. The proposed method, semi-automatic for both closed and open planar profiles, is essentially based on a piecewise application of the Hough transform technique. The Hough transform is a known technique used in image analysis and digital image processing to recognize shapes in images. One of the drawbacks of this technique is the need to identify a potentially approximating family of curves before the recognition algorithm can be successfully applied. Thus, we developed an innovative procedure for the recognition of both closed and open curvilinear profiles in 2D digital images, without knowing neither a family of predefined curves nor a predefined look-up table of a prototypal shape. Our method provides a G1 continuous spline curve – eventually containing C0 junctions where cusps occur – which approximates the sought profile. Edge detection is a widely used tool in image processing with the aim of identifying abrupt changes or discontinuities in a digital image. In the second part of my thesis, I present two original edge detection methods, based on Radial Basis Functions interpolation. For the detection of jump discontinuities in 1D problems, we developed an iterative method based on interpolation with Variably Scaled Kernels (VSKs). This is shown to outperform an existing iterative edge detection method based on multiquadric radial basis function interpolation. To extend our purely one-dimensional edge detector to any dimension, we then introduce an innovative non iterative technique that detects edges by identifying the local maxima of the normalized absolute values of the RBF interpolant coefficients. The RBF interpolant is built-upon the compactly supported C2 Wendland function and exploits its advantageous properties to provide a robust and low-cost method. Numerical examples in 1D and 2D are included to illustrate its effectiveness and efficiency. Image compression is a specific type of data compression with the aim of reducing the amount of data necessary for image storage and transmission. Image compression has an increasingly important role in diverse applications, such as remote sensing, videoconferencing, medical imaging and many more. One of the classical approaches to image compression are multi-scale wavelet based methods. They do not always lead to fully satisfactory results as they do not adapt to the local structure of images, such as edges. Techniques to solve this drawback have been developed in recent works. Because of the need to locally adapt the compression methods to the geometry of image, feature extraction plays a significant role also in this case. In the last part of thesis, I present two original multi-scale image compression algorithms that are map-aided, to ensure a better faithfulness of the reconstruction to the original image. These methods use a prediction step with a multiquadric radial basis function interpolant and WENO scheme to determine the shape parameter. For the first method an edge detection procedure is applied to the original image, from this we obtain an edge map that determines the local prediction step. For the second method, instead, we compute different local reconstructions and we use a map to save the best one.Digital image processing is a vast field of applied mathematics that covers those processes whose inputs and outputs are images and those that extract attributes and patterns from images. In my thesis two different subcategories of digital image processing are investigated: pattern recognition and feature extraction, in particular the recognition of algebraic curves in images and edge detection techniques, and image compression, with particular attention to map-aided techniques. Patter recognition is the study of semi-automated and automated methods for the recognition of pattern and regularities in data. In the first part of my thesis, I present a novel method for the recognition of curvilinear profiles in digital images. The proposed method, semi-automatic for both closed and open planar profiles, is essentially based on a piecewise application of the Hough transform technique. The Hough transform is a known technique used in image analysis and digital image processing to recognize shapes in images. One of the drawbacks of this technique is the need to identify a potentially approximating family of curves before the recognition algorithm can be successfully applied. Thus, we developed an innovative procedure for the recognition of both closed and open curvilinear profiles in 2D digital images, without knowing neither a family of predefined curves nor a predefined look-up table of a prototypal shape. Our method provides a G1 continuous spline curve – eventually containing C0 junctions where cusps occur – which approximates the sought profile. Edge detection is a widely used tool in image processing with the aim of identifying abrupt changes or discontinuities in a digital image. In the second part of my thesis, I present two original edge detection methods, based on Radial Basis Functions interpolation. For the detection of jump discontinuities in 1D problems, we developed an iterative method based on interpolation with Variably Scaled Kernels (VSKs). This is shown to outperform an existing iterative edge detection method based on multiquadric radial basis function interpolation. To extend our purely one-dimensional edge detector to any dimension, we then introduce an innovative non iterative technique that detects edges by identifying the local maxima of the normalized absolute values of the RBF interpolant coefficients. The RBF interpolant is built-upon the compactly supported C2 Wendland function and exploits its advantageous properties to provide a robust and low-cost method. Numerical examples in 1D and 2D are included to illustrate its effectiveness and efficiency. Image compression is a specific type of data compression with the aim of reducing the amount of data necessary for image storage and transmission. Image compression has an increasingly important role in diverse applications, such as remote sensing, videoconferencing, medical imaging and many more. One of the classical approaches to image compression are multi-scale wavelet based methods. They do not always lead to fully satisfactory results as they do not adapt to the local structure of images, such as edges. Techniques to solve this drawback have been developed in recent works. Because of the need to locally adapt the compression methods to the geometry of image, feature extraction plays a significant role also in this case. In the last part of thesis, I present two original multi-scale image compression algorithms that are map-aided, to ensure a better faithfulness of the reconstruction to the original image. These methods use a prediction step with a multiquadric radial basis function interpolant and WENO scheme to determine the shape parameter. For the first method an edge detection procedure is applied to the original image, from this we obtain an edge map that determines the local prediction step. For the second method, instead, we compute different local reconstructions and we use a map to save the best one

Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subjects. MM manifestations are usually identified through hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions (CRAB). In particular, the extent of the bone disease is negatively related to a decreased quality of life in patients and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and Magnetic Resonance Imaging (MRI), is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesions in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review, we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At the present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centers. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the coming years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient’s outcomes with the purpose of finding radiological biomarkers for predicting the prognostic flow and therapeutic response of the disease

Development and definition of a simplified scoring system in patients with multiple myeloma undergoing stem cells transplantation on standard computed tomography: myeloma spine and bone damage score (MSBDS)

In clinical practice, there is the need to optimize imaging usage in MM patients. Accordingly, the aim of this paper was to develop a simple computed tomography (CT) scoring method for MM, able to shorten and simplify the interpretation time with good intra- and inter-reader reliability. This method, named MSBDS (Myeloma Spine and Bone Damage Score) was developed with the final aim to use standard total-body CT in the routine practice of MM centres as a complement of standard evaluations in patients undergoing stem cells transplantation