Intraoperative β-Detecting probe for radio-guided surgery in tumour resection

The development of the β− based radio-guided surgery aims to extend the technique to those tumours where surgery is the only possible treatment and the assessment of the resection would most profit from the low background around the lesion, as for brain tumours. Feasibility studies on meningioma and gliomas already estimated the potentiality of this new treatment. To validate the technique, a prototype of the intraoperative probe detecting β− decays and specific phantoms simulating tumour remnant patterns embedded in healthy tissue have been realized. The response of the probe in this simulated environment is tested with dedicated procedures. This document discusses the innovative aspects of the method, the status of the developed intraoperative β− detecting probe and the results of the preclinical tests

Towards a Radio-guided Surgery with β−\beta^{-} Decays: Uptake of a somatostatin analogue (DOTATOC) in Meningioma and High Grade Glioma

A novel radio guided surgery (RGS) technique for cerebral tumors using $\beta^{-}$ radiation is being developed. Checking the availability of a radio-tracer that can deliver a $\beta^{-}$ emitter to the tumor is a fundamental step in the deployment of such technique. This paper reports a study of the uptake of 90Y labeled (DOTATOC) in the meningioma and the high grade glioma (HGG) and a feasibility study of the RGS technique in these cases.Comment: 21 pages, 5 figure

Measurement of charged particle yields from therapeutic beams in view of the design of an innovative hadrontherapy dose monitor

Particle Therapy (PT) is an emerging technique, which makes use of charged particles to efficiently cure different kinds of solid tumors. The high precision in the hadrons dose deposition requires an accurate monitoring to prevent the risk of under-dosage of the cancer region or of over-dosage of healthy tissues. Monitoring techniques are currently being developed and are based on the detection of particles produced by the beam interaction into the target, in particular: charged particles, result of target and/or projectile fragmentation, prompt photons coming from nucleus de-excitation and back-to-back γ s, produced in the positron annihilation from β + emitters created in the beam interaction with the target. It has been showed that the hadron beam dose release peak can be spatially correlated with the emission pattern of these secondary particles. Here we report about secondary particles production (charged fragments and prompt γ s) performed at different beam and energies that have a particular relevance for PT applications: 12C beam of 80 MeV/u at LNS, 12C beam 220 MeV/u at GSI, and 12C, 4He, 16O beams with energy in the 50–300 MeV/u range at HIT. Finally, a project for a multimodal dose-monitor device exploiting the prompt photons and charged particles emission will be presented

Acoustic analysis in stuttering: a machine-learning study

BackgroundStuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS).ObjectiveWe assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine – SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering.MethodsFifty-three PWS (20 children, 33 younger adults) and 71 age−/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN).ResultsAcoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings.ConclusionAcoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment)

In-vivo range verification analysis with in-beam PET data for patients treated with proton therapy at CNAO

Morphological changes that may arise through a treatment course are probably one of the most significant sources of range uncertainty in proton therapy. Non-invasive in-vivo treatment monitoring is useful to increase treatment quality. The INSIDE in-beam Positron Emission Tomography (PET) scanner performs in-vivo range monitoring in proton and carbon therapy treatments at the National Center of Oncological Hadrontherapy (CNAO). It is currently in a clinical trial (ID: NCT03662373) and has acquired in-beam PET data during the treatment of various patients. In this work we analyze the in-beam PET (IB-PET) data of eight patients treated with proton therapy at CNAO. The goal of the analysis is twofold. First, we assess the level of experimental fluctuations in inter-fractional range differences (sensitivity) of the INSIDE PET system by studying patients without morphological changes. Second, we use the obtained results to see whether we can observe anomalously large range variations in patients where morphological changes have occurred. The sensitivity of the INSIDE IB-PET scanner was quantified as the standard deviation of the range difference distributions observed for six patients that did not show morphological changes. Inter-fractional range variations with respect to a reference distribution were estimated using the Most-Likely-Shift (MLS) method. To establish the efficacy of this method, we made a comparison with the Beam's Eye View (BEV) method. For patients showing no morphological changes in the control CT the average range variation standard deviation was found to be 2.5 mm with the MLS method and 2.3 mm with the BEV method. On the other hand, for patients where some small anatomical changes occurred, we found larger standard deviation values. In these patients we evaluated where anomalous range differences were found and compared them with the CT. We found that the identified regions were mostly in agreement with the morphological changes seen in the CT scan

Diabetic ketoacidosis at the onset of disease during a national awareness campaign: a 2-year observational study in children aged 0-18 years

After a previous survey on the incidence of diabetic ketoacidosis (DKA) at onset of type 1 diabetes in children in 2013-2014 in Italy, we aimed to verify a possible decline in the incidence of DKA at onset during a national prevention campaign

Rigidity in Parkinson's disease: Evidence from biomechanical and neurophysiological measures

: Although rigidity is a cardinal motor sign in patients with Parkinson's disease (PD), the instrumental measurement of this clinical phenomenon is largely lacking, and its pathophysiological underpinning remains still unclear. Further advances in the field would require innovative methodological approaches able to measure parkinsonian rigidity objectively, discriminate the different biomechanical sources of muscle tone (neural or visco-elastic components), and finally clarify the contribution to objective rigidity exerted by neurophysiological responses which have been previously associated with this clinical sign (i.e., the long-latency stretch-induced reflex). Twenty patients with PD (67.3 ± 6.9 years) and 25 age- and sex-matched controls (66.9 ± 7.4 years) were recruited. Rigidity was measured clinically and through a robotic device. Participants underwent robot-assisted wrist extensions at 7 different angular velocities randomly applied, when ON therapy. For each value of angular velocity, several biomechanical (i.e., elastic, viscous and neural components) and neurophysiologic measures (i.e., short- and long-latency reflex and shortening reaction) were synchronously assessed and correlated with the clinical score of rigidity (i.e., Unified Parkinson's Disease Rating Scale - part III subitems for the upper limb). The biomechanical investigation allowed us to measure objective rigidity in PD and estimate the neuronal source of this phenomenon. In patients, objective rigidity progressively increased along with the rise of angular velocities during robot-assisted wrist extensions. The neurophysiological examination disclosed increased long-latency reflexes, but not short-latency reflexes nor shortening reaction, in PD compared with controls. Long-latency reflexes progressively increased according to angular velocities only in patients with PD. Lastly, specific biomechanical and neurophysiological abnormalities correlated with the clinical score of rigidity. Objective rigidity in PD correlates with velocity-dependent abnormal neuronal activity. The observations overall (i.e., the velocity-dependent feature of biomechanical and neurophysiological measures of objective rigidity) would point to a putative subcortical network responsible for objective rigidity in PD which requires further investigation

Handwriting Declines With Human Aging: A Machine Learning Study

Background: Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders. Materials and methods: One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm. Results: Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83). Discussion: Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms

Harmonic Distortion Aspects in Upper Limb Swings during Gait in Parkinson’s Disease

Parkinson’s disease (PD) is responsible for a broad spectrum of signs and symptoms, including relevant motor impairments generally rated by clinical experts. In recent years, motor measurements gathered by technology-based systems have been used more and more to provide objective data. In particular, wearable devices have been adopted to evidence differences in the gait capabilities between PD patients and healthy people. Within this frame, despite the key role that the upper limbs’ swing plays during walking, no studies have been focused on their harmonic content, to which this work is devoted. To this end, we measured, by means of IMU sensors, the walking capabilities of groups of PD patients (both de novo and under-chronic-dopaminergic-treatment patients when in an off-therapy state) and their healthy counterparts. The collected data were FFT transformed, and the frequency content was analyzed. According to the results obtained, PD determines upper limb rigidity objectively evidenced and correlated to lower harmonic contents