Radioimmunotherapy versus autologous hematopoietic stem cell transplantation in relapsed/refractory follicular lymphoma: a Fondazione Italiana Linfomi multicenter, randomized, phase III trial.

BACKGROUND: Optimal consolidation for young patilents with relapsed/refractory (R/R) follicular lymphoma (FL) remains uncertain in the rituximab era, with an unclear benefit of autologous stem cell transplantation (ASCT). The multicenter, randomized, phase III FLAZ12 (NCT01827605) trial compared anti-CD20 radioimmunotherapy (RIT) with ASCT as consolidation after chemoimmunotherapy, both followed by rituximab maintenance. PATIENTS AND METHODS: Patients (age 18-65 years) with R/R FL and without significant comorbidities were enrolled and treated with three courses of conventional, investigator-chosen chemoimmunotherapies. Those experiencing at least a partial response were randomized 1 : 1 to ASCT or RIT before CD34+ collection, and all received postconsolidation rituximab maintenance. Progression-free survival (PFS) was the primary endpoint. The target sample size was 210 (105/group). RESULTS: Between August 2012 and September 2019, of 164 screened patients, 159 were enrolled [median age 57 (interquartile range 49-62) years, 55% male, 57% stage IV, 20% bulky disease]. The study was closed prematurely because of low accrual. Data were analyzed on 8 June 2023, on an intention-to-treat basis, with a 77-month median follow-up from enrollment. Of the 141 patients (89%), 70 were randomized to ASCT and 71 to RIT. The estimated 3-year PFS in both groups was 62% (hazard ratio 1.11, 95% confidence interval 0.69-1.80, P = 0.6662). The 3-year overall survival also was similar between the two groups. Rates of grade ≥3 hematological toxicity were 94% with ASCT versus 46% with RIT (P < 0.001), and grade ≥3 neutropenia occurred in 94% versus 41%, respectively (P < 0.001). Second cancers occurred in nine patients after ASCT and three after radioimmunotherapy (P = 0.189). CONCLUSIONS: Even if prematurely discontinued, our study did not demonstrate the superiority of ASCT versus RIT. ASCT was more toxic and demanding for patients and health services. Both strategies yielded similar, favorable long-term outcomes, suggesting that consolidation programs milder than ASCT require further investigation in R/R FL.

Magnetometer-free Kalman filter for motor-based assessment of prodromal Parkinson's disease.

Observing the kinematics of specific motor tasks, such as finger tapping (FT), provides an objective and consistent quantification of the severity of neurodegenerative diseases. However, the current clinical practice mostly relies on visual observations performed by the clinician. Thus, the assessment is subjective. In this paper, we propose a magnetometer-free Kalman filter (KF) to assess FT features using wearable, inertial sensors. The KF was used to assess features during two different FT tasks, namely forefinger tapping (FTAP) and thumb-forefinger tapping (THFF). The proposed KF was validated against a camera-based reference and compared with a strap-down integration-based method. Comparison between KF method and camera reference showed low discrepancies in terms of root mean square error (RMSE) for considered features: namely number of repetitions (RMSE < 0.7), tapping frequency (RMSE < 0.1 Hz), and amplitude (RMSE < 2.6 deg). An high correlation coefficient between amplitudes was also obtained. The proposed KF performed better than the strap-down integration method on both FT tasks, showing lower RMSE on every feature as well as a higher correlation coefficient. Clinical Relevance- The wearable setup, as well as the proposed magnetometer-free KF, may provide a low-cost, easyto- use, non-invasive motion tracking system for protocols aimed to assess motor performances in neurodegenerative disorders.

Hypersexuality as a tip of the iceberg of a primary psychopathology: a joined position statement of the Italian Society of Andrology and Sexual Medicine (SIAMS) and of the Italian Society of Psychopathology (SOPSI).

In the last years, hypersexual behavior has been broadly scientifically studied. The interest in this topic, belonging to psycho-sexology and sexual medicine, has been due to its still unclear aetiology, nature, and its manifestation in relationship with several organic and psychopathological conditions. So, the specialist (the psychologist, psychiatrist, endocrinologist, neurologist) may encounter some difficulties in diagnosing and managing this symptom. The first main objective of this position statement, which has been developed in collaboration between the Italian Society of Andrology and Sexual Medicine (SIAMS) and the Italian Society of Psychopathology (SOPSI) is to give to the reader evidence about the necessity to consider hypersexuality as a symptom related to another underlying condition. Following this consideration, the second main objective is to give specific statements, for the biopsychosocial assessment and the diagnosis of hypersexual behavior, developed on the basis of the most recent literature evidence. To develop a psycho-pharmacological treatment tailored on patients' needs, our suggestion is to assess the presence of specific comorbid psychopathological and organic conditions, and the impact of pharmacological treatments on the presence of an excess of sexual behavior. Finally, a suggestion of a standardized psychometric evaluation of hypersexuality will be given.

How Dominant Hand and Foot Dexterity May Reveal Dementia Onset: A Motor and Cognitive Dual-Task Study.

The combined provision of an arising number of dementia cases, and the substantial absence of effective treatments, led the scientific community toward the identification of early phases of this condition. Such an effort aims at the recognition of therapeutic windows and the characterization of the disease's different grades. In the last years, Motor and Cognitive Dual-Tasks (MCDT) have been widely used to address the early diagnosis of several neurocognitive disorders, among which dementia. Here we present different protocols: the walking MCDT, the toe-tapping MCDT, and the forefinger-tapping MCDT. Moreover, each task has been performed under different cognitive conditions: no cognitive effort, counting backwards by 1, 3, and 7. In this work, we report the results obtained through the combination of different motor and cognitive tasks, and we present 2 brand-new MCDT protocols, attempting to identify a sweet-spot for early diagnosis of dementia.

Biomechanical parameters assessment for the classification of Parkinson Disease using Bidirectional Long Short-Term Memory.

Nowadays objective and efficient assessment of Parkinson Disease (PD) with machine learning techniques is a major focus for clinical management. This work presents a novel approach for classification of patients with PD (PwPD) and healthy controls (HC) using Bidirectional Long Short-Term Neural Network (BLSTM). In this paper, the SensHand and the SensFoot inertial wearable sensors for upper and lower limbs motion analysis were used to acquire motion data in thirteen tasks derived from the MDS-UPDRS III. Sixty-four PwPD and fifty HC were involved in this study. One hundred ninety extracted spatiotemporal and frequency parameters were applied as a single input against each subject to develop a recurrent BLSTM to discriminate the two groups. The maximum achieved accuracy was 82.4%, with the sensitivity of 92.3% and specificity of 76.2%. The obtained results suggest that the use of the extracted parameters for the development of the BLSTM contributed significantly to the classification of PwPD and HC.

Physiological response to Vibro-Acoustic stimulation in healthy subjects: a preliminary study.

Over the last decade Vibro-Acoustic Therapy (VAT) was used for several clinical applications. This paper investigates the use of AcusticA®, an innovative VAT solution represented by a wooden chaise longue that follows the construction principles of a "musical instrument that stimulates the whole body" in relation to the sound frequencies produced by the music tracks. Ten healthy young subjects were enrolled for this study. Wearable sensors were used to monitor the human physiological response during the VAT session but also during a traditional acoustic therapy (AT) to highlight similarity and differences of those stimulations. Signals from heart activity, brain activity and electrodermal activity were analyzed to investigate the response during the non-stimulated and the stimulated phases. Additionally, two supervised classification algorithms were used to investigate whether the extracted instances could be grouped into two different groups. The results identify a trend of the attention and meditation features extracted from brain activity, which pointed out the relax efficacy of the VAT.Clinical Relevance - There are not significant differences (p<0.05) in the physiological response between the VAT and the AT stimulation, but during the VAT the alpha coefficients were significant different during the stimulated phase. Finally, the classification algorithms were able to classify the groups with an accuracy equal to 100% in the best case.

SensRing, a novel wearable ring-shaped device for objective analysis of reachto-grasp movements.

Reach-to-grasp actions have been recently studied to highlight how intentions influence action planning and shapes the movement kinematics. Reach-to-grasp (RG) kinematics can reveal important information on motor planning and control in several pathologies, including neurodegenerative diseases. Current methods are mainly based on optoelectronic analysis systems, which provide accurate movement tracking but are expensive, time-consuming, and limited to constrained research-oriented space. In this study, we proposed an innovative, non-invasive, and easy-to-use ringshaped wearable system, named SensRing, able to record inertial data during the movement. To ensure accurate and precise measures, which are mandatory for clinical practice, a preliminary technical validation of the SensRing with respect to the Vicon (i.e., gold standard for motion analysis) was performed on two finger tapping exercises. Preliminary results pointed out very low discrepancies in terms of absolute errors (AbsErr) between the values of repetitions (AbsErr≤0.8), frequency (AbsErr=0.04Hz) and amplitude (AbsErr≤2.7deg) measured by the two systems, as well as high correlation between the measures obtained with the inertial and optical system. Therefore, inertial data from the SensRing were used in a "reach-to-grasp and move" protocol to calculate the performance of a group of healthy young subjects during three RG and move sequences. Particularly, subjects were instructed to reach and grasp a bottle to drink (DRINK), to place it on the table (IND) or to pass it to another partner (SOC). Results showed that SensRing could identify that, in the RG phase, different intentions determine different kinematic parameters of grasping the same object. As concerns the phase of moving, if the movement is different (drink vs IND/SOC) it's easier to find differences between the tasks, but also when the action is the same but with different social intent (IND vs SOC) SensRing found a significant difference.

The Iterative Model of Ethical Analysis for Large-Scale Implementation Of ICT Solutions.

This manuscript presents a model and the methodology to understand and define the ethical management of the large-scale implementation of ICT solutions for Active and Healthy Ageing. Based on project expertise, including experience from the Pharaon project Horizon 2020, this model includes an understanding of the main ethical challenges and the development of the necessary guidelines, measures, and tools for different stakeholder profiles. This model extends beyond conventional ethical guidelines, providing a methodology to actively discuss ethical and societal challenges within a project based on interactive and iterative dialogue between the entire value-chain of stakeholders. One of the cornerstones in the analysis of challenges is focused attention on policy and societal issues that emerge during a project. Accordingly, the model includes targeted reflections and tools delivered in the context of the recent Covid-19 pandemic. The tools developed in this process are organised in a guide that can be actively used throughout large-scale implementation projects related to ICT solutions.

Fine Motor Assessment With Unsupervised Learning For Personalized Rehabilitation in Parkinson Disease.

Parkinson disease (PD) is a common neurodegenerative disorders characterized by motor and non-motor impairments. Since the quality of life of PD patients becomes poor while pathology develops, it is imperative to improve the identification of personalized rehabilitation and treatments approaches based on the level of the neurodegeneration process. Objective and precise assessment of the severity of the pathology is crucial to identify the most appropriate treatments. In this context, this paper proposes a wearable system able to measure the motor performance of PD subjects. Two inertial devices were used to capture the motion of the lower and upper limbs respectively, while performing six motor tasks. Forty-one kinematic features were extracted from the inertial signals to describe the performance of each subjects. Three unsupervised learning algorithms (k-Means, Self-organizing maps (SOM) and hierarchical clustering) were applied with a blind approach to group the motor performance. The results show that SOM was the best classifier since it reached accuracy equal to 0.950 to group the instances in two classes (mild vs advanced), and 0.817 considering three classes (mild vs moderate vs severe). Therefore, this system enabled objective assessment of the PD severity through motion analysis, allowing the evaluation of residual motor capabilities and fostering personalized paths for PD rehabilitation and assistance.

Machine Learning based Physical Human-Robot Interaction for Walking Support of Frail People.

In the near future robots will permeate our daily life empowering human beings in several activities of daily living. Particular, service robots could actively support indoor mobility tasks thus to enhance the independent living of citizens. They should be able to provide tailored services to citizens to achieve higher physical human-robot interaction. Too often service robots were designed without taking into account end-users functional requirements, which can change with age and geriatric syndromes. In this paper, we present a robot smart control based on machine learning strategies and adaptable to different handgrip strengths. The smart control was implemented on ASTRO robot conceived to be a companion and to support indoor mobility, among other activities. Particularly, three smart controller strategies were implemented and tested with end users from technical and user point of view. The results show promising results that underline the proposed approach was suitable for the proposed application.

Wearable Sensors for Prodromal Motor Assessment of Parkinson's Disease using Supervised Learning.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. Idiopathic hyposmia (IH), a reduced olfactory sensitivity, is a preclinical marker for the pathology and affects >95% of PD patients. In this paper, SensHand V1 and SensFoot V2, two inertial wearable sensors for upper and lower limbs, were developed to acquire motion data in ten tasks of the MDS-UPDRS III. Fifteen healthy subjects of control, 15 IH people, and 15 PD patients were enrolled. Seventy-one parameters per side were computed by spatiotemporal and frequency data analysis, and the most significant were selected to distinguish among the different classes. Performances of supervised learning algorithms (i.e., Support Vector Machine (SVM), and Random Forest (RF)) were compared on two-group and three-group classification and considering upper and lower limbs separately or together as a full system. Excellent results were obtained for healthy vs. patients classification (accuracy 1.00 for RF, and 0.97 for SVM), and good results were achieved by including IH subjects (0.92 F-measure with RF) within a three-group classification. Overall, the best performances were obtained using the full system with an RF classifier. The system is, thus, suitable to support an objective PD diagnosis. Furthermore, combining motion analysis with a validated olfactory screening test, people at risk for PD can be appropriately analyzed, and subtle changes in motor performance that characterize the prodromal phase and the early PD onset can be identified.

Self-reported recurrent pain and medicine use among 15-year-olds: results from the HBSC Italian study.

BACKGROUND: The prevalence of adolescent pain varies considerably across epidemiological studies, and little information is available on pain-related behaviours among adolescents, including medicine use. The aims of this study were: [1] to examine the prevalence of recurrent pain among 15-year-old adolescents in Italy; [2] to investigate the association between recurrent pain and medicine use among boys and girls; and [3] to evaluate the consistency of these associations across Regions. METHODS: The World Health Organization (WHO) collaborative International Health Behaviour in School-aged Children 2013/2014 study collected self-reported data on pain and medicine use from 13611 15-year-old adolescents in 21 Italian Regions. We used multi-level multivariate logistic regression, stratified by gender, to analyse the association between recurrent pain and medicine use for headache, stomachache, nervousness and difficulties in getting to sleep. RESULTS: On average, across all Regions, almost 45% of adolescents reported recurrent headache, more than 30% reported recurrent backache and approximately 30% reported recurrent stomachache. Although the prevalence of both pain and medicine use was much higher among girls, the association between pain and medicine use was similarly strong in adolescents of both genders. Adolescents with recurrent pain proved more likely to use medicines also for non-corresponding pain, nervousness and difficulties in getting to sleep. The association between recurrent pain and medicine use was consistent across Regions despite large inter-regional differences in the prevalence of both phenomena. CONCLUSIONS: Recurrent pain in adolescence is common nationwide. Adolescents with recurrent pain are more likely to use medicines in general. Recurrent pain and medicine use should be addressed by adolescent health policies.

Development of a Novel Wearable Ring-Shaped Biosensor.

We report on the preliminary results obtained out of a wearable module designed to be encompassed within a ring-shaped system aimed at providing healthcare services. The module is composed of two sensors for the measuring of Galvanic Skin Response (GSR) and Heart Rate Variability (HRV). A first device validation was carried out by involving four subjects who were asked to perform tasks providing different stress-related statuses. A comparison of physiological parameters measured by the module with those measured by a commercial HRV-GSR sensor chosen as gold standard was made. Two out of the three HRV parameters and all of the GSR parameters measured with the module resulted consistent (mostly differing less than 10%) with the same parameters measured by the gold standard. The work reported in this paper set a milestone for the realization of a system exploiting sensor fusion to provide active ageing, stress detection, activity recognition and e-health services has been achieved.

Lenalidomide plus R-CHOP21 in newly diagnosed diffuse large B-cell lymphoma (DLBCL): long-term follow-up results from a combined analysis from two phase 2 trials.

Lenalidomide-RCHOP (R2-CHOP21) has been shown to be safe and effective in patients with untreated diffuse large B-cell lymphoma (DLBCL). The aim of this analysis is to report long-term outcome and toxicities in newly diagnosed DLBCL patients who received R2-CHOP21 in two independent phase 2 trials, conducted by Mayo Clinic (MC) and Fondazione Italiana Linfomi (FIL). All patients received R-CHOP21 plus lenalidomide. Long-term progression-free survival (PFS), time to progression (TTP), overall survival (OS) and late toxicities and second tumors were analyzed. Hundred and twelve patients (63 MC, 49 FIL) were included. Median age was 69 years, 88% were stage III-IV. At a median follow-up of 5.1 years, 5y-PFS was 63.5%, 5y-TTP 70.1% and 5y-OS 75.4%; according to cell of origin (COO): 5y-PFS 52.8% vs 64.5%, 5y-TTP 61.6% vs 69.6% and 5y-OS 68.6% vs 74.1% in germinal center (GCB) vs non-GCB respectively. Four patients experienced grade 4-5 late toxicities. Grade ≤ 3 toxicities were infections (N = 4), thrombosis (N = 1) and neuropathy (N = 3). Seven seconds tumors were observed. Long-term follow-up demonstrates that R2-CHOP21 efficacy was maintained with high rates of PFS, TTP, and OS. Lenalidomide appears to mitigate the negative prognosis of non-GCB phenotype. Incidence of therapy-related secondary malignancies and late toxicities were low.

Objective and automatic classification of Parkinson disease with Leap Motion controller.

BACKGROUND: The main objective of this paper is to develop and test the ability of the Leap Motion controller (LMC) to assess the motor dysfunction in patients with Parkinson disease (PwPD) based on the MDS-UPDRSIII exercises. Four exercises (thumb forefinger tapping, hand opening/closing, pronation/supination, postural tremor) were used to evaluate the characteristics described in MDS-UPDRSIII. Clinical ratings according to the MDS/UPDRS-section III items were used as target. For that purpose, 16 participants with PD and 12 healthy people were recruited in Ospedale Cisanello, Pisa, Italy. The participants performed standardized hand movements with camera-based marker. Time and frequency domain features related to velocity, angle, amplitude, and frequency were derived from the LMC data. RESULTS: Different machine learning techniques were used to classify the PD and healthy subjects by comparing the subjective scale given by neurologists against the predicted diagnosis from the machine learning classifiers. Feature selection methods were used to choose the most significant features. Logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) were trained with tenfold cross validation with selected features. The maximum obtained classification accuracy with LR was 70.37%; the average area under the ROC curve (AUC) was 0.831. The obtained classification accuracy with NB was 81.4%, with AUC of 0.811. The obtained classification accuracy with SVM was 74.07%, with AUC of 0.675. CONCLUSIONS: Results revealed that the system did not return clinically meaningful data for measuring postural tremor in PwPD. In addition, it showed limited potential to measure the forearm pronation/supination. In contrast, for finger tapping and hand opening/closing, the derived parameters showed statistical and clinical significance. Future studies should continue to validate the LMC as updated versions of the software are developed. The obtained results support the fact that most of the set of selected features contributed significantly to classify the PwPD and healthy subjects.

Long-term response to recombinant human growth hormone treatment: a new predictive mathematical method.

INTRODUCTION: Recombinant GH has been offered to GH-deficient (GHD) subjects for more than 30 years, in order to improve height and growth velocity in children and to enhance metabolic effects in adults. AIM: The aim of our work is to describe the long-term effect of rhGH treatment in GHD pediatric patients, suggesting a growth prediction model. MATERIAL AND METHODS: A homogeneous database is defined for diagnosis and treatment modalities, based on GHD patients afferent to Hospital Regina Margherita in Turin (Italy). In this study, 232 GHD patients are selected (204 idiopathic GHD and 28 organic GHD). Each measure is shown in terms of mean with relative standard deviations (SD) and 95% confidence interval (95% CI). To estimate the final height of each patient on the basis of few measures, a mathematical growth prediction model [based on Gompertzian function and a mixed method based on the radial basis functions (RBFs) and the particle swarm optimization (PSO) models] was performed. RESULTS: The results seem to highlight the benefits of an early start of treatment, further confirming what is suggested by the literature. Generally, the RBF-PSO method shows a good reliability in the prediction of the final height. Indeed, RMSE is always lower than 4, i.e., in average the forecast will differ at most of 4 cm to the real value. CONCLUSIONS: In conclusion, the large and accurate database of Italian GHD patients allowed us to assess the rhGH treatment efficacy and compare the results with those obtained in other Countries. Moreover, we proposed and validated a new mathematical model forecasting the expected final height after therapy which was validated on our cohort.

Combining olfactory test and motion analysis sensors in Parkinson's disease preclinical diagnosis: a pilot study.

OBJECTIVES: Preclinical diagnosis of Parkinson's disease (PD) is nowadays a topic of interest as the neuropathological process could begin years before the appearance of motor symptoms. Several symptoms, among them hyposmia, could precede motor features in PD. In the preclinical phase of PD, a subclinical reduction in motor skills is highly likely. In this pilot study, we investigate a step-by-step method to achieve preclinical PD diagnosis. MATERIAL AND METHODS: We used the IOIT (Italian Olfactory Identification Test) to screen a population of healthy subjects. We identified 20 subjects with idiopathic hyposmia. Hyposmic subjects underwent an evaluation of motor skills, at baseline and after 1 year, using motion analysis sensors previously created by us. RESULTS: One subject showed significant worsening in motor measurements. In this subject, we further conducted a dopaminergic challenge test monitored with the same sensors and, finally, he underwent [123 I]-FP/CIT (DaTscan) SPECT brain imaging. The results show that he is probably affected by preclinical PD. CONCLUSIONS: Our pilot study suggests that the combined use of an olfactory test and motor sensors for motion analysis could be useful for a screening of healthy subjects to identify those at a high risk of developing PD.

Development and testing of a new cognitive technological tool for episodic memory: A feasibility study.

Cognitive Rehabilitation Therapy refers to any systemic therapy specifically designed to enhance cognitive performance. Recent studies have shown that physical exercise is beneficial for cognitive activity in patients with degenerative diseases. Therefore, the objective of the present study is to provide training for cognitive functions that take advantage of the physical activity in the execution of the task. A feasibility study concerning the application of a new bioengineering technique in cognitive rehabilitation is presented and it divided into two parts. The first one aims at developing a new cognitive tool, called SmartTapestry (ST), for motor and cognitive rehabilitation. The second part aims at understanding its technical viability and its level of sensitivity in measuring the same cognitive domains covered by the standardized tests. The hypothesis of this study is that, despite the introduction of this new variable, the proposed system has the same sensitivity of the traditional tests. The results suggest a good correlation between the two approaches and that SmartTapestry can train the same cognitive functions of traditional cognitive tasks.