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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Study of the viral removal efficiency in a urban wastewater treatment plant.

Municipal and agricultural wastewater contain a variety of microorganisms and in particular enteric viruses. For the reuse of this treated wastewater it is important to ensure the efficiency of purification treatments and disinfection practices, that have often been insufficient to lower the viral load below the risk level. For this reason, for the routine analysis of recycled waters, the research into pathogenic viruses (e.g. HAV) and classical bacterial parameters (E. coli, enterococci and Salmonella) has to be associated with specific viral indicators such as somatic coliphages, adenovirus and TTV. The results of environmental monitoring, carried out in a wastewater treatment plant, showed the presence of adenovirus DNA in 100% of collected samples and TTV DNA in 95% (19/20) of raw sewage and in 85% (17/20) of the exit samples, while HAV was detected only in 2 samples over 40 (5%). The quantitative analysis has revealed an average reduction of 2 log for adenovirus and 1.58 log for TTV. The bacterial indicators were reduced by 1.74 log and 1.99 log respectively for E. coli and enterococci, while for somatic coliphages an average reduction of 2.2 log was observed. No significant correlation was shown between these parameters, confirming their inadequacy for the virological risk assessment. However the results of adenovirus confirm it as the best indicator to evaluate the efficacy of wastewater depuration plant in eliminating viruses.

Leap motion evaluation for assessment of upper limb motor skills in Parkinson's disease.

The main goal of this study is to investigate the potential of the Leap Motion Controller (LMC) for the objective assessment of motor dysfunctioning in patients with Parkinson's disease (PwPD). The most relevant clinical signs in Parkinson's Disease (PD), such as slowness of movements, frequency variation, amplitude variation, and speed, were extracted from the recorded LMC data. Data were clinically quantified using the LMC software development kit (SDK). In this study, 16 PwPD subjects and 12 control healthy subjects were involved. A neurologist assessed the subjects during the task execution, assigning them a score according to the MDS/UPDRS-Section III items. Features of motor performance from both subject groups (patients and healthy controls) were extracted with dedicated algorithms. Furthermore, to find out the significance of such features from the clinical point of view, machine learning based methods were used. Overall, our findings showed the moderate potential of LMC to extract the motor performance of PwPD.

One-year monthly monitoring of Torque teno virus (TTV) in river water in Italy.

Torque teno virus (TTV) is prevalent worldwide in general populations but at present is not related with any specific pathology. Its presence in faeces and its remarkable environmental stability suggest the possibility of using it as an indicator of faecal contamination in the environment. To evaluate the waterborne spread of TTV and its possible relationship with human pathogen enteric viruses, water samples were collected monthly for a year (May 2004-April 2005) from a river receiving the effluent of the treatment plant of the city of Pisa, concentrated and assayed with bimolecular tests (PCR, RT-PCR). TTV was detected in three samples (25%) while 16% of samples were positive for enteroviruses, 33% for rotaviruses, 8% for noroviruses genotype 1 and 25% for noroviruses genotype 2. Only two TTV samples (June and January) were also positive for rotavirus and norovirus, respectively. The detection of TTV in water confirmed its possible faecal-oral route of transmission but data are still insufficient to draw conclusions about the role of TTV as a viral indicator.

Epidemiological surveillance of human enteric viruses by monitoring of different environmental matrices.

In the aim of studying possible relations between viruses detected in clinical specimens and the ones found in different environmental matrices, in the period May 2004 to April 2005, the collection of faecal samples from gastroenteritis cases and the monthly monitoring of raw and treated wastewater, river water, seawater and mussels were carried out. The viruses considered for environmental monitoring were adenovirus, rotavirus, enterovirus, norovirus, hepatitis A virus (HAV) and Torque teno virus (TTV): they were searched for with PCR and RT-PCR and confirmed by gene sequencing. Faecal coliforms and somatic coliphages' counts were also determined. The surveillance of case detected 45 positive faecal samples out of 255 (17.6%) while 35 of 56 environmental samples (62.5%) resulted positive for at least one of the considered viruses. The detection of the same viral strain in the faeces of gastroenteritis cases and in water was possible for adenovirus and rotavirus, which were also predominant in environmental matrices; thus they could be considered as a reference for risk assessment.

"Atypical" multidrug resistance in human ovarian cancer cell line A2780 selected for resistance to doxorubicin (A2780 DX3).

Human ovarian cancer cells A2780, selected for resistance to doxorubicin (A2780-DX3), are cross-resistant to various other topoisomerase-II-targeted drugs but not to vinblastine. The parental cell line was very sensitive to doxorubicin-, mitoxantrone- or etoposide(VP16)-induced DNA single-strand breaks, under deproteinizing conditions. In contrast, little or no DNA strand breakage was seen in resistant A2780-DX3 cells, even at very high concentrations, indicating a good correlation, with cytotoxicity. No significant alterations in cellular drug uptake were observed in DX3 cells. Further studies showed that the nuclei isolated from resistant cells were also resistant to mitoxantrone- or VP16-induced single-strand breaks, indicating that nuclear modifications in resistant cells are responsible for this resistance. Catalytic activity in crude nuclear extracts from wild-type and DX3 cells was almost equal. However, an assay that specifically measures generation of 5'-protein-linked breaks in 32P-labeled 3 DNA revealed that, DNA cleavage activity in nuclear extract from the DX3 cell line is profoundly resistant to a stimulation by VP16. These data indicate that stimulation of topoisomerase-II-mediated DNA cleavage is responsible for topoisomerase-II-targeted drug-cytotoxicity rather than loss of normal topoisomerase catalytic function. These data support the hypothesis that A2780-DX3 cells display an "atypical" multidrug resistance.

Sister-chromatid exchanges, chromosomal aberrations and cytotoxicity produced by topoisomerase II-targeted drugs in sensitive (A2780) and resistant (A2780-DX3) human ovarian cancer cells: correlations with the formation of DNA double-strand breaks.

Doxorubicin, ellipticine and etoposide are antineoplastic drugs with topoisomerase II inhibitory activity. The relationship between drug-induced sister-chromatid exchanges (SCEs) or chromosomal aberrations (CAs) and cytotoxicity, or drug-induced DNA double-strand breaks (DSBs) and cytotoxicity, or drug-induced SCEs and DSBs was investigated in human ovarian cancer cells sensitive (A2780) and resistant (A2780-DX3) to topoisomerase II inhibitors. 30-min drug treatments produced SCEs, CAs and DSBs in sensitive cells, doxorubicin being more potent than etoposide at equimolar concentrations. The same treatments of resistant (A2780-DX3) cells did not produce chromosomal damage (SCEs, CAs, DSBs) and no cytotoxicity was observed. A plot of cytotoxicity versus SCEs indicated a good correlation between these two parameters for topoisomerase II inhibitors and not for mytomicin C. The plot of DSBs versus SCEs also showed a very good correlation.

Virological control of groundwater quality using biomolecular tests.

Deep groundwater, even if generally protected, could be contaminated by surface or rain water infiltration through soil fractures, septic tanks, cesspits, land irrigation, disposal of wastewater and disposal of muds from depuration systems. The sanitary importance of such possible contamination is related to the different uses of the water and it is at the maximum level when it is intended for human use. Routine microbiological analyses do not consider viruses, only bacterial parameters, as contamination indicators. However, it is known that enteric viruses can survive a long time in deep aquifers and that they may not always be associated with bacterial indicators. The virological analysis of waters intended for drinking use is provided only as an occasional control exercised at the discretion of the sanitary authority. Technological difficulties with obtaining data about groundwater viral contamination led to a study to devise rapid and efficient methods for their detection and the application of these methods to samples from different sources. Four acid nucleic extraction techniques have been tested (classic proteinase K- phenol/chloroform, QIAamp Viral RNA Kit (Qiagen), SV Total RNA Isolation System (Promega) and NucleoSpin Virus L (Macherey-Nagel). Sensitivity and specificity of RT-PCR protocols for entero- (EV), hepatitis A (HAV) and small round structured (SRSV) viruses have been verified. Deep groundwater samples (100 L) were concentrated (2-step tangential flow ultrafiltration) and the concentrate contaminated with serial 10-fold dilutions of a known titre of poliovirus type 3. Extracted RNA was concentrated (microcon-100) and analysed by RT-PCR using specific EV primers and visualising amplification products by agarose gel electrophoresis. In addition, two different methods of RT-PCR for non-cultivable viruses have been tested: (a) RT-PCR and nested RT-PCR for HAV and (b) RT-PCR with generic primers and RT-PCR with specific primers for SRSV. Different specificity tests have been carried out in the presence of some of the commoner microorganisms. The most efficient, sensitive and specific protocols were used to test 35 x 100L deep groundwater samples. Sample concentrates were split with one part treated with chloroform and analysed by cell culture (BGM and Frp/3, derived from FrHK/4, cells) and the other tested by RT-PCR for HAV, EV and SRSV. Results demonstrated the high efficiency of the classic and QIAamp methods. Microcon-100 did not increase the sensitivity of the technique used. The highest sensitivity was observed for RT-PCR with specific primers for SRSV and for nested RT-PCR for HAV. One sample showed a cytopathic effect, not confirmed at the third subculture, while the RT-PCR allowed the detection of echovirus 7. Cell culture did not allow detection of the majority of the enteric viruses while PCR gave sensitive, specific and rapid detection of a range of agents in the same samples. Even if it was impossible to fix a virological quality standard, it would be necessary to find a viral indicator in order to achieve a complete preventive check which would be particularly useful in some cases (e.g. water never used before, after pollution accidents, for seasonal checking).

Assessment of microbial parameters as indicators of viral contamination of aerosol from urban sewage treatment plants.

In order to evaluate possible indicators of viral aerosol contamination in sewage treatment plants, a year-long study was carried out on the relationships between the presence of cytopathogenic viruses and the counts of total bacteria, faecal streptococci and somatic coliphages in samples collected at various distances from the aerosol source (aeration tank). The activated sludge plant studied proved to be a significant source of microbe-bearing aerosol with high levels of viral contamination. When the virus was found in sewage, it was also found in the air, at least in the sites closest to the aeration tank. With regard to the possibility of using the chosen parameters as markers of viral contamination, the total bacteria and faecal streptococci counts were generally positively correlated with viral presence, while coliphage counts yielded no analogous relationship.

Human granulocyte-macrophage colony-stimulating factor is a growth factor active on human ovarian cancer cells.

Human granulocyte-macrophage colony-stimulating factor (GM-CSF) is a regulatory glycoprotein that stimulates the production of granulocytes and macrophages from committed hematopoietic progenitor cells both in vitro and in vivo. In this report, we show that recombinant human GM-CSF enhances colony formation by nonhematopoietic human ovarian cancer cell lines, IGROV-1, A2774, ME-180, Pa-1 and A2780. GM-CSF also enhanced the colony formation by cells obtained from fresh ascites of a patient with ovarian mucinous cystadenocarcinoma and a patient with serous papillary ovarian carcinoma. Our observations were made with GM-CSF concentrations between 0.1 to 1 ng/ml; these concentrations are equivalent to the dosages generally used for bone marrow recovery after chemotherapy.