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"Bacchin, Matteo"
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Incremental value of amyloid-PET versus CSF in the diagnosis of Alzheimer’s disease
PurposeTo compare the incremental diagnostic value of amyloid-PET and CSF (Aβ42, tau, and phospho-tau) in AD diagnosis in patients with mild cognitive impairment (MCI) or mild dementia, in order to improve the definition of diagnostic algorithm.MethodsTwo independent dementia experts provided etiological diagnosis and relative diagnostic confidence in 71 patients on 3 rounds, based on (1) clinical, neuropsychological, and structural MRI information alone; (2) adding one biomarker (CSF amyloid and tau levels or amyloid-PET with a balanced randomized design); and (3) adding the other biomarker.ResultsAmong patients with a pre-biomarker diagnosis of AD, negative PET induced significantly more diagnostic changes than amyloid-negative CSF at both rounds 2 (CSF 67%, PET 100%, P = 0.028) and 3 (CSF 0%; PET 78%, P < 0.001); PET induced a diagnostic confidence increase significantly higher than CSF on both rounds 2 and 3.ConclusionsAmyloid-PET should be prioritized over CSF biomarkers in the diagnostic workup of patients investigated for suspected AD, as it provides greater changes in diagnosis and diagnostic confidence.Trial registrationEudraCT no.: 2014-005389-31
Journal Article
Point mutations of the mitochondrial chaperone TRAP1 affect its functions and pro-neoplastic activity
The mitochondrial chaperone TRAP1 is a key regulator of cellular homeostasis and its activity has important implications in neurodegeneration, ischemia and cancer. Recent evidence has indicated that TRAP1 mutations are involved in several disorders, even though the structural basis for the impact of point mutations on TRAP1 functions has never been studied. By exploiting a modular structure-based framework and molecular dynamics simulations, we investigated the effect of five TRAP1 mutations on its structure and stability. Each mutation differentially impacts long-range interactions, intra and inter-protomer dynamics and ATPase activity. Changes in these parameters influence TRAP1 functions, as revealed by their effects on the activity of the TRAP1 interactor succinate dehydrogenase (SDH). In keeping with this, TRAP1 point mutations affect the growth and migration of aggressive sarcoma cells, and alter sensitivity to a selective TRAP1 inhibitor. Our work provides new insights on the structure-activity relationship of TRAP1, identifying crucial amino acid residues that regulate TRAP1 proteostatic functions and pro-neoplastic activity.
Journal Article
Metabolomic Profile and Antioxidant/Anti-Inflammatory Effects of Industrial Hemp Water Extract in Fibroblasts, Keratinocytes and Isolated Mouse Skin Specimens
Industrial hemp is a multiuse crop whose phytocomplex includes terpenophenolics and flavonoids. In the present study, the phenolic and terpenophenolic compounds were assayed in the water extract of the hemp variety Futura 75. Protective effects were also investigated in human fibroblast and keratinocytes and isolate mouse skin specimens, which were exposed to hydrogen peroxide and/or to the extract (1–500 µg/mL). The results of phytochemical analysis suggested the cannabidiol, cannabidiolic acid and rutin as the prominent phytocompounds. In the in vitro system represented by human keratinocytes and fibroblasts, the hemp extract was found to be able to protect cells from cytotoxicity and apoptosis induced by oxidative stress. Moreover, modulatory effects on IL-6, a key mediator in skin proliferation, were found. In isolated rat skin, the extract reduced hydrogen peroxide-induced l-dopa turnover, prostaglandin-E2 production and the ratio kynurenine/tryptpophan, thus corroborating anti-inflammatory/antioxidant effects. The in silico docking studies also highlighted the putative interactions between cannabidiol, cannabidiolic acid and rutin with tyrosinase and indoleamine-2,3-dioxygenase, involved in l-dopa turnover and tryptophan conversion in kynurenine, respectively. In conclusion, the present findings showed the efficacy of hemp water extract as a skin protective agent. This could be partly related to the extract content in cannabidiol, cannabidiolic acid and rutin.
Journal Article
Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study
Background Postural instability and gait difficulties are key symptoms of Parkinson's disease (PD), elevating the risk of falls substantially. Falls afflict 35% to 90% of PD patients, representing a major challenge in managing the condition. Accurate prediction of fall risk and identification of contributing factors are essential for timely interventions. Objectives Our objective was to develop and validate a machine learning (ML) algorithm across multiple centers in Italy to accurately forecast fall risk and identify related factors using routinely collected clinical data. Methods Patient data from two Italian centers (N = 251) were divided into a training cohort (N = 164) for ML model development and a validation cohort (N = 87). External validation was conducted on a subset of PPMI study patients (N = 65). We compared the performance of logistic regression (LR) and Support Vector Classifier (SVC) models trained on clinical data. The Shapley Additive exPlanations (SHAP) method was employed to examine the predictive power of individual variables. Results In the training set, SVC outperformed LR slightly (AUC: LR = 0.779 ± 0.054, SVC = 0.792 ± 0.056). However, LR demonstrated better prediction accuracy in both internal (AUC: LR = 0.753, SVC = 0.733) and external validation cohorts (AUC: LR = 0.714, SVC = 0.676). SHAP analysis on the LR model revealed associations between fall risk and both motor and non‐motor variables. Conclusions ML‐based models effectively estimate fall risk across different clinical centers, enabling tailored interventions to enhance PD patients' quality of life. Challenges persist in predicting falls in US‐based patients due to demographic and healthcare system differences.
Journal Article
Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study
The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD.
The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis.
The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers.
The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy).
The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.
Journal Article
Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized
The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.
Journal Article
Phenolic Content and Antimicrobial and Anti-Inflammatory Effects of Solidago virga-aurea, Phyllanthus niruri, Epilobium angustifolium, Peumus boldus, and Ononis spinosa Extracts
Prostatitis is an inflammatory condition that is related to multiple infectious agents, including bacteria and fungi. Traditional herbal extracts proved efficacious in controlling clinical symptoms associated with prostatitis. In this context, the aim of the present study was to explore the efficacy of extracts from Solidago virga-aurea, Ononis spinosa, Peumus boldus, Epilobium angustifolium, and Phyllanthus niruri against bacterial (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus cereus) and fungi strains (Candida albicans; C. tropicalis) involved in prostatitis. Additionally, anti-mycotic effects were tested against multiple species of dermatophytes (Trichophyton rubrum, T. tonsurans, T. erinacei, Arthroderma crocatum, A. quadrifidum, A. gypseum, A. currey, and A. insingulare). Antioxidant effects were also evaluated in isolated rat prostates challenged with lipopolysaccharide (LPS), and phytochemical analyses were conducted to identify and quantify selected phenolic compounds, in the extracts. Finally, a bioinformatics analysis was conducted to predict putative human and microbial enzymes targeted by extracts’ phytocompounds and underlying the observed bio-pharmacological effects. The phytochemical analysis highlighted that rutin levels could be crucial for explaining the highest antibacterial activity of P. boldus extract, especially against E. coli and B. cereus. On the other hand, in the E. angustifolium extract, catechin concentration could partially explain the highest efficacy of this extract in reducing lipid peroxidation, in isolated rat prostates stimulated with LPS. Concluding, the results of the present study showed moderate antimicrobial and anti-inflammatory effects induced by water extracts of S. virga-aurea, P. boldus, E. angustifolium, P. niruri, and O. spinosa that could be related, at least partially, to the phenolic composition of the phytocomplex.
Journal Article
Psychometric properties and clinical correlates of the Frontal Behaviour Inventory in progressive supranuclear palsy: data from the PSP-NET
Objectives
Neuropsychiatric symptoms, such as apathy, disinhibition and irritability, are common in Progressive Supranuclear Palsy (PSP). The Frontal Behaviour Inventory (FBI) is a useful instrument for the evaluation of behavioural disorders in neurodegenerative diseases. The main goal of the present study was to explore the psychometric properties of the FBI in PSP.
Design, setting and participants
FBI was administered to the PSP-NET cohort including Italian patients diagnosed according to the Movement Disorder Society criteria. Patients underwent a clinical interview, a motor evaluation, extensive cognitive and behavioural testing.
Results
Two hundred and eight subjects were included in this study. The internal consistency was high (Cronbach’s alpha = 0.868) and no improvement of this value was noted upon removal of any item. FBI showed also good acceptability, reliability and validity. The standard error of measurement (SEM) value for FBI total score was 0.169 [SEM = SD √ (1 – Cronbach’s alpha)]. Factor analysis indicated a five-factor structure:
Apathy
,
Behavioural disorders
,
Impulsivity
,
Motor and speech frontal behaviour
and
Executive disorders
that explained the 54.92% of the total variance. Linear regression analysis showed that global cognitive impairment significantly affects both
Apathy
and
Motor and speech frontal behaviour
factors.
Conclusions
In conclusion, FBI is a reliable and valid tool for the assessment of neuropsychiatric symptoms in PSP, despite some constructs, such as euphoria and irritability, are better measured by the NPI. Two third of the cohort was represented by Richardson’s syndrome, thus our data are mainly applicable to such common phenotype. Such data are useful in both clinical and research settings to plan adequate therapeutic interventions and to improve the quality of life of PSP patients and their caregivers.
Journal Article
Show and Grasp: Few-shot Semantic Segmentation for Robot Grasping through Zero-shot Foundation Models
The ability of a robot to pick an object, known as robot grasping, is crucial for several applications, such as assembly or sorting. In such tasks, selecting the right target to pick is as essential as inferring a correct configuration of the gripper. A common solution to this problem relies on semantic segmentation models, which often show poor generalization to unseen objects and require considerable time and massive data to be trained. To reduce the need for large datasets, some grasping pipelines exploit few-shot semantic segmentation models, which are capable of recognizing new classes given a few examples. However, this often comes at the cost of limited performance and fine-tuning is required to be effective in robot grasping scenarios. In this work, we propose to overcome all these limitations by combining the impressive generalization capability reached by foundation models with a high-performing few-shot classifier, working as a score function to select the segmentation that is closer to the support set. The proposed model is designed to be embedded in a grasp synthesis pipeline. The extensive experiments using one or five examples show that our novel approach overcomes existing performance limitations, improving the state of the art both in few-shot semantic segmentation on the Graspnet-1B (+10.5% mIoU) and Ocid-grasp (+1.6% AP) datasets, and real-world few-shot grasp synthesis (+21.7% grasp accuracy). The project page is available at: https://leobarcellona.github.io/showandgrasp.github.io/
Paper