Recent developments in sentinel lymph node (SLN) and radio occult lesion localization (ROLL) highlight the need for a multimodal contrast agent, providing better presurgical PET imaging and improved intraoperative mapping thanks to fluorescence detection. For this reason, we have studied a trimodal SLN/ROLL targeting agent (^99mTc-⁶⁸Ga-ICG) with commercially available kits of macroaggregated or nanocolloidal albumin (MA/NC-HSA). ⁶⁸Ga PET imaging does provide better spatial resolution and makes it possible to predict signal intensity during surgery. The presence of ^99mTc assesses the efficacy of these compounds in vitro and also during the surgery procedure. The aim of this study was to optimise the labelling and tagging of these two radiopharmaceuticals and assess their yields and stability. Kits of MA/NC-HSA particles (Pulmocis® and NanoAlbumon®) were used for sequential radiolabelling with ^99mTc and ⁶⁸Ga. Fluorescent tagging was performed using indocyanine green, a tricarbocyanine dye. The ITLC radiochemical purity of the trilabelled MA/NC-HSA was >95%. Fluorescent purity was measured by scanning the strips with a PhotoDynamicEye probe. Finally, in vitro stability tests, performed with DTPA and human serum solutions, assessed the efficacy of fluorescent tagging and radiolabelling.

Nanoparticles of Human Serum Albumin (NC) labelled with ^99mTc are widely used in Nuclear Medicine and represent the gold-standard for the intraoperative detection of the sentinel lymph node in many kinds of cancer, mainly breast cancer and melanoma. A significant amount of radionuclides can be incorporated into the HSA particle, due to the multiple binding sites, and HSA-based nanocolloid catabolism is a fast and easy process that results in innocuous degradation products. NCs labelled with different isotopes represent an interesting radiopharmaceutical for extending diagnostic accuracy and surgical outcome, but the knowledge of the chemical bond between NCs and isotopes has not been fully elucidated, including information on its strength and specificity. The aim of this study is to investigate and compare the physicochemical characteristics of the bond between NCs and ^99mTc and ⁶⁸Ga isotopes. Commercial kits of HSA-based nanocolloid particles (NanoAlbumon^®) were used. For this purpose, we have primarily studied the kinetic orders of NC radiolabelling. Langmuir isotherms and pH effect on radiolabelling were tested and the stability of the radiometal complex was verified through competition reactions carried out in presence of different ligands. The future goal of our research is the development of inexpensive and instant kits, easily labelled with a wide spectrum of diagnostic and therapeutic isotopes, thus facilitating the availability of versatile and multipurpose radiopharmaceuticals.

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.

Background: Neurotensin receptors are overexpressed in several cancer types including pancreatic ductal adenocarcinoma. Three NTR subtypes have been cloned: NTR-1, NTR-2 and NTR-3. The most expressed NTR-1 is not present in normal pancreatic tissue and has a low expression in chronic pancreatitis. Objective: Objective of this study was to test in vitro affinity of the new 68Ga labelled neurotensin analogue DOTA-NT-20.3 (fragment 6-13, Ac-Lys(DOTA)-Pro-Arg(N-CH3)-Arg-Pro-Tyr-Tle-Leu) on the human pancreatic ductal adenocarcinoma cell line AsPC-1. Method: For the preparation of 68Ga-DOTA-NT-20.3, 68GaCl3 solution (eluted from 68Ge/68Ga generator) and 50 μg of precursor (Iason, Graz, Austria) water dissolved were used in an automatic synthesis module. The labeled compound was added to cell culture flask and incubated at 37°C. At various time points after tracer addition up to 80min, cells were recovered, rinsed and counted for radioactivity. Results were expressed as percent binding normalized to 200000 cells and affinity parameters were calculated. Results: Labeling yield was ≥98 %. The molar ratio between labelled and total peptide was about 1/400. AsPC-1 cell line showed rapid uptake of the tracer including surface and internalized binding, tending to a plateau phase 80 min after tracer addition (11%/200.000 cells). The Kd (7.335 pmol) and Bmax (90.52 kBq) value indicated high tracer affinity for AsPC-1cell line especially if compared with the literature data regarding other malignancies (e.g. colonic cancer cell line). Binding sites were 1.09x106 sites per cell. Conclusion: New tracer 68Ga-DOTA-NT-20.3 can be a suitable candidate for the clinical use in patients with pancreatic ductal adenocarcinoma.

Background: Macroaggregated human serum albumin (MAA) properties are widely used in nuclear medicine, labelled with ^99mTc. The aim of this study is to improve the knowledge about the morphology, size, dimension and physical–chemical characteristics of MAA and their bond with ^99mTc and ⁶⁸Ga. Methods: Commercial kits of MAA (Pulmocis^®) were used. Characterisation through experiments based on SEM, DLS and Stokes’ Law were carried out. In vitro experiments for Langmuir isotherms and pH studies on radiolabelling were performed and the stability of the radiometal complex was verified through competition reactions. Results: The study settles the MAA dimension within the range 43–51 μm. The Langmuir isotherm reveals for [^99mTc]MAA: Bmax (46.32), h (2.36); for [⁶⁸Ga]MAA: Bmax (44.54), h (0.893). Dual labelling reveals that MAA does not discriminate different radioisotopes. Experiments on pH placed the optimal pH for labelling with ^99mTc at 6. Conclusion: Radiolabelling of MAA is possible with high efficiency. The nondiscriminatory MAA bonds make this drug suitable for radiolabelling with different radioisotopes or for dual labelling. This finding illustrates the need to continue investigating MAA chemical and physical characteristics to allow for secure labelling with different isotopes.

Background: 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. Objective: 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. Methods: 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. Results: 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). Conclusions: 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.