The possibility to combine Low Intensity UltraSound (LIUS) and Nanoparticles (NP) could represent a promising strategy for drugs delivery in tumors difficult to treat overcoming resistance to therapies. On one side the NP can carry drugs that specifically target the tumors on the other the LIUS can facilitate and direct the delivery to the tumor cells. In this study, we investigated whether Very Low Intensity UltraSound (VLIUS), at intensities lower than 120 mW/cm2, might constitute a novel strategy to improve delivery to tumor cells. Thus, in order to verify the efficacy of this novel modality in terms of increase selective uptake in tumoral cells and translate speedily in clinical practice, we investigated VLIUS in three different in vitro experimental tumor models and normal cells adopting three different therapeutic strategies. VLIUS at different intensities and exposure time were applied to tumor and normal cells to evaluate the efficiency in uptake of labeled human ferritin (HFt)-based NP, the delivery of NP complexed Firefly luciferase reported gene (lipoplex-LUC), and the tumor-killing of chemotherapeutic agent. Specifically, we found that specific VLIUS intensity (120 mW/cm2) increases tumor cell uptake of HFt-based NPs at specific concentration (0.5 mg/ml). Similarly, VLIUS treatments increase significantly tumor cells delivery of lipoplex-LUC cargos. Furthermore, of interest, VLIUS increases tumor killing of chemotherapy drug trabectedin in a time dependent fashion. Noteworthy, VLIUS treatments are well tolerated in normal cells with not significant effects on cell survival, NPs delivery and drug-induced toxicity, suggesting a tumor specific fashion. Our data shed novel lights on the potential application of VLIUS for the design and development of novel therapeutic strategies aiming to efficiently deliver NP loaded cargos or anticancer drugs into more aggressive and unresponsive tumors niche.

Background: Intra-arterial cerebral infusion (IACI) of radiotherapeutics is a promising treatment for glioblastoma (GBM) recurrence. We investigated the in silico feasibility and safety of Yttrium-90-Poly(vinyl alcohol)-Microbubble (⁹⁰Y-PVA-MB) IACI in patients with recurrent GBM and compared the results with those of external beam radiation therapy (EBRT). Methods: Contrast-enhanced T1-weighted magnetic resonance imaging (T1W-MRI) was used to delineate the tumor volumes and CT scans were used to automatically segment the organs at risk in nine patients with recurrent GBM. Volumetric Modulated Arc Therapy (VMAT) treatment plans were generated using a clinical treatment planning system. Assuming the relative intensity of each voxel from the MR-T1W as a valid surrogate for the post-IACI ⁹⁰Y-PVA-MB distribution, a specific ⁹⁰Y dose voxel kernel was obtained through Monte Carlo (MC) simulations and convolved with the MRI, resulting in a ⁹⁰Y-PVA-MB-based dose distribution that was then compared with the VMAT plans. Results: The physical dose distribution obtained from the simulation of 1GBq of ⁹⁰Y-PVA-MBs was rescaled to ensure that 95% of the prescribed dose was delivered to 95% or 99% of the target (i.e., A95% and A99%, respectively). The calculated activities were A95% = 269.2 [63.6–2334.1] MBq and A99% = 370.6 [93.8–3315.2] MBq, while the mean doses to the target were 58.2 [58.0–60.0] Gy for VMAT, and 123.1 [106.9–153.9] Gy and 170.1 [145.9–223.8] Gy for A95% and A99%, respectively. Additionally, non-target brain tissue was spared in the ⁹⁰Y-PVA-MB treatment compared to the VMAT approach, with a median [range] of mean doses of 12.5 [12.0–23.0] Gy for VMAT, and 0.6 [0.2–1.0] Gy and 0.9 [0.3–1.5] Gy for the ⁹⁰Y treatments assuming A95% and A99%, respectively. Conclusions: ⁹⁰Y-PVA-MB IACI using MR-T1W appears to be feasible and safe, as it enables the delivery of higher doses to tumors and lower doses to non-target volumes compared to the VMAT approach.

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: “supervised methods, institutional dataset, and predictive and classificatory purposes” (correlation 0.97–8.18 × 10⁻¹; p-value 7.67 × 10⁻⁵⁵–1.28 × 10⁻²²) and the second, Dim2 “not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment” (corr. 0.84–0.47; p-value 5.79 × 10⁻²⁵–-3.59 × 10⁻⁶). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53–0.96) compared to not-supervised methods.

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.