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Acting as ‘antioxidants’, phenolic compounds have been shown to have a potential protective effect against a wide range of noncommunicable diseases. However, their content in plant-based products – and, in turn, their potential benefits – might be affected by the thermal procedures used in food cooking. In light of this, the aim of this work was a characterization of Patata Rossa di Colfiorito (PRC, an Italian PGI red-skinned potato) and an investigation of the impact of cooking and storage on its bioactive compounds, phenolic content, and antioxidant activity. After the harvesting (T0), samples were analyzed using instrumental analysis (e.g., HPLC-ESI-MS/MS) and a set of chemical assays (total phenolic content, 2,2-Diphenyl-1-picrylhydrazyl – DPPH, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) – ABTS, and oxygen radical absorbance capacity – ORAC assays). Analyses were also performed on boiled, pressure-cooked and one-month-stored (T1) products. A commercial red-skinned potato (RSP) was also analyzed.Overall, compared with the raw product, both boiling and pressure-cooking led to a significant increase in bioactive compounds (raw: 168.15 mg kg−1 of fresh matter, FM; boiled: 398.92 mg kg−1 FM; pressure-cooked: 309.24 mg kg−1 FM), total phenolic content and antioxidant activity measured by DPPH and ABTS assays.PRC samples showed higher content in bioactive compounds, TPC values and antioxidant activity compared with commercial RSP (with some sporadic exceptions in ORAC values). Interestingly, one month of domestic storage did not affect the freshly harvested product’s features.In conclusion, these results highlighted the quality of this local product and its property to withstand thermal processing and storage. We believe that the results we have obtained should be taken into consideration by health professionals, at both local and national levels, and that PRC deserves to be further studied to investigate its potential human health benefits, as well.

ABSTRACTMaterial Extrusion (MEX) Additive Manufacturing (AM) has risen as a promising technology to monolithically manufacture smart structures with embedded sensors. Despite all the benefits of MEX AM, 3D printed piezoresistive sensors still suffer from low sensitivity, making them unsuitable for the detection of low values of force, displacement and bending angle. In the present paper, a simple, effective, and inexpensive method to increase the sensitivity in 3D printed sensors is proposed, based on the leveraging of the ironing strategy, which resulted in an improvement in the sensitivity of 83% compared to traditional process parameter selection. The ironing strategy reduced intralayer porosity by 59%, as verified by X-Ray CT. Additionally, the ironing strategy involves an increased healing time, which promotes the polymer chain diffusion between layers, which translated into a greater stability of the sensor when cyclically stressed. Smart structures capable of detecting small forces (0.19 N of resolution against 1.96 N for traditional MEX scenario) and smart auxetic devices have been manufactured, demonstrating the potential of the proposed approach. The present research demonstrates the ability to reduce interlayer voids by using an intrinsic feature of the MEX process and consequently improve electrical performance of 3D printed sensors.

Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of 9−13 N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of 34±14 mm (8.1%L±3.4%L), an average orientation error of 0.40±0.29 rad (23°±17°) and applied pushing forces up to 3 N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

Proteomics is a key analytical method in meat research thanks to its potential in investigating the proteins at interplay in post-mortem muscles. This study aimed to characterize for the first time the differences in early post-mortem muscle proteomes of chickens raised under two farming systems: organic versus antibiotic-free. Forty post-mortem Pectoralis major muscle samples from two chicken strains (Ross 308 versus Ranger Classic) reared under organic versus antibiotic-free farming systems were characterized and compared using two-dimensional electrophoresis and LC-MS/MS mass spectrometry. Within antibiotic-free and organic farming systems, 14 and 16 proteins were differentially abundant between Ross 308 and Ranger Classic, respectively. Within Ross 308 and Ranger Classic chicken strains, 12 and 18 proteins were differentially abundant between organic and antibiotic-free, respectively. Bioinformatics was applied to investigate the molecular pathways at interplay, which highlighted the key role of muscle structure and energy metabolism. Antibiotic-free and organic farming systems were found to significantly impact the muscle proteome of chicken breast meat. This paper further proposes a primary list of putative protein biomarkers that can be used for chicken meat or farming system authenticity.

La presa di coscienza sui cambiamenti climatici, le politiche dello European Green Deal e il PNRR in Italia, hanno generato, tra il 2017 e il 2021, importanti cambi di paradigma strategico-legislativi. Il presente studio si interroga sulla comunicazione di questi temi tramite l’utilizzo degli strumenti dell’information design. Nello studio sono considerate infografiche prodotte in tre livelli di azione (globale, europeo, nazionale) da quattro istituzioni. L’obiettivo è verificarne la coerenza di contenuto e analizzare le strategie di visualizzazione del dato scientifico.

Abstract Background Pancreatic cancer (PC) first‐line therapy often consists of polychemotherapy regimens, but choosing a second‐line therapy after disease progression, especially following first‐line FOLFIRINOX, remains a clinical challenge. This study presents results from a large, multicenter, retrospective analysis of Italian patients with metastatic PC (mPC) treated with Nab‐paclitaxel/Gemcitabine (AG) as second or later line of treatment. Main objective of the study is to identify prognostic factors that could inform treatment decisions. Methods The study included 160 mPC patients treated with AG in 17 Italian institutions. AG was administered according to labelling dose, until disease progression, unacceptable toxicity or patient refusal. Variations in schedules, dose modifications, supportive measures, and response evaluation were determined by individual clinicians' practice. Results AG was well‐tolerated and exhibited promising clinical activity. The overall response rate (ORR) and the disease control rate (DCR) were 22.5% and 45.6%, respectively. Median progression‐free survival (PFS) and overall survival (OS) were 3.9 and 6.8 months, respectively. Among the patients who received AG as a second‐line therapy (n = 111, 66.9%), median PFS and OS were 4.2 and 7.4 months, respectively. Notably, in the 76 patients (68%) receiving AG after first‐line FOLFIRINOX, an ORR of 19.7% and a DCR of 46.0% were observed, resulting in a median PFS of 3.5 and median OS of 5.7 months. The study identified specific clinical or laboratory parameters (LDH, NLR, fasting serum glucose, liver metastases, ECOG PS, and first‐line PFS) as independent prognostic factors at multivariate level. These factors were used to create a prognostic nomogram that divided patients into three risk classes, helping to predict second‐line OS and PFS. Conclusions This study represents the largest real‐world population of mPC patients treated with AG as a second or later line of therapy. It supports the feasibility of this regimen following first‐line FOLFIRINOX, particularly in patients with specific clinical and laboratory characteristics who derived prolonged benefit from first‐line therapy.

IntroductionLymphodepleting chemotherapy (LDC) is critical to CAR T-cell expansion and efficacy. Despite this, there is not a consensus in the literature regarding the optimal LDC regimen, including dose and frequency.MethodsWe retrospectively reviewed consecutive patients at a single institution that received LDC prior to treatment with the CD19 directed CAR T-cell products axicabtagene ciloleucel and tisagenlecleucel. Patients treated at our center received fludarabine 30 mg/m2 and cyclophosphamide 500 mg/m2 for 3 consecutive days prior to May 2019. After this timepoint patients routinely received fludarabine 40 mg/m2 and cyclophosphamide 500 mg/m2 for 2 consecutive days. Clinical data from each cohort were obtained from the electronic medical record and compared for differences in CAR T-cell efficacy and toxicity.ResultsFrom June 2018 to August 2023, LDC was given to 92 patients prior to CD19 directed CAR T-cell therapy for relapsed non-Hodgkin’s lymphoma. Twenty-eight patients received a 3-day regimen, and 64 patients received a 2-day regimen. In the total cohort, 75% of patients received axicabtagene ciloleucel and 25% received tisagenlecleucel. The overall response rates in both the 2-day regimen group and the 3-day regimen group were similar (69% vs 75%, p= 0.21) as were the complete response rates (50% vs 54%, p=0.82). There were no significant differences between the 2-day and 3-day regimens for grade 2–4 cytokine release syndrome (55% vs 50%, p=0.82), grade 2–4 immune effector cell associated-neurotoxicity syndrome (42% vs 29%, p=0.25), or time to resolution of neutropenia or thrombocytopenia. The rate of prolonged platelet recovery lasting greater than 60 days was higher with the 3-day regimen (9% vs 27%, p=0.026).DiscussionAs the number of patients eligible for CAR T-cell therapy continues to increase, optimizing each component of therapy is necessary. We show that a 2-day regimen of LDC with fludarabine and cyclophosphamide is feasible without significant impact on CAR T-cell efficacy or toxicity. Prospective studies are necessary to further determine the most effective LDC regimen.

Vitamin D, a fat-soluble steroid, has increasingly taken a central role due to its crucial role in human health. It is estimated that about 40% of worldwide population are vitamin D deficient. The fish industry produces significant quantities of waste daily, with consequent high environmental impact. The aim of this work is to place a first brick for the fish waste reuse as a source of vitamin D3 extracts to be used for nutraceutical purposes. For this purpose, an UV conversion method for transforming the 7-dehydrocholesterol, highly present in fish, in vitamin D3 has been optimized. The UV wavelength, exposure time, temperature, stirring, and UV intensity were optimized using a surface response design tool. The optimized treatment was applied to five fish species with different fat percentages and the results were very promising reaching vitamin D3 levels >10 times higher than the pre-treatment ones.

The evaluation of landslide hazards in seismic areas is based on a deterministic analysis, which is unable to account for various uncertainties in the analysis process. This paper focuses on the probabilistic local seismic hazard analysis and extends the results to the landslide hazard analysis to consider both the uncertainties of the ground deformations and the strengths. The work studies the areas between Nibbiano and Sant’Erasmo hamlets in the Camerino municipality located in central Italy, where all constructions present evidence of damage caused by both the seismic sequence of 2016–2017 and the slope instability. An exhaustive geological and geophysical investigation has clarified the geological, geomorphological, and hydrogeological characteristics of the area, enabling a new characterization of material stress-strain behaviour. The study reveals that the low stiffness of the debris covers, and their fair degree of permeability contribute to potential instability scenarios triggered by both intense rainfall and the effects of strong earthquakes. The goal was to utilize the results to support local urban planning because in-depth knowledge of the possible evolutionary scenarios of the slopes is fundamental to the management of the degree of danger for structures, especially for people. Moreover, it was shown once again how a multi-source approach, with different investigation techniques, cannot be ignored for the study of the evolution of complex landslides.

Introduction/Justification: Computed tomography (CT) scans are integral to cancer patient diagnosis, revealing changes in body composition linked to survival progression. The conventional approach to body composition analysis using CT scans is labor-intensive and expensive, demanding skilled professionals and licensed software for manual segmentation of Regions of Interest (ROIs). To address these challenges, we introduce a Deep Learning algorithm designed for automated CT image segmentation, presenting an efficient alternative that overcomes the limitations of the current methodology. Beyond the advantages of speed, automation enhances result uniformity and enables uncertainty estimation. In this presentation, we will show preliminary results from our algorithm, highlighting its potential contributions to survival analysis in cancer patients. Objectives: The primary goal of this study was to develop an automated segmentation algorithm for CT scans using Deep Learning models. Materials and Methods: In developing segmentation algorithms, a dataset of 453 CT slices at the L3 lumbar vertebral level from gastric cancer patients was utilized, with an 80% training and 20% testing partition. Employing the UNET+ResNet18 deep learning architecture, supervised training utilized manually generated segmentation masks as references. Four dedicated UNET+ResNet18 algorithms were trained for distinct ROIs: Skeletal Muscle (SM), Intramuscular Adipose Tissue (IMAT), Visceral Adipose Tissue (VAT), and Subcutaneous Adipose Tissue (SAT). Segmentation performance on the test set was evaluated using the Dice Coefficient, underestimation and overestimation percentages, Bland-Altman analyses, and qualitative visual inspection of segmented images. Results: The UNet+ResNet18 models demonstrated superior segmentation performance for SM, VAT, and SAT, achieving mean Dice scores exceeding 0.95. In comparison to manual segmentation, the Deep Learning algorithm exhibited minor average underestimations and overestimations, both below 5% for these tissues. However, IMAT segmentation exhibited relatively lower performance, with a mean Dice score of approximately 0.86 and underestimation and overestimation percentages around 15% and 13%, respectively. The Bland-Altman analysis revealed mean bias and limits of agreement for mean radiodensities of SM, VAT, SAT, and IMAT as follows: 0.14 [-0.82, 1.10] HU, -0.53 [-2.03, 0.98] HU, -0.18 [-1.70, 1.33] HU, and 0.48 [-3.86, 4.82] HU, respectively. Conclusion: The Deep Learning approach provides a standard and fast solution for CT image segmentation, demonstrating good results for SM, VAT and SAT. For these tissues, derived radiomics features could provide valuable insights into the analysis of cancer patient outcomes. Further studies are necessary for enhancing IMAT segmentation, given its challenging small area. Additionally, future investigations should focus on uncertainty estimation in CT images, exploring its impact on segmentation procedures and radiomic feature extraction.

Fusion-based Material Extrusion (MEX) Additive Manufacturing (AM) processes have been extensively used for the fabrication of smart structures with embedded sensors, proving to have several benefits such as reduction in cost, manufacturing time, and assembly. A major issue negatively affecting 3D printed sensors is related to their poor electrical conductivity, as well as inconsistent electrical performance, which leads to electrical power losses amongst other issues. In the present paper, a set of process parameters (ironing, printing temperature, and infill overlap) has been analyzed by performing a Design of Experiment (DoE) factorial plan to minimize the electrical resistance. The best process parameters configuration involves a remarkable reduction of electrical resistance of 47.9%, as well as an improvement of mechanical properties of 31.9% (ultimate tensile strength), 25.8% (elongation at break) and 28.14% (flexural stress). The microstructure of the obtained results has also been analyzed by employing a high-resolution, X-ray Computed Tomography (X-Ray CT) system showing a reduction of intralayer voids of 19.5%. This work demonstrates a clear correlation between process parameters and the corresponding electrical properties, mechanical properties, and internal microstructure. In the present research, it has been shown that i) it is possible to significantly improve the overall 3D printed sensors performance by process parameter selection, and ii) small changes in the microstructure lead to remarkable improvements in electrical and mechanical performance.

Abstract Serum creatinine has been indicated as a potential marker of motor function in SBMA and results form previous longitudinal studies pointed to its decline over time. This is a longitudinal retrospective study investigating creatinine changes over a 36-month-period in 73 patients with SBMA. Severity and progression of the disease was assessed according to serum creatine kinase (CK) values, manual muscle testing (MMT), SBMA functional rating scale (SBMAFRS) score, 6-min-walk test (6MWT) value, and spirometry (forced vital capacity, fVC%) obtained at the baseline and at each of the annual follow-up visits. Baseline serum creatinine concentrations positively correlated with 6MWT, the MMT megascore score of both the upper (ULM) and lower (LLM) limbs and SBMAFRS. No correlation was found with CK or fVC% values. Similar correlation results were achieved at all the subsequent time points. Longitudinal assessments conducted by the generalized estimating equations (GEE) method returned significant changes for SBMAFRS (− 1.41 points per year, p 0.05). We also observed that creatinine levels at baseline did not correlate with changes in the other measures from baseline at each annual visit. Our data do not support a role for serum creatinine as sensitive biomarker of disease progression, and possibily prognosis, in SBMA.

This article aims to present the results of a national, cross-sectional, voluntary, online survey on the presence and roles of associations of breast cancer patients and volunteers in Italian specialist breast centres. The survey was developed according to standard methods. The questionnaire was pre-tested by a random sample of three breast centres, loaded onto the SurveyMonkey platform, and piloted by one volunteer breast centre. The breast centre clinical leads were invited to participate via email. A link to the online instrument was provided. No financial incentives were offered. The results were reported using standard descriptive statistics. The response rate was 82/128 (65%). Members of associations were routinely present in 70% Italian breast centres. Breast centres most often reporting their presence were those certified by the European Society of Breast Cancer Specialists. Patient support (reception and information, listening, identification of needs, and psychological support) was the primary area where associations were reported to offer services. The magnitude of this phenomenon warrants a study to investigate the impact of the activities of associations on the quality of life of patients and on the cost–benefit ratio of the service, and the modes of their interactions with the nursing staff and the medical staff.

Abstract In preclinical studies rapamycin was found to target neuroinflammation, by expanding regulatory T cells, and affecting autophagy, two pillars of amyotrophic lateral sclerosis (ALS) pathogenesis. Herein we report a multicenter, randomized, double-blind trial, in 63 ALS patients who were randomly assigned in a 1:1:1 ratio to receive rapamycin 2 mg/m2/day,1 mg/m2/day or placebo (EUDRACT 2016-002399-28; NCT03359538). The primary outcome, the number of patients exhibiting an increase >30% in regulatory T cells from baseline to treatment end, was not attained. Secondary outcomes were changes from baseline of T, B, NK cell subpopulations, inflammasome mRNA expression and activation status, S6-ribosomal protein phosphorylation, neurofilaments; clinical outcome measures of disease progression; survival; safety and quality of life. Of the secondary outcomes, rapamycin decreased mRNA relative expression of the pro-inflammatory cytokine IL-18, reduced plasmatic IL-18 protein, and increased the percentage of classical monocytes and memory switched B cells, although no corrections were applied for multiple tests. In conclusion, we show that rapamycin treatment is well tolerated and provides reassuring safety findings in ALS patients, but further trials are necessary to understand the biological and clinical effects of this drug in ALS.

In the literature, there is a paucity of methods and tools that allow the identification of biomarkers of authenticity to discriminate organic and non-organic chicken meat products. Shotgun proteomics is a powerful tool that allows the investigation of the entire proteome of a muscle and/or meat sample. In this study, a shotgun proteomics approach using Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) has been applied for the first time to characterize and identify candidate protein biomarkers of authenticity in post-mortem chicken Pectoralis major muscles produced under organic and non-organic farming systems (antibiotic-free). The proteomics characterization was further performed within two chicken strains, these being Ross 308 and Ranger Classic, which differ in their growth rate. From the candidate protein biomarkers, the bioinformatics enrichment analyses revealed significant differences in the muscle proteome between the two chicken strains, which may be related to their genetic background and rearing conditions. The results further provided novel insights on the potential interconnected pathways at interplay that are associated with the differences as a consequence of farming system of chicken strain, such as muscle contraction and energy metabolism. This study could pave the way to more in-depth investigations in proteomics applications to assess chicken meat authenticity and better understand the impact of farming systems on the chicken muscle and meat quality.

The advent of conductive extrudable materials has broadened the range of additive manufacturing applications to include smart devices, circuits, actuators and sensors – all requiring electrical power. 3D printing of components dedicated to energy storage has also gained interest, with the goal of the monolithic printing of batteries directly integrated into subsuming smart components. This review focuses on the state of the art of extrusion-based 3D printed batteries, appearing as the most widespread, inexpensive and simple additive process. The paper is intended to introduce the processes and materials of 3D printing batteries, while highlighting the main manufacturing challenges and associated solutions proposed in literature. Particular attention is dedicated to describing the extrusion-based printers being employed and the required modifications, printing parameters and multi-material capabilities, with the aim of highlighting the most promising solutions required to print composite individual components and complete rechargeable batteries in a single non-assembly step.