Search Results (10)

Meningiomas arise from the meningeal layers covering the central nervous system structures. Although most are benign, meningiomas can still cause neurological morbidity due to the mass effect and compression of the surrounding parenchyma. The prognosis also depends on several factors such as growth pattern or location. Morphological imaging approaches, such as MRI and CT, that emphasize intracranial calcifications, vascular patterns, or invasion of major vessels act as the basis of the diagnosis; PET/CT imaging is a valuable diagnostic tool for assessing somatostatin receptor activity in tumors. It enables the visualization and quantification of somatostatin receptor expression, providing insights into tumor biology, receptor status, and potential therapeutic targets. Aside from radiosurgery and neurosurgical intervention, peptide receptor radionuclide therapy (PRRT) has also shown promising results. Somatostatin receptors 1 and 2 are nearly universally expressed in meningioma tissue. This characteristic is increasingly exploited to identify patients eligible for adjuvant therapy using DOTA-conjugated somatostatin receptor-targeting peptides PET. In the treatment of relapsed/refractory meningiomas, PRRT is increasingly considered a safe and effective therapeutic option. It is often supported by artificial intelligence strategies for dose optimization and side-effect monitoring. The objective of this study is to evaluate the safety and benefits of these strategies based on the latest findings. Full article

Research outputs in higher education institutions (HEIs) are crucially dependent on the research management process. Departing from a SWOT analysis, the main objective of this paper is to analyze the perceptions of stakeholders (researchers, teachers, and senior research managers) regarding the main strengths and weaknesses of HEIs, as well as assess the potential opportunities and threats present in the external environment. It analyzed a total of 462 responses from seven HEIs and two ministries participating in the INNOVA project in Bolivia and Paraguay. The results from the statistical analysis indicate that the respondents tend to identify the traditional obstacles and facilitators to research development, namely, the scarcity and instability of public policies, which permeate the institutions, diminishing the consistency of internal research policies and creating difficulties in access to funding and career development opportunities. Building on the substantial progress made in recent years, the unvirtuous cycle may be halted with political stability and committed action between all the concerned parties. Full article

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images. Full article

Background: Prostate cancer (PCa) is the second most common cancer in men, and radiotherapy (RT) is one of the main treatment options. Although effective, RT can cause toxic side effects. The accurate prediction of dosimetric parameters, enhanced by advanced technologies and AI-based predictive models, is crucial to optimize treatments and reduce toxicity risks. This study aims to explore current methodologies for predictive dosimetric parameters associated with RT toxicity in PCa patients, analyzing both traditional techniques and recent innovations. Methods: A systematic review was conducted using the PubMed, Scopus, and Medline databases to identify dosimetric predictive parameters for RT in prostate cancer. Studies published from 1987 to April 2024 were included, focusing on predictive models, dosimetric data, and AI techniques. Data extraction covered study details, methodology, predictive models, and results, with an emphasis on identifying trends and gaps in the research. Results: After removing duplicate manuscripts, 354 articles were identified from three databases, with 49 shortlisted for in-depth analysis. Of these, 27 met the inclusion criteria. Most studies utilized logistic regression models to analyze correlations between dosimetric parameters and toxicity, with the accuracy assessed by the area under the curve (AUC). The dosimetric parameter studies included Vdose, Dmax, and Dmean for the rectum, anal canal, bowel, and bladder. The evaluated toxicities were genitourinary, hematological, and gastrointestinal. Conclusions: Understanding dosimetric parameters, such as DVH, Dmax, and Dmean, is crucial for optimizing RT and predicting toxicity. Enhanced predictive accuracy improves treatment effectiveness and reduces side effects, ultimately improving patients’ quality of life. Emerging artificial intelligence and machine learning technologies offer the potential to further refine RT in PCa by analyzing complex data, and enabling more personalized treatment approaches. Full article

Gastric cancer (GC) is the second most common cause of cancer-related death worldwide and a serious public health concern. This high death rate is mostly caused by late-stage diagnoses, which lead to poor treatment outcomes. Radiation immunotherapy and targeted therapies are becoming increasingly popular in GC treatment, in addition to surgery and systemic chemotherapy. In this review, we have focused on both in vitro and in vivo research, which presents a summary of recent developments in targeted therapies for gastric cancer. We explore targeted therapy approaches, including integrin receptors, HER2, Claudin 18, and glutathione-responsive systems. For instance, therapies targeting the integrin receptors such as the αvβ3 and αvβ5 integrins have shown promise in enhancing diagnostic precision and treatment efficacy. Furthermore, nanotechnology provides novel approaches to targeted drug delivery and imaging. These include glutathione-responsive nanoplatforms and cyclic RGD peptide-conjugated nanoparticles. These novel strategies seek to reduce systemic toxicity while increasing specificity and efficacy. To sum up, the review addresses the significance of personalized medicine and advancements in gastric cancer-targeted therapies. It explores potential methods for enhancing gastric cancer prognosis and treatment in the future. Full article

Cerebral vascular angiography, or digital subtraction angiography (DSA), is essential for diagnosing neurological conditions but poses radiation risks. This study aims to analyze the impact of examination parameters and patient characteristics on the radiation dose received during DSA to optimize safety and minimize exposure. A retrospective analysis of 251 DSA procedures using the GE Innova IGS 630 dual-plane instrument was conducted. Data on dose area product (DAP) and air kerma (KERMA), along with patient and examination details, were collected. Statistical analyses, including Mann–Whitney, Kruskal–Wallis, and Spearman rank correlation tests, assessed the relationships between variables and radiation dose outcomes. Significant correlations were found between the sides examined (left, right, or both) and DAP (p < 0.0001) and KERMA (p < 0.0001) values, with bilateral studies showing the highest values. The post hoc Dunn tests showed that the ‘L + P’ group significantly differs from both the right group (p < 0.0001 and the left group (p < 0.0001). There is no significant difference between the ‘P’ group and the ‘L’ group (p-value = 0.53). These results suggest that the right and left (both) group have unique KERMA mGy values compared to the other two groups. A strong correlation (rS = 0.87) existed between DAP and KERMA. The number of projections significantly impacted radiation dose (rS = 0.61). Tube parameters (kV and mA) and skull size had low correlations with DAP and KERMA. Optimizing imaging protocols and individualizing parameters can significantly enhance patient safety and diagnostic efficacy while also reducing occupational exposure for medical staff. Full article

Solar energy is widely recognized as one of the most attractive renewable energy sources to support the transition toward a decarbonized society. Use of low- and medium-temperature concentrated solar technologies makes decentralized power production of combined heating and power (CHP) an alternative to conventional energy conversion systems. However, because of the changes in solar radiation and the inertia of the different subsystems, the operation control of concentrated solar power (CSP) plants is fundamental to increasing their overall conversion efficiency and improving reliability. Therefore, in this study, the operation control of a micro-scale CHP plant consisting of a linear Fresnel reflector solar field, an organic Rankine cycle unit, and a phase change material thermal energy storage tank, as designed and built under the EU-funded Innova Microsolar project by a consortium of universities and companies, is investigated. In particular, a fuzzy logic control is developed in MATLAB/Simulink by the authors in order to (i) initially recognize the type of user according to the related energy consumption profile by means of a neural network and (ii) optimize the thermal-load-following approach by introducing a set of fuzzy rules to switch among the different operation modes. Annual simulations are performed by combining the plant with different thermal load profiles. In general, the analysis shows that that the proposed fuzzy logic control increases the contribution of the TES unit in supplying the ORC unit, while reducing the number of switches between the different OMs. Furthermore, when connected with a residential user load profile, the overall electrical and thermal energy production of the plant increases. Hence, the developed control logic proves to have good potential in increasing the energy efficiency of low- and medium-temperature concentrated solar ORC systems when integrated into the built environment. Full article

This paper presents an advanced methodology for the detection of damage in aircraft composite materials based on the sensor fusion of two image-based non-destructive evaluation techniques. Both of the techniques, phased-array ultrasonics and infra-red thermography, are benchmarked on an aircraft-grade painted composite material skin panel with stringers. The sensors systems for carrying out the inspections have been developed and miniaturized for being integrated on a vortex-robotic platform inspector, in the framework of a larger research initiative, the Horizon-2020 ‘CompInnova’ project. Full article

Attention to food waste is an increasingly growing phenomenon today, especially in the context of a circular economy. The InnovaEcoFood project investigates the use of by-products of the Piedmontese rice and wine production chains to valorize their untapped potential in the food sector by applying the Systemic Design approach. We collected, systematized, and visualized a range of solutions for exploiting these by-products, starting from an in-depth literature review on the two value chains. With the support of a consortium of partners from both multidisciplinary industrial and academic sectors, it was possible to validate the links that have been generated. Eventually, the project created food products that integrated these outputs as ingredients (like flour and butter) because they have antioxidant properties and are rich in proteins. InnovaEcoFood has successfully tested how value could be created from waste. Moreover, using rice hull, marc flour, and bran lipid (butter) is of immediate technical and economic feasibility. It could be considered a viable way that deserves further experimentation. Full article

Latent heat thermal energy storage (LHTES) systems allow us to effectively store and release the collected thermal energy from solar thermodynamic plants; however, room for improvements exists to increase their efficiency when in operation. For this reason, in this work, a smart management strategy of an innovative LHTES in a micro-scale concentrated solar combined heat and power plant is proposed and numerically investigated. The novel thermal storage system, as designed and built by the partners within the EU funded Innova MicroSolar project, is subdivided into six modules and consists of 3.8 tons of nitrate solar salt kNO₃/NaNO₃, whose melting temperature is in the range 216 ÷ 223 °C. In this study, the partitioning of the storage system on the performance of the integrated plant is evaluated by applying a smart energy management strategy based on a fuzzy logic approach. Compared to the single thermal energy storage (TES) configuration, the proposed strategy allows a reduction in storage thermal losses and improving of the plant’s overall efficiency especially in periods with limited solar irradiance. The yearly dynamic simulations carried out show that the electricity produced by the combined heat and power plant is increased by about 5%, while the defocus thermal losses in the solar plant are reduced by 30%. Full article