Autorità per le Garanzie nelle Comunicazioni

Social understanding competence develops in sensitive and co‐regulating caregiver interactions. Parental reflective functioning (PRF) and parenting stress can affect children's social understanding. This study investigated if children's social understanding was associated with PRF and parenting stress. Parents of 305 Italian children aged from 24 to 72 months ( M = 48.2, SD = 13.9; 47.9% girls) completed an online survey. Parents completed the following questionnaire: The Parenting Stress Index—Short Form , the Parental Reflective Functioning Questionnaire , and the Children's Social Understanding Scale . Results showed that children's social understanding was predicted by lower parenting stress, b = .002, p = .017, and parent's interest and curiosity about the child's mental states, b = .07, p = .013. Findings confirm that high levels of parenting stress and low PRF constitute unfavorable conditions for preschoolers’ socio‐cognitive development. Thus, the present study can have implication for interventions aimed at improving children's social understanding that should focus on reducing parenting stress and enhancing parental mentalizing.

Introduction Temporal lobe epilepsy is the most common form of focal epilepsy, often associated with cognitive impairments, particularly in memory functions, and depression. Sex and APOE ε4 genotype play a crucial role in modulating cognitive outcomes and depression in various neurological conditions like Alzheimer's disease. However, the combined effects of APOE genotype and sex on cognitive performance and depression in temporal lobe epilepsy have not been previously investigated. Objective This study aims to (i) identify impaired cognitive performance and clinically relevant depression; (ii) explore the interaction between sex and APOE ε4 genotype on cognitive performance and depression in individuals with temporal lobe epilepsy. Methods We used a comprehensive battery of neuropsychological tests to assess domains such as learning and memory, attention, executive functions, language, and visuo-spatial constructional skills and the Hamilton Depression Rating Scale. We also performed APOE genotyping to assess its role in the study. The final sample was composed by fifty-four patients (53.7% female). Cognitive performance and depression were analyzed using normative cut-off scores. To examine the main effects and interactions of sex and APOE ε4 carrier status on neuropsychological test scores and the Hamilton Depression Rating Scale, we also conducted a two-way Analysis of Variance (ANOVA). Results Female APOE ε4 carriers compared to normative cut-offs, exhibited poor performance on multiple test scores, including the MMSE, The Rey Auditory Verbal Learning Test (immediate and delayed recall), The Corsi Block-Tapping Task, The Verbal Fluency Test, The Raven's Standard Progressive Matrices and The Pentagon-copying Test. Males showed impairment only in visuo-spatial short-term memory. ANOVA analysis revealed significant main effects of APOE ε4 status and sex on the MMSE, The Rey Auditory Verbal Learning Test, The Verbal Fluency, The Raven's Standard Progressive Matrices and The Pentagon-copying Test scores. Specifically, female APOE ε4 carriers performed consistently worse than other groups on many tasks. For depression, only an effect of sex emerged. Females scored higher besides APOE genotype. Conclusions These findings underscore the importance of considering both sex and APOE genotype when assessing cognitive performance in patients with temporal lobe epilepsy. The significant cognitive deficits we observed among females carrying the APOE ε4 allele highlight previously unexplored genetic and sex-related influences on cognition. This has potential implications for personalized therapeutic strategies, emphasizing the need for targeted assessment and intervention.

Anger can be deconstructed into distinct components: a tendency to outwardly express it (anger-out) and the capability to manage it (anger control). These aspects exhibit individual differences that vary across a continuum. Notably, the capacity to express and control anger is of great importance to modulate our reactions in interpersonal situations. The aim of this study was to test the hypothesis that anger expression and control are negatively correlated and that both can be decoded by the same patterns of grey and white matter features of a fronto-temporal brain network. To this aim, a data fusion unsupervised machine learning technique, known as transposed Independent Vector Analysis (tIVA), was used to decompose the brain into covarying GM–WM networks and then backward regression was used to predict both anger expression and control from a sample of 212 healthy subjects. Confirming our hypothesis, results showed that anger control and anger expression are negatively correlated, the more individuals control anger, the less they externalize it. At the neural level, individual differences in anger expression and control can be predicted by the same GM–WM network. As expected, this network included lateral and medial frontal regions, the insula, temporal regions, and the precuneus. The higher the concentration of GM–WM in this brain network, the higher the level of externalization of anger, and the lower the anger control. These results expand previous findings regarding the neural bases of anger by showing that individual differences in anger control and expression can be predicted by morphometric features.

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients’ health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.

Social dysfunction is a maladaptive process of coping, problem solving, and achieving one’s goals. A new definition of apathy was cross-linked to social dysfunction, with a reduced goal-directed behavior and social interaction as a separate dimension. We hypothesized that these two neuropsychiatric symptoms may be included in the mild behavioral impairment diagnostic framework, operationalizing and standardizing late-life neuropsychiatric symptom assessment, to improve risk determination of dementia. Social dysfunction and apathy were transdiagnostic and prodromic for late-life cognitive disorders. A transdiagnostic approach could provide a useful mean for a better understanding of apathy and related conditions such as social behavior.

Amphiphilic block copolymers, made of biocompatible polycaprolactone (PCL) and polyethylene glycol (PEG), due to their ability to self‐assemble in water into nanoscopic micelles, have been largely exploited for drug delivery systems (DDS). This study introduces a novel approach by synthesizing a fluorescein isothiocyanate FITC‐labelled PCL‐PEG‐PCL triblock copolymer, with the aim to develop a drug delivery system for natural bioactive. As a proof of concept, the FITC‐labelled PCL‐PEG‐PCL copolymer is applied for the preparation of micelles encapsulating into the core capsaicin (CP), a pungent alkaloid found in chili peppers with diverse therapeutic applications. Challenges associated with CP's solubility, bioavailability, and stability are addressed using this DDS. Comprehensive characterization of FITC‐labelled copolymer is conducted using a range of analytical techniques, including nuclear magnetic resonance (NMR), dynamic light scattering (DLS), high‐performance liquid chromatography (HPLC), Fourier‐transform infrared spectroscopy (FTIR), fluorescence, and confocal laser scanning microscopy (CLSM). Key properties such as critical micelle concentration, CP loading, and release behavior are thoroughly investigated and compared with the characteristics of the unlabeled parent copolymer. This research pioneers the investigation of PCL‐PEG‐PCL triblock copolymers for CP delivery, along with the use of FITC‐labelled variants, opening new avenues for research in drug delivery and nanomedicine.

Background Breast cancer (BC) is a heterogeneous neoplasm characterized by several subtypes. One of the most aggressive with high metastasis rates presents overexpression of the human epidermal growth factor receptor 2 (HER2). A quantitative evaluation of HER2 levels is essential for a correct diagnosis, selection of the most appropriate therapeutic strategy and monitoring the response to therapy. Results In this paper, we propose the synergistic use of SERS and Raman technologies for the identification of HER2 expressing cells and its accurate assessment. To this end, we selected SKBR3 and MDA-MB-468 breast cancer cell lines, which have the highest and lowest HER2 expression, respectively, and MCF10A, a non-tumorigenic cell line from normal breast epithelium for comparison. The combined approach provides a quantitative estimate of HER2 expression and visualization of its distribution on the membrane at single cell level, clearly identifying cancer cells. Moreover, it provides a more comprehensive picture of the investigated cells disclosing a metabolic signature represented by an elevated content of proteins and aromatic amino acids. We further support these data by silencing the HER2 gene in SKBR3 cells, using the RNA interference technology, generating stable clones further analysed with the same combined methodology. Significant changes in HER2 expression are detected at single cell level before and after HER2 silencing and the HER2 status correlates with variations of fatty acids and downstream signalling molecule contents in the context of the general metabolic rewiring occurring in cancer cells. Specifically, HER2 silencing does reduce the growth ability but not the lipid metabolism that, instead, increases, suggesting that higher fatty acids biosynthesis and metabolism can occur independently of the proliferating potential tied to HER2 overexpression. Conclusions Our results clearly demonstrate the efficacy of the combined SERS and Raman approach to definitely pose a correct diagnosis, further supported by the data obtained by the HER2 gene silencing. Furthermore, they pave the way to a new approach to monitor the efficacy of pharmacologic treatments with the aim to tailor personalized therapies and optimize patients’ outcome.

Background In autoimmune diseases (AD), particularly rheumatic and dermatologic ones, Gender makes the difference. Among millions of people affected worldwide, 80% are women [1]. AD can arise in childbearing age and can impact negatively different sides of life and its overall quality, both for the people affected, their families and caregivers. Knowledge on Gender-based Medicine and the importance of a gender approach in Public Health (PH) need to be improved, promoting AD Patients’ empowerment. Women in Italy can count on Genere Donna, the awareness campaign born in 2021 from the close teamwork of the main Italian patients’ associations ANMAR (Associazione Nazionale Malati Reumatici Onlus), APMARR (Associazione Nazionale Persone con Malattie Reumatologiche e Rare aps) and APIAFCO (Associazione Psoriasici Italiani Amici della Fondazione Corazza) and some authoritative experts in AD (5 physicians and a welfare expert). Objectives To improve knowledge on Gender-specific Medicine, to promote patients’ empowerment and the importance of a gender approach in PH also involving PH and Healthcare Professionals (HCP) Representatives and Patients directly, to highlight unmet needs and to share knowledge, becoming more and more a reliable point of reference for Patients, together with spreading update and clear information, validated by experts, about Gender-specific Medicine and rheumatic and dermatologic ADs. Methods Improve contents available on the website highlighting the PH Best Practices in Gender-specific Medicine; strengthen relationships with the Scientific community involving Representatives and being present at their Congresses; foster the direct support from Patients; maintain the accuracy of contents made available. Results Compared to the data presented at EULAR23: +43% website users (280K, 71% female) +31% Reach on social media (9.6Mln), +67% interactions on social media (670K), +9.5% social media community (30K people). Questions received through the “Ask the Expert” service: +27% (289). Sixteen PH and HC Representatives and 4 patients involved producing 20 videos and 22 articles published as Best Practice contents. Moreover, Genere Donna added the patronage from CReI, Collegio dei Reumatologi Italiani (Collegium of Italian Rheumatologists) to the ones from SIR-Società Italiana di Reumatologia (Italian Society of Rheumatology) and SIDeMaST, Società Italiana di Dermatologia e Malattie Sessualmente Trasmesse (Italian Society of Dermatology). In 2023 Genere Donna was present (oral presentation or poster) at 6 National and International scientific meetings. Conclusion The good results reached up to now and the positive feedbacks from Patients, the Scientific Community and PH Representatives drive the project ahead in the future, to improve empowerment of Patients affected by rheumatic and dermatologic ADs and to become more and more a relevant point of reference on Gender-specific Medicine and ADs on the web. REFERENCES [1] Carlo Selmi, Fortissime per natura. Piemme, 2020. • Download figure • Open in new tab • Download powerpoint Figure 1. Acknowledgements Genere Donna counts on an educational grant from UCB Pharma Italy, main sponsor, and Terme di Comano other sponsor. Disclosure of Interests None declared

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology.

The creation of SERS‐active substrates is investigated by self‐assembling hierarchical structures of plasmonic‐assisted nanospheres (HSNs). The hierarchical architecture exploits the advantages of the well‐known ordered configuration of a hexagonally closed‐packed array (CPA) of nanospheres. A further layer of upper nanospheres is used to provide regular and intense surface hotspots, located at the nanogaps between neighboring nanospheres. Numerical analysis is carried out to predict the SERS performances and to identify the more promising configurations, by offering design criteria and a physical insight on the conditions affecting the SERS response of the self‐assembled substrates. Two alternative self‐assembly fabrication methods have been pursued to realize HSNs, namely co‐deposition and sequential deposition. Morphological analysis revealed the formation of well‐ordered hierarchical structures with different ratios between the diameters of the bottom and upper nanospheres. Experimental analysis of the SERS response demonstrates that HSNs can work as cost‐effective SERS substrates with superior performances with respect to the simpler single‐layer CPA configurations.

The widespread extensive use of synthetic polymers has led to a substantial environmental crisis caused by plastic pollution, with microplastics detected in various environments and posing risks to both human health and ecosystems. The possibility of plastic fragments to be dispersed in the air as particles and inhaled by humans may cause damage to the respiratory and other body systems. Therefore, there is a particular need to study microplastics as air pollutants. In this study, we tested a combination of analytical pyrolysis, gas chromatography-mass spectrometry, and gas and liquid chromatography-mass spectrometry to identify and quantify both microplastics and their additives in airborne particulate matter and settled dust within a workplace environment: a WEEE treatment plant. Using this combined approach, we were able to accurately quantify ten synthetic polymers and eight classes of polymer additives. The identified additives include phthalates, adipates, citrates, sebacates, trimellitates, benzoates, organophosphates, and newly developed brominated flame retardants.

One of the fatal diseases that kills a large number of humanity across the globe is brain tumor. If the brain tumor detection is delayed, then the patient has to spend a large amount of money as well as to face severe suffering. Therefore, there is an essential need to detect the brain tumor so that money and life can be saved. The conventional examination of brain images by doctors does not reveal the presence of a tumor in a reliable and accurate manner. To overcome these issues, early and accurate brain tumor identification is of prime importance. A short while ago, methods employing machine learning (ML) and artificial intelligence (AI) were utilized to properly diagnose other diseases using test attributes, electrocardiogram (ECG), electromyography (EMG), Heart Sounds, and other types of signals obtained from the human body. This chapter presents a complete overview of the detection of patient-provided brain MR pictures and classifying patients’ brain tumor using AI and ML approaches. For this pose, brain images obtained from kaggle.com website have been employed for developing various AI and ML classifiers. Through simulation-based experiments conducted on the AI and ML classifiers, performance matrices have been obtained and compared. From the analysis of results reported in the different articles, it is observed that Random Forest exhibit superior detection of brain tumor. There is still further scope for improving the performances as well as developing affordable, reliable, and robust AI-based brain tumor classifiers.

In many physics and engineering applications requiring exceptional precision, the presence of highly reflective coatings with low thermal noise is of utmost significance. These applications include high‐resolution spectroscopy, optical atomic clocks, and investigations into fundamental physics such as gravitational wave detection. Enhancing sensitivity in these experiments relies on effectively reducing the thermal noise originating from the coatings. While ion beam sputtering (IBS) is typically employed for fabricating such coatings, electron beam evaporation can also be utilized and offers certain advantages over IBS, such as versatility and speed. However, a significant challenge in the fabrication process has been the limitations of the quartz crystal monitor used to measure the thickness of the deposited layers. This paper showcases how, through hardware and software upgrades, it becomes achievable to create high‐density coatings with layers as thin as a few angstroms by using electron beam evaporation (OAC75F coater) with a deposition rate of 1 Å/s and ion‐assisted source with a gas mixture of oxygen and argon, using a pressure of about 4 × 10 ⁻⁴ mbar. Furthermore, these upgrades enable the attainment of high levels of precision and uniformity in the thickness of the coatings.