Effects of Long COVID on Psycho-Physical Conditions in the Italian Population: A Statistical and Large Language Model Combined Description.

BACKGROUND: Long COVID refers to the persistence or development of signs and symptoms well after the acute phase of COVID-19. OBJECTIVE OF THE STUDY: To investigate the long-term outcomes of the SARS-CoV-2 infection in terms of psychological, social, and relational consequences within the Italian population. MATERIALS AND METHODS: We conducted an observational, cross-sectional, and multicenter study using an online questionnaire distributed to a sample of the Italian population. By utilizing the Short Form 12 Health Survey (SF-12) and the Hikikomori scale, we assessed perceived quality of life and social isolation, respectively. The questionnaire also included an open-answer question: "What will you remember about the pandemic period?". We used generative artificial intelligence to analyze and summarize the corresponding answers. RESULTS: A total of 1097 people participated in this study. A total of 79.3% (n = 870) of participants declared that they had been hospitalized and 62.8% (n = 689) received home care. Physical symptoms included headaches (43%, n = 472) and asthma (30.4%, n = 334). Additionally, 29.2% (n = 320) developed an addiction during the pandemic and, among these, 224 claimed internet addiction while 73 declared an emotional addiction. Furthermore, 51.8% (n = 568) experienced limitations in carrying out daily life activities. According to the Hikikomori scale, participants with positive SARS-CoV-2 infection exhibited higher levels of isolation compared to the others (p < 0.001). Participants without COVID-19 showed higher levels of emotional support (p < 0.001). Our semiautomatic analysis of the open-ended responses, obtained by a procedure based on a free large language model, allowed us to deduce and summarize the main feelings expressed by the interviewees regarding the pandemic. CONCLUSIONS: The data collected emphasize the urgent need to investigate the consequences of long COVID in order to implement interventions to support psychological well-being.

Exploring the Landscape of Breast Cancer Prevention among Chinese Residents in Italy: An In-Depth Analysis of Screening Adherence, Breast Self-Examination (BSE) Practices, the Role of Technological Tools, and Misconceptions Surrounding Risk Factors and Symptoms.

BACKGROUND: Breast cancer remains a significant health concern among women globally. Despite advancements in awareness and diagnostic techniques, it persists as a leading cause of death, with profound impacts on affected individuals' quality of life. Primary and secondary prevention, including regular screenings and practices like breast self-examination (BSE), are pivotal in ensuring early diagnosis. The national health system (NHS) in Italy offers screenings for women aged 50-69 every two years, managed by the local health authority. However, the participation rates, especially among the Chinese female population residing in Italy, are not well understood. METHODS: Using a snowball method, we electronically disseminated a survey to investigate how Chinese women living in Italy engage with available NHS screening programs. The survey also explores their practice of BSE and the use and impact of technological tools on prevention. Furthermore, the study aims to understand the subjects' depth of knowledge and misconceptions about breast cancer. RESULTS: The data reveal a significant gap in breast cancer screening adherence and knowledge among Chinese women in Italy, with a notable discrepancy between the general population and those who have previously encountered cancer. CONCLUSIONS: The results highlight the urgent need for interventions that are culturally sensitive, stressing that these actions are not only desirable but essential.

Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation.

Prenatal assessment of lung size and liver position is essential to stratify congenital diaphragmatic hernia (CDH) fetuses in risk categories, guiding counseling, and patient management. Manual segmentation on fetal MRI provides a quantitative estimation of total lung volume and liver herniation. However, it is time-consuming and operator-dependent. In this study, we utilized a publicly available deep learning (DL) segmentation system (nnU-Net) to automatically contour CDH-affected fetal lungs and liver on MRI sections. Concordance between automatic and manual segmentation was assessed by calculating the Jaccard coefficient. Pyradiomics standard features were then extracted from both manually and automatically segmented regions. The reproducibility of features between the two groups was evaluated through the Wilcoxon rank-sum test and intraclass correlation coefficients (ICCs). We finally tested the reliability of the automatic-segmentation approach by building a ML classifier system for the prediction of liver herniation based on support vector machines (SVM) and trained on shape features computed both in the manual and nnU-Net-segmented organs. We compared the area under the classifier receiver operating characteristic curve (AUC) in the two cases. Pyradiomics features calculated in the manual ROIs were partly reproducible by the same features calculated in nnU-Net segmented ROIs and, when used in the ML procedure, to predict liver herniation (both AUC around 0.85).          Conclusion: Our results suggest that automatic MRI segmentation is feasible, with good reproducibility of pyradiomics features, and that a ML system for liver herniation prediction offers good reliability.          Trial registration: https://clinicaltrials.gov/ct2/show/NCT04609163?term=NCT04609163&draw=2&rank=1 ; Clinical Trial Identification no. NCT04609163. What is Known: • Magnetic resonance imaging (MRI) is crucial for prenatal congenital diaphragmatic hernia (CDH) assessment. It enables the quantification of the total lung volume and the extent of liver herniation, which are essential for stratifying the severity of CDH, guiding counseling, and patient management. • The manual segmentation of MRI scans is a time-consuming process that is heavily reliant upon the skill set of the operator. What is New: • MRI lung and liver automatic segmentation using the deep learning nnU-Net system is feasible, with good Jaccard coefficient values and satisfactory reproducibility of pyradiomics features compared to manual results. • A feasible ML system for predicting liver herniation could improve prenatal assessments and CDH patient management.

Statistical analysis and generative Artificial Intelligence (AI) for assessing pain experience, pain-induced disability, and quality of life in Parkinson's disease patients.

The Parkinson's Disease (PD) is a chronic neurodegenerative condition characterized by motor symptoms such as tremors, rigidity, and bradykinesia, which can significantly impact various aspects of daily life. Among these aspects, pain is a prominent element. Despite the widespread use of therapies aimed at improving symptoms and quality of life, effective pain management is essential to enhance the quality of life of individuals affected by this disease. However, a detailed understanding of the factors associated with pain in PD is still evolving. In this study, we examined the disability caused by pain and the pain experienced by PD patients using two validated questionnaires, namely the Parkinson's Disease Questionnaire (PDQ) and the King's Parkinson's Disease Pain Questionnaire (KPPQ). Customized questions were also included to further explore the pain experience and management strategies adopted by PD patients. Through statistical analysis, we explored the relationships between questionnaire scores, socio-demographic data, and other relevant variables. Additionally, generative Artificial Intelligence (AI) was employed to gain a deeper understanding of patient responses. The results indicate the extent and impact of pain in PD and provide valuable insights for more targeted and personalized management. This study lays the foundation for future research and the development of interventions aimed at improving the quality of life for individuals affected by this condition.

Clinical Features of Takotsubo Syndrome and Its Differential Diagnostic Criteria in Clinical Nursing Practice: A Review of the Literature.

PURPOSE OF REVIEW: Takotsubo cardiomyopathy (TCM) is a heart disease that mimics the symptoms of a myocardial infarction (MI). The exact cause of TCM is unknown, but the main theory is that the syndrome is triggered by an excessive release of catecholamines, a consequence of factors related to stress or severe emotional distress. The aim of this review is to summarize the various scientific journal articles on the nursing differential diagnosis of TCM, on the specific nurse training (particularly the role of the Advanced Practice Nurse, APN), and on the nursing educational support for the patient after hospital discharge. RECENT FINDINGS: A literature review was conducted on Medline (via PubMed), Web of Science (WoS), Scopus, and Google Scholar databases. Relevant indexed articles that investigated the elements characterizing TCM in nursing differential diagnosis and the role of the APN were identified. RESULTS: Sixteen studies were included in the review; they highlighted the role of the nurse in identifying and educating patients with TCM. Nurses must have a thorough understanding of the syndrome, the onset symptoms, the unusual characteristics, and the probable etiology of TCM in order to recognize and promptly treat patients affected by this syndrome and have the opportunity to educate them after hospital discharge to reduce the possibility of recurrence.

Breast Cancer Prevention: The Key Role of Population Screening, Breast Self-Examination (BSE) and Technological Tools. Survey of Italian Women.

Breast cancer is the most common tumor among women worldwide and still remains the leading cause of death in women in Italy. Although survival from this pathology has increased, this disease and its treatment can have lasting or delayed effects that can greatly affect a woman's quality of life. Primary and secondary prevention are currently the best strategies to combat this cancer: improved lifestyle, early adherence to screening, Breast Self-Examination (BSE), and even now the use of technology, have become among the most important tools to ensure increasingly early diagnosis of this disease, which is a major cause of suffering and premature mortality in women. Indeed, early diagnosis of the disease can lead to a good prognosis and a high survival rate. This study investigates the attitude of Italian women to perform clinical checkups aimed at cancer prevention, particularly adherence to free screening programs offered by the National Health Service (NHS) for women in the 50-69 age group. The knowledge, use and emotional approach toward BSE as a screening tool and the use of dedicated apps for this purpose are also investigated. Low adherence to screening programs, lack of BSE practice, and nonuse of dedicated apps are just some of the results observed in this study. Therefore, it becomes essential to spread the culture of prevention, cancer awareness and the importance of screening throughout life.

How shift work influences anxiety, depression, stress and insomnia conditions in Italian nurses: an exploratory study.

BACKGROUND AND AIM OF THE WORK: Evidence suggested that the nursing profession could be considered as a very complex profession also for nurses themselves. To investigate how shift work influence nurses' health also considering anxiety, depression, stress and insomnia conditions. METHODS: An on-line cohort observational study was conducted during May 2022 to 408 nurses. RESULTS: 408 nurses were on-line recruited. Most of the nurses recruited worked also during the night shift (73.3%) and were very young (p<0.001), as aged less than 30 years (29.2%) and also aged between 31-40 years (29), too. Significant difference was reported in smoking habit, as nurses who worked also during the night reported higher smoking habit then the others (p=0.020). None further significant differences according to sex, age, work experience, nursing education, nursing activity, BMI and shift work was found. Finally, none differences were assessed between anxiety, depression, stress and insomnia conditions according to shift work typologies. CONCLUSIONS: The present study discussed research results already highlighted in the current literature; however, it collected further information and assessed additional differences, so that a more complete picture of the nursing profession could be defined.

Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain.

Back pain is the leading cause of disability worldwide. Its emergence relates not only to the musculoskeletal degeneration biological substrate but also to psychosocial factors; emotional components play a pivotal role. In modern society, people are significantly informed by the Internet; in turn, they contribute social validation to a "successful" digital information subset in a dynamic interplay. The Affective component of medical pages has not been previously investigated, a significant gap in knowledge since they represent a critical biopsychosocial feature. We tested the hypothesis that successful pages related to spine pathology embed a consistent emotional pattern, allowing discrimination from a control group. The pool of web pages related to spine or hip/knee pathology was automatically selected by relevance and popularity and submitted to automated sentiment analysis to generate emotional patterns. Machine Learning (ML) algorithms were trained to predict page original topics from patterns with binary classification. ML showed high discrimination accuracy; disgust emerged as a discriminating emotion. The findings suggest that the digital affective "successful content" (collective consciousness) integrates patients' biopsychosocial ecosystem, with potential implications for the emergence of chronic pain, and the endorsement of health-relevant specific behaviors. Awareness of such effects raises practical and ethical issues for health information providers.

A New Berlin Questionnaire Simplified by Machine Learning Techniques in a Population of Italian Healthcare Workers to Highlight the Suspicion of Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) syndrome is a condition characterized by the presence of repeated complete or partial collapse of the upper airways during sleep associated with episodes of intermittent hypoxia, leading to fragmentation of sleep, sympathetic nervous system activation, and oxidative stress. To date, one of the major aims of research is to find out a simplified non-invasive screening system for this still underdiagnosed disease. The Berlin questionnaire (BQ) is the most widely used questionnaire for OSA and is a beneficial screening tool devised to select subjects with a high likelihood of having OSA. We administered the original ten-question Berlin questionnaire, enriched with a set of questions purposely prepared by our team and completing the socio-demographic, clinical, and anamnestic picture, to a sample of Italian professional nurses in order to investigate the possible impact of OSA disease on healthcare systems. According to the Berlin questionnaire, respondents were categorized as high-risk and low-risk of having OSA. For both risk groups, baseline characteristics, work information, clinical factors, and symptoms were assessed. Anthropometric data, work information, health status, and symptoms were significantly different between OSA high-risk and low-risk groups. Through supervised feature selection and Machine Learning, we also reduced the original BQ to a very limited set of items which seem capable of reproducing the outcome of the full BQ: this reduced group of questions may be useful to determine the risk of sleep apnea in screening cases where questionnaire compilation time must be kept as short as possible.

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.

INTRODUCTION: Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. METHODS AND ANALYTICS: Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. ETHICS AND DISSEMINATION: This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study. REGISTRATION: The study was registered at ClinicalTrials.gov with the identifier NCT04609163.

Aptamers Which Target Proteins: What Proteotronics Suggests to Pharmaceutics.

Aptamers represent a challenging field of research, relevant for diagnosis in macular degeneration, cancer, thrombosis and many inflammatory diseases, and promising in drug discovery and development. Their selection is currently performed by a stable in vitro technology, namely, SELEX. Furthermore, computationalstatistical tools have been developed to complement the SELEX selection; they work both in the preliminary stage of selection, by designing high affinity aptamers for the assigned target, and also in the final stage, analyzing the features of the best performers to implement the selection technique further. A massive use of the in silico approach is, at present, only restricted by the limited knowledge of the specific aptamer-target topology. Actually, only about fifty X-ray structures of aptamer-protein complexes have been experimentally resolved, highlighting how this knowledge has to be improved. The structure of biomolecules like aptamer-protein complexes can be represented by networks, from which several parameters can be extracted. This work briefly reviews the literature, discussing if and how general network parameters in the framework of Proteotronics and graph theory (such as electrical features, link number, free energy change, and assortativity), are important in characterizing the complexes, anticipating some features of the biomolecules. To better explain this topic, a case-study is proposed, constituted by a set of anti-angiopoietin (Ang2) aptamers, whose performances are known from the experiments, and for which two different types of conformers were predicted. A topological indicator is proposed, named Möbius (M), which combines local and global information, and seems able to discriminate between the two possible types of conformers, so that it can be considered as a useful complement to the in vitro screening for pharmaceutical aims.

Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors.

Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.

Evaluation of low-grade glioma structural changes after chemotherapy using DTI-based histogram analysis and functional diffusion maps.

OBJECTIVES: To explore the role of diffusion tensor imaging (DTI)-based histogram analysis and functional diffusion maps (fDMs) in evaluating structural changes of low-grade gliomas (LGGs) receiving temozolomide (TMZ) chemotherapy. METHODS: Twenty-one LGG patients underwent 3T-MR examinations before and after three and six cycles of dose-dense TMZ, including 3D-fluid-attenuated inversion recovery (FLAIR) sequences and DTI (b = 1000 s/mm(2), 32 directions). Mean diffusivity (MD), fractional anisotropy (FA), and tensor-decomposition DTI maps (p and q) were obtained. Histogram and fDM analyses were performed on co-registered baseline and post-chemotherapy maps. DTI changes were compared with modifications of tumour area and volume [according to Response Assessment in Neuro-Oncology (RANO) criteria], and seizure response. RESULTS: After three cycles of TMZ, 20/21 patients were stable according to RANO criteria, but DTI changes were observed in all patients (Wilcoxon test, P ≤ 0.03). After six cycles, DTI changes were more pronounced (P ≤ 0.005). Seventy-five percent of patients had early seizure response with significant improvement of DTI values, maintaining stability on FLAIR. Early changes of the 25th percentiles of p and MD predicted final volume change (R(2) = 0.614 and 0.561, P < 0.0005, respectively). TMZ-related changes were located mainly at tumour borders on p and MD fDMs. CONCLUSIONS: DTI-based histogram and fDM analyses are useful techniques to evaluate the early effects of TMZ chemotherapy in LGG patients. KEY POINTS: • DTI helps to assess the efficacy of chemotherapy in low-grade gliomas. • Histogram analysis of DTI metrics quantifies structural changes in tumour tissue. • Functional diffusion maps (fDMs) spatially localize the changes of DTI metrics. • Changes in DTI histograms and fDMs precede changes in conventional MRI. • Early changes in DTI histograms and fDMs correlate with seizure response.

Robust Intensity Standardization in Brain Magnetic Resonance Images.

The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

Automatic lung segmentation in CT images with accurate handling of the hilar region.

A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent 'fusion' between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the framework of a CAD system. Finally, in the comparison with a two-dimensional segmentation procedure, inter-slice smoothness was calculated, showing that the masks created by the 3D algorithm are significantly smoother than those calculated by the 2D-only procedure.