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BackgroundSurgery is still the standard treatment for breast lesions such as in situ ductal carcinoma (DCIS); however, its survival benefit is minimal, particularly for low-grade DCIS. Surgical complications and related depression status can adversely affect patients’ quality of life. Approximately 25% of breast cancer (BC) cases are in situ forms, with DCIS making up 90% of these. Low and intermediate-grade DCIS often grow slowly and do not always progress clinically significant diseases. Identifying non-invasive lesions could help prevent overtreatment. In this context, new diagnostic tools like vacuum-assisted excision (VAE) could enhance the management of these conditions.MethodsThe prospective VACIS study explores the role of VAE in ensuring the absence of pathology at subsequent surgery and reducing the diagnostic underestimation of breast biopsies for microcalcifications. Patients with suspicious breast microcalcifications up to 15 mm, who are candidates for stereotactic biopsy, will be enrolled and randomised into two groups. The control group will complete the biopsy with typical sampling, aiming to collect some microcalcifications from the target, while the experimental group will focus on the complete removal of the biopsy target (confirmed by mammography on the biopsy table), followed by a second sequence of cleaning samples. Radiograms will confirm lesion removal. Pathologic outcomes at surgery will be compared between the groups, and the percentage of underestimation will be assessed. The sample size is calculated to be 70 patients per group, using statistical tests and multivariate logistic models to detect a significant difference in the absence of pathology. Data collected will include patient age, lesion characteristics, and details of the biopsy, pathology and surgery.DiscussionCurrent surgical treatments for low-and sometimes intermediate-grade DCIS offer limited survival benefits and may hurt patients’ quality of life due to surgery-related complications and associated depression. These lesions often grow slowly and might not become clinically significant, suggesting a need to avoid overtreatment. Improved diagnostics procedures, such as VAE, could help distinguish non-invasive from potentially invasive lesions, reduce biopsy underestimation, enable personalised management and optimise treatment strategies. This study hypothesises that VAE could be a viable alternative to surgery, capable of removing pathology during the biopsy procedure.Clinical trial registrationClinicaltrials.gov, identifier NCT05932758.

Objectives: We investigated the impact of school reopening on SARS-CoV-2 transmission in Italy, Germany, and Portugal in autumn 2022 when the Omicron variant was prevalent. Methods: A prospective international study was conducted using the case reproduction number (Rc) calculated with the time parametrization of Omicron. For Germany and Italy, staggered difference-in-differences analysis was employed to explore the causal relationship between school reopening and Rc changes, accounting for varying reopening dates. In Portugal, interrupted time series analysis was used due to simultaneous school reopenings. Multivariable models were adopted to adjust for confounders. Results: In Italy and Germany, post-reopening Rc estimates were significantly lower compared to those from regions/states that had not yet reopened at the same time points, both in the student population (overall average treatment effect for the treated subpopulation [O-ATT]: -0.80 [95% CI: -0.94;-0.66] for Italy; O-ATT-0.30 [95% CI: -0.36;-0.23] for Germany) and the adult population (O-ATT: -0.04 [95% CI: -0.07;-0.01] for Italy; O-ATT: -0.07 [95% CI: -0.11;-0.03] for Germany). In Portugal, there was a significant decreasing trend in Rc following school reopenings compared to the pre-reopening period (sustained effect: -0.03 [95% CI: -0.04; -0.03] in students; -0.02 [95% CI: -0.03; -0.02] in adults). Conclusions: We found no evidence of a causal relationship between school reopenings in autumn 2022 and Omicron SARS-CoV-2 transmission.

Abstract β2‐microglobulin (β2‐m) is a plasma protein derived from physiological shedding of the class I major histocompatibility complex (MHCI), causing human systemic amyloidosis either due to persistently high concentrations of the wild‐type (WT) protein in hemodialyzed patients, or in presence of mutations, such as D76N β2‐m, which favor protein deposition in the adulthood, despite normal plasma levels. Here we describe a new transgenic Caenorhabditis elegans (C. elegans) strain expressing human WT β2‐m at high concentrations, mimicking the condition that underlies dialysis‐related amyloidosis (DRA) and we compare it to a previously established strain expressing the highly amyloidogenic D76N β2‐m at lower concentrations. Both strains exhibit behavioral defects, the severity of which correlates with β2‐m levels rather than with the presence of mutations, being more pronounced in WT β2‐m worms. β2‐m expression also has a deep impact on the nematodes' proteomic and metabolic profiles. Most significantly affected processes include protein degradation and stress response, amino acids metabolism, and bioenergetics. Molecular alterations are more pronounced in worms expressing WT β2‐m at high concentration compared to D76N β2‐m worms. Altogether, these data show that β2‐m is a proteotoxic protein in vivo also in its wild‐type form, and that concentration plays a key role in modulating pathogenicity. Our transgenic nematodes recapitulate the distinctive features subtending DRA compared to hereditary β2‐m amyloidosis (high levels of non‐mutated β2‐m vs. normal levels of variant β2‐m) and provide important clues on the molecular bases of these human diseases.

Abstract Background Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs. Methods This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables. Results The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables. Conclusions Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855 $$-$$ - 0.887 Dice).

Abstract Background Contradictory results were reported on the role of school closure/reopening on the overall SARS-CoV-2 transmission rate, as well as on which kind and level of mitigation measures implemented in schools may be effective in limiting its diffusion. Some recent studies were reassuring, showing that opening did not increase the community spread, although teachers and families are worried about the high class density. On the other hand, distance learning was associated with a negative impact on learning, sociability and psychological health, especially in vulnerable children. As it becomes clear that the SARS-CoV-2 pandemic will last for a long time, there is a high need for studies and solutions to support safe schools opening based on scientific evidence of harms and benefits. The Lolli-Methode (LM) is a strategy for epidemiological surveillance and early intervention aiming at SARS-CoV-2 outbreaks’ reduction in schools, relying on polymerase chain reaction analysis of saliva samples. Methods In this cluster randomised trial protocol, we aim to determine whether the LM is useful to support schools opening and to reduce clusters and attack rates in schools, compared with the standard of care (SoC) surveillance by public health departments. This multicenter study will enrol 440 classes (around 8800 students, teachers and other personnel) from two countries, cluster randomised to LM or SoC. The samples from the pools will be collected and tested using PCR-based techniques. Test results will be combined with questionnaires filled in by children, parents, schoolteachers, and principals, concerning ongoing mitigation measures, their perceived psychological impact and other health and socio-economic information. An ancillary observational study will be carried out to study the prevalence of SARS-CoV-2 in schools, frequencies and size of clusters and attack rates, to compare the effectiveness of the different preventive measures adopted and to evaluate psychological issues in students and teachers in relation to the pandemic’s containment measures. Discussion By the end of this study, we will have defined and characterised the applicability of the LM for SARS-CoV-2 surveillance, as well as the impact of pandemic preventive measures on children and teachers. Trial registration International Standard Randomised Controlled Trial Number: NCT05396040, 27.05.2022.

IntroductionThe closure of sports centres was implemented as a preventive measure to mitigate the transmission of SARS-CoV-2. Given the observed global decline in physical activity and concurrent rise in sedentary behaviour, even among younger age groups, a retrospective cross-sectional study was undertaken to evaluate the effects of this measure on mental health in children, adolescents, and young adults during the initial phases of the COVID-19 pandemic.MethodsA total of 1,717 non-professional athletes (age range: 6–25; 53.9% males, 44.6% females) completed an online questionnaire including widely used and validated measures for mental health assessment (SDQ and PGWB-S) and questions regarding sociodemographic characteristics (such as gender), physical activity, and screen time. The association between mental health and sociodemographic characteristics, physical activity, and screen time was evaluated by using univariate and multivariable logistic regression models.ResultsIn children and adolescents, the incidence of psychological difficulties was associated with not being physically active (OR = 1.49; 95% CI: 1.09, 2.07; p = 0.015). Engaging in physical activity during the period of closures, particularly if more than twice a week, was significantly associated with less psychological difficulties for children/adolescents (OR = 0.54; 95% CI: 0.35, 0.82; p = 0.004) and psychological symptoms (i.e., psychological well-being lower than the median) for youth/young adults (OR = 0.25; 95% CI: 0.14, 0.45; p < 0.001). More psychological difficulties were also found in males for children and adolescents (OR = 1.37; 95% CI: 1.06, 1.79; p = 0.018). However, young adult males showed less psychological symptoms than females (OR = 0.35; 95% CI: 0.22, 0.55; p = 0.001). Additionally, a greater amount of screen time was associated with a higher incidence of psychological symptoms in the whole sample.ConclusionsOur results confirm the positive impact of physical activity on mental health during the COVID-19 pandemic among younger age groups. They also provide valuable insights into the risk-benefit relationship of interrupting sports activities as a preventive measure for infectious diseases.

Abstract Objective Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior.

Abstract Background We investigated to what extent tube voltage, scanner model, and reconstruction algorithm affect radiomic feature reproducibility in a single-institution retrospective database of computed tomography images of non-small-cell lung cancer patients. Methods This study was approved by the Institutional Review Board (UID 2412). Images of 103 patients were considered, being acquired on either among two scanners, at 100 or 120 kVp. For each patient, images were reconstructed with six iterative blending levels, and 1414 features were extracted from each reconstruction. At univariate analysis, Wilcoxon-Mann-Whitney test was applied to evaluate feature differences within scanners and voltages, whereas the impact of the reconstruction was established with the overall concordance correlation coefficient (OCCC). A multivariable mixed model was also applied to investigate the independent contribution of each acquisition/reconstruction parameter. Univariate and multivariable analyses were combined to analyse feature behaviour. Results Scanner model and voltage did not affect features significantly. The reconstruction blending level showed a significant impact at both univariate analysis (154/1414 features yielding an OCCC < 0.85) and multivariable analysis, with most features (1042/1414) revealing a systematic trend with the blending level (multiple comparisons adjusted p < 0.05). Reproducibility increased in association to image processing with smooth filters, nonetheless specific investigation in relation to clinical endpoints should be performed to ensure that textural information is not removed. Conclusions Combining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.

When researchers are faced with building machine learning (ML) radiomic models, the first choice they have to make is what model to use. Naturally, the goal is to use the model with the best performance. But what is the best model? It is well known in ML that modern techniques such as gradient boosting and deep learning have better capacity than traditional models to solve complex problems in high dimensions. Despite this, most radiomics researchers still do not focus on these models in their research. As access to high-quality and large data sets increase, these high-capacity ML models may become even more relevant. In this article, we use a large dataset of 949 prostate cancer patients to compare the performance of a few of the most promising ML models for tabular data: gradient-boosted decision trees (GBDTs), multilayer perceptions, convolutional neural networks, and transformers. To this end, we predict nine different prostate cancer pathology outcomes of clinical interest. Our goal is to give a rough overview of how these models compare against one another in a typical radiomics setting. We also investigate if multitask learning improves the performance of these models when multiple targets are available. Our results suggest that GBDTs perform well across all targets, and that multitask learning does not provide a consistent improvement.

Background: Several studies suggest a role of gut microbiota in colorectal cancer (CRC) initiation and progression. Vitamin D (vitD) blood levels are also inversely correlated with CRC risk and prognosis. However, these factors’ interplay remains unknown. Methods: 74 CRC patients after standard treatment were randomized to 1-year 2000 IU/day vitD or placebo. Baseline and post-treatment fecal microbiota for shotgun metagenomics sequencing was collected. Coda-lasso and Principal Component Analysis were used to select and summarize treatment-associated taxa and pathways. Associations between vitD and taxa/pathways were investigated with logistic regression. Mediation analysis was performed to study if treatment-associated taxa mediated the effect of supplementation on 25(OH)D levels. Cox proportional-hazards model was used for disease-free survival (DFS). Results: 60 patients were analyzed. Change in alpha diversity (Shannon: p = 0.77; Simpson: p = 0.63) and post-treatment beta diversity (p = 0.70) were comparable between arms. Post-treatment abundances of 63 taxa and 32 pathways differed between arms. The 63 taxa also mediated the effect of supplementation on 25(OH)D (p = 0.02). There were sex differences in vitD levels, microbiota and pathways. Pathways of essential amino acids’ biosynthesis were more abundant in supplemented women. Fusobacterium nucleatum presence at baseline was associated with worse DFS (p = 0.02). Those achieving vitD sufficiency (25(OH)D≥30 ng/ml) had lower post-treatment abundances (p = 0.05). Women were more likely to have F. nucleatum post-treatment (p = 0.02). Conclusions: VitD supplementation may contribute shaping the gut microbiota and the microbiota may partially mediate the effect of supplementation on 25(OH)D. The observed sex-specific differences highlight the necessity of including sex/gender as a variable in microbiome studies.

The relationship between physical exercise and improvement in specific cognitive domains in children and adolescents who play sport has been recently reported, although the effects on visuospatial abilities have not yet been well explored. This study is aimed at evaluating in school-age children practicing artistic gymnastics the visuospatial memory by using a table version of the Radial Arm Maze (table-RAM) and comparing their performances with those ones who do not play any sport. The visuospatial performances of 14 preadolescent girls practicing artistic gymnastics aged between 7 and 10 years and those of 14 preadolescent girls not playing any sport were evaluated in the table-RAM forced-choice paradigm that allows disentangling short-term memory from working memory abilities. Data showed that the gymnasts obtained better performances than control group mainly in the parameters evaluating working memory abilities, such as within-phase errors and spatial span. Our findings emphasizing the role of physical activity on cognitive performances impel to promote physical exercise in educational and recreational contexts as well as to analyse the impact of other sports besides gymnastics on cognitive functioning.

Therapies for epilepsy mainly provide symptomatic control of seizures since most of the available drugs do not target disease mechanisms. Moreover, about one-third of patients fail to achieve seizure control. To address the clinical need for disease-modifying therapies, research should focus on targets which permit interventions finely balanced between optimal efficacy and safety. One potential candidate is the brain-specific enzyme cholesterol 24-hydroxylase. This enzyme converts cholesterol to 24S-hydroxycholesterol, a metabolite which among its biological roles modulates neuronal functions relevant for hyperexcitability underlying seizures.To study the role of cholesterol 24-hydroxylase in epileptogenesis, we administered soticlestat (TAK-935/OV935), a potent and selective brain-penetrant inhibitor of the enzyme, during the early disease phase in a mouse model of acquired epilepsy using a clinically relevant dose. During soticlestat treatment, the onset of epilepsy was delayed and the number of ensuing seizures was decreased by about 3-fold compared to vehicle-treated mice, as assessed by EEG monitoring. Notably, the therapeutic effect was maintained 6.5 weeks after drug wash-out when seizure number was reduced by about 4-fold and their duration by 2-fold. Soticlestat-treated mice showed neuroprotection of hippocampal CA1 neurons and hilar mossy cells as assessed by post-mortem brain histology.High throughput RNA-sequencing of hippocampal neurons and glia in mice treated with soticlestat during epileptogenesis showed that inhibition of cholesterol 24-hydroxylase did not directly affect the epileptogenic transcriptional network, but rather modulated a non-overlapping set of genes that might oppose the pathogenic mechanisms of the disease.In human temporal lobe epileptic foci, we determined that cholesterol 24-hydroxylase expression trends higher in neurons, similarly to epileptic mice, while the enzyme is ectopically induced in astrocytes compared to control specimens. Soticlestat reduced significantly the number of spontaneous seizures in chronic epileptic mice when was administered during established epilepsy.Data show that cholesterol 24-hydroxylase contributes to spontaneous seizures and is involved in disease progression, thus it represents a novel target for chronic seizures inhibition and disease-modification therapy in epilepsy.

Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.

Abstract Non-melanoma skin cancers (NMSC) are more frequent among men, but women (especially those aged

The globular to fibrillar transition of proteins represents a key pathogenic event in the development of amyloid diseases. Although systemic amyloidoses share the common characteristic of amyloid deposition in the extracellular matrix, they are clinically heterogeneous as the affected organs may vary. The observation that precursors of amyloid fibrils derived from circulating globular plasma proteins led to huge efforts in trying to elucidate the structural events determining the protein metamorphosis from their globular to fibrillar state. Whereas the process of metamorphosis has inspired poets and writers from Ovid to Kafka, protein metamorphism is a more recent concept. It is an ideal metaphor in biochemistry for studying the protein folding paradigm and investigating determinants of folding dynamics. Although we have learned how to transform both normal and pathogenic globular proteins into fibrillar polymers in vitro, the events occurring in vivo, are far more complex and yet to be explained. A major gap still exists between in vivo and in vitro models of fibrillogenesis as the biological complexity of the disease in living organisms cannot be reproduced at the same extent in the test tube. Reviewing the major scientific attempts to monitor the amyloidogenic metamorphosis of globular proteins in systems of increasing complexity, from cell culture to human tissues, may help to bridge the gap between the experimental models and the actual pathological events in patients.

To assess the evidence on SARS-CoV2 infection and Covid-19 in relation to deficiency and supplementation of vitamin D, we conducted a systematic review up to April 2021. We summarised data from 38 eligible studies, which presented risk estimates for at least one endpoint, including two RCT and 27 cohort-studies: 205565 patients with information on 25OHD status and 2022 taking vitamin D supplementation with a total of 1197 admitted to the ICU or who needed invasive mechanical ventilation or intubation and hospital stay, and more than 910 Covid-19 deaths. Primary outcomes were severity and mortality and the main aim was to evaluate the association with vitamin D supplementation. Random effects models showed that supplementation was associated with a significant lower risk of both Covid-19 severe disease (SRR 0.38, 95% CI 0.20-0.72, 6 studies) and mortality (SRR 0.35, 95% CI 0.17-0.70, 8 studies). There were no statistically significant dose differences between studies: summary estimates with regular doses remain statistically significant, suggesting that higher doses are not necessary. For patients on vitamin D supplementation, a greater reduction in mortality risk emerged in older individuals and at higher latitudes. Regarding the quality of studies, assessed using the New Castle-Ottawa quality scale, the analysis revealed in most cases no statistically significant differences between low, medium or high quality studies. We found significant associations of vitamin D supplementation with Covid-19, encompassing risks of disease worsening and mortality, especially in seasons characterized by 25OHD deficiency and with not severe patients. Dedicated randomized clinical studies are encouraged to confirm these results.

Background: Stillbirth is a worldwide devastating adverse pregnancy outcome and specific maternal conditions have been associated with an increased risk of fetal death. However, despite the worldwide increased efforts in prevention of stillbirth, little improvements have been achieved in recent years. Our aim was to explore the role of maternal conditions that can be ascertained at the beginning of pregnancy (i.e., demographic and medical conditions/diseases) and estimate their contribution to antepartum stillbirth. An early identification of risk factors could offer to high-risk pregnancies a tailored antenatal surveillance by trained staff leading to a potential reduction of stillbirth rates. Methods: Retrospective case-control study in singleton pregnancies. The difference between fetal survival rates in women with or without risk factors was evaluated. Results: Antepartum stillbirth occurs more frequently in infertile, older women, with systemic diseases. Maternal conditions may affect fetal outcome in a time-dependent manner. Subdividing cases in early stillbirths (before 28 weeks of gestation) and late stillbirth (≥28 weeks of gestation) we observed that early stillbirths are associated with assisted reproductive technologies (Odds Ratio (OR) 3.10; 95% Confidence Interval (CI) 1.43–6.71), maternal age above 35 years (OR 1.59; 95% CI 1.17–2.17) and pre-gestational hypertension (OR 3.68; 95% CI 1.28–10.56). Autoimmune disease (OR 6.55; 95% CI 2.90–14.80), inherited thrombophilia (OR 2.94; 95% CI 1.40–6.18) and pre-gestational diabetes (OR 7.57; 95% CI 2.17–26.35) are independent risk factors for late stillbirths. Further, the risk of stillbirth rises with the increasing of the number of pathological maternal clinical conditions, reaching an OR of 5.27 (95% CI 2.32–11.98) in cases of mother with three or more conditions/diseases. Conclusions: Early awareness of the maternal conditions/diseases addressable at the beginning of pregnancy is crucial to offer a personalized plan for high quality care during gestation; for the prevention of stillbirth, a cared clinical management should acknowledge that pregnancies can be affected more severely and earlier as the number of abnormal maternal conditions increases.

Radiomic analysis of 18F[FDG] PET/CT images might identify predictive imaging biomarkers, however, the reproducibility of this quantitative approach might depend on the methodology adopted for image analysis. This retrospective study investigates the impact of PET segmentation method and the selection of different target lesions on the radiomic analysis of baseline 18F[FDG] PET/CT images in a population of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients. The whole tumor burden was segmented on PET images applying six methods: (1) 2.5 standardized uptake value (SUV) threshold; (2) 25% maximum SUV (SUVmax) threshold; (3) 42% SUVmax threshold; (4) 1.3∙liver uptake threshold; (5) intersection among 1, 2, 4; and (6) intersection among 1, 3, 4. For each method, total metabolic tumor volume (TMTV) and whole-body total lesion glycolysis (WTLG) were assessed, and their association with survival outcomes (progression-free survival PFS and overall survival OS) was investigated. Methods 1 and 2 provided stronger associations and were selected for the next steps. Radiomic analysis was then performed on two target lesions for each patient: the one with the highest SUV and the largest one. Fifty-three radiomic features were extracted, and radiomic scores to predict PFS and OS were obtained. Two proportional-hazard regression Cox models for PFS and OS were developed: (1) univariate radiomic models based on radiomic score; and (2) multivariable clinical–radiomic model including radiomic score and clinical/diagnostic parameters (IPI score, SUVmax, TMTV, WTLG, lesion volume). The models were created in the four scenarios obtained by varying the segmentation method and/or the target lesion; the models’ performances were compared (C-index). In all scenarios, the radiomic score was significantly associated with PFS and OS both at univariate and multivariable analysis (p < 0.001), in the latter case in association with the IPI score. When comparing the models’ performances in the four scenarios, the C-indexes agreed within the confidence interval. C-index ranges were 0.79–0.81 and 0.80–0.83 for PFS radiomic and clinical–radiomic models; 0.82–0.87 and 0.83–0.90 for OS radiomic and clinical–radiomic models. In conclusion, the selection of either between two PET segmentation methods and two target lesions for radiomic analysis did not significantly affect the performance of the prognostic models built on radiomic and clinical data of DLBCL patients. These results prompt further investigation of the proposed methodology on a validation dataset.

The worldwide incidence of melanoma has increased rapidly over the last 50 years. Melanoma is the most common cancer found in the young adult population, and its incidence is very high among geriatric populations. The incidence of melanoma varies by sex, and this factor is also associated with differences in the anatomical site melanoma. Adolescent and young adult women have a higher incidence than men. This may be, in part, due to the greater use of sunbeds, as well as intentional sun exposure among girls and, in general, risky behaviours in seeking to suntan, due to socially-determined aesthetic needs. Indeed, the World Health Organization declared that there is sufficient evidence to classify exposure to ultraviolet radiation (sunbed use and sun exposure) as carcinogenic to humans. Although pigmentation characteristics, such as skin colour, hair and eye colour, freckles and number of common and atypical naevi, do influence susceptibility to melanoma, recommendations regarding prevention should be directed to the entire population and should include avoiding sunbed, covering sun-exposed skin, wearing a hat and sunglasses. Sunscreen use should not be used to prolong intentional sun exposure. Primary prevention should be focused mainly on young adult women, while secondary prevention should be focused mainly on elderly men. In fact, after the age of 40 years, incidence rates reverse, and the incidence of melanoma among men is greater than among women. This is probably due to the fact that men are less likely than women to examine their own skin or present to a dermatologist for skin examination.

Abstract Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

Abstract Dissociation of the native transthyretin (TTR) tetramer is widely accepted as the critical step in TTR amyloid fibrillogenesis. It is modelled by exposure of the protein to non-physiological low pH in vitro and is inhibited by small molecule compounds, such as the drug tafamidis. We have recently identified a new mechano-enzymatic pathway of TTR fibrillogenesis in vitro, catalysed by selective proteolytic cleavage, which produces a high yield of genuine amyloid fibrils. This pathway is efficiently inhibited only by ligands that occupy both binding sites in TTR. Tolcapone, which is bound with similar high affinity in both TTR binding sites without the usual negative cooperativity, is therefore of interest. Here we show that TTR fibrillogenesis by the mechano-enzymatic pathway is indeed more potently inhibited by tolcapone than by tafamidis but neither, even in large molar excess, completely prevents amyloid fibril formation. In contrast, mds84, the prototype of our previously reported bivalent ligand TTR ‘superstabiliser’ family, is notably more potent than the monovalent ligands and we show here that this apparently reflects the critical additional interactions of its linker within the TTR central channel. Our findings have major implications for therapeutic approaches in TTR amyloidosis.

Objective: To investigate the influence of weight excess and blood pressure (BP) on vascular structure and function in a sample of obese children. Methods: We measured office and ambulatory BP (ABPM), carotid intima-media thickness (cIMT), endothelial function by the Flow Mediated Dilation (FMD) technique, carotid distensibility (cDC) (ultrasonograph: LogiQ P5 Pro; software: Multimedia Video Engine II, DSP Lab, Pisa CNR, Italy) (1) and stiffness index (SI) by photopletismography (Pulce Trace PT1000, MicroMedical Ltd, UK) (2) in overweight and obese children (BMI > 90th percentile for sex and age). Results: Seventy children with weight excess were enrolled (age 11.5±2.4 years; female n: 30). cDC showed inverse correlation with BMI and waist circumference (rSpearman = −0.403 and rs = −0.346, respectively), 24h-SBP (rs = −0.449) and nighttime-SBP (rs = −0.490). SI inversely correlated with BMI (rs = −0.359) and waist/height ratio (rs = −0.303). When comparing normotensive and hypertensive children, as defined on the basis of the ABPM, no significant differences in vascular tests were found, although cDC tended to be lower in hypertensive. In normotensive subjects (n: 53) cIMT directly correlated with nighttime-SBP and DBP. Most of the correlations remained significant after adjustment for age, sex, BMI and BP. Conclusions: These data suggest that arterial elasticity is negatively affected by weight excess and 24h-BP levels even in childhood. In the normotensive subgroup it is detectable an effect of BP also on arterial structure.

Abstract The mechanisms underlying transthyretin‐related amyloidosis in vivo remain unclear. The abundance of the 49–127 transthyretin fragment in ex vivo deposits suggests that a proteolytic cleavage has a crucial role in destabilizing the tetramer and releasing the highly amyloidogenic 49–127 truncated protomer. Here, we investigate the mechanism of cleavage and release of the 49–127 fragment from the prototypic S52P variant, and we show that the proteolysis/fibrillogenesis pathway is common to several amyloidogenic variants of transthyretin and requires the action of biomechanical forces provided by the shear stress of physiological fluid flow. Crucially, the non‐amyloidogenic and protective T119M variant is neither cleaved nor generates fibrils under these conditions. We propose that a mechano‐enzymatic mechanism mediates transthyretin amyloid fibrillogenesis in vivo. This may be particularly important in the heart where shear stress is greatest; indeed, the 49–127 transthyretin fragment is particularly abundant in cardiac amyloid. Finally, we show that existing transthyretin stabilizers, including tafamidis, inhibit proteolysis‐mediated transthyretin fibrillogenesis with different efficiency in different variants; however, inhibition is complete only when both binding sites are occupied.

PURPOSE:The aim of this study was to investigate the prognostic performance of multiparametric magnetic resonance imaging (mpMRI) and Prostate Imaging Reporting and Data System (PIRADS) score in predicting pathologic features in a cohort of patients eligible for active surveillance who underwent radical prostatectomy. METHODS:A total of 223 patients who fulfilled the criteria for "Prostate Cancer Research International: Active Surveillance", were included. Mp-1.5 Tesla MRI examination staging with endorectal coil was performed at least 6-8 weeks after TRUS-guided biopsy. In all patients, the likelihood of the presence of cancer was assigned using PIRADS score between 1 and 5. Outcomes of interest were: Gleason score upgrading, extra capsular extension (ECE), unfavorable prognosis (occurrence of both upgrading and ECE), large tumor volume (≥ 0.5 ml), and seminal vesicle invasion (SVI). Receiver Operating Characteristic (ROC) curves and Decision Curve Analyses (DCA) were performed for models with and without inclusion of PIRADS score. RESULTS:Multivariate analysis demonstrated the association of PIRADS score with upgrading (P < 0.0001), ECE (P < 0.0001), unfavorable prognosis (P < 0.0001), and large tumor volume (P = 0.002). ROC curves and DCA showed that models including PIRADS score resulted in greater net benefit for almost all the outcomes of interest, with the only exception of SVI. CONCLUSIONS:mpMRI and PIRADS scoring are feasible tools in clinical setting and could be used as decision-support systems for a more accurate selection of patients eligible for AS.

Metabolic acidosis correction in advanced renal failure slows renal function decline attributed to tubulointerstitial damage (TID) reduction. No study evaluated if spontaneous baseline high urinary pH (UpH) is renoprotective in patients with normal renal function and without metabolic acidosis. The study tested this hypothesis in idiopathic membranous nephropathy (IMN). Eighty-five patients (follow-up 81 ± 54 months) measured UpH, serum creatinine, eGFR, protein/creatinine ratio, fractional excretion of albumin, IgG, α1-microglobulin, and urinary N-acetyl-β-D-glucosaminidase (β-NAG)/creatinine ratio. Twenty-eight patients (33%) had UpH ≥ 6.5 and 57 (67%) pH < 6.5; high versus low UpH patients had significantly lower values of the tubulointerstitial damage (TID) markers FE α1m and β-NAG and significantly better baseline renal function. These differences persisted over time in a subset of 38 patients with 5 measurements along 53 ± 26 months. In 29 patients with nephrotic syndrome (NS) treated with supportive therapy (follow-up: 80 ± 52 months) renal function was stable in 10 high and significantly worse in 19 low UpH patients. Steroids + cyclophosphamide treatment in 35 NS patients masks the renoprotection of high UpH. Conclusions. In IMN high and persistent UpH is associated with reduction of the proteinuric markers of tubulointerstitial damage and baseline better renal function in all patients and in NS patients treated only with supportive therapy during disease course. The factors associated with high pH-dependent renoprotection were lower values of TID markers, eGFR ≥ 60 mL/min, BP < 140/90 mmHg, and age < 55 years.