Spatial Olfactory Memory and Spatial Olfactory Navigation, Assessed with a Variant of Corsi Test, Is Modulated by Gender and Sporty Activity

Many studies have focused on navigation, spatial skills, and the olfactory system in comparative models, including those concerning the relationship between them and physical activity. Although the results are often in contrast with each other, it is assumed that physical activity can affect cognition in different ways-both indirectly and through a certain influence on some brain structures. In contrast, there is little research that focuses on the relationship between spatial abilities and olfactory abilities in humans. This research aimed to evaluate and compare the performance in working memory tasks of athletes and non-athletes who require good visual-spatial navigation, olfactory-spatial navigation, and olfactory-semantic skills. The study involved 236 participants (83 athletes) between the ages of 18 and 40. All subjects were matched by age or sex. The standard Corsi Block Tapping Test (CBTT) was administrated to investigate the visual-spatial memory. Olfactory-spatial navigation and olfactory-semantic skills were assessed with two modified versions of CBTT: Olfactory CBTT (OCBTT) and Semantic-Olfactory CBTT (SOCBTT) respectively. The results show differences between the CORSI conditions in direction of a poor performance for athletes. A gender effect in favor of men was also found, particularly in the classic version of the CBTT. Both groups performed better in the classic version of the CBTT than OCBTT and SOCBTT. The mean of SOCBTT results is markedly lower, perhaps due to the different information processing systems needed to perform this kind of task. It is possible to explain how sports practice can affect tasks that require spatial skills and olfactory perception differently, thus supporting new hypotheses and opening new scientific horizons

An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review

A significant proportion of patients with COVID-19 pneumonia could develop acute respiratory distress syndrome (ARDS), thus requiring mechanical ventilation, and resulting in a high rate of intensive care unit (ICU) admission. Several complications can arise during an ICU stay, from both COVID-19 infection and the respiratory supporting system, including barotraumas (pneumothorax and pneumomediastinum), superimposed pneumonia, coagulation disorders (pulmonary embolism, venous thromboembolism, hemorrhages and acute ischemic stroke), abdominal involvement (acute mesenteric ischemia, pancreatitis and acute kidney injury) and sarcopenia. Imaging plays a pivotal role in the detection and monitoring of ICU complications and is expanding even to prognosis prediction. The present pictorial review describes the clinicopathological and radiological findings of COVID-19 ARDS in ICU patients and discusses the imaging features of complications related to invasive ventilation support, as well as those of COVID-19 itself in this particularly fragile population. Radiologists need to be familiar with COVID-19's possible extra-pulmonary complications and, through reliable and constant monitoring, guide therapeutic decisions. Moreover, as more research is pursued and the pathophysiology of COVID-19 is increasingly understood, the role of imaging must evolve accordingly, expanding from the diagnosis and subsequent management of patients to prognosis prediction

The role of MRI in the detection of local recurrence: Added value of multiparametric approach and Signal Intensity/Time Curve analysis

Objective: The aim of the study was to evaluate the accuracy of multiparametric Magnetic Resonance Imaging (mpMRI) in the detection of local recurrence of prostate cancer (PCa) with the evaluation of the added value of signal Intensity/Time (UT) curves.Materials and methods: A retrospective analysis of 22 patients undergoing mpMRI from 2015 to 2020 was carried out, with the following inclusion criteria: performing trans rectal ultrasound guided biopsy within 3 months in the case of positive or doubtful findings and undergoing biopsy and/or clinical follow-up for 24 months in the case of negative results. The images were reviewed, and the lesions were catalogued according to morphological, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) features.Results: The presence of local recurrence was detected in 11/22 patients (50%). Greater diameter, hyperintensity on DWI, positive contrast enhancement and type 2/3 signal UT curves were more frequently observed in patients with local recurrence (all p < 0.05). Of all the sequences, DCE was the most accurate; however, the combination of DCE and DWI showed the best results, with a sensitivity of 100%, a specificity of 82%, a negative predictive value of 100% and a positive predictive value of 85%.Conclusions: The utility of MRI in the detection of local recurrence is tied to the multiparametric approach, with all sequences providing useful information. A combination of DCE and DWI is particularly effective. Moreover, specificity could be additionally improved using analysis of the signal UT curves

Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

Featured Application The present study demonstrates that semi-automatic segmentation enables the identification of regions of interest affected by SARS-CoV-2 infection for the extraction of prognostic features from chest CT scans without suffering from the inter-operator variability typical of segmentation, hence offering a valuable and informative second opinion. Machine Learning methods allow identification of the prognostic features potentially reusable for the early detection and management of other similar diseases. (1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation

The Role of Fast and Deep PSA Response in Castration-sensitive Prostate Cancer

Background: Outcomes of castration-sensitive prostate cancer (CSPC) have improved owing to new therapies and early treatment, previously reserved for castration-resistant disease (CRPC). Prostatic-specific antigen (PSA) remains the most used marker to follow-up patients under treatment, but only limited data are available about the prognostic role of its changes over time and the impact of response to subsequent therapies. This analysis aims to assess the prognostic role of the magnitude and velocity of PSA response in CSPC and describe how this may affect the outcome to subsequent treatment outcomes in CRPC. Patients and methods: A retrospective analysis was performed on patients with de novo CSPC referring to six oncology centers in Italy. Clinical and pathological features were recorded. PSA response (PSA50), defined as a decrease > 50% compared to baseline, PSA velocity (PSAv), defined as any decrease in PSA levels over time and the deep and fast PSA response (4mPSA50), defined as the PSA response reached within the threshold of 4 months from the beginning of androgen deprivation therapy (ADT) have been evaluated for their impact on survival. Survivals were estimated using the Kaplan-Meier method and compared across groups using the log-rank test. Cox proportional-hazard models, stratified according to baseline characteristics, were used to estimate hazard ratios for overall survival (OS). Results: A totals of 94.4% of patients had PSA50, which was correlated to longer OS compared to patients without PSA50 (56.0 vs. 14.8 months; p<0.001). The median PSAv was 6.9 (ng/dl)/month, which was predictive for longer OS: Each decrease of 1 (ng/dl)/month was able to improve OS by 0.2% (HR=0.998, 95%CI=0.997-1.000; p=0.008). A total of 47.9% of patients reached 4mPSA50, with a median OS and progression-free survival (PFS) to ADT-based therapy of 101.0 and 23.4 months compared to 41.9 and 11.0 months for those who did not (p<0.001), respectively. The independent prognostic role of 4mPSA50 was retained even when evaluated in multivariable analysis adjusted for other baseline characteristics and early docetaxel for CSPC. In CRPC, 4mPSA50 evaluated during CSPC retains its prognostic role even if it does not predict a different outcome between patients treated with abiraterone/enzalutamide or taxanes. Conclusion: Achieving a deep and fast PSA response correlates with a better outcome in patients with de novo mCSPC, also positively influencing the prognosis of the subsequent first-line therapy for CRPC disease

A Liver Index and its Relationship to Indices of HCC Aggressiveness

A Hepatocellular (HCC) Aggressiveness Index was recently constructed, consisting of the sum of the scores for the 4 clinical parameters of maximum tumor size, multifocality, presence of portal vein thrombus and blood alphafetoprotein levels. It was observed that there was an association with several liver function tests. We have now formed a Liver Index from the 4 liver parameters with the highest hazard ratios with respect to HCC aggressiveness, namely: blood total bilirubin, gamma glutamyl transpeptidase (GGTP), albumin and platelet levels (cirrhosis surrogate). We found that the scores for the Liver Index related significantly to survival, but also to the Aggressiveness Index and to its individual HCC components as well as showing significant trends with the components. These results support the hypothesis that liver function is not only an important prognostic factor in HCC patients, but may also be involved in HCC biology and aggressiveness. Blood albumin, GGTP, albumin and platelet levels were used to create a Liver Index that related significantly to parameters of HCC aggressiveness

Development and Validation of a New Prognostic System for Patients with Hepatocellular Carcinoma

BACKGROUND: Prognostic assessment in patients with hepatocellular carcinoma (HCC) remains controversial. Using the Italian Liver Cancer (ITA.LI.CA) database as a training set, we sought to develop and validate a new prognostic system for patients with HCC. METHODS AND FINDINGS: Prospective collected databases from Italy (training cohort, n = 3,628; internal validation cohort, n = 1,555) and Taiwan (external validation cohort, n = 2,651) were used to develop the ITA.LI.CA prognostic system. We first defined ITA.LI.CA stages (0, A, B1, B2, B3, C) using only tumor characteristics (largest tumor diameter, number of nodules, intra- and extrahepatic macroscopic vascular invasion, extrahepatic metastases). A parametric multivariable survival model was then used to calculate the relative prognostic value of ITA.LI.CA tumor stage, Eastern Cooperative Oncology Group (ECOG) performance status, Child-Pugh score (CPS), and alpha-fetoprotein (AFP) in predicting individual survival. Based on the model results, an ITA.LI.CA integrated prognostic score (from 0 to 13 points) was constructed, and its prognostic power compared with that of other integrated systems (BCLC, HKLC, MESIAH, CLIP, JIS). Median follow-up was 58 mo for Italian patients (interquartile range, 26-106 mo) and 39 mo for Taiwanese patients (interquartile range, 12-61 mo). The ITA.LI.CA integrated prognostic score showed optimal discrimination and calibration abilities in Italian patients. Observed median survival in the training and internal validation sets was 57 and 61 mo, respectively, in quartile 1 (ITA.LI.CA score 64 1), 43 and 38 mo in quartile 2 (ITA.LI.CA score 2-3), 23 and 23 mo in quartile 3 (ITA.LI.CA score 4-5), and 9 and 8 mo in quartile 4 (ITA.LI.CA score > 5). Observed and predicted median survival in the training and internal validation sets largely coincided. Although observed and predicted survival estimations were significantly lower (log-rank test, p < 0.001) in Italian than in Taiwanese patients, the ITA.LI.CA score maintained very high discrimination and calibration features also in the external validation cohort. The concordance index (C index) of the ITA.LI.CA score in the internal and external validation cohorts was 0.71 and 0.78, respectively. The ITA.LI.CA score's prognostic ability was significantly better (p < 0.001) than that of BCLC stage (respective C indexes of 0.64 and 0.73), CLIP score (0.68 and 0.75), JIS stage (0.67 and 0.70), MESIAH score (0.69 and 0.77), and HKLC stage (0.68 and 0.75). The main limitations of this study are its retrospective nature and the intrinsically significant differences between the Taiwanese and Italian groups. CONCLUSIONS: The ITA.LI.CA prognostic system includes both a tumor staging-stratifying patients with HCC into six main stages (0, A, B1, B2, B3, and C)-and a prognostic score-integrating ITA.LI.CA tumor staging, CPS, ECOG performance status, and AFP. The ITA.LI.CA prognostic system shows a strong ability to predict individual survival in European and Asian populations

Wide spetcrum mutational analysis of metastatic renal cell cancer : a retrospective next generation sequencing approach

Renal cell cancer (RCC) is characterized by histological and molecular heterogeneity that may account for variable response to targeted therapies. We evaluated retrospectively with a next generation sequencing (NGS) approach using a pre-designed cancer panel the mutation burden of 32 lesions from 22 metastatic RCC patients treated with at least one tyrosine kinase or mTOR inhibitor. We identified mutations in the VHL, PTEN, JAK3, MET, ERBB4, APC, CDKN2A, FGFR3, EGFR, RB1, TP53 genes. Somatic alterations were correlated with response to therapy. Most mutations hit VHL1 (31,8%) followed by PTEN (13,6%), JAK3, FGFR and TP53 (9% each). Eight (36%) patients were wild-type at least for the genes included in the panel. A genotype concordance between primary RCC and its secondary lesion was found in 3/6 cases. Patients were treated with Sorafenib, Sunitinib and Temsirolimus with partial responses in 4 (18,2%) and disease stabilization in 7 (31,8%). Among the 4 partial responders, 1 (25%) was wild-type and 3 (75%) harbored different VHL1 variants. Among the 7 patients with disease stabilization 2 (29%) were wild-type, 2 (29%) PTEN mutated, and single patients (14% each) displayed mutations in VHL1, JAK3 and APC/CDKN2A. Among the 11 non-responders 7 (64%) were wild-type, 2 (18%) were p53 mutated and 2 (18%) VHL1 mutated. No significant associations were found among RCC histotype, mutation variants and response to therapies. In the absence of predictive biomarkers for metastatic RCC treatment, a NGS approach may address single patients to basket clinical trials according to actionable molecular specific alterations.Peer reviewe

Diabetes and Obesity as Independent Risk Factors for Osteoporosis: Updated Results from the ROIS/EMEROS Registry in a Population of Five Thousand Post-Menopausal Women Living in a Region Characterized by Heavy Environmental Pressure

EMEROS/ROIS Registry is a project funded by ISBEM and supported by Medicina Futura Research