Explore the vast range of titles available.

Background Diaphragmatic assessment by ultrasound (US) is a non-invasive and useful method in the clinical management of patients with Amyotrophic Lateral Sclerosis (ALS). The aim of our observational study was to evaluate the impact of serial assessment of the diaphragmatic function by US on long-term outcomes in a series of patients suffering from ALS and to correlate US indices of diaphragmatic function and respiratory function tests with these outcomes. Methods A cohort of 39 consecutive patients has been followed up to 24 months. Both lung volume (forced vital capacity, FVC) and diaphragmatic pressure generating capacity (by sniff inspiratory nasal pressure (SNIP) and by both US thickening fraction, ΔTdi, and the ratio of the thickening fraction between tidal volume and maximal lung capacity, ΔTmax) were recorded at baseline and every 3 months. Parameters were then correlated with outcomes (nocturnal hypoventilation, daily hypercapnia, start of ventilatory support (NIV), and death at 1 year) over time. Results The occurrence of ΔTmax > 0.75 increased the risk to start NIV (HR = 5.6, p  = 0.001) and to die (HR = 3.7, p  = 0.0001) compared with patients maintaining lower values. Moreover, compared with the occurrence of FVC < 50% of predicted, ΔTmax > 0.75 appeared slightly better correlated with NIV commencement within 6 months. Conclusions Serial diaphragmatic assessment by ultrasound is a useful and accurate method to predict the initiation of NIV earlier in patients with ALS.

The Global Earthquake Model aims to combine the main features of state-of-the-art science, global collaboration and buy-in, transparency and openness in an initiative to calculate and communicate earthquake risk worldwide. One of the first steps towards this objective has been the open-source development and release of software for seismic hazard and risk assessment called the OpenQuake engine. This software comprises a set of calculators capable of computing human or economic losses for a collection of assets, caused by a given scenario event, or by considering the probability of all possible events that might happen within a region within a certain time span. This paper provides an insight into the current status of the development of this tool and presents a comprehensive description of each calculator, with example results.

A high incidence of thrombotic events has been reported in patients with coronavirus disease (COVID-19), which is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. We report 3 clinical cases of patients in Italy with COVID-19 who developed abdominal viscera infarction, demonstrated by computed tomography.

COVID-19 prognostic factors include age, sex, comorbidities, laboratory and imaging findings, and time from symptom onset to seeking care. The study aim was to evaluate indices combining disease severity measures and time from disease onset to predict mortality of COVID-19 patients admitted to the emergency department (ED). All consecutive COVID-19 patients who underwent both computed tomography (CT) and chest X-ray (CXR) at ED presentation between 27/02/2020 and 13/03/2020 were included. CT visual score of disease extension and CXR Radiographic Assessment of Lung Edema (RALE) score were collected. The CT- and CXR-based scores, C-reactive protein (CRP), and oxygen saturation levels (sO2) were separately combined with time from symptom onset to ED presentation to obtain severity/time indices. Multivariable regression age- and sex-adjusted models without and with severity/time indices were compared. For CXR-RALE, the models were tested in a validation cohort. Of the 308 included patients, 55 (17.9%) died. In multivariable logistic age- and sex-adjusted models for death at 30 days, severity/time indices showed good discrimination ability, higher for imaging than for laboratory measures (AUCCT = 0.92, AUCCXR = 0.90, AUCCRP = 0.88, AUCsO2 = 0.88). AUCCXR was lower in the validation cohort (0.79). The models including severity/time indices performed slightly better than models including measures of disease severity not combined with time and those including the Charlson Comorbidity Index, except for CRP-based models. Time from symptom onset to ED admission is a strong prognostic factor and provides added value to the interpretation of imaging and laboratory findings at ED presentation.

PurposeTo study whether a new combination of different warming kits is clinically effective for vitrified human blastocysts.MethodsThis is a longitudinal cohort study analysing two hundred fifty-five blastocysts warming cycles performed between January and October 2018. Embryos were vitrified using only one brand of ready-to-use kits (Kitazato), whereas the warming procedure was performed with three of the most widely used vitrification/warming kits (Kitazato, Sage and Irvine) after patient stratification for oocyte source. The primary endpoint was survival rate, while the secondary endpoints were clinical pregnancy, live birth and miscarriage rates.ResultsWe observed a comparable survival rate across all groups of 100% (47/47) in KK, 97.6% (49/50) in KS, 97.6% (41/42) in KI, 100% (38/38) in dKK, 100% (35/35) in dKS and 100% (43/43) in dKI. Clinical pregnancy rates were also comparable: 38.3% (18/47) in KK, 49% (24/49) in KS, 56.1% (23/ 41) in KI, 47.4% (18/38) in dKK, 31.4% (11/35) in dKS and 48.8% (21/ 43) in dKI. Finally, live birth rates were 29.8% (14/47) in KK, 36.7% (18/49) in KS, 46.3% (19/41) in KI, 36.8% (14/38) in dKK, 25.7% (9/35) in dKS and 41.9% (18/43) in dKI, showing no significant differences.ConclusionThis study confirmed the efficacy of applying a single warming protocol, despite what the “industry” has led us to believe, supporting the idea that it is time to proceed in the cryopreservation field and encouraging embryologists worldwide to come out and reveal that such a procedure is possible and safe.

Anthracyclines remain a cornerstone of cancer therapy but are associated with a significant risk of cardiotoxicity, which can lead to overt heart failure. The risk is modulated by cumulative dose, pre-existing cardiovascular disease, and patient-specific factors. As cancer survival improves, the long-term cardiovascular consequences of anthracycline exposure have become a growing concern, underscoring the need for effective preventive strategies. This narrative review examines lifestyle and pharmacological interventions aimed at mitigating anthracycline-induced cardiotoxicity. Evidence suggests that structured exercise programs and antioxidant-rich diets may enhance cardiovascular resilience, while beta-blockers, renin-angiotensin system inhibitors, and dexrazoxane remain central pharmacological options. Emerging therapies, including sodium-glucose co-transporter 2 inhibitors and sacubitril/valsartan, show promise but require further investigation. A comprehensive approach that integrates lifestyle modifications with pharmacological strategies within a multidisciplinary cardio-oncology framework may provide optimal protection, improving long-term cardiovascular outcomes in cancer patients receiving anthracyclines.

Inflammatory burden is associated with COVID-19 severity and outcomes. Residual computed tomography (CT) lung abnormalities have been reported after COVID-19. The aim was to evaluate the association between inflammatory burden during COVID-19 and residual lung CT abnormalities collected on follow-up CT scans performed 2–3 and 6–7 months after COVID-19, in severe COVID-19 pneumonia survivors. C-reactive protein (CRP) curves describing inflammatory burden during the clinical course were built, and CRP peaks, velocities of increase, and integrals were calculated. Other putative determinants were age, sex, mechanical ventilation, lowest PaO2/FiO2 ratio, D-dimer peak, and length of hospital stay (LOS). Of the 259 included patients (median age 65 years; 30.5% females), 202 (78%) and 100 (38.6%) had residual, predominantly non-fibrotic, abnormalities at 2–3 and 6–7 months, respectively. In age- and sex-adjusted models, best CRP predictors for residual abnormalities were CRP peak (odds ratio [OR] for one standard deviation [SD] increase = 1.79; 95% confidence interval [CI] = 1.23–2.62) at 2–3 months and CRP integral (OR for one SD increase = 2.24; 95%CI = 1.53–3.28) at 6–7 months. Hence, inflammation is associated with short- and medium-term lung damage in COVID-19. Other severity measures, including mechanical ventilation and LOS, but not D-dimer, were mediators of the relationship between CRP and residual abnormalities.

Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.

Objective: Interstitial fibrosis/tubular atrophy (IFTA) is a common, irreversible, and progressive form of chronic kidney allograft injury, and it is considered a critical predictor of kidney allograft outcomes. The extent of IFTA is estimated through a graft biopsy, while a non-invasive test is lacking. The aim of this study was to evaluate the feasibility and accuracy of an MRI radiomic-based machine learning (ML) algorithm to estimate the degree of IFTA in a cohort of transplanted patients. Approach: Patients who underwent MRI and renal biopsy within a 6-month interval from 1 January 2012 to 1 March 2021 were included. Stable MRI sequences were selected, and renal parenchyma, renal cortex and medulla were segmented. After image filtering and pre-processing, we computed radiomic features that were subsequently selected through a LASSO algorithm for their highest correlation with the outcome and lowest intercorrelation. Selected features and relevant patients’ clinical data were used to produce ML algorithms using 70% of the study cases for feature selection, model training and validation with a 10-fold cross-validation, and 30% for model testing. Performances were evaluated using AUC with 95% confidence interval. Main results: A total of 70 coupled tests (63 patients, 35.4% females, mean age 52.2 years) were included and subdivided into a wider cohort of 50 for training and a smaller cohort of 20 for testing. For IFTA ≥ 25%, the AUCs in test cohort were 0.60, 0.59, and 0.54 for radiomic features only, clinical variables only, and a combined radiomic–clinical model, respectively. For IFTA ≥ 50%, the AUCs in training cohort were 0.89, 0.84, and 0.96, and in the test cohort, they were 0.82, 0.83, and 0.86, for radiomic features only, clinical variables only, and the combined radiomic–clinical model, respectively. Significance: An ML-based MRI radiomic algorithm showed promising discrimination capacity for IFTA > 50%, especially when combined with clinical variables. These results need to be confirmed in larger cohorts.

The study of materials for space exploration is one of the most interesting targets of international space agencies. An essential tool for realizing light junctions is epoxy adhesive (EA), which provides an elastic and robust material with a complex mesh of polymeric chains and crosslinks. In this work, a study of the structural and chemical modification of a commercial two-part flexible EA (3M™ Scotch-Weld™ EC-2216 B/A Gray), induced by 60Co gamma radiation, is presented. Combining different spectroscopic techniques, such as the spectroscopic Fourier transform infrared spectroscopy (FTIR), the THz time-domain spectroscopy (TDS), and the electron paramagnetic resonance (EPR), a characterization of the EA response in different regions of the electromagnetic spectrum is performed, providing valuable information about the structural and chemical properties of the polymers before and after irradiation. A simultaneous dissociation of polymeric chain and crosslinking formation is observed.The polymer is not subject to structural modification at an absorbed dose of 10 kGy, in which only transient free radicals are observed. Differently, between 100 and 500 kGy, a gradual chemical degradation of the samples is observed together with a broad and long-living EPR signal appearance. This study also provides a microscopic characterization of the material useful for the mechanism evaluation of system degradation.