Classifying structural alterations of the cytoskeleton by spectrum enhancement and descriptor fusion.

A classifier capable of ranking structural alterations of the cytoskeleton is developed. Images of cytoskeletal microtubules obtained from the epifluorescence microscopy of primary culture rat hepatocytes are analyzed. Morphological descriptors are extracted by contour and mass fractal analysis, direct methods, and spectrum enhancement. All methods are designed and tuned to make the extracted morphological descriptors insensitive to absolute fluorescence intensities. Spectrum enhancement is a nonlinear filter that involves spatial differentiation of the gray-scale image followed by conversion of power spectral density to the logarithmic scale and averaging over arcs in the reciprocal domain. Enhanced spectra exhibit local maxima that correspond to the structured microtubule bundles of a normal cytoskeleton. Descriptor fusion for classification is achieved by means of multivariate analysis. The classifier is trained by image sets representing normal ("negative control") microtubules and those altered by exposure to a fungicide at the highest dose of the experiment design. Some sensitivity and validation tests, including discriminant functions analysis, are applied to the classifier. The latter is applied to recognize images of microtubules not used in the training stage and comes from treatments at lower concentrations and shorter times. As a result, structural alterations are ranked and structural recovery after treatment is quantified. The method has potential use in quantitative, morphology-based tests on the cytoskeleton treated either by anticancer drugs or by cytotoxic agents

Molecular detection of TP53, Ki-Ras and p16INK4A promoter methylation in plasma of patients with colorectal cancer and its association with prognosis. Results of a 3-year GOIM (Gruppo Oncologico dell'Italia Meridionale) prospective study.

BACKGROUND:Despite the improvement in detection and surgical therapy in the last years, the outcome of patients affected by colorectal carcinoma (CRC) remains limited by metastatic relapse. The aim of this study was to investigate the presence of free tumor DNA in the plasma of CRC patients in order to understand its possible prognostic role. PATIENTS AND METHODS: Ki-Ras, TP53 mutations and p16(INK4A) methylation status were prospectively evaluated in tumor tissues and plasma of 66 CRC patients. RESULTS: In 50 of the 66 primitive tumor cases (76%) at least one significant alteration was identified in Ki-Ras and/or TP53 and/or p16(INK4A) genes. Eighteen of the 50 patients presented the same alteration both in the plasma and in the tumor tissue. At univariate analysis, Ki-Ras mutations proved to be significantly related to quicker relapse (P <0.01), whereas only a trend towards statistical significance (P = 0.083) was observed for the TP53 mutations CONCLUSIONS: Detection of Ki-Ras and TP53 mutation in plasma should be significantly related to disease recurrence. These data suggest that patients with a high risk of recurrence can be identified by means of the analysis of tumor-derived plasma DNA with the use of fairly non-invasive techniques

Lopinavir/Ritonavir and Darunavir/Cobicistat in Hospitalized COVID-19 Patients: Findings From the Multicenter Italian CORIST Study

Background: Protease inhibitors have been considered as possible therapeutic agents for COVID-19 patients. Objectives: To describe the association between lopinavir/ritonavir (LPV/r) or darunavir/cobicistat (DRV/c) use and in-hospital mortality in COVID-19 patients. Study Design: Multicenter observational study of COVID-19 patients admitted in 33 Italian hospitals. Medications, preexisting conditions, clinical measures, and outcomes were extracted from medical records. Patients were retrospectively divided in three groups, according to use of LPV/r, DRV/c or none of them. Primary outcome in a time-to event analysis was death. We used Cox proportional-hazards models with inverse probability of treatment weighting by multinomial propensity scores. Results: Out of 3,451 patients, 33.3% LPV/r and 13.9% received DRV/c. Patients receiving LPV/r or DRV/c were more likely younger, men, had higher C-reactive protein levels while less likely had hypertension, cardiovascular, pulmonary or kidney disease. After adjustment for propensity scores, LPV/r use was not associated with mortality (HR = 0.94, 95% CI 0.78 to 1.13), whereas treatment with DRV/c was associated with a higher death risk (HR = 1.89, 1.53 to 2.34, E-value = 2.43). This increased risk was more marked in women, in elderly, in patients with higher severity of COVID-19 and in patients receiving other COVID-19 drugs. Conclusions: In a large cohort of Italian patients hospitalized for COVID-19 in a real-life setting, the use of LPV/r treatment did not change death rate, while DRV/c was associated with increased mortality. Within the limits of an observational study, these data do not support the use of LPV/r or DRV/c in COVID-19 patients

Unraveling spatial and temporal heterogeneities of very slow rock-slope deformations with targeted DInSAR analyses

Spaceborne radar interferometry is a powerful tool to characterize landslides at local and regional scales. However, its application to very slow rock slope deformations in alpine environments (displacement rates < 5 cm/year) remains challenging, mainly due to low signal to noise ratio, atmospheric disturbances, snow cover effects, and complexities resulting from heterogeneous displacement in space and time. Here we combine SqueeSARTM data, targeted multi-temporal baseline DInSAR, GPS data, and detailed field morpho-structural mapping, to unravel the kinematics, internal segmentation, and style of activity of the Mt. Mater deep-seated gravitational slope deformation (DSGSD) in Valle Spluga (Italy). We retrieve slope kinematics by performing 2D decomposition (2D InSAR) of SqueeSARTM products derived from Sentinel-1 data acquired in ascending and descending orbits. To achieve a spatially-distributed characterization of DSGSD displacement patterns and activity, we process Sentinel-1 A/B images (2016-2019) with increasing temporal baselines (ranging from 24-days to 1-year) and generate several multi-temporal interferograms. Unwrapped displacement maps are validated using ground-based GPS data. Interferograms derived with different temporal baselines reveal a strong kinematic and morpho-structural heterogeneity and outline nested rockslides and active sectors, that arise from the background displacement signal of the main DSGSD. Seasonal interferograms, supported by GPS displacement measurements, reveal non-linear displacement trends suggesting a complex response of different slope sectors to rainfall and snowmelt. Our analyses clearly outline a composite slope instability with different nested sectors possibly undergoing different evolutionary trends towards failure. The results herein outline the potential of a targeted use of DInSAR for the detailed investigation of very slow rock slope deformations in different geological and geomorphological settings.ISSN:2072-429

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect&rsquo;s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field

Efficacy of 90Yttrium-ibritumomab tiuxetan in relapsed/refractory extranodal marginal-zone lymphoma

We evaluated clinical activity of 90Yttrium-ibritumomab (90Y-ibritumomab) tiuxetan in extranodal marginal-zone lymphoma. From May 2004 to April 2011, 30 patients affected by relapsed/refractory marginal-zone lymphoma-arisen at any extranodal site-received 90Y-ibritumomab tiuxetan at the activity of 0.4mCi/kg. Median age was 57years. At time of treatment, 13 out of 30 patients had disseminated disease (stage III/IV). All patients had received a previous treatment with a maximum of 7. Overall response rate was 90%: 23 patients achieved a complete response (77%); partial response occurred in 4 patients (13%), stable disease in 2 patients (7%) and 1 progression (3%). With a median follow-up of 5.3years, median time to relapse was not reached; 2 patients relapsed after complete response; 18 out of 23 complete responses are still responders after >3years, 12 of them after >5years. 90Y-ibritumomab tiuxetan seems to be active in patients with extranodal marginal-zone lymphoma relapsed/refractory to conventional treatment including radiotherapy. These results suggest that radioimmunotherapy could represent a possible option for the treatment in this subset of patients. © 2013 John Wiley & Sons, Ltd