Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging

Nuclear Shield: A Multi-Enzyme Task-Force for Nucleus Protection

In eukaryotic cells the nuclear envelope isolates and protects DNA from molecules that could damage its structure or interfere with its processing. Moreover, selected protection enzymes and vitamins act as efficient guardians against toxic compounds both in the nucleoplasm and in the cytosol. The observation that a cytosolic detoxifying and antioxidant enzyme i.e. glutathione transferase is accumulated in the perinuclear region of the rat hepatocytes suggests that other unrecognized modalities of nuclear protection may exist. Here we show evidence for the existence of a safeguard enzyme machinery formed by an hyper-crowding of cationic enzymes and proteins encompassing the nuclear membrane and promoted by electrostatic interactions

Acute diverticulitis management: evolving trends among Italian surgeons. A survey of the Italian Society of Colorectal Surgery (SICCR)

Acute diverticulitis (AD) is associated with relevant morbidity/mortality and is increasing worldwide, thus becoming a major issue for national health systems. AD may be challenging, as clinical relevance varies widely, ranging from asymptomatic picture to life-threatening conditions, with continuously evolving diagnostic tools, classifications, and management. A 33-item-questionnaire was administered to residents and surgeons to analyze the actual clinical practice and to verify the real spread of recent recommendations, also by stratifying surgeons by experience. CT-scan remains the mainstay of AD assessment, including cases presenting with recurrent mild episodes or women of child-bearing age. Outpatient management of mild AD is slowly gaining acceptance. A conservative management is preferred in non-severe cases with extradigestive air or small/non-radiologically drainable abscesses. In severe cases, a laparoscopic approach is preferred, with a non-negligible number of surgeons confident in performing emergency complex procedures. Surgeons are seemingly aware of several options during emergency surgery for AD, since the rate of Hartmann procedures does not exceed 50% in most environments and damage control surgery is spreading in life-threatening cases. Quality of life and history of complicated AD are the main indications for delayed colectomy, which is mostly performed avoiding the proximal vessel ligation, mobilizing the splenic flexure and performing a colorectal anastomosis. ICG is spreading to check anastomotic stumps' vascularization. Differences between the two experience groups were found about the type of investigation to exclude colon cancer (considering the experience only in terms of number of colectomies performed), the size of the peritoneal abscess to be drained, practice of damage control surgery and the attitude towards colovesical fistula

Disease-specific and general health-related quality of life in newly diagnosed prostate cancer patients: The Pros-IT CNR study

Background: The National Research Council (CNR) prostate cancer monitoring project in Italy (Pros-IT CNR) is an observational, prospective, ongoing, multicentre study aiming to monitor a sample of Italian males diagnosed as new cases of prostate cancer. The present study aims to present data on the quality of life at time prostate cancer is diagnosed. Methods: One thousand seven hundred five patients were enrolled. Quality of life is evaluated at the time cancer was diagnosed and at subsequent assessments via the Italian version of the University of California Los Angeles-Prostate Cancer Index (UCLA-PCI) and the Short Form Health Survey (SF-12). Results: At diagnosis, lower scores on the physical component of the SF-12 were associated to older ages, obesity and the presence of 3+ moderate/severe comorbidities. Lower scores on the mental component were associated to younger ages, the presence of 3+ moderate/severe comorbidities and a T-score higher than one. Urinary and bowel functions according to UCLA-PCI were generally good. Almost 5% of the sample reported using at least one safety pad daily to control urinary loss; less than 3% reported moderate/severe problems attributable to bowel functions, and sexual function was a moderate/severe problem for 26.7%. Diabetes, 3+ moderate/severe comorbidities, T2 or T3-T4 categories and a Gleason score of eight or more were significantly associated with lower sexual function scores at diagnosis. Conclusions: Data collected by the Pros-IT CNR study have clarified the baseline status of newly diagnosed prostate cancer patients. A comprehensive assessment of quality of life will allow to objectively evaluate outcomes of different profile of care

High-Repetition Millimeter-Wave Passive Remote Sensing of Humidity and Hydrometeor Profiles from Elliptical Orbit Constellations

The potential of an elliptical-orbit Flower Constellation of Millimeter-Wave Radiometers (FLORAD) for humidity profile and precipitating cloud observations is analyzed and discussed. The FLORAD mission scientific requirements are aimed at the retrieval of hydrological properties of the troposphere, specifically water vapor, cloud liquid content, rainfall, and snowfall profiles. This analysis is built on the results already obtained in previous works and is specifically devoted to evaluate the possibility of (i) deploying an incremental configuration of a Flower constellation of six minisatellites, optimized to provide the maximum revisit time over the Mediterranean area or, more generally, midlatitudes (between +/- 35 degrees and +/- 65 degrees); and (ii) evaluating in a quantitative way the accuracy of a one-dimensional variational data assimilation (1D-Var) Bayesian retrieval scheme to derive hydrometeor profiles at quasi-global scale using an optimized set of millimeter-wave frequencies. The obtained results show that a revisit time over the Mediterranean area (latitude 25 degrees 45 ', longitude-10 degrees 35 'degrees) of less than about 1 and 0.5 h can be obtained with four satellites and six satellites in Flower elliptical orbits, respectively. The accuracy of the retrieved hydrometeor profiles over land and sea for a winter and summer season at several latitudes shows the beneficial performance from using a combination of channels at 89, 118, 183, and 229 GHz. A lack of lower frequencies, such as those below 50 GHz, reduces the sounding capability for cloud lower layers, but the temperature and humidity retrievals provide a useful hydrometeor profile constraint. The FLORAD mission is fully consistent with the Global Precipitation Mission (GPM) scope and may significantly increase its space-time coverage. The concept of an incremental Flower constellation can ensure the flexibility to deploy a spaceborne system that achieves increasing coverage through separate launches of member spacecrafts. The choice of millimeter-wave frequencies provides the advantage of designing compact radiometers that comply well with the current technology of minisatellites (overall weight less than 500 kg). The overall budget of the FLORAD small mission might become appealing as an optimal compromise between retrieval performances and system complexity

Predicting brain age with complex networks: From adolescence to adulthood

: In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64 years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson’s correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89 and Mean Absolute Error MAE 2.19 years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE 2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging