First cases of combined full robotic partial nephrectomy and colorectal resections: Results and new perspectives

Background: Nowadays the robotic platform is widespread in general surgery, urology, and gynecology. Combined surgery may represent an alternative to sequential procedures and it allows the treatment, at the same time, of coexisting lesions; in this perspective, full-robotic multiorgan surgery is starting to gain interest from surgeons worldwide. Methods: Between April and June 2019, two patients presenting with synchronous colorectal and kidney cancers underwent, respectively, full-robotic right colectomy with right partial nephrectomy and anterior rectal resection with left partial nephrectomy. Surgeries were performed by both the general surgery and urology team. Results: No intraoperative complications were registered and the postoperative course was uneventful in both cases. Conclusions: Combined multiple organ surgery with full robotic technique is safe and offers oncological adequate results. A multi-team surgical pre-planning is mandatory to reduce invasiveness and operative time. To the best of our knowledge, these are the first reports of full robotic partial nephrectomy combined with colorectal procedures

Psychobiological evidence of the stress resilience fostering properties of a cosmetic routine

Everyday life psychosocial stressors contribute to poor health and disease vulnerabilty. Means alternative to pharmacotherapy that are able to foster stress resilience are more and more under the magnifying glass of biomedical research. The aim of this study was to test stress resilience fostering properties of the self-administration of a cosmetic product enriched with essential oils. On day 0, fourty women, 25-50 years old, self-administered both the enriched cosmetic product (ECP) and a placebo one (PCP). Then, women were randomized for daily self-administration (from day 1 to 28) of either ECP

Cooperation in wild Barbary macaques: factors affecting free partner choice

A key aspect of cooperation is partner choice: choosing the best available partner improves the chances of a successful cooperative interaction and decreases the likelihood of being exploited. However, in studies on cooperation subjects are rarely allowed to freely choose their partners. Group-living animals live in a complex social environment where they can choose among several social partners differing in, for example, sex, age, temperament, or dominance status. Our study investigated whether wild Barbary macaques succeed to cooperate using an experimental apparatus, and whether individual and social factors affect their choice of partners and the degree of cooperation. We used the string pulling task that requires two monkeys to manipulate simultaneously a rope in order to receive a food reward. The monkeys were free to interact with the apparatus or not and to choose their partner. The results showed that Barbary macaques are able to pair up with a partner to cooperate using the apparatus. High level of tolerance between monkeys was necessary for the initiation of successful cooperation, while strong social bond positively affected the maintenance of cooperative interactions. Dominance status, sex, age, and temperament of the subjects also affected their choice and performance. These factors thus need to be taken into account in cooperative experiment on animals. Tolerance between social partners is likely to be a prerequisite for the evolution of cooperation

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study

Dornier lithotripter S - The first 50 treatments in our department

INTRODUCTION: We assessed the short-term efficacy of extracorporeal shockwave lithotripsy with the Dornier Lithotripter S in the treatment of renal and ureteral stones. MATERIALS AND METHODS: Between February and April 2003, 32 renal and 19 ureteral stones were treated. Patients were evaluated 1 and 3 months afterwards. Stone size and location, total number of shockwaves and the stone-free rate were taken into consideration. RESULTS: The stone-free rate for ureteral stones was 63% at 1 month and 84.2% at 3 months. The stone-free rate for renal calculi was 75% at 1 month and 87.5% at 3 months. The overall stone-free rate was 70.6% at 1 month and 86.3% at 3 months. Analgesia was necessary in 12 patients (23.5%). No serious complications were seen, except for one steinstrasse. CONCLUSIONS: The Dornier Lithotripter S is very effective in the treatment of renal and ureteral calculi