walk through programming for industrial applications

Abstract Collaboration between humans and robots is increasingly desired in several application domains, including the manufacturing domain. The paper describes a software control architecture for industrial robotic applications allowing human-robot cooperation during the programming phase of a robotic task. The control architecture is based on admittance control and tool dynamics compensation for implementing walk-through programming and manual guidance. Further steps to integrate this system on a real set-up include the robot kinematics and a socket communication that sends a binary file to the robot

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naive Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO(2) ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naive Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naive Bayes algorithm with 14 features chosen a priori.Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naive Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia

Second Report (1998-2006) of the International Registry of Hand and Composite Tissue Transplantation

Since May 2002 all groups performing hand transplantations have supplied detailed information to the International Registry on Hand and Composite Tissue Transplantation. This report provides a review of all hand transplants performed to date. From September 1998 to February 2006 eighteen male patients underwent 24 hand/forearm/digit transplantations (eleven unilateral and four bilateral hand transplantations, two bilateral forearm transplantations, one thumb transplantation). The level of amputation was mostly at the distal forearm or wrist. Patient average age was 32. Time since hand loss ranged from 2 months to 22 years. Immunosuppressive therapy included tacrolimus, mycophenolate mofetil, rapamycin and steroids; polyclonal or monoclonal antibodies were used for induction. Topical immunosuppression was administered in some patients. Follow-up period ranged from 34 to 85 months. Patient survival was 100%. Graft survival was 100% at 1 and 2 years. Two cases of graft failure at a later date occurred and were caused by severe inflammation and progressive rejection in a non-compliant patient. In addition, 6 hands were lost due to a rejection process as the Chinese recipients did not take their immunosuppressive treatment. These failures were communicated in January 2006. Acute rejection episodes occurred in 12 patients within the first year. Rejection was completely reversible in all compliant patients. Side-effects included opportunistic infections and metabolic complications. No life-threatening complications or malignancies were reported. As it would have been very difficult to analyse transplantation functional results in a standardized way, the Registry has performed a functional score system. All patients had achieved protective sensation and in 17 of them also discriminative sensation. Extrinsic and intrinsic muscle recovery enabled patients to perform most daily activities and 90% of the recipients returned to work, and improved manual skills allowed them not only to resume their previous jobs but also, in some cases, to find more suitable employment. Fifteen recipients reported an improvement of their quality of life and we evaluated as a very important point as patient satisfaction and well-being are mandatory goals of hand transplantation. © 2007 Elsevier B.V. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

The international registry on hand and composite tissue transplantation

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Toward cell therapy using placenta-derived cells: Disease mechanisms, cell biology, preclinical studies, and regulatory aspects at the round table

Among the many cell types which may prove useful to regenerative medicine, mounting evidence suggests that human term placenta-derived cells will join the list of significant contributors. In making new cell therapy-based strategies a clinical reality, it is fundamental that no a priori claims are made regarding which cell source is preferable for a particular therapeutic application. Rather, ongoing comparisons of the potentiality and characteristics of cells from different sources should be made to promote constant improvement in cell therapies, and such comparisons will likely show that individually-tailored cells can address disease-specific clinical needs. The principle underlying such an approach is resistance to the notion that comprehensive characterization of any cell type has been achieved, neither in terms of phenotype nor risks-to-benefits ratio. Tailoring cell therapy approaches to specific conditions also requires an understanding of basic disease mechanisms and close collaboration between translational researchers and clinicians, to identify current needs and shortcomings in existing treatments. To this end, the international workshop entitled "Placenta-derived stem cells for treatment of inflammatory diseases: moving toward clinical application" was held in Brescia, Italy, in March 2009, and aimed to harness an understanding of basic inflammatory mechanisms inherent in human diseases with updated findings regarding biological and therapeutic properties of human placenta-derived cells, with particular emphasis on their potential for treating inflammatory diseases. Finally, steps required to allow their future clinical application according to regulatory aspects including good manufacturing practice (GMP) were also considered. In September, 2009, the International Placenta Stem Cell Society (IPLASS) was founded to help strengthen the research network in this field