Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke

The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective

Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People

In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error. We apply this idea to the prediction of future glucose values in diabetes, which is a delicate task as unstable predictions can leave the patient in doubt and make him/her take the wrong action, threatening his/her life. The study is conducted on type 1 and type 2 diabetic people, with a focus on predictions made 30-minutes ahead of time. First, we confirm the superiority, in the context of glucose prediction, of the LSTM model by comparing it to other state-of-the-art models (Extreme Learning Machine, Gaussian Process regressor, Support Vector Regressor). Then, we show the importance of making stable predictions by smoothing the predictions made by the models, resulting in an overall improvement of the clinical acceptability of the models at the cost in a slight loss in prediction accuracy. Finally, we show that the proposed approach, outperforms all baseline results. More precisely, it trades a loss of 4.3\% in the prediction accuracy for an improvement of the clinical acceptability of 27.1\%. When compared to the moving average post-processing method, we show that the trade-off is more efficient with our approach

Mycophenolate mofetil versus azathioprine for prevention of acute rejection in renal transplantation (MYSS): a randomised trial.

BACKGROUND: Mycophenolate mofetil has replaced azathioprine in immunosuppression regimens worldwide to prevent graft rejection. However, evidence that its antirejection activity is better than that of azathioprine has been provided only by registration trials with an old formulation of ciclosporin and steroid. We aimed to compare the antirejection activity of these two drugs with a new formulation of ciclosporin. METHODS: The mycophenolate steroids sparing multicentre, prospective, randomised, parallel-group trial compared acute rejections and adverse events in recipients of cadaver-kidney transplants over 6-month treatment with mycophenolate mofetil or azathioprine along with ciclosporin microemulsion (Neoral) and steroids (phase A), and over 15 more months without steroids (phase B). The primary endpoint was occurrence of acute rejection episodes. Analysis was by intention to treat. FINDINGS: 168 patients per group entered phase A. 56 (34%) assigned mycophenolate mofetil and 58 (35%) assigned azathioprine had clinical rejections (risk reduction [RR] on mycophenolate mofetil compared with azathioprine 13.7% [95% CI -25.7% to 40.7%], p=0.44). 88 patients in the mycophenolate mofetil group and 89 in the azathioprine group entered phase B. 14 (16%) taking mycophenolate mofetil and 11 (12%) taking azathioprine had clinical rejections (RR -16.2%, [-157.5% to 47.5%], p=0.71). Average per-patient costs of mycophenolate mofetil treatment greatly exceeded those of azathioprine (phase A 2665 Euros [SD 586] vs Euros 184 [62]; phase B 5095 Euros [2658] vs 322 Euros [170], p<0.0001 for both). INTERPRETATION: In recipients of cadaver kidney-transplants given ciclosporin microemulsion, mycophenolate mofetil offers no advantages over azathioprine in preventing acute rejections and is about 15 times more expensive. Standard immunosuppression regimens for transplantation should perhaps include azathioprine rather than mycophenolate mofetil, at least for kidney graft

Personalizing Cancer Pain Therapy: Insights from the Rational Use of Analgesics (RUA) Group

Introduction: A previous Delphi survey from the Rational Use of Analgesics (RUA) project involving Italian palliative care specialists revealed some discrepancies between current guidelines and clinical practice with a lack of consensus on items regarding the use of strong opioids in treating cancer pain. Those results represented the basis for a new Delphi study addressing a better approach to pain treatment in patients with cancer. Methods: The study consisted of a two-round multidisciplinary Delphi study. Specialists rated their agreement with a set of 17 statements using a 5-point Likert scale (0 = totally disagree and 4 = totally agree). Consensus on a statement was achieved if the median consensus score (MCS) (expressed as value at which at least 50% of participants agreed) was at least 4 and the interquartile range (IQR) was 3–4. Results: This survey included input from 186 palliative care specialists representing all Italian territory. Consensus was reached on seven statements. More than 70% of participants agreed with the use of low dose of strong opioids in moderate pain treatment and valued transdermal route as an effective option when the oral route is not available. There was strong consensus on the importance of knowing opioid pharmacokinetics for therapy personalization and on identifying immediate-release opioids as key for tailoring therapy to patients’ needs. Limited agreement was reached on items regarding breakthrough pain and the management of opioid-induced bowel dysfunction. Conclusion: These findings may assist clinicians in applying clinical evidence to routine care settings and call for a reappraisal of current pain treatment recommendations with the final aim of optimizing the clinical use of strong opioids in patients with cancer

An Integrated Model for Supporting Aware Decisions of Companies in a Circular and Sustainable Economy Transition

The world of business is rapidly changing, not only thanks to digitization and technological transformation, but also to address challenges related to the environment and climate change, and to reduce its impact in terms of waste, emissions, and raw materials. The COVID-19 crisis and the European Green New Deal have also accelerated this transformation process. In this context, companies must be able to evaluate their commitment and contribution to sustainable development, and to adopt lower impact business models. To achieve this aim, companies need easy and accessible measurement tools. The tools currently available are based on quantitative or statistical approaches and require the process of large amounts of data. This approach is easily accessible to large companies, while small companies or craft businesses may be scared off, as they may lack the structures and expertise. This study fills this gap by presenting an innovative and easy-to-access methodology for assessing sustainability in companies. Through a qualitative assessment of interdependence among nine categories grouping multiple environmental, social, and governance indicators, companies can evaluate their impact on the 17 SDGs and on the 3 ESG dimensions. The result can be used by the companies to design strategies for their businesses and plan future actions to improve circular models, thanks to the awareness and benefits gained from the analysis. The methodology has been applied to the case study of Ohoskin © 2021