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

Mathematics Teachers’ Perception on Modular Distance Learning: A Phenomenological Study

This study aimed to capture the lived experiences of Mathematics teachers on modular distance learning. It explored mathematics teachers' perception of modular distance learning, describe the challenges, and struggles in the new normal, unveil the coping mechanisms of mathematics teachers on the demand of modular distance learning, and verify the effects of modular distance learning on students' mathematics performance. This phenomenological study used Colaizzi’s method of data analysis to interpret and analyze the data. Nine (9) teacher-participants were recruited using purposive sampling technique. The study used the interview method in gathering data. In this study, teacher-participants perceived that modular distance learning is one of the best ways to continue students' learning. However, students' mathematics performance using modular distance learning is far below the expected standard. Identified contributing factors were students' attitudes, teachers' workloads, digital infrastructure and gadgets, slow internet connections, lack of parents' support, and students' motivation. The findings of this study can be used as a basis or springboard for revision of implementing guidelines of modular distance learning enactment policies that would provide intervention to the existing identified problem

Efficient Proofs of Knowledge for Threshold Relations

Recently, there has been great interest towards constructing efficient zero-knowledge proofs for practical languages. In this work, we focus on proofs for threshold relations, in which the prover is required to prove knowledge of witnesses for k out of ℓ statements. The main contribution of our work is an efficient and modular transformation that starting from a large class of Σ -protocols and a corresponding threshold relation Rk,ℓ, provides an efficient Σ -protocol for Rk,ℓ with improved communication complexity w.r.t. prior results. Our transformation preserves statistical/perfect honest-verifier zero knowledge

Shielded Computations in Smart Contracts Overcoming Forks

In this work, we consider executions of smart contracts for implementing secure multi-party computation (MPC) protocols on forking blockchains (e.g., Ethereum), and we study security and delay issues due to forks. In this setting, the classical double-spending problem tells us that messages of the MPC protocol should be confirmed on-chain before playing the next ones, thus slowing down the entire execution. Our contributions are twofold: For the concrete case of fairly tossing multiple coins with penalties, we notice that the lottery protocol of Andrychowicz et al. (S&P ’14) becomes insecure if players do not wait for the confirmations of several transactions. In addition, we present a smart contract that instead retains security even when all honest players immediately answer to transactions appearing on-chain. We analyze the performance using Ethereum as testbed.We design a compiler that takes any “digital and universally composable” MPC protocol (with or without honest majority), and transforms it into another one (for the same task and same setup) which maintains security even if all messages are played on-chain without delays. The special requirements on the starting protocol mean that messages consist only of bits (e.g., no hardware token is sent) and security holds also in the presence of other protocols. We further show that our compiler satisfies fairness with penalties as long as honest players only wait for confirmations once. By reducing the number of confirmations, our protocols can be significantly faster than natural constructions

La nascita e i primi passi del P.I.D.D.AM. - Laboratorio permanente per la Promozione e l'Innovazione Didattica delle Discipline geografiche, ambientali e territoriali

Collana "Geo-innovare - Serie generale" - n. 1