ORCID Profile
0000-0002-7984-514X
Current Organisations
University of Oxford
,
College of Pharmaceutical Sciences, Dayananda Sagar University
,
Sleep, Cognition and Neuroimaging Laboratory
,
Harvard Medical School
,
Northeastern University
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Publications
Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
Publisher: BMJ
Date: 18-05-2022
Rehabilitation versus surgical reconstruction for non-acute anterior cruciate ligament injury (ACL SNNAP): a pragmatic randomised controlled trial
Publisher: Elsevier BV
Date: 08-2022
Matrix Metalloproteinases in Chemoresistance: Regulatory Roles, Molecular Interactions, and Potential Inhibitors
Publisher: Hindawi Limited
Date: 09-05-2022
DOI: 10.1155/2022/3249766
Abstract: Cancer is one of the major causes of death worldwide. Its treatments usually fail when the tumor has become malignant and metastasized. Metastasis is a key source of cancer recurrence, which often leads to resistance towards chemotherapeutic agents. Hence, most cancer-related deaths are linked to the occurrence of chemoresistance. Although chemoresistance can emerge through a multitude of mechanisms, chemoresistance and metastasis share a similar pathway, which is an epithelial-to-mesenchymal transition (EMT). Matrix metalloproteinases (MMPs), a class of zinc and calcium-chelated enzymes, are found to be key players in driving cancer migration and metastasis through EMT induction. The aim of this review is to discuss the regulatory roles and associated molecular mechanisms of specific MMPs in regulating chemoresistance, particularly EMT initiation and resistance to apoptosis. A brief presentation on their potential diagnostic and prognostic values was also deciphered. It also aimed to describe existing MMP inhibitors and the potential of utilizing other strategies to inhibit MMPs to reduce chemoresistance, such as upstream inhibition of MMP expressions and MMP-responsive nanomaterials to deliver drugs as well as epigenetic regulations. Hence, manipulation of MMP expression can be a powerful tool to aid in treating patients with chemo-resistant cancers. However, much still needs to be done to bring the solution from bench to bedside.
Evidence-Based Evaluation of Practice and Innovation in Physical Therapy Using the IDEAL-Physio Framework
Publisher: Oxford University Press (OUP)
Date: 23-10-2017
DOI: 10.1093/PTJ/PZX103
Abstract: The IDEAL framework is an established method for initial and ongoing evaluations of innovation and practice for complex health care interventions. First derived for surgical sciences and embedded at a global level for evaluating surgery/surgical devices, the IDEAL framework is based on the principle that innovation and evaluation in clinical practice can, and should, evolve together in an ordered manner: from conception to development and then to validation by appropriate clinical studies and, finally, longer-term follow-up. This framework is highly suited to other complex, nonpharmacological interventions, such as physical therapist interventions. This perspective outlines the application of IDEAL to physical therapy in the new IDEAL-Physio framework. The IDEAL-Physio framework comprises 5 stages. In stage 1, the idea phase, formal data collection should begin. Stage 2a is the phase for iterative improvement and adjustment with thorough data recording. Stage 2b involves the onset of formal evaluation using systematically collected group or cohort data. Stage 3 is the phase for formal comparative assessment of treatment, usually involving randomized studies. Stage 4 involves long-term follow-up. The IDEAL-Physio framework is recommended as a method for guiding and evaluating both innovation and practice in physical therapy, with the overall goal of providing better evidence-based care.
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
Publisher: Springer Science and Business Media LLC
Date: 05-2022
DOI: 10.1038/S41591-022-01772-9
Abstract: A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
Beyond the RCT: When are Randomized Trials Unnecessary for New Therapeutic Devices, and What Should We Do Instead?
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 07-07-2021
Related Organisations
University Of Oxford
Location: United Kingdom of Great Britain and Northern Ireland
College Of Pharmaceutical Sciences, Dayananda Sagar University
Location: India
Related Funding Activities
No related grants have been discovered for Arsenio Paez.