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Despite human saliva representing a convenient and non-invasive clinical substrate for disease diagnosis and biomonitoring, its widespread utilization has been hampered by technical challenges. The non-Newtonian, heterogenous and highly viscous nature of clinical saliva samples complicate the development of automated fluid handling processes that are vital for accurate diagnoses. Furthermore, conventional saliva processing methods are either resource and/or time intensive precluding certain testing capabilities in low- and middle-income countries, with these challenges aggravated during a pandemic outbreak. The conventional approaches can also potentially alter analyte structure, reducing application opportunities in Point-of-Care diagnostics. To overcome these challenges, we introduce the SHEAR saliva collection device that preprocesses saliva for enhanced interfacing with downstream assays. We demonstrate the devices impact on reducing salivas viscosity, improving sample uniformity and, increasing diagnostic performance of COVID-19 Rapid Antigen Tests. Importantly, in addition to reporting technical advances and to address downstream implementation factors, we conducted a formal user experience study, which resulted in generally positive comments. Effective implementation of this device could be of support to realize the potential of saliva, particularly in large-scale and/or resource-limited settings for global and community health diagnostics.
RESUMO
Background/Aims@#Constipation can be a chronic condition that impacts daily functioning and quality of life (QoL). To aid healthcare providers in accurately assessing patient symptoms and treatment outcomes, patient-related outcome measures (PROMs) have been increasingly adopted in clinical settings. This review aims to (1) evaluate the methodological quality and measurement properties of constipationrelated PROMs, using the COnsensus-based Standards for the selection of health Measurement INtruments (COSMIN) criteria; and (2) assess the modes of digital dissemination of constipation-related PROMs. @*Methods@#PubMed, Embase, and PsycINFO databases were searched and 11 011 records ranging from 1989 to 2020 were screened by 2 independent reviewers. A total of 26 studies (23 PROMs; 18 measuring symptom-related items and 5 measuring constipation-related QoL items) were identified for the review and assessed. @*Results@#There were multiple variations between PROMs, including subtypes of constipation, methods of administration, length of PROM and recall period. While no PROM met all the COSMIN quality standards for development and measurement properties, 5 constipationrelated PROMs received at least 4 (out of 7) sufficient ratings. Only 2 PROMs were developed in Asia. Five PROMs were administered through digital methods during the validation process but methods of adapting the PROMs into digital formats were not reported. @*Conclusions@#The constipation-related PROMs identified in this review present varying quality of development and validation, with an overall need for improvement. Further considerations should be given towards more consistent methodology and reporting of PROM development, increase in culturally-specific PROMs, and better reporting of protocol for the digitization of PROMs.
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ObjectivesWe aimed to harness IDentif.AI 2.0, a clinically actionable AI platform to rapidly pinpoint and prioritize optimal combination therapy regimens against COVID-19. MethodsA pool of starting candidate therapies was developed in collaboration with a community of infectious disease clinicians and included EIDD-1931 (metabolite of EIDD-2801), baricitinib, ebselen, selinexor, masitinib, nafamostat mesylate, telaprevir (VX-950), SN-38 (metabolite of irinotecan), imatinib mesylate, remdesivir, lopinavir, and ritonavir. Following the initial drug pool assessment, a focused, 6-drug pool was interrogated at 3 dosing levels per drug representing nearly 10,000 possible combination regimens. IDentif.AI 2.0 paired prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus (propagated, original strain, B.1.351 and B.1.617.2 variants) and Vero E6 assay with a quadratic optimization workflow. ResultsWithin 3 weeks, IDentif.AI 2.0 realized a list of combination regimens, ranked by efficacy, for clinical go/no-go regimen recommendations. IDentif.AI 2.0 revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived. ConclusionsIDentif.AI 2.0 rapidly revealed promising drug combinations for clinical translation. It pinpointed dose-dependent drug synergy behavior to play a role in trial design and realizing positive treatment outcomes. IDentif.AI 2.0 represents an actionable path towards rapidly optimizing combination therapy following pandemic emergence. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/21259321v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@f8a159org.highwire.dtl.DTLVardef@12908b7org.highwire.dtl.DTLVardef@fb6485org.highwire.dtl.DTLVardef@8493c3_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights- When novel pathogens emerge, the immediate strategy is to repurpose drugs. - Good drugs delivered together in suboptimal combinations and doses can yield low or no efficacy, leading to misperception that the drugs are ineffective. - IDentif.AI 2.0 does not use in silico modeling or pre-existing data. - IDentif.AI 2.0 pairs optimization with prospectively acquired experimental data using a SARS-CoV-2/Vero E6 assay. - IDentif.AI 2.0 pinpoints EIDD-1931 as a foundation for optimized anti-SARS-CoV-2 combination therapies.
RESUMO
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) has led to the rapid initiation of urgently needed clinical trials of repurposed drug combinations and monotherapies. These regimens were primarily relying on mechanism-of-action based selection of drugs, many of which have yielded positive in vitro but largely negative clinical outcomes. To overcome this challenge, we report the use of IDentif.AI, a platform that rapidly optimizes infectious disease (ID) combination therapy design using artificial intelligence (AI). In this study, IDentif.AI was implemented on a 12-drug candidate therapy search set representing over 530,000 possible drug combinations. IDentif.AI demonstrated that the optimal combination therapy against SARS-CoV-2 was comprised of remdesivir, ritonavir, and lopinavir, which mediated a 6.5-fold improvement in efficacy over remdesivir alone. Additionally, IDentif.AI showed hydroxychloroquine and azithromycin to be relatively ineffective. The identification of a clinically actionable optimal drug combination was completed within two weeks, with a 3-order of magnitude reduction in the number of tests typically needed. IDentif.AI analysis was also able to independently confirm clinical trial outcomes to date without requiring any data from these trials. The robustness of the IDentif.AI platform suggests that it may be applicable towards rapid development of optimal drug regimens to address current and future outbreaks.
