Electrophysiological characterization of drug response in hSC-derived cardiomyocytes using voltage-sensitive optical platforms

Introduction: Voltage-sensitive optical (VSO) sensors offer a minimally invasive method to study the time course of repolarization of the cardiac action potential (AP). This Comprehensive in vitro Proarrhythmia Assay (CiPA) cross-platform study investigates protocol design and measurement variability of VSO sensors for preclinical cardiac electrophysiology assays. Methods: Three commercial and one academic laboratory completed a limited study of the effects of 8 blinded compounds on the electrophysiology of 2 commercial lines of human induced pluripotent stem-cell derived cardiomyocytes (hSC-CMs). Acquisition technologies included CMOS camera and photometry; fluorescent voltage sensors included di-4-ANEPPS, FluoVolt and genetically encoded QuasAr2. The experimental protocol was standardized with respect to cell lines, plating and maintenance media, blinded compounds, and action potential parameters measured. Serum-free media was used to study the action of drugs, but the exact composition and the protocols for cell preparation and drug additions varied among sites. Results: Baseline AP waveforms differed across platforms and between cell types. Despite these differences, the relative responses to four selective ion channel blockers (E-4031, nifedipine, mexiletine, and JNJ 303 blocking IKr, ICaL, INa, and IKs, respectively) were similar across all platforms and cell lines although the absolute changes differed. Similarly, four mixed ion channel blockers (flecainide, moxifloxacin, quinidine, and ranolazine) had comparable effects in all platforms. Differences in repolarisation time course and response to drugs could be attributed to cell type and experimental method differences such as composition of the assay media, stimulated versus spontaneous activity, and single versus cumulative compound addition. Discussion: In conclusion, VSOs represent a powerful and appropriate method to assess the electrophysiological effects of drugs on iPSC-CMs for the evaluation of proarrhythmic risk. Protocol considerations and recommendations are provided toward standardizing conditions to reduce variability of baseline AP waveform characteristics and drug responses

An Empirical Comparison of Consumer Innovation Adoption Models: Implications for Subsistence Marketplaces

So called “pro-poor” innovations may improve consumer wellbeing in subsistence marketplaces. However, there is little research that integrates the area with the vast literature on innovation adoption. Using a questionnaire where respondents were asked to provide their evaluations about a mobile banking innovation, this research fills this gap by providing empirical evidence of the applicability of existing innovation adoption models in subsistence marketplaces. The study was conducted in Bangladesh among a geographically dispersed sample. The data collected allowed an empirical comparison of models in a subsistence context. The research reveals the most useful models in this context to be the Value Based Adoption Model and the Consumer Acceptance of Technology model. In light of these findings and further examination of the model comparison results the research also shows that consumers in subsistence marketplaces are not just motivated by functionality and economic needs. If organizations cannot enhance the hedonic attributes of a pro-poor innovation, and reduce the internal/external constraints related to adoption of that pro-poor innovation, then adoption intention by consumers will be lower

AI-based skin cancer detection: the balance between access and overutilization

Gregoor et al. evaluated the healthcare implications and costs of an AI-enabled mobile health app for skin cancer detection, involving 18,960 beneficiaries of a Netherlands insurer. They report a 32% increase in claims for premalignant and malignant skin lesions among app users, largely attributed to benign skin lesions and leading to higher annual costs for app users (€64.97) compared to controls (€43.09). Cost-effectiveness analysis showed a comparable cost to dermatologist-based diagnosis alone. This editorial emphasizes the balance in AI-based dermatology between increased access and increased false positives resulting in overutilization. We suggest refining the diagnostic schemas with new referral pathways to capitalize on potential savings. We also discuss the importance of econometric analysis to evaluate the adoption of new technologies, as well as adapting payment models to mitigate the risk of overutilization inherent in AI-based diagnostics such as skin cancer detection

Digital health technology in clinical trials

Digital health technologies (DHTs) have brought several significant improvements to clinical trials, enabling real-world data collection outside of the traditional clinical context and more patient-centered approaches. DHTs, such as wearables, allow the collection of unique personal data at home over a long period. But DHTs also bring challenges, such as digital endpoint harmonization and disadvantaging populations already experiencing the digital divide. A recent study explored the growth trends and implications of established and novel DHTs in neurology trials over the past decade. Here, we discuss the benefits and future challenges of DHT usage in clinical trials

Promoting racial equity in digital health: applying a cross-disciplinary equity framework

Even as innovation occurs within digital medicine, challenges around equity and racial health disparities remain. Golden et al. evaluate structural racism in their recent paper focused on reproductive health. They recommend a framework to Remove, Repair, Restructure, and Remediate. We propose applying the framework to three areas within digital medicine: artificial intelligence (AI) applications, wearable devices, and telehealth. With this approach, we can continue to work towards an equitable future for digital medicine