Explore the vast range of titles available.

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

While a role for sleep in declarative memory processing is established, the qualitative nature of this consolidation benefit, and the physiological mechanisms mediating it, remain debated. Here, we investigate the impact of sleep physiology on characteristics of episodic memory using an item- (memory elements) and context- (contextual details associated with those elements) learning paradigm; the latter being especially dependent on the hippocampus. Following back-to-back encoding of two word lists, each associated with a different context, participants were assigned to either a Nap-group, who obtained a 120-min nap, or a No Nap-group. Six hours post-encoding, participants performed a recognition test involving item-memory and context-memory judgments. In contrast to item-memory, which demonstrated no between-group differences, a significant benefit in context-memory developed in the Nap-group, the extent of which correlated both with the amount of stage-2 NREM sleep and frontal fast sleep-spindles. Furthermore, a difference was observed on the basis of word-list order, with the sleep benefit and associated physiological correlations being selective for the second word-list, learned last (most proximal to sleep). These findings suggest that sleep may preferentially benefit contextual (hippocampal-dependent) aspects of memory, supported by sleep-spindle oscillations, and that the temporal order of initial learning differentially determines subsequent offline consolidation.

Evidence indicates that sleep after learning is critical for the subsequent consolidation of human memory. Whether sleep before learning is equally essential for the initial formation of new memories, however, remains an open question. We report that a single night of sleep deprivation produces a significant deficit in hippocampal activity during episodic memory encoding, resulting in worse subsequent retention. Furthermore, these hippocampal impairments instantiate a different pattern of functional connectivity in basic alertness networks of the brainstem and thalamus. We also find that unique prefrontal regions predict the success of encoding for sleep-deprived individuals relative to those who have slept normally. These results demonstrate that an absence of prior sleep substantially compromises the neural and behavioral capacity for committing new experiences to memory. It therefore appears that sleep before learning is critical in preparing the human brain for next-day memory formation—a worrying finding considering society's increasing erosion of sleep time.

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well‐established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample‐level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

Chemotherapy / radiation induced oral mucositis (OM) poses significant clinical challenges, often leading to pain, malnutrition, treatment delays, and reduced quality of life. Regenerative medicine has been shown to have beneficial value in wound care. Hence, the current study was aimed at evaluating the safety and therapeutic efficacy of Mesenchymal Stem (Stromal) Cell derived Conditioned Media (MSC-CM) formulations (5%, 10%, and 15%) in preclinical models of 5-fluorouracil (5-FU)–induced OM and radiation wounds. The 28-day oral toxicity studies in rats/mice established that the formulations across treatment groups produced no treatment-related adverse effects. The NOAEL was established at 100% MSC-CM concentration. In the 5-FU based OM model, treatment with MSC-CM formulations demonstrated dose-dependent benefits, including significant reductions in ulcer size/ severity, regeneration of mucosal epithelium, and improvements in hematological, biochemical, hepatic, renal, and immunological parameters compromised by 5-FU. Histopathological evaluation revealed that chemotherapy-induced mucosal hyperplasia and blister formation were markedly reduced following MSC-CM treatment. These findings suggested enhanced tissue repair, possibly mediated through autophagy-associated mechanisms. The 15% formulation was most efficacious, yielding 100% survival, a mean ulcer healing score of 71.9%, and near-complete mucosal recovery, followed by 10% and 5% formulations. In the radiation wound healing model, administration of 10% and 15% MSC-CM formulations was well tolerated, as evidenced by stable body weights, and produced visible reductions in redness, hair loss, wound wetness, and oozing compared to controls. Notably, the 10% formulation promoted accelerated wound closure, with significant wound healing as early as day 4–7. Mechanistically, this could be attributed to induction of secretory IL-20 and IL-17 A/F in mice after wound healing gel (WHG) treatment at site of injury, which led to repair and regeneration of radiation-induced wounds. Collectively, these findings establish MSC-CM based topical formulations as safe and effective therapeutic candidates for mitigating chemotherapy and radiation induced toxicities, enhancing tissue regeneration, thereby supporting its translational potential in oral mucositis.