Oral and dental health is an important indicator and determinant of an individual’s overall well-being. Untreated oral diseases can lead to severe systemic complications. Monitoring the oral environment and identifying biochemical and physiological patterns associated with disease states, such as periodontitis, gingivitis, caries, and oral cancers, is essential for early diagnosis and effective intervention. This review evaluates the current clinical needs in biochemical and physiological monitoring for oral healthcare and state-of-the-art biosensors capable of continuous analyte measurement. We surveyed the relevant biomarkers for common oral and dental diseases in patients compared to healthy controls. The design and performance of recent biosensing devices for these target analytes are reviewed and evaluated. For biochemical sensing, we found intraoral biosensors for high-abundance small molecules, such as ions and metabolites, have advanced significantly in recent years. However, robust sensing technologies for low-abundance analytes, including cytokines and other inflammatory biomarkers, remain limited and require further development in sensing mechanisms, bio-interfaces, and device integration. For physiological sensing, particularly the measurement of forces in tooth movement, recent developments in force sensor technologies have substantially improved measurement accuracy over traditional techniques. Despite these advancements, current platforms still face limitations in achieving long-term, real-time monitoring of mechanical conditions within the oral cavity due to challenges related to biocompatibility and device miniaturization. In conclusion, while notable progress has been made in biosensing for oral applications, continued research in device integration with clinical practices is essential to realize robust and clinically deployable biosensor systems that can advance precision oral healthcare.

Long-range intercellular communication is essential for multicellular biological systems to regulate multiscale cell–cell interactions and maintain life. Growing evidence suggests that intercellular calcium waves (ICWs) act as a class of long-range signals that influence a broad spectrum of cellular functions and behaviors. Importantly, mechanical signals, ranging from single-molecule-scale to tissue-scale in vivo, can initiate and modulate ICWs in addition to relatively well-appreciated biochemical and bioelectrical signals. Despite these recent conceptual and experimental advances, the full nature of underpinning mechanotransduction mechanisms by which cells convert mechanical signals into ICW dynamics remains poorly understood. This review provides a systematic analysis of quantitative ICW dynamics around three main stages: initiation, propagation, and regeneration/relay. We highlight the landscape of upstream molecules and organelles that sense and respond to mechanical stimuli, including mechanosensitive membrane proteins and cytoskeletal machinery. We clarify the roles of downstream molecular networks that mediate signal release, spread, and amplification, including adenosine triphosphate (ATP) release, purinergic receptor activation, and gap junction (GJ) communication. Furthermore, we discuss the broad pathophysiological implications of ICWs, covering pathophysiological processes such as cancer metastasis, tissue repair, and developmental patterning. Finally, we summarize recent advances in optical imaging and artificial intelligence (AI)/machine learning (ML) technologies that reveal the precise spatial-temporal-functional dynamics of ICWs and ATP waves. By synthesizing these insights, we offer a comprehensive framework of ICW mechanobiology and propose new directions for mechano-therapeutic strategies in disease diagnosis, cancer immunotherapies, and drug discovery.

Real-time biosensors that can continuously measure circulating biomolecules in vivo would provide valuable insights into a patients’ health status and their response to therapeutics even when there is considerable variability in pharmacokinetics and pharmacodynamics across patient populations. Unfortunately, current real-time biosensors are limited to a handful of analytes (e.g. glucose and blood oxygen) and are limited in sensitivity (high nanomolar). In this work, we describe a general approach for continuously and simultaneously measuring multiple analytes with picomolar sensitivity and sub-second temporal resolution. As exemplars, we report the simultaneous detection of glucose and insulin at picomolar concentrations in live diabetic rats. Using our system, we demonstrate the capacity to resolve inter-individual differences in the pharmacokinetic responses to insulin and discriminate profiles from different insulin formulations at a high temporal resolution. Critically, our approach is general and could be readily modified to continuously and simultaneously measure other circulating analytes in vivo by swapping the affinity reagents, thus making it a versatile tool for biomedical research.

Continuous biochemical sensing provides valuable insights into an individual’s physiological state and the mechanisms underlying pathophysiological changes. However, most existing bioanalytical methods are not compatible with continuous biochemical sensing. A major technical challenge lies in achieving rapid measurement readouts while maintaining high specificity and sensitivity in complex biological fluids. Sensitive molecular detection typically requires slow analyte–binder dissociation and long incubation to reach equilibrium, whereas rapid and frequent measurements demand fast association–dissociation kinetics that are difficult to reconcile for low-abundance analytes. To address this challenge, we introduce a sensing mechanism termed photothermal recycling (PTR), which mimics the thermal cycling process in polymerase chain reaction. Using plasmonic photothermal effects, PTR rapidly recycles binders to enable frequent measurements. We demonstrate a digital PTR assay capable of multi-hour biochemical monitoring with subpicomolar(pM) sensitivity in buffer, diluted serum, and saliva. This approach leverages localized thermal energy to dynamically modulate biomolecular recognition, offering a new bioanalytical paradigm for continuous biochemical sensing across diverse application settings.

Chronic critical illness (CCI) is a disease state in which, following an initial insult, a patient neither recovers nor dies but instead remains in a state of critical illness. CCI is characterized by prolonged organ dysfunction, weight loss, and persistent increased vulnerability to infection. Recent data has shown that patients with CCI generally exhibit persistent immune dysfunction, characterized by prolonged elevation of specific pro-inflammatory cytokines. In this paper, we introduce a host response model that couples hematopoiesis dynamics with immune response to infection. Specifically, we incorporate the reactions between pro-inflammatory and anti-inflammatory signals with specific hematopoietic stem cell compartments with a reduced model of acute inflammation. We found that a maladaptive hematopoietic response to pathogenic insult is able to qualitatively reproduce similar behavior to that seen in CCI patients, namely the presence of a persistent, elevated level of pro-inflammatory cytokines. This suggests that maladaptive hematopoietic responses in vivo may play a role in the development of CCI.