Continuous biomarker monitoring is revolutionizing chronic disease management, with glucose monitoring for diabetes as the primary example. Given the success of this approach, a transition to continuous protein monitoring (CPM, a real-time, implantable or wearable technology) could similarly advance precision medicine. In this work, we review state-of-the-art CPM platforms and their prospective clinical impact across both chronic disorders—metabolic, cardiovascular, autoimmune, and neurodegenerative—and acute crises, such as sepsis and transplant dysfunction. We also highlight remaining barriers to widespread adoption, including sensor stability, robust machine learning models for live interpretation, and responsible data handling for patient privacy. With continued engineering and clinical validation, emerging biosensor technologies could transform disease management, facilitating earlier interventions and individualizing treatment strategies, ultimately improving patient outcomes.

Flexible microfluidics have found extensive utility in the biological and biomedical fields. A leading substrate material for compliant devices is polydimethylsiloxane (PDMS). Despite its many advantages, PDMS is inherently hydrophobic and consequently its use in passive (pumpless) microfluidics becomes problematic. To this end, many physical and chemical modifications have been introduced to render PDMS hydrophilic, ranging from amphiphilic molecule additions to surface plasma treatments. However, when transitioning from lab benchtop to realized medical devices, these modifications must exhibit long-term stability. Unfortunately, these modifications are often presented but their mechanisms and long-term stability are not studied in detail. We have investigated an array of PDMS modifications, utilizing contact angle goniometry to study surface energy over a 30-day evolution study. Samples were stored in air and water, and Fourier Transform Infrared-Attenuated Total Reflectance (FTIR-ATR) analysis was used to confirm surface functional group uniformity. We have identified preferred modification techniques for long-lasting PDMS devices and characterized often overlooked material stability.

Adrenocortical cancer is an aggressive endocrine malignancy with an incidence of 0.72 to 1.02 per million people/year, and a very poor prognosis with a five-year survival rate of 22%. As an orphan disease, clinical data are scarce, meaning that drug development and mechanistic research depend especially on preclinical models. While a single human ACC cell line was available for the last three decades, over the last five years, many new in vitro and in vivo preclinical models have been generated. Herein, we review both in vitro (cell lines, spheroids, and organoids) and in vivo (xenograft and genetically engineered mouse) models. Striking leaps have been made in terms of the preclinical models of ACC, and there are now several modern models available publicly and in repositories for research in this area.

Neuroendocrine neoplasia (NENs) are a complex and heterogeneous group of cancers that can arise from neuroendocrine tissues throughout the body and differentiate them from other tumors. Their low incidence and high diversity make many of them orphan conditions characterized by a low incidence and few dedicated clinical trials. Study of the molecular and genetic nature of these diseases is limited in comparison to more common cancers and more dependent on preclinical models, including both in vitro models (such as cell lines and 3D models) and in vivo models (such as patient derived xenografts (PDXs) and genetically-engineered mouse models (GEMMs)). While preclinical models do not fully recapitulate the nature of these cancers in patients, they are useful tools in investigation of the basic biology and early-stage investigation for evaluation of treatments for these cancers. We review available preclinical models for each type of NEN and discuss their history as well as their current use and translation.

Computational modeling can provide a mechanistic and quantitative framework for describing intracellular spatial heterogeneity of solutes such as oxygen partial pressure (pO₂). This study develops and evaluates a finite-element model of oxygen-consuming mitochondrial bioenergetics using the COMSOL Multiphysics program. The model derives steady-state oxygen (O₂) distributions from Fickian diffusion and Michaelis–Menten consumption kinetics in the mitochondria and cytoplasm. Intrinsic model parameters such as diffusivity and maximum consumption rate were estimated from previously published values for isolated and intact mitochondria. The model was compared with experimental data collected for the intracellular and mitochondrial pO₂ levels in human cervical cancer cells (HeLa) in different respiratory states and under different levels of imposed pO₂. Experimental pO₂ gradients were measured using lifetime imaging of a Förster resonance energy transfer (FRET)-based O₂ sensor, Myoglobin-mCherry, which offers in situ real-time and noninvasive measurements of subcellular pO₂ in living cells. On the basis of these results, the model qualitatively predicted (1) the integrated experimental data from mitochondria under diverse experimental conditions, and (2) the impact of changes in one or more mitochondrial processes on overall bioenergetics.