We report a method for fabricating inexpensive microfluidic platforms on paper using laser treatment. Any paper with a hydrophobic surface coating (e.g., parchment paper, wax paper, palette paper) can be used for this purpose. We were able to selectively modify the surface structure and property (hydrophobic to hydrophilic) of several such papers using a CO(2) laser. We created patterns down to a minimum feature size of 62±1 µm. The modified surface exhibited a highly porous structure which helped to trap/localize chemical and biological aqueous reagents for analysis. The treated surfaces were stable over time and were used to self-assemble arrays of aqueous droplets. Furthermore, we selectively deposited silica microparticles on patterned areas to allow lateral diffusion from one end of a channel to the other. Finally, we demonstrated the applicability of this platform to perform chemical reactions using luminol-based hemoglobin detection.

This paper presents a minimally invasive implantable pressure sensing transponder for continuous wireless monitoring of intraocular pressure (IOP). The transponder is designed to make the implantation surgery simple while still measuring the true IOP through direct hydraulic contact with the intraocular space. Furthermore, when IOP monitoring is complete, the design allows physicians to easily retrieve the transponder. The device consists of three main components: 1) a hypodermic needle (30 gauge) that penetrates the sclera through pars plana and establishes direct access to the vitreous space of the eye; 2) a micromachined capacitive pressure sensor connected to the needle back-end; and 3) a flexible polyimide coil connected to the capacitor forming a parallel LC circuit whose resonant frequency is a function of IOP. Most parts of the sensor sit externally on the sclera and only the needle penetrates inside the vitreous space. In vitro tests show a sensitivity of 15 kHz/mmHg with approximately 1-mmHg resolution. One month in vivo implants in rabbits confirm biocompatibility and functionality of the device.

Although electrogastrography (EGG) could be a critical tool in the diagnosis of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived from abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus-to-duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k-nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>75%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching, which could provide a methodology to diagnose GI diseases and symptoms such as nausea and vomiting.

Islet transplantation for type 1 diabetes treatment has been limited by the need for lifelong immunosuppression regimens. This challenge has prompted the development of macroencapsulation devices (MEDs) to immunoprotect the transplanted islets. While promising, conventional MEDs are faced with insufficient transport of oxygen, glucose, and insulin because of the reliance on passive diffusion. Hence, these devices are constrained to two-dimensional, wafer-like geometries with limited loading capacity to maintain cells within a distance of passive diffusion. We hypothesized that convective nutrient transport could extend the loading capacity while also promoting cell viability, rapid glucose equilibration, and the physiological levels of insulin secretion. Here, we showed that convective transport improves nutrient delivery throughout the device and affords a three-dimensional capsule geometry that encapsulates 9.7-fold-more cells than conventional MEDs. Transplantation of a convection-enhanced MED (ceMED) containing insulin-secreting β cells into immunocompetent, hyperglycemic rats demonstrated a rapid, vascular-independent, and glucose-stimulated insulin response, resulting in early amelioration of hyperglycemia, improved glucose tolerance, and reduced fibrosis. Finally, to address potential translational barriers, we outlined future steps necessary to optimize the ceMED design for long-term efficacy and clinical utility.