Ultra-thin and ultra-porous nanofiber networks as a basement-membrane mimic.

Current basement membrane (BM) mimics used for modeling endothelial and epithelial barriers in vitro do not faithfully recapitulate key in vivo physiological properties such as BM thickness, porosity, stiffness, and fibrous composition. Here, we use networks of precisely arranged nanofibers to form ultra-thin (∼3 µm thick) and ultra-porous (∼90%) BM mimics for blood-brain barrier modeling. We show that these nanofiber networks enable close contact between endothelial monolayers and pericytes across the membrane, which are known to regulate barrier tightness. Cytoskeletal staining and transendothelial electrical resistance (TEER) measurements reveal barrier formation on nanofiber membranes integrated within microfluidic devices and transwell inserts. Further, significantly higher TEER values indicate a biological benefit for co-cultures formed on the ultra-thin nanofiber membranes. Our BM mimic overcomes critical technological challenges in forming co-cultures that are in proximity and facilitate cell-cell contact, while still being constrained to their respective sides. We anticipate that our nanofiber networks will find applications in drug discovery, cell migration, and barrier dysfunction studies.

Spatiotemporal estimations of temperature rise during electroporation treatments using a deep neural network.

The nonthermal mechanism for irreversible electroporation has been paramount for treating tumors and cardiac tissue in anatomically sensitive areas, where there is concern about damage to nearby bowels, ducts, blood vessels, or nerves. However, Joule heating still occurs as a secondary effect of applying current through a resistive tissue and must be minimized to maintain the benefits of electroporation at high voltages. Numerous thermal mitigation protocols have been proposed to minimize temperature rise, but intraoperative temperature monitoring is still needed. We show that an accurate and robust temperature prediction AI model can be developed using estimated tissue properties (bulk and dynamic conductivity), known geometric properties (probe spacing), and easily measurable treatment parameters (applied voltage, current, and pulse number). We develop the 2-layer neural network on realistic 2D finite element model simulations with conditions encompassing most electroporation applications. Calculating feature contributions, we found that temperature prediction is mostly dependent on current and pulse number and show that the model remains accurate when incorrect tissue properties are intentionally used as input parameters. Lastly, we show that the model can predict temperature rise within ex vivo perfused porcine livers, with error <0.5 °C. This model, using easily acquired parameters, is shown to predict temperature rise in over 1000 unique test conditions with <1 °C error and no observable outliers. We believe the use of simple, readily available input parameters would allow this model to be incorporated in many already available electroporation systems for real-time temperature estimations.

Real-Time Temperature Rise Estimation during Irreversible Electroporation Treatment through State-Space Modeling.

To evaluate the feasibility of real-time temperature monitoring during an electroporation-based therapy procedure, a data-driven state-space model was developed. Agar phantoms mimicking low conductivity (LC) and high conductivity (HC) tissues were tested under the influences of high (HV) and low (LV) applied voltages. Real-time changes in impedance, measured by Fourier Analysis SpecTroscopy (FAST) along with the known tissue conductivity and applied voltages, were used to train the model. A theoretical finite element model was used for external validation of the model, producing model fits of 95.8, 88.4, 90.7, and 93.7% at 4 mm and 93.2, 58.9, 90.0, and 90.1% at 10 mm for the HV-HC, LV-LC, HV-LC, and LV-HC groups, respectively. The proposed model suggests that real-time temperature monitoring may be achieved with good accuracy through the use of real-time impedance monitoring.

Experimental and Numerical Investigation of Parameters Affecting High-Frequency Irreversible Electroporation for Prostate Cancer Ablation.

While the primary goal of focal therapy for prostate cancer (PCa) is conserving patient quality of life by reducing oncological burden, available modalities use thermal energy or whole-gland radiation which can damage critical neurovascular structures within the prostate and increase risk of genitourinary dysfunction. High-frequency irreversible electroporation (H-FIRE) is a promising alternative ablation modality that utilizes bursts of pulsed electric fields (PEFs) to destroy aberrant cells via targeted membrane damage. Due to its nonthermal mechanism, H-FIRE offers several advantages over state-of-the-art treatments, but waveforms have not been optimized for treatment of PCa. In this study, we characterize lethal electric field thresholds (EFTs) for H-FIRE waveforms with three different pulse widths as well as three interpulse delays in vitro and compare them to conventional irreversible electroporation (IRE). Experiments were performed in non-neoplastic and malignant prostate cells to determine the effect of waveforms on both targeted (malignant) and adjacent (non-neoplastic) tissue. A numerical modeling approach was developed to estimate the clinical effects of each waveform including extent of nonthermal ablation, undesired thermal damage, and nerve excitation. Our findings indicate that H-FIRE waveforms with pulse durations of 5 and 10 µs provide large ablations comparable to IRE with tolerable levels of thermal damage and minimized muscle contractions. Lower duration (2 µs) H-FIRE waveforms exhibit the least amount of muscle contractions but require increased voltages which may be accompanied by unwanted thermal damage.