DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology.

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

Unrolled-DOT: an interpretable deep network for diffuse optical tomography.

Significance: Imaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning. Aim: We used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch. Approach: Our model "Unrolled-DOT" uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers. Results: In experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers. Conclusion: We demonstrated a learning-based ToF-DOT inverse solver that achieves state-of-the-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.

A wireless millimetric magnetoelectric implant for the endovascular stimulation of peripheral nerves.

Implantable bioelectronic devices for the simulation of peripheral nerves could be used to treat disorders that are resistant to traditional pharmacological therapies. However, for many nerve targets, this requires invasive surgeries and the implantation of bulky devices (about a few centimetres in at least one dimension). Here we report the design and in vivo proof-of-concept testing of an endovascular wireless and battery-free millimetric implant for the stimulation of specific peripheral nerves that are difficult to reach via traditional surgeries. The device can be delivered through a percutaneous catheter and leverages magnetoelectric materials to receive data and power through tissue via a digitally programmable 1 mm × 0.8 mm system-on-a-chip. Implantation of the device directly on top of the sciatic nerve in rats and near a femoral artery in pigs (with a stimulation lead introduced into a blood vessel through a catheter) allowed for wireless stimulation of the animals' sciatic and femoral nerves. Minimally invasive magnetoelectric implants may allow for the stimulation of nerves without the need for open surgery or the implantation of battery-powered pulse generators.

Deep learning extended depth-of-field microscope for fast and slide-free histology.

Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells-a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.

Nanowire-mediated delivery enables functional interrogation of primary immune cells: application to the analysis of chronic lymphocytic leukemia.

A circuit level understanding of immune cells and hematological cancers has been severely impeded by a lack of techniques that enable intracellular perturbation without significantly altering cell viability and function. Here, we demonstrate that vertical silicon nanowires (NWs) enable gene-specific manipulation of diverse murine and human immune cells with negligible toxicity. To illustrate the power of the technique, we then apply NW-mediated gene silencing to investigate the role of the Wnt signaling pathway in chronic lymphocytic leukemia (CLL). Remarkably, CLL-B cells from different patients exhibit tremendous heterogeneity in their response to the knockdown of a single gene, LEF1. This functional heterogeneity defines three distinct patient groups not discernible by conventional CLL cytogenetic markers and provides a prognostic indicator for patients' time to first therapy. Analyses of gene expression signatures associated with these functional patient subgroups reveal unique insights into the underlying molecular basis for disease heterogeneity. Overall, our findings suggest a functional classification that can potentially guide the selection of patient-specific therapies in CLL and highlight the opportunities for nanotechnology to drive biological inquiry.

On-chip gas detection in silicon optical microcavities.

We demonstrate a chip-scale photonic system for the room-temperature detection of gas composition and pressure using a slotted silicon microring resonator. We measure shifts in the resonance wavelength due to the presence and pressure of acetylene gas and resolve differences in the refractive index as small as 10(-4) in the near-IR. The observed sensitivity of this device (enhanced due to the slot-waveguide geometry) agrees with the expected value of 490 nm/refractive index unit.

Far-field control of radiation from an individual optical nanocavity: analogue to an optical dipole.

We demonstrate long-range control of the radiative lifetime of a silicon optical nanocavity using a metallic atomic force microscope probe. We extract changes in the radiative lifetime from changes in the cavity's transmittivity resulting from probe-cavity interaction over distances of several optical wavelengths. Analogous to atomic systems, the cavity acts as an individual radiating dipole with a radiative rate that is modified by a metallic interface.

Role of radiation and surface plasmon polaritons in the optical interactions between a nano-slit and a nano-groove on a metal surface.

Through full-vectorial simulations and analytical models, we investigate the role of radiation and surface plasmon polaritons (SPP) in the optical interaction between a nano-slit and a parallel nano-groove on a metal surface. We quantitatively confirm the radiaton as the interaction mechanism in perfect electrical conductors (PEC), and verify the role of radiation and SPP in the slit-groove interaction in silver. While the contribution of SPP dominates for the nano-slit and nano-groove placed far apart, the radiation plays a significant role for the nano-slit and nano-groove with smaller separations comparable to one wavelength. We present the first quantitative of the individual contributions of the radiation and SPP on the transmission through the nano-slit.