The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.

Porous silicon nanoparticles containing both Pd and iron oxide nanoparticles are prepared and studied as magnetically recoverable catalysts for organic reductions. The Pd nanoparticles are generated in situ by electroless deposition of Pd(NH), where the porous Si skeleton acts as both a template and as a reducing agent and the released ammonia ligands raise the local pH to exert control over the size of the Pd nanoparticles. The nanocomposites are characterized by transmission electron microscopy, energy-dispersive X-ray spectroscopy, nitrogen adsorption, X-ray diffraction, superconducting quantum interference device magnetization, and dynamic light scattering. The nanocomposite consists of a porous Si nanoparticle (150 nm mean diameter) containing ∼20 nm pores, uniformly decorated with a high loading of surfactant-free Pd nanoparticles (12 nm mean diameter) and superparamagnetic γ-FeO nanoparticles (∼7 nm mean diameter). The reduction of 4-nitrophenol to 4-aminophenol by sodium borohydride is catalyzed by the nanocomposite, which is stable through the course of the reaction. Catalytic reduction of the organic dyes methylene blue and rhodamine B is also demonstrated. The conversion efficiency and catalytic activity are found to be superior to a commercial Pd/C catalyst compared under comparable reaction conditions. The composite catalyst can be recovered from the reaction mixture by applying an external magnetic field due to the existence of the superparamagnetic iron oxide nanoparticles in the construct. The recovered particles retain their catalytic activity.

Si-doped and Si, N-codoped carbon nanomaterials exhibited much higher electrocatalytic activity and longer-term stability for the oxygen reduction reaction than non-doped carbon nanomaterials, due to the changes in net charges and the energy characteristics of the Si-containing carbon framework and the adsorption mode of oxygen.

No abstract

The coffee-ring effect, ubiquitously present in the drying process of aqueous droplets, impedes the performance of a myriad of applications involving precipitation of particle suspensions in evaporating liquids on solid surfaces, such as liquid biopsy combinational analysis, microarray fabrication, and ink-jet printing, to name a few. We invented the methodology of laser-induced differential evaporation to remove the coffee-ring effect. Without any additives to the liquid or any morphology modifications of the solid surface the liquid rests on, we have eliminated the coffee-ring effect by engineering the liquid evaporation profile with a CO2 laser irradiating the apex of the droplets. The method of laser-induced differential evaporation transitions particle deposition patterns from coffee-ring patterns to central-peak patterns, bringing all particles (e.g. fluorescent double strand DNAs) in the droplet to a designated area of 100 μm diameter without leaving any stains outside. The technique also moves the drying process from the constant contact radius (CCR) mode to the constant contact angle (CCA) mode. Physical mechanisms of this method were experimentally studied by internal flow tracking and surface evaporation flux mapping, and theoretically investigated by development of an analytical model.

No abstract

No abstract

No abstract