Dual Ion Releasing Nanoparticles for Modulating Osteogenic Cellular Microenvironment of Human Mesenchymal Stem Cells.

In this study we developed a dual therapeutic metal ion-releasing nanoparticle for advanced osteogenic differentiation of stem cells. In order to enhance the osteogenic differentiation of human mesenchymal stem cells (hMSCs) and induce angiogenesis, zinc (Zn) and iron (Fe) were synthesized together into a nanoparticle with a pH-sensitive degradation property. Zn and Fe were loaded within the nanoparticles to promote early osteogenic gene expression and to induce angiogenic paracrine factor secretion for hMSCs. In vitro studies revealed that treating an optimized concentration of our zinc-based iron oxide nanoparticles to hMSCs delivered Zn and Fe ion in a controlled release manner and supported osteogenic gene expression (RUNX2 and alkaline phosphatase) with improved vascular endothelial growth factor secretion. Simultaneous intracellular release of Zn and Fe ions through the endocytosis of the nanoparticles further modulated the mild reactive oxygen species generation level in hMSCs without cytotoxicity and thus improved the osteogenic capacity of the stem cells. Current results suggest that our dual ion releasing nanoparticles might provide a promising platform for future biomedical applications.

A new etching process for zinc oxide with etching rate and crystal plane control: experiment, calculation, and membrane application.

The etching process can be a useful method for the morphology control of nanostructures. Using precise experiments and theoretical calculations, we report a new ZnO etching process triggered by the reaction of ZnO with transition metal cations and demonstrate that the etching rate and direction could be controlled by varying the kind of transition metal cation. In addition, the developed etching process was introduced to form a thin and uniform ZnO layer, which was utilized for the fabrication of an improved propylene-selective ZIF-8 membrane via conversion seeding and secondary growth.

Deep-Learning Technique To Convert a Crude Piezoresistive Carbon Nanotube-Ecoflex Composite Sheet into a Smart, Portable, Disposable, and Extremely Flexible Keypad.

An extremely simple bulk sheet made of a piezoresistive carbon nanotube (CNT)-Ecoflex composite can act as a smart keypad that is portable, disposable, and flexible enough to be carried crushed inside the pocket of a pair of trousers. Both a rigid-button-imbedded, rollable (or foldable) pad and a patterned flexible pad have been introduced for use as portable keyboards. Herein, we suggest a bare, bulk, macroscale piezoresistive sheet as a replacement for these complex devices that are achievable only through high-cost fabrication processes such as patterning-based coating, printing, deposition, and mounting. A deep-learning technique based on deep neural networks (DNN) enables this extremely simple bulk sheet to play the role of a smart keypad without the use of complicated fabrication processes. To develop this keypad, instantaneous electrical resistance change was recorded at several locations on the edge of the sheet along with the exact information on the touch position and pressure for a huge number of random touches. The recorded data were used for training a DNN model that could eventually act as a brain for a simple sheet-type keypad. This simple sheet-type keypad worked perfectly and outperformed all of the existing portable keypads in terms of functionality, flexibility, disposability, and cost.

An extremely simple macroscale electronic skin realized by deep machine learning.

Complicated structures consisting of multi-layers with a multi-modal array of device components, i.e., so-called patterned multi-layers, and their corresponding circuit designs for signal readout and addressing are used to achieve a macroscale electronic skin (e-skin). In contrast to this common approach, we realized an extremely simple macroscale e-skin only by employing a single-layered piezoresistive MWCNT-PDMS composite film with neither nano-, micro-, nor macro-patterns. It is the deep machine learning that made it possible to let such a simple bulky material play the role of a smart sensory device. A deep neural network (DNN) enabled us to process electrical resistance change induced by applied pressure and thereby to instantaneously evaluate the pressure level and the exact position under pressure. The great potential of this revolutionary concept for the attainment of pressure-distribution sensing on a macroscale area could expand its use to not only e-skin applications but to other high-end applications such as touch panels, portable flexible keyboard, sign language interpreting globes, safety diagnosis of social infrastructures, and the diagnosis of motility and peristalsis disorders in the gastrointestinal tract.

Classification of crystal structure using a convolutional neural network.

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

Efficacy of latanoprost in patients with chronic angle-closure glaucoma and no visible ciliary-body face: a preliminary study.

The aim of this study was to evaluate the efficacy of 0.005% latanoprost in lowering intraocular pressure (IOP) in patients with chronic angle-closure glaucoma (CACG) and no visible ciliary-body face. Fourteen eyes of 14 Korean patients with CACG with 360 degrees of peripheral anterior synechiae (PAS) and an IOP greater than 21 mmHg without medication were treated with 0.005% latanoprost once-daily. All patients completed 3 months of treatment with latanoprost. The IOP, which was 30.3 +/- 4.5 (mean +/- standard deviation) mmHg at baseline, decreased to 22.6 +/- 4.9 mmHg after 1 week, 19.6 +/- 5.5 mmHg after 1 month, 19.4 +/- 4.9 mmHg after 2 months, and 21.5 +/- 5.9 mmHg after 3 months of treatment with latanoprost (P < 0.01 for each). Ultrasound biomicroscopy of the anterior chamber angle showed anterior bowing of the iris with total occlusion of the angle by PAS, except for 5 eyes with focal microscopic openings to the ciliary-body face at various angles. Adverse ocular events were well-tolerated and transient. In this preliminary study, treatment with 0.005% latanoprost once-daily resulted in a significant reduction in IOP in CACG patients with 360 degrees of PAS on gonioscopy. Our results suggest that latanoprost may be considered as a therapy of choice in these rare cases.

Effect of cataract extraction on frequency doubling technology perimetry.

PURPOSE: To investigate the effect of cataract extraction on the results of frequency doubling technology (FDT) perimetry in healthy subjects. DESIGN: Single-center, prospective, case series. METHODS: We performed FDT threshold C20-1 and Humphrey Swedish Interactive Threshold Algorithm (SITA)-fast programs within 1 month before and 2 months after phacoemulsification in 52 consecutive nonglaucomatous patients. Global indexes, including mean deviation and mean sensitivity of FDT and mean deviation of SITA-fast, were compared before and after cataract surgery. Mean sensitivity of FDT at various visual field grid locations was also compared before and after cataract surgery, as were localized indexes of both FDT and SITA-fast. RESULTS: Mean deviation improved after cataract surgery in both FDT threshold (from -7.09 dB--2.16 dB) and SITA-fast program (from -6.14 dB--2.87 dB). The mean sensitivity at 17 grid locations when the FDT threshold program was used significantly improved postoperatively (from 19.6 dB-26.13 dB). Postoperative improvements in mean sensitivity were also significant at all different sectors, but there were no significant differences of improvement among them. Postoperative pattern standard deviations from FDT and SITA-fast did not differ significantly from their preoperative values. CONCLUSIONS: Cataracts induced a generalized reduction of sensitivity in FDT threshold as well as SITA-fast perimetry. Cataract removal did not change pattern standard deviation of FDT or of SITA-fast. Caution should be taken when interpreting the results of FDT perimetry in the eyes with cataract.