Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection.

Due to its ability to record position, intensity, and intensity distribution information, camera-based monitoring of nanoparticles in optical traps can enable multi-parametric morpho-optical characterization at the single-particle level. However, blurring due to the relatively long (10s of microsecond) integration times and aliasing from the resulting limited temporal bandwidth affect the detected particle position when considering nanoparticles in traps with strong stiffness, leading to inaccurate size predictions. Here, we propose a ResNet-based method for accurate size characterization of trapped nanoparticles, which is trained by considering only simulated time series data of nanoparticles' constrained Brownian motion.

Effect of Anisotropic Structural Depth on Orientation and Differentiation Behavior of Skeletal Muscle Cells.

Extensive research has been conducted to examine how substrate topological factors are involved in modulating the cell behavior. Among numerous topological factors, the vital influence of the touchable depth of substrates on cell behaviors has already been extensively characterized, but the response of cells to the topological structure at untouchable depth is still elusive. Herein, the influences of substrate depth on myoblast behaviors are systematically investigated using substrates with depths ranging from touchable depth (microgrooved) to untouchable depth (microbridges).

Rapid Intracellular Detection and Analysis of Lipid Droplets' Morpho-Chemical Composition by Phase-Guided Raman Sampling.

Intracellular lipid droplets (LDs) are dynamic, complex organelles involved in nearly all aspects of cellular metabolism. In situ characterization methods are primarily limited to fluorescence imaging, which yields limited chemical information, or Raman spectroscopy, which provides excellent chemical profiling but very low throughput. Here, we propose a new paradigm where locations of both large and small droplets are obtained automatically from high-resolution phase images and fed into a galvomirror-controlled Raman sampling arm to obtain the full spectrum of each LD efficiently.

A critical evaluation of compressed line-scan Raman imaging.

The weak signal strength of Raman imaging leads to long imaging times. To increase the speed of Raman imaging, line scanning and compressed Raman imaging methods have been proposed. Here we combine both line scanning and compressed sensing to further increase the speed.

Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes.

Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise.

Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks.

With the advent of hyperspectral Raman imaging technology, especially the rapid and high-resolution imaging schemes, datasets with thousands to millions of spectra are now commonplace. Standard preprocessing and regression methods such as least squares approaches are time consuming and require input from highly trained operators. Here we propose a solution to this analytic bottleneck through a convolutional neural network trained fully on synthetic data and then applied to experimental measurements, including cases where complete spectral information is missing ( an underdetermined model).

Combined Morpho-Chemical Profiling of Individual Extracellular Vesicles and Functional Nanoparticles without Labels.

Biological nanoparticles are important targets of study, yet their small size and tendency to aggregate makes their heterogeneity difficult to profile on a truly single-particle basis. Here we present a label-free system called 'Raman-enabled nanoparticle trapping analysis' (R-NTA) that optically traps individual nanoparticles, records Raman spectra and tracks particle motion to identify chemical composition, size, and refractive index. R-NTA has the unique capacity to characterize aggregation status and absolute chemical concentration at the single-particle level.

Maskless Surface Modification of Polyurethane Films by an Atmospheric Pressure He/O₂ Plasma Microjet for Gelatin Immobilization.

A localized maskless modification method of polyurethane (PU) films through an atmospheric pressure He/O₂ plasma microjet (APPμJ) was proposed. The APPμJ system combines an atmospheric pressure plasma jet (APPJ) with a microfabricated silicon micronozzle with dimension of 30 μm, which has advantages of simple structure and low cost. The possibility of APPμJ in functionalizing PU films with hydroxyl (⁻OH) groups and covalent grafting of gelatin for improving its biocompatibility was demonstrated.

Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy.

Raman spectroscopy is a label-free method of obtaining detailed chemical information about samples. Its compatibility with living tissue makes it an attractive choice for biomedical analysis, yet its translation from a research tool to a clinical tool has been slow, hampered by fundamental Raman scattering issues such as long integration times and limited penetration depth. In this review we detail the how combining Raman spectroscopy with other techniques yields multimodal instruments that can help to surmount the translational barriers faced by Raman alone.

Fabrication of SiN Thin Film of Micro Dielectric Barrier Discharge Reactor for Maskless Nanoscale Etching.

The prevention of glow-to-arc transition exhibited by micro dielectric barrier discharge (MDBD), as well as its long lifetime, has generated much excitement across a variety of applications. Silicon nitride (SiN) is often used as a dielectric barrier layer in DBD due to its excellent chemical inertness and high electrical permittivity. However, during fabrication of the MDBD devices with multilayer films for maskless nano etching, the residual stress-induced deformation may bring cracks or wrinkles of the devices after depositing SiN by plasma enhanced chemical vapor deposition (PECVD).

[CT perfusion imaging evaluation on hemodynamic changes of acute spontaneous intracerebral hemorrhage surrounding tissues].

Objective: To discuss the hemodynamic changes in patients with acute supratentorial spontaneous intracerebral hemorrhage (within 72 hours) by using 320-slice of low-dose volume CT perfusion imaging.

Methods: Twenty-six patients of The First Affiliated Hospital of Wenzhou Medical University during December 2012 to December 2013 with acute supratentorial SICH diagnosed by plain CT scanning and clinic were enrolled. With hematoma maximum level for reference, the hematoma volume, edema area and perfusion defect area were measured, and the perfusion parameters values of the marginal area and outer area of the intracerebral hematoma and contralateral mirror area were measured, including cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT) and time-to-peak (TTP), and rCBF, rCBV, rMTT and rTTP were calculated by ipsilateral/contralateral value.