Biomedical optics express最新文献

Elastic resonator interference stress microscopy (ERISM) enables label-free, non-invasive measurements of cellular forces by detecting the cell-induced deformation of an optical microcavity using optical interference. Here, we present an improved microcavity design that utilizes a high-refractive-index elastomer, eliminating the need for the top metal layer required in traditional ERISM microcavities. This simplifies the fabrication process, offers improved control over mechanical properties, and enhances the resolution of ERISM measurements. The design is compatible with various bio-coatings, maintains long-term cell viability, and significantly improves optical transmission. This enables integrated and confocal fluorescence imaging with improved contrast over traditional ERISM microcavities.

Vocal fold paralysis (VFP) is characterized by impaired vocal fold movement, commonly resulting from nerve damage during surgical procedures. Current diagnostic methods rely on endoscopic examinations requiring specialized physicians, reducing accessibility and potentially delaying treatment. We propose a non-contact optical sensing method using speckle pattern analysis for VFP identification. Our approach uses external laser illumination and a camera that captures speckle patterns, providing a non-invasive and real-time assessment. The technique uses spectral analysis enhanced by sliding window scanning to extract amplitude peaks across vocal fold regions. Our clinical measurements on 10 subjects (3 healthy and 7 VFP patients) demonstrate identical bilateral voice frequencies, but amplitude varies significantly according to the paralysis side. Healthy subjects presented amplitude ratios close to 1, while VFP patients showed distinct asymmetric patterns: ratios below 0.5 for right-sided paralysis and above 2 for left-sided paralysis, enabling effective VFP detection and localization with potential for clinical implementation.

In glioma surgery, the quality of tumor resection largely determines patient prognosis. Accurate intraoperative discrimination of glial tumors from normal brain tissue and delineation of tumor margins is a major challenge. Given the inherent biochemical differences between tumor and normal tissue, imaging techniques based on cellular autofluorescence represent a promising approach to address this challenge. The aim of this study was to evaluate the ability of macroscopic fluorescence lifetime imaging, macro-FLIM, to discriminate between different classes of glioma (glioblastoma, astrocytoma, oligodendroglioma) and normal brain tissue and to identify glioma cells in the peritumoral region. The study was performed on 110 freshly excised tissue samples from 53 patients. Macro-FLIM images were acquired in the NAD(P)H spectral channel (ex. 375 nm, em. 435-485 nm) using a confocal laser macroscanner. In human performance, the sensitivity of macro-FLIM in discriminating glioblastoma from normal tissue was 92.3% (AUC 0.905), astrocytoma and oligodendroglioma - 62.5% (AUC 0.796 and 0.687). To automatically classify the macro-FLIM images, the Random Forests machine learning algorithm was developed, which reliably discriminated glioblastoma from all normal (82.4% sensitivity, AUC 0.86), astrocytoma from white matter (80.3% sensitivity, AUC 0.857), and oligodendroglioma from gray matter (89.2% sensitivity, AUC 0.875). In addition, the classification model demonstrated the ability to detect areas of tumor infiltration within the peritumoral white matter. The current results demonstrate the potential of NAD(P)H-based macro-FLIM combined with machine learning as a surgical guidance tool to improve glioma resection.

Optical coherence tomography (OCT) is a widely used imaging technique in ophthalmology, capable of non-invasive, high-resolution imaging of retinal tissues. However, OCT images are often degraded by speckle noise, resulting in poor image quality. Deep learning-based denoising models have become the mainstream approach, but existing methods tend to oversmooth images and lose high-frequency details, making it difficult to recover the true retinal structure. This paper proposes a high-frequency enhanced diffusion model based on the cold diffusion framework, named THFN-OCT (time-enhanced high-frequency network for OCT denoising). The model decouples frequency-domain information and processes it separately, with cross-domain connections to preserve high-frequency details while ensuring denoising performance. In addition, considering the different impacts of each diffusion timestep on frequency components, we design a timestep-aware attention module that uses the timestep t to guide the reconstruction. Experiments on two public OCT retinal denoising datasets and one private dataset show that the proposed method outperforms existing denoising algorithms.

This study presents a hybrid deep learning (DL) approach for designing and optimizing a photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) multi-analyte biosensor. Using a simulation based on the finite element method (FEM), we generated a comprehensive data set that captures various sensor parameters and refractive index (RI) values. A hybrid recurrent neural network long-short-term memory (RNN-LSTM) model was developed to predict confinement loss (CL), which showed superior performance with an MSE of 0.0014, an MAE of 0.0188, and an R² of 0.9510 compared to other DL and machine learning (ML) models. The proposed model shows a maximum amplitude sensitivity (AS) of 3102.41 RIU^-1, a wavelength sensitivity (WS) of 10,000 nm/RIU, and a sensor resolution (SR) of 1 × 10^-5. The effectiveness of the model was validated through extensive analysis, including ablation studies and SHAP-based explainability analysis. Our findings highlight the potential of DL to improve multi-analyte biosensor design and performance prediction.

Nonlinear optical (NLO) imaging platforms traditionally rely on refractive microscope objectives, which suffer from chromatic aberrations and temporal dispersion of pulsed excitation light. These issues degrade spatial imaging properties and signal brightness. Furthermore, the limited transmission range of refractive materials restricts NLO imaging, especially for applications requiring short- to mid-wave infrared excitation. While reflective microscope objectives offer an achromatic solution and broader transmission range (from visible to mid-infrared), conventional Schwarzschild designs have a central obscuration, which limits transmission throughput, imparts diffraction effects into the images, and, more generally, hinders the adoption of reflective optics in NLO microscopy. We overcome these obscuration-based limitations by developing a reflective microscope objective using freeform mirrors in a non-coaxial geometry. This obscuration-free design boasts a 0.65 numerical aperture (NA), near diffraction-limited imaging performance, and offers significantly improved transmission with wider fields-of-view. We demonstrate its utility by integrating it into a standard laser-scanning microscope and performing NLO microscopy across a wide range of excitation wavelengths. Our freeform microscope objective outperforms standard reflective designs, providing an achromatic, dispersion-free alternative to refractive lenses for NLO imaging.

Cellular reactive oxygen species (ROS), a key parameter involved in cell metabolism, signaling, and apoptosis, whose detection is necessary to achieve in a variety of biological processes. However, current ROS detection methods, including fluorescence, colorimetry, and electrochemical methods, are difficult to achieve in-situ non-invasive detection due to their reliance on invasive probes or destructive sampling. In this study, we propose an in-situ non-invasive ROS detection integrating the Raman spectrum and a bidirectional gated recurrent unit (Bi-GRU) deep learning model during HepG2 cell apoptosis. The Bi-GRU model leverages bidirectional gating mechanisms to capture long-term dependencies in Raman spectra while incorporating both forward and backward spectral information for enhanced feature extraction. After training with spectral data of HepG2 cells in various apoptotic states, the R² (coefficient of determination) of the Bi-GRU model reaches 0.8511, which outperforms that of traditional methods such as KNN (0.2607), PLS (0.4720), and RNN (0.7724). In the present study, we not only realized the in-situ and non-invasive cellular ROS detection but also expanded the application of artificial intelligence in the field of cellular medicine. Importantly, this will provide a new research idea for further understanding the physiological state of cells and the mechanism of drug action.

The metabolic effects of anesthetic agents like propofol on cancer cells remain poorly understood despite the widespread use of anesthesia during cancer diagnosis and treatment. Fluorescence lifetime imaging microscopy (FLIM) was used to analyze propofol-induced metabolic changes in triple-negative breast cancer cells (MDA-MB-231) by monitoring endogenous NAD(P)H and FAD fluorescence lifetimes. FLIM of propofol-treated MDA-MB-231 cells revealed concentration-dependent shifts in metabolic states that were supported by Seahorse extracellular flux analysis. While the flux analysis provided population-averaged metabolic data, FLIM enabled high-resolution, dynamic mapping of metabolism in live cancer cells. Our study highlights FLIM as a label-free tool for investigating anesthetic-induced metabolic alterations in cells, offering insights into the potential metabolic mechanisms of propofol.

Predicting postoperative visual acuity (VA) from optical quality metrics is essential for the evaluation and development of intraocular lenses (IOLs). Metrics based on modulation transfer function (MTF) integration are commonly used but have important limitations: they neglect the phase of the optical transfer function (OTF) and fail to account for the distribution of contrast across spatial frequencies. As a result, they may overestimate visual performance-especially under defocus-by including phase-reversed or low-frequency contrast that contributes little to visual acuity. In this study, we measured the optical profiles of five IOLs using a new metrology device (NIMO-TEMPO), simulated their polychromatic through-focus OTFs, and computed several metrics, including MTF area, OTF area, visual Strehl ratio, and a perceptually weighted real part of the OTF (wOTF). These metrics were then correlated with through-focus VA curves extracted from publicly available FDA clinical trial data. Our results show that phase-sensitive metrics, particularly those that weight frequencies according to neural relevance, mitigate some of the limitations of conventional MTF-based approaches by suppressing spurious contrast and better reflecting the spatial content most relevant to VA. These findings highlight the need for physiologically meaningful, phase-aware metrics in both research and regulatory contexts.

Vascular-targeted photodynamic therapy (V-PDT) is a promising treatment for benign vascular proliferative disorders. However, its efficacy largely depends on clinicians' experience due to the lack of reliable methods for efficacy prediction. To provide an objective prediction approach, a hyperspectral imaging (HSI) system was developed to achieve real-time, non-invasive visualization of V-PDT dose parameters, including photosensitizer distribution, oxygen concentration, and vasoconstriction. Based on these measurements, we proposed a photodynamic therapy efficacy prediction index (PEPI)-a new metric that integrates the dynamic changes of both photosensitizer and oxygen throughout the treatment process, thereby providing critical insights for optimizing treatment protocols. Experimental results obtained in vivo using a dorsal skinfold window model demonstrate that the system accurately detects V-PDT dose parameters, and the proposed efficacy prediction parameters exhibit a strong positive correlation with treatment outcomes. This work highlights the potential of hyperspectral imaging to advance V-PDT toward more precise, individualized, and effective clinical applications, paving the way for its broader adoption in the field of precision medicine.