Journal of Biophotonics最新文献

Spontaneous Raman spectroscopy is a well-established diagnostic tool, allowing for the identification of all Raman active species with a single measurement. Yet, it may suffer from low-signal intensity and fluorescent background. In contrast, coherent anti-Stokes Raman scattering (CARS) offers laser-like signals, but the traditional approach lacks the multiplex capability of spontaneous Raman spectroscopy. We present an ultrabroadband CARS setup which aims at exciting the full spectrum (300–3700 cm⁻¹) of biological molecules. A dual-output optical parametric amplifier provides a ~7 fs pump/Stokes and a ~700 fs probe pulse. CARS spectra of DMSO, ethanol, and methanol show great agreement with spontaneous Raman spectroscopy and superiority in fluorescent environments. The spectral resolution proves sufficient to differentiate between the complex spectra of L-proline and hydroxyproline. Moreover, decay constants in the sub picosecond range are determined for individual Raman transitions, providing an additional approach for sample characterization.

The study utilized Fourier transform infrared (FTIR) spectroscopy coupled with chemometrics to investigate protein composition and structural changes in the blood serum of patients with polycythemia vera (PV). Principal component analysis (PCA) revealed distinct biochemical properties, highlighting elevated absorbance of phospholipids, amides, and lipids in PV patients compared to healthy controls. Ratios of amide I/amide II and amide I/amide III indicated alterations in protein structures. Support vector machine analysis and receiver operating characteristic curves identified amide I as a crucial predictor of PV, achieving 100% accuracy, sensitivity, and specificity, while amide III showed a lower predictive value (70%). PCA analysis demonstrated effective differentiation between PV patients and controls, with key wavenumbers including amide II, amide I, and CH lipid vibrations. These findings underscore the potential of FTIR spectroscopy for diagnosing and monitoring PV.

Here we introduce a Raman spectroscopy approach combining multi-spectral imaging and a new fluorescence background subtraction technique to image individual Raman peaks in less than 5 seconds over a square field-of-view of 1-centimeter sides with 350 micrometers resolution. First, human data is presented supporting the feasibility of achieving cancer detection with high sensitivity and specificity – in brain, breast, lung, and ovarian/endometrium tissue – using no more than three biochemically interpretable biomarkers associated with the inelastic scattering signal from specific Raman peaks. Second, a proof-of-principle study in biological tissue is presented demonstrating the feasibility of detecting a single Raman band – here the CH₂/CH₃ deformation bands from proteins and lipids – using a conventional multi-spectral imaging system in combination with the new background removal method. This study paves the way for the development of a new Raman imaging technique that is rapid, label-free, and wide field.

Different approaches on wound healing have been developed over the years but they suffer from high costs and adverse effects for the patients. The current paper was designed to study low dose PDT, a novel healing approach, in an in vitro fibroblasts wound healing model. Chloroaluminum phthalocyanine (AlClPc) was used as photosensitizer and was activated by a red diode laser at 661 nm. After PDT optimization, wound closure rate and reactive oxygen species were quantified by image processing and analysis. Our results revealed that wound healing rates were significantly higher in PDT treated groups than in the control. Additionally, the study revealed that a prolonged ROS increase did not promote wound closure, while a small increase acted as a trigger, resulting in faster wound closure. Concluding, low dose PDT using AlClPc enhances wound healing in vitro in a ROS dependent manner, allowing the assumption of similar positive effects in vivo.

Fundus photography (FP) is a crucial technique for diagnosing the progression of ocular and systemic diseases in clinical studies, with wide applications in early clinical screening and diagnosis. However, due to the nonuniform illumination and imbalanced intensity caused by various reasons, the quality of fundus images is often severely weakened, brings challenges for automated screening, analysis, and diagnosis of diseases. To resolve this problem, we developed strongly constrained generative adversarial networks (SCGAN). The results demonstrate that the quality of various datasets were more significantly enhanced based on SCGAN, simultaneously more effectively retaining tissue and vascular information under various experimental conditions. Furthermore, the clinical effectiveness and robustness of this model were validated by showing its improved ability in vascular segmentation as well as disease diagnosis. Our study provides a new comprehensive approach for FP and also possesses the potential capacity to advance artificial intelligence-assisted ophthalmic examination.

A number of hardware-based and software-based strategies have been suggested to eliminate motion artifacts for improvement of 3D-optical coherence tomography (OCT) image quality. However, the hardware-based strategies have to employ additional hardware to record motion compensation information. Many software-based strategies have to need additional scanning for motion correction at the expense of longer acquisition time. To address this issue, we propose a motion artifacts correction and motion estimation method for OCT volumetric imaging of anterior segment, without requirements of additional hardware and redundant scanning. The motion correction effect with subpixel accuracy for in vivo 3D-OCT has been demonstrated in experiments. Moreover, the physiological information of imaging object, including respiratory curve and respiratory rate, has been experimentally extracted using the proposed method. The proposed method offers a powerful tool for scientific research and clinical diagnosis in ophthalmology and may be further extended for other biomedical volumetric imaging applications.

Nail fold capillaroscopy is an important means of monitoring human health. Panoramic nail fold images improve the efficiency and accuracy of examinations. However, the acquisition of panoramic nail fold images is seldom studied and the problem manifests of few matching feature points when image stitching is used for such images. Therefore, this paper presents a method for panoramic nail fold image stitching based on vascular contour enhancement, which first solves the problem of few matching feature points by pre-processing the image with contrast-constrained adaptive histogram equalization (CLAHE), bilateral filtering (BF), and sharpening algorithms. The panoramic images of the nail fold blood vessels are then successfully stitched using the fast robust feature (SURF), fast library of approximate nearest neighbors (FLANN) and random sample agreement (RANSAC) algorithms. The experimental results show that the panoramic image stitched by this paper's algorithm has a field of view width of 7.43 mm, which improves the efficiency and accuracy of diagnosis.

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

A Mueller matrix polarimetry system at 532 nm wavelength is developed for noninvasive glucose sensing in turbid media such as human's fingertip. The system extracts mean absorbance and anisotropic properties, demonstrated numerically and experimentally with phantom glucose samples. It is found that mean absorbance ($�A_e$), depolarization index (Δ), and linear dichroism (LD) show linear variation with glucose concentration 100–500 mg/dL. In addition, LightTools simulations indicate proportional scaling of scattering effects with $�A_e$, Δ, and LD. Real-world tests on fingertip show a strong correlation between these properties and blood glucose levels with a mean absolute relative deviation (MARD) of 12.56% and a correlation coefficient (R²) of 0.875 in prediction by a neural network (NN) model, highlighting the advantages of Mueller matrix in extracting more parameters related to blood glucose.

Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.