A single-mode-fiber (SMF)-multimode-fiber (MMF)-tri-core-fiber (TCF) Michelson probe structure is proposed for trace detection of canine distemper virus (CDV). One end of the TCF is cut flat and fused with the multimode fiber, and the other end is coated with a silver film to enhance the reflection, and an optic-fiber sensing probe with SMF-MMF-TCF structure is obtained. The (PDDA/PSS) 3 multilayer film is modified on the surface of the fiber by layer-by-layer self-assembly method as a polyelectrolyte binder to immobilize CDV antibodies to form a (PDDA/PSS) 3/CDV antibody composite membrane for specific detection of CDV antigens. The response-recovery test of the sensor is performed to verify its repeatability. The detection limit, the sensitivity, and the linear fitting degree for CDV antigen are 0.1236 pg/ml, 1.1776 dB/(pg/ml), and 0.9899, respectively. At the same time, the stability, selectivity, and clinical samples of the sensors were also verified.

Considering difficulties of achieving vertical incidence of beam in different positions of skin, it’s significant to study potential effects of incidence angles of laser on incisions. Surgical platform with a 1064nm continuous fiber laser was established. Incident angle was adopted and real-time temperature fluctuations in laser operating area could be monitored. The rats were treated with laser at day 0 and day 3 after incision modeling, and H&E, Masson, Sirius Red and Immuno-histochemical staining and enzyme-linked immunosorbent assay were adopted at day 3,7,14 to analyze the performance of healing. Laser with energy density of 67.54J/mm 2 can effectively accelerate wound healing in vivo, in which laser with incident angle around 60° can effectively avoid scar hyperplasia. Therefore, the use of low energy laser with small deflection angle has a good clinical application prospect in promoting wound healing.

Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution, facilitating evaluation. We imaged fresh ex vivo skin tissues (normal and neoplastic) from Mohs surgery. FF-OCT features were defined and diagnostic accuracy for malignancies was performed by the two experts OCT readers via a blinded analysis. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 BCCs and 17 SCCs). The average reader diagnostic accuracy was 88.1%, a sensitivity of 93.7%, and a specificity of 58.3%. The AI model achieved a diagnostic accuracy of 87.6%±5.9%, sensitivity of 93.2%±2.1%, and specificity of 81.2%±9.2%. A mean intersection-over-union of 60.3%±10.1% was achieved delineating nodular BCC from normal. We envision FF-OCT for rapid evaluation of surgical margins and AI tumor detection leading to widespread technique integration.

Colorectal cancer is one of the most prevalent malignancies worldwide, and long-term chronic colitis is considered to be an important factor in the etiological study of colorectal cancer. As an emerging detection technology, the non-invasive, non-ionizing, and fingerprint spectroscopic features of terahertz technology have tremendous application prospects in disease diagnosis. Therefore, this study aims to explore the value of terahertz technology in colitis-related cancers by constructing an inflammation-associated mouse colorectal cancer model. In this paper, we found that the terahertz spectroscopy technique can effectively distinguish colitis-associated cancer from normal tissue, and for the first time, we searched that 1.8 THz may be the best terahertz frequency for diagnosing colorectal cancer in mice, suggesting that we can use the terahertz technique for rapid diagnosis of colorectal cancer histopathology.

The aim of this study was to develop a clinical SWIR reflectance handpiece to assess the activity of lesions on the occlusal surfaces. The time-resolved reflectivity of ten active and ten arrested occlusal caries lesions on extracted teeth was monitored at 1470 nm using a benchtop system and a modified clinical prototype during forced air drying. The presence of a highly mineralized surface zone measured with microcomputed tomography (microCT) was used to indicate lesion activity. Multiple kinetic parameters were extracted from the acquired SWIR time versus intensity dehydration curves and used to assess lesion activity. Three parameters: delay, %I fin, and rate calculated from the SWIR dehydration curves were significantly different (P < 0.05) between active and arrested lesions. The modified clinical probe was able to completely dehydrate all the active lesion areas in the occlusal pits and fissures in less than 30 seconds.

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Optical coherence tomography angiography (OCTA) has successfully demonstrated its viability for clinical applications in dermatology. Due to the high optical scattering property of skin, extracting high-quality OCTA images from skin tissues requires at least six-repeated scans. While the motion artifacts from the patient and the free hand-held probe can lead to a low-quality OCTA image. Our deep-learning-based scan pipeline enables fast and high-quality OCTA imaging with 0.3-second data acquisition. We utilize a fast scanning protocol with a 60 µm/pixel spatial interval rate and introduce Angiography-Reconstruction-Transformer (ART) for 4× super-resolution of low transverse resolution OCTA images. The ART outperforms state-of-the-art networks in OCTA image super-resolution and provides a lighter network size. ART can restore microvessels while reducing the processing time by 85%, and maintaining improvements in structural similarity and peak-signal-to-noise ratio. This study represents that ART can achieve fast and flexible skin OCTA imaging while maintaining image quality.

The Optical Kerr Effect was investigated for the first time in biological tissues. This nonlinear optical effect was explored in both human brain and avian breast tissues using a time-resolved femtosecond pump-probe Optical Kerr Gate. The Optical Kerr Effect describes the nonlinear change in a material’s refractive index in response to an electric field. It is fundamental to spectral effects that are commonly used in biological science. The tested tissues produced a unique ultrafast (700-800 fs) doubled peaked Kerr signal, which is indicative of temporal interplay between the different components (electronic plasma, and molecular) that make up the Kerr index. Temporal properties varied between samples suggesting that this method could be used as a new diagnostic. Understanding this Kerr behavior can help improve current spectral diagnostic techniques, such as SRS, and potentially create a new Kerr based biopsy method for the detection of diseased tissues, such as cancer and Alzheimer’s.

During thyroid surgery fast and reliable intra-operative pathological feedback has the potential to avoid a two-stage procedure and significantly reduce health care costs in patients undergoing a diagnostic hemithyroidectomy (HT). We explored higher harmonic generation (HHG) microscopy, which combines second harmonic generation (SHG), third harmonic generation (THG), and multiphoton excited autofluorescence (MPEF) for this purpose. With a compact, portable HHG microscope, images of freshly excised healthy tissue, benign nodules (follicular adenoma) and malignant tissue (papillary carcinoma, follicular carcinoma and spindle cell carcinoma) were recorded. The images were generated on unprocessed tissue within minutes and show relevant morphological thyroid structures in good accordance with the histology images. The thyroid follicle architecture, cells, cell nuclei (THG), collagen organization (SHG) and the distribution of thyroglobulin and/or thyroid hormones T3 or T4 (MPEF) could be visualized. We conclude that SHG/THG/MPEF imaging is a promising tool for clinical intraoperative assessment of thyroid tissue.

A computational modeling is employed for quantitative assessment of weld formation and area of tissue temperature necrosis during the human skin laser soldering. The evaluation is carried out depending on the components composition of using solders, including bovine serum albumin (BSA), indocyanine green (ICG), and carbon nanotubes (CNT), as well as the angle of incidence of laser light and its pulse duration. The influence of CNT on the change of thermodynamic characteristics of albumin denaturation and the rate of formation of the laser weld is investigated. The obtained results suggest to limit the duration of laser light pulse by temperature relaxation time to minimize transfer of thermal energy to reduce the heating of human skin tissues. The developed model has a great potential for further optimization of laser soldering of biological tissues technology with greater efficiency in minimizing the weld area.

Capillary Refill Time (CRT) assesses peripheral perfusion in resource-limited settings. However, the repeatability and reproducibility of CRT measurements are limited for individuals with darker skin. This paper presents quantitative CRT measurements demonstrating good performance and repeatability across all Fitzpatrick skin phototypes. The study involved 22 volunteers and utilized controlled compression at 7 kPa, an RGB video camera, and cocircular polarized white LED light. CRT was determined by calculating the time constant of an exponential regression applied to the mean pixel intensity of the green (G) channel. An adaptive algorithm identifies the optimal regression region for noise reduction, and flags inappropriate readings. The results indicate that 80% of the CRT readings fell within a 20% range of the expected CRT value. The repetition standard deviation was 17%. These findings suggest the potential for developing reliable and reproducible quantitative CRT methods for robust measurements in patient triage, monitoring, and telehealth applications.

For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In this study, we propose a label-free method that combines optical diffraction tomography (ODT) and deep learning (DL) to automate the histological quantification process. The DL model classifies ODT image patches with 95% accuracy, and the collective prediction generates a whole-slide map of red blood cells and fibrin. The resulting whole-slide composition displays an average error of 1.1% and does not experience staining variability, facilitating faster analysis with reduced labor. The present approach will enable rapid and quantitative evaluation of blood clot composition, expediting the preclinical research and diagnosis of cardiovascular diseases.

We present a method to use a birefringent crystal for generating two illumination beams in a digital scanned laser light-sheet microscopy (DSLM) system. Upon this, a conventional confocal DSLM can be easily upgraded to a dual-slit confocal DSLM with two-fold imaging speed. We have implemented this method to our bidirectional DSLM system, locating two identical calcite crystals on both illumination paths from both sides of the sample. The neurons of in vivo larval zebrafish have been fast imaged with sterling image quality, especially ~2.5 times higher contrast, compared to the conventional DSLM.

Vascular network labeling in transparent tissues provides more complete information on blood vessels. To achieve a fast and efficient method for vascular network labeling in transparent tissues, we compared various vascular labeling methods under different tissue clearing protocols. FITC-dextran labeling and CUBIC cleaning treatment were found to be the best options for vascular network labeling in cleared mouse tissues. Satisfactory labeling of vascular networks in various organs can be achieved by selecting FITC-dextran with different molecular weights and different administration methods.

To alleviate the ill-posed of the inverse problem in Fluorescent molecular tomography (FMT), many regularization methods based on L 2 or L 1 norm have been proposed. Whereas, the quality of regularization parameters affect the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L 2 or L 1 norm and achieve good reconstruction performance.

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 cm-1 to 850 cm-1. The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.