In fiber-based confocal microscopy, using two separate fibers for illumination and collection enables the use of a few-mode fiber to achieve an effect similar to opening the pinhole in a conventional confocal microscope. In some Fourier-domain applications, however, or when a spectral measurement is involved, the coherent light detection would lead to noticeable spectral modulation artifacts that result from differential mode delay, an effect caused by the multimode propagation in the collection fiber. After eliminating these artifacts by using mode-dependent polarization control, we demonstrate effective spectrally encoded imaging with improved signal efficiency and lower speckle noise, and only a minor, negligible reduction in lateral and axial resolutions.

Accurate and efficient determination of malachite green (MG) in aquaculture is crucial for ensuring environment and food safety. Herein, we present a dual-response fluorescence probe based on an Ag/PMMA/Eu nanocomposite for the sensitive detection of MG with low concentration and single droplet. The luminescence properties of the Ag/PMMA/Eu nanocomposite and the fluorescence resonance energy transfer (FRET) effect between Eu and MG are significantly improved due to the localized surface plasmon resonance (LSPR) effect. The dual-response system enables the detection of MG through both luminescence intensity and energy transfer efficiency using the Ag/PMMA/Eu nanocomposite as a detection platform in the range of 0-10.78 µmol/L. The detection limit reaches as low as 0.5 nmol/L, a significant improvement over the 0.11 µmol/L limit achievable by pure Eu film alone, demonstrating superior sensitivity compared to traditional fluorescence detection techniques. The results indicate that the nanocomposite significantly boosts the sensitivity of the dual-mode sensors. In addition, the sensor successfully detects MG residues in lake water, highlighting the Ag/PMMA/Eu nanocomposite's potential to advance high sensitivity, selectivity, stability, and accurate detection in food security and biological analysis.

We present a implementation method of light-sheet microscopy utilizing a highly miniaturized device that produces light-sheet illumination while immersed in the sample container. Our miniaturized plane illuminator (MPI) internally equips a two-axis beam-scanning mechanism based on a magnetostatically driven optical fiber cantilever. A light sheet is produced by fast scanning of the focused beam in an axis while the illumination plane can move in the other axis for positioning and 3D imaging. Our MPI device is so compact in a 1.5 mm-thick needle form that it can be conveniently placed in the right vicinity of the imaging sample. Because the illumination is directly given in the sample-surrounding medium, a great deal of operational flexibility is obtained with an uncompromised beam quality. We could build a light-sheet microscopy system with a conventional inverted microscope frame by attaching our MPI upgrade kit as an add-on module. In this study, the optical and electromechanical characteristics of our MPI device were carefully investigated. As well, light-sheet microscopy imaging of various samples was performed to validate the practical power of our technique. We found our MPI can provide a low-cost and easy-to-use imaging mode, and make the light-sheet microscopy more available in various applications.

Deep-UV microscopy enables high-resolution, label-free molecular imaging by leveraging biomolecular absorption properties in the UV spectrum. Recent advances in UV-imaging hardware have renewed interest in this technique for quantitative live cell imaging applications. However, UV-induced photodamage remains a concern for longitudinal dynamic imaging studies. Here, we quantify UV phototoxicity with several cell types at notable UV wavelengths. We find that the fluence required for cell death via UV phototoxicity with continuous UV exposure varies with cell type and wavelength from ∼0.5µJ/µm to 2µJ/µm, but is independent of typical illumination power/radiant flux of UV microscopy (e.g., 0.1-20 nW/µm). We also show results from fractionation studies that reveal cell repair following UV exposure, which increases the tolerance to UV radiation by a factor of 2 or more, depending on the fractionation paradigm. Results further show that UV tolerance exceeds ANSI guidelines for maximum permissible exposure. Finally, we calculate imaging limits for a typical application of UV microscopy, such as hematology analysis. Together, this work provides UV fluence thresholds that can serve as guidelines for nondestructive, longitudinal, and dynamic deep-UV microscopy experiments.

Functional near-infrared spectroscopy (fNIRS) -based hyperscanning is a popular new technology in the field of social neuroscience research. In recent years, studying human social interaction from the perspective of inter-brain networks has received increasing attention. In the present study, we proposed a new approach named the hyper-brain independent component analysis (HB-ICA) for detecting the inter-brain networks from fNIRS-hyperscanning data. HB-ICA is an ICA-based, data-driven method, and can be used to search the inter-brain networks of social interacting groups containing multiple participants. We validated the method by using both simulated data and in vivo fNIRS-hyperscanning data. The results showed that the HB-ICA had good performance in detecting the inter-brain networks in both simulation and in-vivo experiments. Our approach provided a promising tool for studying the neural mechanism of human social interactions.

Gastric cancer is a leading cause of cancer-related deaths globally. As mortality rates continue to rise, predicting cancer survival using multimodal data-including histopathological images, genomic data, and clinical information-has become increasingly crucial. However, extracting effective predictive features from this complex data has posed challenges for survival analysis due to the high dimensionality and heterogeneity of histopathology images and genomic data. Furthermore, existing methods often lack sufficient interaction between intra- and inter-modal features, significantly impacting model performance. To address these challenges, we developed a deep learning-based multimodal feature fusion model, MultiDeepsurv, designed to predict the survival of gastric cancer patients by integrating histopathological images, clinical data, and gene expression data. Our approach includes a two-branch hybrid network, GLFUnet, which leverages the attention mechanism for enhanced pathology image representation learning. Additionally, we employ a graph convolutional neural network (GCN) to extract features from gene expression data and clinical information. To capture the correlations between different modalities, we utilize the SFusion fusion strategy that employs a self-attention mechanism to learn potential correlations across modalities. Finally, these deeply processed features are fed into Cox regression models for an end-to-end survival analysis. Comprehensive experiments and analyses conducted on a gastric cancer cohort from The Cancer Genome Atlas (TCGA) demonstrate that our proposed MultiDeepsurv model outperforms other methods in terms of prognostic accuracy, with a C-index of 0.806 and an AUC of 0.842.

Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis. Herein, we demonstrate a Gramian angular summation field and MobileNet V2 (GASF-MobileNet)-assisted surface-enhanced Raman spectroscopy (SERS) technique for analyzing exosomes, aimed at precise diagnosis of lung cancer. Specifically, a composite substrate was synthesized for SERS detection of exosomes based on TiCTx Mxene and the array of gold-silver core-shell nanocubes (MGS), that combines sensitivity and signal stability. The employment of MXene facilitates the non-selective capture and enrichment of exosomes. To overcome the issue of potentially overlooking spatial features in spectral data analysis, 1-D spectra were first transformed into 2-D images through GASF. By using transformed images as the input data, a deep learning model based on the MobileNet V2 framework extracted spectral features from higher dimensions, which identified different non-small cell lung cancer (NSCLC) cell lines with an overall accuracy of 95.23%. Moreover, the area under the curve (AUC) for each category exceeded 0.95, demonstrating the great potential of integrating label-free SERS with deep learning for precise lung cancer differential diagnosis. This approach allows routine cancer management, and meanwhile, its non-specific analysis of SERS signatures is anticipated to be expanded to other cancers.

Cardiovascular imaging with camera-on-tip endoscopes has the potential to provide physiologically relevant data on the tissue state and device placement that can improve clinical outcomes. In this work, we review the unmet clinical need for image-based cardiovascular diagnostics and guidance for minimally invasive procedures. We present a 7 Fr camera-on-tip endoscope with fibre-coupled multispectral illumination that includes methods for imaging in a blood-filled field of view (FOV). We demonstrate that the endoscope can be navigated from the femoral artery to cardiac regions such as the left atrium and left ventricle in a porcine model, where images of the cardiac walls are recorded. We further show that physiologically relevant parameters such as heart rate and respiration can be extracted from the images and that changes to tissue state can be inferred from the imaging data. Finally, a methodology for merging the imaging data with diffuse reflection spectroscopy (DRS) recorded through the optical fibre is outlined.

Radiation therapy (RT) is widely used for cancer treatment but is found with side effects of radiation dermatitis and fibrosis thereby calling for timely assessment. Nevertheless, current clinical assessment methods are found to be subjective, prone to bias, and accompanied by variability. There is, therefore, an unmet clinical need to explore a new assessment technique, ideally portable and affordable, making it accessible to less developed regions too. We developed an affordable (16764 CNY) and portable high-resolution ((3.91 μm) darkfield polarization-sensitive multispectral imaging (PS-MSI) microscope. The implementation of the Monte Carlo simulation on the PS multi spectra allows the quantitative analysis of physiological parameters (i.e., blood volume fraction (BVF) and oxygen saturation of hemoglobin) at different skin layers for the dermatitis assessment. Further derivation of the degree of linear polarization (DOLP) reflects randomly distributed collagen fibers associated with fibrosis for the fibrosis assessment. PS-MSI microscope developed revealed a significant decrease (p < 0.001, analysis of variance, ANOVA) in the DOLP associated with fibrosis like scar tissue, and significant ( < 0.001, ANOVA) increases in BVF and oxygen saturation of hemoglobin accompanying artificially induced dermatitis. One-dimensional convolutional neural network implemented on the DOLP and multiple spectra achieved accuracies of 96% and 92.2%, respectively, for the classification of the artificially induced skin dermatitis and fibrosis like scar, demonstrating the potential of the affordable PS-MSI microscope developed for objective, unbiased and consistent assessment of radiation dermatitis and fibrosis in the clinics.

Optical diffraction tomography enables label-free, 3D refractive index (RI) imaging of biological samples. We present a novel, cost-effective approach to ODT that employs a modular design incorporating a self-reference holographic capture module. This two-part system consists of an illumination module and a capture module that can be seamlessly integrated with any life-science microscope using an automated alignment protocol. The illumination module employs a galvo-scanner system, providing precise control over the angular illumination, while the capture module utilises the principle of self-reference off-axis holography. The design has a compact form factor, simple alignment, and reduced cost. Furthermore, our system offers the capability to switch between two imaging modalities, ODT and real-time synthetic aperture digital holographic microscopy (SA-DHM), a unique feature not found in other setups. Experimental results are provided using a kidney cancer cell line. Experimental results are provided using a kidney cancer cell line.

To measure the influence of ganglion cell layer (GCL) thickness on the changes in size and red blood cell (RBC) flow in small retinal vessels evoked by full-field flicker. We used a dual-beam adaptive optics scanning laser ophthalmoscope to image 11 healthy young controls in two retinal areas with significantly different GCL thicknesses. All capillaries and arterioles of the superficial vascular plexus were responsive to the flicker stimulation. Average lumen dilation and RBC flow changes were greater in capillaries than in arterioles (vasodilation: 10.9%, 6.7%; RBC flow: 51%, 38%, respectively). No statistically significant differences regarding relative lumen diameter, RBC velocity, or RBC flow were found with respect to GCL thickness, or vessel size.

Multiphoton fluorescence microscopy (MFM), renowned for its noninvasiveness and high spatiotemporal resolution, is extensively applied in brain structure imaging in vivo. Three-photon fluorescence (3PF) imaging, excited at the NIR-III window, can penetrate the deepest mouse cerebrovascular. Evans blue, a substance known for its low toxicity, high water solubility, and resistance to metabolism, is frequently employed to assess blood-brain barrier (BBB) permeability. However, its suitability for multiphoton fluorescence imaging of mouse cerebrovascular at the NIR-III window in vivo remains unexplored. In this paper, we conduct a comprehensive analysis of the multiphoton excitation and emission characterization of Evans blue when excited at the NIR-III window. Our findings indicate that 1) Evans blue can generate 3PF signals; 2) it exhibits a substantial three-photon action cross-section ( ) in plasma; 3) its three-photon emission spectrum measured in vivo agrees with that measured in plasma ex vivo. Drawing upon these findings, we successfully demonstrated the application of 3PF imaging of mouse brain vasculature labeled with Evans blue. Notably, the maximum depth of cerebrovascular is 1550 μm beneath the brain surface, spanning the entire gray matter layer and white matter layer and extending into the hippocampus. Evans blue is thus highly ideal for brain cerebrovascular 3PF imaging in vivo.

Two-photon phosphorescence lifetime microscopy has been a key tool for studying cerebral oxygenation in mice. However, the accuracy of the partial pressure of oxygen (pO) measurements is affected by out-of-focus signal. In this work, we applied reconfigurable differential aberration imaging to characterize and correct for out-of-focus signal contamination in intravascular pO imaging. Our results show that signal contamination is higher in more oxygenated vessels and that it could be effectively removed using the proposed method.

The study aimed to identify differences in the biochemical composition of corneal stroma lenses across varying degrees of myopia using Raman spectrum characteristics. Corneal stroma lens samples from 38 patients who underwent small incision lens extraction (SMILE) surgery, were categorized into low (n = 9, spherical power -3.00D), moderate (n = 23, spherical power < -3.00D and > -6.00D), and high myopia (n = 6, spherical power ≦-6.00D) groups. A custom-built microscopic confocal Raman system (MCRS) was used to collect Raman spectra, which were processed by smoothing, denoising, and baseline calibrating to refine raw data. Independent sample t-tests were used to analyze spectral feature peaks among sample types. Significant differences ( < 0.001) were found in multiple Raman spectral characteristic peaks (854 cm, 937 cm, 1002 cm, 1243 cm, 1448 cm, and 2940 cm) between low and high myopia samples, particularly at 2940 cm. Differences were also found between low and moderate, and moderate and high myopia samples, although fewer than between low and high myopia samples. The three-classification model, particularly with PLS-KNN training, exhibited superior discriminative performance with accuracy rates of 95%. Similarly, the two-classification model for low and high myopia achieved high accuracy with PLS-KNN (94.4%) compared to PCA-KNN (93.3%). PLS dimensionality reduction slightly outperformed PCA, enhancing classification accuracy. In addition, in both reduction methods, the KNN algorithm demonstrated the highest accuracy and performance. The optimal PLS-KNN classification model showed AUC values of 0.99, 0.98, and 1.00 for ROC curves corresponding to low, moderate, and high myopia, respectively. Classification accuracy rates were 89.7% and 96.9%, and 100% for low and high myopia, respectively. For the two-classification model, accuracy reached 94.4% with an AUC of 0.98, indicating strong performance in distinguishing between high and low myopic corneal stroma. We found significant biochemical differences such as collagen, lipids, and nucleic acids in corneal stroma lenses across varying degrees of myopia, suggesting that Raman spectroscopy holds substantial potential in elucidating the pathogenesis of myopia.

Optically-pumped magnetometer (OPM) has been of increasing interest for biomagnetic measurements due to its low cost and portability compared with superconducting quantum interference devices (SQUID). Miniaturized spin-exchange-relaxation-free (SERF) OPMs typically have limited bandwidth (less than a few hundred Hertz), making it difficult to measure high-frequency biomagnetic signals such as the magnetocardiography (MCG) signal of the mouse. Existing experiments mainly use SQUID systems to measure the signal. In this paper, we introduce a prototype miniaturized single-beam SERF magnetometer with a bandwidth of ∼ 1 kHz. Instead of operating the OPM in a closed-loop mode to improve the bandwidth of the OPM, which usually has a poorer performance in high-frequency range, we use the power-broadening effects to shorten the spin relaxation time and thus a faster response to the magnetic fields to be measured. Combined with light power stabilizations to improve both the sensitivity and stability, our magnetometer has a low noise floor of 30 fT / Hz, which has been successfully adopted to measure the MCG signal of the mouse.

Abnormal corneal nerve function and associated disease is a significant public health concern. It is associated with prevalent ocular surface diseases, including dry eye disease. Corneal nerve dysfunction is also a common side effect of refractive surgeries, as well as a symptom of diseases that cause peripheral neuropathies. Here, we demonstrate calcium imaging of mouse corneal nerves expressing GCaMP6f, a genetically encoded calcium indicator. A custom fluorescence imaging and stereotactic system was designed, allowing for non-contact imaging of the mouse cornea with an air objective. Dynamic imaging of neuronal activity is demonstrated in the various layers of the cornea and in response to local anesthetic administration. This approach demonstrates a less invasive means of assessing corneal nerve function than has been previously used, and has significant potential for studying the effects of ocular diseases, refractive surgeries, and peripheral neuropathies on corneal nerve function, as well as the effectiveness of various therapies to treat corneal nerve dysfunction.

Conventional scanned optical coherence tomography (OCT) suffers from the frame rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. To overcome this limitation, we propose two variants of machine learning (ML)-based adaptive scanning approaches: one using a ConvLSTM-based sequential prediction model and another leveraging a temporal attention unit (TAU)-based parallel prediction model for scene dynamics prediction. These models are integrated with a kinodynamic path planner based on the clustered traveling salesperson problem to create two versions of ML-based adaptive scanning pipelines. Through experimental validation with novel deterministic phantoms based on a digital light processing board, our techniques achieved mean frame rate speed-ups of up to 40% compared to conventional raster scanning and the probabilistic adaptive scanning method without compromising image quality. Furthermore, these techniques reduced scene-dependent manual tuning of system parameters to demonstrate better generalizability across scenes of varying types, including those of intrasurgical relevance. In a real-time surgical tool tracking experiment, our technique achieved an average speed-up factor of over 3.2× compared to conventional scanning methods, without compromising image quality.

A fair comparison of multiple live cell cultures requires examining them under identical environmental conditions, which can only be done accurately if all cells are prepared simultaneously and studied at the same time and place. This contribution introduces a multiplexed lensless digital holographic microscopy system (MLS), enabling synchronous, label-free, quantitative observation of multiple live cell cultures with single-cell precision. The innovation of this setup lies in its ability to robustly compare the behaviour, i.e., migratory pathways, of cells cultured or contained in different ways (with varied stimuli applied), making it a valuable tool for dynamic biomedical diagnostics on a cellular level. The system's design allows for potential expansion to accommodate as many samples as needed, thus broadening its application scope in future quantitative diagnostics on global multi-culture cellular behaviours via their localized single-cell spatiotemporal optical signatures. We believe that our method has the potential to empower reliable live cell multi-culture comparisons through simultaneous quantitative imaging, enhancing label-free investigations into cell cultures and the effects of biochemical or physical stimuli over large areas, and unlocking novel mechanistic understandings through high-throughput time-lapse observations.

[This corrects the article on p. 4909 in vol. 15, PMID: 39346983.].

A miniature electrically tuneable liquid crystal component is used to steer light from -1° to +1° and then to inject into a simple tapered fiber. This allows the generation of various propagation modes, their leakage, and selective illumination of the surrounding medium at different depth levels without using mechanical movements nor deformation. The performance of the device is characterized in a reference fluorescence medium (Rhodamine 6G) as well as in a mouse brain (medullary reticular formation and mesencephalic locomotor regions) during in-vivo experiments as a proof of concept. This device may be further miniaturized to be applied to freely behaving animals for the dynamic selective excitation or inhibition of different brain regions.

The choroid, a critical vascular layer beneath the retina, is essential for maintaining retinal function and monitoring chorioretinal disorders. Existing imaging methods, such as indocyanine green angiography (ICGA) and optical coherence tomography (OCT), face significant limitations, including contrast agent requirements, restricted field of view (FOV), and high costs, limiting accessibility. To address these challenges, we developed a nonmydriatic, contrast agent-free fundus camera utilizing transcranial near-infrared (NIR) illumination. This system achieves a wide snapshot FOV of up to 185° eye-angle (130° visual-angle) without pharmacological pupillary dilation or contrast agents. By montaging two HDR images, the effective FOV can exceed 220° eye-angle (160° visual angle). Employing high dynamic range (HDR) imaging, the device ensures uniform contrast and enhanced choroidal visualization by correcting illumination inhomogeneity. The system demonstrated imaging performance comparable to ICGA when tested on healthy participants and patients with choroidal conditions, offering improved accessibility and affordability. This innovation holds promise for advancing the screening, diagnosis, and management of choroidal disorders, particularly in underserved settings.