RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization.

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.

A Multimode Microfiber Specklegram Biosensor for Measurement of CEACAM5 through AI Diagnosis.

Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)-1 within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.

Magnetic microspheres enhanced peanut structure cascaded lasso shaped fiber laser biosensor for cancer marker-CEACAM5 detection in serum.

The detection of trace cancer markers in body fluids such as blood/serum is crucial for cancer diseases screening and treatment, which requires high sensitivity and specificity of biosensors. In this study, a peanut structure cascaded lasso (PSCL) shaped fiber sensing probe based on fiber laser demodulation method was proposed to specifically detect the carcinoembryonic antigen related cell adhesion molecules 5 (CEACAM5) protein in serum. Thanks for the narrow linewidth and high signal-to-noise ratio (SNR) of the laser spectrum, it is easier to distinguish small spectral changes than interference spectrum. Adding the antibody modified magnetic microspheres (MMS) to form the sandwich structure of "antibody-antigen-antibody-MMS", and amplified the response caused by biomolecular binding. The limit of detection (LOD) for CEACAM5 in buffer could reach 0.11 ng/mL. Considering the common threshold of 5 ng/mL for CEA during medical screening and the cut off limit of 2.5 ng/mL for some kits, the LOD of proposed biosensor meets the actual needs. Human serum samples from a hospital were used to validate the real sensing capability of proposed biosensor. The deviation between the measured value in various serum samples and the clinical value ranged from 1.9 to 9.8 %. This sensing scheme holds great potential to serve as a point of care testing (POCT) device and extend to more biosensing applications.

Loss-balanced parallel decoding network for retinal fluid segmentation in OCT.

As a leading cause of blindness worldwide, macular edema (ME) is mainly determined by sub-retinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) accumulation, and therefore, the characterization of SRF, IRF, and PED, which is also known as ME segmentation, has become a crucial issue in ophthalmology. Due to the subjective and time-consuming nature of ME segmentation in retinal optical coherence tomography (OCT) images, automatic computer-aided systems are highly desired in clinical practice. This paper proposes a novel loss-balanced parallel decoding network, namely PadNet, for ME segmentation. Specifically, PadNet mainly consists of an encoder and three parallel decoder modules, which serve as segmentation, contour, and diffusion branches, and they are employed to extract the ME's characteristics, the contour area features, and to expand the ME area from the center to edge, respectively. A new loss-balanced joint-loss function with three components corresponding to each of the three parallel decoding branches is also devised for training. Experiments are conducted with three public datasets to verify the effectiveness of PadNet, and the performances of PadNet are compared with those of five state-of-the-art methods. Results show that PadNet improves ME segmentation accuracy by 8.1%, 11.1%, 0.6%, 1.4% and 8.3%, as compared with UNet, sASPP, MsTGANet, YNet, RetiFluidNet, respectively, which convincingly demonstrates that the proposed PadNet is robust and effective in ME segmentation in different cases.

Fiber Laser-Based Lasso-Shaped Biosensor for High Precision Detection of Cancer Biomarker-CEACAM5 in Serum.

Detection of trace tumor markers in blood/serum is essential for the early screening and prognosis of cancer diseases, which requires high sensitivity and specificity of the assays and biosensors. A variety of label-free optical fiber-based biosensors has been developed and yielded great opportunities for Point-of-Care Testing (POCT) of cancer biomarkers. The fiber biosensor, however, suffers from a compromise between the responsivity and stability of the sensing signal, which would deteriorate the sensing performance. In addition, the sophistication of sensor preparation hinders the reproduction and scale-up fabrication. To address these issues, in this study, a straightforward lasso-shaped fiber laser biosensor was proposed for the specific determination of carcinoembryonic antigen (CEA)-related cell adhesion molecules 5 (CEACAM5) protein in serum. Due to the ultra-narrow linewidth of the laser, a very small variation of lasing signal caused by biomolecular bonding can be clearly distinguished via high-resolution spectral analysis. The limit of detection (LOD) of the proposed biosensor could reach 9.6 ng/mL according to the buffer test. The sensing capability was further validated by a human serum-based cancer diagnosis trial, enabling great potential for clinical use. The high reproduction of fabrication allowed the mass production of the sensor and extended its utility to a broader biosensing field.

Single-cell HER2 quantification via instant signal amplification in microdroplets.

Accurate and ultrasensitive evaluation of human epidermal growth factor receptor 2 (HER2) protein is key to early diagnosis and subtype differentiation of breast cancer. Single-cell analyses to reduce ineffective targeted therapies due to breast cancer heterogeneity and improve patient survival remain challenging. Herein, we reported a novel droplet microfluidic combined with an instant cation exchange signal amplification strategy for quantitative analysis of HER2 protein expression on single cells. In the 160 µm droplets produced by a tapered capillary bundle, abundant Immuno-CdS labeled on HER2-positive cells were replaced by Ag + to obtain Cd2+ that stimulated Rhod-5N fluorescence. This uniformly distributed and instantaneous fluorescence amplification strategy in droplets improves sensitivity and reduces signal fluctuation. Using HER2 modified PS microsphere to simulate single cells, we obtained a linear fitting of HER2-modified concentration and fluorescence intensity in microdroplets with the limit detection of 11.372 pg mL-1. Moreover, the relative standard deviation (RSD) was 4.2-fold lower than the traditional immunofluorescence technique (2.89% vs 12.21%). The HER2 protein on SK-BR-3 cells encapsulated in droplets was subsequently quantified, ranging from 9862.954 pg mL-1 and 205.26 pg mL-1, equivalent to 9.795 × 106 and 2.038 × 105 protein molecules. This detection system provides a universal platform for single-cell sensitive quantitative analysis and contributes to the evaluation of HER2-positive tumors.

Adaptive Fiber-Ring Lasers Based on Isopropanol Filled Microfiber Coupler for High-Sensitivity Temperature Sensing.

We demonstrated a new method for temperature measurement inside a fiber ring laser (FRL) cavity. Different from traditional FRL temperature sensing system which need additional filter working as a sensor, a micro-fiber coupler (MFC) was designed as a beam splitter, filter, and temperature sensor. In addition, isopropanol, a liquid with very high photothermal coefficient, is selectively filled in the MFC in order to improve the sensitivity of the system on temperature. In the dynamic range of 20-40 °C, we obtained a good temperature sensitivity of -1.29 nm/°C, with linear fitting up to 0.998. Benefiting from the advantages of laser sensing, the acquired laser has a 3 - dB bandwidth of less than 0.2 nm and a signal-to-noise ratio (SNR) of up to 40 dB. The proposed sensor has a low cost and high sensitivity, which is expected to be used in biomedical health detection, real-time monitoring of ocean temperature, and other application scenarios.

Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm.

This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial-temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.

Experimental investigation of inter-core crosstalk tolerance of MIMO-OFDM/OQAM radio over multicore fiber system.

In this paper, the feasibility of space division multiplexing for optical wireless fronthaul systems is experimentally demonstrated by implementing high speed MIMO-OFDM/OQAM radio signals over 20km 7-core fiber and 0.4m wireless link. Moreover, the impact of optical inter-core crosstalk in multicore fibers on the proposed MIMO-OFDM/OQAM radio over fiber system is experimentally evaluated in both SISO and MIMO configurations for comparison. The experimental results show that the inter-core crosstalk tolerance of the proposed radio over fiber system can be relaxed to -10 dB by using the proposed MIMO-OFDM/OQAM processing. These results could guide high density multicore fiber design to support a large number of antenna modules and a higher density of radio-access points for potential applications in 5G cellular system.

Rapid SERS monitoring of lipid-peroxidation-derived protein modifications in cells using photonic crystal fiber sensor.

We proposed a side channel photonic crystal fiber (SC-PCF) based Surface enhanced Raman spectroscopy (SERS) platform which is able to accurately monitor lipid peroxidation derived protein modifications in cells. This platform incorporates linoleamide alkyne (LAA), which is oxidized and subsequently modifies proteins in cells with alkyne functional group upon lipid peroxidation. By loading the side channel of SC-PCF with a mixture of gold nanoparticles and LAA treated cells, and subsequently measuring the interference-free alkyne Raman peak from these proteins in cells, strong SERS signal was obtained. The platform provides a method for the rapid monitoring of lipid peroxidation derived protein modification in cells.

Highly sensitive SERS detection and quantification of sialic acid on single cell using photonic-crystal fiber with gold nanoparticles.

An ultrasensitive surface enhanced Raman spectroscopy (SERS) based sensing platform was developed to detect the mean sialic acid level on the surface of single cell with sensitivity as low as 2 fmol. This platform adopted the use of an interference-free Raman tag, 4-(dihydroxyborophenyl) acetylene (DBA), which selectively binds to sialic acid on the cell membrane. By loading the side channel of a photonic crystal fiber with a mixture of gold nanoparticles and DBA-tagged HeLa cell, and subsequently propagating laser light through the central solid core, strong SERS signal was obtained. This SERS technique achieved accurate detection and quantification of concentration of sialic acid on a single cell, surpassing previously reported methods that required more than 10(5) cells. Moreover, this platform can be developed into a clinical diagnostic tool to potentially analyze sialic acid-related diseases such as tumor malignancy and metastasis in real-time.

Review of diverse optical fibers used in biomedical research and clinical practice.

Optical fiber technology has significantly bolstered the growth of photonics applications in basic life sciences research and in biomedical diagnosis, therapy, monitoring, and surgery. The unique operational characteristics of diverse fibers have been exploited to realize advanced biomedical functions in areas such as illumination, imaging, minimally invasive surgery, tissue ablation, biological sensing, and tissue diagnosis. This review paper provides the necessary background to understand how optical fibers function, to describe the various categories of available fibers, and to illustrate how specific fibers are used for selected biomedical photonics applications. Research articles and vendor data sheets were consulted to describe the operational characteristics of conventional and specialty multimode and single-mode solid-core fibers, double-clad fibers, hard-clad silica fibers, conventional hollow-core fibers, photonic crystal fibers, polymer optical fibers, side-emitting and side-firing fibers, middle-infrared fibers, and optical fiber bundles. Representative applications from the recent literature illustrate how various fibers can be utilized in a wide range of biomedical disciplines. In addition to helping researchers refine current experimental setups, the material in this review paper will help conceptualize and develop emerging optical fiber-based diagnostic and analysis tools.