Synovial fluid research based on SERS and SERRS for enhanced detection of biomarkers in staged osteoarthritis.

Surface-enhanced (resonance) Raman scattering (SER(R)S) can extremely enhance Raman intensity of samples, which is helpful for detecting synovial fluid (SF) that does not show Raman activity under normal conditions. In this study, SER(R)S spectra of SF from three different osteoarthritis (OA) stages were collected and analyzed for OA progress, finding that the content of collagen increased throughout the disease, while non-collagen proteins and polysaccharides decreased sharply at advanced OA stage accompanied by the increase of phospholipid. The spectral features and differences were enhanced by salting-out and centrifugation. Much more information on biomolecules at different OA stages was disclosed by using SERRS for the first time, these main trace components (ß-carotene, collagen, hyaluronic acid, nucleotide, and phospholipid) can be used as potential biomarkers. It indicates that SERRS has a more comprehensive ability to assist SERS in seeking micro(trace) biomolecules as biomarkers and facilitating accurate and efficient diagnosis and mechanism research of OA.

NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis.

Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.

Monitoring Amyloidogenesis with a 3D Deep-Learning-Guided Biolaser Imaging Array.

Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet microlaser to monitor the amyloidogenesis process and evaluate the efficacy of anti-amyloid drugs. The lasing wavelength changes accordingly with the amyloid peptide folding behaviors and nanostructure conformations in the droplet resonator. A 3D deep-learning strategy was developed to directly image minute spectral shifts through a far-field camera. By extracting 1D color information and 2D features from the laser images, the progression of the amyloidogenesis process could be monitored using arrays of laser images from microdroplets. The training set, validation set, and test set of the multimodal learning model achieved outstanding classification accuracies of over 95%. This study shows the great potential of deep-learning-empowered peptide microlaser yields for protein misfolding studies and paves the way for new possibilities for high-throughput imaging of cavity biosensing.

Motor-like microlasers functioning in biological fluids.

Microlasers integrated with biological systems have received tremendous attention for their intense light intensity and narrow linewidth recently, serving as a powerful tool for studying complex dynamics and interactions in scattered biological micro-environments. However, manipulation of microlasers with controllable motions and versatile functions remains elusive. Herein, we introduce the concept of motor-like microlasers formed by magnetic-doped liquid crystal droplets, in which the direction and velocity could be controlled by altering internal magnetic nanoparticles or external magnetic fields. Both translational and rotatory motions of the lasing resonator could be continually changed in real-time. Lasing-encoded motors carrying different functions and lasing wavelengths were also achieved. Finally, we demonstrate the potential of motor-like microlasers by functioning as a localized stimulation emission light source to stimulate or illuminate living cells, providing a novel approach for switching on/off light emissions and subcellular imaging. Laser emitting micromotors offer a facile system for precise manipulation of microlasers in biological fluids, providing new insight into the development of programmable on-chip laser devices and laser-emitting intelligent systems.

Size-dependent neurotoxicity of micro- and nanoplastics in flowing condition based on an in vitro microfluidic study.

With the widespread presence of plastic wastes, knowledge about the potential environmental risks and bioavailability of micro- or nanoplastics fragmented from large analogs is of utmost importance. As the particle size matters in mediating endocytic mechanism and particle internalization, we first studied the effects of polystyrene microparticles (PS-MPs, 1 µm) and polystyrene nanoparticles (PS-NPs, 100 nm) of two different sizes at varying concentrations of 5, 25 and 75 µg/mL on the mouse hippocampal neuronal HT22 cells. The in vitro study showed efficient cellular uptake of PS-MPs and PS-NPs of both sizes. The adverse effects of cellular metabolic activity as reflective of excess Reactive Oxygen Species (ROS) and cell cycle S phase arresting were observed especially at the greater concentration of smaller-sized PS particles, consequently leading to mild cytotoxicity. We further evaluated the dynamic particle-cell interaction with a continuous supply of PS particles using a microfluidic device. By recapitulating the in vivo mechanical microenvironments while allowing homogeneous distribution of PS particles, the dynamic exposure to PS particles of both sizes under flowing conditions resulted in much lesser viability of neural cells than the traditional static exposure. As the flowing dynamics may avoid the gravitational settling of particles and allow more efficient cellular uptake, the size distribution, together with the exposure configurations, contributed significantly to the determination of the PS particle cytotoxicity. The on-chip investigation and a better understanding of particle translocation mechanisms would offer very much to the risk assessment of PS particles on human health.

Polarized Micro-Raman Spectroscopy and 2D Convolutional Neural Network Applied to Structural Analysis and Discrimination of Breast Cancer.

Raman spectroscopy has been efficiently used to recognize breast cancer tissue by detecting the characteristic changes in tissue composition in cancerization. In addition to chemical composition, the change in bio-structure may be easily obtained via polarized micro-Raman spectroscopy, aiding in identifying the cancerization process and diagnosis. In this study, a polarized Raman spectral technique is employed to obtain rich structural features and, combined with deep learning technology, to achieve discrimination of breast cancer tissue. The results reconfirm that the orientation of collagen fibers changes from parallel to vertical during breast cancerization, and there are significant structural differences between cancerous and normal tissues, which is consistent with previous reports. Optical anisotropy of collagen fibers weakens in cancer tissue, which is closely related with the tumor's progression. To distinguish breast cancer tissue, a discrimination model is established based on a two-dimensional convolutional neural network (2D-CNN), where the input is a matrix containing the Raman spectra acquired at a set of linear polarization angles varying from 0° to 360°. As a result, an average discrimination accuracy of 96.01% for test samples is achieved, better than that of the KNN classifier and 1D-CNN that are based on non-polarized Raman spectra. This study implies that polarized Raman spectroscopy combined with 2D-CNN can effectively detect changes in the structure and components of tissues, innovatively improving the identification and automatic diagnosis of breast cancer with label-free probing and analysis.

Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network.

As the most common cancer in women, breast cancer is becoming lethal worldwide. However, the current breast diagnosis technologies are not enough to meet the requirements in clinic due to some shortages of early-stage insensitiveness, time consumption and relying on the doctor's experience, etc. It's necessary to develop a creative method for the automatical diagnosis of breast cancer. Therefore, Raman spectroscopy and one-dimensional convolutional neural network (1D-CNN) algorithm were combined together for the first time to classify the healthy and cancerous breast tissues in this study. First, a number of Raman spectra were collected from breast samples of 20 patients for spectral analysis. Then, a 1D-CNN model was developed and trained for classification. In addition, the Fisher Discrimination Analysis (FDA) and Support Vector Machine (SVM) classifiers were trained and tested with the same spectral data for comparison. The best classification performance, namely the overall diagnostic accuracy of 92%, the sensitivity of 98% and the specificity of 86%, has been achieved by using 1D-CNN model. This study proves that 1D-CNN combined with Raman spectroscopy can classify breast tissues effectively and automatically and lay the foundation for automated cancer diagnosis in the future.

Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms.

Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.

Fourier transform infrared spectroscopy research on subchondral bone in osteoarthritis.

Osteoarthritis (OA) is not only related to the degradation of articular cartilage, but also possibly to the changes of subchondral bone. The purpose of this study was to assess whether specific differences could be resolved from bone composition, as also contributed to OA. These differences were assessed by using Fourier transform infrared spectroscopy (FTIRS). The main parameters including mineral content, carbonate content, crystallinity, collagen cross-linking ratio (XLR) and acid phosphate content were represented with characteristic peak integration. It was found that mineral and carbonate content varied significantly with depths at different OA stages. Mineral content increased with depth in healthy samples, while carbonate content showed opposite trend. The mineral content reduced obviously with OA duration, which was different with carbonate decreasing only at early stage of OA. In addition, the content of acid phosphate, collagen maturity (XLR) and crystallinity slight varied with the OA aggravation. Therefore, the changes in subchondral bone were significantly associated with cartilage degeneration and OA, the associated parameters should be targeted for OA therapies.