Single-model multi-tasks deep learning network for recognition and quantitation of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman scattering (SERS) spectroscopy analysis has long been the central task of nanoscience and nanotechnology to realize the ultrasensitive recognition/quantitation applications. Recently, the blooming of artificial intelligence algorithms provides an edge tool to revolutionize the spectroscopy analysis, especially for multiple substances analysis and large-scale data handling. In this study, a single-model multi-tasks deep learning network is proposed to simultaneously achieve the qualitative recognition and quantitative analysis of SERS spectroscopy. The SERS spectra of two kinds of hypoglycemic drugs (PHE, ROS) and the corresponding mixtures are collected, respectively, with the concentration grade from 10-4 M to 10-8 M. Based on the SERS spectroscopy dataset, the loss functions and hyperparameters of the multi-tasks classifications model are optimized, and the recognition accuracies are tested by simulation experiments. It is demonstrated that the accuracy rates of qualitative and quantitative analysis can reach up to 99.0% and 98.4%, respectively. Moreover, the practical feasibility of this multi-tasks model is demonstrated by using it to achieve qualitative and quantitative analysis of PHE and ROS in complex serum matrix. Overall, this single-model multi-tasks deep learning network shows significant potential for the recognition and quantitation of SERS spectroscopy, which provides the algorithmic and experimental basis for large-scale and multiple substances SERS spectra analysis.

Satellite Cells are Activated in a Rat Model of Radiation-Induced Muscle Fibrosis.

Radiation-induced muscle fibrosis is a long-term side effect of radiotherapy that significantly affects the quality of life and even reduces the survival of cancer patients. We have demonstrated that radiation induces satellite cell (SC) activation at the molecular level; however, cellular evidence in a rat model of radiation-induced muscle fibrosis was lacking. In this study, we evaluated SC activation in vivo and investigated whether radiation affects the proliferation and differentiation potential of SCs in vitro. For in vivo studies, Sprague-Dawley rats were randomly divided into six groups (n = 6 per group): non-irradiated controls, 90 Gy/1 week-, 90 Gy/2 weeks-, 90 Gy/4 weeks-, 90 Gy/12 weeks- and 90 Gy/24 weeks-postirradiation groups. Rats received a single dose of radiation in the left groin area and rectus femoris tissues were collected in the indicated weeks. Fibrosis, apoptosis, and autophagy were evaluated by Masson's trichrome staining, TUNEL staining, and electron microscopy, respectively. SC activation and central nuclear muscle fibers were evaluated by immunofluorescence staining and hematoxylin and eosin staining. IL-1ß concentrations in serum and irradiated muscle tissue samples were determined by ELISA. For in vitro studies, SCs were isolated from rats with radiation-induced muscle fibrosis and their proliferation and differentiation were evaluated by immunofluorescence staining. In vivo, fibrosis increased over time postirradiation. Apoptosis and autophagy levels, IL-1ß concentrations in serum and irradiated skin tissues, and the numbers of SCs and central nuclear muscle fibers were increased in the irradiated groups when compared with the control group. In vitro, cultured SCs from irradiated muscle were positive for the proliferation marker Pax7, and differentiated SCs were positive for the myogenic differentiation marker MyHC. This study provided cellular evidence of SC activation and proliferation in rats with radiation-induced muscle fibrosis.

NF-κB pathway activation during endothelial-to-mesenchymal transition in a rat model of doxorubicin-induced cardiotoxicity.

Doxorubicin is a commonly used anthracycline chemotherapeutic agent; however, its application is limited owing to its cardiotoxicity. Current clinical treatments cannot efficiently or fully prevent doxorubicin-induced toxicity, primarily because its pathogenesis and mechanisms of action remain unknown. In this study, we established a rat model of chronic doxorubicin-induced cardiotoxicity, in which the severity of cardiac fibrosis and hydroxyproline levels increased in a time-dependent manner. Doxorubicin damaged the mitochondria and blood vessels and induced autophagy. Cells undergoing endothelial-to-mesenchymal transition (EndoMT)and those expressing endothelial cell and myofibroblast markers were simultaneously observed in vitro and in rats treated with doxorubicin. The NF-κB pathway was activated during EndoMT, andp65 and p-p65 were strongly expressed in the nucleus of endothelial cells in vitro. Taken together, these results suggest that vascular injury and cardiac fibrosis are characteristic symptoms of doxorubicin-induced cardiotoxicity. The NF-κB pathway-associated EndoMT may influence the pathogenesis of doxorubicin-induced cardiotoxicity, and the constituents of this pathway may be potential therapeutic targets to prevent the development of this condition.

Hydrogel-based microbeads for Raman-encoded suspension array using the reversed-phase suspension polymerization method and ultraviolet light curing.

A one-step synthesis using the reversed-phase suspension polymerization method and ultraviolet light curing is proposed for preparing the Raman-encoded suspension array (SA). The encoded microcarriers are prepared by doping the Raman reporter molecules into an aqueous phase, and then dispersing the aqueous phase in an oil phase and curing by ultraviolet light irradiation. The multiplexed biomolecule detection and various concentration experiments confirm the qualitative and quantitative analysis capabilities of the Raman-encoded SA with a limit of detection of 52.68 pM. The narrow bandwidth of the Raman spectrum can achieve a large number of codes in the available spectral range and the independence between the encoding channel and the fluorescent label channel provides the encoding method with high accuracy. This preparation method is simple and easy to operate, low in cost, and high in efficiency. A large number of hydrogel-based encoding microbeads could be quickly obtained with good biocompatibility. Most importantly, concentrating plenty of Raman reporter molecules inside the microbeads increases the signal intensity and means the molecular assembly is not limited by the functional groups; thus, the types of materials available for Raman encoding method are expanded. Furthermore, the signal intensity-related encoding method is verified by doping different proportions of Raman reporter molecules with our proposed synthesis method, which further increases the detection throughput of Raman-encoded SA. Graphical Abstract.

Detection of Macromolecular Content in a Mixed Solution of Protein Macromolecules and Small Molecules Using a Weak Measurement Linear Differential System.

In this paper, we propose a linear differential detection system based on a frequency domain weak measurement. The system can be used for detecting optical substances. Moreover, we completed an experiment to detect himan serum albumin (HSA) content in a mixture of human serum albumin and l-proline via dialysis. This work also proves the differential function of the system. This experiment can be further extended to detecting protein content in a mixed solution that contains protein macromolecules and various small molecules. It is very important for detecting molecules without photomarking in solutions of complex biological samples. In this paper, the system has an optical resolution of 1.39 × 10-5, and resolution of 4.06 × 10-8 mol/L for himan serum protein solution.

Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning.

A deep learning network called "residual neural network" (ResNet) was used to decode Raman spectra-encoded suspension arrays (SAs). With narrow bandwidths and stable signals, Raman spectra have ideal encoding properties. The different Raman reporter molecules assembled micro-quartz pieces (MQPs) were grafted with various biomolecule probes, which enabled simultaneous detection of numerous target analytes in a single sample. Multiple types of mixed MQPs were measured by Raman spectroscopy and then decoded by ResNet to acquire the type information of analytes. The good classification performance of ResNet was verified by a t-distributed stochastic neighbor embedding (t-SNE) diagram. Compared with other machine learning models, these experiments showed that ResNet was obviously superior in terms of classification stability and training convergence to different datasets. This method simplified the decoding process and the classification accuracy reached 100%.

Establishment and characterization of a radiation-induced dermatitis rat model.

Radiation-induced dermatitis is a common and serious side effect after radiotherapy. Current clinical treatments cannot efficiently or fully prevent the occurrence of post-irradiation dermatitis, which remains a significant clinical problem. Resolving this challenge requires gaining a better understanding of the precise pathophysiology, which in turn requires establishment of a suitable animal model that mimics the clinical condition, and can also be used to investigate the mechanism and explore effective treatment options. In this study, a single dose of 90 Gy irradiation to rats resulted in ulceration, dermal thickening, inflammation, hair follicle loss, and sebaceous glands loss, indicating successful establishment of the model. Few hair follicle cells migrated to form epidermal cells, and both the severity of skin fibrosis and hydroxyproline levels increased with time post-irradiation. Radiation damaged the mitochondria and induced both apoptosis and autophagy of the skin cells. Therefore, irradiation of 90 Gy can be used to successfully establish a rat model of radiation-induced dermatitis. This model will be helpful for developing new treatments and gaining a better understanding of the pathological mechanism of radiation-induced dermatitis. Specifically, our results suggest autophagy regulation as a potentially effective therapeutic target.

Dual-wavelength digital holographic phase and fluorescence microscopy combining with Raman spectroscopy for micro-quartz pieces-based dual-channel encoded suspension array.

Dual-wavelength digital holographic phase and fluorescence microscopy (DW-DHPFM), combining with Raman spectroscopy, is designed to achieve the detection and analysis of biomolecules with a new dual-channel encoding method. This employs the Raman reporter molecules assembled micro-quartz pieces (MQPs) as microcarriers of suspension array (SA). The dual-wavelength digital holographic phase microscopy (DW-DHPM) and Raman spectroscopy are served as the decoding platforms, and the fluorescence microscopy is used to quantify target analytes. Considering the independence between encoding and label signal, the above two encoding channels could effectively avoid the crosstalk in immunoassay process, and the combination of two encoding methods expand the encoding capacity with a considerable magnitude. Accurate and stable decoding abilities are verified by multiplexed immunoassay experiment and the quantitative analysis of targets with high-sensitivity is confirmed by concentration gradient experiments.

Optimization of a quantum weak measurement system with digital filtering technology.

In this paper, we propose a post-Gaussian filtering theory for weak measurement in the frequency domain, and propose a highly deformed digital filtering technique that can be used to optimize sensors based on weak-frequency measurement techniques. We completed the experimental verification based on the weak measurement total internal reflection sensor. The experimental results show that digital filtering technology can optimize the system in the working range, sensitivity, and resolution of the frequency domain weak measurement system, so that it can reach 0.210 rad, 3210.9 nm/RIU, and 7.12×10-7 RIU, respectively.

Rapid Separation of Enantiomeric Impurities in Chiral Molecules by a Self-Referential Weak Measurement System.

We propose a self-referential fast detection scheme for a frequency domain weak measurement system for the detection of enantiomeric impurities in chiral molecules. In a transmissive weak measurement system, the optical rotation (OR) is used to modify the pre-selected polarization state and the post-selection polarization state. We obtained the sum and difference of the optical rotations produced by the sample and the standard by rotating the quarter wave plate in the system. Then, we estimate the ratio of chiral molecules to enantiomeric impurities using the ratio of the central wavelength shifts caused by the addition and subtraction states described above. In this paper, our system has an optical resolution of 1.88 × 10-5°. At the same time, we completed the detection of the ratio of the two substances in the mixture of L-proline and D-proline in different proportions, which proved that our system can quickly detect the content of enantiomeric impurities in chiral molecules.

Optimization of a quantum weak measurement system with its working areas.

Phase-sensitive weak measurement systems have been receiving an increasing amount of attention. In this paper, we introduce a series of weak measurement working areas. By adjusting the pre-selection and post-selection states and the total phase difference between vertically polarized light and horizontally polarized light, the measurement of the weak value is amplified by several times in one system. Its applicability is verified in a label-free total internal reflection system. The original sensitivity and resolution are improved at different working areas, reaching 1.85 um/refractive index unit (RIU) and 6.808 × 10-7 RIU, respectively.

Metaproteomics reveals the major microbial players and their biogeochemical functions in a productive coastal system in the northern South China Sea.

We study the metaproteome of the GF/F-prefiltered fraction of a microbial community from Shantou coast summer surface waters using a shotgun proteomic approach. Spectra attributed to the marine Roseobacter clade (MRC), the oligotrophic marine Gammaproteobacteria (OMG) group and Flavobacteria dominated in the microbial community, accounting for 21.0%, 23.2% and 12.7% of all of the detected spectra, respectively, whereas the SAR 92 clade accounted for 50% of the OMG group. The abundance of TonB-dependent receptors (TBDRs) was detected and the majority of TBDRs were attributed to the OMG, whereas a large number of ABC transporters matched to the MRC, which suggests niche separation in the microbial community. Expression of proteorhodopsin and RagB/SusD from Flavobacteria facilitates their attachment and growth on algal-derived organic matter. Taurine and glycine betaine appear to be an important source of carbon and nitrogen for the Rhodobacteraceae and SAR11 cluster. The detection of carbon monoxide dehydrogenase, formate dehydrogenase, O-acetylhomoserine sulfhydrylase and sulfur oxidation protein from the MRC demonstrated that members of the MRC play important roles in coastal ocean biogeochemical cycles. This study provides the first insight into functional processes occurring in microbial communities in coastal waters in the South China Sea.