Gastric cancer is usually diagnosed at late stage and has a high mortality rate, whereas early detection of gastric cancer could bring a better prognosis. Conventional gastric cancer diagnostic methods suffer from long diagnostic times, severe trauma, and a high rate of misdiagnosis and rely heavily on doctors’ subjective experience. Raman spectroscopy is a label-free molecular vibrational spectroscopy technique that identifies the molecular fingerprint of various samples based on the inelastic scattering of monochromatic light. Because of its advantages of non-destructive, rapid, and accurate detection, Raman spectroscopy has been widely studied for benign and malignant tumor differentiation, tumor subtype classification, and section pathology diagnosis. This paper reviews the applications of Raman spectroscopy for the in vivo and in vitro diagnosis of gastric cancer, methodology related to the spectroscopy data analysis, and presents the limitations of the technique.

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Raman spectroscopy is a rapid analysis method of biological samples without labeling and destruction. At present, the commonly used Raman spectrum classification models include CNN, RNN, etc. The transformer has not been used for Raman spectrum identification. This paper introduces a new method of transformer combined with Raman spectroscopy to identify deep-sea cold seep microorganisms at the single-cell level. We collected the Raman spectra of eight cold seep bacteria, each of which has at least 500 spectra for the training of transformer model. We compare the transformer classification model with other deep learning classification models. The experimental results show that this method can improve the accuracy of microbial classification. Our average isolation level accuracy is more than 97%.

Single cell isolation and cultivation play an important role in studying physiology, gene expression and functions of microorganisms. A series of single-cell isolation technologies have been developed, among which single-cell ejection technology is one of the most promising. Single cell ejection technology has applied Laser Induced Forward Transfer Technique (LIFT) to isolate bacteria but the viability (or recovery rate) of cells after sorting has not been clarified in the current research progress. In this work, to keep the cells alive as much as possible, we propose a three-layer LIFT system (top layer: 25-nm aluminum film; second layer: 3 μm agar media; third layer: liquid containing bacterial) for the isolation and cultivation of single Gram-negative ( E. coli ), Gram-positive ( Lactobacillus rhamnosus GG, LGG), and eukaryotic microorganisms ( Saccharomyces cerevisiae ). The experiment results showed that the average survival rates for ejected pure single cells were 63% for Saccharomyces cerevisiae , 22% for E. coli DH5α, and 74% for LGG. In addition, we successfully isolated and cultured the GFP expressing E. coli JM109 from the mixture containing complex communities of soil bacteria by fluorescence signal. The average survival rate of E. coli JM109 was demonstrated to be 25.3%. In this study, the isolated and cultured single colonies were further confirmed by colony PCR and sequencing. Such precise sorting and cultivation technique of live single microbial cells could be coupled with other microscopic approaches to isolate single microorganisms with specific functions, revealing their roles in the natural community. Importance We developed a laser induced forward transfer (LIFT) technology to accurately isolate single live microbial cells. The cultivation recovery rates of the ejected single cells were 63% for Saccharomyces cerevisiae , 22% for E. coli DH5α, and 74% for Lactobacillus rhamnosus GG (LGG). Coupled LIFT with fluorescent microscope, we demonstrated that single cells of GFP expressing E. coli JM109 were sorted according to fluorescence signal from a complex community of soil bacteria, and subsequently cultured with 25% cultivation recovery rate. This single cell live sorting technology could isolate single microbes with specific functions, revealing their roles in the natural community.

Colon cancer is one of the most common malignant tumors worldwide. Understanding the underlying molecular mechanisms is crucial for the development of therapeutic strategies for the treatment of patients with colon cancer. In the present study, a novel tumor suppressive microRNA, miR-192, was demonstrated to be markedly downregulated in colon cancer cells compared with normal colon cells. By overexpressing miR-192 in colon cancer HCT-116 cells, the results of the present study revealed that miR-192 inhibits cell proliferation, migration and invasion. Bioinformatics were used to determine the target gene of miR-192 and Ras-related protein Rab-2A (RAB2A) was identified as a downstream target of miR-192. Following the determination of the role of the miR-192-RAB2A pathway in colon cancer, small molecules that may regulate miR-192 were screened and the results demonstrated that simvastatin is an activator of miR-192. Furthermore, simvastatin upregulated miR-192 and inhibited the expression of downstream targets of miR-192, which subsequently led to suppressed proliferation, migration and invasion of colon cancer cells. In conclusion, the present study identified a novel colon cancer cell suppressor, as well as a small-molecule activator of the tumor suppressor miR-192, which may represent a therapeutic strategy for the treatment of patients with colon cancer. Materials and methodsCells and reagents. The HCT-116, HT-29, SW480 and RKO human colon cancer cell lines, as well as the FHC normal colon epithelial cell line and the 293T cell line, were acquired from the Shanghai Institute for Biological Sciences (Shanghai, China). The cells were cultured in McCoy's 5A (modified) MicroRNA-192 acts as a tumor suppressor in colon cancer and simvastatin activates miR-192 to inhibit cancer cell growth

Beer spoilage bacteria have been a headache for major breweries. In order to rapidly identify spoilage bacteria and improve the sensitivity and signal-to-noise ratio of bacterial SERS detection. Using the...

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the “fingerprint” of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto‐encoder (VAE), and long short‐term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal‐to‐noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.

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