Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.

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Rapid diagnosis and quarantine of influenza virus mutant-infected people is critical to contain the fatal viral infection spread because effective antiviral drugs are normally not available. Conventional methods, however, cannot be used for the diagnosis because these methods need predefined labels, likely also unavailable for just emerging viruses. Here, we propose label-free identification of cells infected with different influenza viruses based on surface-enhanced Raman spectroscopy (SERS) and principal component analysis (PCA). Viral envelope proteins that are displayed on the surface of cells after infection of influenza viruses were targeted for this identification. Cells that expressed the envelope proteins of A/WSN/33 H1N1 or A/California/04/2009 H1N1 influenza viruses produced distinct SERS signals. Cells that displayed combinations of the envelope proteins from these two viral variants, an indication of emergence of a new virus, also generated characteristic SERS patterns. However, the cell’s own surface proteins often hindered the identification of virally infected cells by producing SERS peaks similar to viral ones. PCA of the obtained SERS patterns could effectively capture the virus-specific signal components from the jumbled SERS peaks. Our study demonstrates a potential of combination of SERS and PCA to identify newly emerging influenza viruses through sensing the cells infected with the viruses.

BackgroundThis study aimed to determine the association between shift work and inflammatory markers, which are independent risk factors of cardiovascular diseases, in male manual workers at a display manufacturing company.MethodsThis study was conducted between June 1 and July 31, 2015 on 244 male manual workers aged 20–39 years old at a display manufacturing company and investigated age, marital status, education level, alcohol consumption habit, smoking habit, regular exercise habit, sleep duration, sleep debt, sleep insufficiency, past medical history, current and past shift work experience, duration of shift work, and weekly work hours through face-to-face interviews using structured questionnaires and performed blood tests. Study participants were divided into daytime, former shift, and current shift workers based on the work schedule. Chi-square tests and one-way analyses of variance were performed to compare inflammatory markers and cardiovascular disease risk factors, and analyses of covariance were conducted after adjusting for variables potentially affecting inflammatory markers.ResultsHigh-sensitivity C-reactive protein (hs-CRP; mean ± standard deviation) levels in daytime, former shift, and current shift workers were 0.65 ± 0.43, 0.75 ± 0.43, and 0.86 ± 0.72 mg/L, respectively (p = 0.029). The leukocyte count (mean ± standard deviation) was 5,556 ± 1,123, 6,210 ± 1,366, and 6,530 ± 1,216 cells/μL, respectively (p < 0.001). Both hs-CRP level and leukocyte count were significantly higher in current shift workers than in daytime workers, and leukocyte count was higher in former shift workers than in daytime workers. After adjusting for variables potentially affecting inflammatory markers, hs-CRP levels (adjusted mean ± standard deviation) in daytime and current shift workers were 0.59 ± 0.06 and 0.92 ± 0.07 mg/L, respectively (p = 0.002). The leukocyte count (adjusted mean ± standard deviation) was 5,557 ± 124 and 6,498 ± 144 cells/μL, respectively (p < 0.001).ConclusionsA significant association between shift work and increases in inflammatory markers was confirmed. Because chronic low-grade inflammation plays an important role in the development of cardiovascular diseases, regular follow-up of inflammatory markers as a marker of cardiovascular diseases in shift workers may serve as an early indicator in predicting the effects of shift work on health.

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