A Comparative Study of the Effects of Electronic Cigarette and Traditional Cigarette on the Pulmonary Functions of C57BL/6 Male Mice.

INTRODUCTION: Electronic cigarettes (E-cigs) are in a controversial state. Although E-cig aerosol generally contains fewer harmful substances than smoke from burned traditional cigarettes, aerosol along with other compounds of the E-cigs may also affect lung functions and promote the development of lung-related diseases. We investigated the effects of E-cig on the pulmonary functions of male C57BL/6 mice and reveal the potential underlying mechanisms. METHODS: A total of 60 male C57BL/6 mice were randomly divided into four groups. They were exposed to fresh-air, traditional cigarette smoke, E-cig vapor with 12 mg/mL of nicotine, and E-cig with no nicotine for 8 weeks. Lung functions were evaluated by using quantitative analysis of the whole body plethysmograph, FlexiVent system, lung tissue histological and morphometric analysis, and RT-PCR analysis of mRNA expression of inflammation-related genes. In addition, the effects of nicotine and acrolein on the survival rate and DNA damage were investigated using cultured human alveolar basal epithelial cells. RESULTS: Exposure to E-cig vapor led to significant changes in lung functions and structures including the rupture of the alveolar cavity and enlarged alveolar space. The pathological changes were also accompanied by increased expression of interleukin-6 and tumor necrosis factor-α. CONCLUSIONS: The findings of the present study indicate that the safety of E-cig should be further evaluated. IMPLICATIONS: Some people currently believe that using nicotine-free E-cigs is a safe way to smoke. However, our research shows that E-cigs can cause lung damage regardless of whether they contain nicotine.

Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.

To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calculations, but the latter have better repeatability and efficiency. We investigate the relationship between IVDD parameters and demographic information (age, gender, position and IVDD grade) in a large population. Considering these parameters present strong correlation with IVDD grade, we establish a quantitative criterion for IVDD. This fully automated quantitation system for IVDD may provide more precise information for clinical practice, clinical trials, and mechanism investigation. It also would increase the number of patients that can be monitored.

[Application of liquid chip-mass spectrometry technology for screening serum biomarker proteins in lung squamous cell carcinoma].

OBJECTIVE: To screen the serum biomarker proteins of lung squamous cell carcinoma (SCCs) by liquid chip-mass spectrometry technology. METHODS: All serum samples, including 34 SCCs, 46 benign lung diseases (BLDs) and 44 healthy individuals, were analyzed by CLINPROT system in order to study the serum protein expression profiles. Then the discriminatory proteins were detected by FlexAnalysis 3.0 software. Biomarkers were identified by liquid chromatography-tandem mass spectrometry (LCMS/MS). RESULTS: Comparing the differential serum expression proteins between SCCs and healthy individuals, and SCCs and BLDs respectively. Ninety-six differential protein peaks [mass-to-charge ration (M/Z) between 800 and 10 000] were found between SCCs and healthy individuals. In these protein peaks, the expression of protein peaks at 4054.13 M/Z and 4267.46 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from healthy individuals by the frame of axes. Similarly, 99 differential protein peaks were automatically detected between SCCs and BLDs. In these protein peaks, the expression of protein peaks at 5065.27 M/Z and 4054.02 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from BLDs by the frame of axes. Identified by LC-MS/MS, 1778 M/Z and 1865 M/Z might be assayed jointly and corresponded to complements C3 fragment or C3f precursor. CONCLUSIONS: Differential protein expressions existed between SCCs versus healthy individuals and SCCs versus BLD patients. It is feasible to screen the diagnostic serum biomarkers of SCC with a high sensitivity and specificity by using CLINPROT system.

Holographic Three-Dimensional Imaging of Terra-Cotta Warrior Model Using Fractional Fourier Transform.

Holographic three-dimensional (3D) imaging of Terra-Cotta Warrior model using Fractional Fourier Transform is introduced in this paper. Phase holograms of Terra-Cotta Warrior model are calculated from 60 horizontal viewing-angles by the use of fractional Fourier transform (FRT). Multiple phase holograms are calculated for each angle by adding proper pseudorandom phase to reduce the speckle noise of a reconstructed image. Experimental system based on high-resolution phase-only spatial light modulator (SLM) is built for 3D image reconstruction from the calculated phase holograms. The texture of the Terra-Cotta Warrior model is rough. The calculation of rough texture is optimized in order to show better model details. The effects of computing distance and layer thickness on imaging quality are analyzed finally.

Glycoprotein nonmetastatic B as a prognostic indicator in small cell lung cancer.

Glycoprotein nonmetastatic melanoma B (GPNMB) is a type I transmembrane glycoprotein which is overexpressed in many tumors and seems to play a critical role in metastasis of malignant tumors. The purpose of this study was to determine GPNMB expression in small cell lung cancer (SCLC) and analyze the prognostic value in patients with SCLC. A total of 132 cases of SCLCs were analyzed immunohistochemically on tissue microarrays (TMAs). Patients were divided into weak-positive and strong-positive GPNMB groups. In addition, serum GPNMB was evaluated by enzyme-linked immunosorbent assay (ELISA). The average serum GPNMB concentration was 1054.15 ± 363.71 pg/mL in the weak-positive group, 2611.52 ± 457.57 pg/mL in the strong-positive group, and 427.61 ± 273.9 pg/mL in the control. The strong-positive group showed significantly higher serum GPNMB levels than the weak-positive group and healthy control (p < 0.01). Overall survival in the weak-positive GPNMB group was significantly longer than in the strong-positive group (27 months vs 15 months, p < 0.01). These results suggest that the expression of GPNMB may be useful as a prognostic indicator in patients with SCLC.

Biomarker discovery and identification from non-small cell lung cancer sera.

Currently, serum biomarkers might usually be thought not to be used for early detection of lung cancer by some researchers. In this study, we used a highly optimized ClinProt-matrix-assisted laser desorption/ionization time-of flight mass spectrometer (MALDI-TOF-MS) to screen non-small cell lung carcinoma (NSCLC) markers in serum. A training set of spectra derived from 45 NSCLC patients, 24 patients with benign lung diseases (BLDs) and 21 healthy individuals, was used to develop a proteomic pattern that discriminated cancer from non-cancer effectively. A test set, including 74 cases (29 NSCLC patients and 45 controls), was used to validate this pattern. After cross-validation, the classifier showed sensitivity and specificity, 86.20% and 80.00%, respectively. Remarkably, 100% of early stage serum samples could be correctly classified as lung cancer. Furthermore, the differential peptides of 1865Da and 4209Da were identified as element of component 3 and eukaryotic peptide chain release factor GTP-binding subunit ERF, respectively. The patterns we described and peptides we identified may have clinical utility as surrogate markers for detection and classification of NSCLC.