Aging characteristics of colorectal cancer based on gut microbiota

Abstract Background Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). Methods A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age‐differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age‐differentiated bacteria. Results The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age‐related differential bacteria and model of CRC patients based on the 58 age‐related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease‐differentiated bacteria and the age‐differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). Conclusion Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC

Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non‐small cell lung cancer

Abstract Background The aim of the present study was to compare the predictive accuracy of PD‐L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non‐small cell lung cancer (NSCLC). Methods In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)‐treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured. Results In part 1, the AUC of combined biomarkers (0.75) was higher than PD‐L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD‐L1 plus TMB (PD‐L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD‐L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD‐L1, tTMB‐high, wild‐type EGFR, and positive PD‐L1 plus TMB were associated with prolonged progression‐free survival (PFS). Conclusion Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings

Detection Probability Analysis of True Random Coding Photon Counting Lidar

With the wide application of lidar in the future, the problem of crosstalk between lidars will become more serious. True random coding photon counting lidar with high anti-crosstalk ability will play an important role in solving this problem. In this paper, based on the working principle of Gm-APD, the detection probability theoretical model of true random coding photon counting lidar is built, and the impact of jitter on detection probability is considered for the first time. The influence of mean echo photon number, mean pulse count density, sequence length and pulse width on detection probability is analyzed. Monte Carlo simulation and experimental results are highly consistent with the theoretical model, which proves the correctness of the detection probability theoretical model. This theoretical model provides an effective means to evaluate the system performance

Detection Probability Analysis of True Random Coding Photon Counting Lidar

With the wide application of lidar in the future, the problem of crosstalk between lidars will become more serious. True random coding photon counting lidar with high anti-crosstalk ability will play an important role in solving this problem. In this paper, based on the working principle of Gm-APD, the detection probability theoretical model of true random coding photon counting lidar is built, and the impact of jitter on detection probability is considered for the first time. The influence of mean echo photon number, mean pulse count density, sequence length and pulse width on detection probability is analyzed. Monte Carlo simulation and experimental results are highly consistent with the theoretical model, which proves the correctness of the detection probability theoretical model. This theoretical model provides an effective means to evaluate the system performance

Observations of Atmospheric Aerosol and Cloud Using a Polarized Micropulse Lidar in Xi’an, China

A polarized micropulse lidar (P-MPL) employing a pulsed laser at 532 nm was developed by the Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences). The optomechanical structure, technical parameters, detection principle, overlap factor calculation method, and inversion methods of the atmospheric boundary layer (ABL) depth and depolarization ratio (DR) were introduced. Continuous observations using the P-MPL were carried out at Xi’an Meteorological Bureau, and the observation data were analyzed. In this study, we gleaned much information on aerosols and clouds, including the temporal and spatial variation of aerosols and clouds, aerosol extinction coefficient, DR, and the structure of ABL were obtained by the P-MPL. The variation of aerosols and clouds before and after a short rainfall was analyzed by combining time-height-indication (THI) of range corrected signal (RCS) and DR was obtained by the P-MPL with profiles of potential temperature (PT) and relative humidity (RH) detected by GTS1 Digital Radiosonde. Then, the characteristics of tropopause cirrus cloud were discussed using the data of DR, PT, and RH. Finally, a haze process from January 1st to January 5th was studied by using aerosol extinction coefficients obtained by the P-MPL, PT, and RH profiles measured by GTS1 Digital Radiosonde and the time-varying of PM2.5 and PM10 observed by ambient air quality monitor. The source of the haze was simulated by using the NOAA HYSPLIT Trajectory Model