Antimicrobial resistance spectrum and virulence characterization of Escherichia coli, Klebsiella pneumoniae and Proteus mirabilis isolated from asymptomatic and diarrheal rhesus monkeys.

This study aims to deepen our understanding of the drug resistance and virulence characterization among gut bacteria in asymptomatic and diarrheal captive rhesus macaques (RMs). A total of 31 samples, including 8 asymptomatic RMs, 10 diarrheal RMs, and 1 dead RM, were collected from a breeding base in Sichuan, China, for bacterial isolation. As a result, Escherichia coli (n = 23), Klebsiella (n = 22), Proteus mirabilis (n = 10), Enterococcus (n = 10), Salmonella (n = 2), and Staphylococcus (n = 2) were isolated. All isolates were subjected to antimicrobial susceptibility testing and whole-genome sequencing, among which some E. coli, K. pneumoniae, and P. mirabilis were subjected to the Galleria mellonella and mice infection testing. The antimicrobial resistance rates of levofloxacin, enrofloxacin, and cefotaxime in diarrhea-associated isolates were higher than those of asymptomatic isolates. Consistent with the antimicrobial resistance phenotype, diarrheal isolates had a higher prevalence rate to qnrS1, blaTEM-1B and blaCTX-M-27 than asymptomatic isolates. Furthermore, compared with asymptomatic isolates, diarrheal isolates demonstrated a higher pathogenic potential against larvae and mice. Additionally, sequence types (STs) 14179-14181 in E. coli and ST 625 and ST 630-631 in Klebsiella aerogenes were firstly characterized. Our evidence underscores the considerable challenge posed by high rates of bacterial drug resistance in the effective treatment of diarrheal RMs.

Impact of Beijing's "Coal to Electricity" program on ambient PM2.5 and the associated reactive oxygen species (ROS).

The Beijing "Coal to Electricity" program provides a unique opportunity to explore air quality impacts by replacing residential coal burning with electrical appliances. In this study, the atmospheric ROS (Gas-phase ROS and Particle-phase ROS, abbreviated to G-ROS and P-ROS) were measured by an online instrument in parallel with concurrent PM2.5 sample collections analyzed for chemical composition and cellular ROS in a baseline year (Coal Use Year-CUY) and the first year following implementation of the "Coal to Electricity" program (Coal Ban Year-CBY). The results showed PM2.5 concentrations had no significant difference between the two sampling periods, but the activities of G-ROS, P-ROS, and cellular ROS in CBY were 8.72 nmol H2O2/m3, 9.82 nmol H2O2/m3, and 2045.75 µg UD /mg PM higher than in CUY. Six sources were identified by factor-analysis from the chemical components of PM2.5. Secondary sources (SECs) were the dominant source of PM2.5 in the two periods, with 15.90% higher contribution in CBY than in CUY. Industrial Emission & Coal Combustion sources (Ind. & CCs), mainly from regional transport, also increased significantly in CBY. The contributions of Aged Sea Salt & Residential Burning sources to PM2.5 decreased 5.31% from CUY to CBY. The correlation results illustrated that Ind. & CCs had significant positive correlations with atmospheric ROS, and SECs significantly associated with cellular ROS, especially nitrates (r = 0.626, p = 0.000). Therefore, the implementation of the "Coal to Electricity" program reduced PM2.5 contributions from coal and biomass combustion, but had little effect on the improvement of atmospheric and cellular ROS.

Coordination effect enabled palladium-catalyzed regioselective O-alkylation of 2-pyridones.

A novel tactic for the regioselective O-alkylation of 2-pyridones has been realized through palladium catalysis in moderate to high yields. The coordination effect between palladium and nitrogen on the pyridine ring plays a versatile role.

Prediction of the oxidation potential of PM2.5 exposures from pollutant composition and sources.

The inherent oxidation potential (OP) of atmospheric particulate matter has been shown to be an important metric in assessing the biological activity of inhaled particulate matter and is associated with the composition of PM2.5. The current study examined the chemical composition of 388 personal PM2.5 samples collected from students and guards living in urban and suburban areas of Beijing, and assessed the ability to predict OP from the calculated metrics of carcinogenic risk, represented by ELCR (excess lifetime cancer risk), non-carcinogenic risk represented by HI (hazard index), and the composition and sources of the particulate matter using multiple linear regression methods. The correlations between calculated ELCR and HI and the measured OP were 0.37 and 0.7, respectively. HI was a better predictor of OP than ELCR. The prediction models based on pollutants (Model_1) and pollution sources (Model_2) were constructed by multiple linear regression method, and Pearson correlation coefficients between the predicted results of Model_1 and Model_2 with the measured volume normalized OP are 0.81 and 0.80, showing good prediction ability. Previous investigations in Europe and North America have developed location-specific relationships between the chemical composition of particulate matter and OP using regression methods. We also examined the ability of relationships between OP and composition, sources, developed in Europe and North America, to predict the OP of particulate matter in Beijing from the composition and sources determined in Beijing. The relationships developed in Europe and North America provided good predictive ability in Beijing and it suggests that these relationships can be used to predict OP from the chemical composition measured in other regions of the world.

Estimation of commercial cooking emissions in real-world operation: Particulate and gaseous emission factors, activity influencing and modelling.

Measurements of real-world cooking emission factors (CEFs) were rarely reported in recent year's studies. However, the needs for accurately estimating CEFs to produce cooking emission inventories and further implement controlling measures are urgent. In this study, we collected cooking emission aerosols from real-world commercial location operations in Beijing, China. 2 particulate (PM2.5, OC) and 2 gaseous (NMHC, OVOCs) CEF species were examined on influencing activity conditions of cuisine type, controlling technology, operation scales (represented by cook stove numbers), air exhausting volume, as well as location and operation period. Measured NMHC emission factors (Non-barbecue: 8.19 ± 9.06 g/h and Barbecue: 35.48 ± 11.98 g/h) were about 2 times higher than PM2.5 emission factors (Non-barbecue: 4.88 ± 3.43 g/h and Barbecue: 15.48 ± 7.22 g/h). T-test analysis results showed a significantly higher barbecued type CEFs than non-barbecued cuisines for both particulate and gaseous emission factor species. The efficacy of controlling technology was showing an average of 50 % in decreasing PM2.5 CEFs while a 50 % in increasing OC particulate CEFs. The effects of controlling equipment were not significant in removing NMHC and OVOCs exhaust concentrations. CEF variations within cook stove numbers and air exhausting volume also reflected a comprehensive effect of operation scale, cuisine type and control technology. The simulations among activity influencing factors and CEFs were further determined and estimated using hierarchical multiple regression model. The R square of this simulated model for PM2.5 CEFs was 0.80 (6.17 × 10-9) with standardized regression coefficient of cuisine type, location, sampling period, control technology, cook stove number (N) and N2 of 5.18 (0.02), 5.33 (0.02), 1.93 (0.19), 9.29 (4.18 × 10-6), 9.10 (1.71 × 10-3) and -1.18 (2.43 × 10-3), respectively. In perspective, our study provides ways of better estimating CEFs in real operation conditions and potentially highlighting much more importance of cooking emissions on air quality and human health.

Comparative analysis of clonal evolution among patients with right- and left-sided colon and rectal cancer.

Tumor multiregion sequencing reveals intratumor heterogeneity (ITH) and clonal evolution playing a key role in tumor progression and metastases. Large-scale high-depth multiregional sequencing of colorectal cancer, comparative analysis among patients with right-sided colon cancer (RCC), left-sided colon cancer (LCC), and rectal cancer (RC), as well as the study of lymph node metastasis (LN) with extranodal tumor deposits (ENTDs) from evolutionary perspective remain weakly explored. Here, we recruited 68 patients with RCC (18), LCC (20), and RC (30). We performed high-depth whole-exome sequencing of 206 tumor regions including 176 primary tumors, 19 LN, and 11 ENTD samples. Our results showed ITH with a Darwinian pattern of evolution and the evolution pattern of LCC and RC was more complex and divergent than RCC. Genetic and evolutionary evidences found that both LN and ENTD originated from different clones. Moreover, ENTD was a distinct entity from LN and evolved later.

Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer.

BACKGROUND: Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features. This study aimed to use deep neural networks for computed tomography (CT) diagnosis of perigastric metastatic lymph nodes (PGMLNs) to simulate the recognition of lymph nodes by radiologists, and to acquire more accurate identification results. METHODS: A total of 1371 images of suspected lymph node metastasis from enhanced abdominal CT scans were identified and labeled by radiologists and were used with 18,780 original images for faster region-based convolutional neural networks (FR-CNN) deep learning. The identification results of 6000 random CT images from 100 gastric cancer patients by the FR-CNN were compared with results obtained from radiologists in terms of their identification accuracy. Similarly, 1004 CT images with metastatic lymph nodes that had been post-operatively confirmed by pathological examination and 11,340 original images were used in the identification and learning processes described above. The same 6000 gastric cancer CT images were used for the verification, according to which the diagnosis results were analyzed. RESULTS: In the initial group, precision-recall curves were generated based on the precision rates, the recall rates of nodule classes of the training set and the validation set; the mean average precision (mAP) value was 0.5019. To verify the results of the initial learning group, the receiver operating characteristic curves was generated, and the corresponding area under the curve (AUC) value was calculated as 0.8995. After the second phase of precise learning, all the indicators were improved, and the mAP and AUC values were 0.7801 and 0.9541, respectively. CONCLUSION: Through deep learning, FR-CNN achieved high judgment effectiveness and recognition accuracy for CT diagnosis of PGMLNs. TRIAL REGISTRATION: Chinese Clinical Trial Registry, No. ChiCTR1800016787; http://www.chictr.org.cn/showproj.aspx?proj=28515.

Comparison of PM2.5 emission rates and source profiles for traditional Chinese cooking styles.

The number of restaurants is increasing rapidly in recent years, especially in urban cities with dense populations. Particulate matter emitted from commercial and residential cooking is a significant contributor to both indoor and outdoor aerosols. The PM2.5 emission rates and source profiles are impacted by many factors (cooking method, food type, oil type, fuel type, additives, cooking styles, cooking temperature, source surface area, pan, and ventilation) discussed in previous studies. To determine which cooking activities are most influential on PM2.5 emissions and work towards cleaner cooking, an experiment design based on multi-factor and level orthogonal tests was conducted in a laboratory that is specifically designed to resemble a professional restaurant kitchen. In this cooking test, four main parameters (the proportion of meat in ingredients, flavor, cooking technique, oil type) were chosen and five levels for each parameter were selected to build up 25 experimental dishes. Concentrations of PM2.5 emission rates, organic carbon/elemental carbon (OC/EC), water-soluble ions, elements, and main organic species (PAHs, n-alkanes, alkanoic acids, fatty acids, dicarboxylic acids, polysaccharides, and sterols) were investigated across 25 cooking tests. The statistical significance of the data was analyzed by analysis of variance (ANOVA) with ranges calculated to determine the influence orders of the 4 parameters. The PM2.5 emission rates of 25 experimental dishes ranged from 0.1 to 9.2 g/kg of ingredients. OC, EC, water-soluble ions (WSI), and elements accounted for 10.49-94.85%, 0-1.74%, 10.09-40.03%, and 0.04-3.93% of the total PM2.5, respectively. Fatty acids, dicarboxylic acids, n-alkanes, alkanoic acids, and sterols were the most abundant organic species and accounted for 2.32-93.04%, 0.84-60.36%, 0-45.05%, and 0-25.42% of total PM2.5, respectively. There was no significant difference between the 4 parameters on PM2.5 emission rates, while a significant difference was found in WSI, elements, n-alkanes, and dicarboxylic acids according to ANOVA. Cooking technique was found to be the most influential factor for PM2.5 source profiles, followed by the proportion of meat in ingredients and oil type which resulted in significant difference of 183.19, 185.14, and 115.08 g/kg of total PM2.5 for dicarboxylic acids, n-alkanes, and WSI, respectively. Strong correlations were found among PM2.5 and OC (r = 0.854), OC and sterols (r = 0.919), PAHs and n-alkanes (r = 0.850), alkanoic acids and fatty acids (r = 0.877), and many other species of PM2.5.

Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network.

BACKGROUND: In this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated. METHODS: The ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared. RESULTS: The artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). This finding indicates a higher diagnostic accuracy (p = 0.0434). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists (p > 0.05). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026). CONCLUSIONS: Compared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.

Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks.

MRI is the gold standard for confirming a pelvic lymph node metastasis diagnosis. Traditionally, medical radiologists have analyzed MRI image features of regional lymph nodes to make diagnostic decisions based on their subjective experience; this diagnosis lacks objectivity and accuracy. This study trained a faster region-based convolutional neural network (Faster R-CNN) with 28,080 MRI images of lymph node metastasis, allowing the Faster R-CNN to read those images and to make diagnoses. For clinical verification, 414 cases of rectal cancer at various medical centers were collected, and Faster R-CNN-based diagnoses were compared with radiologist diagnoses using receiver operating characteristic curves (ROC). The area under the Faster R-CNN ROC was 0.912, indicating a more effective and objective diagnosis. The Faster R-CNN diagnosis time was 20 s/case, which was much shorter than the average time (600 s/case) of the radiologist diagnoses.Significance: Faster R-CNN enables accurate and efficient diagnosis of lymph node metastases. Cancer Res; 78(17); 5135-43. ©2018 AACR.

Acute changes in a respiratory inflammation marker in guards following Beijing air pollution controls.

The adverse respiratory health effects of PM2.5 have been studied. However, the epidemiological evidence for the association of specific PM2.5 sources with health outcomes is still limited. This study investigated the association between PM2.5 components and sources with a biomarker of acute respiratory inflammation (FeNO) in guards. Personal exposure was estimated by microenvironment samplers and FeNO measurements were carried out before, during and after the Victory Day Military Parade in Beijing. Four sources were determined by factor analysis, including urban pollution, dust, alloy steel abrasion and toxic metals. A mixed-effect model was used to estimate the associations of FeNO with PM2.5 sources and chemical constituents, controlling for age, BMI, smoke activity, physical activity, waist circumference, temperature and relative humidity. In summary, large concentration decreases in PM2.5 concentration and PM2.5 chemical constituents were observed in both roadside and indoor environments during the air control periods, immediately followed by statistically significant decreases in FeNO of roadside guards and patrol guards. Besides, statistically significant increases in FeNO were found to be associated with interquartile range (IQR) increases in some pollutants, with an increase of 1.45ppb (95% CI: 0.69, 2.20), 0.65ppb (95% CI: 0.13, 1.17), 1.48ppb (95% CI: 0.60, 2.35), 0.82ppb (95% CI: 0.44, 1.20), 0.77ppb (95% CI: 0.42, 1.11) in FeNO for mass, sulfate, BC, Ca2+ and Sm, respectively. In addition, compared to alloy steel abrasion and toxic metals, urban pollution and dust factors were more associated with acute airway inflammation for highly-exposed populations.