[Analysis of pathogen spectrum and antimicrobial resistance of pathogens associated with hospital-acquired infections collected from 11 teaching hospitals in 2018].

Objective: To investigate the spectrum and antimicrobial resistance of major pathogens causing nosocomial infections in China, 2018. Methods: Non-duplicated nosocomial cases as well as pathogens causing bloodstream infections (BSI), hospital-acquired pneumonia (HAP) and intra-abdominal infections (IAI) from 11 teaching hospitals across China were collected. The minimum inhibitory concentrations (MICs) of clinically significant strains were determined by agar dilution method or broth microdilution method. The Clinical and Laboratory Standards Institute (CLSI) M100-S29 criteria were used for interpretation, and the WHONET-5.6 software was used in data analysis. Results: A total of 1 590 cases were collected, including 831 cases from BSI, 450 cases from HAP and 309 cases from IAI. The most prevalent pathogens causing BSI were Escherichia coli (29.2%, 243/831), Klebsiella pneumoniae (16.2%, 135/831) and Staphylococcus aureus (10.1%, 84/831); the most prevalent pathogens causing IAI were E. coli (26.2%, 81/309), Enterococcus faecium (15.5%, 48/309) and K. pneumoniae (13.3%, 41/309); while Acinetobacter baumanii (24.7%, 111/450), Pseudomonas aeruginosa (20.7%, 93/450) and K. pneumoniae (16.2%, 73/450) were dominated in HAP. All S. aureus were susceptible to tigecycline, linezolid, daptomycin and glycopeptides; 77.8% (105/135) of S. aureus strains were susceptible to ceftaroline. Methicillin-resistant S. aureus (MRSA) accounted for 29.6% (40/135) of all the S. aureus, and was lower than the accounted rate of methicillin-resistant coagulase-negative Staphylococcus (MRCNS) (83.7%, 41/49). One E. faecium strain (1.1%, 1/95) resistant to vacomycin and teicoplanin and one E. faecalis strain (2.3%, 1/43) resistant to linezolid was found. The prevalence of extended-spectrum ß-lactamase (ESBL) was 56.1% (193/344) in E. coli and 22.1% (55/249) in K. pneumonia; the rate of carbapenem resistant E. coli and K. pneumonia was 4.1% (14/344) and 22.9% (57/249), respectively; the percentage of ceftazidime/avibactam resistant E. coli and K. pneumonia was 2.3% (8/344) and 2.0% (5/249), respectively; the percentage of colistin resistant E. coli and K. pneumonia was 1.5% (5/344) and 7.6% (19/249), respectively; no E. coli and K. pneumonia strains were found resistant to tigecycline. The rate of carbapenem resistant A. baumanii and P. aeruginosa were 78.9% (146/185) and 36.7% (66/180), respectively. A. baumanii showed low susceptibility to the antimicrobial agents except colistin (99.5%, 184/185) and tigecycline (91.4%, 169/185). Colistin, amikacin and ceftazidime/avibactam demonstrated high antibacterial activity against P. aeruginosa with susceptility rate of 100% (180/180), 93.3% (168/180) and 85.6% (154/180), respectively. Conclusions: Nosocomial Gram-negative pathogens show high susceptibilities to tigecycline, colistin and ceftazidime/avibactam in vitro. Antimicrobial resistance in A. baumannii is a serious problem. The prevalence of carbapenem-resistant Enterobacteriaceae has increased, which should be monitored continuously in China.

[Epidemiological characteristic and current status of surgical treatment for esophageal cancer by analysis of national registry database].

Objective: To investigate the epidemiological characteristics and current status of surgical management for esophageal cancer in China. Methods: A national database was setup through a network platform. The clinical data of esophageal cancer treated by surgery was collected from 70 major hospitals in China between January 2009 and December 2014. Results: Complete data of 8 181 cases of esophageal cancer patients who underwent surgery were recorded in the database and recruited in the analysis. Among them, 6 052 cases were male and 2 129 were female, the average age was 60.5 years.The epidemiological investigation results showed that 148 cases (1.8%) had history of psychological trauma, 7 527 cases (92.0%) were lower social economic status, 5 072 cases (62.0%) were short of fresh vegetables and fruits, 6 544 cases (80.0%) ate rough food frequently, 3 722 cases (45.5%) drank untreated water directly from lake or river or shallow well, 3 436 cases (42.0%) had a unhealthy eating habit, including habits of eating food fast (507 cases, 6.2%), eating hot food or drinking hot tea/soup (998 cases, 12.2%), eating fried food (1 939 cases, 23.7%), 4 410 cases (53.9%) had the habits of smoking cigarettes and 2 822 cases (34.5%) drank white wine frequently.The pathological results showed that 7 813 cases (95.5%) were squamous cell carcinoma, 267 cases were adenocarcinoma (3.3%), 25 cases were adenosquamous cell carcinoma (0.3%) and 50 cases were small cell carcinoma (0.6%). A total of 1 800 cases (22.0%) received preoperative neoadjuvant therapy due to locally advanced disease or difficulty of resection. The esophagectomies were performed through left thoracotomy approach in 5 870 cases (71.8%), through right chest approach in 2 215 cases (27.1%), and the remain 96 cases (1.2%) received surgery though other approaches.A total of 8 001 cases (97.8%) underwent radical resection, the other 180 cases (2.2%) received palliative resection. The 30-day postoperative mortality rate was 0.5%, the overall ≥ grade Ⅱ postoperative complication rate was 11.6% (951 cases). The 1-yr, 3-yr, and 5-yr overall actual survival rates were 82.6%, 61.6%, and 52.9%, respectively. Conclusions: The data analysis of the national database for esophageal cancer shows that bad eating habits or eating rough food without enough nutrients, lower social and economic status, drinking white wine and smoking cigarettes frequently may be correlated with tumorigenesis of esophageal cancer. However, strong evidences produced by prospective observation studies are needed. Overall, the long-term survival of esophageal cancer patients has been improved gradually due to the application of advanced surgical techniques and reasonable multimodality treatment.

Analysis of a registry database for esophageal cancer from high-volume centers in China.

Esophageal cancer has a high incidence among malignancies in China, but a comprehensive picture of the status of its surgical management in China has hitherto not been available. A nationwide database has recently been established to address this issue. METHOD: A National Database was setup through a network platform, and data was collected from 70 high-volume centers (>100 esophagectomies/per year) across China. Data was entered between January 2009 and December 2014, and was analyzed in June 2015 after a minimal follow-up of 6 months for all patients. 8181 patients with complete data who received surgery for primary esophageal cancer on the Database were included in the analysis. RESULT: In this series, there were 6052 males and 2129 females, with a mean age of 60.5 years (range: 22-90 years). The pathology in 95.5% of patients was squamous cell carcinoma. The pathological stage distribution was 1.2% in stage 0, 2.5% in Ia, 11.5% in Ib, 14.8% in IIa, 36.1% in IIb, 19.3% in IIIa, 8.3% in IIIb, 6.2% in IIIc. 1800 patients (22.0%) with locally advanced disease received preoperative neoadjuvant therapy and 3592 patients (43.9%) underwent postoperative adjuvant chemotherapy and/or radiotherapy. The esophagectomies were performed through left thoracotomy approach in 5870 cases (72.6%), through right chest approach in 2215 cases (27.4%) including right thoracotomy (21.3%) and VATS (6.1%). The 30-day postoperative mortality rate was 0.6% (43 patients), and the overall postoperative complication rate was 11.6% (951 patients). The 1-, 3-, and 5-year overall survival rates were 82.6%, 61.6%, and 52.9%, respectively. CONCLUSION: This National Registry Database from high-volume centers provides a comprehensive picture of surgical management for esophageal cancer in China for the first time. Squamous cell carcinoma predominates, but there is heterogeneity with respect to the surgical approach and perioperative oncologic management. Overall, surgical mortality and morbidity rates are low, and good survival rates have been achieved due to improvement of surgical treatment technology in recent years.

PRINS Long Noncoding RNA Involved in IP-10-Mediated Allograft Rejection in Rat Kidney Transplant.

BACKGROUND: Previously, high levels of CXCR3+ T-cell recruitment was demonstrated in the prolonged ischemia-accelerated acute allograft rejection in rat kidney transplant. In the present study, the effect of chemokine IP-10 was investigated and the expression of chemokine-related PRINS (Psoriasis susceptibility-related RNA gene induced by stress) lncRNA determined in the allografts subjected to ischemia. METHODS: F344-to-Lewis rat kidney transplantation was performed, and renal grafts were stored for 2 hours or 16 hours. Samples were removed at 24 hours and 7 days after operation. Cellular infiltration was determined with the use of immunohistochemistry, and messenger RNA expression was assessed with the use of real-time polymerase chain reaction. RESULTS: The 16-hour-ischemia kidney displayed acute tubule damage and up-regulation of PRINS lncRNA expression. On day 7, IP-10 expression and CD3-positive T cells were increased in allografts compared with control samples, which were inhibited by the IP-10 antibody treatment accompanied by reduced serum creatinine. CONCLUSIONS: These observations provide evidence for IP-10 in a regulatory role in cold ischemia-elicited acute allograft rejection and in PRINS lncRNA expression. Our data enhance the understanding of the mechanism underlying between prolonged ischemia and acute rejection.

Predominance of Cryptococcus neoformans var. grubii multilocus sequence type 5 and emergence of isolates with non-wild-type minimum inhibitory concentrations to fluconazole: a multi-centre study in China.

There are few data on the molecular epidemiology of cryptococcosis in China. Here we investigated the species distribution, molecular types and antifungal susceptibilities of 312 Cryptococcus neoformans species complex isolates from ten hospitals over 5 years. Isolates were identified by internal transcribed spacer (ITS) sequencing and by two matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) systems. Multilocus sequence typing (MLST) was used to verify species/variety and to designate molecular types. Susceptibility to six antifungal drugs was determined by the Sensititre YeastOne™ method. Cryptococcus neoformans was the predominant species (305/312 isolates (97.8%), all were ITS type 1, serotype A), of which 89.2% (272/305) were C. neoformans var. grubii MLST sequence type (ST) 5 and 6.2% (19/305) were ST31. Other C. neoformans var. grubii STs were rare but included six novel STs. Only two strains were C. neoformans var. neoformans (both serotype AD). Cryptococcus gattii was uncommon (n = 7, four ITS types) and comprised five MLST STs including one novel ST. For C. neoformans var. grubii, the proportion of isolates with non-wild-type MICs to fluconazole significantly rose in the fourth study year (from 0% (0/56 isolates) in the first year to 23.9% (17/71) in the fourth year), including five isolates with fluconazole MICs of ≥32 mg/L. The study has provided useful data on the species epidemiology and their genetic diversity and antifungal susceptibility. The proportional increase in isolates with non-wild-type MICs to fluconazole is noted.

Resistance trends among clinical isolates in China reported from CHINET surveillance of bacterial resistance, 2005-2014.

With the aim of gathering temporal trends on bacterial epidemiology and resistance from multiple laboratories in China, the CHINET surveillance system was organized in 2005. Antimicrobial susceptibility testing was carried out according to a unified protocol using the Kirby-Bauer method or automated systems. Results were analyzed according to Clinical and Laboratory Standards Institute (CLSI) 2014 definitions. Between 2005 and 2014, the number of bacterial isolates ranged between 22,774 and 84,572 annually. Rates of extended-spectrum ß-lactamase production among Escherichia coli isolates were stable, between 51.7 and 55.8%. Resistance of E. coli and Klebsiella pneumoniae to amikacin, ciprofloxacin, piperacillin/tazobactam and cefoperazone/sulbactam decreased with time. Carbapenem resistance among K. pneumoniae isolates increased from 2.4 to 13.4%. Resistance of Pseudomonas aeruginosa strains against all of antimicrobial agents tested including imipenem and meropenem decreased with time. On the contrary, resistance of Acinetobacter baumannii strains to carbapenems increased from 31 to 66.7%. A marked decrease of methicillin resistance from 69% in 2005 to 44.6% in 2014 was observed for Staphylococcus aureus. Carbapenem resistance rates in K. pneumoniae and A. baumannii in China are high. Our results indicate the importance of bacterial surveillance studies.

Bursts of Terahertz Radiation from Large-Scale Plasmas Irradiated by Relativistic Picosecond Laser Pulses.

Powerful terahertz (THz) radiation is observed from large-scale underdense preplasmas in front of a solid target irradiated obliquely with picosecond relativistic intense laser pulses. The radiation covers an extremely broad spectrum with about 70% of its energy located in the high frequency regime over 10 THz. The pulse energy of the radiation is found to be above 100 µJ per steradian in the laser specular direction at an optimal preplasma scale length around 40-50 µm. Particle-in-cell simulations indicate that the radiation is mainly produced by linear mode conversion from electron plasma waves, which are excited successively via stimulated Raman scattering instability and self-modulated laser wakefields during the laser propagation in the preplasma. This radiation can be used not only as a powerful source for applications, but also as a unique diagnostic of parametric instabilities of laser propagation in plasmas.

Bright betatron X-ray radiation from a laser-driven-clustering gas target.

Hard X-ray sources from femtosecond (fs) laser-produced plasmas, including the betatron X-rays from laser wakefield-accelerated electrons, have compact sizes, fs pulse duration and fs pump-probe capability, making it promising for wide use in material and biological sciences. Currently the main problem with such betatron X-ray sources is the limited average flux even with ultra-intense laser pulses. Here, we report ultra-bright betatron X-rays can be generated using a clustering gas jet target irradiated with a small size laser, where a ten-fold enhancement of the X-ray yield is achieved compared to the results obtained using a gas target. We suggest the increased X-ray photon is due to the existence of clusters in the gas, which results in increased total electron charge trapped for acceleration and larger wiggling amplitudes during the acceleration. This observation opens a route to produce high betatron average flux using small but high repetition rate laser facilities for applications.

Molecular analysis and frequency of Staphylococcus aureus virulence genes isolated from bloodstream infections in a teaching hospital in Tianjin, China.

Staphylococcus aureus is an important cause of bloodstream infections worldwide. We examined the prevalence of genes that encode erythromycin ribosome methylase and bacterial toxins in S. aureus collected from bloodstream infections. Sixty different S. aureus isolates were obtained from blood cultures of patients who were admitted to a Teaching Hospital in Tianjin from January 2006 to August 2011. The susceptibility of the isolates to 16 antibiotics was tested. Methicillin-resistant S. aureus (MRSA) was identified using the disk diffusion method with cefoxitin. PCR was used to detect genes that encode the staphylococcal enterotoxins, Panton-Valentine leukocidin, toxic shock syndrome toxin 1 and erythromycin ribosome methylase. Molecular analysis of the MRSA strains was done using pulsed-field gel electrophoresis (PFGE) and staphylococcal cassette chromosome mec (SCCmec) typing. The positivity rates of mecA, ermA, ermB, and ermC in the isolates were 13/60, 10/60, 18/60, and 18/60, respectively. Among the 60 isolates, 30 harbored enterotoxin genes, with sea as the most frequent toxin gene (33%), followed by sec (15%), sed (12%), and seb (5%). The see and tst genes were not found in any of the isolates. The pvl gene was detected in four strains. Eleven MRSA isolates were of the SCCmec type III; two MRSA isolates could not be determined through SCCmec typing. PFGE analysis of the 13 MRSA isolates produced 8 distinct pulsotypes. Virulence genes and erythromycin ribosome methylase genes were highly prevalent in these isolates. The PFGE results demonstrated that the MRSA spread through cloning, mainly involving SCCmec type III.

QSAR study of neuraminidase inhibitors based on heuristic method and radial basis function network.

Neuraminidase (NA) is a critical enzyme of the influenza virus and many inhibitors targeting this enzyme are quite efficient anti-influenza agents. In this paper, quantitative structure-activity relationship (QSAR) method was used to predict the activity of different kinds of 46 NA inhibitors. Heuristic method (HM) and radial basis function network (RBFNN) were first used to build linear and nonlinear models, respectively. The prediction results were in agreement with the experimental value. The proposed model is simple and can be extended to other QSAR investigations.

Synthesis and characterizations of amorphous carbon nanotubes by pyrolysis of ferrocene confined within AAM templates.

Amorphous carbon nanotubes (a-CNTs) are synthesized by pyrolysis of ferrocene confined in the nanopores of the anodic alumina membrane (AAM) and characterized by field emission scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), electron energy-loss spectroscopy (EELS), and Raman spectroscopy. It is shown that the a-CNT has an ultrathin amorphous wall (approximately 3 nm) and a relatively large diameter (approximately 50 nm), and is capsulated with iron oxide nanoparticles. It is found that the growth of the a-CNTs is governed mainly by the template limitation effect. Electrical transport measurements on individual a-CNTs demonstrate that the a-CNT may be connected with electrodes via either ohmic or Schottky contacts, and the resisitivity of the a-CNTs was measured to be 4.5 x 10(-3) Omega cm.

Quantitative structure-toxicity relationships (QSTRs): a comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis.

Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).

Application of support vector machine (SVM) for prediction toxic activity of different data sets.

As a new method, support vector machine (SVM) were applied for prediction of toxicity of different data sets compared with other two common methods, multiple linear regression (MLR) and RBFNN. Quantitative structure-activity relationships (QSAR) models based on calculated molecular descriptors have been clearly established. Among them, SVM model gave the highest q(2) and correlation coefficient R. It indicates that the SVM performed better generalization ability than the MLR and RBFNN methods, especially in the test set and the whole data set. This eventually leads to better generalization than neural networks, which implement the empirical risk minimization principle and may not converge to global solutions. We would expect SVM method as a powerful tool for the prediction of molecular properties.

Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines.

The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor.

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine.

Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R(2) was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.

Study of probabilistic neural networks to classify the active compounds in medicinal plants.

Probabilistic neural networks (PNNs) were utilized for the classifications of 102 active compounds from diverse medicinal plants with anticancer activity against human rhinopharyngocele cell line KB. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using factor correlation analysis and forward stepwise regression was used to construct the prediction models. Linear discriminant analysis (LDA) was also utilized to construct the classification model to compare the results with those obtained by PNNs. The accuracy of the training set, the cross-validation set, and the test set given by PNNs and LDA were 100, 92.3, 90.9% and 71.8, 92.3, 54.5%, respectively, which indicated that the results obtained by PNNs agree well with the experimental values of these compounds and also revealed the superiority of PNNs over LDA approach for the classification of anticancer activities of compounds. The models built in this work would be of potential help in the design of novel and more potent anticancer agents.

Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine.

A new method support vector machine (SVM) and the heuristic method (HM) were used to develop the nonlinear and linear models between the capacity factor (logk) and seven molecular descriptors of 75 peptides for the first time. The molecular descriptors representing the structural features of the compounds only included the constitutional and topological descriptors, which can be obtained easily without optimizing the structure of the molecule. The seven molecular descriptors selected by the heuristic method in CODESSA were used as inputs for SVM. The results obtained by SVM were compared with those obtained by the heuristic method. The prediction result of the SVM model is better than that of heuristic method. For the test set, a predictive correlation coefficient R = 0.9801 and root-mean-square error of 0.1523 were obtained. The prediction results are in very good agreement with the experimental values. But the linear model of the heuristic method is easier to understand and ready to use for a chemist. This paper provided a new and effective method for predicting the chromatography retention of peptides and some insight into the structural features which are related to the capacity factor of peptides.