[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 β-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.

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.

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)).

Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity

The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to construct quantitative linear and nonlinear models to compare with the results obtained by SVM. The root-mean-square (rms) errors in heat capacity predictions for the whole data set given by MLR, RBFNNs, and SVM were 4.648, 4.337, and 2.931 heat capacity units, respectively. The prediction results are in good agreement with the experimental value of heat capacity; also, the results reveal the superiority of the SVM over MLR and RBFNNs models.

Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression

Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one involves an application of SVM to the development of a QSAR model for the prediction of toxicities of 153 phenols, and the second investigation deals with the QSAR model between the structures and the activities of a set of 85 cyclooxygenase 2 (COX-2) inhibitors. For each application, the molecular structures were described using either the physicochemical parameters or molecular descriptors. In both studied cases, the predictive ability of the SVM model is comparable or superior to those obtained by MLR and RBFNN. The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies.

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.

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.

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.

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.

QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines

The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.

3D QSAR studies on antimalarial alkoxylated and hydroxylated chalcones by CoMFA and CoMSIA

The 3D QSAR analyses of antimalarial alkoxylated and hydroxylated chalcones were first conducted by Comparative molecular field analysis (CoMFA) and Comparative similarity indices analysis (CoMSIA) to determine the factors required for the activity of these compounds. Satisfactory results were obtained after performing a leave-one-out (LOO) cross-validation study with cross-validation q(2) and conventional r(2) values of 0.740 and 0.972 by the CoMFA model, 0.714 and 0.976 by the CoMSIA model, respectively. The results provided the tools for predicting the affinity of related compounds, and for guiding the design and synthesis of novel and more potent antimalarial agents.

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.

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.

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R2) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.

Study of the Quantitative Structure-Mobility Relationship of Carboxylic Acids in Capillary Electrophoresis Based on Support Vector Machines

The support vector machines (SVM), as a novel type of learning machine, were used to develop a quantitative structure-mobility relationship (QSMR) model of 58 aliphatic and aromatic carboxylic acids based on molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) were also utilized to construct the linear and the nonlinear model to compare with the results obtained by SVM. The root-mean-square errors in absolute mobility predictions for the whole data set given by MLR, RBFNNs, and SVM were 1.530, 1.373, and 0.888 mobility units (10(-5) cm(2) S(-1) V(-1)), respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and RBFNNs models for the prediction of the absolute mobility of carboxylic acids. Moreover, the models we proposed could also provide some insight into what structural features are related to the absolute mobility of aliphatic and aromatic carboxylic acids.

An Accurate QSPR Study of O−H Bond Dissociation Energy in Substituted Phenols Based on Support Vector Machines

The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as inputs for the SVM model. The root-mean-square (rms) errors in BDE predictions for the training, test, and overall data sets were 3.808, 3.320, and 3.713 BDE units (kJ mol(-1)), respectively. The results obtained by Gaussian-kernel SVM were much better than those obtained by multiple linear regression, radial basis function neural networks, linear-kernel SVM, and other QSPR approaches.

Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs

The support vector machine (SVM), as a novel type of a learning machine, for the first time, was used to develop a QSPR model that relates the structures of 35 amino acids to their isoelectric point. Molecular descriptors calculated from the structure alone were used to represent molecular structures. The seven descriptors selected using GA-PLS, which is a sophisticated hybrid approach that combines GA as a powerful optimization method with PLS as a robust statistical method for variable selection, were used as inputs of RBFNNs and SVM to predict the isoelectric point of an amino acid. The optimal QSPR model developed was based on support vector machines, which showed the following results: the root-mean-square error of 0.2383 and the prediction correlation coefficient R=0.9702 were obtained for the whole data set. Satisfactory results indicated that the GA-PLS approach is a very effective method for variable selection, and the support vector machine is a very promising tool for the nonlinear approximation.

[The effect of artesunate in preventing the populations from Schistosoma japonicum infection during flood-control]

OBJECTIVE

To evaluate the prophylactic effect of artesunate in high-risk populations who had contacted the infested water.

METHODS

From the 7th day post-exposure to infested water, a total of 17,031 people who had contacted the infested water for more than 20 days had been treated with artesunate at a dose of 300 mg once a week for three successive weeks. On day 17 after the last medication, they were examined for schistosomiasis and followed-up 60 days later.

RESULTS

No acute schistosomiasis case was found. ELISA-positive was found in 204(1.20%) and COPT > 3% was found in 195 cases (1.14%). The side-effects were slight.

CONCLUSION

Artesunate is highly effective and safe for the prevention of schistosomiasis.

QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-kappa B mediated gene expression based on support vector machines

The support vector machine, as a novel type of learning machine, for the first time, was used to develop a QSAR model of 57 analogues of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate (EPC), an inhibitor of AP-1 and NF-kappa B mediated gene expression, based on calculated quantum chemical parameters. The quantum chemical parameters involved in the model are Kier and Hall index (order3) (KHI3), Information content (order 0) (IC0), YZ Shadow (YZS) and Max partial charge for an N atom (MaxPCN), Min partial charge for an N atom (MinPCN). The mean relative error of the training set, the validation set, and the testing set is 1.35%, 1.52%, and 2.23%, respectively, and the maximum relative error is less than 5.00%.

Diagnosing breast cancer based on support vector machines

The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.

Quantitative prediction of liquid chromatography retention of N-benzylideneanilines based on quantum chemical parameters and radial basis function neural network

Based on quantum chemical parameters and a simple numerical coding, the liquid chromatography retention of bifunctionally substituted N-benzylideneaniles (NBA) has been predicted using a radial basis function neural network (RBFNN) model. The quantum chemical parameters involved in the model are dipole moment (m), energies of the highest occupied and lowest unoccupied molecular orbitals (E(homo,) E(lumo)), net charge of the most negative atom (Q(min)), sum of absolute values of the charges of all atoms in two given functional groups (Delta), total energy of the molecule (E(T)), weight of the molecule (W), and numerical coding (N). N was used to indicate the different positions of two substituents. The predictive values are consistent with the experimental results. The mean relative error of the testing set is 1.6%, and the maximum relative error is less than 5.0%. In this work the success of the whole modeling process only depends on the optimization of the spread parameter in network.

Microdetermination of proteins with the arsenazo-DBN-Al(III) complex by Rayleigh light-scattering technique and application of the method

The determination of proteins with arsenazo-DBN and Al3+ by Rayleigh light-scattering (RLS) is described. The weak RLS of arsenazo-DBN and BSA can be enhanced greatly by addition of Al3+ in the pH range 5.3-7.0; this resulted in two enhanced RLS signals at 420-440 nm and 460-480 nm. The reaction between arsenazo-DBN, Al3+, and proteins was studied and a new method was developed for quantitative determination of proteins. This method is very sensitive (0.34-41.71 microg mL(-1) for bovine serum albumin, BSA, and 0.29-53.41 microg mL(-1) for human serum albumin, HSA), rapid (< 2 min), simple (one step), and tolerant of most interfering substances. The effects of different surfactants were also examined. When these proteins were determined in four human serum samples the maximum relative error was not more than 2% and the recovery was between 97 and 103%.

[Determination of enantiomeric purity for lactic acid in fermentation broth by Rhizopus oryzae with high performance liquid chromatography]

A procedure for the resolution of DL-lactic acid and the determination of D-isomer ratio in L-lactic acid fermentation broth by Rhizopus oryzae is described. The effects of pH of mobile phase and concentration of chiral mobile phase additives on resolution of DL-lactic acid were investigated. The optical isomers of lactic acid were resolved by RP-HPLC with 2,3,6-tri-O-beta-cyclodextrin(TM-beta-CD) as a chiral mobile phase additive, and C18 column as stationary phase, and detected at wavelength 210 nm. The results showed that a correction factor should be introduced into the equation for calculation of the percentage of D-lactic acid, because the UV absorption of D-lactic acid and L-lactic acid might not be the same when TM-beta-CD was present. Quantitation was achieved with external standard method, the average recovery was 100.4%, and the relative standard deviation was 0.82%. This method can be used for the determination of the percentage of D-isomer in L-lactic acid fermentation broth by Rhizopus oryzae, and it is simple, rapid and accurate. The results showed that the mass fraction of D-isomer in the fermented broth increased during the period of storage.

[Determination of lactic acid in fermentation broth of Rhizopus oryzae by reversed-phase high performance liquid chromatography (RP-HPLC)]

A method for determining lactic acid in fermentation broth of Rhizopus oryzae by RP-HPLC is described. The operating conditions were Wakosil-II 5 C18 RS column(4.6 mm i.d. x 150 mm, 5 microns) at room temperature, 0.01 mol/L phosphoric acid solution (pH 2.5) as mobile phase with a flow rate of 1.0 mL/min and UV detection at 210 nm. The retention time of lactic acid was 3.820 min. This method is simple, rapid and accurate. The results will not be affected by other components in the broth. The relative standard deviation was 0.22% (n = 5), and the recovery was over 99%.

[The medium optimization of xylitol fermentation based on neural networks and genetic algorithms]

Using genetic algorithms(GA) for medium optimization of xylitol fermentation, and coupling neural networks model for predicting xylitol concentration is introduced. The medium compose determined by GA is as input data of the neural networks, while the output data predicted by neural networks is as suitable value of GA for predicting. The optimum medium is further validated by experimentation. The good result, which save the experimental workload and charge, enhance the level of xylitol fermentation as well as reduced the medium consume is obtained.

[Study on taxol release in the two-liquid-phase cultures of Taxus cuspidata]

Effects of rare earth compound (ammonium sulphate), organic solvents(oleic acid and dibutylphthalate) and the integrated function of the rare earth compound and organic solvents were studied on taxol release in the Taxus cuspidata suspension cultures. And then effects of different organic solvents(paraffin, organic acid, alcohol and ester), their volumetric fraction and phase toxicity were studied on taxol release in the two-liquid-phase cultures of Taxus cuspidata. The results showed that the addition of the rare earth compound or the organic solvents could strengthen obviously taxol release, especially the organic solvents. But the addition of the rare earth compound could not strengthen further taxol release in the twoliquid-phase cultures of Taxus cuspidata. Therefore the organic solvents were very good permeabilizing reagents, which could enhance obviously secondary metabolite in the twoliquid-phase cultures of plant cells. Release percentage of taxol was increased into more than 75% from 40% of the control.

Electrophoretic behavior study and determination of some active components in Chinese medicinal preparations by capillary electrophoresis

The determination of icariin (IC), rhein (RH), chrysophanol (CH), physcion (PHY), glycyrrhetic acid (GE), and glycyrrhizic acid (GI), in traditional Chinese preparations, Anshen Bunao oral liquid and Maren pill, has been investigated by micellar electrokinetic capillary electrophoresis. With borate buffer (10 mM), SDS (20 mM) and acetonitrile (10%) as background electrolyte (pH 9.55), 20 kV applied voltage and 254 nm UV detection, the six active compounds were completely separated within 10 min. The effects of buffer pH, concentration of borate, SDS and modifier on electrophoretic behavior and separation are discussed. Regression equations revealed linear relationships (correlation coefficients: 0.9960-0.9999) between the peak-area of each component and the content. In addition, the levels of the six active compounds in two kinds of traditional Chinese medicinal preparations were easily determined with recoveries of from 94.7% to 106.4%.

Prediction of programmed-temperature retention values of naphthas by artificial neural networks

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.