Neonatal sepsis is a global health problem that seriously affects the body health and life safety of newborns. It has a higher incidence in preterm infants,especially for early-onset sepsis (EOS) within 72 hours of birth. The diagnosis of neonatal EOS requires a series of examinations,and early and accurate diagnosis can improve clinical outcomes and reduce antibiotic overuse in a timely manner. At present,the commonly used biomarkers and traditional blood culture methods for EOS diagnosis have certain shortcomings,so it is urgent to find new molecular diagnostic methods. This article summarizes and compares the early and novel diagnostic methods of neonatal EOS,in order to provide a reference for clinical practice.

Objective To examine the quality of Zukamu preparations through the national drug sampling and testing,and further understand their current quality status and existing problems.This work is benefit for improving the quality standard of Zukamu preparations and providing technical support for the drug regulatory authorities.Methods Samples of Zukamu preparations were collected from a total of 29 provinces in China,and were tested for description,identification,other requirements(weight variation,particle size,determination of water,disprsion,and microbial limit items),and assay in accordance with the national pharmaceutical standards.The test data were analyzed to evaluate the quality status of the Zukamu preparations,and exploratory research was carried out to address the problems found in the test.Results A total of 97 batches of Zukamu preparations were sampled,and the passing rate was 100.0%according to the current quality standard.Exploratory study,revealed that Zukamu preparation were subject to 4 testing standards,with uneven test items,missing items,poor operability,and lack of exclusivity in some items.The test based on the existing standards can't comprehensively evaluate the quality of the preparation.Conclusions Based on the national drug sampling and testing,combined with exploratory research on drug safety,authenticity and effectiveness,it is recommended to unify the quality standards of Zukamu preparations by combining with the work of standard improving,revising the identification method of thin-layer chromatography,increasing the content determination,and establishing the quick test method,thereby effectively evaluating and controling the quality of the samples of Zukamu preparations.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Based on the results of the National Drug Sampling and Inspection Programme,we summarized the history,the standard collection,the production enterprises and the dosage form specifications,the quality standard study,the pharmacological and pharmacodynamic study,and the clinical application study of Wuwei Qingzhuo preparation of Mongolian medicine and Shiliu Jianwei preparation of Zang medicine,to provide the basis for improved quality standards for both preparations.The development of these two preparations was searched and analyzed through literature.The available information shows that there is very little research on the two preparations and insufficient pharmacological experimental and clinical experimental data.The two preparations are basically the same in prescription and efficacy.However,the quality standards are very different,which are not conducive to the quality control of the two and their related dosage forms.And it is suggested that the Chinese Pharmacopoeia should take the situation of this category into comprehensive consideration,and unify the quality standards of the two preparations.

Objective To examine the quality of Zukamu preparations through the national drug sampling and testing,and further understand their current quality status and existing problems.This work is benefit for improving the quality standard of Zukamu preparations and providing technical support for the drug regulatory authorities.Methods Samples of Zukamu preparations were collected from a total of 29 provinces in China,and were tested for description,identification,other requirements(weight variation,particle size,determination of water,disprsion,and microbial limit items),and assay in accordance with the national pharmaceutical standards.The test data were analyzed to evaluate the quality status of the Zukamu preparations,and exploratory research was carried out to address the problems found in the test.Results A total of 97 batches of Zukamu preparations were sampled,and the passing rate was 100.0%according to the current quality standard.Exploratory study,revealed that Zukamu preparation were subject to 4 testing standards,with uneven test items,missing items,poor operability,and lack of exclusivity in some items.The test based on the existing standards can't comprehensively evaluate the quality of the preparation.Conclusions Based on the national drug sampling and testing,combined with exploratory research on drug safety,authenticity and effectiveness,it is recommended to unify the quality standards of Zukamu preparations by combining with the work of standard improving,revising the identification method of thin-layer chromatography,increasing the content determination,and establishing the quick test method,thereby effectively evaluating and controling the quality of the samples of Zukamu preparations.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Based on the results of the National Drug Sampling and Inspection Programme,we summarized the history,the standard collection,the production enterprises and the dosage form specifications,the quality standard study,the pharmacological and pharmacodynamic study,and the clinical application study of Wuwei Qingzhuo preparation of Mongolian medicine and Shiliu Jianwei preparation of Zang medicine,to provide the basis for improved quality standards for both preparations.The development of these two preparations was searched and analyzed through literature.The available information shows that there is very little research on the two preparations and insufficient pharmacological experimental and clinical experimental data.The two preparations are basically the same in prescription and efficacy.However,the quality standards are very different,which are not conducive to the quality control of the two and their related dosage forms.And it is suggested that the Chinese Pharmacopoeia should take the situation of this category into comprehensive consideration,and unify the quality standards of the two preparations.

Neonatal sepsis is a global health problem that seriously affects the body health and life safety of newborns. It has a higher incidence in preterm infants,especially for early-onset sepsis (EOS) within 72 hours of birth. The diagnosis of neonatal EOS requires a series of examinations,and early and accurate diagnosis can improve clinical outcomes and reduce antibiotic overuse in a timely manner. At present,the commonly used biomarkers and traditional blood culture methods for EOS diagnosis have certain shortcomings,so it is urgent to find new molecular diagnostic methods. This article summarizes and compares the early and novel diagnostic methods of neonatal EOS,in order to provide a reference for clinical practice.

This study was aimed at understanding the serotypes,virulence,drug resistance,and genetics of the pathogenic genes from 29 strains of foodborne Listeria monocytogenes in Guizhou Province in 2022.The minimum inhibitory concentration(MIC)values against eight antibiotics were determined with the microbroth dilution method,and whole genome sequencing was performed on 29 L.monocytogenes strains isolated from food microbiology and foodborne disease surveillance efforts in the province in 2022.The genome sequences were assembled,and bioinformatics analyses were conducted to examine genealogy,serogroups,sequence analysis(ST type),clonal groups(CC type),resistance genes,and virulence genes.A total of 29 strains of L.monocytogenes had resistance to zero to eight antibiotics,and carried six resistance genes.All strains of L.monocytogenes carried fosX,mprF,Lin,and norB.The 29 strains of L.monocytogenes belonged to 2 lineages(Ⅰ and Ⅱ):17 strains belonged to lineage Ⅱ,which was the dominant strain,and 12 strains belonged to lineage Ⅰ.The strains were classified into 13 ST types,among which ST8 was dominant,accounting for 31.03％(9/29 strains),and was followed by ST619 and ST121,accounting for three strains each.The strains were di-vided into four serogroups,with 15 strains in serogroups 1/2a and 3a;11 strains in serogroups 1/2b,3b,and 7;2 strains in serogroups 1/2c and 3c;and 1 strain in serogroups 4b,4d,and 4e.The strains were divided into 12 CC types,and one unsub-divided CC type,ST2348,among which CC8 was dominant,accounting for 27.59％(nine strains).A total of 25 virulence genes were found,which belonged to three virulence islands(LIPI-1,LIPI-2,and LIPI-3)and 23 CL types,including one or two strains each,and three CL types including two strains each.Foodborne L.monocytogenes in Guizhou Province has a low level of drug resistance,carries a high number of virulence genes,and shows genetic diversity in serogroups and molecular phe-notypes.These findings should strengthen the continuous surveillance efforts for Listeria monocytogenes.

Objective:To effectively quantify and evaluate the quality of different deformation registration algorithms, in order to enhance the possibility of implementing deformation registration in clinical practice.Methods:The Jacobian determinant mean (JDM) is proposed based on the Jacobian determinant (JD) of displacement vector field (DVF), and the Jacobian determinant error (DJDE) is introduced by incorporating the JD of the inverse DVF. The optical flow method (OF-DIR) and fast demons method with elastic regularization (FD-DIR) were tested on nasopharyngeal and lung cancer datasets. Finally, JDM and DJDE with the Jacobian determinant negative percentage (JDNP), inverse consistency error (ICE) and normalized mean square error (NMSE) were used to evaluate the registration algorithms and compare the differences evaluation indicators in different tumor images and different algorithms, and the receiver operating curve (ROC) was analyzed in evaluation.Results:In lung cancer, OF-DIR outperformed FD-DIR in terms of JDM, NMSE, DJDE and ICE, and the difference was statistically significant( z = -2.24, -4.84, t = 4.01, 6.54, P<0.05). In nasopharyngeal carcinoma, DJDE, ICE and NMSE of OF-DIR were superior to FD-DIR, and the difference was statistically significant ( t = 4.46, -7.49, z = -2.22, P<0.05), but there was no significant difference in JDM ( P>0.05). In lung cancer and nasopharyngeal carcinoma, JDNP of OF-DIR was worse than that of FD-DIR, and the difference was statistically significant ( z = -4.29, -4.02, P<0.01). In addition, DJDE is more specific and sensitive on ROC curve (AUC=0.77), and has different performance result for tumor images at different sites. Conclusions:The JDM and DJDE evaluation metrics proposed are effective for deformation registration algorithms. OF-DIR is suitable for both lung cancer and nasopharyngeal carcinoma, while the influence of organ motion on the registration effect should be considered when using FD-DIR.