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.

Hematologic malignancies,such as lymphoma,myeloma,and myeloid neoplasms,can occur in extramedullary tissues.Traditional histopathological morphology and immunohistochemical staining have lim-itations,including time-consuming specimen processing,prolonged reporting cycles,and relatively low sensi-tivity in cases of limited cell numbers.Flow cytometry offers significant advantages in detecting tissue sam-ples,such as rapid processing,shorter reporting cycles,and high accuracy and sensitivity,making it an effective complement to histopathological and immunohistochemical methods.However,the application of flow cytome-try in tissue sample detection currently lacks standardized protocols for sample collection and preservation,single-cell suspension preparation,antibody panel design for limited samples,data analysis,and result repor-ting.To promote the standardized application of flow cytometry in detecting hematologic tumor cells in tissue samples,the Cell Analysis Professional Committee of the Chinese Society of Biotechnology organized experts to develop the Chinese Expert Consensus on Flow Cytometry for Detecting Hematologic Tumor Cells in Tis-sue Samples(hereinafter referred to as the Consensus).This Consensus elaborates on the technical aspects of flow cytometry for tissue sample detection,covering sample processing,antibody panel design,data analysis,reporting content,and quality management.It particularly emphasizes recommended antibody panels and data analysis methods for flow cytometry when tissue sample cell counts are low.This article aims to interpret the key points of the Consensus to facilitate its better application in clinical practice.

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.

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 investigate the value of serum free light chain(sFLC)and serum calcium ion in the diagnosis and prognosis of multiple myeloma(MM).Methods:Forty patients with MM treated in Henan Provincial People's Hospital from January 2018 to January 2022 were selected as the observation group,and 40 healthy volunteers were selected as the control group.The differences of sFLC-κ,sFLC-λ,sFLC-κ/λ,serum calcium ions,etc between the two groups were compared.Meanwhile,the differences of sFLC-κ,sFLC-λ,sFLC-κ/λ,serum calcium ions,etc in different international staging systems(ISS),chemotherapy efficacy and prognosis patients were analyzed.Results:The levels of sFLC-κ[(98.39±21.19)vs(12.01±4.45)mg/L],sFLC-λ[(210.20±45.54)vs(14.10±5.11)mg/L]and proportions of hypocalcemia(65％vs 0)in the observation group were significantly higher than those in the control group(P＜0.05),while sFLC-κ/λ ratio[(0.44±0.10)vs(0.87±0.12)]and serum calcium ions[(1.98±0.46)vs(2.42±0.40)mmol/L]were significantly lower than those in the control group(P＜0.05).The sFLC-κ,sFLC-λ,the proportion of hypocalcemia and the course of hypocalcemia in ISS stage Ⅲ patients in the observation group were significantly higher than those in stage Ⅰ and Ⅱ patients(P＜0.05),while sFLC-κ/λ ratio,and serum calcium ions were significantly lower than those in stage Ⅰ and Ⅱ patients(P＜0.05).The levels of sFLC-κ[(107.76±21.22)vs(94.67 ±20.11)mg/L],sFLC-λ[(245.54±41.12)vs(205.54±50.22)mg/L]of patients with hypocalcemia in the observation group was significantly higher than those without hypocalcemia(P＜0.05),while the sFLC-κ/λ ratio was significantly lower than those without hypocalcemia[(0.42±0.04)vs(0.47±0.06);P＜0.05].The levels of sFLC-κ[(107.29±20.14)vs(91.11±18.92)mg/L],sFLC-λ[(247.98±42.26)vs(179.29±39.32)mg/L]in patients with ineffective chemotherapy were significantly higher than those in patients with effective chemotherapy(P＜0.05),while the sFLC-κ/λ ratio was significantly lower than those in patients with effective chemotherapy[(0.43± 0.10)vs(0.50±0.09);P＜0.05)].The area under the ROC curve for sFLC-κ,sFLC-λ,sFLC-κ/λ predicting ineffective chemotherapy was 0.803,0.793 and 0.699 respectively,P＜0.05.There was no significant difference in sFLC-κ,sFLC-λ,sFLC-κ/λ ratio,serum calcium ion,hypocalcemia ratio and hypocalcemia course between survival and death patients(P＞0.05).Conclusion:sFLC and serum calcium are related to 1SS stage of MM patients.sFLC level has a certain value to predict the curative effect of chemotherapy in MM patients.However,the prognostic values of sFLC and serum calcium are not yet confirmed for MM patients.