ObjectiveThis paper aims to clarify the material basis of Gualou Niubangtang and establish a quantitative analysis method for its main constituents， providing a reference for the overall quality control of this preparation. MethodsThe constituents in the formula were systematically characterized based on ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. Identification was performed by matching with the UNIFI 9.6 software and utilizing database platforms such as PubChem， ChemicalBook， and ChemSpider， combined with relevant literature reports. A quantitative analysis method for the seven main constituents in Gualou Niubangtang was established by using high performance liquid chromatography （HPLC）. ResultsUPLC-Q-TOF-MS/MS analysis identified 155 constituents， including 69 flavonoids， 36 terpenoids， 23 phenylpropanoids， 8 phenylethanoid glycosides， and 19 other types of constituents. In the established quantitative analysis method， the seven main constituents showed good linearity within their respective linear ranges. The precision， repeatability， stability， and spike recovery all met the required standards. The results showed that the content ranges of geniposide， liquiritin， hesperidin， arctiin， baicalin， oroxylin A-7-O-β-D-glucuronide， and wogonoside in 15 batches of Gualou Niubangtang were 13.67-21.25， 1.20-7.64， 5.45-7.45， 22.97-33.51， 29.95-39.07， 2.58-4.80， and 6.56-9.31 mg·g^-1， respectively. ConclusionThis study successfully characterizes and attributes multi-category constituents in Gualou Niubangtang， clarifying that its material basis is primarily composed of flavonoids， terpenoids， phenylethanoid glycosides， and phenylpropanoids. Furthermore， it enables the quantification of seven constituents within the formula. This work lays a foundation for research on the quality control， action mechanism， and clinical application of this formula.

Objective:To evaluate the clinical features of dystonia in patients with different types of atypical Parkinson syndrome (APS).Methods:A total of 104 patients with APS admitted in the Department of Neurology, Beijing Hospital from January 2015 to June 2023 were enrolled in the study, including 57 cases of multiple system atrophy (MSA), 38 cases of progressive supranuclear palsy (PSP) and 9 cases of corticobasal degeneration (CBD). Among 104 cases there were 63 males (60.6%), the mean age of patients was (62.3±8.9) years (54 to 73 years). The sex, age at onset, disease duration, first symptom, clinical features of dystonia and other neurological signs, response to levodopa therapy, numbers of Hoehn & Yahr scale≥3 after 3 years of disease, and MRI findings were documented in patients with different type APS.Results:The overall frequency of dystonia in this series was 45.2%(47/104), and 33.3% (19/57) for MSA group, 50.0% (19/38) for PSP group, 9/9 for CBD group. The types of dystonia were anterocollis, retrocollis, blepharospasm, oromandibular, foot/limb dystonia, Pisa syndrome and myoclonus. In all 47 cases presenting dydtonia, dystonia was not the first complaint and it did not respond to levodopa therapy.Conclusion:In this series of atypical Parkinson syndrome, dystonia is a common feature of the disease, while it is not the first symptom at disease onset, and usually does not respond to levodopa therapy.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective:To evaluate the clinical features of dystonia in patients with different types of atypical Parkinson syndrome (APS).Methods:A total of 104 patients with APS admitted in the Department of Neurology, Beijing Hospital from January 2015 to June 2023 were enrolled in the study, including 57 cases of multiple system atrophy (MSA), 38 cases of progressive supranuclear palsy (PSP) and 9 cases of corticobasal degeneration (CBD). Among 104 cases there were 63 males (60.6%), the mean age of patients was (62.3±8.9) years (54 to 73 years). The sex, age at onset, disease duration, first symptom, clinical features of dystonia and other neurological signs, response to levodopa therapy, numbers of Hoehn & Yahr scale≥3 after 3 years of disease, and MRI findings were documented in patients with different type APS.Results:The overall frequency of dystonia in this series was 45.2%(47/104), and 33.3% (19/57) for MSA group, 50.0% (19/38) for PSP group, 9/9 for CBD group. The types of dystonia were anterocollis, retrocollis, blepharospasm, oromandibular, foot/limb dystonia, Pisa syndrome and myoclonus. In all 47 cases presenting dydtonia, dystonia was not the first complaint and it did not respond to levodopa therapy.Conclusion:In this series of atypical Parkinson syndrome, dystonia is a common feature of the disease, while it is not the first symptom at disease onset, and usually does not respond to levodopa therapy.

Objective To explore the prescription law of Professor Wang Yinglin for treating pediatric cough based on the Carma algorithm and complex network.Methods The prescriptions of children with cough as the chief complaint who were treated by Professor Wang in the outpatient department from November 2022 to May 2023 were taken as the research object.Carma algorithm and complex network were used to analyze the main prescriptions of Professor Wang for treating children's cough,and explore the prescription law of Professor Wang for treating children's cough.Results A total of 420 cases were included,with an average age of 6.5 years old.Among them,there were 158 males and 262 females,involving 420 prescriptions,97 kinds of Chinese materia medica,a total frequency of 4 665,and 37 drugs with a frequency of use>20.By analyzing the drug combination derived from Carma analysis of algorithms and clinical verification,it was found that Professor Wang commonly used two drug combinations to treat children's cough:Poria-Exocarpium Citri Rubrum,Scrophulariae Radix-Imperatae Rhizoma,Peucedani Radix-Cynanchi Stauntonii Rhizoma,Perillae Fructus-Asteris Radix,Saposhnikoviae Radix-Liquidambaris Fructus;three medicine combination:Perillae Fructus-Asteris Radix-Semen Lepidii,Poria-Cablin Potchouli Herb-Exocarpium Citri Rubrum,Magnoliae Flos-Saposhnikoviae Radix-Liquidambaris Fructus;the combination of four drugs included Poria-Scrophulariae Radix-Exocarpium Citri Rubrum-Imperatae Rhizoma,Poria-Adenophorae Radix-Exocarpium Citri Rubrum-Scrophulariae Radix;five medicine combinations:Poria-Scrophulariae Radix-Exocarpium Citri Rubrum-Imperatae Rhizoma-Adenophorae Radix,Poria-Scrophulariae Radix-Exocarpium Citri Rubrum-Imperatae Rhizoma-Cablin Potchouli Herb;six medicine combinations:Poria-Scrophulariae Radix-Exocarpium Citri Rubrum-Imperatae Rhizoma-Adenophorae Radix-Folium Eriobotryae,Poria-Scrophulariae Radix-Exocarpium Citri Rubrum-Imperatae Rhizoma-Adenophorae Radix-Isatidis Radix,Poria-Scrophulariae Radix-Exocarpium Citri Rubrum-Imperatae Rhizoma-Cablin Potchouli Herb-Saposhnikoviae Radix,Folium Eriobotryae-Perillae Fructus-Asteris Radix-Semen Lepidii-Peucedani Radix-Cynanchi Stauntonii Rhizoma,Glehniae Radix-Crataegi Fructus-Stemonae Radix-Bulbus Lilii-Bulbus Fritillariae Cirrhosae-Ophiopogonis Radix.Complex network analysis found that the core drugs were:Adenophorae Radix,Poria,Exocarpium Citri Rubrum,Scrophulariae Radix,Imperatae Rhizoma,Folium Eriobotryae,Bulbus Fritillariae Thunbergii,Isatidis Radix,Peucedani Radix,Cynanchi Stauntonii Rhizoma,Stemonae Radix,Bambusae Concretio Silicea,Cablin Potchouli Herb.Five core prescriptions were obtained by multi-scale backbone network analysis.Conclusion Professor Wang's treatment of pediatric cough varies depending on the medical history,symptoms,and location of the disease,with different prescriptions.New diseases are often considered based on pathogenic factors,with phlegm heat as the main treatment,and the efficacy is mostly achieved by purging the lungs and resolving phlegm;phlegm heat gradually subsides,and residual pathogens are not cleared.The main approach is to eliminate residual pathogens and replenish qi and yin;long term illness mainly focuses on supplementing qi and nourishing yin.