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.

ObjectiveTo evaluate the application effect of morphology driven preparation technique in indirect restoration of posterior teeth. Methods84 patients with dental defects were selected and divided into the control group and the experimental group randomly， with 42 patients in each group （a total of 84 teeth）. Traditional preparation and morphology driven preparation techniques were used to complete dental preparation and adhesive glass ceramic restorations， respectively. The satisfaction， masticatory function， World Dental Federation （FDI） Scores for edge fracture and fixation，edge adaptability index，periodontal bleeding index （BI） and plaque index （PLI） of the two groups of patients were evaluated and various indicator data were recorded and statistically analyzed at 3 and 6 months after restoration completion. ResultsThere was no statistically significant difference in satisfaction between the two groups after 3 and 6 months of repair； Both groups of data showed a significant improvement in masticatory function. There was no statistical difference after 3 and 6 months of repair. However， a statistically significant difference in masticatory function was noted after 6 months of repair compared with before and after 3 months of repair. As the repair time increased， the masticatory effect improved significantly. There was no statistically significant difference in the edge fracture and fixation index between the two groups at 3 months after repair， but there was a statistically significant difference between the two groups at 6 months with the experimental group outperforming the control group（P<0.05）. The edge adaptability， BI， and PLI index were statistically significant between the two groups after 3 and 6 months of repair and the experimental group had a significant advantage （P<0.05）. ConclusionBoth types of tooth preparation techniques can improve patient masticatory function and reach high satisfaction after repair. However， morphology driven preparation technique has significant advantages in edge adaptability， edge fracture and fixation index scores and improve periodontal conditions. This technique is an effective method for improving dental preparation in adhesive indirect restoration of posterior teeth.

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.

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.

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.