Based on the theory of "spleen functions as wei qi"， this paper believes that the disease mechanism of allergic rhinitis （AR） in children is the nasal dysfunction caused by the loss of spleen's wei qi. The root cause of AR is the failure of splenic transportation as well as its inability to properly distribute nutrients. The inducement of AR is the invasion of pathogenic qi coupled with insecurity of the wei exterior. The key to AR recurrence lies in the deficiency of healthy qi and lingering of pathogenic qi， with pathogenic qi lodging inside the body. The treatment should adhere to the principle of helping the spleen restore wei qi. During the acute phase， the treatment should dispel wind， conso-lidate the wei qi， and relieve stuffy orifices， and the modified Qufeng Tongqiao Decoction （祛风通窍汤） is used. During the remission phase， the treatment should fortify the spleen， raise the clear， and harmonize the wei qi， and the modified Yuhan Decoction （御寒汤） is applied. During the recovery phase， the treatment should reinforce the healthy qi， consolidate the constitution， and strengthen the wei qi， and the modified Huangqi Jianzhong Decoction （黄芪建中汤） is employed.

Objective:To investigate the differences in cross-frequency coupling (CFC) characteristics of electroencephalography (EEG) between narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2).Methods:A total of 23 NT1 and 31 NT2 patients were included from the Chinese Clinical Sleep Database (CCSD) between October 2022 and September 2023. All participants underwent overnight polysomnography and a multiple sleep latency test. CFC features were extracted from EEG signals during polysomnography, encompassing various combinations of sleep stages, electrode pairs, frequency bands, and coupling types. Feature selection was performed using elastic net regularization. The Spearman correlation between key CFC features and the Epworth Sleepiness Scale (ESS) scores was analyzed. Finally, a support vector classification (SVC) model was constructed to distinguish NT1 from NT2, and leave-one-out cross-validation was used to assess the generalization performance.Results:Among all coupling features during non-rapid eye movement sleep stage 1 (N1), the fronto-occipital θ-α1 and central δ-α1 couplings showed the highest absolute coefficients, reaching 1.13 and 1.10, respectively. In the NT1 group, the α1-β2 imaginary part of phase-locking value (iPLV) of the F3-C3 pair during N1 was significantly positively correlated with ESS scores ( r=0.52, P=0.012). In the machine learning classification task, the SVC model achieved an accuracy of 85% using leave-one-out cross-validation. Conclusion:The CFC features during the sleep-wake transition stage play an important role in distinguishing NT1 from NT2 and show a significant correlation with excessive daytime sleepiness (EDS) in NT1. CFC may serve as a potential biomarker for differentiating narcolepsy subtypes and provide new insights into the mechanisms and clinical evaluation of EDS.

Objective:To investigate the differences in cross-frequency coupling (CFC) characteristics of electroencephalography (EEG) between narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2).Methods:A total of 23 NT1 and 31 NT2 patients were included from the Chinese Clinical Sleep Database (CCSD) between October 2022 and September 2023. All participants underwent overnight polysomnography and a multiple sleep latency test. CFC features were extracted from EEG signals during polysomnography, encompassing various combinations of sleep stages, electrode pairs, frequency bands, and coupling types. Feature selection was performed using elastic net regularization. The Spearman correlation between key CFC features and the Epworth Sleepiness Scale (ESS) scores was analyzed. Finally, a support vector classification (SVC) model was constructed to distinguish NT1 from NT2, and leave-one-out cross-validation was used to assess the generalization performance.Results:Among all coupling features during non-rapid eye movement sleep stage 1 (N1), the fronto-occipital θ-α1 and central δ-α1 couplings showed the highest absolute coefficients, reaching 1.13 and 1.10, respectively. In the NT1 group, the α1-β2 imaginary part of phase-locking value (iPLV) of the F3-C3 pair during N1 was significantly positively correlated with ESS scores ( r=0.52, P=0.012). In the machine learning classification task, the SVC model achieved an accuracy of 85% using leave-one-out cross-validation. Conclusion:The CFC features during the sleep-wake transition stage play an important role in distinguishing NT1 from NT2 and show a significant correlation with excessive daytime sleepiness (EDS) in NT1. CFC may serve as a potential biomarker for differentiating narcolepsy subtypes and provide new insights into the mechanisms and clinical evaluation of EDS.

Objective To explore indicators of risks for preoperative adverse cardiovascular events(PACE)in elderly patients who underwent non-cardiac surgery(NCS).Methods The clinical data of elderly patients who had undergone NCS at Tianjin Medical University General Hospital from January 2022 to March 2024 were analyzed retrospectively,including gender,age,medical history,operation ways,and preoperative creatinine(Cr),hypersensitive C-reactive protein(hs-CRP),hypersensitivity cTnT(hs-cTnT),MB isoenzyme of creatine kinase(CK-MB),N-terminal pro-brain natriuretic peptide(NT-proBNP)and D-Dimer,prothrombin time(PT),activated partial thromboplastin time(APTT),fibrinogen(Fib).To measure the revised cardiac risk index(RCRI),the 30-day prognosis of the patients was followed up.Patients were divided into PACE group and non-PACE group according to the occurrence of PACE,and the clinical data between the two groups were compared.Multiple Logistics regression was used to identify independent risk factors for PACE,and the receiver operator characteristic curve(ROC curve)was drawn to evaluate the predictive value of each risk factor for PACE risk in elderly patients underwent NCS.Results A total of 112 elderly patients underwent NCS were enrolled,including 9 cases in the PACE group(incidence rate 8.03%)and 103 cases in the non-PACE group.Patients'age(years old:75.1±6.4 vs.69.4±7.2,P=0.002),preoperative incidence of coronary heart disease[44.4%(4/9)vs.28.2%(29/103),P=0.004],incidence of heart failure[22.2%(2/9)vs.6.8%(7/103),P=0.003],NT-proBNP[(ng/L):485.17(196.10,787.53)vs.98.41(73.81,160.35),P=0.001],hs-cTnT[(μg/L):0.043(0.023,0.087)vs.0.005(0.003,0.008),P=0.002],and D-Dimer[(μg/L):2 953(2 674,3 570)vs.514(260,1 010),P=0.001]levels in the PACE group were significantly higher than those in the non-PACE group,the proportion of RCRI 2-3 scores in PACE group were significantly higher than those in non-PACE group[2 scores:44.4%(4/9)vs.18.4%(19/103),3 scores:33.3%(3/9)vs.14.6%(15/103),P=0.047].As shown in multiple Logistic regression analysis,age,preoperative NT-proBNP levels and RCRI scores≥2 were independent risk factors affecting the prognosis for PACE occurrence[odds ratio(OR)and 95%confidence interval(95%CI)were 1.163(1.032-1.310),1.021(1.008-1.031)and 1.261(1.003-1.585),P values were 0.013,0.001,0.048,respectively].As shown in ROC curve analysis,age,NT-proBNP and RCRI had predictive value for the occurrence of PACE in NCS patients,the area under the curve(AUC)and 95%CI of age,NT-proBNP and RCRI were 0.795(0.675-0.915),0.908(0.838-0.979)and 0.735(0.556-0.912),with P=0.030,<0.001 and 0.020 respectively.The optimal cut-off values were:age≥69.5 years(sensitivity was 88.9%,specificity was 56.3%);NT-proBNP≥177.0 ng/L(sensitivity was 88.9%,specificity was 83.5%);RCRI≥1.5 points(sensitivity was 77.8%,specificity was 67.0%).Conclusions Age,NT-proBNP levels and RCRI have certain predictive values for the occurrence of PACE in NCS patients undergoing NCS,advanced age,higher NT-proBNP levels,and elevated RCRI scores were associated with increased PACE risk in elderly patients undergoing NCS.While age showed high sensitivity but limited specificity,combining NT-proBNP(with high diagnostic accuracy)and RCRI(with objective scoring)improved preoperative PACE risk prediction.

Objective:To evaluate the efficacy and safety of hypomethylating agents (HMA) in patients with myelodysplastic syndromes (MDS) .Methods:A total of 409 MDS patients from 45 hospitals in Zhejiang province who received at least four consecutive cycles of HMA monotherapy as initial therapy were enrolled to evaluate the efficacy and safety of HMA. Mann-Whitney U or Chi-square tests were used to compare the differences in the clinical data. Logistic regression and Cox regression were used to analyze the factors affecting efficacy and survival. Kaplan-Meier was used for survival analysis. Results:Patients received HMA treatment for a median of 6 cycles (range, 4-25 cycles) . The complete remission (CR) rate was 33.98% and the overall response rate (ORR) was 77.02%. Multivariate analysis revealed that complex karyotype ( P=0.02, OR=0.39, 95% CI 0.18-0.84) was an independent favorable factor for CR rate. TP53 mutation ( P=0.02, OR=0.22, 95% CI 0.06-0.77) was a predictive factor for a higher ORR. The median OS for the HMA-treated patients was 25.67 (95% CI 21.14-30.19) months. HMA response ( P=0.036, HR=0.47, 95% CI 0.23-0.95) was an independent favorable prognostic factor, whereas complex karyotype ( P=0.024, HR=2.14, 95% CI 1.10-4.15) , leukemia transformation ( P<0.001, HR=2.839, 95% CI 1.64-4.92) , and TP53 mutation ( P=0.012, HR=2.19, 95% CI 1.19-4.07) were independent adverse prognostic factors. There was no significant difference in efficacy and survival between the reduced and standard doses of HMA. The CR rate and ORR of MDS patients treated with decitabine and azacitidine were not significantly different. The median OS of patients treated with decitabine was longer compared with that of patients treated with azacitidine (29.53 months vs 20.17 months, P=0.007) . The incidence of bone marrow suppression and pneumonia in the decitabine group was higher compared with that in the azacitidine group. Conclusion:Continuous and regular use of appropriate doses of hypomethylating agents may benefit MDS patients to the greatest extent if it is tolerated.

Objective To explore indicators of risks for preoperative adverse cardiovascular events(PACE)in elderly patients who underwent non-cardiac surgery(NCS).Methods The clinical data of elderly patients who had undergone NCS at Tianjin Medical University General Hospital from January 2022 to March 2024 were analyzed retrospectively,including gender,age,medical history,operation ways,and preoperative creatinine(Cr),hypersensitive C-reactive protein(hs-CRP),hypersensitivity cTnT(hs-cTnT),MB isoenzyme of creatine kinase(CK-MB),N-terminal pro-brain natriuretic peptide(NT-proBNP)and D-Dimer,prothrombin time(PT),activated partial thromboplastin time(APTT),fibrinogen(Fib).To measure the revised cardiac risk index(RCRI),the 30-day prognosis of the patients was followed up.Patients were divided into PACE group and non-PACE group according to the occurrence of PACE,and the clinical data between the two groups were compared.Multiple Logistics regression was used to identify independent risk factors for PACE,and the receiver operator characteristic curve(ROC curve)was drawn to evaluate the predictive value of each risk factor for PACE risk in elderly patients underwent NCS.Results A total of 112 elderly patients underwent NCS were enrolled,including 9 cases in the PACE group(incidence rate 8.03%)and 103 cases in the non-PACE group.Patients'age(years old:75.1±6.4 vs.69.4±7.2,P=0.002),preoperative incidence of coronary heart disease[44.4%(4/9)vs.28.2%(29/103),P=0.004],incidence of heart failure[22.2%(2/9)vs.6.8%(7/103),P=0.003],NT-proBNP[(ng/L):485.17(196.10,787.53)vs.98.41(73.81,160.35),P=0.001],hs-cTnT[(μg/L):0.043(0.023,0.087)vs.0.005(0.003,0.008),P=0.002],and D-Dimer[(μg/L):2 953(2 674,3 570)vs.514(260,1 010),P=0.001]levels in the PACE group were significantly higher than those in the non-PACE group,the proportion of RCRI 2-3 scores in PACE group were significantly higher than those in non-PACE group[2 scores:44.4%(4/9)vs.18.4%(19/103),3 scores:33.3%(3/9)vs.14.6%(15/103),P=0.047].As shown in multiple Logistic regression analysis,age,preoperative NT-proBNP levels and RCRI scores≥2 were independent risk factors affecting the prognosis for PACE occurrence[odds ratio(OR)and 95%confidence interval(95%CI)were 1.163(1.032-1.310),1.021(1.008-1.031)and 1.261(1.003-1.585),P values were 0.013,0.001,0.048,respectively].As shown in ROC curve analysis,age,NT-proBNP and RCRI had predictive value for the occurrence of PACE in NCS patients,the area under the curve(AUC)and 95%CI of age,NT-proBNP and RCRI were 0.795(0.675-0.915),0.908(0.838-0.979)and 0.735(0.556-0.912),with P=0.030,<0.001 and 0.020 respectively.The optimal cut-off values were:age≥69.5 years(sensitivity was 88.9%,specificity was 56.3%);NT-proBNP≥177.0 ng/L(sensitivity was 88.9%,specificity was 83.5%);RCRI≥1.5 points(sensitivity was 77.8%,specificity was 67.0%).Conclusions Age,NT-proBNP levels and RCRI have certain predictive values for the occurrence of PACE in NCS patients undergoing NCS,advanced age,higher NT-proBNP levels,and elevated RCRI scores were associated with increased PACE risk in elderly patients undergoing NCS.While age showed high sensitivity but limited specificity,combining NT-proBNP(with high diagnostic accuracy)and RCRI(with objective scoring)improved preoperative PACE risk prediction.

The management of emerging infectious diseases has always been given a high priority in public health. Identification of the epidemiological characteristics and transmission patterns of emerging infectious diseases is of great significance to contain the disease transmission and reduce the damages to public health and socioeconomic developments. Currently, infectious disease dynamics models are mainly established based on infectious disease surveillance data to predict the epidemiological patterns and trends of emerging infectious diseases; however, many model-based predictions fail to achieve the expected results due to the presence of multiple uncertain factors during the integrated management of infectious diseases. This review describes the basic principles and variables of common infectious disease dynamics models, including the susceptible-infected-recovered ( SIR ) model, susceptible-infected-removed-susceptible ( SIRS ) model, susceptible-exposed-infected-removed ( SEIR ) model and improved SEIR model, compares the advantages and disadvantages of these models, and summarizes the advances of the infectious disease dynamics models in the prediction of trends in incidence of emerging infectious diseases, so as to provide insights into the effective application of infectious disease dynamics models in the management of infectious diseases.

Few studies have described the key features and prognostic roles of lung microbiota in patients with severe community-acquired pneumonia (SCAP). We prospectively enrolled consecutive SCAP patients admitted to ICU. Bronchoscopy was performed at bedside within 48 h of ICU admission, and 16S rRNA gene sequencing was applied to the collected bronchoalveolar lavage fluid. The primary outcome was clinical improvements defined as a decrease of 2 categories and above on a 7-category ordinal scale within 14 days following bronchoscopy. Sixty-seven patients were included. Multivariable permutational multivariate analysis of variance found that positive bacteria lab test results had the strongest independent association with lung microbiota (R² = 0.033; P = 0.018), followed by acute kidney injury (AKI; R² = 0.032; P = 0.011) and plasma MIP-1β level (R² = 0.027; P = 0.044). Random forest identified that the families Prevotellaceae, Moraxellaceae, and Staphylococcaceae were the biomarkers related to the positive bacteria lab test results. Multivariable Cox regression showed that the increase in α-diversity and the abundance of the families Prevotellaceae and Actinomycetaceae were associated with clinical improvements. The positive bacteria lab test results, AKI, and plasma MIP-1β level were associated with patients' lung microbiota composition on ICU admission. The families Prevotellaceae and Actinomycetaceae on admission predicted clinical improvements.
Acute Kidney Injury/complications* ; Bacteria/classification* ; Chemokine CCL4/blood* ; Community-Acquired Infections/microbiology* ; Humans ; Lung ; Microbiota/genetics* ; Pneumonia, Bacterial/diagnosis* ; Prognosis ; RNA, Ribosomal, 16S/genetics*

OBJECTIVE:To establish a method for simultaneous determination of 7 constituents in Wikstroemia indica,and to conduct principal component analysis and cluster analysis. METHODS:HPLC method was adopted. The determination was per-formed on Diamonsil Platisil ODS column with mobile phase consisted of acetonitrile-0.15% triethylamine solution(pH adjusted to 6.0 with phosphoric acid,gradient elution)at the flow rate of 1.0 mL/min. The detection wavelength was set at 280 nm,and col-umn temperature was 40 ℃. The sample size was 10 μL. The principal component analysis and cluster analysis were conducted for the results of content determination by SPSS 22.0 statistical software. RESULTS:The linear ranges were 2.688-53.76μg/mL for nar-ingin(r=0.9998),5.052-101.00 μg/mL for myricetin(r=0.9999),2.052-41.04 μg/mL for arctiin(r=0.9999),2.108-42.16 μg/mL(r=0.9999),5.112-102.20 μg/mL(r=0.9999),0.820-16.42 μg/mL(r=0.9999),2.070-41.40 μg/mL(r=0.9999),respec-tively. The limits of quantitation were no higher than 1.0720 μg/mL,the limits of detection were no higher than 0.3318 μg/mL. RSDs of precision,stability and reproducibility tests were all lower than 2.0%. The recoveries were 97.8%-102.5%(RSD=1.8%, n=6),97.2%-102.0%(RSD=2.0%,n=6),95.2%-100.1%(RSD=1.7%,n=6),95.2%-99.3%(RSD=1.6%,n=6), 97.0%-100.8%(RSD=1.3%,n=6),95.5%-98.6%(RSD=1.1%,n=6),95.0%-99.3%(RSD=1.8%,n=6),respectively. Three main components were belong to the samples of 10 batches of medicinal materials. The samples of medicinal materials from 10 pro-ducing area could be divided into 2 categories. The quality of W. indica from Qingyuan Guangdong and Guiyang Guizhou were bet-ter than others. CONCLUSIONS:The method is simple,precise,stable and reproducible,and it can be used for simultaneous de-termination of 7 constituents in medicinal material. The quality of W. indica from different regions are quite different.