ObjectiveTo study the changing law of appearance color and physicochemical properties of Angelicae Sinensis Radix Carbonisata（ASRC） during the processing by color space method combined with statistical analysis， so as to provide reference for determining the processing endpoint and evaluating the quality of the decoction pieces. MethodsTaking processing time（4， 8， 12， 16 min） and temperature（180， 200， 220， 240 ℃） as factors， ASRC decoction pieces with different processing degrees were prepared in a completely randomized design. Then， the brightness value（L^*）， red-green value（a^*）， yellow-blue value（b^*）， and total chromaticity value （E^*ab） of the decoction pieces were determined by spectrophotometer， the color difference value（ΔE） was calculated， and the data of colorimetric values were analyzed by discriminant analysis. At the same time， the pH， charcoal adsorption， and contents of tannins， 5-hydroxymethylfurfural（5-HMF）， tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide of ASRC with different processing degrees were determined by pH meter， ultraviolet and visible spectrophotometry and ultra-high performance liquid chromatography（UPLC）. Principal component analysis（PCA） was used to analyze the data of physicochemical indexes， after determining the processing technology of ASRC， the canonical discriminant function was established to distinguish the decoction pieces with different processing degrees， and leave-one-out cross validation was conducted. Finally， Pearson correlation analysis was used to explore the correlation between various physicochemical indexes and chromaticity values. ResultsWith the prolongation of the processing time， L^*， a^*， b^* and E^*ab all showed a decreasing trend， and the established discriminant model based on color parameters was able to distinguish ASRC with different processing degrees. The pH showed an increasing trend with the prolongation of processing time， and the charcoal adsorption， and the contents of tannins， 5-HMF， and tryptophan all showed an increasing and then decreasing trend. Among them， the charcoal adsorption， contents of tannin and 5-HMF reached their maximum values successively after processing for 8-12 min. While the contents of chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H and ligustilide decreased with the increase of processing time， with a decrease of 60%-80% at 8 min of processing. Therefore， the optimal processing time should be determined to be 8-12 min. PCA could clearly distinguish ASRC with different processing degrees， while temperature had no significant effect on the processing degree. The 12 batches of process validation results（10 min， 180-240 ℃） showed that except for 3 batches identified as class Ⅱ light charcoal， all other batches were identified as class Ⅲ standard charcoal， and the chromaticity values of each batch of ASRC were within the reference range of class Ⅱ-Ⅲ sample chromaticity values. The correlation analysis showed that the chromaticity values were negatively correlated with pH and charcoal adsorption， and positively correlated with contents of tryptophan， chlorogenic acid， ferulic acid， senkyunolide I， senkyunolide H， and ligustilide. And both pH and charcoal adsorption were negatively correlated with the contents of the above components， but the charcoal adsorption was positively correlated with the content of 5-HMF. ConclusionThe chromaticity values and the contents of various physicochemical indicators of ASRC undergo significant changes with the prolongation of processing time， and there is a general correlation between chromaticity values and various physicochemical indicators. Based on the changes in color and physicochemical indicators， the optimal processing time for ASRC is determined to be 8-12 min. This study reveals the dynamic changes of the relevant indexes in the processing of ASRC， which can provide a reference for the discrimination of the processing degree and the quantitative study of the processing endpoint.

Objective:To retrieve, evaluate, and integrate the best evidence of general anesthesia health education for adult patients undergoing elective surgery, so as to provide a basis for clinical health education guidance.Methods:Evidence-based questions were established based on the population, intervention, professional, outcome, setting and type of evidence (PIPOST) model. All evidence on general anesthesia health education for adult patients undergoing elective surgery were retrieved from databases or professional association websites such as Cochrane Library, British Medical Journal (BMJ) Best Practice, UpToDate, Agency for Healthcare Research and Quality, Guidelines International Network, National Institute for Health and Clinical Excellence, Clinical Guidelines Library, PubMed, China National Knowledge Infrastructure, WanFang Data, Medlive, American Society of Anesthesiologists, and Society of Anesthesiology of the Chinese Medical Association. The search period was from database establishment to June 18, 2023. Two researchers screened and evaluated the quality of the included literature, extracted and integrated the evidence to form the best evidence for general anesthesia health education in adult patients undergoing selective surgery.Results:A total of 10 articles were included, including four guidelines, three clinical decision-making, and three expert consensus. A total of 41 pieces of evidence on general anesthesia health education for adult patients undergoing elective surgery were extracted, including six aspects of anesthesia overview, pre-anesthesia evaluation, pre-anesthesia preparation, anesthesia process and cooperation, anesthesia recovery period management, and postoperative management.Conclusions:The best evidence of general anesthesia health education for adult patients undergoing selective surgery summarized can provide a basis for comprehensive and systematic education of anesthesia health educators.

Objective:To discusses the variations of 7Be, 137Cs and 210Pb activity concentrations in the air in Beijing from March 16, 2021 to March 31, 2023, and their correlation with temperature, precipitation, relative humidity, barometric pressure, PM10 (only limited to 137Cs) and other meteorological parameters. Methods:A total of 60 aerosol samples were collected using the HRHA01-SFS1000/A ultra-large flowair sampler in the automatic radiation monitoring station. The activity concentrations of 7Be, 137Cs and 210Pb in the aerosol samples were measured by GMX-60 low background anti-Compton high purity germanium gamma spectrometer. Pearson correlation coefficient was used to show the correlation of the relevant parameters. Results:The seasonal mean values of 7Be activity concentration were spring 4.31 mBq/m 3, summer 3.53 mBq/m 3, autumn 3.09 mBq/m 3 and winter 2.45 mBq/m 3, respectively, ranging from 1.17 to 7.79 mBq/m 3, with the overall mean of (3.36±1.33) mBq/m 3. The activity concentration of 137Cs ranged from 0.39 to 8.49 μBq/m 3, with an average of (0.59±1.47) μBq/m 3. The average activity concentrations of 210Pb were spring 0.53 mBq/m 3, summer 0.44 mBq/m 3, autumn 0.72 mBq/m 3 and winter 0.75 mBq/m 3, respectively, ranging from 0.21 to 1.36 mBq/m 3, with an average of (0.56±0.26) mBq/m 3. The activity concentration of 7Be was correlated with temperature and pressure ( r=0.38, -0.40), the activity concentration of 137Cs was correlated with precipitation ( r=-0.41), and the activity concentration of 210Pb was correlated with temperature and pressure ( r=-0.31, 0.37). The variation of 137Cs activity concentration in the air showed an obvious seasonal pattern, and the peak value generally appears in spring of each year (March to May), which was related to the frequent spring dust in Beijing. The activity concentration of 210Pb in the air was affected by coal combustion heating in winter, and has a peak value during November to March. Conclusions:The activity concentrations of 7Be, 137Cs and 210Pb in the air in Beijing are within the normal range, showing a seasonal trend.

Endoscopy ; Health Services Needs and Demand

Humans ; Cytomegalovirus ; Cytomegalovirus Infections ; Antigens, Viral ; Immunity ; Autoimmune Diseases ; CD8-Positive T-Lymphocytes

Objective To investigate and evaluate the changes in total α and total β radioactivity levels in drinking water in Beijing, China, 2012—2021. Methods The test results of total α and total β radioactivity levels at 14 monitoring sites from 9 groundwater sources and 5 surface water sources in Beijing, 2012—2021 were collected. The radioactivity levels in the two types of water sources were compared. Statistical charts were used to show the monitoring situation at sampling sites in different regions during different periods, and related issues were explored. Results The total α and total β activity concentrations measured at monitoring sites from some water sources in Beijing, 2012—2021, were less than the total α and total β guideline values specified in the Standards for Drinking Water Quality (GB 5749—2006) (total α: 0.5 Bq/L, total β: 1.0 Bq/L). The mean total α activity concentration in the groundwater was significantly higher than that in the surface water. The total α and total β radioactivity levels in the reservoir D in the surface water were slightly higher than those in the other surface water. Conclusion In the past decade from 2012 to 2021, the total α and total β radioactivity levels in some water sources in Beijing were generally in a good condition and fluctuated within the range of environmental background values, without significant changes on the whole.

Objective:To establish an acute kidney injury (AKI) prediction model in patients after cardiac surgery by extreme gradient boosting (XGBoost) machine learning model, and to explore the risk and protective factors for AKI in patients after cardiac surgery.Methods:All patients who underwent cardiac surgery in Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ) database were enrolled, and they were divided into AKI group and non-AKI group according to whether AKI developed within 14 days after cardiac surgery. Their clinical characteristics were compared. Based on five-fold cross-validation, XGBoost and Logistic regression were used to establish the prediction model of AKI after cardiac surgery. And the area under the receiver operator characteristic curve (AUC) of the models was compared. The output model of XGBoost was interpreted by Shapley additive explanations (SHAP).Results:A total of 6 912 patients were included, of which 5 681 (82.2%) developed AKI within 14 days after the operation, and 1 231 (17.8%) did not. Compared with the non-AKI group, the main characteristics of AKI group included older age [years: 68.0 (59.0, 76.0) vs. 62.0 (52.0, 71.0)], higher incidence of emergency admission and complicated with obesity and diabetes (52.4% vs. 47.8%, 9.0% vs. 4.0%, 32.0% vs. 22.2%), lower respiratory rate [RR; bpm: times/min: 17.0 (14.0, 20.0) vs. 19.0 (15.0, 22.0)], lower heart rate [HR; bpm: 80.0 (67.0, 89.0) vs. 82.0 (71.5, 93.0)], higher blood pressure [mmHg (1 mmHg ≈ 0.133 kPa): 80.0 (70.7, 90.0) vs. 78.0 (70.0, 88.0)], higher hemoglobin (Hb), blood glucose, blood K + level and serum creatinine [SCr; Hb (g/L): 122.0 (109.0, 136.0) vs. 120.0 (106.0, 135.0), blood glucose (mmol/L): 7.3 (6.1, 8.9) vs. 6.8 (5.7, 8.5), blood K + level (mmol/L): 4.2 (3.9, 4.7) vs. 4.2 (3.8, 4.6), SCr (μmol/L): 88.4 (70.7, 106.1) vs. 79.6 (70.7, 97.2)], lower albumin (ALB) and triacylglycerol [TG; ALB (g/L): 38.0 (35.0, 41.0) vs. 39.0 (37.0, 42.0), TG (mmol/L): 1.4 (1.0, 2.0) vs. 1.5 (1.0, 2.2)] as well as higher incidence of multiple organ dysfunction syndrome (MODS) and sepsis (30.6% vs. 16.2%, 3.3% vs. 1.9%), with significant differences (all P < 0.05). In the output model of Logistic regression, important predictors were lactic acid [Lac; odds ratio ( OR) = 1.062, 95% confidence interval (95% CI) was 1.030-1.100, P = 0.005], obesity ( OR = 2.234, 95% CI was 1.900-2.640, P < 0.001), male ( OR = 0.858, 95% CI was 0.794-0.928, P = 0.049), diabetes ( OR = 1.820, 95% CI was 1.680-1.980, P < 0.001) and emergency admission ( OR = 1.278, 95% CI was 1.190-1.380, P < 0.001). Receiver operator characteristic curve (ROC curve) analysis showed that the AUC of the Logistic regression model for predicting AKI after cardiac surgery was 0.62 (95% CI was 0.61-0.67). After optimizing the XGBoost model parameters by grid search combined with five-fold cross-validation, the model was trained well with no overfitting or overfitting. ROC analysis showed that the AUC of XGBoost model for predicting AKI after cardiac surgery was 0.77 (95% CI was 0.75-0.80), which was significantly higher than that of Logistic regression model ( P < 0.01). After SHAP treatment, in the output model of XGBoost, age and ALB were the most important predictors of the final outcome, where age was the risk factor (average |SHAP value| was 0.434), and ALB was the protective factor (average |SHAP value| was 0.221). Conclusions:Age is an important risk factor for AKI after cardiac surgery, and ALB is a protective factor. The performance of machine learning in predicting cardiac and vascular surgery-associated AKI is better than the traditional Logistic regression. XGBoost can analyze the more complex relationship between variables and outcomes, and can predict the risk of postoperative AKI more accurately and individually.

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Under the leadership of the Beijing Winter Olympic Organizing Committee and the Beijing Municipal Health Commission, the Beijing Emergency Center, as the designated medical institution for the 2022 Beijing Winter Olympic Games, has completed the first aid support task of this Winter Olympic Games with other medical institutions. The author systematically analyzed the development of each link in the pre-hospital emergency support for the 2022 Beijing Winter Olympic Games, summarizes the key links of the entire Winter Olympics cycle, such as the construction of the organizational system, the formulation of support plans, and the training of support personnel, and analyzed the results of related work, so as to provide reference for the pre-hospital emergency support for China to host large-scale international events in the future.

Objective:To investigate the clinical features and influencing factors of long-term prognosis of tuberculous meningitis(TBM), and to provide a recommendation for treatment and early intervention of TBM.Methods:Clinical data of TBM patients were retrospectively collected at Peking Union Medical College Hospital from January 2014 to December 2021. Patients who were followed-up more than one year were divided into two groups according to modified Rankin Scale (mRS). Risk factors associated with long-term prognosis were analyze by conditional logistic stepwise regression.Results:A total of 60 subjects were enrolled including 33 (55%) males and 27 (45%) females with age 15-79 (44.5±19.8) years. There were 30 cases (50%) complicated with encephalitis, 21 cases (35%) with miliary tuberculosis. The diagnosis was microbiologically confirmed in 22 patients (36.7%), including 5 cases (22.7%, 5/22) by acid-fast staining, 8 cases (36.4%, 8/22) by Mycobacterium tuberculosis (MTB) culture, and 20 cases (90.9%, 20/22) by molecular biology. The median follow-up period was 52(43, 66 ) months in 55 cases surviving more than one year. Among them, 40 cases (72.7%) were in favorable group (mRS 0-2) and 15 cases (27.3%) were in unfavorable group (mRS 3-6) with poor prognosis. The mortality rate was 20% (11/55). Elderly ( OR=1.06, P=0.048 ) , hyponatremia( OR=0.81, P=0.020), high protein level in cerebrospinal fluid (CSF) ( OR=3.32, P=0.033), cerebral infarction( OR=10.50, P=0.040) and hydrocephalus( OR=8.51, P=0.049) were associated with poor prognosis in TBM patients. Conclusions:The mortality rate is high in patients with TBM. Molecular biology tests improves the sensitivity and shorten the diagnosis time of TBM. Elderly, hyponatremia, high protein level in CSF, cerebral infarction and hydrocephalus are independent risk factors of long-term survival in TBM patients.