OBJECTIVE To provide a reference for optimizing and promoting the quality standards of Shechuan naolitong granules. METHODS Fifteen batches of Shechuan naolitong granules were used as samples to establish HPLC fingerprints using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）. Similarity evaluation and common peak identification were performed， and orthogonal partial least squares discriminant analysis （OPLS-DA） was used to assess quality differences among different batches and to screen quality differential components. Using salvianolic acid B（SAB） as the internal reference， quantitative analysis of multi-components by single-marker （QAMS） was developed to simultaneously determine geniposidic acid （GA）， chlorogenic acid （CA）， vaccarin （VA）， ferulic acid （FA） and senkyunolide I （SI）. The results were compared with those obtained by the external standard method. RESULTS A total of 13 common peaks were identified in the HPLC fingerprints of 15 batches of samples， and the similarities of the spectra were all above 0.96. Seven chromatographic peaks were identified as GA （peak 3）， CA （peak 6）， VA （peak 8）， FA （peak 9）， SI （peak 11）， SAB（peak 12） and TA（peak 13）. OPLS-DA indicated that the differential quality markers among 15 batches were peaks 5， 11 （SI）， and 12 （SAB）.Using SAB as the internal reference， the relative correction factors for GA， CA， VA， FA and SI were calculated as 1.058 4， 0.594 3， 0.643 3， 0.342 7 and 0.262 8， respectively. The mean content of GA， CA， VA， FA， SI and SAB across the 15 batches of samples were 0.155 0， 0.085 4， 0.140 3， 0.071 8， 0.072 7， 1.276 3 mg/g， respectively， showing no significant difference compared with the ESM （P＞0.05）. CONCLUSIONS The established HPLC fingerprint and QAMS are simple， efficient and economical， providing a reference for the quality control and further development of Shechuan naolitong granules.

This article systematically studied the moxibustion methods embedded in the 15 medical books of the Mawangdui medical literature. Starting from the origin of the medical moxibustion culture in Mawangdui， this paper comprehensively analyzed the records of moxibustion. By analyzing the Classic of Moxibustion to Eleven Foot-arm Channels （《足臂十一脉灸经》） and Classic of Moxibustion to Eleven Yin-yang Channels （《阴阳十一脉灸经》） as well as Formulas for Fifty-two Diseases （《五十二病方》）， which have relatively rich records of moxibustion， it is found that moxibustion involves more than 170 diseases and syndromes， covering 12 categories such as limb diseases related to meridians and collaterals， lung system diseases， and heart and brain diseases. In summary， the distinctive characte-ristics of moxibustion in Mawangdui medical books include primitive simplicity， combination of witch culture and medical practices， philosophical deduction， centripetal circulation， diverse moxibustion materials， extensive cove-rage of diseases and syndromes， taboos in moxibustion， and the integration of prevention and treatment. The ideas and applications of moxibustion mainly manifest in preventing diseases before they occur and preventing changes in existing diseases， distinguishing and treating each disease based on different causes， and combining treatment based on meridians， collaterals and comprehensive diseases.

Objective:To design a hospital pre-triage system based on the Spark big data platform and the improved Adaboost algorithm,and to pre-triage patients in the hospital in advance and accelerate the process of medical treatment.Methods:Based on the Spark big data platform,the basic data from patients entering the hospital for the first time was collected in real time,and the blockchain technology was applied to the whole process of data collection,storage and transmission,and the data was analyzed by the improved the Adaboost algorithm.The outpatient data of The 940th Hospital of the PLA Joint Logistics Support Force in the 10 years from 2011 to 2020 were used as the dataset to quickly identify and guide patients to seek medical treatment in the hospital.The application effect of the hospital pre-triage system based on the Spark big data platform and the improved Adaboost algorithm was analyzed.Results:When the custom limit weight threshold of the improved Adaboost algorithm was set to 0.52,the algorithm accuracy reached a peak of 95.56%,and the accuracy of pre-test triage was 4.24%higher than that of the traditional Adaboost algorithm.The average waiting time for patients was shortened from 0.8 h before the triage to 0.5 h,and the average consultation time for patients was shortened from 6 min before the triage to 4.8 min.Conclusion:The hospital pre-triage system based on the big data platform and the improved Adaboost algorithm can pre-triage patients before diagnosis in advance,improve the efficiency and accuracy of the triage,and relieve the hospital visiting pressure.

Objective:To design a medical big data center based on data warehouse(DW)and data service platform,to integrate information resources between different information systems and organizational structures,to build a secure channel for data sharing,and to meet the needs of clinical application data.Methods:Based on data flow direction,and according to hospital clinical services,operation management,and scientific research development,a top-down data application layer,data service layer,DW layer,and operational data storage(ODS)layer design architecture was adopted to design a medical big data center based on DW and data service platform.Guided by hospital data and business,according to the"object-event-report"data splitting logic,the activities corresponding to the roles in each subject domain were disassembled and sorted out to facilitate quick invocation in clinical applications.Results:The medical big data center was equipped with basic modules for patient master index management and master data management,covering 16 major business subject domains and 52 business subdomains,including hospital's clinical services,hospital management,and patient identification.The medical big data center application included clinical data center,operation data center and scientific research data center,and clearly defined the correlation logic between major categories of information,centrally managed the whole life cycle of service application program interfaces,combined master data information,comprehensively managed medical data,realized the normalization of hospital data,and established high-quality data assets and flexible DW models with the help of big data technology.Conclusion:The medical big data center based on DW and data service platform can integrate different information systems of the hospital with data within the hospital,realize the convenient invocation of interface services and the standardized and persistent management of hospital data,and ensure the data applications needs of clinical application,operational decision-making,and scientific research analysis.

Objective To investigate the in vitro anti-inflammatory effects of Sophora davidii Hance leaves total alkaloids(SDLTAs)and possible molecular mechanisms.Methods The lipopolysaccharide(LPS)-induced inflammation model of RAW264.7 cells was used,and different concentrations of SDLTAs(50,100 and 200 μg/mL)were administered,and the effect of SDLTAs on cellular NO expression was detected by the Griess method;ELISA method was used to detect the effect of SDLTAs on the expression of IL-6,TNF-α and IL-1β;The expression of iNOS,NF-κB p65 and IκBα mRNA was detected by RT-qPCR;Western blotting was used to detecte the expres-sion of p-p38,p-p65 and p-JNK in the cells and NF-κB p65 in the nucleus.Results SDLTAs could significantly inhibit the LPS-induced inflammatory response in RAW264.7 cells.SDLTAs significantly decreased the secretion of NO,IL-6,TNF-α and IL-1β in cells(P<0.01),and significantly decreased the mRNA expressions of iNOS,NF-κB p65 and IκBα in cells(P<0.01).Significantly decreased the protein expression of p-p38,p-p65 and p-JNK in cells and NF-κB p65 in nucleus(P<0.01).Conclusion SDLTAs can exert anti-inflammatory effects by regulating the MAPK/NF-κB signalling pathway.

In recent years, the artificial intelligence machine learning and deep learning technology have made leap progress. Using clinical decision support system for auxiliary diagnosis and treatment is the inevitable developing trend of wisdom medical. Clinicians tend to ignore the interpretability of models while pursuing its high accuracy, which leads to the lack of trust of users and hamper the application of clinical decision support system. From the perspective of explainable artificial intelligence, the authors make some preliminary exploration on the construction of clinical decision support system in the field of liver disease. While pursuing high accuracy of the model, the data governance techniques, intrinsic interpretability models, post-hoc visualization of complex models, design of human-computer interactions, providing knowledge map based on clinical guidelines and data sources are used to endow the system with interpretability.

Objective:To investigate the application value of machine learning algorithms for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 277 patients with HCC who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University between May 2015 and December 2018 were collected. There were 235 males and 42 females, aged (56±10)years, with a range from 33 to 80 years. Patients underwent preoperative magnetic resonance imaging examination. According to the random numbers showed in the computer, all the 277 HCC patients were divided into training dataset consisting of 193 and validation dataset consisting of 84, with a ratio of 7∶3. Machine learning algorithms, including logistic regression nomogram, support vector machine (SVM), random forest (RF), artificial neutral network (ANN) and light gradient boosting machine (LightGBM), were used to develop models for preoperative prediction of MVI. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) analysis of risk factors for tumor MVI of the training dataset; (3) construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the paired t test. Count data were described as absolute numbers, and comparison between groups was analyzed using the chi-square test. Univariate and multivariate analyses were performed using the Logistic regression model. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: there were 157 males and 36 females in the training dataset, 78 males and 6 females in the validation dataset, showing a significant difference in the sex between the training dataset and validation dataset ( χ2=6.028, P<0.05). (2) Analysis of risk factors for tumor MVI of the training dataset: of the 193 patients, 108 had positive MVI, and 85 had negative MVI. Results of univariate analysis showed that age, the number of tumors, tumor diameter, satellite lesions, tumor margin, alpha fetaprotein (AFP), alkaline phosphatase (ALP), fibrinogen were related factors for tumor MVI [ odds ratio ( OR)=0.971, 2.449, 1.368, 4.050, 2.956, 4.083, 2.532, 1.996, 95% confidence interval ( CI): 0.943-1.000, 1.169-5.130, 1.180-1.585, 1.316-12.465, 1.310-6.670, 2.214-7.532, 1.016-6.311, 1.323-3.012, P<0.05]. Results of multivariate analysis showed that AFP>20 μg/L, multiple tumors, larger tumor diameter, unsmooth tumor margin were independent risk factors for tumor MVI ( OR=3.680, 3.100, 1.438, 3.628, 95% CI: 1.842-7.351, 1.334-7.203, 1.201-1.721, 1.438-9.150, P<0.05). Larger age was associated with lower risk of preoperative tumor MVI ( OR=0.958, 95% CI: 0.923-0.994, P<0.05). (3) Construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction: ①machine learning algorithm prediction models involving logistic regression nomogram, SVM, RF, ANN and LightGBM were constructed based on results of multivariate analysis including age, AFP, the number of tumors, tumor diameter, tumor margin, and consistency analysis of the logistic regression nomogram prediction model showed a good stability. For the training dataset and validation dataset, the area under curve (AUC) of logistic regression nomogram model, SVM model, RF model, ANN model, LightGBM model was 0.812, 0.794, 0.807, 0.814, 0.810 and 0.784, 0.793, 0.783, 0.803, 0.815, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.731-0.849, 0.744-0.860, 0.752-0.867, 0.747-0.862, Z=0.995, 0.245, 0.130, 0.102, P>0.05) and (95% CI: 0.690-0.873, 0.679-0.865, 0.702-0.882, 0.715-0.891, Z=0.325, 0.026, 0.744, 0.803, P>0.05)]. ② Clinicopathological factors were selected using RF, LightGBM machine learning algorithm to construct corresponding prediction models. According to importance scale of factors to prediction models, factors with importance scale>0.01 were selected to construct RF model, including age, tumor diameter, AFP, white blood cell, platelet, total bilirubin, aspartate transaminase, γ-glutamyl transpeptidase, ALP, and fibrinogen. Factors with importance scale>5.0 were selected to construct LightGBM model, including age, tumor diameter, AFP, white blood cell, ALP, and fibrinogen. Due to lack of factor selection ability, factors based on results of univariate analysis were secected to construct SVM model and ANN model, including age, the number of tumors, tumor diameter, satellite lesions, tumor margin, AFP, ALP, and fibrinogen. For the training dataset and validation dataset, the AUC of SVM model, RF model, ANN model, LightGBM model was 0.803, 0.838, 0.793, 0.847 and 0.810, 0.802, 0.802, 0.836, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.740-0.857, 0.779-0.887, 0.729-0.848, 0.789-0.895, Z=0.421, 0.119, 0.689, 1.517, P>0.05) and (95% CI: 0.710-0.888, 0.700-0.881, 0.701-0.881, 0.740-0.908, Z=0.856, 0.458, 0.532, 1.306, P>0.05)]. Conclusion:Machine learning algorithms can predict MVI of HCC preoperatively, but its application value needs to be further verified by large sample data from multi centers.

Objective To observe the changes of potassium ion (K+), lactic acid (Lac) and glucose (Glu) in swine with traumatic hemorrhagic shock (THS) inside the dry-heat environment and to explore its possible mechanism. Methods A total of 40 local Landrace piglets were randomly(random number) divided equally into 4 groups: the normal temperature sham operation group (NS), the normal temperature traumatic hemorrhagic shock group (NTHS), the dry-heat sham operation group (DS group) and the dry-heat traumatic hemorrhagic shock group (DTHS). The experiment was carried out in the artifi cia climate cabin simulated the special environment of northwest of China. After exposed to their respective environment[dry-heat environment: (40.5±0.5), plus(10±2)% humidity; normal temperature environment: (25±0.5), plus(35±5)% humidity] for 3 h. Laparotomy were performed in swine of all groups, and then splenectomy and partial hepatectomy were performed only in NTHS and DTHS. The process of exsanguination from the external iliac artery was established to make the MAP reaching to 40-50 mmHg, and thus the traumatic hemorrhagic shock model of swine was successfully made. Blood samples were collected from external iliac artery at different intervals including the time just after exposure for 3 h and the successful establishment of traumatic hemorrhagic shock model (0 h) and then every 30 min after 0 h, serum levels of K+, Lac and Glu were detected. The features of varied serum K+, Lac and Glu were observed in each group. All data were statistically analyzed using One-way ANOVA and Pearson correlation analysis. Results After exposed , the level of serum K+inside the dry-heat environment was higher than that of swine inside the normal temperature group ( P<0.01), however the Glu level was lower in the swine inside dry-heat environment than that of swine inside the normal temperature ( P<0.01).The level of serum K+and Lac of DTHS group were rapidly increased from the establishment of the model to the death in about 3 h, while those of NTHS group were increased slowly. The level of K+and Lac were positively correlated in the two groups amd the correlation coeffi cient were rDTHS=0.927 (P<0.01) and rNTHS=0.539 (P<0.01),respectively. The level of Glu was progressively decrease in DTHS group, while in NTHS group, it was not noticeable. The level of K+and Glu were negatively correlated in the two group, the correlation coeffi cient were rDTHS=-0.804 (P<0.01) and rNTHS=0.420 (P<0.01),respectively. Conclusions The changes of serum K+, Lac and Glu occurred sooner and more obvious in traumatic hemorrhagic shock models inside dry heat environment (DTHS) group than those in NTHS group. The level of serum K+positively correlated with Lac, however, negatively correlated with Glu, which suggested that hyperkalemia and acidosis should be paid more attention to the treatment of traumatic hemorrhagic shock inside the dry heat environment, and the hypoglycemia should be treated at the same time.

Objective To study the changes of oxidative stress and caspase-3 in swine with traumatic hemorrhagic shock in dry-heat environment of desert.Methods A total of 48 Landrace small swine were randomly(random number)divided into 2 groups(n=24 in each group), and then the traumatic hemorrhagic shock was established in room temperature environment and in dry-heat environmentin swine.Dry-heat environment traumatic hemorrhagic shock group (DHS), which was made in an artificial experiment cabin mimic the reality included swine exposed in the dry-heat environment of desert for 3 h (T0, n=6), T1 (50 min after shock modeling, n=6), T2 (100 min after shock modeling, n=6), T3 (150 min after shock modeling, n=6).At each interval, blood sample was collected to detect urea nitrogen (BUN) and creatinine, urine sample was collected to detect neutrophil gelatinase-associated lipoprotein (NGAL), kidney tissue samples were collected to evaluate renal morphological and tubular scores, as well as to detect catalase (CAT), superoxide dismutase (SOD) and malondialdehyde (MDA).Western blot was used to detect the level of caspase-3.Traumatic hemorrhagic shock group of room temperature environment (RTS) was established and variety of assays were carried out as same as those deteced in the dry-heat environment group.Results Compared with the room temperature environment exposed group,kidney damage index, antioxidant and caspase-3 were increased in desert dry-heat environment exposed for 3 h group, but there were no statistically significant difference(P> 0.05).And from T1 then on, the levels of NGAL, CAT and SOD in DHS groups were increased which were significant different from those in RTS group (P<0.05 or P<0.01).There were significant differences in BUN and creatinine at T2 between two groups(P<0.05).At T3, caspase-3 protein content in DHS group was significantly different from that in RTS group (P<0.01).Correlation analysis showed that the NGAL level was correlated with the levels to MDA (rRTS=0.935, rDHS =0.858, P<0.01) in RTS group and DHS group.Compared with RTS group, renal tissue under light microscope showed that Bowman appeared dilated with degeneration and exfoliated epithelial cells, proximal tubule epithelial shedding, and interstitial edema in DHS group.Electron microscope showed that mitochondria became pleomorphic, endoplasmic reticulum with fold broadening.Conclusions When traumatic hemorrhagic shock happened in the desert dry-heat environment, desert dry-heat environment can aggravate kidney damage, possibly by reducing the renal tissue antioxidant enzyme content and increase renal tissue caspase-3 activity to promote renal tissue apoptosis.Antioxidant stress and apoptosis may be an important role in the prevention of the secondary kidney injury induced by traumatic hemorrhagic shock in dry-heat environment.