BACKGROUND: Severe stroke has high rates of mortality and morbidity. This study aimed to investigate the clinical course, causes of worsening, and outcomes of severe ischemic stroke. METHODS: This prospective, multicenter cohort study enrolled adult patients admitted ≤30 days after ischemic stroke from nine hospitals in China between September 2017 and December 2019. Severe stroke was defined as a score of ≥15 on the National Institutes of Health Stroke Scale (NIHSS). Clinical worsening was defined as an increase of 4 in the NIHSS score from baseline. Unfavorable functional outcome was defined as a modified Rankin scale score ≥3 at 3 months and 1 year after stroke onset, respectively. We performed Logistic regression to explore baseline features and reperfusion therapies associated with clinical worsening and functional outcomes. RESULTS: Among 4201 patients enrolled, 854 patients (20.33%) had severe stroke on admission. Of 3347 patients without severe stroke on admission, 142 (4.24%) patients developed severe stroke in hospital. Of 854 patients with severe stroke on admission, 33.95% (290/854) experienced clinical worsening (median time from stroke onset: 43 h, Q1-Q3: 20-88 h), with brain edema (54.83% [159/290]) as the leading cause; 24.59% (210/854) of these patients died by 30 days, and 81.47% (677/831) and 78.44% (633/807) had unfavorable functional outcomes at 3 months and 1 year respectively. Reperfusion reduced the risk of worsening (adjusted odds ratio [OR]: 0.24, 95% confidence interval [CI]: 0.12-0.49, P  <0.01), 30-day death (adjusted OR: 0.22, 95% CI: 0.11-0.41, P  <0.01), and unfavorable functional outcomes at 3 months (adjusted OR: 0.24, 95% CI: 0.08-0.68, P <0.01) and 1 year (adjusted OR: 0.17, 95% CI: 0.06-0.50, P  <0.01). CONCLUSIONS: Approximately one-fifth of patients with ischemic stroke had severe neurological deficits on admission. Clinical worsening mainly occurred in the first 3 to 4 days after stroke onset, with brain edema as the leading cause of worsening. Reperfusion reduced the risk of clinical worsening and improved functional outcomes. REGISTRATION ClinicalTrials.gov , NCT03222024.
Humans ; Male ; Female ; Prospective Studies ; Ischemic Stroke/mortality* ; Aged ; Middle Aged ; Aged, 80 and over ; Stroke ; Brain Ischemia

Objective To establish an HPLC fingerprint of Dingdang Qigui Decoction and analyze and evaluate it using chemical pattern recognition technology;To determine the contents of 5 effective chemical components in Dingdang Qigui Decoction;To provide a basis for its quality control.Methods The analysis was performed on Agilent 5 TC-C18(2)column(250 mm×4.6 mm).The mobile phase comprised of acetonitrile-0.1%phosphoric acid aqueous solution with the gradient elution at a flow rate of 1.0 mL/min.The detection wavelength was set at 260 nm.The column temperature was maintained at 30℃and the injection volume was 10 μL.SPSS 26.0 and SIMCA 14.1 were used to perform clustering analysis and principal component analysis on the 10 batches of Didang Qigui Decoction.The landmark components for inter batch differences were selected through orthogonal partial least squares discriminant analysis(OPLS-DA).Results The HPLC fingerprint with eighteen common peaks of Didang Qigui Decoction in 10 batches of sample was established,and the similarities of samples were between 0.828 and 0.989.Five indicative components were identified and quantitatively analyzed by comparing with the reference substances,which were paeoniflorin,mauroisoflavone glucoside,hesperidin,cinnamaldehyde and aloe rhodopsin.The linear ranges was 10.000 0-320.000 0 μg/mL,2.500 0-80.000 0 μg/mL,10.000 0-320.000 0 μg/mL,10.000 0-320.000 0 μg/mL,0.078 1-5.000 0 μg/mL,respectively,and their mean recovery ranged from 100.30%to 104.09%.Clustering analysis and principal component analysis divided 10 batches of samples from Didang Qigui Decoction into 2 categories.Through OPLS-DA screening,hairy pistil isoflavone glycosides,paeoniflorin,and hesperidin were selected as landmark components for quality differences.Conclusion The quality evaluation method for Didang Qigui Decoction established in this study is simple,sensitive,accurate,and reproducible,which can provide a basis for the quality evaluation of Didang Qigui Decoction.

Objective To introduce a locating device for the entry point of intramedullary nail based on the inertial navigation technology,which utilizes multi-dimensional angle information to assist in rapid and accurate positioning of the ideal direction of femoral anterograde intramedullary nails'entry point,and to verify its clinical value through clinical tests.Methods After matching the locating module with the developing board,which are the two components of the locating device,they were placed on the skin surface of the proximal femur of the affected side.Anteroposterior fluoroscopy was performed.The developing angle corresponding to the ideal direction of entry point was selected based on the X-ray image,and then the yaw angle of the locating module was reset to zero.After resetting,the locating module was combined with the surgical instrument to guide the insertion angle of the guide wire.The ideal direction of entry point was accurately located based on the angle guidance.By setting up an experimental group and a control group for clinical surgical operations,the number of guide wire insertion times,surgical time,fluoroscopy frequency,and intraoperative blood loss with or without the locating device was recorded.Results Compared to the control group,the experimental group showed significant improvement in the number of guide wire insertion times,surgical time,fluoroscopy frequency,and intraoperative blood loss,with a statistically significant difference(P<0.01).Conclusion The locating device can assist doctors in quickly locating the entry point of intramedullary nail,effectively reducing the fluoroscopy frequency and surgical time by improving the success rate of the guide wire insertion with one shot,improving surgical efficiency,and possessing certain clinical value.

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.

ObjectiveTo investigate the application of Mengchao Liver Disease-Brain System version 2.0 in clinical diagnosis and treatment. MethodsThis study was conducted among 160 patients who were admitted to the internal medicine and surgical departments from June 9 to 21， 2021， and their data were automatically captured by the intelligent information system of Southeast Big Data Institute of Hepatobiliary Health， Mengchao Hepatobiliary Hospital of Fujian Medical University. The completeness and accuracy of Mengchao Liver Disease-Brain System version 2.0 were evaluated based on the intelligent diagnostic tools such as auxiliary diagnosis of chronic hepatitis B， interpretation of liver fibrosis， staging model of chronic hepatitis B， auxiliary diagnosis of liver cirrhosis， auxiliary staining of liver cirrhosis， auxiliary diagnosis of primary liver cancer， BCLC stage of primary liver cancer， Chinese staging of primary liver cancer， Child-Pugh score， and APRI score. ResultsAll auxiliary diagnostic tools had a complete rate of 94.17% in terms of the extraction of correct key dimensions within the test period. The artificial intelligence report had a structured accuracy of 97.55% in capturing data and an accuracy rate of 91.61% in text processing. ConclusionMengchao Liver Disease-Brain System version 2.0 provides an innovative mode for the construction of big data platform in medical specialties and has a high accuracy as an auxiliary diagnostic tool in clinical diagnosis and treatment.

Objective:To characterize the histopathological subtypes and their clinicopathological parameters of gender and onset age by common, rare and sparse primary esophageal malignant tumors (PEMT).Methods:A total of 272 437 patients with PEMT were enrolled in this study, and all of the patients were received radical surgery. The clinicopathological information of the patients was obtained from the database established by the State Key Laboratory of Esophageal Cancer Prevention & Treatment from September 1973 to December 2020, which included the clinical treatment, pathological diagnosis and follow-up information of esophagus and gastric cardia cancers. All patients were diagnosed and classified by the criteria of esophageal tumor histopathological diagnosis and classification (2019) of the World Health Organization (WHO). The esophageal tumors, which were not included in the WHO classification, were analyzed separately according to the postoperative pathological diagnosis. The χ 2 test was performed by the SPSS 25.0 software on count data, and the test standard α=0.05. Results:A total of 32 histopathological types were identified in the enrolled PEMT patients, of which 10 subtypes were not included in the WHO classification. According to the frequency, PEMT were divided into common (esophageal squamous cell carcinoma, ESCC, accounting for 97.1%), rare (esophageal adenocarcinoma, EAC, accounting for 2.3%) and sparse (mainly esophageal small cell carcinoma, malignant melanoma, etc., accounting for 0.6%). All the common, rare, and sparse types occurred predominantly in male patients, and the gender difference of rare type was most significant (EAC, male∶ female, 2.67∶1), followed with common type (ESCC, male∶ female, 1.78∶1) and sparse type (male∶ female, 1.71∶1). The common type (ESCC) mainly occurred in the middle thoracic segment (65.2%), while the rare type (EAC) mainly occurred in the lower thoracic segment (56.8%). Among the sparse type, malignant melanoma and malignant fibrous histiocytoma were both predominantly located in the lower thoracic segment (51.7%, 66.7%), and the others were mainly in the middle thoracic segment.Conclusion:ESCC is the most common type among the 32 histopathological types of PEMT, followed by EAC as the rare type, and esophageal small cell carcinoma and malignant melanoma as the major sparse type, and all of which are mainly occur in male patients. The common type of ESCC mainly occur in the middle thoracic segment, while the rare type of EAC mainly in the lower thoracic segment. The mainly sparse type of malignant melanoma and malignant fibrous histiocytoma predominately occur in the lower thoracic segment, and the remaining sparse types mainly occur in the middle thoracic segment.

Deep learning is a process in which machine learning obtains new knowledge and skills by simulating the learning behavior of human brain through massive data training and analysis. With the development of medical technology, a large amount of data has been accumulated in the medical field, and the research on data may help to understand the relationships and rules within data and predict the onset and prognosis of human diseases. Deep learning can find the hidden information in data and has been increasingly used in the medical field. Primary liver cancer is a malignant tumor with high incidence and mortality rates, poor prognosis, and a high recurrence rate, and early diagnosis, timely treatment, and prediction of recurrence have always been the research hotspots in recent years. This article reviews the advances in the application of deep learning in the diagnosis and recurrence of liver cancer from the aspects of risk prediction, postoperative recurrence, and survival risk prediction.

In order to understand the evolution of the diagnosis and treatment plans of corona virus disease 2019 (COVID-19), and provide convenience for medical staff in actual diagnosis and treatment, this paper uses the 9 diagnosis and treatment plans of COVID-19 issued by the National Health Commission during the period from January 26, 2020 to August 19, 2020 as research data to perform comparative analysis and visual analysis. Based on text mining, this paper obtained the text similarity and summarized its evolution law by expressing and measuring the similarity of the overall diagnosis and treatment plans of COVID-19 and the same modules, which provides reference for clinical diagnosis and treatment practice and other diagnosis and treatment plan formulation.
COVID-19 ; Data Mining ; Humans ; SARS-CoV-2

Objective:To clarify the characteristics of renal cortical microcirculation and its relationship with the expression of plasma endothelial microparticle (EMP) in septic rats, and to evaluate the effect of Xuebijing injection as an adjuvant therapy of antibiotics on septic AKI.Methods:The 8-10 weeks old specific pathogen free (SPF) male Sprague-Dawley (SD) rats were divided into sham operation group (Sham group), positive drug control group and Xuebijing group by the random number table method, with 10 rats in each group. The cecal ligation and puncture (CLP) with large ligation (ligated 75% of the cecum) was used to prepare a rat high-grade sepsis model; in the Sham group, the cecum was stretched without ligation or puncture. Due to the high mortality of CLP with large ligation, Xuebijing injection (4 mL/kg, 12 hours per time) and imipenem/cilastatin injection (90 mg/kg, 6 hours per time) were administered to the rats in the Xuebijing group via the tail vein immediately after the model was produced. Normal saline and imipenem/cilastatin were administered to the rats by the same methods in the positive drug control group. The rats in the Sham group were treated with the same volume of normal saline as any of the other two groups at the same frequency. At 48 hours after model reproduction, the mean arterial pressure (MAP) and blood lactic acid (Lac) of the rats were measured. The renal cortical microcirculation was monitored by using side stream dark-field imaging. Renal hypoxia signals were assessed by pimonidazole chloride immunohistochemistry. Plasma EMP levels were determined by using flow cytometry, and then the correlation between EMP and microcirculation parameters of renal cortex was analyzed. At the same time, the serum creatinine (SCr) was measured, and the renal injury score (Paller score) was used to evaluate the severity of renal tissue pathological damage.Results:Compared with the Sham group, perfused vessel density (PVD), microvascular flow index (MFI) and MAP in the positive drug control group and the Xuebijing group decreased significantly, the positive expression of hypoxia probe (pimonidazole) increased, Lac, EMP, Paller score and SCr increased significantly. However, compared with the positive drug control group, the renal cortical microcirculation in the Xuebijing group was improved significantly, PVD and MFI were increased significantly [PVD (mm/mm 2): 16.20±1.20 vs. 9.77±1.12, MFI: 2.46±0.05 vs. 1.85±0.15, both P < 0.05], Lac was reduced significantly (mmol/L: 4.81±1.23 vs. 6.08±1.09, P < 0.05), MAP increased slightly [mmHg (1 mmHg = 0.133 kPa): 84.00±2.00 vs. 80.00±2.00, P > 0.05], suggested that Xuebijing injection improved renal microcirculation perfusion in septic rats, and this effect did not depend on the change of MAP. The positive expression of pemonidazole in renal cortex of the Xuebijing group was significantly lower than that of the positive drug control group [(35.89±1.13)% vs. (44.93±1.37) %, P < 0.05], suggested that Xuebijing injection alleviated renal hypoxia. The plasma EMP levels of rats in the Xuebijing group were significantly lower than those in the positive drug control group (×10 6/L: 3.49±0.17 vs. 5.78±0.22, P < 0.05), and the EMP levels were significantly negatively correlated with PVD and MFI ( r values were -0.94 and -0.95, respectively, both P < 0.05), indicated that the increase of plasma EMP was highly correlated with renal microcirculation disorder, and Xuebijing injection inhibited the increase of plasma EMP levels. The Paller score in the Xuebijing group was significantly lower than that in the positive drug control group (46.90±3.84 vs. 62.70±3.05, P < 0.05), and the level of SCr was also significantly lower than that in the positive drug control group (μmol/L: 121.1±12.4 vs. 192.7±23.9, P < 0.05), which suggested that Xuebijing injection relieved kidney injury and improved renal function in septic rats. Conclusion:As an adjuvant therapy of antibiotics, Xuebijing injection could inhibit the expression of plasma EMP in rats with sepsis, improve renal cortex microcirculation, and reduce kidney injury.

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.