ObjectiveTo establish a theoretical system of pharmacokinetic and spectrokinetic dose-time characterization of traditional Chinese medicine（TCM）. By analyzing the pharmacokinetic and spectrokinetic behaviors of Lonicerae Flos， Houttuyniae Herba injection， Lonicerae Japonicae Flosand Buyang Huanwutang， this paper compared the similarities and differences of the three methods for characterizing the dose-time relationship， namely half-life， statistical moment and statistics， in order to find the most suitable method for characterizing the relationship. MethodTen mice were randomly selected from 100 Kunming mice as the blank group， and the remaining mice were coated with xylene in the auricle to establish the acute inflammation model of ear swelling. After successful modeling， the mice were gavaged with aqueous extract of Lonicerae Flos（30 g∙kg^-1）， and the blank group was gavaged with an equal volume of normal saline. The plasma of mice was collected at different time points to determine the content changes of components. At the same time， the pharmacokinetic results of Houttuyniae Herba injection， Lonicerae Japonicae Flos and Buyang Huanwutang were included， and the pharmacokinetic and spectrokinetic parameters were calculated. Then the difference in the time of calculating 95% total component content of metabolism by half-life method， statistical moment method and statistical method was compared. ResultOn the basis of the half-life method， the mathematical expressions of statistical moment method and statistical method suitable for the characterization of dose-time relationship of multi-component system of TCM were established. The results showed that the pharmacokinetic parameters of the individual components in Lonicerae Flos varied， with cryptochlorogenic acid and rutin showing a two-compartment model and the other components showing a one-compartment model. After calculation of spectrokinetic similarity， the metabolic patterns among the components contained in Houttuyniae Herba injection， Lonicerae Japonicae Flos， Lonicerae Flos and Buyang Huanwutang were different and varied greatly in vivo. The time to metabolize 95% of the total components of the four research subjects in vivo was calculated by the half-life method， statistical moment method and statistical method， and it was found that the difference between statistical moment method and half-life method was large， and the difference between statistical moment method and statistical method was small. ConclusionStatistical method not only reflects the characteristics of statistical moment method， characterizes the dispersion degree of each component， but also can be associated with fingerprint to form spectrokinetics， characterizing the dose-time relationship of 95% of drug components， which is a more desirable method to characterize the dose-time relationship of the component groups in TCM.

Objective To propose a heart sound segmentation method based on multi-feature fusion network. Methods Data were obtained from the CinC/PhysioNet 2016 Challenge dataset (a total of 3 153 recordings from 764 patients, about 91.93% of whom were male, with an average age of 30.36 years). Firstly the features were extracted in time domain and time-frequency domain respectively, and reduced redundant features by feature dimensionality reduction. Then, we selected optimal features separately from the two feature spaces that performed best through feature selection. Next, the multi-feature fusion was completed through multi-scale dilated convolution, cooperative fusion, and channel attention mechanism. Finally, the fused features were fed into a bidirectional gated recurrent unit (BiGRU) network to heart sound segmentation results. Results The proposed method achieved precision, recall and F1 score of 96.70%, 96.99%, and 96.84% respectively. Conclusion The multi-feature fusion network proposed in this study has better heart sound segmentation performance, which can provide high-accuracy heart sound segmentation technology support for the design of automatic analysis of heart diseases based on heart sounds.

ObjectiveTo study the characteristics of imprinting template of flavonoid clusters in four Chinese medicines attributed to the lung meridian， and to establish an in vitro experimental approach for the study of the attribution of Chinese medicines to the lung meridian. MethodBased on 13 Chinese medicines， including Xanthii Fructus， Houttuyniae Herba， Fagopyri Dibotryis Rhizoma， Belamcandae Rhizoma and so on， which only belong to the lung meridian in Chinese Materia Medica（the 13^th Five-Year planning textbook of general higher education）， we identified four representative Chinese medicines， namely Houttuyniae Herba， Fagopyri Dibotryis Rhizoma， Belamcandae Rhizoma and Mori Cortex， and set up their fingerprints by high performance liquid chromatography（HPLC） and calculated the molecular connectivity indices of various components in the four Chinese medicines， the similarity to their mean value was calculated by included angle cosine method， so as to establish the quantitative relationship of construction versus imprinting ability， and to determine the order of each component in the lung meridian. A total of 7 reference substances， including chlorogenic acid， rutin， quercetin， isoquercitrin， hyperoside， epicatechin， and iridin， were selected to validate the overall conformational relationships of flavonoids of the model， as well as its predictive ability. ResultHouttuyniae Herba， Fagopyri Dibotryis Rhizoma， Belamcandae Rhizoma and Mori Cortex contained a total of 437 chemical components with an average molecular connectivity index similarity of 0.995 6. The four Chinese medicines contained a total of 204 flavonoids with an average molecular connectivity index similarity of 0.978 0， which was second only to the alkaloids with 0.985 1. The retention time（t_R） of the 7 reference substances showed a good conformational relationship with the similarity of the molecular connectivity index（t_R=831.4×S-790.3， r=0.861 4， P<0.01）， which was applicable to the in vitro attribution study of the position， similarity， and relative similarity with t_R of the cluster of 98.04% of flavonoids. Accordingly， the 1^st position was kuwanon D， with a similarity of molecular connectivity index of 0.987 7 and a t_R of 30.88 min， the 200^th position was chlorogenic acid， with a similarity of molecular connectivity index of 0.958 2 and a t_R of 6.36 min. The total first-order moment of the four Chinese medicines calculated by total statistical moment method of fingerprint was 24.26 min， ranked 21， which could characterize 99.19% of the whole， and the total first-order moment of the total peak area of the 7 reference substances in the four Chinese medicines was 20.00 min， with a rank of 46， which could characterize 98.68% of the whole. ConclusionFlavonoid clusters are suitable probes for the characterization of imprinting template for the study of the lung meridian， which can be established a quantitative imprinting method for meridian tropism of Chinese medicines in vitro.

Objective:To explore the application of a scenario-based onsite first-aid skills station in objective structured clinical examination (OSCE).Methods:Based on common scenarios and cases in medical practice, an evaluation framework of the OSCE onsite first-aid skills station—containing assessment indicators, exam room setting, examiner training, and assessment process—was designed to evaluate the onsite first-aid competencies of medical graduates of the five-year program for three consecutive years. SPSS 24.0 was used to perform the Kruskal-Wallis test and Pearson correlation analysis to calculate the correlation between course examination scores and OSCE onsite first-aid skills station assessment scores. Excel was used to calculate the difficulty index and discrimination index of test items.Results:The graduates' OSCE onsite first-aid skills station assessment scores were improved year by year, with a mean score of about 80 points. The station assessment items showed a moderate difficulty level (0.7-0.8), a good discrimination level (>0.4), and good internal consistency (Cronbach's α>0.7). The examiners and examinees had a high recognition of the design and effectiveness of this station assessment method. There was a positive correlation between the OSCE scores and corresponding course scores (2016, r=0.245, P=0.001; 2017, r=0.108, P=0.026; 2018, r=0.198, P=0.006). Conclusions:Through scientific scoring and strict examination management, the OSCE scenario-based onsite first-aid skills station can effectively evaluate examinees' injury treatment competencies in different situations, which can provide a reference for course teaching.

Trigeminal neuralgia(TN)is a common neurological disease in clinical practice,which often causes unbearable pain to patients.This paper reported a rare case of idiopathic TN after external carotid artery stenting(ECAS)and analyzed the cause.It is considered to be related to the compression and impact to the trigeminal nerve from the blood flow of the maxillary artery which had been improved after ECAS.TN was effectively relieved after treatment with carbamazepine,and not any discomfort was observed at 1-week follow-up after discharge.Currently,TN of this patient has been well controlled.Since there are no any relevant reports in clinical practice,this case report is provided to clinical physicians for reference and discussion.

Objective To observe the value of feature pyramid network(FPN)for automatic segmentation and semantic feature classification of spontaneous intracerebral hemorrhage(sICH)hematoma showed on non-contrast CT.Methods Non-contrast CT images of 408 sICH patients in hospital A(training set)and 103 sICH patients in hospital B(validation set)were retrospectively analyzed.Deep learning(DL)segmentation model was constructed based on FPN to segment the hematoma region,and its efficacy was assessed using intersection over union(IoU),Dice similarity coefficient(DSC)and accuracy.Then DL classification model was established to identify the semantic features of sICH hematoma.Receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the efficacy of DL classification model for recognizing semantic features of sICH hematoma.Results The IoU,DSC and accuracy of DL segmentation model for 95％sICH hematoma in training set was 0.84±0.07,0.91±0.04 and(88.78±8.04)％,respectively,which was 0.83±0.07,0.91±0.05 and(88.59±7.76)％in validation set,respectively.The AUC of DL classification model for recognizing irregular shape,uneven density,satellite sign,mixed sign and vortex sign of sICH hematoma were 0.946-0.993 and 0.714-0.833 in training set and validation set,respectively.Conclusions FPN could accurately,effectively and automatically segment hematoma of sICH,hence having high efficacy for identifying semantic features of sICH hematoma.

Objective To observe the value of 3D Res2Net deep learning model for predicting volume doubling time(VDT)of solid pulmonary nodule.Methods Chest CT data of 734 patients with solid pulmonary nodules were retrospectively analyzed.The patients were divided into progressive group(n=218)and non-progressive group(n=516)according to whether lung nodule volume increased by ≥25％during follow-up or not,also assigned into training set(n=515)and validation set(n=219)at a ratio of 7∶3.Then a clinical model was constructed based on clinical factors being significantly different between groups,CT features model was constructed based on features of nodules on 2D CT images using convolutional neural network,and 3D Res2Net model was constructed based on Res2Net network using 3D CT images as input.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated.Taken actual VDT as gold standard,the efficacy of the above models for predicting solid pulmonary nodule'VDT≤400 days were evaluated.Results No significant difference of predicting efficacy for solid pulmonary nodule'VDT≤400 days was found among clinical model,CT feature model and 3D Res2Net model,the AUC of which was 0.689,0.698 and 0.734 in training set,0.692,0.714 and 0.721 in validation set,respectively.3D Res2Net model needed 5-7 s to predict VDT of solid pulmonary nodules,with an average time of(5.92±1.08)s.Conclusion 3D Res2Net model could be used to predict VDT of solid pulmonary nodules,which might obviously reduce manual interpreting time.

Objective     To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods     A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results     The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion     This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.

Objective    To explore the application of Tsetlin Machine (TM) in heart beat classification. Methods    TM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electro-cardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. Results    The classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. Conclusion    TM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.

Objective:To compare research hotspots in digital transformation of higher education in China and abroad and analyze the research trends through the bibliometric method and text analysis method, and to offer advice and suggestions for digital transformation of higher education in China.Methods:The relevant literature in Web of Science and China National Knowledge Infrastructure from the establishment of the databases to April 1, 2023 were retrieved. Citespace and VOSviewer were used to visually compare the research hotspots and trends in digital transformation of higher education in China and abroad from the perspectives of number of published papers, journal, author, country, cooperation network, keyword co-occurrence and clustering, and keyword burst.Results:The results showed that both international and domestic researchers paid attention to the practical significance and development model of digital transformation of higher education in the context of rapid development of science and technology and the post-pandemic era. International researchers emphasized the advantages and disadvantages of digital transformation from the aspects of connotation, practice logic, and theoretical framework, while Chinese researchers focused on clarifying the principles related to digital transformation, summarizing the experience of other countries, and exploring the development path. The application of educational technology and the digital literacy of teachers and students had become research hotspots in China and abroad.Conclusion:The research on digital transformation of higher education has gradually shifted from theoretical system to practice effect. In the future, with the continuous deepening of theoretical research, how to improve the effectiveness of digital transformation of higher education is a research direction worthy of attention.