Objective To observe the effect of Baduanjin exercise on depression,sleep quality and life quality of patients with breast cancer in the rehabilitation period.Methods A total of 76 breast can-cer patients in postoperative rehabilitation were randomly divided into an intervention group of 38 with 2 dropping out,and a control group of 38 with 3 dropouts.Both groups received routine nursing and rehabilitation after breast cancer surgery,while the intervention group additionally practised Baduanjin for 6 weeks.The Beck Depression Inventory-Ⅱ(BDI-Ⅱ-C),Pittsburgh Sleep Quality Index(PSQI)and Quality of Life Questionnaire Core 30(EORTC QLQ-C30)were used to evaluate both groups be-fore,as well as 3 and 6 weeks after intervention.Results After 3-week intervention,the average BDI-Ⅱ-C score,the total PSQI score and the scores of all dimensions except for the hypnotic drug dimen-sion of the intervention group was significantly lower than before treatment(P<0.05),and the control group at the same time point(P<0.05),while the scores of physical,emotional,cognitive,social and role function in EORTC QLQ-C30 were significantly higher than the latter(P<0.05).Three weeks lat-er,the average BDI-Ⅱ-C score,the total PSQI score and the scores of all dimensions except for the hypnotic drug dimension of the intervention group was significantly lower than before treatment(P<0.05),and the control group at the same time point(P<0.05),while the various scores of EORTC QLQ-C30 were significantly higher than the latter(P<0.05).Compared with after 3-week intervention,after 6-week intervention,the average BDI-Ⅱ-C score,the total PSQI score and the scores of its all dimensions except for the sleep disorder dimension of the intervention group decreased significantly,while all dimension scores of EORTC QLQ-C30 except the cognitive function dimension increased sig-nificantly(P<0.05 for all).Conclusion Baduanjin is feasible in improving the sleep and life quality of patients in the rehabilitation period after breast cancer surgery,and relieving their depression.

Objective To summarize the best evidence of nutrition management in patients with severe pneumonia,aiming to offer evidence-based guidance for clinical healthcare professionals.Methods All evidence on nutrition management in patients with severe pneumonia was retrieved from various databases and websites including BMJ Best Practice,UpToDate,National Guideline Clearinghous(NGC),Guidelines International Network(GIN),Registered Nurses'Association of Ontario(RNAO),National Institute for Health and Care Excellence(NICE),Cochrane Library,OVID,PubMed,Embase,Web of Science,CINAHL,Chinese Medical Journal Full-text Database,CNKI,VIP,Wanfang,CBM,American Society for Parenteral and Enteral Nutrition(ASPEN),European Society for Clinical Nutrition and Metabolism(ESPEN),Society of Critical Care Medicine(SCCM)and European Society of Intensive Care Medicine(ESICM).The retrieved evidence included clinical decisions,guidelines,systematic reviews,expert consensuses and evidence summaries.The search period ranged from January 1st,2012 to December 31st,2022.There were 2 researchers who independently assessed the quality of the included studies and then extracted and summarized the evidence by topic.Results A total of 13 articles were involved,including 3 clinical decisions,4 guidelines,1 systematic review,and 5 expert consensuses.A total of 24 pieces of evidence were summarized across 6 aspects which encompassed team building,nutrition screening and assessment,nutritional requirements,nutrition intervention,nutrition monitoring,and health education.Conclusion The production process of this evidence summary followed standardized procedures,ensuring comprehensive content.Medical professionals should integrate clinical conditions,patient factors,and family preferences to select the most optimal evidence in order to enhance patient prognosis and improve medical quality.

Methods: After screening the dataset provided in the published literature, this study included 495 CHMs and their 8 075 compounds. Three molecular descriptors were used to represent the compounds: the molecular access system (MACCS), extended connectivity fingerprint (ECFP), and two-dimensional (2D) molecular descriptors computed by the RDKit open-source toolkit (RDKit_2D). A homogeneous graph with CHMs as nodes was constructed and a classification model for the cold and hot medicinal properties of CHMs was developed based on a GCN using the molecular descriptor information of the compounds as node features. Finally, using accuracy and F1 score to evaluate model performance, the GCN model was experimentally compared with the traditional machine learning approaches, including decision tree (DT), random forest (RF), k-nearest neighbor (KNN), Naïve Bayes classifier (NBC), and support vector machine (SVM). MACCS, ECFP, and RDKit_2D molecular descriptors were also adopted as features for comparison. Results: The experimental results show that the GCN achieved better performance than the traditional machine learning approach when using MACCS as features, with the accuracy and F1 score reaching 0.836 4 and 0.845 3, respectively. The accuracy and F1 score have increased by 0.8690 and 0.8120, respectively, compared with the lowest performing feature combination OMER (only the combination of MACCS, ECFP, and RDKit_2D). The accuracy and F1 score of DT, RF, KNN, NBC, and SVM are 0.505 1 and 0.501 8, 0.616 2 and 0.601 5, 0.676 8 and 0.624 3, 0.616 2 and 0.607 1, 0.636 4 and 0.622 5, respectively. Conclusion In this study, by introducing molecular descriptors as features, it is verified that molecular descriptors and fingerprints play a key role in classifying the cold and hot medicinal properties of CHMs. Meanwhile, excellent classification performance was achieved using the GCN model, providing an important algorithmic basis for the in-depth study of the “structure-property” relationship of CHMs.

With bio-materials being widely applied in clinical practice, acellular dermal matrix (ADM), one of the most competitive techniques in tissue engineering, has been developed rapidly in recent years, gaining popularity for its advantages in clinical application. What’s more, micronized acellular dermal matrix (mADM) has received an increasing amount of attention for its unique morphological characteristics, particularly in the field of cosmetic injection. Compared with other techniques, mADM has the advantages of great safety and sustainability, with less minimal invasion. However, further practical validation is still needed to determine the most suitable media for mADM injection as well as the most appropriate ratio of media to mADM transplantation. This paper reviews the relevant research findings.

With bio-materials being widely applied in clinical practice, acellular dermal matrix (ADM), one of the most competitive techniques in tissue engineering, has been developed rapidly in recent years, gaining popularity for its advantages in clinical application. What’s more, micronized acellular dermal matrix (mADM) has received an increasing amount of attention for its unique morphological characteristics, particularly in the field of cosmetic injection. Compared with other techniques, mADM has the advantages of great safety and sustainability, with less minimal invasion. However, further practical validation is still needed to determine the most suitable media for mADM injection as well as the most appropriate ratio of media to mADM transplantation. This paper reviews the relevant research findings.

Objective To use feature selection and Likert grading method to quantify the data of lung cancer medical records,to construct a deep extreme learning machine model optimized by the sparrow search algorithm,to classify and predict the syndrome types of traditional Chinese medicine medical record data of lung cancer,and to provide scientific and effective research on syndrome type classification of traditional Chinese medicine.means.Methods The medical records of 497 cases diagnosed with lung cancer from January 2015 to December 2021 were collected from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine,and 412 medical records were screened as the research objects.Syndromic factors of different syndromes were summarized by feature selection and feature importance ranking,and the syndrome factors were quantified by Likert grading method.Build a deep extreme learning machine optimized based on the sparrow search algorithm,and train and test the model.Finally,the model built in this paper is compared with other machine learning models according to three evaluation criteria.Results The average classification accuracy of the SSA-DELM model established in this paper was 88.44%,while the average accuracy of the support vector machine and Bayesian network was 83.39%and 84.53%,respectively.The recall rate and F1 value of the SSA-DELM model on the five syndrome types are mostly above 80%,which is also better than other traditional machine learning models.Conclusion The results of the study show that the use of feature selection combined with Likert grading method to quantify the lung cancer medical record data,compared with the 0-1 processing data,can show the characteristics of the data,improve the accuracy of the classification model,SSA-DELM new Compared with other traditional machine learning classification models,the model has better representation learning ability and learning speed.This model not only provides a scientific and technical means for the clinical treatment of lung cancer,but also provides a useful reference for the informatization and intelligent development of TCM syndrome differentiation and treatment.

Bioluminescence is a widespread phenomenon in nature, and luminescent organisms can be found both on land and in the ocean. Among them, luciferase based bioluminescence systems have been widely studied, inspiring the exploration of genetic and epigenetic aspects and the development of a series of related assays for in vivo and in vitro studies. This paper summarizes the recent developments of luciferase based bioluminescence assays in terms of bioluminescence systems, types of luciferases, and the development and application of luciferase bioluminescence assays.

With bio-materials being widely applied in clinical practice, acellular dermal matrix (ADM), one of the most competitive techniques in tissue engineering, has been developing rapidly in recent years, gaining popularity for its advantages in clinical application. What ’ s more, micronized acellular dermal matrix (mADM ) has received an increasing amount of attention for its unique morphological characteristics, particularly in the field of cosmetic injection. Compared with other techniques, mADM is of greater safety and sustainability, with less minimal invasion. However, further practical validation is still needed to determine the most suitable media for mADM injection as well as the most appropriate ratio of media to mADM transplantation. This paper reviews the relevant research findings.

With bio-materials being widely applied in clinical practice, acellular dermal matrix (ADM), one of the most competitive techniques in tissue engineering, has been developing rapidly in recent years, gaining popularity for its advantages in clinical application. What ’ s more, micronized acellular dermal matrix (mADM ) has received an increasing amount of attention for its unique morphological characteristics, particularly in the field of cosmetic injection. Compared with other techniques, mADM is of greater safety and sustainability, with less minimal invasion. However, further practical validation is still needed to determine the most suitable media for mADM injection as well as the most appropriate ratio of media to mADM transplantation. This paper reviews the relevant research findings.

OBJECTIVE：To comprehensi vely evaluate the application progress of network pharmacology methods in TCM compound prescription research ，and to provide reference for modernization of TCM compound prescription research. METHODS ： Taking“network pharmacology ”and“TCM compound prescription ”as keywords ，the literatures were retrieved from CNKI ， Wanfang database and VIP during May 2006 to May 2020. Screening literature ，the databases ，analysis platforms ，and software used of the literature were summarized ；on the basis of quantitative analysis ，the application of network pharmacology in the research of traditional Chinese medicine compound were summarized. RESULTS & CONCLUSIONS ：There were a total of 761 valid literatures ，among which the number of literatures that could be retrieved in 2019 reached 313. In the modernization research of TCM compound prescription ，network pharmacology methods were mainly usedmechanism ，material basis of pharmacodynamics ， compatibility law ，compound optimization ，and“effect-toxic”network. Commonly used databasesand platforms included traditional chinese medicine information database （TCMSP and TCMID ），therapeutic target database （TTD，OMIM），drug targets and target prediction platform （Drugbank，SwissTargetPrediction，TargetNet，PharmMapper），network pharmacology analysis and prediction software and platform （CytoScape），etc. Network pharmacology method was widely used in the field of TCM compound prescription research ，and provided new ideas and methods for the modernization of TCM compound prescription research. In the future，the related research can be combined with the pharmacokinetic parameters ，the efficacy of active compounds and related basic experiments ，use the weighted method to carry out network pharmacology analysis ，and integrate the information of multiple databases to improve the scientificity of research results.