Animal experiments are essential tools in biomedical research, serving as a bridge between basic research and clinical trials. Systematic reviews and meta-analyses (SRs/MAs) of animal experiments are crucial methods for integrating evidence from animal experiment, which can facilitate the translation of findings into clinical research, reduce translational risks, and promote resource integration in basic research. With the continuous development of the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology, its application in SRs/MAs of animal experiments has gained increasing attention. This article first outlines the principles and specific applications of the GRADE methodology in SRs/MAs of animal experiments, including qualitative descriptive systematic reviews, meta-analyses, and network meta-analyses. It then deeply analyzes the misuse of the GRADE methodology in practice, including incorrect evidence grading, improper classification of evidence, misapplication in qualitative systematic reviews, inconsistencies between the documentation of the upgrading and downgrading process and results, and inappropriate use for making recommendations. Furthermore, this article comprehensively discusses the factors influencing the grading of evidence certainty in SRs/MAs of animal experiments, including the impact of bias risk, indirectness, inconsistency, imprecision, and publication bias on evidence downgrading, as well as the role of large effect sizes and cross-species consistency in evidence upgrading. Finally, in response to the issues discussed, improvement strategies are proposed, including further research and optimization of the GRADE methodology for SRs/MAs of animal experiments, the development of reporting guidelines tailored to the characteristics of SRs/MAs in animal experiment research, and enhanced professional training for researchers in the GRADE methodology. This article aims to improve the quality of evidence in SRs/MAs of animal experiments, strengthen their reliability in clinical decision-making, and promote the more efficient translation of findings from animal experiment research into clinical practice.

BACKGROUND:Cardiac dysfunction due to spinal cord injury is an important factor of death in patients with spinal cord injury;however,the specific mechanism is still not clear.Therefore,revealing the mechanism of cardiac dysfunction in spinal cord injury patients is of great significance to improve their quality of life and survival rate. OBJECTIVE:To investigate the mechanism of luteolin in improving serum-induced myocardial cell death in spinal cord injury rats. METHODS:Allen's impact instrument was used to damage the spine T9-T11 of male SD rats to establish a spinal cord injury model meanwhile a sham operation group was set as the control group.The serum of rats of each group was collected.H9c2 cells were divided into a blank control group,a sham operated rat serum group,a spinal cord injury rat serum group and a luteolin pretreatment group.The cells in blank control group were only cultured with ordinary culture medium.The cells in the sham operated rat serum group were treated with medium containing 10%serum from sham operated rat.The cells in the spinal cord injury rat serum group were treated with medium containing 10%serum from spinal cord injury rat.The cells in the luteolin pretreatment group were precultured with a final concentration of 20 μmol/L luteolin for 4 hours and then changed to a medium containing 10%rat serum from spinal cord injury rat.After 24 hours of culture,the survival rate of each group of H9c2 cells was measured by CCK-8 assay.Western blot assay was used to detect the expression of autophagy related protein LC3 and p62 in H9c2 cells in each group. RESULTS AND CONCLUSION:Compared with the blank control group,there was no significant change in cell survival rate in the sham operated rat serum group(P>0.05).Compared with the sham operated rat serum group,the cell survival rate(P<0.01)and the expression of LC3 protein(P<0.05)in spinal cord injury rat serum group was significantly reduced,and the expression of p62 protein was significantly increased(P<0.05).Compared with the spinal cord injury rat serum group,the survival rate of cells in the luteolin pretreatment group significantly increased(P<0.000 1);the expression of LC3 protein significantly increased(P<0.05),and the expression of p62 protein significantly decreased(P<0.05).The results indicate that luteolin may improve myocardial cell death induced by serum from rats with spinal cord injury by promoting autophagy.

BACKGROUND:Gradient artificial bone repair scaffolds can mimic unique anatomical features in musculoskeletal tissues,showing great potential for repairing injured musculoskeletal tissues. OBJECTIVE:To review the latest research advances in gradient artificial bone repair scaffolds for tissue engineering in the musculoskeletal system and describe their advantages and fabrication strategies. METHODS:The first author of the article searched the Web of Science and PubMed databases for articles published from 2000 to 2023 with search terms"gradient,bone regeneration,scaffold".Finally,76 papers were analyzed and summarized after the screening. RESULTS AND CONCLUSION:(1)As an important means of efficient and high-quality repair of skeletal system tissues,gradient artificial bone repair scaffolds are currently designed bionically for the natural gradient characteristics of bone tissue,bone-cartilage,and tendon-bone tissue.These scaffolds can mimic the extracellular matrix of native tissues to a certain extent in terms of structure and composition,thus promoting cell adhesion,migration,proliferation,differentiation,and regenerative recovery of damaged tissues to their native state.(2)Advanced manufacturing technology provides more possibilities for gradient artificial bone repair scaffold preparation:Gradient electrospun fiber scaffolds constructed by spatially differentiated fiber arrangement and loading of biologically active substances have been developed;gradient 3D printed scaffolds fabricated by layered stacking,graded porosity,and bio-3D printing technology;gradient hydrogel scaffolds fabricated by in-situ layered injections,simple layer-by-layer stacking,and freeze-drying method;and in addition,there are also scaffolds made by other modalities or multi-method coupling.These scaffolds have demonstrated good biocompatibility in vitro experiments,were able to accelerate tissue regeneration in small animal tests,and were observed to have significantly improved histological structure.(3)The currently developed gradient artificial bone repair scaffolds have problems such as mismatch of gradient scales,unclear material-tissue interactions,and side effects caused by degradation products,which need to be further optimized by combining the strengths of related disciplines and clinical needs in the future.

BACKGROUND:LncRNA-TNFRSF13C,an important factor in B cell development and function,is expressed in periodontal tissues of patients with periodontitis,but the specific mechanism is still unclear. OBJECTIVE:To investigate the mechanism of lncRNA-TNFRSF13C regulating miR-1246 on hypoxia-inducible factor 1α in periodontal cells. METHODS:Human periodontal ligament cells(hPDLCs)were treated with lipopolysaccharide and divided into group A(hPDLCs cell lines without transfection),group B(hPDLCs cell lines transfected with TNFRSF13C NC-siRNA),group C(hPDLCs cell lines transfected with TNFRSF13C-siRNA),group D(hPDLCs cell line transfected with miR-1246 mimics),group E(hPDLCs cell line transfected with miR-1246 siRNA),group F(hPDLCs cell line transfected with TNFRSF13C-siRNA+miR-1246 mimics),and group G(hPDLCs cell line transfected with TNFRSF13C-siRNA+miR-1246 siRNA).The relative expression of lncRNA-TNFRSF13C and miR-1246 in each group was detected by qRT-PCR.Cell counting kit-8 assay was used to detect cell viability.Apoptosis was detected by flow cytometry.Expression of hypoxia-inducible factor 1α and vascular endothelial growth factor proteins was detected by western blot.The correlation between lncRNA-TNFRSF13C and miR-1246 was analyzed by Pearson,and the targeting relationship was analyzed by dual-luciferase reporter assay. RESULTS AND CONCLUSION:There was no significant difference in human periodontal ligament cell activity,apoptosis rate and protein indexes between groups A and B(P>0.05).Compared with group B,hPDLCS cell activity in group C was increased,and apoptosis rate and the expression of hypoxia-inducible factor 1α and vascular endothelial growth factor proteins were decreased(P<0.05).Compared with group C,hPDLCS cell activity in group D was decreased,and apoptosis rate and the expression of hypoxia-inducible factor 1α and vascular endothelial growth factor proteins were increased(P<0.05).Compared with group D,the cell activity of group E was increased(P<0.05).The cell activity in group F was lower than that in group E,and the apoptosis rate was reduced in both groups E and F(P<0.05).Compared with group F,the cell activity of group G was increased,and the apoptosis rate and the expression of hypoxia-inducible factor 1α and vascular endothelial growth factor were decreased(P<0.05).LncRNA-TNFRSF13C was positively correlated with miR-1246(P<0.05).Compared with the TNFRSF13C-siRNA group,the fluorescence activity of miR-1246-wt in the TNFRSF13C-NC group was reduced(P>0.05);compared with the miR-1246-NC group,the fluorescence activities of hypoxia-inducible factor 1α-wt and vascular endothelial growth factor-wt in the miR-1246 mimics group were increased(P<0.05).To conclude,down-regulation of lncRNA-TNFRSF13C can promote the activity of periodontal cells treated with lipopolysaccharide,reduce apoptosis,and inhibit hypoxia-inducible factor 1α and vascular endothelial growth factor.The mechanism is related to the regulation of miR-1246 activity.

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.

ObjectiveTo establish a risk recognition model for coronary artery stenosis by using a machine learning method and to identify the key causative factors. MethodsPatients aged ≥18 years，diagnosed with coronary heart disease through coronary angiography from January 2013 to May 2020 in two prominent hospitals in Shanxi Province， were continuously enrolled. Logistic regression，back propagation neural network （BPNN）， and random forest（RF）algorithms were used to construct models for detecting the causative factors of coronary artery stenosis. Sensitivity （TPR）， specificity （TNR）， accuracy （ACC）， positive predictive value （PV+）， negative predictive value （PV-）， area under subject operating characteristic curve （AUC）， and calibration curve were used to compare the discrimination and calibration performance of the models. The best model was then employed to predict the main risk variables associated with coronary stenosis. ResultsThe RF model exhibited superior comprehensive performance compared to logistic regression and BPNN models. The TPR values for logistic regression，BPNN，and RF models were 75.76%， 74.30%， and 93.70%， while ACC values were 74.05%， 72.30%， and 79.49%， respectively. The AUC values were：logistic regression 0.739 9； BPNN 0.723 1； RF 0.752 2. Manifestations such as chest pains，abnormal ST segments on ECG，ventricular premature beats with hypertension， atrial fibrillation， regional wall motion abnormalities（RWMA） by color echocardiography， aortic regurgitation（AR）， pulmonary insufficiency （PI）， family history of cardiovascular diseases，and body mass index（BMI）were identified as top ten important variables affecting coronary stenosis according to the RF model. ConclusionsRandom forest model shows the best comprehensive performance in identification and accurate assessment of coronary artery stenosis. The prediction of risk factors affecting coronary artery stenosis can provide a scientific basis for clinical intervention and help to formulate further diagnosis and treatment strategies so as to delay the disease progression.

[Objective] To perform genetic analysis on samples with weak agglutination and mixed agglutination of ABO blood group antigens in neonates, and to investigate the molecular biological characteristics of ABO subtypes in neonates. [Methods] Serological identification of ABO blood group was performed by tube method and microcolumn gel method. The ABO exons 2-7 were amplified by PCR, and the amplified products were sequenced by Sanger sequencing method to determine the genotype. [Results] Among the ABO blood group serological results of 14 neonates, 8 cases showed weakened A antigen, and 6 cases showed weakened B antigen. Seven samples were identified with ABO subtype alleles, with genotypes as A102/B101+c.538C>T, Aw26/B102, A205/O02, A205/B101(2 cases), Aw26/O02, B(A)06/O01, B101/O01(3 cases), A102/O01(2 cases), A102/B101 and B101/O02. Additionally, three other family members were also found to carry B(A)06 allele in a pedigree investigation. [Conclusion] For samples showing weakened antigens in ABO blood type identification of neonates, it is necessary to consider the possibility of ABO subtype in addition to age factors, and genetic testing can be used to prevent missed detection of ABO subtypes in neonates.

The development of traditional Chinese medicine (TCM) diagnosis and treatment has undergone multiple paradigms, evolving from sporadic experiential practices to systematic approaches in syndrome differentiation and treatment and further integration of disease and syndrome frameworks. TCM is a vital component of the medical system, valued alongside Western medicine. Treatment based on syndrome differentiation embodies both personalized treatment and holistic approaches; however, the inconsistency and lack of stability in syndrome differentiation limit clinical efficacy. The existing integration of diseases and syndromes primarily relies on patchwork and embedded systems, where the full advantages of synergy between Chinese and Western medicine are not fully realized. Recently, driven by the development of diagnosis and treatment concepts and advances in analytical technology, Western medicine has been rapidly transforming from a traditional biological model to a precision medicine model. TCM faces a similar need to progress beyond traditional syndrome differentiation and disease-syndrome integration toward a more precise diagnosis and treatment paradigm. Unlike the micro-level precision trend of Western medicine, precision diagnosis and treatment in TCM is primarily reflected in data-driven applications that incorporate information at various levels, including precise syndrome differentiation, medication, disease management, and efficacy evaluation. The current priority is to accelerate the development of TCM precision diagnosis and treatment technology platforms and advance discipline construction in this area.

ObjectiveTo investigate the influencing factors for hepatic hydrothorax （HH） before intervention for primary hepatic carcinoma （PHC）， and to construct and assess the nomogram risk prediction model. MethodsA retrospective analysis was performed for the clinical data of 353 hospitalized patients who attended the Third People’s Hospital of Kunming for the first time from October 2012 to October 2021 and there diagnosed with PHC， and according to the presence or absence of HH， they were divided into HH group with 153 patients and non-HH group with 200 patients. General data and the data of initial clinical testing after admission were collected from all PHC patients. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. After the multicollinearity test was performed for the variables with statistical significance determined by the univariate analysis， the multivariate Logistic regression analysis was used to identify independent influencing factors. The “rms” software package was used to construct a nomogram risk prediction model， and the Hosmer-Lemeshow test and the receiver operating characteristic （ROC） curve were used to assess the risk prediction model； the “Calibration Curves” software package was used to plot the calibration curve， and the “rmda” software package was used to plot the clinical decision curve and the clinical impact curve. ResultsAmong the 353 patients with PHC， there were 153 patients with HH， with a prevalence rate of 43.34%. Child-Pugh class B （odds ratio ［OR］=2.652， 95% confidence interval ［CI］： 1.050 — 6.698， P=0.039）， Child-Pugh class C （OR=7.963， 95%CI： 1.046‍ ‍—‍ ‍60.632， P=0.045）， total protein （OR=0.947， 95%CI： 0.914‍ ‍—‍ ‍0.981， P=0.003）， high-sensitivity C-reactive protein （OR=1.007， 95%CI： 1.001‍ ‍—‍ ‍1.014， P=0.025）， and interleukin-2 （OR=0.801， 95%CI： 0.653‍ ‍—‍ ‍0.981， P=0.032） were independent influencing factors for HH before PHC intervention， and a nomogram risk prediction model was established based on these factors. The Hosmer-Lemeshow test showed that the model had a good degree of fitting （χ²=5.006， P=0.757）， with an area under the ROC curve of 0.752 （95%CI： 0.701‍ ‍—‍ ‍0.803）， a sensitivity of 78.40%， and a specificity of 63.50%. The calibration curve showed that the model had good consistency in predicting HH before PHC intervention， and the clinical decision curve and the clinical impact curve showed that the model had good clinical practicability within a certain threshold range. ConclusionChild-Pugh class， total protein， interleukin-2， and high-sensitivity C-reactive protein are independent influencing factors for developing HH before PHC intervention， and the nomogram model established based on these factors can effectively predict the risk of developing HH.

Psychological factors have become significant contributors to the onset and progression of insomnia. This article explored the treatment of insomnia from the perspective of “liver governs wei qi （defensive qi）”. The concept of “liver governs wei qi （defensive qi）” is summarized in three aspects， firstly， the liver assists the spleen and stomach in transformation and transportation， governing the generation of wei qi； secondly， the liver aids lung qi diffusion and dispersion， governing the distribution of wei qi； thirdly， the liver regulates circadian rhythms， governing the circulation of wei qi. It is proposed that the clinical treatment of insomnia should focus on the following methods： for regulating the liver to harmonize the five viscera， and facilitate the circulation of wei qi， medicinals entering the liver channel include Chaihu （Bupleuri radix）， Baishao （Paeoniae Radix Alba）， Zhizi （Gardeniae Fructus）， and Suanzaoren （Ziziphi Spinosae Semen） could be commonly used； for nourishing the liver， the treatment should align with the day-night rhythm， and herbs such as Baihe （Lilium）， Hehuan （Albizia julibrissin）， and Yejiaoteng （Polygoni multiflori caulis） are commonly used； for soothing the liver and address both mental and physical health to calm wei qi， treatment should advocate verbal counseling， psychological regulation， and health education. Ultimately， this treatment approach can free liver qi to flow， soothe qi movement， restore the motion of wei qi， regulate during day and night， balance yin and yang， and resolve insomnia effectively.