Liver ischemia-reperfusion injury (LIRI) is a pathological process involving multiple injury factors and cell types, with different stages. Currently, protective drugs targeting a single condition are limited in efficacy, and interventions on immune cells will also be accompanied by a series of side effects. In the current bottleneck research stage, the multi-target and obvious clinical efficacy of Chinese medicine (CM) is expected to become a breakthrough point in the research and development of new drugs. In this review, we summarize the roles of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in various stages of hepatic ischemia-reperfusion and on various types of cells. Combined with the current research progress in reducing ROS/RNS with CM, new therapies and mechanisms for the treatment of hepatic ischemia-reperfusion are discussed.
Reperfusion Injury/drug therapy* ; Reactive Oxygen Species/metabolism* ; Reactive Nitrogen Species/metabolism* ; Humans ; Liver/drug effects* ; Animals ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Atractylodes lancea is a perennial herbaceous plant of Asteraceae, with rhizomes for medical use. However, A. lancea plants from different habitats have great variability, and the germplasm resources of A. lancea are unclear and mixed during production. Therefore, it is urgent to protect new varieties of A. lancea. The distinctness, uniformity, and stability(DUS) testing of new plant varieties is the foundation of plant variety protection, and the DUS testing guidelines are the technical basis for variety approval agencies to conduct DUS testing. In this study, the phenotypic traits of 94 germplasm accessions of A. lancea were investigated considering the breeding and variety characteristics of A. lancea in China. The traits were classified and described, and 24 traits were preliminarily determined, including 20 basic traits that must be tested and four traits selected to be tested. The 20 basic traits included 3 quality traits, 5 false quality traits, and 12 quantitative traits, corresponding to 1 plant traits, 2 stem traits, 8 leaf traits, 6 flower traits, and 3 seed traits. The measurement ranges and coefficients of variation of eight quantitative traits were determined, on the basis of which the grading criteria and codes of the traits were determined and assigned. The guidelines has guiding significance for the trait evaluation, utilization, and breeding of new varieties of A. lancea.
Atractylodes/growth & development* ; China ; Phenotype ; Guidelines as Topic ; Plant Breeding

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective: To investigate the effect of activation of the stimulator of interferon genes(STING) pathway on macrophage polarization function and its role in T-cell response. Methods: Mouse macrophage RAW264.7 cells were used.STING signaling related proteins in RAW264.7 macrophage treated with STING agonist diABZI were analyzed by Western blot,including TANK-binding kinase-1(TBK1),interferon regulatory factor-3(IRF3),STING,p-TBK1,p-IRF3,p-STING.The polarization of macrophage RAW264.7 cells treated with diABZI was analyzed by flow cytometry.Co-culture of diABZI-treated RAW264.7 macrophage and T cells was applied to evaluate the change of T cell response. Results: STING signaling related proteins were upregulated in macrophage RAW264.7 cells treated with diABZI for 3 hours.The expression of CD86 was upregulated on the surface of macrophages after 12 hours of diABZI treatment,and the CD86/CD206 ratio was elevated,which presented the M1 polarization phenotype.When coculturing diABZI-treated macrophage RAW264.7 cells with T cells,the cytokine secretion ability of T cells including CD4+T and CD8+T cells was enhanced and the expression of CD107a in CD8+T cells was upregulated. Conclusion STING signaling induces M1 polarization of macrophages which enhance the function of T cells,especially CD8+T cell immune response.

Objective To achieve intelligent recognition and segmentation of common craniocerebral inju-ries(hereinafter referred to as"segmentation")by training convolutional neural network DeepLabV3+model based on CT images of blunt craniocerebral injury(BCI),and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.Methods A total of 5 486 CT images of BCI from living persons were collected as the training set,validation set and test set for model training and performance evaluation.Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to seg-ment the five types of craniocerebral injuries including scalp hematoma,skull fracture,epidural hema-toma,subdural hematoma,and brain contusion.Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers.The five types CT images of all BCI except the blind test set were manually labeled;then,each dataset was inputted into the model to train the model.The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set,and the generalization ability was evaluated based on the Dice value of the test set.According to the accuracy,precision and F1 value of the blind test set,the segmentation performance of the model for five types of BCI was evaluated.Results After training and optimizing the model,the average Dice values of the final optimal model to scalp hematoma,skull fracture,epidural hematoma,subdural hematoma and brain contusion segmen-tation were 0.766 4,0.812 3,0.938 7,0.782 7 and 0.858 1,respectively,all greater than 0.75,meeting the expected requirements.External validation showed that the F1 values were 93.02%,89.80%,87.80%,92.93%and 86.57%in living CT images,respectively;83.92%,44.90%,76.47%,64.29%and 48.89%in cadaveric CT images,respectively.The above suggested that the model was able to accu-rately segment various types of craniocerebral injury on living CT images,while its segmentation ability was relatively poor on cadaveric CT images,but still able to accurately segment scalp hematoma,epidu-ral hematoma and subdural hematoma.Conclusion Deep learning model trained on CT images can be used for BCI segmentation.However,the direct use of living persons'BCI models for the identifica-tion of cadaveric BCI has some limitations.This study provides a new approach for intelligent segmen-tation of virtual anatomical data for BCI.

Objective:To investigate associations of serum β 2-microglobulin (β 2M) with baseline stroke severity and outcome at 3 months after onset in patients with acute ischemic stroke (AIS). Methods:Patients with first-ever AIS admitted to the Department of Neurology, the Affiliated Municipal Hospital of Xuzhou Medical University from January 2018 to May 2022 were included retrospectively. The severity of stroke was evaluated according to the National Institute of Health Stroke Scale score at admission. ≤8 was defined as mild stroke, and >8 was defined as moderate to severe stroke. At 3 months after onset, the modified Rankin Scale was used to evaluate the clinical outcome, ≤2 points was defined as good outcome and >2 points was defined as poor outcome. Multivariate logistic regression analysis was used to determine associations of β 2M with the severity and outcome of stroke. Results:A total of 463 patients with AIS were enrolled, including 263 males (56.68%), aged 62.89±11.77 years. There were 319 patients (68.89%) in the mild stroke group and 144 (31.10%) in the moderate to severe stroke group; 304 patients (65.66%) in the good outcome group and 159 (34.34%) in the poor outcome group. Univariate analysis showed that the β 2M in the moderate to severe stroke group and the poor outcome group were significantly higher than those in the mild stroke group and the good outcome group, respectively (all P<0.05). Multivariate logistic regression analysis showed that β 2M was an independent influencing factor for stroke severity (odds ratio 2.476, 95% confidence interval 1.621-3.783; P<0.001) and poor outcome (odds ratio 5.179, 95% confidence interval 2.941-11.121; P<0.001) in patients with AIS. Conclusion:There is a significant independent correlation between high serum β 2M and the severity and poor outcome of stroke in patients with AIS.

The prevalence of Lyme disease in endogenous populations in Urumqi,Xinjiang was investigated.In total,795 serum samples were collected from residents of three townships in the surrounding area of Urumqi City from 2022 to 2023,which included 383 from Lucaogou Town,145 from Shuixigou Town,and,267 from Tori Township.Serum levels of IgG and IgM antibodies were screened with an enzyme linked immunosorbent assay(ELISA)and confirmed by western blot(WB)analysis.Clinical data of WB-positive indi-viduals were collected and comprehensive analysis was con-ducted for case diagnosis.The chi square test was used for statistical analysis of the results and the P＜0.05 was consid-ered statistically significant.In total,110(13.84％)of 795 samples were positive.The positivity rates was higher in females than males[16.26％(73/449)vs.10.69％(37/346),x2=5.076,P=0.024],while there was no significant difference among age groups(x2=2.569,P=0.766).The positivity rates for serum antibodies in Shuixigou Town,Lucaogou Town,and Tuoli Township were 17.98％(48/267),14.48％(21/145),and 10.70％(41/383),respectively,with a significantly higher rate in Tuoli Township than Lucaogou Town(x2=7.041,P=0.008).Of 110 individuals who were initially positive for IgG and IgM antibodies with the ELISA,82(10.31％)were con-firmed positive by WB analysis.In total,20(2.52％)patients were diagnosed with Lyme disease based on clinical manifesta-tions.Lyme disease is epidemic among the population in Urumqi,as the infection rate is higher than the national average.Hence,continued surveillance is recommended for prevention of Lyme disease.

Confirmed cases of Lyme disease(LD)in Ji'an City,Jiangxi Province and the clinical manifestations and region-al distribution were assessed to provide a basis for timely diagnosis and treatment.In total,133 serum samples were collected from patients with suspected LD in Ji'an Central People's Hospital from December 2021 to August 2022.Serum antibodies a-gainst Borrelia burgdorferi were detected with a two-step testing process.In addition,a specific gene of B.burgdorferi in samples was identified by nested PCR.Sequencing analysis was conducted to confirm the positive samples.Overall,25(18.80％)serum samples were positive for B.burgdorferi nucleic acid and antibodies,which included 20(15.04％)positive for antibodies and 6(4.51％)positive for nucleic acids.The sequences of the 5S-23S rRNA gene spacer of 6 samples were con-sistent with the corresponding sequences of Borrelia yangtzensis and one of the six samples was also positive for antibodies.The 25 positive samples were collected from 9 counties in Ji'an City,with the majority in Jizhou District(44％).All positive patients may experience symptoms related to LD,including joint disorders,neurological disorders,infectious fever,der-matitis,and/or chest pain.The most common symptom was joint disorders(72％).Six cases positive for B.yangtzensis nucleic acids were mainly collected in the Jizhou District,of which four had joint lesions and two had infectious fever.This study confirms the existence of LD in Jiangxi Province,which may be the first confirmation of human infection with B.yan-gtzensis in southern China.Hence,doctors in this region should consider the possibility of LD for cases presenting with related symptoms.Although LD may occur in this region,further investigations and monitoring are warranted.