The specific mechanisms of interferon regulatory factor 8(IRF8)in porcine intestinal in-nate immunity and resistance to enteric virus infection remain to be elucidated.To investigate the immunoregulatory role of IRF8,establishing an IRF8 gene knockout porcine intestinal epithelial cell(IPEC-J2)monoclonal cell line is of significant importance.This study initially aimed to obtain recombinant adeno-associated virus rAAV-sgIRF8-eGFP capable of knocking out the IRF8 gene through co-transfection of HEK-293T cells with three plasmids.Subsequently,IPEC-J2 cells were infected with the virus,and those expressing eGFP were selected by flow cytometry and cultured to form monoclonal cell lines.These cell lines were then identified by Sanger sequencing and West-ern blot techniques.Lastly,qPCR analysis was used to measure the expression levels of interferon factors IFN-α,IFN-β,IFN-γ and IFN-λ,providing preliminary insights into the impact of IRF8 gene knockout on IPEC-J2 cell immunity.The results demonstrated successful generation of rAAV-sgIRF8-eGFP,which successfully infected IPEC-J2 cells leading to eGFP fluorescence.Flow cytometry followed by cell culture led to the establishment of two monoclonal cell lines,IRF8-KO1 and IRF8-KO3.Sanger sequencing revealed a five-base deletion in IRF8-KO1 and a seven-base dele-tion in IRF8-KO3.Western blot confirmed the absence of IRF8 protein expression in IRF8-KO1,making it an ideal candidate monoclonal cell line.qPCR analysis of interferon factors indicated sig-nificant decrease in IFN-γ(P＜0.05)and IFN-λ(P＜0.01)transcription level in IRF8-knockout cells,while the transcription levels of IFN-α and IFN-β remained relatively unchanged.This study successfully established an IRF8 gene knockout IPEC-J2 monoclonal cell line,providing a founda-tion for further research on IRF8-related porcine intestinal immune regulation and mechanisms of intestinal virus infection.

Objective To investigate the role of circular RNAs(circRNA)in Alzheimer's disease(AD)and its potential mechanism.Methods Six-month-old APP/PS1 mouse model of AD and wild type(WT)mice were subjected and then randomly divided into WT group,WT+circ-Olfm1 knockout group,AD group(transgenic APP/PS1 mice),AD+circ-Olfm1 knockout group,AD+FOXO3a knockout group,with 3 mice in each group.① The total RNA of mouse brain was extracted,and the differential expression of circRNAs and mRNAs between the AD mice and WT mice was detected,and the obtained circRNAs and mRNAs were analyzed with gene ontology(GO)analysis.② RT-qPCR was used to detect the expression of the top 10 up-regulated and down-regulated circRNAs,as well as the expression of circ-Olfm1 and miR-330-5p.③ Lentiviral vectors were prepared and stereotaxically injected into the cortex or hippocampus of WT and AD mice to knock out circ-Olfm1 gene.Water maze test was used to evaluate the effect of circ-Olfm1 knockout on cognitive function,and immunofluorescence assay was employed to observe the deposition of amyloid β(Aβ)plaque in the brain.④ The interaction between circ-Olfm1 and miR-330-5p was verified by double luciferase reporter gene analysis.⑤ The protein levels of AMPK and FOXO3a were detected by Western blotting.⑥ Transmission electron microscopy was utilized to observe the mitochondria of the hippocampus.⑦ The levels of inflammatory factors IL-6,IL-1β and TNF-α were detected by ELISA.Results There were totally 52 differentially expressed circRNAs identified between the AD and WT mice,including 28 up-regulated and 24 down-regulated(fold change>1.5,P<0.05).These differentially expressed genes are mainly involved in signal transduction,learning and memory and other functions.circ-Olfm1 was identified as the most significantly differentially expressed circRNA,which is highly expressed in the neurons and up-regulated in the cerebral cortex and hippocampus of the AD mice.Knockout of circ-Olfm1 reduced the number of Aβ plaques in the cerebral cortex and hippocampus of AD mice(P<0.01).In starBase database,there are complementary sequences observed between circ-Olfm1 and miR-330-5p.Western blotting showed that the addition of Aβ42 significantly increased the expression of AMPK and FOXO3a in the neuronal cells(P<0.01).And silencing circ-Olfm1 led to decreased expression of AMPK and FOXO3a in neuronal cells+Aβ42(P<0.01).ELISA revealed that knockout of FOXO3a significantly increased the levels of inflammatory factors IL-6,IL-1β,and TNF-α(P<0.01).Transmission electron microscopy displayed that knocking FOXO3a out significantly aggravated mitochondrial damage(P<0.01).Conclusion circ-Olfm1 is up-regulated in the brain tissue and neurons+Aβ42 of AD rats,and the mechanism of cognitive impairment in AD rats may be through its regulating FOXO3a protein.

The specific mechanisms of interferon regulatory factor 8(IRF8)in porcine intestinal in-nate immunity and resistance to enteric virus infection remain to be elucidated.To investigate the immunoregulatory role of IRF8,establishing an IRF8 gene knockout porcine intestinal epithelial cell(IPEC-J2)monoclonal cell line is of significant importance.This study initially aimed to obtain recombinant adeno-associated virus rAAV-sgIRF8-eGFP capable of knocking out the IRF8 gene through co-transfection of HEK-293T cells with three plasmids.Subsequently,IPEC-J2 cells were infected with the virus,and those expressing eGFP were selected by flow cytometry and cultured to form monoclonal cell lines.These cell lines were then identified by Sanger sequencing and West-ern blot techniques.Lastly,qPCR analysis was used to measure the expression levels of interferon factors IFN-α,IFN-β,IFN-γ and IFN-λ,providing preliminary insights into the impact of IRF8 gene knockout on IPEC-J2 cell immunity.The results demonstrated successful generation of rAAV-sgIRF8-eGFP,which successfully infected IPEC-J2 cells leading to eGFP fluorescence.Flow cytometry followed by cell culture led to the establishment of two monoclonal cell lines,IRF8-KO1 and IRF8-KO3.Sanger sequencing revealed a five-base deletion in IRF8-KO1 and a seven-base dele-tion in IRF8-KO3.Western blot confirmed the absence of IRF8 protein expression in IRF8-KO1,making it an ideal candidate monoclonal cell line.qPCR analysis of interferon factors indicated sig-nificant decrease in IFN-γ(P＜0.05)and IFN-λ(P＜0.01)transcription level in IRF8-knockout cells,while the transcription levels of IFN-α and IFN-β remained relatively unchanged.This study successfully established an IRF8 gene knockout IPEC-J2 monoclonal cell line,providing a founda-tion for further research on IRF8-related porcine intestinal immune regulation and mechanisms of intestinal virus infection.

Traumatic supraorbital fissure syndrome (TSOFS) is a symptom complex caused by nerve entrapment in the supraorbital fissure after skull base trauma. If the compressed cranial nerve in the supraorbital fissure is not decompressed surgically, ptosis, diplopia and eye movement disorder may exist for a long time and seriously affect the patients′ quality of life. Since its overall incidence is not high, it is not familiarized with the majority of neurosurgeons and some TSOFS may be complicated with skull base vascular injury. If the supraorbital fissure surgery is performed without treatment of vascular injury, it may cause massive hemorrhage, and disability and even life-threatening in severe cases. At present, there is no consensus or guideline on the diagnosis and treatment of TSOFS that can be referred to both domestically and internationally. To improve the understanding of TSOFS among clinical physicians and establish standardized diagnosis and treatment plans, the Skull Base Trauma Group of the Neurorepair Professional Committee of the Chinese Medical Doctor Association, Neurotrauma Group of the Neurosurgery Branch of the Chinese Medical Association, Neurotrauma Group of the Traumatology Branch of the Chinese Medical Association, and Editorial Committee of Chinese Journal of Trauma organized relevant experts to formulate Chinese expert consensus on the diagnosis and treatment of traumatic supraorbital fissure syndrome ( version 2024) based on evidence of evidence-based medicine and clinical experience of diagnosis and treatment. This consensus puts forward 12 recommendations on the diagnosis, classification, treatment, efficacy evaluation and follow-up of TSOFS, aiming to provide references for neurosurgeons from hospitals of all levels to standardize the diagnosis and treatment of TSOFS.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To investigate the effects of amylin,also known as islet amyloid polypeptide(IAPP),on learning and memory abilities and the phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt)signaling pathway in APP/PS1 mice.Methods A total of 20 APP/PS1 mice were randomly divided into Alzheimer's disease(AD)group and IAPP group,with 10 mice in each group.The mice in the latter group were given an intraperitoneal injection of 0.5 μmol/L IAPP,and those of the former group received same dose of PBS.Both interventions were given once per day,for 10 weeks.Morris water maze test was used to measure the learning and memory abilities;HE staining was employed to observe the pathological changes in the hippocampus;Transmission electron microscopy was utilized to observe the ultrastructure of hippocampal neurons;Biochemical assay were conducted to detect the contents of glutathione peroxidase(GSH-Px),malondialdehyde(MDA)and superoxide dismutase(SOD)in hippocampal tissues;ELISA was applied to measure the levels of inflammatory factors such as IL-1β,IL-6,and TNF-α as well as content of Aβ42 in hippocampal tissues;And Western blotting was conducted to detect the expression of PI3K/Akt proteins.Results Compared with the AD group,significantly shorter platform latency(P＜0.01),increased number of traversing the platform and longer time to explore the hidden platform(P＜0.01)were observed in the IAPP group,but no such difference was seen in the swimming speed of the mice.HE staining displayed that the IAPP group had more and well-arranged nerve cells in the hippocampal tissue when compared with the AD group(P＜0.05).Lower Aβ protein expression(P＜0.01),reduced oxidative stress and decreased contents of inflammatory factors(P＜0.01)in hippocampal tissue were observed in the IAPP group than the AD group.The IAPP group showed clearer structure of neuronal mitochondria,reduced vacuolization,and better arranged microtubules and microfilaments,and elevated expression of p-PI3K/PI3K and p-Akt/Akt proteins when compared with the AD group(P＜0.01).Conclusion Amylin can reduce oxidative stress and inflammatory responses,improve learning and memory abilities in AD mice,and promote the activity of PI3K/Akt signaling pathway.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.