BACKGROUND: Recombinant adeno-associated virus 2(rAAV-2) has attracted considerable attention due to its nonpathogenic nature in contrast to other viral vectors such as adenoviral and retroviral vectors in gene therapy attempts.OBJECTIVE: To explore rAAV-2 transduction to bone marrow mesenchymalstem cell(BMSC) in vitro and evaluate the possibility of using rAAV-2 as a vector for gene therapy of acute myelogenous leukemia(AML).DESIGN: An open experiment with cells as the observational subjects.SETTING: Department of Hematology, Nanfang Hospital, Southern Medical University.MATERIALS: The experiment was conducted in the Department of Hematology, Nanfang Hospital, Southern Medical University from February to July 2004. We used passages 3 to 5 BMSCs derived from six de novo AML patients and four healthy volunteers in this study.METHODS: BMSC was isolated from 6 to 10 mL of bone marrow aspirates obtained from the iliac crests of the patients who had been diagnosed as having de novo AML and from those of healthy volunteers. The acquired BMSC was infected by rAAV-2 which contained enhanced green fluorescent protein (rAAV-2-eGFP) at different multiplicity of infection(MOI) (MOI = 1 × 102,1 × 103, 1 × 104, 1 × 105, 1 × 106, 1 × 107) . Then we observed through phase contrast fluorescent microscope and flow cytometer to evaluate green fluorescent protein(GFP) expression 10 to 14 days after transduction. GFP expression was observed as the rAAV-2-eGFP transduced BMSC cultured in vitro. We also observed the in vitro gene expression profile of GFP in rAAV-2-eGFP transduced BMSC which was selected by neomycin ( G418). First, we confirmed GFP expression in BMSC through phase contrast fluorescent microscope, then on flow cytometer to detect the percentage of GFP expression.MAIN OUTCOME MEASURES: The efficiency of rAAV-2-eGFP transduction to BMSC. GFP expression was observed through phase contrast fluorescent microscope and flow cytometer at different time points after transduction.rAAV-2-eGFP to BMSC derived from normal volunteers and AML patients had no significant differences. GFP began to express 10 to 14 days after transduction, and the transduction efficiency ranged from 0. 3% to 1.4%. By changing infection condition, we could not make a higher transduction efficiency( P ＞ 0.05) . One round infection of BMSC by rAAV-2-eGFP at a MOI of 1 × 105 was ( 1. 030 ± 0. 034) %, 3 rounds of infection of BMSC by rAAV-2-eGFP at a MOI of 1 × 105 was (1. 140 ±0. 036)%, and coinfected by LipofectAMINE was (1. 380 ± 0. 054)%. However, 293 cell line which was the package cell of rAAV-2 could be efficiently transduced by AML patients transduced by rAAV-2-eGFP at MOI = 1 × 105: The percentage of GFP expression cell gradually decreased from 1.14% at day 12 after transduction to 0. 6% as cell passaged from 2 to 3, and maintained at a level of 0. 5% to 0. 6% later on till 61 days after transduction. After selected by neomycin(G418) 1 month later, rAAV-2-eGFP transduced BMSCs could maintain a long-term GFP expression at a level of 6.0% in vitro without significant decay within 100 days of observation period after transduction.CONCLUSION: The advantages of rAAV-2 mediated gene transduction lie in safety, no immune response to the host, and long-term expression maintained by the target gene. rAAV-2 and BMSC can be used for in vitro gene therapy, and as a systemic gene delivery system, it might be an alternative for systemic gene therapy in the future.

OBJECTIVETo investigate the expressions of glucose-6-phosphatase (G6Pase) and phosphoenolpyruvate carboxykinase (PEPCK) in the liver of mice with hyperhomocysteinemia (HHcy) and explore the mechanism of gluconeogenesis induced by homocysteine.

METHODSFifty mice were randomly divided into normal control group (n=25) and HHcy group (n=25) and fed with normal food and food supplemented with 1.5% methionine, respectively. After 3 months of feeding, the fasting blood glucose and insulin levels were determined, and HOMA insulin resistance index (HOMA-IR) was calculated. The expressions of G6Pase and PEPCK in the liver of mice were detected using RT-PCR and Western blotting.

RESULTSThe fasting blood glucose and insulin levels and HOMA-IR were significantly higher in HHcy group than in the control group (P<0.05). RT-PCR and Western blotting showed that the hepatic expressions of G6Pase and PEPCK mRNA and proteins increased significantly in HHcy group compared with those in the control group (P<0.05).

CONCLUSIONHomocysteine promotes gluconeogenesis to enhance glucose output and contribute to the occurrence of insulin resistance.

Animals ; Gluconeogenesis ; Glucose-6-Phosphatase ; metabolism ; Homocysteine ; blood ; Hyperhomocysteinemia ; metabolism ; Insulin Resistance ; Liver ; metabolism ; Male ; Mice ; Mice, Inbred Strains ; Phosphoenolpyruvate Carboxykinase (ATP) ; metabolism

Objective:To identify the pathogenic gene and inheritance pattern in a pedigree of congenital iris coloboma with congenital cataract.Methods:The method of pedigree investigation was adopted.A pedigree of congenital iris coloboma with congenital cataract was collected by Yunnan Disabled Rehabilitation Center and the 2nd Afliated Hospital of Kunming Medical University in February 2020.Ophthalmic examinations were carried out on the female proband, her parents, her children and her husband, and the clinical diagnosis was made.Genomic DNA was extracted from peripheral blood samples collected from the family members.The suspected pathogenic gene in the proband and her husband was screened by whole exome sequencing and was identified by bioinformatics analysis.The amino acid conservation was analyzed by UGENE software.The impact of the mutation on protein translation was predicted using MutationTaster software.The pathogenicity of the mutation was assessed according to the American College of Medical Genetics (ACMG) Standards and Guidelines.Pathogenic gene and mutations were verified by Sanger sequencing.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of the 2nd Afliated Hospital of Kunming Medical University (No.PJ-2020-61).Written informed consent was obtained from each subject or custodian.Results:The proband showed large iris defects in both eyes with only a small amount of observable iris tissue in the periphery, lens cortical opacity and posterior capsule opacification, accompanied by nystagmus.A novel heterozygous frameshift variation c. 415dupA (p.R139fs) was located in exon 8 of PAX6 gene, and the variation was conservative across multiple species.The variation was in the highly conserved region of PAX6 gene and caused the dysfunction of PAX6 protein.The variation was graded as PVS1+ PM2+ PP1, a pathogenic variation, based on ACMG guidelines.The pedigree was consistent with co-segregation, indicating that the novel variation was pathogenic.The proband and her children were diagnosed, but her parents were phenotypically normal, in accordance with autosomal dominant inheritance. Conclusions:The novel frameshift variation c.415dupA (p.R139fs) on the exon 8 of PAX6 gene is responsible for congenital iris coloboma with congenital cataract in the pedigree.This is the first report of this novel variation in PAX6 gene.

Objective To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.Methods We proposed a two-stage carotid plaque detection method based on deep convolutional neural network(SM-YOLO).A series of algorithms such as median filtering,histogram equalization,and Gamma transformation were used to preprocess the dataset to improve image quality.In the first stage of the model construction,a candidate plaque set was built based on the YOLOX_l target detection network,using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes.In the second stage,the Histogram of Oriented Gradient(HOG)features and Local Binary Pattern(LBP)features were extracted and fused,and a Support Vector Machine(SVM)classifier was used to screen the candidate plaque set to obtain the final detection results.This model was compared quantitatively and visually with several target detection models(YOLOX_l,SSD,EfficientDet,YOLOV5_l,Faster R-CNN).Results SM-YOLO achieved a recall of 89.44%,an accuracy of 90.96%,a F1-Score of 90.19%,and an AP of 92.70%on the test set,outperforming other models in all performance indicators and visual effects.The constructed model had a much shorter detection time than the Faster R-CNN model(only one third of that of the latter),thus meeting the requirements of real-time detection.Conclusion The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

Objective To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.Methods We proposed a two-stage carotid plaque detection method based on deep convolutional neural network(SM-YOLO).A series of algorithms such as median filtering,histogram equalization,and Gamma transformation were used to preprocess the dataset to improve image quality.In the first stage of the model construction,a candidate plaque set was built based on the YOLOX_l target detection network,using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes.In the second stage,the Histogram of Oriented Gradient(HOG)features and Local Binary Pattern(LBP)features were extracted and fused,and a Support Vector Machine(SVM)classifier was used to screen the candidate plaque set to obtain the final detection results.This model was compared quantitatively and visually with several target detection models(YOLOX_l,SSD,EfficientDet,YOLOV5_l,Faster R-CNN).Results SM-YOLO achieved a recall of 89.44%,an accuracy of 90.96%,a F1-Score of 90.19%,and an AP of 92.70%on the test set,outperforming other models in all performance indicators and visual effects.The constructed model had a much shorter detection time than the Faster R-CNN model(only one third of that of the latter),thus meeting the requirements of real-time detection.Conclusion The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

Objective To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging(MRI)deep learning features with clinical features for preoperative prediction of cytokeratin 19(CK19)status of hepatocellular carcinoma(HCC).Methods A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status.A single sequence multi-scale feature fusion deep learning model(MSFF-IResnet)and a multi-scale and multi-modality feature fusion model(MMFF-IResnet)were established based on the hepatobiliary phase(HBP),diffusion weighted imaging(DWI)sequences of enhanced MRI images,and the clinical features significantly correlated with CK19 status.The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery.Results Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio(P=0.029)and incomplete tumor capsule(P=0.028)were independent predictors of CK19 expression in HCC.The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models,and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%,an accuracy of 80.6%,a sensitivity of 80.1%and a specificity of 81.2%.Conclusion The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC,demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.

Objective To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging(MRI)deep learning features with clinical features for preoperative prediction of cytokeratin 19(CK19)status of hepatocellular carcinoma(HCC).Methods A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status.A single sequence multi-scale feature fusion deep learning model(MSFF-IResnet)and a multi-scale and multi-modality feature fusion model(MMFF-IResnet)were established based on the hepatobiliary phase(HBP),diffusion weighted imaging(DWI)sequences of enhanced MRI images,and the clinical features significantly correlated with CK19 status.The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery.Results Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio(P=0.029)and incomplete tumor capsule(P=0.028)were independent predictors of CK19 expression in HCC.The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models,and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%,an accuracy of 80.6%,a sensitivity of 80.1%and a specificity of 81.2%.Conclusion The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC,demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.

OBJECTIVE: To investigate the consistency and diagnostic performance of magnetic resonance imaging (MRI) for detecting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and the validity of deep learning attention mechanisms and clinical features for MVI grade prediction. METHODS: This retrospective study was conducted among 158 patients with HCC treated in Shunde Hospital Affiliated to Southern Medical University between January, 2017 and February, 2020. The imaging data and clinical data of the patients were collected to establish single sequence deep learning models and fusion models based on the EfficientNetB0 and attention modules. The imaging data included conventional MRI sequences (T₁WI, T₂WI, and DWI), enhanced MRI sequences (AP, PP, EP, and HBP) and synthesized MRI sequences (T1mapping-pre and T1mapping-20 min), and the high-risk areas of MVI were visualized using deep learning visualization techniques. RESULTS: The fusion model based on T1mapping-20min sequence and clinical features outperformed other fusion models with an accuracy of 0.8376, a sensitivity of 0.8378, a specificity of 0.8702, and an AUC of 0.8501 for detecting MVI. The deep fusion models were also capable of displaying the high-risk areas of MVI. CONCLUSION The fusion models based on multiple MRI sequences can effectively detect MVI in patients with HCC, demonstrating the validity of deep learning algorithm that combines attention mechanism and clinical features for MVI grade prediction.
Humans ; Carcinoma, Hepatocellular ; Retrospective Studies ; Liver Neoplasms ; Magnetic Resonance Imaging ; Algorithms