Objective To investigate the effects of sericin pretreatment on the expression of extracellular matrix(ECM) associated protein in diabetic nephropathy(DN) rats' kidney.Methods Sixty six male SD rats were randomly divided into 3 groups(n=12):normal control group,DN model group and sericine pretreatment group.DN rats model in model group and sericine pretreatment group were established by intraperitoneally injection of streptozotocin(STZ).Blood glucose≥16.7 mmol/L was taken as the standard of successful modelization.The rats in sericine pretreatment group were lavaged with sericine(2.4 g·kg~(-1)·d~(-1)) for 35 days before injecting STZ.The enzymic method was used to measure the blood glucose.Type Ⅳ collagen(cⅣ)and laminin(LN)content in the serum were detected by ELBA.The expression of transforming growth factor-β_1,(TGF-β_1)and tissue inhibitors of maprix metalloproteinase-1(TMP-1) protein in the kidney was observed by immunohistochemical staining.The expression of Smad 3 protein in the kidney was detected by Western blot.Results Compared with normal control rats,the blood glucose,cⅣ and LN content in the serum,TGF-β_1,TIMP-1 and Smad 3 expression in the kidney of the model group rats increased obviously(P<0.01).The blood glucose,cⅣ and LN content in the serum,TGF-β_1,TMP-1 and Smad3 expression in the kidney of rats in sericine pretreatment group were significantly lower than those of the rats in model group(P<0.01).Conclusion Sericin pretreatment can inhibit the activation of TGF-β/Smad 3 signal pathway in the kidney of DN rats,and prevent the decrease of MMPs activity induced by up-regulation of TIMP-1.So sericin can prevent accumulation of ECM and glomerulosclerosis during DN,and has satisfactory apotropaic effects on the development of DN.

AIM: To study the differentiation of rat bone mesenchymal stem cells (MSCs) into cardiomyocytes in vitro. METHODS: MSCs were isolated and purified from the bone marrow of rats by density gradient centrifugation and adhering to the plastic culture. The third passage MSCs were treated by 5-azacytidine (5-aza). The induced cells were evaluated by immunocytochemistry staining and RT-PCR analysis. RESULTS: After being induced by 5-aza, some MSCs became bigger and longer. The connection of the cells were formed on day 14.The direction of the cells arraying was similar gradually. The induced cells were stained positively for desmin, ?-actin and troponin I. RT-PCR showed that these cells expressed ? myosin heavy chain. CONCLUSION: 5-aza can induce MSCs to differentiate into cardiomyocytes in vitro.

OBJECTIVETo investigate the alkaloids from Aconitum handelianum.

METHODThe column chromatographic methods were employed for the isolation and purification of the chemical constituents. The structures were elucidated by spectroscopic methods.

RESULTEight diterpenoid alkaloids were isolated and identified as acoforine (1), acoforestinine (2), 14-O-acetylsachaconitine (3), vilmorrianine C (4), vilmorrianine D (5), talatizamine (6), chasmanine (7) and yunaconitine (8).

CONCLUSIONAll compounds were isolated from this plant for the first time.

Aconitum ; chemistry ; Alkaloids ; analysis ; isolation & purification ; Diterpenes ; analysis ; isolation & purification ; Drugs, Chinese Herbal ; analysis ; isolation & purification

Background: Feline panleukopenia virus (FPV) is a widespread and highly infectious pathogen in cats with a high mortality rate. Although Yanji has a developed cat breeding industry, the variation of FPV locally is still unclear. Objectives: This study aimed to isolate and investigate the epidemiology of FPV in Yanji between 2021 and 2022. Methods: A strain of FPV was isolated from F81 cells. Cats suspected of FPV infection (n = 80) between 2021 and 2022 from Yanji were enrolled in this study. The capsid protein 2 (VP2) of FPV was amplified. It was cloned into the pMD-19T vector and transformed into a competent Escherichia coli strain. The positive colonies were analyzed via VP2 Sanger sequencing. A phylogenetic analysis based on a VP2 coding sequence was performed to identify the genetic relationships between the strains. Results: An FPV strain named YBYJ-1 was successfully isolated. The virus diameter was approximately 20–24 nm, 50% tissue culture infectious dose = 1 × 10 −4.94 /mL, which caused cytopathic effect in F81 cells. The epidemiological survey from 2021 to 2022 showed that 27 of the 80 samples were FPV-positive. Additionally, three strains positive for CPV-2c were unexpectedly found. Phylogenetic analysis showed that most of the 27 FPV strains belonged to the same group, and no mutations were found in the critical amino acids. Conclusions A local FPV strain named YBYJ-1 was successfully isolated. There was no critical mutation in FPV in Yanji, but some cases with CPV-2c infected cats were identified.

Objective:To investigate the application value of machine learning algorithms for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 277 patients with HCC who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University between May 2015 and December 2018 were collected. There were 235 males and 42 females, aged (56±10)years, with a range from 33 to 80 years. Patients underwent preoperative magnetic resonance imaging examination. According to the random numbers showed in the computer, all the 277 HCC patients were divided into training dataset consisting of 193 and validation dataset consisting of 84, with a ratio of 7∶3. Machine learning algorithms, including logistic regression nomogram, support vector machine (SVM), random forest (RF), artificial neutral network (ANN) and light gradient boosting machine (LightGBM), were used to develop models for preoperative prediction of MVI. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) analysis of risk factors for tumor MVI of the training dataset; (3) construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the paired t test. Count data were described as absolute numbers, and comparison between groups was analyzed using the chi-square test. Univariate and multivariate analyses were performed using the Logistic regression model. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: there were 157 males and 36 females in the training dataset, 78 males and 6 females in the validation dataset, showing a significant difference in the sex between the training dataset and validation dataset ( χ2=6.028, P<0.05). (2) Analysis of risk factors for tumor MVI of the training dataset: of the 193 patients, 108 had positive MVI, and 85 had negative MVI. Results of univariate analysis showed that age, the number of tumors, tumor diameter, satellite lesions, tumor margin, alpha fetaprotein (AFP), alkaline phosphatase (ALP), fibrinogen were related factors for tumor MVI [ odds ratio ( OR)=0.971, 2.449, 1.368, 4.050, 2.956, 4.083, 2.532, 1.996, 95% confidence interval ( CI): 0.943-1.000, 1.169-5.130, 1.180-1.585, 1.316-12.465, 1.310-6.670, 2.214-7.532, 1.016-6.311, 1.323-3.012, P<0.05]. Results of multivariate analysis showed that AFP>20 μg/L, multiple tumors, larger tumor diameter, unsmooth tumor margin were independent risk factors for tumor MVI ( OR=3.680, 3.100, 1.438, 3.628, 95% CI: 1.842-7.351, 1.334-7.203, 1.201-1.721, 1.438-9.150, P<0.05). Larger age was associated with lower risk of preoperative tumor MVI ( OR=0.958, 95% CI: 0.923-0.994, P<0.05). (3) Construction of machine learning algorithm prediction models and comparison of their accuracy of preoperative tumor MVI prediction: ①machine learning algorithm prediction models involving logistic regression nomogram, SVM, RF, ANN and LightGBM were constructed based on results of multivariate analysis including age, AFP, the number of tumors, tumor diameter, tumor margin, and consistency analysis of the logistic regression nomogram prediction model showed a good stability. For the training dataset and validation dataset, the area under curve (AUC) of logistic regression nomogram model, SVM model, RF model, ANN model, LightGBM model was 0.812, 0.794, 0.807, 0.814, 0.810 and 0.784, 0.793, 0.783, 0.803, 0.815, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.731-0.849, 0.744-0.860, 0.752-0.867, 0.747-0.862, Z=0.995, 0.245, 0.130, 0.102, P>0.05) and (95% CI: 0.690-0.873, 0.679-0.865, 0.702-0.882, 0.715-0.891, Z=0.325, 0.026, 0.744, 0.803, P>0.05)]. ② Clinicopathological factors were selected using RF, LightGBM machine learning algorithm to construct corresponding prediction models. According to importance scale of factors to prediction models, factors with importance scale>0.01 were selected to construct RF model, including age, tumor diameter, AFP, white blood cell, platelet, total bilirubin, aspartate transaminase, γ-glutamyl transpeptidase, ALP, and fibrinogen. Factors with importance scale>5.0 were selected to construct LightGBM model, including age, tumor diameter, AFP, white blood cell, ALP, and fibrinogen. Due to lack of factor selection ability, factors based on results of univariate analysis were secected to construct SVM model and ANN model, including age, the number of tumors, tumor diameter, satellite lesions, tumor margin, AFP, ALP, and fibrinogen. For the training dataset and validation dataset, the AUC of SVM model, RF model, ANN model, LightGBM model was 0.803, 0.838, 0.793, 0.847 and 0.810, 0.802, 0.802, 0.836, respectively, showing no significant difference between SVM model and logistic regression nomogram model, between RF model and logistic regression nomogram model, between ANN model and logistic regression nomogram model, between LightGBM model and logistic regression nomogram model [(95% CI: 0.740-0.857, 0.779-0.887, 0.729-0.848, 0.789-0.895, Z=0.421, 0.119, 0.689, 1.517, P>0.05) and (95% CI: 0.710-0.888, 0.700-0.881, 0.701-0.881, 0.740-0.908, Z=0.856, 0.458, 0.532, 1.306, P>0.05)]. Conclusion:Machine learning algorithms can predict MVI of HCC preoperatively, but its application value needs to be further verified by large sample data from multi centers.

With its high efficiency for site-specific genome editing and easy manipulation, the clustered regularly interspaced short palindromic repeats (CRISPR)/ CRISPR associated protein 9 (CAS9) system has become the most widely used gene editing technology in biomedical research. In addition, significant progress has been made for the clinical development of CRISPR/CAS9 based gene therapies of human diseases, several of which are entering clinical trials. Here we report that CAS9 protein can function as a genome mutator independent of any exogenous guide RNA (gRNA) in human cells, promoting genomic DNA double-stranded break (DSB) damage and genomic instability. CAS9 interacts with the KU86 subunit of the DNA-dependent protein kinase (DNA-PK) complex and disrupts the interaction between KU86 and its kinase subunit, leading to defective DNA-PK-dependent repair of DNA DSB damage via non-homologous end-joining (NHEJ) pathway. XCAS9 is a CAS9 variant with potentially higher fidelity and broader compatibility, and dCAS9 is a CAS9 variant without nuclease activity. We show that XCAS9 and dCAS9 also interact with KU86 and disrupt DNA DSB repair. Considering the critical roles of DNA-PK in maintaining genomic stability and the pleiotropic impact of DNA DSB damage responses on cellular proliferation and survival, our findings caution the interpretation of data involving CRISPR/CAS9-based gene editing and raise serious safety concerns of CRISPR/CAS9 system in clinical application.