The monoamine oxidase mutant A-1 (F210V/L213C) from Aspergillus niger showed some catalytic activity on mexiletine. To futher improve its activity, the mutant was subjected to directed evolution with MegaWHOP PCR (Megaprimer PCR of Whole Plasmid) and selection employing a high-throughput agar plate-based colorimetric screen. This approach led to the identification of a mutant ep-1, which specific activity was 189% of that for A-1. The ep-1 also showed significantly improved enantioselectivity, with the E value increased from 101 to 282; its kinetic k(cat)/K(m) value increased from 0.001 51 mmol/(L x s) to 0.002 89 mmol/(L x s), suggesting that catalytic efficiency of ep-1 had been improved. The mutant showed obviously higher specific activities on 7 of all tested 11 amines substrates, and the others were comparable. Sequence analysis revealed that there was a new mutation T162A on ep-1. The molecular dynamics simulation indicated that T162A may affect the secondary structure of the substrate channel and expand the binding pocket.
Aspergillus niger ; enzymology ; Catalysis ; Kinetics ; Monoamine Oxidase ; genetics ; metabolism ; Mutation ; Polymerase Chain Reaction ; Protein Engineering ; Protein Structure, Secondary ; Substrate Specificity

Objective:To study the anti-inflammatory and analgesic effects of different solvent extracts of Folium Pyrrosiae. Meth-ods:Water extract and 75% alcohol extract of Folium Pyrrosiae were obtained. Mouse auricle swelling model induced by xylene was used to observe the anti-inflammation. The analgesic effect was tested by acetic acid writhing test and hot plate test. Results:The eth-anol extract of Folium Pyrrosiae could markedly inhibit the mouse auricle swelling induced by xylene (P<0. 01), and had the ability to inhibit the twisting induced by acetic acid in the mice (P <0. 05). The ethanol extract of Folium Pyrrosiae could increase the threshold of pain in the mice significantly after the 1-hour and 2-hour treatment (P<0. 05). The water extract of Folium Pyrrosiae could inhibit the mouse auricle swelling induced by xylene and the writhing reaction induced by acetic acid (P<0. 05). The water ex-tract of Folium Pyrrosiae could increase the threshold of pain in the mice significantly after the 1-hour treatment (P<0. 05). Conclu-sion:Folium Pyrrosiae has obvious analgesic and anti-inflammatory effects.

Objective The aim of this study was to investigate the co-operative role of HGF and IL-1ra in metastatic processes by interactions between colon cancer cells and stromal cells in their microenvironment.Methods Expression of IL-1α,HGF and c-Met mRNA and proteins were determined by RT-PCR and Western blot.The effect of HGF on metastatic potential was evaluated by proliferation,invasion,and angiogenesis assays using an in vitro system consisting of co-cultured tumor cells and stromal cells.Results IL-1α expression was closely correlated with metastatic potential,and cancer cell-derived IL-1α significantly promoted HGF expression by fibroblasts (P ＜ 0.01).HGF enhanced the migration and proliferation of human umbilical vein endothelial cells (HUVECs),and angiogenesis (P ＜ 0.01).The high liver-metastatic colon cancer cell line (HT-29),which secretes IL-1 α,significantly enhanced angiogenesis compared to the low liver-metastatic cell line (CaCo-2),which does not produce IL-1 α (P ＜ 0.01).IL-1 ra significantly inhibit migration,proliferation and angiogenesis (P ＜ 0.01).Conclusions Autocrine IL-1α and paracrine HGF enhance the metastatic potential of colon cancer cells;IL-1ra inhibit the metastatic potential of colon cancer cells by blocking IL-1α and HGF signaling pathways.

To investigate the relationship between the plasma biomarker proteins and the states of Zang-Fu organs in patients with phlegm or blood stagnation syndromes due to hyperlipidemia and atherosclerosis.

OBJECTIVETo explore the effect of colon cancer cell-derived interleukin-1α on the migration and proliferation of human umbilical vein endothelial cells as well as the role of IL-1α and IL-1ra in the angiogenesis process.

METHODSWestern blot was used to detect the expression of IL-1α and IL-1R1 protein in the colon cancer cell lines with different liver metastatic potential. We also examined how IL-1α and IL-1ra influence the proliferation and migration of umbilical vascular endothelial cells assessed by PreMix WST-1 assay and migration assay, respectively. Double layer culture technique was used to detect the effect of IL-1α on the proliferation and migration of vascular endothelial cells and the effect of IL-1ra on the vascular endothelial cells.

RESULTSWestern blot analysis showed that IL-1α protein was only detected in highly metastatic colon cancer HT-29 and WiDr cells, but not in the lowly metastatic CaCo-2 and CoLo320 cells.Migration assay showed that there were significant differences in the number of penetrated cells between the control (17.9±3.6) and 1 ng/ml rIL-1α group (23.2±4.2), 10 ng/ml rIL-1α group (31.7±4.5), and 100 ng/ml rIL-1α group (38.6±4.9), showing that it was positively correlated with the increasing concentration of rIL-1α (P<0.01 for all). The proliferation assay showed that the absorbance values were 1.37±0.18 in the control group, and 1.79±0.14 in the 1 ng/ml rIL-1α group, 2.14±0.17 in the 10 ng/ml rIL-1α group, and 2.21±0.23 in the 100 ng/ml rIL-1α group, showing a positive correlation with the increasing concentration of rIL-1α(P<0.01 for all). IL-1ra significantly inhibited the proliferation and migration of vascular endothelial cells (P<0.01). The levels of VEGF protein were (1.697±0.072) ng/ml, (3.507±0.064)ng/ml and (4.139±0.039)ng/ml in the control, HUVECs+ IL-1α and HUVECs+ HT-29 co-culture system groups, respectively, showing a significant difference between the control and HUVECs+ 10 pg/ml rIL-1α groups and between the control and HUVECs+ HT-29 groups (P<0.01 for both).

CONCLUSIONSOur findings indicate that colon cancer cell-derived IL-1α plays an important role in the liver metastasis of colon cancer through increased VEGF level of the colon cancer cells and enhanced vascular endothelial cells proliferation, migration and angiogenesis, while IL-1ra can suppress the effect of IL-1α and inhibit the angiogenesis in colon cancer.

Blotting, Western ; Caco-2 Cells ; Cell Line, Tumor ; Cell Movement ; physiology ; Cell Proliferation ; physiology ; Coculture Techniques ; Colonic Neoplasms ; blood supply ; metabolism ; pathology ; Human Umbilical Vein Endothelial Cells ; cytology ; Humans ; Interleukin 1 Receptor Antagonist Protein ; metabolism ; physiology ; Interleukin-1alpha ; metabolism ; physiology ; Liver Neoplasms ; secondary ; Neovascularization, Pathologic ; etiology

Objective:To construct an prediction model based on magnetic resonance imaging (MRI) machine learning algorithm and radiomics and investigate its application value in predicting lymphovascular invasion (LVI) status of rectal cancer without lymph node metastasis.Methods:The retrospective cohort study was conducted. The clinicopathological data of 204 rectal cancer patients without lymph node metastasis who were admitted to Gansu Provincial Hospital from February 2016 to January 2024 were collected. There were 123 males and 81 females, aged (61±7)years. All 204 patients were randomly divided into the training dataset of 163 cases and the testing dataset of 41 cases by a ratio of 8∶2 using the electronic computer randomization method. The training dataset was used to construct the prediction model, and the testing dataset was used to validate the prediction model. The clinical prediction model, radiomics model and joint prediction model were constructed based on the selected clinical and/or imaging features. Measurement data with normal distribution were represented as Mean± SD. Count data were described as absolute numbers, and the chi-square test or Fisher exact probability were used for comparison between the groups. Comparison of ordinal data was conducted using the nonparameter rank sum test. The inter-class correlation coefficient (ICC) was used to evaluate the consistency of the radiomics features of the two doctors, and ICC >0.80 was good consistency. Univariate analysis was conducted by corres-ponding statistic methods. Multivariate analysis was conducted by Logistic stepwise regression model. The receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC), Delong test, decision curve and clinical impact curve were used to evaluate the diagnostic efficiency and clinical utility of the model. Result:(1) Analysis of factors affecting LVI status of patients. Of the 204 rectal cancer patients without lymph node metastasis, there were 71 cases with positive of LVI and 133 cases with negative of LVI. Results of multivariate analysis showed that gender, platelet (PLT) count and carcinoembryonic antigen (CEA) were independent factors affecting LVI status of rectal cancer without lymph node metastasis in training dataset [ odds ratio=2.405, 25.062, 2.528, 95% confidence interval ( CI) as 1.093-5.291, 2.748-228.604, 1.181-5.410, P<0.05]. (2) Construction of clinical prediction model. The clinical prediction model was conducted based on the results of multivariate analysis including gender, PLT count and CEA. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of clinical prediction model were 0.721 (95% CI as 0.637-0.805), 0.675, 0.632 and 0.698 for the training dataset, and 0.795 (95% CI as 0.644-0.946), 0.805, 1.000 and 0.429 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of clinical prediction model between the training dataset and the testing dataset ( Z=-0.836, P>0.05). (3) Construction of radiomics model. A total of 851 radiomics features were extracted from 204 patients, and seven machine learning algorithms, including logistic regression, support vector machine, Gaussian process, logistic regression-lasso algorithm, linear discriminant analysis, naive Bayes and automatic encoder, were used to construct the prediction model. Eight radiomics features were finally selected from the optimal Gaussian process learning algorithm to construct a radiomics prediction model. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.857 (95% CI as 0.800-0.914), 0.748, 0.947 and 0.642 for the training dataset, and 0.725 (95% CI as 0.571-0.878), 0.634, 1.000 and 0.444 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of radiomics prediction model between the training dataset and the testing dataset ( Z=1.578, P>0.05). (4) Construction of joint prediction model. The joint prediction model was constructed based on the results of multivariate analysis and the radiomics features. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.885 (95% CI as 0.832-0.938), 0.791, 0.912 and 0.726 for the training dataset, and 0.857 (95% CI as 0.731-0.984), 0.854, 0.714 and 0.926 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of joint prediction model between the training dataset and the testing dataset ( Z=0.395, P>0.05). (5) Performance comparison of three prediction models. Results of the Hosmer-Lemeshow goodness-of-fit test showed that all of the clinical prediction model, radiomics prodiction model and joint prediction model having good fitting degree ( χ2=1.464, 12.763, 10.828, P>0.05). Results of Delong test showed that there was no signifi-cant difference in the AUC between the clinical prediction model and the joint prediction model or the radiomics model ( Z=1.146, 0.658, P>0.05), and there was a significant difference in the AUC between the joint prediction model and the radiomics model ( Z=2.001, P<0.05). Results of calibra-tion curve showed a good performance in the joint prediction model. Results of decision curve and clinical impact curve showed that the performance of joint prediction model in predicting LVI status of rectal cancer without lymph node metastasis was superior to the clinical prediction model and the radiomics model. Conclusions:The clinical prediction model is constructed based on gender, PLT count and CEA. The radiomics predictive model is constructed based on 8 selected radiomics features. The joint prediction model is constructed based on the clinical prediction model and the radiomics predictive model. All of the three models can predict the LVI status of rectal cancer with-out lymph node metastasis, and the joint prediction model has a superior predictive performance.

Objective:To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their families, and provide data support for clinical decision-making.Methods:A retrospective study was conducted. The clinical information of stroke patients ( n = 53?793) were extracted from the Yidu cloud big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to July 31, 2023. Combined with the results of single factor screening and the opinions of experts with senior professional titles in neurology, the input variable was determined, and the output variable was the National Institutes of Health Stroke Scale (NIHSS) representing the severity of the disease at admission. Python 3.7 was used to build DeepFM algorithm model, and five data mining models including Logistic regression, CART decision tree, C5.0 decision tree, Bayesian network and deep neural network (DNN) were built at the same time. The original data were randomly divided into 80% training set and 20% test set, which were used to train and test the models, adjust the parameters of each model, respectively calculate the accuracy, sensitivity and F-index of the six models, carry out the comprehensive comparison and evaluation of the model. The receiver operator characteristic curve (ROC curve) and calibration curve were drawn, compared the prediction performance of DeepFM model and the other five algorithms. In addition, the data of stroke patients ( n = 1?028) were extracted from Dalian Central Hospital for external verification of the model. Results:A total of 14?015 stroke patients with complete information were selected, including 11?212 in the training set and 2?803 in the testing set. After univariate screening, 14 indicators were included to construct the model, including gender, age, recurrence, physical impairment, facial problems, speech disorders, head reactions, disturbance of consciousness, visual disorders, abnormal cough and swallowing, high risk factor, family history, smoking history and drinking history. DeepFM model adopted the two-order crossover feature. The number of hidden layers in DNN layer was 3. Dropout was used to discard the neurons in the neural network. Rule was used as the activation function. Each layer used Dense full connection. The objective function was random gradient descent. The number of iterations was 15. There were 133?922 training parameters in total. Comparing the predictive value of the six models showed that the accuracy of DeepFM model was 0.951, the sensitivity was 0.992, the specificity was 0.814, the F-index was 0.950, and the area under the curve (AUC) was 0.916. The accuracy of the other five data mining models were between 0.771-0.780, the sensitivity were between 0.978-0.987, the F-index were between 0.690-0.707, and the AUC were between 0.568-0.639. The calibration curve of the DeepFM model was more aligned with the ideal curve than the other five data mining models. Suggesting that the prediction performance of DeepFM model was the best. External validation was conducted on the DeepFM model, and its accuracy was 0.891, indicating good generalization performance of the model.Conclusion:The pre-hospital non-invasive screening prediction model based on DeepFM can accurately predict the severity grading of stroke patients, and has potential application value in rapid screening and early clinical decision-making of stroke.

Objective To summarize the preliminary application results of ultrasound-guided biliary drainage tube replacement, present the corresponding technical points, and discuss the operation strategy and clinical application value. Methods The clinical data of 60 patients who underwent ultrasound-guided biliary drainage tube replacement in Qilu Hospital of Shandong University between August 2014 and August 2020 were retrospectively analyzed. The operation procedure, clinical applications, and postoperative complications were summarized and analyzed. Results Fifty-eight of the 60 patients (96.67%) were successfully replaced with drainage tubes along the original sinus. Among them, dilated sinus tracts of 47 patients were placed with coarse-grade drainage tubes, and dilated sinus tracts of the remaining 11 patients were placed with the original type of drainage tubes, with the mean operation time of 15.8(12.0-19.0) min under local anesthesia. In total, bile was drained from 28 patients receiving PTCD drainage, 23 patients receiving gallbladder drainage, and 9 patients receiving T-tube drainage. The post-operation evaluation revealed that the drainage situation has improved, with a 100% effective rate. No obvious abnormality was found in the postoperative follow-up visit. Conclusion The replacement of drainage tube under ultrasound guidance is simple, safe and feasible, and it provides further promotion in clinical practice with sufficient data support.