Effects of dihydroartemisinin (DHA) on the expression level of Alpha-7 .3 giardin mRNA in C2 Giardia lam-blia was investigated in this study to explore the damage to skeleton protein of C 2 Giardia lamblia .Giardia lamblia was culti-vated respectively for 2 ,4 ,8 ,and 12 hours with modified TYI-S-33 medium containing 100 μg/mL and 200 μg/mL DHA , while the control group performed in the same experimental conditions without DHA .The expressive quantity of Alpha-7 .3 gi-ardin mRNA was determined by using real-time reverse transcription PCR ,and then we found that the expressive quantities of Alpha-7 .3 giardin mRNA with DHA were significantly lower than those in the control group .It’s suggested that dihydroarte-misinin has obvious inhibitory effect on the expression level of Alpha-7 .3 giardin mRNA in C2 Giardia lamblia .The actions of dihydroartemisinin on skeleton protein of C2 Giardia lamblia are effective .

Objective:To compare and analyze the clinical diagnostic values of five thyroid nodule malignant risk stratification guidelines.Methods:From October 2019 to October 2021, 926 cases of patients with 1 027 thyroid nodules were recruited in the Second Affiliated Hospital of Xi ′an Jiaotong University. All nodules were categorized individually according to 2015 American Thyroid Association for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults Guidelines(ATA guidelines), 2016 the Korean Society of Radiology and the Korean Society of Thyroid Radiology Thyroid Imaging Reporting and Data Systems(K-TIRADS), 2017 European Thyroid Association Thyroid Imaging Reporting and Data Systems(Eu-TIRADS), 2017 American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR TI-RADS), and 2020 Chinese Thyroid Imaging Reporting and Data System (C-TIRADS). The pathological results were taken as the gold standard, the malignancy ratio of nodules of different categories in each system was calculated. ROC curves were plotted to evaluate the diagnostic efficiencies of different systems, and DeLong test was used to compare the areas under ROC curves. The sensitivity and specificity of different systems were calculated when the maximum point of the Youden index was the optimal cut-off value.Results:In the same stratified system, there were statistically significant differences in the malignant proportion of nodules of different grades ( P<0.05). The malignant proportion of nodules in the high-risk group showed no significant difference among different stratified systems ( P>0.05). Except for C-TIRADS, the malignant proportion of nodules was increased with the increase of diagnostic grade at each grade of the other four stratification systems. ROC curve showed that AUCs of ATA guidelines, K-TIRADS, EU-TIRADS, ACR TI-RADS and C-TIRADS were 0.814, 0.819, 0.814, 0.820 and 0.802, respectively, there was no statistical significance in AUC of different stratification systems (all P>0.05). The optimal truncation values in differentiating benign and malignant nodules were middle-risk malignant nodules, moderately suspicious malignant nodules, middle-risk malignant nodules, class 4 and class 4B. The diagnostic of five stratification systems showed that ATA guidelines had the highest sensitivity (0.784), C-TIRADS had the highest specificity (0.854). Conclusions:The five stratified systems have similar efficacy in differentiating benign and malignant thyroid nodules, and all of them have good diagnostic value.

Objective:To construct an explainable artificial intelligence(AI) model of risk characteristics of papillary thyroid carcinoma(PTC), and to explore its value of it combined with clinical features in predicting cervical lymph node metastasis(CLNM) in PTC patients.Methods:From January 2021 to September 2022, 422 patients(422 nodules) with pathologically confirmed PTC underwent thyroidectomy and neck lymph node dissection in the Second Affiliated Hospital of Xi′an Jiaotong University were retrospectively collected, the patients were randomly divided into training set and test set according to the ratio of 7∶3. Ultrasonographic features highly correlated with PTC risk characteristics were extracted by traditional machine learning method, and an intelligent prediction model with optimal probability of risk characteristics was established. Then, a risk model for predicting CLNM of PTC patients was constructed in combination with clinical features. The diagnostic effectiveness of the model was evaluated by drawing a ROC curve and calculating the area under curve (AUC).Results:In the AI explaineable model of PTC risk characteristics in the test set, the intelligent diagnosis model of calcification based on logistic regression classification showed the highest diagnostic efficiency, with an AUC of 0.87 ( P<0.05). Compared with the probability model of risk characteristic of PTC alone, the comprehensive model combined with clinical characteristics showed higher diagnostic efficiency in predicting CLNM of PTC patients, with AUC of 0.97, diagnostic critical value of 0.15, corresponding accuracy, sensitivity and specificity of 92.65%, 92.76% and 92.54%, respectively (all P<0.05). Conclusions:The explaineble risk characteristics of PTC AI model combined with clinical features can effectively predict the cervical lymph node metastasis of PTC, and then provide effective information for clinical decision-making of PTC patients.

Objective: To isolate, purify and culture fibroblastic reticular cells (FRCs) of mouse in spleen, to develop a reliable and robust method to immortalize primary mouse FRCs, to filter stable FRCs cell lines, to prove that the clones can be infected by SFTSV in vitro. Methods: After purifying FRCs by fluorescence activated cell sorting (FACS) from autoMACS-enriched stroma cells of mouse spleen, we infected FRCs by simian virus 40 large T antigen in vitro, screened the FRCs clones with puromycin, compared primary and immortalized FRCs by RNA sequencing(RNA-seq) technology, infected the clones with severe fever with thrombocytopenia syndrome virus (SFTSV) in vitro. Results: We succeed in culturing purified FRCs from spleen, isolated four stable FRCs clones, two of which have a purity of 99%, survived for more than 50 passages, express the key FRCs marker podoplanin and do not express CD31 and CD45. Clone 01 lost the typical FRCs-like morphology, the rate of expansion of which is quite different from that of primary FRCs and Clone 02. Clone 02 can be infected with SFTSV, which has the same gene expression pattern and immunophenotype with primary FRCs. Conclusions The stable FRCs clone Clone 02 has FRCs-like morphology and express key FRCs surface markers podoplanin (GP38 or PDPN) and do not express endothelial cell markers CD31 and leukocyte common antigen CD45. The RNA expression profiles identified by RNA-seq are also characteristic of FRCs. Infected with SFTSV in vitro, Clone 02 will be a new platform to study SFTSV.