Objective To observe the efficacy of nomogram model based on enhanced MRI radiomics,deep learning(DL)and clinical features for differentiating spinal tuberculosis and pyogenic spondylitis.Methods Totally 59 cases of spinal tuberculosis and 66 of pyogenic spondylitis were retrospectively enrolled.Radiomics,DL and clinical features relevant to differentiating spinal tuberculosis and pyogenic spondylitis were selected.Then a predictive model was constructed using logistic regression based on the selected optimal features,and a comprehensive nomogram model was developed through combination of the above features.The effectiveness of these models for distinguishing spinal tuberculosis from pyogenic spondylitis were visualized based on receiver operating characteristic curves,calidration curves and decision curves.Results The nomogram model demonstrated the highest area under the curve(AUC)in both training set and test set,with AUC of 0.997 and 0.920,respectively.In test set,DeLong test indicated that the difference of AUC between the nomogram model and clinical model was significant(P=0.002),while no significant difference was observed between the nomogram model and the other models(all P＞0.05).The nomogram model provided the highest overall net benefit and exhibited good calibration for distinguishing spinal tuberculosis from pyogenic spondylitis.Conclusion Nomogram model based on enhanced MRI radiomics,DL and clinical features demonstrated high efficacy for differentiating spinal tuberculosis from pyogenic spondylitis.

Objective To observe the efficacy of nomogram model based on enhanced MRI radiomics,deep learning(DL)and clinical features for differentiating spinal tuberculosis and pyogenic spondylitis.Methods Totally 59 cases of spinal tuberculosis and 66 of pyogenic spondylitis were retrospectively enrolled.Radiomics,DL and clinical features relevant to differentiating spinal tuberculosis and pyogenic spondylitis were selected.Then a predictive model was constructed using logistic regression based on the selected optimal features,and a comprehensive nomogram model was developed through combination of the above features.The effectiveness of these models for distinguishing spinal tuberculosis from pyogenic spondylitis were visualized based on receiver operating characteristic curves,calidration curves and decision curves.Results The nomogram model demonstrated the highest area under the curve(AUC)in both training set and test set,with AUC of 0.997 and 0.920,respectively.In test set,DeLong test indicated that the difference of AUC between the nomogram model and clinical model was significant(P=0.002),while no significant difference was observed between the nomogram model and the other models(all P＞0.05).The nomogram model provided the highest overall net benefit and exhibited good calibration for distinguishing spinal tuberculosis from pyogenic spondylitis.Conclusion Nomogram model based on enhanced MRI radiomics,DL and clinical features demonstrated high efficacy for differentiating spinal tuberculosis from pyogenic spondylitis.

OBJECTIVE To observe the effects of Panax quinquefolium-Acorus calamus on learning and memory abilities in diabetes mellitus(DM) mice and investigate the mechanism of Panax quinquefolium-Acorus calamus in treating diabetic cognitive impairment(DCI) through network pharmacology and animal experiments. METHODS Diabetic mouse model was established by intraperitoneal injection of streptozotocin(80 mg·kg−1), followed by 8 weeks of oral administration and assessment of drug efficacy using the Morris water maze. The active ingredients and targets of Panax quinquefolium-Acorus calamus were collected using TCMSP, Swiss Target Prediction, and Gene Cards. The protein-protein interaction network of "Traditional Chinese Medicine-Ingredient-Disease targets" was constructed using the String platform and Cytoscape, visualized, and subjected to enrichment analysis using the Metascape database. The anti-DCI mechanism of Panax quinquefolium-Acorus calamus was examined through ELISA and Western blotting, while changes in hippocampal neurons of diabetic mice were observed using HE staining. RESULTS Panax quinquefolium-Acorus calamus reduced the escape latency of diabetic mice(P<0.05), without significant impact on swimming speed. Network pharmacology results indicated that the main components of Panax quinquefolium-Acorus calamus in treating DCI were ginsenoside Re, ginsenoside Rh2, and shanjin phenol, which regulated the Nrf2-Keap1 signaling pathway to treat DCI. Animal experiments demonstrated that Panax quinquefolium-Acorus calamus increased SOD activity(P<0.05), decreased MDA levels(P<0.01), enhanced the expression of HO-1, Keap1, Nrf2 in mouse brain(P<0.01), and alleviated the loosening of granule cell arrangement and nuclear condensation in the hippocampal CA1, CA3, and DG regions. CONCLUSION Using animal experiments combined with network pharmacology, this study preliminarily elucidates the potential targets and mechanisms of Panax quinquefolium-Acorus calamus in intervening DCI, and predictes the molecular basis for its intervention in DCI through molecular docking, providing insights for further in-depth research on Panax quinquefolium-Acorus calamus.

Objective To observe the value of multiparametric MRI-based radiomics model and deep learning(DL)model for distinguishing benign and malignant myxoid soft tissue tumors(MSTT).Methods A total of 141 MSTT patients confirmed with pathology were retrospectively collected.The patients were randomly divided into training set(n=98,including 51 cases of malignant MSTT and 47 cases of benign MSTT)and test set(n=43,including 22 cases of malignant MSTT and 21 cases of benign MSTT)at the ratio of 7∶3.Based on T1WI and fat suppression(FS)-T2WI in training set,radiomics features and DL features were extracted and selected,then a radiomics model and a DL model were constructed,respectively.Receiver operating characteristic(ROC)curves,calibration curves and decision curves were drawn,and the discrimination,calibration and net benefit of these 2 models were compared.Results In training set,the radiomics model for differentiating benign and malignant MSTT was constructed according to 9 optimal radiomics features,including 2 first order features,1 shape feature,3 gray level co-occurrence matrix features,1 gray level dependence matrix feature and 2 gray level size zone matrix features,while DL model was built based on 7 optimal DL features.In test set,the area under the ROC curve of radiomics model and DL model was 0.758 and 0.911,respectively,the latter was higher than the former(P=0.017).Both models had good calibration,and DL model had higher overall net benefit.Conclusion Compared with radiomics model,DL model based on MRI had better ability to differentiating benign and malignant MSTT,also higher overall net benefit.

Objective    To analyze the correlation between folate receptor-positive circulating tumor cells (FR+CTC) and the benign or malignant lesions of the lung, and to establish a malignant prediction model for pulmonary neoplasm based on clinical data, imaging and FR+CTC tests. Methods    A retrospective analysis was done on 1 277 patients admitted to the Affiliated Hospital of Qingdao University from September 2018 to December 2019, including 518 males and 759 females, with a median age of 57 (29-85) years. They underwent CTC examination of peripheral blood and had pathological results of pulmonary nodules and lung tumors. The patients were randomly divided into a trial group and a validation group. Univariate and multivariate analyses were performed on the data of the two groups. Then the nomogram prediction model was established and verified internally and externally. Receiver operating characteristic (ROC) curve was used to test the differentiation of the model and calibration curve was used to test the consistency of the model. Results    Totally 925 patients suffered non-small cell lung cancer and 113 patients had benign diseases in the trial group; 219 patients suffered non-small cell lung cancer and 20 patients had benign diseases in the verification group. The FR+CTC in the peripheral blood of non-small cell lung cancer patients was higher than that found in the lungs of the patients who were in favorite conditions (P<0.001). Multivariate analysis showed that age≥60 years, female, FR+CTC value>8.7 FU/3 mL, positive pleural indenlation sign, nodule diameter, positive burr sign, consolidation/tumor ratio<1 were independent risk factors for benign and malignant lung tumors with a lesion diameter of ≤4 cm. Thereby, the nomogram prediction model was established. The area under the ROC curve (AUC) of the trial group was 0.918, the sensitivity was 86.36%, and the specificity was 83.19%. The AUC value of the verification group was 0.903, the sensitivity of the model was 79.45%, and the specificity was 90.00%, indicating nomogram model discrimination was efficient. The calibration curve also showed that the nomogram model calibration worked well. Conclusion    FR+CTC in the peripheral blood of non-small cell lung cancer patients is higher than that found in the lungs of the patients who carry benign pulmonary diseases. The diagnostic model of clinical stage Ⅰ non-small cell lung cancer established in this study owns good accuracy and can provide a basis for clinical diagnosis.

Objective: To compare blood levels of NT-proBNP and uric acid (UA) in patients with pulmonary thromboembolism (PTE) and chronic heart failure (CHF). Methods: A prospective research was conducted in 288 acute dyspnea patients treated in our hospital from 2010-06 to 2015-05. The patients were divided into 2 groups based on clinical diagnosis: PTE group,n=107 and CHF group, n=181. Blood levels of NT-proBNP and UA were examined in all patients, statistical analysis was performed by SPSS 17.0 software, independent samplet test or variance analysis were used to make comparison between 2 groups. Results: There were more male patients as 64/107 (59.8%) in PTE group and 103/181 (56.9%) in CHF group. Compared with CHF group, PTE group had the lower blood levels of NT-proBNP (2421.7±1678.1) pg/ml vs (6964.3±3873.1) pg/ml and UA (340.6±121.3) μmol/L vs (492.1±166.2) μmol/L, allP<0.01. Conclusion: In our research, blood levels of NT-proBNP and UA were lower in PTE patients than CHF patients; with general background, such phenomenon might be helpful to distinguish PTE and CHF in acute dyspnea patients in clinical practice.

Nourishing kidney-yin (NKY) granules and warming kidney-yang (WKY) granules represent one of the prescriptions that prescribed in treating primary osteoporosis (POP) in light of tonifying kidney and nourishing essence principle as well as the theory of "treating both the disease and traditional Chinese medicine (TCM) syndrome".Both granules were created through the systematic analysis of clinic prescriptions by Professor Shi Qi.Consequently clinical investigations have well established that NKY granules significant improved bone mineral density (BMD) as well as relieved the kidney-yin deficiency syndromes in POP patients.Meanwhile,WKY granules relieve kidney-yang deficiency syndrome and the quality of life (QOL).What is more,pharmacological study established the application of common cnidium fruit,and fructus ligustri lucidi alleviated bone loss in OVX-induced mice.In addition,investigation with effective components identified that both NKY and WKY granules play systematic pharmacological effects on bone remodeling by regulating the expression of BMP/Smad,Wnt/β-catenin,RANKL/RANK/OPG axis,and Notch.The drug discovery was performed by the lead of traditional Chinese medicine (TCM) theory.It is one successful transformation investigation based on pharmacological effects,clinical intervention,animal model,cell culture and molecular investigation.

OBJECTIVETo monitor human cytomegalovirus (HCMV) drug resistance in recipients of hematopoietic stem cell transplantation by phenotypic and genotypic methods.

METHODSHCMV clinical isolates was isolated from the urine of hematopoietic stem cell transplantation recipients treated with GCV. Tissue cell infection median dose (TCID50) of the isolates was calculated using Reed-Muench method, and their drug susceptibility was determined by plaque reduction assay. We amplified the UL97 DNA fragment of the virus by nested PCR followed by automated DNA sequencing.

RESULTSHCMV clinical strain isolated from the urine samples of the recipients using a human fibroblast cell line showed a TCID50 value of 10(-4.618)/0.1 ml and a 50% inhibitory concentration (IC50) to GCV of 5.847 µmol/L, suggesting its sensitivity to GCV. Alignment with the AD169 DNA reference sequence identified 4 point mutations of the virus at 1509 (T-C), 1575 (C-T), 1794 (T-C), and 1815 (C-G), and only the last mutation resulted in one amino acid mutation to D605E. No gene mutation was found in relation to GCV resistance.

CONCLUSIONSPhenotypic and genotypic assays were established to examine antiviral drug resistance of HCMV in recipients of hematopoietic stem cell transplantation. We did not find any drug resistance of the clinical HCMV isolate.

Antiviral Agents ; pharmacology ; Cell Line ; Cytomegalovirus ; drug effects ; genetics ; isolation & purification ; Drug Resistance, Viral ; genetics ; Ganciclovir ; pharmacology ; Genes, Viral ; Genotype ; Hematopoietic Stem Cell Transplantation ; Humans ; Mutation ; Phosphotransferases (Alcohol Group Acceptor) ; genetics

Objective To monitor human cytomegalovirus (HCMV) drug resistance in recipients of hematopoietic stem cell transplantation by phenotypic and genotypic methods. Methods HCMV clinical isolates was isolated from the urine of hematopoietic stem cell transplantation recipients treated with GCV. Tissue cell infection median dose (TCID50) of the isolates was calculated using Reed-Muench method, and their drug susceptibility was determined by plaque reduction assay. We amplified the UL97 DNA fragment of the virus by nested PCR followed by automated DNA sequencing. Results HCMV clinical strain isolated from the urine samples of the recipients using a human fibroblast cell line showed a TCID50 value of 10-4.618/0.1ml and a 50%inhibitory concentration (IC50) to GCV of 5.847 μmol/L, suggesting its sensitivity to GCV. Alignment with the AD169 DNA reference sequence identified 4 point mutations of the virus at 1509 (T-C), 1575 (C-T), 1794 (T-C), and 1815 (C-G), and only the last mutation resulted in one amino acid mutation to D605E. No gene mutation was found in relation to GCV resistance. Conclusions Phenotypic and genotypic assays were established to examine antiviral drug resistance of HCMV in recipients of hematopoietic stem cell transplantation. We did not find any drug resistance of the clinical HCMV isolate.

Objective To monitor human cytomegalovirus (HCMV) drug resistance in recipients of hematopoietic stem cell transplantation by phenotypic and genotypic methods. Methods HCMV clinical isolates was isolated from the urine of hematopoietic stem cell transplantation recipients treated with GCV. Tissue cell infection median dose (TCID50) of the isolates was calculated using Reed-Muench method, and their drug susceptibility was determined by plaque reduction assay. We amplified the UL97 DNA fragment of the virus by nested PCR followed by automated DNA sequencing. Results HCMV clinical strain isolated from the urine samples of the recipients using a human fibroblast cell line showed a TCID50 value of 10-4.618/0.1ml and a 50%inhibitory concentration (IC50) to GCV of 5.847 μmol/L, suggesting its sensitivity to GCV. Alignment with the AD169 DNA reference sequence identified 4 point mutations of the virus at 1509 (T-C), 1575 (C-T), 1794 (T-C), and 1815 (C-G), and only the last mutation resulted in one amino acid mutation to D605E. No gene mutation was found in relation to GCV resistance. Conclusions Phenotypic and genotypic assays were established to examine antiviral drug resistance of HCMV in recipients of hematopoietic stem cell transplantation. We did not find any drug resistance of the clinical HCMV isolate.