Objective To explore the pulsed wave Doppler (PWD) manifestations of acute experimental incomplete testicular torsion. Methods Eight healthy dogs were surgically modeled to obtain unilateral acute testis torsion from 180° to 630°, respectively. The peak systole velocity (PSV) and resistance index (RI) of intratesticular artery, capsular artery and arteria spermatica interna were measured with PWD before torsion, 2 h, 4 h and 6 h after torsion. Testes were examined pathologically after the experiment. Results PSV and RI of capsular artery and intratesticular artery decreased gradually with the extension of torsional time (P<0.05). PSV and RI of superior, torsional and inferior segments of arteria spermatica interna varied greatly and showed no strong regularity (P＞0.05) . The pathological results of testes showed no distinct changes or just mild interstitial hyperemia and edema. Conclusion The decrease of PSV and RI of capsular artery and intratesticular artery, especially RI have referential value in the judgement of acute experimental incomplete testicular torsion, whereas changes of PSV and RI of superior, torsional and inferior segments of arteria spermatica interna are not reliable.

Objective To observe the effect of oxidized low-density lipoproteins (Ox-LDL),15-Deoxy-△ 12,14-prostaglandin J2 ( 15d-PGJ2 ),leukotrienes B4 ( LTB4 ) on mRNA expressions of peroxisome proliferator activated receptor γ2 ( PPARγ2 ),receptor activator of NF-κB ligand (RANKL),alkaline phosphatase ( ALP),and osteoprotegerin(OPG) in osteoblastic cells of rats; and to investigate the influence of these PPARγ2 endogenous ligands on bone metabolism.Methods Rat osteoblastic cells were cultured in vitro for 24 h in medium with different PPARγ2 endogenous ligands at various concentrations ( the final concentrations of Ox-LDL were 0,12.5,25,50μg/ml; the final concentrations of 15 d-PGJ2 were 0,10,20,30 μmol/L; the final concentrations of LTB4 were 0,0.1,1.0,10 μ mol/L).RT-PCR was performed to determine the mRNA expressions of PPARγ2,RANKL,ALP,and OPG in osteoblastic cells.Results RT-PCR analysis showed that Ox-LDL,15d-PGJ2,and LTB4 all down-regulated the mRNA expressions of RANKL,ALP,and OPG,while up-regulated the mRNA expressions of PPARγ2 in osteoblastic cells in a dose-dependent manner.Significant differences were found in interclass comparisons( P＜0.05 or P＜ 0.01 ).Conclusions These findings suggest that Ox-LDL,15d-PGJ2,and LTB4 suppress the expressions of osteogenic genes through activating the transcription activity of PPARγ2,and this may be a plausible mechanism of senile osteoporosis.

ObjectiveTo investigate the value of combined measurement of serum alpha-fetoprotein (AFP), Dickkopf-1 (DKK1), and cytoskeleton-associated protein 4 (CKAP4) in the diagnosis of hepatocellular carcinoma (HCC). MethodsA total of 122 patients with HCC (76 patients in the early stage), 152 patients with liver cirrhosis, and 105 patients with chronic hepatitis B, who were admitted to The First Affiliated Hospital of Soochow University from January 2013 to December 2017, were enrolled, and 101 individuals who underwent physical examination during the same period of time were enrolled as healthy control group. A one-way analysis of variance was used for comparison of continuous data between multiple groups, and the least significant difference t-test was used for further comparison between two groups; the chi-square test was used for comparison of categorical data between multiple groups. A binary logistic regression analysis was used to obtain the new variable of predicted probability, and the receiver operating characteristic (ROC) curve analysis was performed for each index and predicted probability to investigate the area under the ROC curve (AUC), sensitivity, and specificity of the three indices used alone or in combination. ResultsThe HCC group had significantly higher serum levels of AFP, DKK1, and CKAP4 than the liver cirrhosis group, the chronic hepatitis B group, and the healthy control groups (F=121.618, 84.559, and 91.769, P＜0.001). The combination of AFP, DKK1, and CKAP4 had an AUC of 0.967 (95% confidence interval ［CI］: 0.950-0.984), a sensitivity of 0.869, and a specificity of 0.980, which were significantly higher than the AUCs, sensitivities, and specificities of the three indices used alone (all P＜0.05). The combination of the three indices had an AUC of 0.965 (95%CI: 0.942-0.988), a sensitivity of 0.868, and a specificity of 0.980 in the diagnosis of early-stage HCC, which were significantly higher than the AUCs, sensitivities, and specificities of the three indices used alone (all P＜0.05). ConclusionCombined measurement of serum AFP, DKK1, and CKAP4 improves the accuracy, sensitivity, and specificity of HCC diagnosis and thus has an important clinical value in the screening for and early diagnosis of HCC.

Objective To evaluate the feasibility of utilizing dose-volume histogram (DVH) prediction models of organs at risk (OARs) to deliver automatic treatment planning of prostate cancer.Methods The training set included 30 cases randomly selected from a database of 42 cases of prostate cancer receiving treatment planning.The bladder and rectum were divided into sub-volumes (Ai) of 3 mm in layer thickness according to the spatial distance from the boundary of planning target volume (PTV).A skewed normal Gaussian function was adopted to fit the differential DVH of Ai,and a precise mathematical model was built after optimization.Using the embedded C++ subroutine of Pinnacle scripa,ahe volume of each Ai of the remaining validation set for 12 patients was obtained to predict the DVH parameters of these OARa,ahich were used as the objective functions to create personalized Pinnacle script.Finalla,automatic plans were generated using the script.The dosimetric differences among the original clinical plannina,aredicted value and the automatic treatment planning were statistically compared with paired t-test.Results DVH residual analysis demonstrated that predictive volume fraction of the bladder and rectum above 6 000 cGy were lower than those of the original clinical planning.The automatic treatment planning significantly reduced the V70,V60,V50 of the bladder and the V70 and V60 of the rectum than the original clinical planning (all P＜0.05),the coverage and conformal index (CI) of PTV remained unchangea,and the homogeneity index (HI) was slightly decreased with no statistical significance (P＞ 0.05).Conclusion The automatic treatment planning of the prostate cancer based on the DVH prediction models can reduce the irradiation dose of OARs and improve the treatment planning efficiency.

Objective:To evaluate narrow band imaging-magnifying endoscopy (NBI-ME) for the further assessment of lesions of low-grade intraepithelial neoplasia (LGIN) in the gastric biopsy.Methods:Data of 180 patients who underwent NBI-ME before endoscopic submucosal dissection (ESD) for biopsy of gastric LGIN at the First Affiliated Hospital of Soochow University from January 2017 to October 2020 were analyzed retrospectively. Taking the pathological results after ESD as the gold standard, the sensitivity, the specificity, the positive predictive value, the negative predictive value, and the accuracy of NBI-ME in predicting the pathological upgrading of gastric LGIN lesions after ESD were calculated, and the receiver operator characteristic (ROC) curve was drawn.Results:Among 180 gastric LGIN lesions, 115 (63.89%) were pathological upgraded and 65 (36.11%) were not after ESD. There were 10 missed diagnoses, 19 misdiagnoses, and 151 correct diagnoses in NBI-ME examination before ESD. The sensitivity, the specificity, the positive predictive value, the negative predictive value, and the accuracy of NBI-ME in predicting the pathological upgrading of gastric LGIN lesions after ESD were 91.3% (105/115), 70.8% (46/65), 84.7% (105/124), 82.1%(46/56) and 83.9% (151/180), respectively. The area under the ROC curve was 0.810 (95% CI: 0.737-0.883). Conclusion:Further NBI-ME examination of gastric LGIN lesions diagnosed by biopsy pathology can accurately predict whether the lesions have pathological upgrading after ESD, which is of important guiding significance for the patients to choose the treatment strategy of further follow-up or endoscopic resection.

Objective:To develop a deep learning model for predicting three-dimensional (3D) voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT).Methods:A total of 110 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 90 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net was constructed to predict 3D dose distributions. Three types of 3D matrices from CT images, structure sets and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of IMRT dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: the average dose prediction bias and mean absolute errors (MAEs)of all voxels within the body, the dice similarity coefficients (DSCs), Hausdorff distance(HD 95) and mean surface distance (MSD) of different isodose surfaces were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; the dosimetric index (DI) including homogeneity index, conformity index, V50, V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired-samples t test. Results:For the 20 testing cases, the average prediction bias ranged from -2.12% to 2.88%, and the MAEs varied from 2.55% to 5.75%. The DSCs value was above 0.9 for all isodose surfaces, the average MSD ranged from 0.21 cm to 0.45 cm, and the average HD 95 varied from 0.61 cm to 1.54 cm. There was no statistically significant difference for all DIs, except for bladder Dmean. Conclusions:This study developed a deep learning model based on 3D U-Res-Net by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT.

OBJECTIVE: In order to distinguish the traditional Chinese medicine Bupleurum falcatum and its adulterants effectively and develop a better understanding of the factors affecting synonymous codon usage, codon usage patterns of chloroplast genome, we determine the complete chloroplast (cp) genome of B. falcatum and clarify the main factors that influence codon usage patterns of 78 genes in B. falcatum chloroplast genome. METHODS: The total genomic DNA of fresh leaves from a single individual of B. falcatum was extracted with EASYspin plus Total DNA Isolation Kit and 2 μg genome DNA was sequenced using Illumina Hiseq 2500 Sequencing Platform. The cp genome of B. falcatum was reconstructed with MITObim v1.8 and annotated in the program CPGAVAS2 with default parameters. Python script and Codon W were used to calculate the codon usage bias parameters. RESULTS: The full length of B. falcatum cp genome was 155 851 bp, 132 different genes were annotated in this cp genome containing 80 protein-coding genes, 30 tRNA genes, and four rRNA genes. The codon usage models tended to use A/T-ending codons. The neutrality plot, ENC plot, PR2-Bias plot and correspondence analysis showed that both compositional constraint under selection and mutation could affect the codon usage models in B. falcatum cp genome. Furthermore, three optimal codons were identified and most of these three optimal codons ended with G/U. CONCLUSION The cp genome of B. falcatum has been characterized and the codon usage bias in B. falcatum cp genome is influenced by natural selection, mutation pressure and nucleotide composition. The results will provide much more barcode information for species discrimination and lay a foundation for future research on codon optimization of exogenous genes, genetic engineering and molecular evolution in B. falcatum.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.