Objective To observe the value of 80 kVp combined with artificial intelligence iterative reconstruction(AIIR)in lower extremity CT angiography(CTA)of diabetic foot.Methods Sixty patients with diabetic foot were prospectively enrolled and divided into experimental group and control group(each n=30).Lower extremity CTA was performed using 80 kVp combined with AIIR in experimental group,while using 120 kVp combined with hybrid iterative reconstruction(HIR)in control group.The subjective and objective evaluation results of imaging quality,as well as the radiation dose of scanning were compared between groups.Results Compared with control group,images in experimental group showed higher subjective scores(5[5,5]vs.4[4,4]),displayed more branches of lower limb arteries(both P＜0.05),while the radiation dose decreased by 59.76％ ([8.29±0.53]mSv vs.[20.60±2.42]mSv).CT values,signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of each lower extremity artery segments were higher,while the noise of each lower extremity artery segments in experimental group were lower than those in control group(all P＜0.05).Conclusion 80 kVp combined with AIIR could reduce radiation dose and improve imaging quality of lower extremity CTA of diabetic foot,hence being helpful to displaying small arteries of lower extremity,such as dorsalis pedis arteries and common plantar metatarsal arteries.

Objective To observe the value of 80 kVp combined with artificial intelligence iterative reconstruction(AIIR)in lower extremity CT angiography(CTA)of diabetic foot.Methods Sixty patients with diabetic foot were prospectively enrolled and divided into experimental group and control group(each n=30).Lower extremity CTA was performed using 80 kVp combined with AIIR in experimental group,while using 120 kVp combined with hybrid iterative reconstruction(HIR)in control group.The subjective and objective evaluation results of imaging quality,as well as the radiation dose of scanning were compared between groups.Results Compared with control group,images in experimental group showed higher subjective scores(5[5,5]vs.4[4,4]),displayed more branches of lower limb arteries(both P＜0.05),while the radiation dose decreased by 59.76％ ([8.29±0.53]mSv vs.[20.60±2.42]mSv).CT values,signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of each lower extremity artery segments were higher,while the noise of each lower extremity artery segments in experimental group were lower than those in control group(all P＜0.05).Conclusion 80 kVp combined with AIIR could reduce radiation dose and improve imaging quality of lower extremity CTA of diabetic foot,hence being helpful to displaying small arteries of lower extremity,such as dorsalis pedis arteries and common plantar metatarsal arteries.

Objective:To compare the image quality and the diagnostic efficiency for breast malignant lesions using simultaneous multi-slice single shot echo planar imaging (SMS+SS-EPI) and readout segment of long variable echo trains (RESOLVE) for breast diffusion-weighted imaging (DWI).Methods:This study was a cross-sectional study. Clinical and imaging data of 102 patients with breast lesion from March 2021 to February 2023 in the First Hospital Affiliated to Ningbo University were prospectively analyzed. All patients underwent routine breast MRI scans and cross-sectional RESOLVE and SMS+SS EPI sequence DWI, and the image quality of 2 types sequences of DWI was evaluated. The subjective evaluation was based on a 5-point scale, including geometric distortion, artifact blurring, fat suppression, overall image quality, and lesion conspicuity of the breast. The objective evaluation included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) value of the lesion. Wilcoxon signed rank sum test was used to compare the subjective and objective parameters between the two sequences. Finally, the receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the effectiveness of ADC values for diagnosing breast malignant lesions based on two sequence images.Results:All 102 female patients had single lesions, aged from 25 to 68 years and 60 lesions were malignant and 42 were benign. The acquisition time for SMS+SS-EPI sequence imaging was 1 min 50 s, and the acquisition time for RESOLVE sequence imaging was 3 min 43 s.The subjective scores from both SMS+SS-EPI and RESOLVE sequence were over than 3 points. The geometric distortion, artifact blurring, and overall image quality scores of RESOLVE sequence images were higher than those of SMS+SS-EPI (all P<0.001). The overall lesion conspicuity score, malignant lesion conspicuity score, and benign lesion conspicuity score of DWI and ADC images combined with SMS+SS-EPI sequence were higher than those of the RESOLVE sequence (all P<0.05). There were no statistically difference in SNR, CNR, overall ADC value, malignant ADC value, and benign ADC value between SMS+SS-EPI and RESOLVE sequence DWI images of breast lesions (all P>0.05). The AUC of RESOLVE sequence ADC value in diagnosis of breast cancer was 0.973, the sensitivity was 0.929, and the specificity was 0.915; The AUC of SMS+SSC-EPI sequence ADC value in diagnosis of breast cancer was 0.956, the sensitivity was 0.977, and the specificity was 0.850. Conclusions:In breast DWI, the subjective score of SMS+SS EPI image quality can basically meet the diagnostic requirements and the conspicuity of lesions is better than that of the RESOLVE sequence. Both have good diagnostic efficacy for malignant breast lesions.

Objective To preliminarily analyze the prediction efficiency of multimodal MRI?based radiomics model for preoperative glioma IDH1 gene expression type. Methods The MRI data of 108 surgery?proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T1WI,T2WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T1WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray?level co?occurrence matrix(GLCM), the run?length matrix(RUN), the absolute gradient(GRA),the auto?regressive model(ARM) and wavelets transform (WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross?validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI?based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI?based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI?based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10?fold cross?validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s?4) of T2WI, S(4, 4) inverse difference of gray uniformity measurement (InvDfMom), S(5, 0) Entropy (entropy), WavEnHH_s?4 of T1WI enhancement, S(1, 1) InvDfMom, S(1, -1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity,sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI?based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI?based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively.

Objective: To preliminarily analyze the prediction efficiency of multimodal MRI-based radiomics model for preoperative glioma IDH1 gene expression type. Methods: The MRI data of 108 surgery-proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T₁WI,T₂WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T₁WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray-level co-occurrence matrix(GLCM), the run-length matrix(RUN), the absolute gradient(GRA),the auto-regressive model(ARM) and wavelets transform(WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross -validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI-based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI-based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI-based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results: Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10-fold cross-validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s-4) of T₂WI, S(4,4) inverse difference of gray uniformity measurement (InvDfMom),S(5,0) Entropy (entropy),WavEnHH_s-4 of T₁WI enhancement, S(1,1) InvDfMom,S(1, -1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity, sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI-based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI-based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively.

Objective: To investigate the infection status and genotype distribution of cervical human papillomavirus (HPV) in women of different ethnic groups and different ages in Yili, Xinjiang Uygur Autonomous Region (Xinjiang). Methods: By using the convenient sampling method, 54 760 women from November 2015 to May 2017 seeking for service in gynecological clinics in a general hospital in Yili, Xinjiang, were selected as the research subjects, and 3 445 samples of cervical mucous exfoliative cells were collected, and the social information of their ethnic and age was collected at the same time. The inclusion criteria were those with sexual life, cervical integrity, and ethnic groups for Han or Uygur or Kazak. PCR-reverse dot blot hybridization was used to detect HPV genotyping in exfoliated cells, and chi-square test was used to compare the difference of HPV positive rate among different ethnic groups. Then, according to ethnicity and age, the differences in positive rates of different ages and ethnic groups were compared in each layer. Results: The positive rate of HPV was 25.6% (882 cases), of which the Han, Uygur and Kazakh were 27.9% (564 cases), 22.9% (196 cases) and 21.6% (122 cases), and the difference was statistically significant (χ²=13.80, P=0.001). The most prevalent high-risk genotypes of Han women were HPV16/52/58, accounting for 24.8% (140 cases), 17.7% (100 cases) and 9.8% (55 cases), respectively. The most prevalent high-risk genotypes of Uygur women were HPV16/52/53, accounting for 34.2% (67 cases), 12.8% (25 cases), 9.2% (18 cases), respectively. The most prevalent high-risk genotypes of Kazak were HPV16/52/53, accounting for 37.7% (46 cases), 17.2% (21 cases), 12.3% (15 cases), respectively. The highest rate of HPV in Uygur patients aged ≥61 years was 41.5% (22 cases), and the lowest in group 36-40 years old, 15.9% (21 cases), the difference between different age groups was statistically significant (χ²=35.01, P<0.001). Conclusion The positive rate of HPV infection among Han, Uygur and Kazak in Yili Prefecture of Xinjiang was different, and the HPV positive genotype differs among different ethnic groups.

Objective To investigate the effect of hydrogen on mitochondrial membrane potential during focal cerebral ischemia-reperfusion (I/R) in rats.Methods Eighty-four healthy adult male SPF Sprague-Dawley rats,weighing 220-240 g,were divided into 3 groups (n=28 each) using a random number table:sham operation group (Sham group),I/R group and hydrogen group (H2 group).Focal cerebral I/R was produced by mid-cerebral artery occlusion in I/R and H2 groups.Hydrogen 10 ml/kg was intraperitoneally injected immediately after onset of reperfusion and at 12 h of reperfusion in group H2.Neurological deficit was scored at 24 h of reperfusion.The rats were then sacrificed and brain tissues in cortex were removed for microscopic examination of the pathological changes and for determination of cerebral infarct size (by TFC staining),nerve cell apoptosis (by TUNEL),mitochondrial membrane potential (by JC-1 staining) and expression of Bcl-2 and Bax (by Western blot).Apoptotic index (AI) was calculated.Results Compared with Sham group,the neurological deficit score,percentage of cerebral infarct size and AI were significantly increased,the mitochondrial membrane potential was decreased,Bax expression in brain tissues was up-regulated,and Bcl-2 expression in brain tissues was down-regulated in I/R and H2 groups (P＜0.05).Compared with I/R group,the neurological deficit score,percentage of cerebral infarct size and AI were significantly decreased,the mitochondrial membrane potential was increased,Bax expression in brain tissues was down-regulated,and Bcl-2 expression in brain tissues was up-regulated (P＜0.05),and the pathological changes of brain tissues were significantly attenuated in H2 group.Conclusion The mechanism by which hydrogen reduces focal cerebral I/R injury is related to decreasing dissipation of mitochondrial membrane potential and inhibiting nerve cell apoptosis in rats.

Objective To study the effect of hydrogen on mitochondrial function in cerebral cortex after cerebral ischemia/reperfusion(I/R)injury in rats. Methods 48 male SD rats were randomly divided into sham operation group(sham group),brain I/R injury group(MOD group),hydrogen treatment group(H2group). 24 hours after reperfusion,the neurological deficit scoring was performed. The changes of mitochondrial membrane potential(△ψm),permeability openness(MPTP),ROS production rate and mitochondrial swelling were detected. Results Compared with the Sham group,neurological deficit score,MPTP openness,mitochondrial swelling degree and ROS production rate were increased in the MOD group(P<0.01),△ψm levels were reduced(P<0.01). Compared with the MOD group,the neurological deficit score,MPTP openness,mitochondrial swelling degree and ROS production rate were decreased in H2group(P<0.01),△ψm levels increased(P<0.01). Conclusions Simul-taneous intraperitoneal injection of pure hydrogen can reduce the generation of reactive oxygen species,protect the mitochondrial function of neuronal cells in the ischemic region after brain I/R,improvement the rat brain I/R after the neurological scoring.

OBJECTIVETo investigate the association of parameters in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using reference region model with prognostic factors and molecular subtypes of breast cancer.

METHODSMRI and pathological data of 50 patients with pathologically confirmed invasive ductal carcinoma of the breast were retrospectively analyzed. Reference region model was applied to analyze pharmacokinetic quantitative parameters including volume transfer constant (RR K), rate constant (K) and the ratio of Kto extracellular space volume (K/V). The associations of the above parameters with prognostic factors and molecular subtypes of breast cancer were analyzed.

RESULTSRR Kand Kwere significantly higher in patients of histological grade 3 compared with those of histological grade 1 & 2 (all<0.05); and the patients with estrogen receptor (ER)-negative and/or progesterone receptor (PR)-negative also had higher RR Kand Kthan those with ER-positive or PR-positive (all<0.05). For immunohistochemistry, RR Kand Kwere significantly higher in triple negative breast cancer compared with luminal type breast cancer (all<0.05).

CONCLUSIONSHigh RR Kand Kare associated with poor prognosis of breast cancer, and which can also be used to distinguish molecular subtypes of breast cancer.