Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.

Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.

Purpose To investigate the clinical value of multi-center digital mammography radiomics nomogram model in predicting triple-negative breast cancer(TNBC).Materials and Methods The digital mammograms of 462 patients with pathologically confirmed breast cancer from November 2016 to March 2022 were retrospectively analyzed,including 243 cases from Yijishan Hospital of Wannan Medical College(institution 1),106 cases from Fuyang People's Hospital(institution 2)and 113 cases from Taihe People's Hospital(institution 3).According to the results of immunohistochemistry,a total of 349 breast cancer patients in institution 1 and institution 2 were randomly divided into the training group(244 cases,including 41 TNBC and 203 non-TNBC)and the validation group(105 cases,including 18 TNBC and 87 non-TNBC)according to the ratio of 7∶3,113 breast cancer patients(24 TNBC and 89 non-TNBC)from institution 3 were included in the external validation group.Comparing the mediolateral oblique and cranial cauda digital mammography images,the mammography imaging with larger lesion areas were selected,and the image segmentation and radiomics feature extraction were performed.The radiomics model was constructed by using Logistic regression.The clinicopathological parameters and radiomics scores were used to construct a nomogram.Receiver operating characteristic and decision curve analysis were used to evaluate the model performance.To compare The predictive performance between the models was compared.Results Finally,four radiomics features closely related to TNBC were selected to construct an radiomics model.The area under the curve,sensitivity and specificity of TNBC predicted by the radiomics model in training group,validation group and external test group were 0.868,90.24%and 72.91%,0.827,72.22%and 75.86%,0.837,70.83%and 78.65%,respectively.The area under the curve,sensitivity and specificity of TNBC predicted by the combined model in the training group,validation group and external test group were 0.903,80.49%and 86.70%,0.890,77.78%and 88.51%,0.870,62.50%and 85.39%,respectively.The combined model was better than the single image omics model in predicting TNBC,and the difference was statistically significant between the training group and the verification group(Z=2.061,2.064,both P<0.05),but not between the external test group(Z=1.223,P=0.221).In three group,decision curve analysis showed that the nomogram predicted a higher net benefit than the radiomics model for triple-negative breast cancer.Conclusion The radiomics model has high diagnostic efficiency in predicting TNBC,and the nomogram model combined with the radiomics score and histological grading can further improve the prediction efficiency.

The Crimean-congo hemorrhagic fever virus(CCHFV)is a geographically widespread fatal pathogen.Identification of the epitope regions of the virus is important for the diagnosis and epidemiological studies of CCHFV infections.In this study,expression vectors carrying series truncated fragments of the NP(nueleoeapsid protein)gene from the S fragment of CCHFV strain YL04057 were constructed.The recombinant proteins were expressed in E.coli and purified for detection.The antigenic of the truncated fragments of NP was detected with a polyclonai serum(rabbit)and 2 monoclonal(mAbs)(14B7 and 43E5)against CCHFV by Western-blot analyses.The results showed that the three expressed constructs,which all contained the region 235AA to 305AA could be detected by mAbs polyclonal serum.The results suggest that region 235-305 aa of NP is a highly antigenic region and is highly conserved in the NP protein.