Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Objective To explore the efficacy of a model combining clinicopathological characteristics and multiparametric MRI features for predicting BRCA-mutated breast cancer(BC).Methods A total of 256 BC patients were retrospectively selected and divided into BRCA mutation group(116 cases)and BRCA wild group(140 cases)based on the BRCA results.Chi-square tests or independ-ent sample t-tests were used to compare the differences in clinicopathological characteristics and multiparametric MRI features between the BRCA mutation group and the wild group.Risk factors for BRCA-mutated BC were identified through univariate and multivariate logistic regression ananlyses,and a combined predictive model was constructed.Receiver operating characteristic(ROC)curve was used to ana-lyze the diagnostic efficacy of the model.Results There were statistically significant differences in T stage,human epidermal growth factor receptor 2(HER-2),Ki-67,non-mass enhancement,enhancement pattern,time-signal intensity curve(TIC)type,and apparent diffusion coefficient(ADC)values between the BRCA mutation group and the wild group.Univariate logistic regression analysis showed that T stage,HER-2,Ki-67,non-mass enhancement,enhancement pattern,TIC type,and ADC values were risk factors for BRCA-mutated BC(P<0.05).Multivariate logistic regression analysis revealed that T stage,HER-2,Ki-67,enhancement pattern,and TIC type were independent risk factors for BRCA-mutated BC(P<0.05).The combined model incorporating T stage,HER-2,Ki-67,enhancement pattern,and TIC type had the best diagnostic efficacy in predicting BRCA-mutated BC,with an area under the curve(AUC)of 0.751.Conclusion The combined model integrating T stage,HER-2,Ki-67,enhancement pattern,and TIC type has good efficacy in predicting BRCA-mutated BC.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Purpose To explore the feasibility of targeted diagnosis and localization of prostate cancer via background free differential ultrasound molecular imaging based on prostate-specific membrane antigen (PSMA) targeted ultrasound nanobubbles (NB). Materials and Methods Targeted PSMA-NB and non-targeted NB were constructed. The targeting ability of PSMA-NB on human prostate tumor 22RV1 cells (PSMA positive expression) and PC-3 cells (PSMA negative expression) was determined in vitro. Ten nude mouse models of human prostate tumor 22RV1 cells (n=5) and PC-3 cells (n=5) were constructed. PSMA-NB was injected into the rat tail vein,and in-situ blasting was performed. Ultrasound molecular images before and after blasting were collected,using destruction supplement post-processing technology to obtain and compare the differential ultrasound molecular imaging effects between the two groups. Results The particle size of PSMA-NB and NB were (363.7±24.4) nm and (236.0±55.2) nm,with statistical difference (t=3.19,P=0.007),respectively. Cell targeting results showed that PSMA-NB only adhered to the nucleus with positive PSMA-expression. Animal experiments indicated that the differential ultrasonic molecular images of PSMA positive expression group only showed the highly enhanced area of contrast agent at the tumor site,with no background noise. Conclusion Background free differential ultrasound molecular images can be used for precise targeted diagnosis and localization of PSMA positive prostate cancer,which is constructed based on PSMA targeted ultrasound nanobubbles.

Purpose To explore the feasibility of targeted diagnosis and localization of prostate cancer via background free differential ultrasound molecular imaging based on prostate-specific membrane antigen (PSMA) targeted ultrasound nanobubbles (NB). Materials and Methods Targeted PSMA-NB and non-targeted NB were constructed. The targeting ability of PSMA-NB on human prostate tumor 22RV1 cells (PSMA positive expression) and PC-3 cells (PSMA negative expression) was determined in vitro. Ten nude mouse models of human prostate tumor 22RV1 cells (n=5) and PC-3 cells (n=5) were constructed. PSMA-NB was injected into the rat tail vein,and in-situ blasting was performed. Ultrasound molecular images before and after blasting were collected,using destruction supplement post-processing technology to obtain and compare the differential ultrasound molecular imaging effects between the two groups. Results The particle size of PSMA-NB and NB were (363.7±24.4) nm and (236.0±55.2) nm,with statistical difference (t=3.19,P=0.007),respectively. Cell targeting results showed that PSMA-NB only adhered to the nucleus with positive PSMA-expression. Animal experiments indicated that the differential ultrasonic molecular images of PSMA positive expression group only showed the highly enhanced area of contrast agent at the tumor site,with no background noise. Conclusion Background free differential ultrasound molecular images can be used for precise targeted diagnosis and localization of PSMA positive prostate cancer,which is constructed based on PSMA targeted ultrasound nanobubbles.

Objective:To measure the morphological parameters of the fetal vertebral centrum ossification centers (COC) in the second-third trimester using MRI susceptibility weighted imaging (SWI), and to explore the growth and development trajectory of the vertebrae.Methods:Fetus in the second-third trimester with normal vertebrae development were prospectively and continuously included in Shandong Provincial Hospital Affiliated to Shandong First Medical University from December 2015 to December 2021, and the SWI scanning of fetal spine was performed. The following morphometric parameters of the C4, T6, L3, S1 vertebrae COC were measured, including sagittal diameter, transverse diameter, height, cross-sectional area and volume. The linear and nonlinear regression analysis was used to derive the best-fit curve for each parameters and gestational age.Results:A total of 112 fetuses were recruited with gestatonal age 21-39 (29.4±3.9) weeks, including 30 cases of C4, 58 cases of T6, 92 cases of L3, 62 cases of S1. Fetal spine in utero with global curvature was kyphosis, presenting two primary curves (thoracic and sacral kyphosis). The morphological parameters sagittal diameter, transverse diameter, height, cross-sectional area and volume of C4 followed the quadratic polynomial rule during 25 to 38 weeks (R 2=0.938, 0.943, 0.952, 0.957, 0.982). During 21 to 38 weeks, the sagittal diameter, transverse diameter and height of the T6 followed the exponential growth pattern (R 2=0.915, 0.923, 0.849) and the growth of the area and volume followed the quadratic polynomial growth pattern (R 2=0.943, 0.961). The L3 followed the quadratic polynomial rule during 21 to 39 weeks (R 2=0.910, 0.916, 0.914, 0.942, 0.948) The sagittal diameter, transverse diameter and height of the S1 followed the linear growth pattern (R 2=0.905, 0.911, 0.922) and the area and volume followed the quadratic polynomial growth pattern (R 2=0.930, 0.964) during 23 to 39 weeks. Conclusions:The growth and development of C4, T6, L3 and S1 COC of fetus in the second-third trimester has a good correlation with gestational age. The growth of fetal vertebral COC in the early stage is slow, but with the growth of gestational age, the growth rate of vertebral bodies accelerates.

Objective:To study the effect of cyclin Y (CCNY) on the lesion degree of patients with liver cancer and its correlation with preoperative and postoperative liver injury.Methods:The clinical data of 60 patients with liver cancer (liver cancer group) and 69 patients with liver cirrhosis (liver cirrhosis group) in Hangzhou Hospital of Zhejiang Medical and Health Group from January 2015 to October 2017 were retrospectively analyzed. In liver cirrhosis group, Child-Pugh liver function grade A was in 33 cases (liver cirrhosis grade A group), grade B was in 21 cases (liver cirrhosis grade B group), grade C was in 15 cases (liver cirrhosis grade C group). The serum total bilirubin (TBIL), alanine aminotransferase (ALT), albumin, cholinesterase, γ-glutamyl transpeptidase (GGT) and total bile acid were detected by automatic biochemical analyzer. The serum CCNY was detected by WB method, and compared with 40 healthy subjects (healthy control group).Results:Compared with those in healthy control group, the albumin and cholinesterase in liver cirrhosis grade A, B and C groups were significantly decreased, the ALT, TBIL, GGT, total bile acid and CCNY were significantly increased, , and the changes were more obvious with the severity of liver disease, and there were statistical differences ( P<0.05). Pearson correlation analysis result showed that the CCNY was positive correlation with TBIL, ALT, total bile acid and GGT in patients with liver cancer ( r = 0.544, 0.612, 0.553 and 0.539; P<0.05), and CCNY was negative correlation with albumin and cholinesterase ( r = - 0.478 and - 0.620, P<0.05). In patients with liver cancer, before operation and 1, 2 and 7 d after operation, the CCNY was 3.01 ± 1.10, 7.24 ± 2.57, 6.29 ± 1.78 and 4.01 ± 1.52, ALT was (98.74 ± 16.79), (430.55 ± 197.62), (255.73 ± 26.77) and (121.89 ± 20.42) U/L, respectively; the CCNY and ALT 1 and 2 d after operation were significantly higher than those before operation, those 2 d after operation were significantly lower than those 1 d after operation, those 7 d after operation were significantly lower than those 2 d after operation, and there were statistical differences ( P<0.05); there were no statistical difference between 7 d after operation and before operation ( P>0.05). The expression of CCNY before operation and 1 d, 2 d, 7 d after operation was positive correlation with ALT in patients with liver cancer ( r = 0.669, 0.821, 0.663 and 0.642; P<0.01). Conclusions:The more severe the degree of liver lesions in patients with liver cancer, the higher the serum CCNY, and the higher the expression of CCNY, the more severe the degree of liver injury in patients with liver cancer due to surgery, which is positively correlated with liver injury indexes.

Objective:To investigate the relationship between rs5896 polymorphism of prothrombin gene and renal calculi in the elderly patients.Methods:Ninety elderly patients with kidney stones admitted to Hangzhou Hospital of Zhejiang Medical and Health Group from January 2014 to December 2016 were selected as observation group, and another 80 healthy people during the same period were selected as control group.The prothrombin gene rs5896 polymorphism was detected by RT-PCR, and the differences between the two groups were compared.Results:The T frequency of rs5896 allele in the observation group (64.44%) was higher than that in the control group (46.25%) (χ 2=5.687), and the TT genotype (54.44%) was higher than that in the control group (38.75%) (χ 2=4.187, P<0.05). TT genotype (77.78%) at rs5896 locus was more than single stone (38.89%), and CC genotype (16.67%) was less than single stone (37.04%) (χ 2=13.171, 4.363, all P<0.05). The serum uric acid level of TT type[(434.21±78.13)μmol/L] was higher than that of CC+ CT type[(245.73±54.20)μmol/L] ( t=13.038, P<0.05). The serum creatinine, calcium, phosphorus and potassium levels of different genotypes had not statistically significant differents (all P>0.05). The urine level of uric acid in TT-type [(415.32±56.63)μmol/L] was higher than that in CC+ CT type [(267.31±67.18)μmol/L] ( t=11.343, P<0.05), but there were no statistically significant differences in the changes of calcium, phosphorus and potassium in different genotypes of urine (all P>0.05). Conclusion:The T gene frequency of prothrombin gene rs5896 is higher and TT genotype frequency is higher in elderly patients with renal calculi.It is suggested that T gene may be a susceptible gene and that this site may lead to renal calculi through uric acid metabolic disorder, which is worthy of clinical reference.

Objective: To establish the loop-mediated isothermal amplification (LAMP) method for detection of variola virus. Methods: One set of primers were designed for recognizing 5 distinct sequences on variola virus-specific gene HA. To optimize the reaction temperature and primers screening, and the sensitivity and specificity of this method for variola virus (VARV) detection were evaluated. Results: Rapid detection of variola virus by LAMP assay was completed within 60 min at 63 ℃. The sensitivity of LAMP with detection limits of 1 pg/μl was 10 times higher than that of PCR, that is, the LAMP sensitivity was 3.37×10⁵ copies/μl, and the PCR sensitivity was 3.37×10⁶ copies/μl. and the result of 2 kinds of other virus were negative, showing that it had a good specificity. Conclusions The method reported here demonstrates a potential and valuable means for detection of VARV. The LAMP assay is suitable for wide-area sample screening and on-site detection of variola virus in grassroots units, for on-site and primary quarantine, medical units for rapid diagnosis.