Objective To evaluate an ultrasound-based deep learning radiomics nomogram(DLR_Nomo-gram)for non-invasively differentiating between type Ⅰ and type Ⅱ epithelial ovarian cancer(EOC)before surgery.Methods In this study,a cohort of 195 patients diagnosed with EOC was analyzed.Participants were randomly divided into a training set and a testing set at an 8∶2 ratio.Following data preprocessing,region of interest(ROI)delineation,feature extraction and selection,as well as the clipping and extraction of the maximum section sonogram for each sample,three initial models were developed:the radiomics signature(Rad_Sig),the deep transfer learning signature(DTL_Sig),and the clinical signature(Clinic_Sig).Subsequently,an integrated model—referred to as the DLR_Nomogram—was constructed by combining Rad_Sig,DTL_Sig,and Clinic_Sig,and was presented in the form of a nomogram.The performance of the model was evaluated using the receiver operating characteristic(ROC)curve and the corresponding area under the curve(AUC).Results In the testing set,the DLR_Nomogram demonstrated superior predictive performance(AUC:0.951,95%CI:0.876～1.000)compared to Rad_Sig(AUC:0.709,95%CI:0.539～0.880),DTL_Sig(AUC:0.842,95%CI:0.712～0.972),and Clinic_Sig(AUC:0.916,95%CI:0.827～1.000).The Hosmer-Lemeshow goodness-of-fit test for the DLR_Nomogram resulted in a p-value exceeding 0.05,indicating adequate model calibration.Moreover,decision curve analysis revealed that the DLR_No-mogram offers a higher net clinical benefit across a defined range of threshold probabilities.Conclusions The ultrasound-based DLR_Nomogram exhibits a robust ability to differentiate between Type Ⅰ and Type Ⅱ EOC,and may serve as a valuable clinical tool for guiding individualized preoperative diagnostic and therapeutic decision-making.

Objective To evaluate an ultrasound-based deep learning radiomics nomogram(DLR_Nomo-gram)for non-invasively differentiating between type Ⅰ and type Ⅱ epithelial ovarian cancer(EOC)before surgery.Methods In this study,a cohort of 195 patients diagnosed with EOC was analyzed.Participants were randomly divided into a training set and a testing set at an 8∶2 ratio.Following data preprocessing,region of interest(ROI)delineation,feature extraction and selection,as well as the clipping and extraction of the maximum section sonogram for each sample,three initial models were developed:the radiomics signature(Rad_Sig),the deep transfer learning signature(DTL_Sig),and the clinical signature(Clinic_Sig).Subsequently,an integrated model—referred to as the DLR_Nomogram—was constructed by combining Rad_Sig,DTL_Sig,and Clinic_Sig,and was presented in the form of a nomogram.The performance of the model was evaluated using the receiver operating characteristic(ROC)curve and the corresponding area under the curve(AUC).Results In the testing set,the DLR_Nomogram demonstrated superior predictive performance(AUC:0.951,95%CI:0.876～1.000)compared to Rad_Sig(AUC:0.709,95%CI:0.539～0.880),DTL_Sig(AUC:0.842,95%CI:0.712～0.972),and Clinic_Sig(AUC:0.916,95%CI:0.827～1.000).The Hosmer-Lemeshow goodness-of-fit test for the DLR_Nomogram resulted in a p-value exceeding 0.05,indicating adequate model calibration.Moreover,decision curve analysis revealed that the DLR_No-mogram offers a higher net clinical benefit across a defined range of threshold probabilities.Conclusions The ultrasound-based DLR_Nomogram exhibits a robust ability to differentiate between Type Ⅰ and Type Ⅱ EOC,and may serve as a valuable clinical tool for guiding individualized preoperative diagnostic and therapeutic decision-making.

ObjectiveTo study the differences in volatile oil content of bran-processed Atractylodes lancea and its standard decoction concentrate and freeze-dried powder， as well as the differences in the types and contents of chemical components in volatile oil， and to clarify the quality value transmitting. MethodTen batches of A. lancea rhizoma were collected and prepared into raw products and bran-processed products of A. lancea， standard decoction concentrate and freeze-dried powder of bran-processed A. lancea in order to extract the volatile oil， and the transfer rate of volatile oil in each sample was calculated. Quantitative analysis of the main chemical components（β-eudesmol， atractylon， atractylodin） in each volatile oil was performed by gas chromatography（GC） on the HP-5 quartz capillary column（0.32 mm×30 m， 0.25 μm） with a flame ionization detector（FID）， a split ratio of 10∶1 and a temperature program（initial temperature at 80 ℃， hold for 1 min， rise to 150 ℃ at 10 ℃·min^-1， hold for 10 min， rise to 155 ℃ at 0.5 ℃·min^-1， hold for 5 min， rise to 240 ℃ at 8.5 ℃·min^-1， hold for 8 min）. Cluster analysis and principal component analysis（PCA） were used to explore the overall differences in types and contents of chemical components between the standard decoction concentrate and freeze-dried powder. ResultThe transfer rates of volatile oil in the bran-processed products， standard decoction concentrate and freeze-dried powder were 70.51%， 1.57% and 40.90%， respectively. The average transfer rates of β-eudesmol， atractylon and atractylodin in the volatile oil of bran-processed A. lancea were 58.45%， 48.49% and 55.64%， respectively. In the standard decoction concentrate， only β-eudesmol and atractylodin were detected， and their average transfer rates were 0.22% and 0.10%， respectively. And only β-eudesmol was detected in the freeze-dried powder with the average transfer rate of 8.37%. The results of cluster analysis and PCA showed that there are obvious differences in the types and contents of chemical components between the standard decoction concentrate and freeze-dried powder. ConclusionThe quality value transmitting between bran-processed A. lancea and its standard decoction concentrate and freeze-dried powder is stable， and if the freeze-dried powder is selected as the reference material of dispensing granules， appropriate amount of volatile oil should be added back to make it consistent with the quality of the standard decoction concentrate.

Objective:To investigate the relationship between telomere length of bone marrow mononuclear cells and prognosis of patients with acute myeloid leukemia (AML) who received allogeneic hematopoietic stem cell transplantation (allo-HSCT).Methods:Telomere length of bone marrow mononuclear cells before transplantation, after transplantation and before donor mobilization as well as information related to follow-up of 33 AML patients who received allo-HSCT in the Affiliated Hospital of Guizhou Medical University between June 2020 and June 2021 were retrospectively analyzed. Telomere length was detected by using telomeric terminal restriction fragment (TRF) method. Telomere length was compared among patients with different prognoses. The recurrence within 1 year was treated as the gold standard and receiver operating characteristic (ROC) curve was used to analyze the effect of telomere length before transplantation or before donor mobilization in the judgement of the recurrence within 1 year after transplantation. The patients were stratified according to the optimal threshold value of telomere length for patients or donors, and Kaplan-Meier method was used to compare the progression-free survival (PFS) of patients with different stratification, and log-rank test was performed.Results:The median age of 33 patients was 34 years (14-61 years), and there were 17 males and 16 females; 31 patients were initially diagnosed with AML, 1 patient transferred from myelodysplastic syndrome (MDS) to AML, and 1 patient transferred from chronic granulocytic leukemia (CML) to AML; 14 received identical sibling transplantation and 19 received haploidentical sibling transplantation. The median age of the donors was 30 years (20-65 years), including 24 males and 9 females. Telomere length of bone marrow mononuclear cells before mobilization in 33 donors was longer than that in patients before transplantation (33 cases) and at +30 d after transplantation (31 cases) [(6.67±0.31) kb, (6.40±0.33) kb, (6.48±0.33) kb, respectively; all P < 0.05], and the difference between patients before and at +30 d after transplantation was not statistically significant ( t = 0.89, P = 0.378), and the telomere length of bone marrow mononuclear cells in 11 patients +180 d after transplantation was (6.66±0.18) kb. The incidence of acute graft-versus-host disease (aGVHD) after transplantation was 45.5% (15/33), the incidence of infection with clear imaging and pathogenic basis was 39.4% (13/33), the mortality rate within 1 year after transplantation was 3.0% (1/33), and the recurrence rate within 1 year after transplantation was 15.2% (5/33). There were no statistically significant differences in telomere length of donor pre-mobilization bone marrow mononuclear cells between the groups with and without aGVHD and between the infected and non-infected groups (all P > 0.05).Compared with patients who had not relapsed within 1 year after transplantation, telomere length of donor pre-mobilization bone marrow mononuclear cells was shorter in patients who relapsed within 1 year after transplantation [(6.39±0.19) kb vs. (6.72±0.30) kb, t = -3.23, P = 0.011], telomere length was longer in patients before transplantation [(6.75±0.16) kb vs. (6.35±0.36) kb, t = 4.17, P = 0.001]. ROC curve analysis showed that the optimal threshold values for telomere length of pre-transplantation and donor pre-mobilization bone marrow mononuclear cells were 6.48 and 6.42 kb, respectively for patients who relapsed within 1 year after transplantation. PFS in patients with pre-transplantation bone marrow mononuclear cells telomere length < 6.48 kb was better than that in patients with telomere length ≥ 6.48 kb ( P = 0.003); PFS in patients with pre-mobilization bone marrow mononuclear cells telomere length>6.42 kb was better than that in patients with telomere length ≤ 6.42 kb ( P < 0.001). Conclusions:In allo-HSCT for AML, patients have an increased risk of relapse within 1 year after transplantation when their pre-transplantation bone marrow mononuclear cells telomere length is long and the donor bone marrow mononuclear cells telomere length is short.

Objective To evaluate the performance of virtual touch tissue quantification imaging (VTQ) for differential diagnosing in uterine hysteromyoma and adenomyoma. Methods Blood flow parameters and SWV (shear wave velocities) from 42 regular hysteromyomas,14 abnormal hysteromyomas and 25 uterine adenomyomas were compared and analyzed through color Doppler. Results The SWV of regular hysteromyomas was on average of (2.72 ± 0.31) m/s;that of abnormal hysteromyomas (1.88 ± 0.25) m/s;while the SWV of uterine adenomyomas was on average of 1.91 ± 0.27 m/s. The mean SWV in regular hysteromyomas group was significantly higher than that in abnormal hysteromyomas group and uterine adenomyomas group (P < 0.05). There was no significant difference of SWV between abnormal hysteromyomas group and uterine adenomyomas group (P > 0.05).The resistance index and SWV of uterine hysteromyomas and adenomyomas had positive correlation (r = 0.753,0.544,0.506,P < 0.05). Conclusion VTQ can quantify the stiffiness of uterine hysteromyoma and adenomyoma.