Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: To investigate the relationship between serum alanine transaminase (ALT) and carotid intima media thickness (cIMT) in adolescents, providing evidence for early prevention and control strategies for cardiovascular structural abnormalities in adolescents. Methods: Data were obtained from the third follow up survey (from October to November 2023) of the "Huantai Children Cardiovascular Health Cohort", including 1 153 healthy adolescents with complete information. Analysis of variance (ANOVA) was used to compare cIMT across different ALT level groups, and multiple linear regression was employed to analyze the relationship between serum ALT levels and cIMT. Results: Significant differences in cIMT were observed among Q 1, Q 2, and Q 3 ALT level groups [(0.56±0.04) (0.57±0.04) (0.59±0.04)mm, respectively; F=3.61, P <0.01]. After adjusting for covariates, multiple linear regression revealed a positive correlation between ALT levels and cIMT ( β=0.18, P <0.01). Gender subgroup analysis showed similar results in males ( β=0.19, P <0.01), but no statistically significant association was found in females ( β=0.07, P = 0.54). Conclusions Elevated serum ALT levels are associated with an increased risk of vascular structural damage in adolescents, especially in boys. Early detection and control of abnormal serum ALT levels can help to reduce early vascular structural damage and further reduce the risk of adverse cardiovascular events.

Objective: To investigate the association of serum alanine aminotransferase (ALT) with left ventricular hypertrophy (LVH) in adolescents, and to provide scientific evidence for the early screening and intervention strategy of cardiac structure damage. Methods: Data were obtained from the third follow up survey (October 2023) of the "Huantai Childhood Cardiovascular Health Cohort Study", including 1 156 healthy adolescents aged 12-17 with complete information. The sample population was stratified into low ( Q 1 group), medium ( Q 2 group), and high ( Q 3 group) ALT levels based on tertiles within the same gender and age groups. Inter group comparisons were conducted using analysis of variance and trend test. A multivariate Logistic regression model was used to analyze the association between ALT levels and LVH, and stratified analyses were performed by gender and age groups. Results: With the increase of ALT quantile level, the detection rate of LVH showed an increasing trend ( Q 1: 3.7%; Q 2: 10.6%; Q 3: 16.7%, Z= 5.89 , P <0.01). After adjusting for potential covariates, compared with the ALT group ( Q 1), the group ( Q 3) increased the risk of developing LVH in adolescents ( OR=2.09, 95%CI =1.21-4.12). Stratified analyses by age and sex showed a significant association only in boys and younger individuals aged 12 to 14 years [ OR (95% CI ) were 2.64(1.04-7.67) and 3.24( 1.35 -9.06), both P <0.05)]. Conclusion Elevated serum ALT levels are associated with an increased risk of LVH in adolescents, and early detection and control of abnormal liver enzyme levels can help reduce early vascular structural damage and prevent adverse cardiovascular events.

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

Objective To investigate the impact of whole body vibration training combined with intermittent blood flow restriction on the motor function and community activity in elderly stroke patients with hemiplegia.Methods A total of 80 convalescent hemiplegic patients after stroke who were hospitalized in the Rehabilitation Medicine Department of Yichang Central People's Hospital(Xiling Campus)from October 2021 to December 2023 were included and randomly divided into control group(n=25),vibration group(n=28)and combination group(n=27)using the random number table method.All patients received conventional rehabilitation training,on the basis;the vabration group received whole body vibration training,while the combination group received whole body vibration training combined with intermittent blood flow restriction.Before and after 6 weeks of training,the balance function was evaluated with Berg balance scale(BBS),while the gait function was tested with 6-minute walking test(6MWT)and the Community Balance and Mobility scale(CB&M)was used to assess the community activity ability.The community balance and mobility(CB&M)were evaluated in the first and third month after discharge.Results There was no significant difference in BBS,CB&M scores and 6MWT walking distance between the three groups before training(P>0.05).After 6 weeks of training,the three groups showed significant increases in the BBS,CB&Mscores and the walk distance of 6MWT(P<0.001).Furthermore,compared with the control group,the combination group and the vibration group were significantly beter(P<0.05),and there was no signifi-cant difference between the combination group and the vibration group(P>0.05).At the first and third month of follw-up after discharge,the CB&M scores of the three groups were significantly higher than those of the control group(P<0.05),while there was no significant difference between the CB&M scores of the combination group and the vibration group at the first month of follow-up(P>0.05).But the CB&M scores of the combination group was higher than those of the vibration group at the third month of follow-up,and the difference was statistically signifi-cant(P<0.05).Conclusion On the basis of conventional rehabilitation,whole body vibration training combined with intermittent blood flow restriction can significantly enhance balance function,balance confidence,walking ability and mobility in early stage of community life for elderly stroke patients with hemiplegia,potentially facilitat-ing their return to community life.

Objective:To investigate the expression of kinase-like gene CT10 regulatory fac-tor(CRKL)in intrahepatic cholangiocarcinoma(ICC)and its effect on the proliferation and invasion of intrahepatic cholangiocarcinoma cells.Methods:qRT-PCR detected CRKL transcription in ICC pa-tient tissues and adjacent tissues.Immunohistochemistry assessed CRKL expression in ICC tissues and adjacent tissues,and analyzed its relationship with clinicopathological features.For the inhibition of CRKL expression in QBC939 cells by siRNA technology,the effect of CRKL silencing on AKT and ERK signaling pathway was measured by Western blot.CRKL's influence on QBC939 cell prolifera-tion and invasion was analyzed by MTT,clonogenesis,and Transwell assays.Results:The expres-sion of CRKL in ICC tissues was up-regulated,and there were statistically significant differences in CRKL expression in ICC with different clinicopathological features such as tumor size,lymph node metastasis and TNM stage.Cellular experiments showed a significant decrease in the phosphoryla-tion of AKT and ERK upon inhibition of CRKL,and the proliferation and invasion ability of ICC cells was significantly diminished(P＜0.05).Conclusion:CRKL promotes ICC cell proliferation and invasion through AKT and ERK pathways,offering new molecular targets and directions for targeted therapy.