The China Cardiovascular Health and Disease Report 2024 has recently been released,offering a comprehensive and detailed assessment of the current status of cardiovascular diseases(CVD)and their associated risk factors in China.The report reveals that the number of CVD cases,as well as the overall and age-standardized incidence rates,continue to increase.Among these conditions,atherosclerotic cardiovascular disease(ASCVD)constitutes a major contributor to CVD-related mortality.Although ASCVD is largely preventable and controllable,the management of key modifiable risk factors-such as smoking,obesity,hypertension,dyslipidemia,and diabetes-remains suboptimal.This article systematically summarizes the prevalence,current management practices,and evidence-based prevention strategies for each major ASCVD risk factor,aiming to reinforce for clinicians the impor-tance of not only treating established ASCVD but also proactively guiding patients in the prevention and control of its underlying risk factors,thereby reducing the overall burden of ASCVD in terms of both prevalence and mortality.

The China Cardiovascular Health and Disease Report 2024 has recently been released,offering a comprehensive and detailed assessment of the current status of cardiovascular diseases(CVD)and their associated risk factors in China.The report reveals that the number of CVD cases,as well as the overall and age-standardized incidence rates,continue to increase.Among these conditions,atherosclerotic cardiovascular disease(ASCVD)constitutes a major contributor to CVD-related mortality.Although ASCVD is largely preventable and controllable,the management of key modifiable risk factors-such as smoking,obesity,hypertension,dyslipidemia,and diabetes-remains suboptimal.This article systematically summarizes the prevalence,current management practices,and evidence-based prevention strategies for each major ASCVD risk factor,aiming to reinforce for clinicians the impor-tance of not only treating established ASCVD but also proactively guiding patients in the prevention and control of its underlying risk factors,thereby reducing the overall burden of ASCVD in terms of both prevalence and mortality.

Interventional therapy for hepatic malignant tumours primarily includes cardiovascular system surgery,diges-tive system surgery,and various diagnostic and therapeutic procedures.Referring to the"International Classification of Diseases,Ninth Edition,Clinical Modification,ICD-9-CM-3"(2011 revision),the relevant surgical procedure codes on the front page of the case are contained mainly in the 9th chapter for cardiovascular system surgery,the 11th chapter for digestive system surgery,and the 18th chapter for various diagnostic and therapeutic procedures and other related chapters on diagnostic and therapeutic procedures.It has become a challenge for the history coders to complete the first page of interventional surgery cases by reviewing the surgery records,extracting the surgical steps,and then identifying the appropriate surgery codes and their sequencing.Accord-ing to the current classification of interventional therapy for hepatic malignant tumors,it is suggested to conduct the search and re-trieval using the keywords like"arteriography""embolization""perfusion""implantation""destruction"and"ablation".

Objectives:To explore the prognostic significances of geriatric nutrition risk index (GNRI), controlling nutritional status (CONUT), and prognostic nutritional index (PNI) in patients with heart failure.Methods:This was a retrospective study. Patients with heart failure admitted to the Department of Cardiology, the first affiliated hospital of Sun Yat-sen University from September 16, 2013 to December 28, 2017 were enrolled. Clinical data of patients were collected and patients were followed up. GNRI, CONUT and PNI scores of patients were calculated. Cox proportional risk model was used to evaluate the relationship between the above three nutritional status scores and the risk of all-cause death in patients with heart failure, and restricted cubic spline analysis was used to verify the relationship. Subgroup analysis was performed based on left ventricular ejection fraction, N-terminal pro-B-type natriuretic peptide, number of heart failure comorbidities, systolic blood pressure, creatinine, body mass index, use of angiotensin-converting enzyme inhibitors/angiotensin receptor antagonists, use of β-blockers, and use of spironolactone.Results:A total of 2 016 patients with heart failure aged (67.7±13.0) years old were enrolled, including 1 230 (61.0%) males. All patients were followed up until September 15, 2021. Multivariate Cox proportional hazard models showed that increased GNRI score was significantly associated with reduced risk of all-cause mortality in patients with heart failure ( HR=0.989, 95% CI 0.982-0.996, P=0.001), while CONUT and PNI scores were not associated with all-cause mortality (both P>0.05). Restricted cubic spline analysis showed no U-shaped relationship between the scores and all-cause death. Subgroup analysis suggested that the prognostic value of GNRI score was predominant in patients not using angiotensin-converting enzyme inhibitor/angiotensin receptor blocker ( Pinteraction=0.024), while there was no statistically significant difference between groups in other subgroup analyses ( Pinteraction>0.05). Conclusions:Increased GNRI score is associated with reduced risk of all-cause mortality in patients with heart failure and may serve as an effective tool to assess nutritional status in patients with heart failure.

Objective: Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test. Methods: The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration. Results: Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT. Conclusion DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.

Objective: Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test. Methods: The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration. Results: Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT. Conclusion DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.

Objective: Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test. Methods: The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration. Results: Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT. Conclusion DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.

Objective: Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test. Methods: The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration. Results: Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT. Conclusion DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.

Objective: Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test. Methods: The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration. Results: Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT. Conclusion DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.

Objective: To explore the influence of birthweight and delivery mode on overweight/obesity and abdominal obesity in primary school students, and to provide evidence for childhood obesity prevention. Methods: After physical examination, questionnaire survey was conducted among 3 361 students and their parents from 3 primary schools in Guangzhou selected through stratified clustering sampling. Information about birthweight and delivery mode was collected. Logistic regression model was used to analyze the association of birthweight and delivery mode with overweight/obesity and abdominal obesity. The multiplicative interaction term and the Delta method was used to explore the potential interaction. Results: The prevalence rate of overweight and obesity for primary school students was 21.33%, and the rate of abdominal obesity was 12.08%. Multivariate Logistic regression analysis showed that delivery by cesarean section was associated with 44% increased risk of overweight/obesity(OR=1.44, 95%CI=1.16-1.80), as compared to vaginal delivery. Higher birthweight was linked to increased risk of overweight/obesity(OR=1.62, 95%CI=1.09-2.42). There was no significant association of birth weight and delivery mode with abdominal obesity in multivariate analysis(P>0.05). Additionally, a positive additive interaction was seen between delivery mode and parental obesity for students risk of overweight/obesity(RERI=0.33, 95%CI=0.02-0.65), as well as for the risk of abdominal obesity(RERI=0.39, 95%CI=0.12-0.65). Conclusion Cesarean section delivery and higher birthweight are linked to increased risk of overweight and obesity in primary school students. The synergistic effect of cesarean section and parental obesity may increase the risk of childhood obesity.