Purpose To evaluate the effectiveness of deep learning image reconstruction(DLIR)algorithms in reconstructing lumbar vertebrae images from abdominal CT scans,aiming to reduce radiation dose and eliminate the need for repeat lumbar CT examinations.Materials and Methods A retrospective collection was conducted from March to May 2024 in the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine.Thirty-two patients who underwent both abdominal and lumbar CT scans in a supine head-first position were enrolled.The abdominal CT(DLIR group)utilized a tube voltage of 120 kVp and a current of 200 mA with high-intensity DLIR for lumbar reconstruction.The standard lumbar CT(lumbar group)used the same voltage with a tube current of 260 mA and was reconstructed using 60%weighted adaptive statistical iterative reconstruction.Objective assessments was used to measure the CT values,noise(standard deviation,SD value),signal-to-noise ratio and contrast-to-noise ratio(excluding adipose tissue)at the third lumbar vertebral pedicle level and the L2/L3 intervertebral disc level for muscle,adipose tissue,cancellous bone,intervertebral discs,dura mater and cortical bone.Subjective assessments employed a five-point scale to evaluate image contrast,noise and sharpness.Results The volume CT dose index in lumbar group and DLIR group were 15.25 mGy and 11.74 mGy,respectively.There was no statistical difference in CT values between the structures of both groups(all P>0.05).Compared with the lumbar group,the DLIR group showed significant reductions in SD values across the measured tissues by 31.09%,35.66%,13.48%,27.82%,24.93%and 15.09%(t=5.09-7.21,all P<0.05).The signal-to-noise ratio improved by 36.40%,52.31%,16.56%,34.13%,38.39%and 18.81%,and the contrast-to-noise ratio improved by 51.70%,51.32%,36.24%,34.47%and 53.56%(t=-9.58--4.23,all P<0.001).The DLIR group significantly outperformed the lumbar group in image contrast[4.45(4.00,5.00)points vs.4.75(4.00,5.00)points],image noise[4.06(4.00,4.00)points vs.4.39(4.00,5.00)points],and spatial resolution of fine structures[4.00(4.00,4.00)points vs.4.27(4.00,5.00)points](Z=-3.80,-4.38,-3.55,all P<0.001).Conclusion Using high-intensity DLIR for abdominal examinations can achieve high-quality lumbar CT images with a 25%reduction in radiation dose,enabling simultaneous abdominal and lumbar scanning in a single session.

BACKGROUND: Valvular heart disease (VHD) has become increasingly common with the aging in China. This study aimed to evaluate regional differences in the clinical features, management strategies, and outcomes of patients with VHD across different regions in China. METHODS: Data were collected from the China-VHD Study. From April 2018 to June 2018, 12,347 patients who presented with moderate or severe native VHD with a median of 2 years of follow-up from 46 centers at certified tertiary hospitals across 31 provinces, autonomous regions, and municipalities in Chinese mainland were included in this study. According to the locations of the research centers, patients were divided into five regional groups: eastern, southern, western, northern, and central China. The clinical features of VHD patients were compared among the five geographical regions. The primary outcome was all-cause mortality or rehospitalization for heart failure. Kaplan-Meier survival analysis was used to compare the cumulative incidence rate. RESULTS: Among the enrolled patients (mean age, 61.96 years; 6877 [55.70%] male), multiple VHD was the most frequent type (4042, 32.74%), which was mainly found in eastern China, followed by isolated mitral regurgitation (3044, 24.65%), which was mainly found in northern China. The etiology of VHD varied significantly across different regions of China. The overall rate of valve interventions was 32.67% (4008/12,268), with the highest rate in southern China at 48.46% (205/423). In terms of procedure, the proportion of transcatheter valve intervention was relatively low compared to that of surgical treatment. Patients with VHD in western China had the highest incidence of all-cause mortality or rehospitalization for heart failure. Valve intervention significantly improved the outcome of patients with VHD in all five regions (all P  <0.05). CONCLUSIONS: This study revealed that patients with VHD in China are characterized by significant geographic disparities in clinical features, treatment, and clinical outcomes. Targeted efforts are needed to improve the management and prognosis of patients with VHD in China according to differences in geographical characteristics. REGISTRATION ClinicalTrials.gov , NCT03484806.
Aged ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology* ; Heart Valve Diseases/therapy* ; Kaplan-Meier Estimate ; Tertiary Care Centers ; Treatment Outcome

Medication reconciliation plays a key role in improving patient medication safety,reducing inappropriate polypharmacy,and promoting the high-quality development of pharmaceutical services.Compared to advanced international guidelines,China's medication reconciliation service standards have deficiencies in areas such as definition and process design,and multidisciplinary team building.There is a need to establish a comprehensive medication reconciliation effect evaluation index system,develop pharmacist-led multidisciplinary teams,promote the advancement of artificial intelligence and big data technologies,and strengthen outpatient and community medication reconciliation coverage,thereby contributing to the high-quality development of pharmaceutical services in China.

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Refinement and standardisation of the management of clinical diagnostic and treatment operations is a key aspect of achieving high-quality development in hospitals.By analysing the management status quo of clinical diagnosis and treatment operations in hospitals,it combed the problems existing in this field.Based on the closed-loop management model,it proposed measures and recommendations to promote the continuous optimisation of the management of clinical diagnostic operations in hospitals.Hospitals should establish hospital-level operation catalog and conduct classified management,authorize operators and dynamically adjust them,carry out operation quality management,pay attention to information management of operation management,and combine operation management with physician performance management.

Purpose To evaluate the effectiveness of deep learning image reconstruction(DLIR)algorithms in reconstructing lumbar vertebrae images from abdominal CT scans,aiming to reduce radiation dose and eliminate the need for repeat lumbar CT examinations.Materials and Methods A retrospective collection was conducted from March to May 2024 in the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine.Thirty-two patients who underwent both abdominal and lumbar CT scans in a supine head-first position were enrolled.The abdominal CT(DLIR group)utilized a tube voltage of 120 kVp and a current of 200 mA with high-intensity DLIR for lumbar reconstruction.The standard lumbar CT(lumbar group)used the same voltage with a tube current of 260 mA and was reconstructed using 60%weighted adaptive statistical iterative reconstruction.Objective assessments was used to measure the CT values,noise(standard deviation,SD value),signal-to-noise ratio and contrast-to-noise ratio(excluding adipose tissue)at the third lumbar vertebral pedicle level and the L2/L3 intervertebral disc level for muscle,adipose tissue,cancellous bone,intervertebral discs,dura mater and cortical bone.Subjective assessments employed a five-point scale to evaluate image contrast,noise and sharpness.Results The volume CT dose index in lumbar group and DLIR group were 15.25 mGy and 11.74 mGy,respectively.There was no statistical difference in CT values between the structures of both groups(all P>0.05).Compared with the lumbar group,the DLIR group showed significant reductions in SD values across the measured tissues by 31.09%,35.66%,13.48%,27.82%,24.93%and 15.09%(t=5.09-7.21,all P<0.05).The signal-to-noise ratio improved by 36.40%,52.31%,16.56%,34.13%,38.39%and 18.81%,and the contrast-to-noise ratio improved by 51.70%,51.32%,36.24%,34.47%and 53.56%(t=-9.58--4.23,all P<0.001).The DLIR group significantly outperformed the lumbar group in image contrast[4.45(4.00,5.00)points vs.4.75(4.00,5.00)points],image noise[4.06(4.00,4.00)points vs.4.39(4.00,5.00)points],and spatial resolution of fine structures[4.00(4.00,4.00)points vs.4.27(4.00,5.00)points](Z=-3.80,-4.38,-3.55,all P<0.001).Conclusion Using high-intensity DLIR for abdominal examinations can achieve high-quality lumbar CT images with a 25%reduction in radiation dose,enabling simultaneous abdominal and lumbar scanning in a single session.

Refinement and standardisation of the management of clinical diagnostic and treatment operations is a key aspect of achieving high-quality development in hospitals.By analysing the management status quo of clinical diagnosis and treatment operations in hospitals,it combed the problems existing in this field.Based on the closed-loop management model,it proposed measures and recommendations to promote the continuous optimisation of the management of clinical diagnostic operations in hospitals.Hospitals should establish hospital-level operation catalog and conduct classified management,authorize operators and dynamically adjust them,carry out operation quality management,pay attention to information management of operation management,and combine operation management with physician performance management.

Medication reconciliation plays a key role in improving patient medication safety,reducing inappropriate polypharmacy,and promoting the high-quality development of pharmaceutical services.Compared to advanced international guidelines,China's medication reconciliation service standards have deficiencies in areas such as definition and process design,and multidisciplinary team building.There is a need to establish a comprehensive medication reconciliation effect evaluation index system,develop pharmacist-led multidisciplinary teams,promote the advancement of artificial intelligence and big data technologies,and strengthen outpatient and community medication reconciliation coverage,thereby contributing to the high-quality development of pharmaceutical services in China.

Redox-sensitive cysteine(RSC)thiol plays an important role in many biological processes such as photosynthesis,cellular metabolism,and transcription.Therefore,it is necessary to identify red-ox-sensitive cysteine accurately.However,traditional redox-sensitive cysteine identification is very ex-pensive and time-consuming.At present,there is an urgent need for a mathematical calculation method to identify sequence information and redox-sensitive cysteines quickly and accurately.Here,we devel-oped an effective predictor called iRSC-PseAAC,which used the dimension reduction algorithm LDA combined with the support vector machine to predict redox-sensitive cysteine sites.In the cross-validation results,the specificity(Sp),sensitivity(Sn),accuracy(Acc)and Matthews correlation coefficient(MCC)were 0.841,0.868,0.859 and 0.692 respectively.In the independent dataset results,the Sp,Sn,Acc and MCC were 0.906,0.882,0.890 and 0.767 respectively.compared with existing prediction methods,iRSC-PseAAC had obvious improvement effect.The method proposed for this study can also be used for many problems in computational proteomics.

OBJECTIVE: To develop and validate a user-friendly risk score for older mitral regurgitation (MR) patients, referred to as the Elder-MR score. METHODS: The China Senile Valvular Heart Disease (China-DVD) Cohort Study functioned as the development cohort, while the China Valvular Heart Disease (China-VHD) Study was employed for external validation. We included patients aged 60 years and above receiving medical treatment for moderate or severe MR (2274 patients in the development cohort and 1929 patients in the validation cohort). Candidate predictors were chosen using Cox's proportional hazards model and stepwise selection with Akaike's information criterion. RESULTS: Eight predictors were identified: age ≥ 75 years, body mass index < 20 kg/m², NYHA class III/IV, secondary MR, anemia, estimated glomerular filtration rate < 60 mL/min per 1.73 m², albumin < 35 g/L, and left ventricular ejection fraction < 60%. The model displayed satisfactory performance in predicting one-year mortality in both the development cohort (C-statistic = 0.73, 95% CI: 0.69-0.77, Brier score = 0.06) and the validation cohort (C-statistic = 0.73, 95% CI: 0.68-0.78, Brier score = 0.06). The Elder-MR score ranges from 0 to 15 points. At a one-year follow-up, each point increase in the Elder-MR score represents a 1.27-fold risk of death (HR = 1.27, 95% CI: 1.21-1.34, P < 0.001) in the development cohort and a 1.24-fold risk of death (HR = 1.24, 95% CI: 1.17-1.30, P < 0.001) in the validation cohort. Compared to EuroSCORE II, the Elder-MR score demonstrated superior predictive accuracy for one-year mortality in the validation cohort (C-statistic = 0.71 vs. 0.70, net reclassification improvement = 0.320, P < 0.01; integrated discrimination improvement = 0.029, P < 0.01). CONCLUSIONS The Elder-MR score may serve as an effective risk stratification tool to assist clinical decision-making in older MR patients.