Diabetic cardiomyopathy （DCM） is a common chronic complication of diabetes， categorized as consumptive thirst in traditional Chinese medicine （TCM）， and it subsequently gives rise to chest impediment. DCM is characterized by a complex pathogenesis and exhibits long-term progression with poor prognosis in clinical practice. Astragali Radix， as a medicinal material with edible value， possesses diverse active components including saponins， polysaccharides， and flavonoids. It has the effects of tonifying Qi， elevating Yang， promoting fluid production， nourishing blood， expressing toxin， and expelling pus. Modern pharmacological studies have confirmed that Astragali Radix and its active components exert therapeutic effects in various aspects such as inhibition of inflammation， alleviation of stress injury， regulation of programmed cell death， modulation of glucose and lipid metabolism， inhibition of myocardial fibrosis， and regulation of gut microbiota and metabolites， treating DCM in a multi-component， multi-target， and multi-pathway manner. The compatibility of Astragali Radix exerts both synergistic and antagonistic effects. The compound preparations and extracts of Astragali Radix are widely used in clinical practice， and the development of novel nano and gel materials and extracellular vesicles helps to improve the oral bioavailability， providing more possibilities for the clinical application of Astragali Radix in DCM， though its potential for clinical translation requires ongoing exploration. This review summarizes the TCM theoretical basis of Astragali Radix in the treatment of DCM， and integrates modern pharmacological research results to delve into the effects and mechanisms of the herb combinations， compound preparations， extracts， and new composite materials of Astragali Radix in the treatment of DCM， aiming to provide a reference for in-depth research and new drug development of Astragali Radix for DCM.

Objective·To construct and validate a nomogram for distinguishing primary Sj?gren's syndrome(PSS)from immunoglobulin G4-related sialadenitis(IgG4-RS)based on ultrasound scoring parameters and clinical indicators.Methods·A total of 141 patients with PSS and 31 patients with IgG4-RS were retrospectively recruited from Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine from January 2018 to December 2023.The ultrasound scoring parameters,including parotid gland ultrasound(PGUS)score,submandibular gland ultrasound(SMGUS)score,and salivary gland ultrasound(SGUS)score,along with clinical indicators such as gender,age,anti-SSB/La antibody,anti-SSA/Ro60 antibody,anti-SSA/Ro52 antibody,IgG,and rheumatoid factor(RF),were collected.The optimal US scoring parameters and clinical indicators were screened by least absolute shrinkage and selection operator(LASSO)regression,and a nomogram model was constructed to distinguish PSS from IgG4-RS.The internal validation of the model was carried out through bootstrap method.Receiver operator characteristic(ROC)curve,calibration curve and decision curve analysis(DCA)were used to estimate the discrimination,calibration,and clinical utility of the nomogram model,respectively.Results·LASSO regression identified six major variables:gender,age,anti-SSA/Ro60 antibody,anti-SSA/Ro52 antibody,PGUS score,and SMGUS score.These variables were used to construct the nomogram.ROC curve of the nomogram showed that the area under the curve(AUC)was 0.976,indicating the nomogram had strong discrimination ability.The bootstrap method was used for internal validation with 1 000 resampling iterations,and the average absolute error was 0.018.Calibration curve demonstrated good agreement between predicted and observed values.DCA indicated that the nomogram had certain clinical utility.Conclusion·The nomogram based on ultrasound scoring parameters and clinical indicators demonstrates excellent discrimination and calibration in differentiating PSS from IgG4-RS.It has the potential to assist in clinical diagnosis and decision-making.

Objective·To construct and validate a nomogram for distinguishing primary Sj?gren's syndrome(PSS)from immunoglobulin G4-related sialadenitis(IgG4-RS)based on ultrasound scoring parameters and clinical indicators.Methods·A total of 141 patients with PSS and 31 patients with IgG4-RS were retrospectively recruited from Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine from January 2018 to December 2023.The ultrasound scoring parameters,including parotid gland ultrasound(PGUS)score,submandibular gland ultrasound(SMGUS)score,and salivary gland ultrasound(SGUS)score,along with clinical indicators such as gender,age,anti-SSB/La antibody,anti-SSA/Ro60 antibody,anti-SSA/Ro52 antibody,IgG,and rheumatoid factor(RF),were collected.The optimal US scoring parameters and clinical indicators were screened by least absolute shrinkage and selection operator(LASSO)regression,and a nomogram model was constructed to distinguish PSS from IgG4-RS.The internal validation of the model was carried out through bootstrap method.Receiver operator characteristic(ROC)curve,calibration curve and decision curve analysis(DCA)were used to estimate the discrimination,calibration,and clinical utility of the nomogram model,respectively.Results·LASSO regression identified six major variables:gender,age,anti-SSA/Ro60 antibody,anti-SSA/Ro52 antibody,PGUS score,and SMGUS score.These variables were used to construct the nomogram.ROC curve of the nomogram showed that the area under the curve(AUC)was 0.976,indicating the nomogram had strong discrimination ability.The bootstrap method was used for internal validation with 1 000 resampling iterations,and the average absolute error was 0.018.Calibration curve demonstrated good agreement between predicted and observed values.DCA indicated that the nomogram had certain clinical utility.Conclusion·The nomogram based on ultrasound scoring parameters and clinical indicators demonstrates excellent discrimination and calibration in differentiating PSS from IgG4-RS.It has the potential to assist in clinical diagnosis and decision-making.