Efficacy of astragalus combined with renin-angiotensin-aldosterone system blockers in the treatment of stage III diabetic nephropathy: a systematic review and meta-analysis.

OBJECTIVE: To determine the efficacy and safety of Astragalus combined with renin-angiotensin-aldosterone system (RAAS) blockers in treating stage III diabetic nephropathy (DN) by meta-analysis. METHODS: PubMed, Embase, Cochrane Library, Wiley, and Web of Science databases were searched for articles published between August 2007 and August 2022. Clinical studies on Astragalus combined with RAAS blockers for the treatment of stage III DN were included. Meta-analysis was performed by RevMan 5.1 and Stata 14.3 software. RESULTS: A total of 32 papers were included in this meta-analysis, containing 2462 patients from randomized controlled trials, with 1244 receiving the combination treatment and 1218 solely receiving RAAS blockers. Astragalus combined with RAAS blockers yielded a significantly higher total effective rate (TER) (mean difference [MD] 3.63, 95% confidence interval [CI] 2.59-5.09) and significantly reduced urinary protein excretion rate (UPER), serum creatinine (Scr), blood urine nitrogen (BUN) and glycosylated hemoglobin (HbAlc) levels. In subgroup analysis, combining astragalus and angiotensin receptor blocker significantly lowered fasting plasma glucose (FPG) and 24 h urinary protein (24hUTP) levels, compared with the combined astragalus and angiotensin-converting enzyme inhibitor treatment. Meanwhile, the latter significantly decreased the urinary microprotein (ß2-MG). Importantly, the sensitivity analysis confirmed the study's stability, and publication bias was not detected for UPER, BUN, HbAlc, FPG, or ß2-MG. However, the TER, SCr, and 24hUTP results suggested possible publication bias. CONCLUSIONS: The astragalus-RAAS blocker combination treatment is safe and improves outcomes; however, rigorous randomized, large-scale, multi-center, double-blind trials are needed to evaluate its efficacy and safety in stage III DN.

Renin-angiotensin-aldosterone system (RAAS) inhibitors are commonly used to treat diabetic neuropathy (DN) and Astragalus membranaceus components are known to improve DN symptoms.We aimed to establish the efficacy and safety of using Astragalus combined with RAAS inhibitors.Astragalus combined with RAAS inhibitors enhances the total effective rate of diabetic neuropathy response to treatment and reduces urinary protein excretion rate, serum creatinine, blood urea nitrogen and HbAlc.Sensitivity analysis affirms study stability, while publication bias was detected for total effective rate, serum creatinine, and 24 h urinary protein levels.

A Differential Pressure Sensor Coupled with Conductance Sensors to Evaluate Pressure Drop Prediction Models of Gas-Water Two-Phase Flow in a Vertical Small Pipe.

In the process of production logging to evaluate fluid flow inside pipe, logging tools that force all flow to pass through a small measuring pipe are commonly utilized for measuring mixture density. For these logging tools, studying the fluid flow phenomenon inside the small diameter pipe and improving the prediction accuracy of pressure drop are beneficial to accurately measure mixture density. In this paper, a pressure drop prediction system is designed based on a combination of an eight-electrode rotating electric field conductance sensor (REFCS), plug-in cross-correlation conductance sensor, and differential pressure sensor. This combination overcomes the limitation of the existing pressure drop prediction model that the inlet flow velocity needs to be known. An experiment is conducted in a flow loop facility with 20 mm inner diameter small pipe. The responses of the combination sensors are collected. The REFCS is used to identify flow pattern and measure water holdup. During which five flow patterns are identified by recurrence plot method, i.e., slug flow, bubble flow, churn flow, bubble-slug transitional flow, and slug-churn transitional flow. The mixture velocity of two-phase flow is determined by the plug-in conductance sensor. The differential pressure sensor provides a differential pressure fluctuation signal. Five models of prediction of pressure drop are evaluated. The mixture friction factor of gas-water two-phase flow is obtained by a fitting method based on the measured parameters and flow pattern identification using the optimal model. Then, the pressure drop can be predicted according to the measurement results of a conductance sensor and fitting relationship. The results of pressure drop prediction show that the model proposed by Ansari et al. presents a higher accuracy compared with the other four differential pressure models with the absolute average percentage deviation (AAPD) of less than 2.632%. Moreover, the accuracy of pressure drop prediction of the Zhang et al. model is improved by using the mixture friction factor.