Objective To evaluate the effectiveness and safety of Dan hong injection for treating diabetic nephropathy (DN). Methods We electronically searched CENTRAL (the second issue 2009), MEDLINE (1980-2009.6), EMBASE (1980-2009.6), CNKI (1994-2009.6), CBM (1990-2009.6), WANG FANG (1984-2009.6) and VIP (1989-2009.6) to June 2009, and reference lists of all papers identified also were checked. Randomized controlled trials (RCTs), quasi- RCTs and cross-over studies were identified and assessed according to the Cochrane Handbook for Systematic Reviews of Interventions, and then Revman 5.0 was used to undertake meta analysis. Results Ten RCTs of 736 patients of diabetic nephropathy were included. Meta analyses showed that Dan hong injection was superior to routine treatment in decreasing UAER [MD= – 27.08, 95%CI (– 30.40, – 24.02)], Hb1AC [MD= – 1.12, 95%CI (– 1.67, – 0.56)] and FDP [MD= – 2.28, 95%CI (– 2.70, – 1.86)]; and Dan hong had similar effects in reducing the 24 hours proteinuria [MD= – 0.03, 95%CI (– 0.10, 0.04)], serum creatinine [MD= – 1.00, 95%CI (– 7.86, 5.86)], endogenous creatinine clearance rate [MD= – 2.30, 95%CI (– 15.51, 10.91)], blood cholesterol [MD= – 0.10, 95%CI (– 0.68, 0.48)], blood triglyceride [MD= – 0.15, 95%CI (– 0.36, 0.05)] and fasting blood sugar [MD= – 0.10, 95%CI (– 0.68, 0.48)]; However, Dan hong had more effective action on decreasing the UAER [MD= – 13.14, 95%CI (– 18.00, – 8.27)], blood cholesterol (Plt;0.05) and triglyceride (Plt;0.05) comparing with Dan shen, and on reducing 24 hour albuminuria (Plt;0.05) and FDP (Plt;0.05) with Mai luoning; no significant adverse effects or allergic reactions were reported. Conclusion Dan hong can improve UAER of DN; conclusive results cannot be made about the effectiveness and safety of dan hong for diabetic nephropathy according to limited existing trials. Larger and higher quality randomized controlled trials are needed to assess the available evidence.
In colorectal cancer histopathology, the collaborative perception of microscopic and salient lesions is critical for effective diagnosis and improved patient prognosis. However, existing deep learning methods struggle to simultaneously capture microscopic glandular disorganization and salient tissue lesions. To address this limitation, a colorectal cancer diagnosis network based on dynamic gland-aware and tissue soft-clustering (DGTSNet) is proposed. The method employs dynamic gland-aware convolution to explicitly perceive gland boundaries and dynamically adjust sampling offsets, while incorporating a continuous-domain constraint to prevent out-of-bound sampling, thereby enhancing the perception of microscopic glandular disorders. Meanwhile, a tissue soft-clustering module is utilized to adaptively generate clustering prototypes and guide pixels toward relevant prototype centroids according to semantic similarity, suppressing irrelevant background interference and enhancing responses to significant tissue lesions. Finally, a cascaded sparse coupling module is introduced to collaboratively modulate cross-semantic feature representations along both the spatial and channel dimensions, while constructing differentiable masks to suppress low-contribution semantics, thereby achieving collaborative coupling of cross-semantic features. Experimental results demonstrated that the proposed method achieved superior performance on the Chaoyang, Kather-5K, and EBHI datasets, as well as a real-world clinical validation cohort, outperforming multiple baseline models. The study shows that the proposed method can effectively enhance the joint perception of microscopic glandular disorders and significant tissue lesions, providing an effective solution for colorectal cancer diagnosis.