Tailored lipid nanoparticles (LNPs)-mediated small interfering RNA (siRNA) nanomedicines show promise in treating liver disease, such as acute liver injury (ALI) and non-alcoholic steatohepatitis (NASH). However, constructing LNPs that address biosafety concerns, ensure efficient delivery, and target specific hepatic subcellular fractions has been challenging. To evade above obstacles, we develop three novel self-degradable "gemini-like" ionizable lipids (SS-MA, SS-DC, SS-MH) by incorporating disulfide bonds and modifying the length of ester bond and tertiary amino head. Our findings reveal that the disulfide-bond-bridged LNPs exhibit reduction-responsive drug release, improving both biosafety and siRNA delivery efficiency. Furthermore, the distance of ester bond and tertiary amino head significantly influences the LNPs' pK _a, thereby affecting endosomal escape, hemolytic efficiency, absorption capacity of ApoE, uptake efficiency of hepatocytes and liver accumulation. We also develop the modified-mannose LNPs (M-LNP) to target liver macrophages specifically. The optimized M-MH_LNP@TNFα exhibits potential in preventing ALI by decreasing tumor necrosis factor α (TNFα) levels in the macrophages, while MH_LNP@DGAT2 could treat NASH by selectively degrading diacylglycerol O-acyltransferase 2 (DGAT2) in the hepatocytes. Our findings provide new insights into developing novel highly effective and low-toxic "gemini-like" ionizable lipids for constructing LNPs, potentially achieving more effective treatment for hepatic diseases.

Objective:To investigate the high-risk factors affecting the prognosis of patients with pT 1-2N 1M 0 after mastectomy, establish a nomogram prediction model, perform risk stratification, and screen the radiotherapy benefit populations. Methods:Clinical data of 936 patients with pT 1-2N 1M 0 breast cancer undergoing mastectomy in the Fourth Hospital of Hebei Medical University from January 2010 to December 2016 were retrospectively analyzed and 908 cases had complete follow-up data. They were divided into the radiotherapy (RT) group ( n=583) and non radiotherapy (NRT) group ( n=325) according to the radiotherapy. After propensity score matching (PSM) was performed 1 vs. 1, 298 cases were assigned into the RT group and 298 in the NRT group. Overall survival (OS) and disease-free survival (DFS) were compared between two groups using log-rank test. Nomogram prediction model was established, the survival differences were compared among different risk groups, and the radiotherapy benefit populations were screened. Results:Univariate analysis showed that the 5- and 8-year OS and DFS in the RT group were significantly better than those in the NRT group (both P<0.001). Multivariate analysis showed that age, tumor quadrant, number of lymph node metastases, T staging, and Ki-67 level were the independent prognostic factors for OS. Age, tumor quadrant, and T staging were the independent prognostic factors for DFS. The OS nomogram analysis showed that the OS of patients in the high-risk group was significantly improved by post-mastectomy radiotherapy (PMRT) ( P=0.001), while PMRT did not show an advantage in the low- and medium-risk groups ( P=0.057, P=0.099). The DFS nomogram analysis showed that DFS was significantly improved by PMRT in patients in the medium- and high-risk groups ( P=0.036, P=0.001), whereas the benefits from PMRT were not significant in the low-risk group ( P=0.475). Conclusions:For patients with pT 1-2N 1M 0 breast cancer after mastectomy, age ≤ 40 years, tumor located in the inner quadrant or central area, T 2 staging, 2-3 lymph node metastases, Ki-67>30% are the high-risk factors affecting clinical prognosis. The nomogram prediction model can screen the populations that can benefit from PMRT, providing reference for clinical decision-making.