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 develop a Nomogram prediction model for post-stroke delirium (PSD) in patients with acute stroke and to verify the effectiveness of the prediction model. Methods A total of 400 patients with acute stroke,admitted to the Department of Neurology in our hospital between June 2022 and March 2023,were retrospectively included in the study as the training group. Independent risk factors for PSD were identified by Logistic regression analysis. A calibration model was constructed to evaluate the consistency of the model. The area under ROC curve (AUC) was used to evaluate the accuracy of the prediction model. Between April and July 2023,172 patients with acute stroke were selected,as the validation group,for external validation of the model. Results The incidence of PSD was found at 27.50％ in the training group and 26.74％ in the validation group. A Nomogram prediction model was constructed with the five predictors:cerebrovascular interventional surgery,hypersensitive C-reactive protein,smoking,National Institutes of Health Stroke Scale (NIHSS) score and age. The calibration curve was found close to the ideal curve with an AUC at 0.797 for the training group,and the risk prediction corresponding to the maximum Youden index was 0.554 with a predicted threshold of 134.63. The calibration curve of the validation group was also found close to the ideal curve,with an AUC of 0.844. Conclusion The nomogram prediction model for PSD in patients with acute stroke demonstrates a good risk prediction value for risk assessment,which can help medical staff to effectively predict the risk for PSD in patients with acute stroke and to take corresponding measures in prevention of PSD.

Objective To develop a Nomogram prediction model for post-stroke delirium (PSD) in patients with acute stroke and to verify the effectiveness of the prediction model. Methods A total of 400 patients with acute stroke,admitted to the Department of Neurology in our hospital between June 2022 and March 2023,were retrospectively included in the study as the training group. Independent risk factors for PSD were identified by Logistic regression analysis. A calibration model was constructed to evaluate the consistency of the model. The area under ROC curve (AUC) was used to evaluate the accuracy of the prediction model. Between April and July 2023,172 patients with acute stroke were selected,as the validation group,for external validation of the model. Results The incidence of PSD was found at 27.50％ in the training group and 26.74％ in the validation group. A Nomogram prediction model was constructed with the five predictors:cerebrovascular interventional surgery,hypersensitive C-reactive protein,smoking,National Institutes of Health Stroke Scale (NIHSS) score and age. The calibration curve was found close to the ideal curve with an AUC at 0.797 for the training group,and the risk prediction corresponding to the maximum Youden index was 0.554 with a predicted threshold of 134.63. The calibration curve of the validation group was also found close to the ideal curve,with an AUC of 0.844. Conclusion The nomogram prediction model for PSD in patients with acute stroke demonstrates a good risk prediction value for risk assessment,which can help medical staff to effectively predict the risk for PSD in patients with acute stroke and to take corresponding measures in prevention of PSD.

Objective:To explore the effects of embodied emotion priming on attentional bias of individuals with depression tendency.Methods:From June to December 2018, a total of 91 college students with depression tendency were recruited to participate in the experiment.A 3(embodied emotion priming: positive priming, negative priming and no priming) × 2 (emotional face: happy and sad) mixed design was adopted to measure the attentional bias of individuals with depression tendency using the dot probe paradigm. SPSS 22.0 statistical software was used for repeated measurement analysis of variance.Results:In terms of attentional bias, the interaction effect between embodied emotion priming types and emotional faces was significant ( F(2, 88)=5.97, P=0.004, ηp2=0.119). Further simple effect analysis showed that, under the happy-face condition, participants' attentional bias reaction time(△RT) was significantly higher when primed with embodied positive emotion than those primed with embodied negative emotion((14.30±18.23)ms, (-6.53±38.17)ms, P<0.05). The participants' attentional bias △RT was significantly lower when primed with embodied negative emotion than participants with no priming ((-6.53±38.17)ms, (9.16±30.62)ms, P<0.05). Under the sad-face condition, the participants' attentional bias △RT was significantly higher when primed with embodied negative emotion((28.22±35.33)ms) than participants primed with embodied positive emotion((11.71±29.24)ms, P<0.05) and no priming ((7.63±30.60)ms, P<0.05). Conclusion:Embodied emotion priming can affect the attentional bias of individuals with depression tendency.