Sustained inflammatory responses are closely related to various severe diseases,and inhibiting the excessive activation of inflammasomes and pyroptosis has significant implications for clinical treatment.Natural products have garnered considerable concern for the treatment of inflammation.Huanglian-Wumei decoction(HLWMD)is a classic prescription used for treating inflammatory diseases,but the necessity of their combination and the exact underlying anti-inflammatory mechanism have not yet been elucidated.Inspired by the supramolecular self-assembly strategy and natural drug compatibility theory,we successfully obtained berberine(BBR)-chlorogenic acid(CGA)supramolecular(BCS),which is an herbal pair from HLWMD.Using a series of characterization methods,we confirmed the self-assembly mechanism of BCS.BBR and CGA were self-assembled and stacked into amphiphilic spherical supra-molecules in a 2:1 molar ratio,driven by electrostatic interactions,hydrophobic interactions,and π-πstacking;the hydrophilic fragments of CGA were outside,and the hydrophobic fragments of BBR were inside.This stacking pattern significantly improved the anti-inflammatory performance of BCS compared with that of single free molecules.Compared with free molecules,BCS significantly attenuated the release of multiple inflammatory mediators and lipopolysaccharide(LPS)-induced pyroptosis.Its anti-inflammatory mechanism is closely related to the inhibition of intracellular nuclear factor-kappaB(NF-κB)p65 phosphorylation and the noncanonical pyroptosis signalling pathway mediated by caspase-11.

Sustained inflammatory responses are closely related to various severe diseases, and inhibiting the excessive activation of inflammasomes and pyroptosis has significant implications for clinical treatment. Natural products have garnered considerable concern for the treatment of inflammation. Huanglian-Wumei decoction (HLWMD) is a classic prescription used for treating inflammatory diseases, but the necessity of their combination and the exact underlying anti-inflammatory mechanism have not yet been elucidated. Inspired by the supramolecular self-assembly strategy and natural drug compatibility theory, we successfully obtained berberine (BBR)-chlorogenic acid (CGA) supramolecular (BCS), which is an herbal pair from HLWMD. Using a series of characterization methods, we confirmed the self-assembly mechanism of BCS. BBR and CGA were self-assembled and stacked into amphiphilic spherical supramolecules in a 2:1 molar ratio, driven by electrostatic interactions, hydrophobic interactions, and π-π stacking; the hydrophilic fragments of CGA were outside, and the hydrophobic fragments of BBR were inside. This stacking pattern significantly improved the anti-inflammatory performance of BCS compared with that of single free molecules. Compared with free molecules, BCS significantly attenuated the release of multiple inflammatory mediators and lipopolysaccharide (LPS)-induced pyroptosis. Its anti-inflammatory mechanism is closely related to the inhibition of intracellular nuclear factor-kappaB (NF-κB) p65 phosphorylation and the noncanonical pyroptosis signalling pathway mediated by caspase-11.

Lyase acylation is a post-translational modification of proteins that plays a key role in cellular function,gene transcription and cellular metabolism.Meanwhile,lysine acylation is also involved in mul-tiple biological processes in life forms,and its abnormality may be associated with the occurrence and de-velopment of many diseases.Therefore,prediction of lysine acylation sites is important for the diagnosis and treatment of diseases.Although biomedical experiments can detect lysine acylation sites with high precision,they are costly and time-consuming.To address this problem,researchers have developed more convenient and efficient computational methods as an alternative to traditional biomedical experi-mental techniques.In this study,we developed a prediction model iKcr-RG based on a deep learning ap-proach,which employs a two-branching strategy using both ResNet and BiGRU to extract both local and global feature information from the original sequence encoding.To further improve the performance of the model,we innovatively designed a feature fusion method.After these optimizations,this study demon-strates stronger robustness and stability in unbalanced data.In the independent dataset results,specificity(Sp),sensitivity(Sn),accuracy(Acc)and Matthews correlation coefficient(MCC)were 0.8109,0.7902,0.7940,and 0.4978 respectively.The iKcr-RG model was better than existing prediction mod-els in predicting lysine acylation sites,and this study provides new ideas and methods for the application of deep learning in bioinformatics.

Objective To investigate the practical effects of pediatric rapid sequence intubation(RSI)nursing coordination training based on the LSPPDM(learn,see,practice,prove,do,maintain)framework in order to provide evidence for optimizing pediatric RSI nursing training programs.Methods Nurses from the intensive care unit(ICU)of Children's Hospital,Fudan University during Feb 2023 and Jan 2024 were divided into the experimental group(n=35)and the control group(n=35)by block randomization.The experimental group received LSPPDM framework-based training,while the control group underwent conventional training with theoretical lectures and procedural demonstrations.Outcomes included training satisfaction,theoretical knowledge and procedural skill assessment scores,team collaboration compliance and RSI procedure time were compared between the two groups.Results The experimental group demonstrated significantly higher training satisfaction(123.80±2.04 vs.117.26±9.82,P<0.05),superior post-training theoretical knowledge and procedural skills(P<0.05),enhanced team collaboration compliance(P<0.05),and shorter RSI completion time(P<0.05)compared with the control group.Conclusion Pediatric RSI nursing coordination training based on the LSPPDM framework can effectively increase training satisfaction,promote theoretical and procedural skills and reduce completion time in nurses.

Objective To investigate the practical effects of pediatric rapid sequence intubation(RSI)nursing coordination training based on the LSPPDM(learn,see,practice,prove,do,maintain)framework in order to provide evidence for optimizing pediatric RSI nursing training programs.Methods Nurses from the intensive care unit(ICU)of Children's Hospital,Fudan University during Feb 2023 and Jan 2024 were divided into the experimental group(n=35)and the control group(n=35)by block randomization.The experimental group received LSPPDM framework-based training,while the control group underwent conventional training with theoretical lectures and procedural demonstrations.Outcomes included training satisfaction,theoretical knowledge and procedural skill assessment scores,team collaboration compliance and RSI procedure time were compared between the two groups.Results The experimental group demonstrated significantly higher training satisfaction(123.80±2.04 vs.117.26±9.82,P<0.05),superior post-training theoretical knowledge and procedural skills(P<0.05),enhanced team collaboration compliance(P<0.05),and shorter RSI completion time(P<0.05)compared with the control group.Conclusion Pediatric RSI nursing coordination training based on the LSPPDM framework can effectively increase training satisfaction,promote theoretical and procedural skills and reduce completion time in nurses.

Lyase acylation is a post-translational modification of proteins that plays a key role in cellular function,gene transcription and cellular metabolism.Meanwhile,lysine acylation is also involved in mul-tiple biological processes in life forms,and its abnormality may be associated with the occurrence and de-velopment of many diseases.Therefore,prediction of lysine acylation sites is important for the diagnosis and treatment of diseases.Although biomedical experiments can detect lysine acylation sites with high precision,they are costly and time-consuming.To address this problem,researchers have developed more convenient and efficient computational methods as an alternative to traditional biomedical experi-mental techniques.In this study,we developed a prediction model iKcr-RG based on a deep learning ap-proach,which employs a two-branching strategy using both ResNet and BiGRU to extract both local and global feature information from the original sequence encoding.To further improve the performance of the model,we innovatively designed a feature fusion method.After these optimizations,this study demon-strates stronger robustness and stability in unbalanced data.In the independent dataset results,specificity(Sp),sensitivity(Sn),accuracy(Acc)and Matthews correlation coefficient(MCC)were 0.8109,0.7902,0.7940,and 0.4978 respectively.The iKcr-RG model was better than existing prediction mod-els in predicting lysine acylation sites,and this study provides new ideas and methods for the application of deep learning in bioinformatics.