Microbial infections are a prevalent challenge in the prevention and treatment of oral diseases. Antibiotic therapy faces clinical limitations due to its single-target mechanism and tendency to induce resistance with repeated use, necessitating novel antibacterial strategies. Stimuli-responsive antibacterial materials, whose antimicrobial activity can be modulated by external stimuli, offer advantages such as remote controllability, potential for localized precision treatment, and a reduced risk of inducing resistance. Among these materials, mechanical force-triggered piezoelectric materials exhibit significant antibacterial activity in the biomedical field owing to their unique piezoelectric effect, excellent stability, and good biocompatibility. Research has shown that piezoelectric materials can convert mechanical energy into electrical energy in response to external forces, which enables antibacterial effects without requiring an external power source. The underlying mechanisms primarily include direct electric field effects, generation of reactive oxygen species, and immune modulation. Preliminary applications in treating oral infections (e.g., dental caries, periodontitis, and peri-implantitis) have confirmed their stability and biocompatibility, establishing a foundation for clinical translation. However, long-term efficacy and biosafety in the complex oral microenvironment require further validation. Future research should focus on optimizing material preparation protocols to enhance antibacterial efficacy and stability, further investigating the underlying antimicrobial mechanisms, and systematically evaluating their therapeutic outcomes and safety profiles across various types of oral infections. This review summarizes the antibacterial effects, mechanisms, stability, safety, and research progress of piezoelectric materials in the stomatologic field, aiming to provide new insights for further research and application in this area.

Grounded in organizational change-complex systems theory,it investigates the pathways and mechanisms for deep integration of Artificial Intelligence(AI)into clinical management.Addressing structural challenges in current clinical management systems,it propose a dynamic three-phase model"unfreezing-changing-refreezing"driven by AI technologies.By deconstructing systemic contradictions arising from technological penetration e.g.,multi-agent coordination,ethical risks,and responsibility ambiguity,a layered governance framework and dynamic regulatory mechanisms are established.Through synergistic evolution of technology,organization,and institution,an adaptive transition in clinical management paradigms can be achieved,ultimately fostering an AI-augmented healthcare ecosystem that balances efficiency with safety.

The primary role of transfer RNA(tRNA)is to connect a specific amino acid to its 3' end, use its anticodon to match the codon on messenger RNA(mRNA), and deliver the corresponding amino acid to the ribosome for protein synthesis.tRNA exists in two forms: precursor tRNA and mature tRNA.When acted upon by enzymes like Dicer, elaC ribonuclease Z 2(ELAC2), angiopoietin(ANG), and other ribonucleases, tRNA is broken down into tRNA-derived stress-induced RNA(tiRNA)and tRNA-derived fragments(tRF).Recent advancements in RNA sequencing technology have led to increased interest in tiRNA and tRF, shedding light on their roles in various physiological and pathological processes.tRNA-derived small molecules(tsRNA)function similarly to microRNA(miRNA), influencing gene expression and protein synthesis.They show promise as diagnostic markers and potential therapeutic targets for age-related diseases.This review offers a comprehensive analysis of tsRNA classification, biological functions, research advancements, and clinical applications in age-related conditions.

Objective:To investigate the risk factors of pediatric sepsis-induced coagulopathy（pSIC），and to construct a nomogram prediction model for early prediction of pSIC.Methods:Using a cross-sectional retrospective cohort design，children with sepsis who were hospitalized in PICU of the Second People's Hospital of Liaocheng Subsidiary to Shandong First Medical University from January 2017 to December 2023 were selected as the study objects，and the diagnosis of sepsis met the diagnostic criteria for childhood sepsis of the 2015 edition.According to the diagnostic criteria of pSIC，the children with sepsis were divided into common sepsis group and pSIC group.The clinical data of both groups were compared，such as general condition，inflammatory indicators，coagulation indicators，sequential organ failure assessment（pSOFA），pSIC score，PICU duration，etc.The risk factors of pSIC were initially screened by Lasso regression analysis，and the independent risk factors were screened by multivariate Logistic regression analysis.R software was used to construct the risk prediction nomogram and evaluate the model.Results:A total of 150 children with sepsis were included in the study，including 121 in the common sepsis group and 29 in the pSIC group.Lasso regression and multivariate Logistic regression analysis showed that pSOFA，prothrombin time（PT），alanine aminotransferase（ALT），blood urea nitrogen（BUN），mean platelet volume/platelet（MPV/PLT）and pediatric critical illness score(PCIS) were independent risk factors for pSIC（all P<0.05）.Since the sources of the pSIC score overlaped with those of pSOFA and PT, only four indicators including ALT，BUN，MPV/PLT and PCIS were used to construct a nomogram model for predicting pSIC.The consistency index of the nomogram model was 0.98，and the area under the receiver operating characteristic curve was 0.975（95% CI 0.952-0.999）.The calibration curve was shown as a straight line with slope close to 1，indicating that the nomogram model had good accuracy in predicting pSIC.The clinical decision curve indicated that the nomogram model had good clinical applicability. Conclusion:pSOFA，PT，ALT，BUN，MPV/PLT and PCIS were all independent risk factors for pSIC.The risk prediction nomogram model of pSIC based on ALT，BUN，MPV/PLT and PCIS can predict the occurrence of pSIC，and provide reference for early clinical recognition and intervention.

Objective:To investigate the clinical application effect of Zishen Yutai Pill in patients with repeated implantation failure (RIF) undergoing frozen-thawed embryo transfer (FET) cycles.Methods:A retrospective case-control study was conducted, selecting 744 cycles of patients with RIF at the Department of Female Reproductive Medicine,State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health, Shandong University from October 2017 to April 2023. The patients were divided into experimental group (treated with Zishen Yutai Pill, n=279) and control group (treated without Zishen Yutai Pill, n=465) based on whether Zishen Yutai Pill was added to luteal support. The pregnancy outcomes between the two groups were compared. Based on the different endometrial preparation protocols in the FET cycles, the patients were used down-regulation protocol ( n=271) or non-down-regulation protocol ( n=473). The pregnancy outcomes of the two groups in each protocol were compared. Results:There were no statistically significant differences between the two groups in terms of biochemical pregnancy rate, clinical pregnancy rate, embryo implantation rate, early miscarriage rate, late miscarriage rate, and live birth rate (all P>0.05). However, the biochemical pregnancy rate [69.0% (58/84)], clinical pregnancy rate [59.5% (50/84)], and embryo implantation rate [59.4% (57/96)] in the experimental group of the down-regulated protocol were significantly higher than those in control group [56.1% (105/187), P=0.045; 46.5% (87/187), P=0.048; 44.9% (92/205), P=0.019], with statistically significant differences. In the non-down-regulated protocol, there were no statistically significant differences in pregnancy outcomes between the two groups (all P>0.05). Conclusion:In the FET down-regulated protocol, Zishen Yutai Pill can significantly improve the clinical pregnancy rate and the embryo implantation rate in patients with RIF, thereby improving pregnancy outcomes.

Objective:To investigate the clinical application effect of Zishen Yutai Pill in patients with repeated implantation failure (RIF) undergoing frozen-thawed embryo transfer (FET) cycles.Methods:A retrospective case-control study was conducted, selecting 744 cycles of patients with RIF at the Department of Female Reproductive Medicine,State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health, Shandong University from October 2017 to April 2023. The patients were divided into experimental group (treated with Zishen Yutai Pill, n=279) and control group (treated without Zishen Yutai Pill, n=465) based on whether Zishen Yutai Pill was added to luteal support. The pregnancy outcomes between the two groups were compared. Based on the different endometrial preparation protocols in the FET cycles, the patients were used down-regulation protocol ( n=271) or non-down-regulation protocol ( n=473). The pregnancy outcomes of the two groups in each protocol were compared. Results:There were no statistically significant differences between the two groups in terms of biochemical pregnancy rate, clinical pregnancy rate, embryo implantation rate, early miscarriage rate, late miscarriage rate, and live birth rate (all P>0.05). However, the biochemical pregnancy rate [69.0% (58/84)], clinical pregnancy rate [59.5% (50/84)], and embryo implantation rate [59.4% (57/96)] in the experimental group of the down-regulated protocol were significantly higher than those in control group [56.1% (105/187), P=0.045; 46.5% (87/187), P=0.048; 44.9% (92/205), P=0.019], with statistically significant differences. In the non-down-regulated protocol, there were no statistically significant differences in pregnancy outcomes between the two groups (all P>0.05). Conclusion:In the FET down-regulated protocol, Zishen Yutai Pill can significantly improve the clinical pregnancy rate and the embryo implantation rate in patients with RIF, thereby improving pregnancy outcomes.

Objective:To investigate the risk factors of pediatric sepsis-induced coagulopathy（pSIC），and to construct a nomogram prediction model for early prediction of pSIC.Methods:Using a cross-sectional retrospective cohort design，children with sepsis who were hospitalized in PICU of the Second People's Hospital of Liaocheng Subsidiary to Shandong First Medical University from January 2017 to December 2023 were selected as the study objects，and the diagnosis of sepsis met the diagnostic criteria for childhood sepsis of the 2015 edition.According to the diagnostic criteria of pSIC，the children with sepsis were divided into common sepsis group and pSIC group.The clinical data of both groups were compared，such as general condition，inflammatory indicators，coagulation indicators，sequential organ failure assessment（pSOFA），pSIC score，PICU duration，etc.The risk factors of pSIC were initially screened by Lasso regression analysis，and the independent risk factors were screened by multivariate Logistic regression analysis.R software was used to construct the risk prediction nomogram and evaluate the model.Results:A total of 150 children with sepsis were included in the study，including 121 in the common sepsis group and 29 in the pSIC group.Lasso regression and multivariate Logistic regression analysis showed that pSOFA，prothrombin time（PT），alanine aminotransferase（ALT），blood urea nitrogen（BUN），mean platelet volume/platelet（MPV/PLT）and pediatric critical illness score(PCIS) were independent risk factors for pSIC（all P<0.05）.Since the sources of the pSIC score overlaped with those of pSOFA and PT, only four indicators including ALT，BUN，MPV/PLT and PCIS were used to construct a nomogram model for predicting pSIC.The consistency index of the nomogram model was 0.98，and the area under the receiver operating characteristic curve was 0.975（95% CI 0.952-0.999）.The calibration curve was shown as a straight line with slope close to 1，indicating that the nomogram model had good accuracy in predicting pSIC.The clinical decision curve indicated that the nomogram model had good clinical applicability. Conclusion:pSOFA，PT，ALT，BUN，MPV/PLT and PCIS were all independent risk factors for pSIC.The risk prediction nomogram model of pSIC based on ALT，BUN，MPV/PLT and PCIS can predict the occurrence of pSIC，and provide reference for early clinical recognition and intervention.

The primary role of transfer RNA(tRNA)is to connect a specific amino acid to its 3' end, use its anticodon to match the codon on messenger RNA(mRNA), and deliver the corresponding amino acid to the ribosome for protein synthesis.tRNA exists in two forms: precursor tRNA and mature tRNA.When acted upon by enzymes like Dicer, elaC ribonuclease Z 2(ELAC2), angiopoietin(ANG), and other ribonucleases, tRNA is broken down into tRNA-derived stress-induced RNA(tiRNA)and tRNA-derived fragments(tRF).Recent advancements in RNA sequencing technology have led to increased interest in tiRNA and tRF, shedding light on their roles in various physiological and pathological processes.tRNA-derived small molecules(tsRNA)function similarly to microRNA(miRNA), influencing gene expression and protein synthesis.They show promise as diagnostic markers and potential therapeutic targets for age-related diseases.This review offers a comprehensive analysis of tsRNA classification, biological functions, research advancements, and clinical applications in age-related conditions.

Grounded in organizational change-complex systems theory,it investigates the pathways and mechanisms for deep integration of Artificial Intelligence(AI)into clinical management.Addressing structural challenges in current clinical management systems,it propose a dynamic three-phase model"unfreezing-changing-refreezing"driven by AI technologies.By deconstructing systemic contradictions arising from technological penetration e.g.,multi-agent coordination,ethical risks,and responsibility ambiguity,a layered governance framework and dynamic regulatory mechanisms are established.Through synergistic evolution of technology,organization,and institution,an adaptive transition in clinical management paradigms can be achieved,ultimately fostering an AI-augmented healthcare ecosystem that balances efficiency with safety.

Objective:This study aimed to evaluate the immunoprotective effect of anti-rabies virus cocktail monoclonal antibody Zamerovimab/Mazorelvimab after rabies exposure.Methods:The dynamic data of rabies virus neutralizing antibody (RVNA) were analyzed in the Zamerovimab/Mazorelvimab Chinese phase Ⅲ study (clinical trial registration number: CTR20201819).Results:The full analysis set showed that RVNA geometric mean titers (GMT) on the 4 th, 8 th, 15 th, 43 rd, and 99 th day in the Zamerovimab/Mazorelvimab group were 4.413 IU/ml, 5.178 IU/ml, 17.062 IU/ml, 14.672 IU/ml, and 2.836 IU/ml, respectively, while those in the human rabies immunoglobulin (HRIG) group were 0.299 IU/ml, 0.451 IU/ml, 11.374 IU/ml, 18.063 IU/ml, and 6.769 IU/ml, respectively. The positive rates of RVNA on the 4 th, 8 th, 15 th, 43 rd, and 99 th day in the Zamerovimab/Mazorelvimab group were 99.9%, 99.6%, 100%, 100%, and 97.4%, respectively, while those in the HRIG group were 23.3%, 34.1%, 97.6%, 99.6%, and 98.4%, respectively. Conclusions:Compared with HRIG, Zamerovimab/Mazorelvimab cocktail monoclonal antibody reached the required protection level of RVNA very soon, thus effectively provided an immediate neutralizing effect of passive immunization therapies against rabies virus.