Objective To explore the relationship between PANoptosis and hepatic ischemia-reperfusion injury (HIRI), and to screen the key genes of PANoptosis in HIRI. Methods PANoptosis-related differentially expressed genes (PDG) were obtained through the Gene Expression Omnibus database and GeneCards database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the biological pathways related to PDG. A protein-protein interaction network was constructed. Key genes were selected, and their diagnostic value was assessed and validated in the HIRI mice. Immune cell infiltration analysis was performed based on the cell-type identification by estimating relative subsets of RNA transcripts. Results A total of 16 PDG were identified. GO analysis showed that PDG were closely related to cellular metabolism. KEGG analysis indicated that PDG were mainly enriched in cellular death pathways such as apoptosis and immune-related signaling pathways such as the tumor necrosis factor signaling pathway. GSEA results showed that key genes were mainly enriched in immune-related signaling pathways such as the mitogen-activated protein kinase (MAPK) signaling pathway. Two key genes, DFFB and TNFSF10, were identified with high accuracy in diagnosing HIRI, with areas under the curve of 0.964 and 1.000, respectively. Immune infiltration analysis showed that the control group had more infiltration of resting natural killer cells, M2 macrophages, etc., while the HIRI group had more infiltration of M0 macrophages, neutrophils, and naive B cells. Real-time quantitative polymerase chain reaction results showed that compared with the Sham group, the relative expression of DFFB messenger RNA in liver tissue of HIRI group mice increased, and the relative expression of TNFSF10 messenger RNA decreased. Cibersort analysis showed that the infiltration abundance of naive B cells was positively correlated with DFFB expression (r=0.70, P=0.035), and the infiltration abundance of M2 macrophages was positively correlated with TNFSF10 expression (r=0.68, P=0.045). Conclusions PANoptosis-related genes DFFB and TNFSF10 may be potential biomarkers and therapeutic targets for HIRI.

Objectives:To summarize the relevant evidence of non-invasive cardiac output monitoring management in critically ill patients and provide evidence-based basis for strengthening the standardization and accuracy of non-invasive cardiac output monitoring by clinical medical staff.Methods:We searched UpToDate,British Medical Journal Best Practice Database,The UK National Institute of Clinical Medicine guideline library,PubMed,Embase,American Society of Critical Care Medicine,American Association of Critical Care Nurses,Wanfang database,China Knowledge Network,SinoMed and other databases to collect relevant clinical decisions,guidelines,best practices,evidence summaries,systematic reviews,expert consensuses and randomized controlled trials related to non-invasive cardiac output monitoring management.The search period is from the inception to August 2023.After screening and quality evaluation by the evidence-based team,relevant evidence that meets the standards was extracted.Results:A total of 11 articles were obtained,including 7 systematic reviews,4 expert consensus.Finally,20 best evidences were obtained about the non-invasive cardiac output monitoring management in critically ill patients,including the patients suitable for non-invasive cardiac output monitoring,correlation with the invasive cardiac output monitoring,and the source of error in the monitoring process,involving 5 aspects such as monitoring population,clinical application,interference factors,precautions and personnel training.Conclusions:Clinical medical staffshould strengthen the training of non-invasive cardiac output monitoring technology in critically ill patients,and appropriate practical evidence should be selected in combination with the specific clinical situation to improve the application standardization and measurement accuracy of non-invasive cardiac output monitoring in critically ill patients.

Objective To investigate the effects of different doses of 3,3'-iminodipropionitrile(IDPN)in mice with Tourette syndrome(TS)to optimize the dosage and establish a stable TS model.Methods Thirty-two male C57BL/6J mice were divided randomly into a control group and a model group.The model group was further subdivided and administered low-dose(300 mg/kg),medium-dose(350 mg/kg),and high-dose(400 mg/kg)IDPN,respectively,while the control group received an equal volume of saline by intraperitoneal injection for 7 days.Modeling effectiveness was assessed on days 0 and 7 using stereotypy scoring,the number of head and body twitches,and open-field testing.Dopamine and tumor necrosis factor(TNF-α)levels in serum and brain tissue were measured by enzyme-linked immunosorbent assay.The morphology of striatum and hippocampus tissues were observed by hematoxylin/eosin(HE)staining.Results The stereotypy scores indicated successful modeling of TS in the medium-and high-dose groups.Significant behavioral changes in the open-field test were only detected in the high-dose IDPN group(P＜0.05).Serum dopamine levels were significantly increased(P＜0.05)in the model group,and TNF-α levels were significantly elevated in the medium-and high-dose groups(P＜0.05),but there was no significant difference in brain-tissue levels(P＞0.05).HE staining showed that the neurons and glial cells in the striatum and hippocampus were morphologically normal in the control group,but there were some neurodegenerative changes and a few swollen neuronal cell bodies in the striatum and hippocampus in the model group,and obvious lymphocyte infiltration in the striatum and hippocampus in the high-dose group.Conclusions Through systematic comparison of varying IDPN dosages in establishing a TS model,this study identified 400 mg/kg as the optimal dosage for effective model induction.These findings provide data to support dose optimization in the TS model and offer valuable references for ensuring the smooth progress of early-stage experiments,which could aid the evaluation of the therapeutic effects of subsequent drug interventions.

Objective To investigate the effects of different doses of 3,3'-iminodipropionitrile(IDPN)in mice with Tourette syndrome(TS)to optimize the dosage and establish a stable TS model.Methods Thirty-two male C57BL/6J mice were divided randomly into a control group and a model group.The model group was further subdivided and administered low-dose(300 mg/kg),medium-dose(350 mg/kg),and high-dose(400 mg/kg)IDPN,respectively,while the control group received an equal volume of saline by intraperitoneal injection for 7 days.Modeling effectiveness was assessed on days 0 and 7 using stereotypy scoring,the number of head and body twitches,and open-field testing.Dopamine and tumor necrosis factor(TNF-α)levels in serum and brain tissue were measured by enzyme-linked immunosorbent assay.The morphology of striatum and hippocampus tissues were observed by hematoxylin/eosin(HE)staining.Results The stereotypy scores indicated successful modeling of TS in the medium-and high-dose groups.Significant behavioral changes in the open-field test were only detected in the high-dose IDPN group(P＜0.05).Serum dopamine levels were significantly increased(P＜0.05)in the model group,and TNF-α levels were significantly elevated in the medium-and high-dose groups(P＜0.05),but there was no significant difference in brain-tissue levels(P＞0.05).HE staining showed that the neurons and glial cells in the striatum and hippocampus were morphologically normal in the control group,but there were some neurodegenerative changes and a few swollen neuronal cell bodies in the striatum and hippocampus in the model group,and obvious lymphocyte infiltration in the striatum and hippocampus in the high-dose group.Conclusions Through systematic comparison of varying IDPN dosages in establishing a TS model,this study identified 400 mg/kg as the optimal dosage for effective model induction.These findings provide data to support dose optimization in the TS model and offer valuable references for ensuring the smooth progress of early-stage experiments,which could aid the evaluation of the therapeutic effects of subsequent drug interventions.

Objectives:To summarize the relevant evidence of non-invasive cardiac output monitoring management in critically ill patients and provide evidence-based basis for strengthening the standardization and accuracy of non-invasive cardiac output monitoring by clinical medical staff.Methods:We searched UpToDate,British Medical Journal Best Practice Database,The UK National Institute of Clinical Medicine guideline library,PubMed,Embase,American Society of Critical Care Medicine,American Association of Critical Care Nurses,Wanfang database,China Knowledge Network,SinoMed and other databases to collect relevant clinical decisions,guidelines,best practices,evidence summaries,systematic reviews,expert consensuses and randomized controlled trials related to non-invasive cardiac output monitoring management.The search period is from the inception to August 2023.After screening and quality evaluation by the evidence-based team,relevant evidence that meets the standards was extracted.Results:A total of 11 articles were obtained,including 7 systematic reviews,4 expert consensus.Finally,20 best evidences were obtained about the non-invasive cardiac output monitoring management in critically ill patients,including the patients suitable for non-invasive cardiac output monitoring,correlation with the invasive cardiac output monitoring,and the source of error in the monitoring process,involving 5 aspects such as monitoring population,clinical application,interference factors,precautions and personnel training.Conclusions:Clinical medical staffshould strengthen the training of non-invasive cardiac output monitoring technology in critically ill patients,and appropriate practical evidence should be selected in combination with the specific clinical situation to improve the application standardization and measurement accuracy of non-invasive cardiac output monitoring in critically ill patients.

Objective:To establish a set of artificial intelligence (AI) verification rules for blood routine analysis.Methods:Blood routine analysis data of 18 474 hospitalized patients from the First Hospital of Jilin University during August 1st to 31st, 2019, were collected as training group for establishment of the AI verification rules,and the corresponding patient age, microscopic examination results, and clinical diagnosis information were collected. 92 laboratory parameters, including blood analysis report parameters, research parameters and alarm information, were used as candidate conditions for AI audit rules; manual verification combining microscopy was considered as standard, marked whether it was passed or blocked. Using decision tree algorithm, AI audit rules are initially established through high-intensity, multi-round and five-fold cross-validation and AI verification rules were optimized by setting important mandatory cases. The performance of AI verification rules was evaluated by comparing the false negative rate, precision rate, recall rate, F1 score, and pass rate with that of the current autoverification rules using Chi-square test. Another cohort of blood routine analysis data of 12 475 hospitalized patients in the First Hospital of Jilin University during November 1sr to 31st, 2023, were collected as validation group for validation of AI verification rules, which underwent simulated verification via the preliminary AI rules, thus performance of AI rules were analyzed by the above indicators. Results:AI verification rules consist of 15 rules and 17 parameters and do distinguish numeric and morphological abnormalities. Compared with auto-verification rules, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score of AI rules in training group were 22.7%, 1.6%, 74.5%, 1.3%, 75.7%, 97.2%, 93.5%, 94.7%, 94.1, respectively.All of them were better than auto-verification rules, and the difference was statistically significant ( P<0.001), and with no important case missed. In validation group, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score were 19.2%, 8.2%, 70.1%, 2.5%, 72.6%, 89.2%, 70.0%, 88.3%, 78.1, respectively, Compared with the auto-verification rules, The false negative rate was lower, the false positive rate and the recall rate were slightly higher, and the difference was statistically significant ( P<0.001). Conclusion:A set of the AI verification rules are established and verified by using decision tree algorithm of machine learning, which can identify, intercept and prompt abnormal results stably, and is moresimple, highly efficient and more accurate in the report of blood analysis test results compared with auto-vefication.