Based on network pharmacology,molecular docking technology and experimental valida-tion to explore the therapeutic efficacy and mechanism of action of baicalin(BC)on kidney injury caused by Klebsiella pneumoniae(KP)infection.The inhibitory activity of BC against KP was de-termined by in vitro experiments;a mouse kidney injury model was established,and the therapeu-tic effect was preliminarily verified by ophthalmoscopy and pathological histology;three pro-in-flammatory factors,namely,TNF-α,IL-10,and IL-1β,were detected by ELISA;and the cyber-pharmacology technology was utilized by PubChem,TCMSP,STRING,Cytoscape,AutoDocks and other databases and software to construct the PPI network as well as to perform GO function and KEGG enrichment analyses;and molecular docking technology was used to assess the binding ac-tivity of the drugs to the core targets and to speculate on the signaling pathways of the drug action.The results showed that BC had a better inhibitory effect on KP in the in vitro experiments;path-ological histology showed a significant therapeutic effect of BC;compared with the infected group,the content of pro-inflammatory factors TNF-α,IL-10,and IL-1β in the baicalin treatment group were significantly decreased(P≤0.05).Twenty-four core targets and 11 pathways of action were screened by network pharmacology,and BC docked stably with the acquired core targets TP53,PTGS2,MAPK1,MAPK8,TNF,BCL2,and IGF1 molecules,and it was speculated that BC might exert its antibacterial and anti-inflammatory effects through the signaling pathways of PI3K-Akt,MAPK,HIF-1,and NF-kappa B,etc.This study lays the foundation for further research on the mechanism of action of baicalin on renal injury.

Based on network pharmacology,molecular docking technology and experimental valida-tion to explore the therapeutic efficacy and mechanism of action of baicalin(BC)on kidney injury caused by Klebsiella pneumoniae(KP)infection.The inhibitory activity of BC against KP was de-termined by in vitro experiments;a mouse kidney injury model was established,and the therapeu-tic effect was preliminarily verified by ophthalmoscopy and pathological histology;three pro-in-flammatory factors,namely,TNF-α,IL-10,and IL-1β,were detected by ELISA;and the cyber-pharmacology technology was utilized by PubChem,TCMSP,STRING,Cytoscape,AutoDocks and other databases and software to construct the PPI network as well as to perform GO function and KEGG enrichment analyses;and molecular docking technology was used to assess the binding ac-tivity of the drugs to the core targets and to speculate on the signaling pathways of the drug action.The results showed that BC had a better inhibitory effect on KP in the in vitro experiments;path-ological histology showed a significant therapeutic effect of BC;compared with the infected group,the content of pro-inflammatory factors TNF-α,IL-10,and IL-1β in the baicalin treatment group were significantly decreased(P≤0.05).Twenty-four core targets and 11 pathways of action were screened by network pharmacology,and BC docked stably with the acquired core targets TP53,PTGS2,MAPK1,MAPK8,TNF,BCL2,and IGF1 molecules,and it was speculated that BC might exert its antibacterial and anti-inflammatory effects through the signaling pathways of PI3K-Akt,MAPK,HIF-1,and NF-kappa B,etc.This study lays the foundation for further research on the mechanism of action of baicalin on renal injury.

Objective:To investigate the incidence and risk factors of coma in patients with hypoglycemia (≤3.9 mmol/L).Methods:A retrospective study was conducted. Patients aged 20 years and older with blood glucose levels ≤3.9 mmol/L, and measured by emergency physicians from January 2020 to December 2022 were collected. Baseline patient data, clinical values collected on-site, and treatment outcomes were analyzed. The Glasgow Coma Scale (GCS) was used to determine if patients were comatose, with GCS ≤8 classified as the coma group and GCS >8 as the non-coma group. Further analysis was conducted on the resuscitated coma group to identify factors affecting patient recovery. Patients were divided into eight age groups, seven time periods within 24 h, and six blood glucose level groups to calculate the incidence of coma. A multivariate logistic regression model was constructed to analyze independent risk factors for coma in hypoglycemic patients.Results:A total of 754 patients with blood glucose levels ≤3.9 mmol/L were collected, with 425 cases of coma and 329 non-coma cases, resulting in a coma probability of 56.37% (95% CI: 52.82%-59.91%). Patients in the coma group were older ( P<0.001) and had a higher prevalence of diabetes compared to the non-coma group (82.12% vs. 67.78%, P<0.001). The age of all patients was (73.05±15.20) years, with the 61-90 years age groups being the most prone to hypoglycemia and coma. In terms of time distribution, the high-incidence periods for hypoglycemia and coma were 0-6 o’clock, 6-9 o’clock, and 14-18 o’clock. The primary causes of hypoglycemia included reduced energy intake after insulin injection (12.07%), improper use of insulin (6.37%), and reduced energy intake (6.23%), with 71.09% of cases having unknown causes. Additionally, 18.44% of patients used insulin before the onset of hypoglycemia, with a higher proportion in the coma group compared to the non-coma group (22.12% vs. 13.68%, P=0.003). The initial blood glucose level of all patients was (2.13±0.85) mmol/L, with lower levels observed in the coma group compared to the non-coma group ( P<0.001). The probabilities of coma occurrence corresponding to blood glucose levels were: 1.1-1.5 mmol/L (72.97%), 1.6-2.0 mmol/L (68.90%), 2.1-2.5 mmol/L (54.10%), 2.6-3.0 mmol/L (38.20%), 3.1-3.5 mmol/L (37.50%), and 3.6-3.9 mmol/L (19.40%). Multivariate logistic regression analysis indicated that age ( OR=1.021, 95% CI: 1.010-1.033, P<0.001), insulin use before onset ( OR=1.948, 95% CI: 1.142-3.323, P=0.014), and blood glucose concentration ( OR=0.426, 95% CI: 0.347-0.522, P<0.001) were independent predictors of coma in hypoglycemic patients. The investigation revealed that after intravenous injection of 50% glucose solution, 215 of 425 coma patients regained consciousness (50.58%), and the recovery time was (18.43±9.09) min. Patients in the recovery group were younger and had lower initial blood glucose levels compared to the non-recovery group (both P<0.05), while recovery group re-measured blood glucose levels were higher than those in the non-recovery group ( P=0.002). Conclusions:The probability of coma in hypoglycemic patients was high, with insulin use being a common trigger. Proper use of insulin is essential to prevent hypoglycemia and coma.