Background: At present ,the anesthetist usally use Controlled hypotension tecnical reduced blood loss and improved surgical field. Dexmedetomidine and esmolol have been proved effective for controlled hypotension. This meta-analysis aimed to evaluate the effects of dexmedetomidine versus esmolol on the controlled hypotension during surgery.Methods: All articles were retrieved from PubMed, Embase, Cochrane Library up to January 2020. JL and HD independently screened, extracted and evaluated the literature that meet the inclusion and exclusion criteria, and used RevMan 5.3 for statistic analysis.Results: Eight randomised trials, including 418 patients were selected in this meta-anaysis. And it’s results showed:1) The dexmedetomidine group was more favorable in reducing blood loss (MD 49.48 with 95% CI [-5.44,104.40], P =0.08）;2）Compared with the esmolol group，HR （MD 3.30 with 95% CI [2.11,4.50], P <0.00001）,requiring of fentany(SMD 5.96 with 95% CI [3.43,8.48], P <0.00001）were considerably lower in the dexmedetomidine group，while emergence time （MD -6.69 with 95% CI [-9.61,-3.76], P <0.0001)was more longer in the dexmedetomidine group . 3）Compared with the esmolol group，duration of surgery（MD -0.12 with 95% CI [-6.24,6.01], P = 0.97），MAP（ MD 2.29 with 95% CI [-2.02,6.60],P =0.30），the recovery period（MD -0.52 with 95% CI [-3.07,2.04], P = 0.69）were not statistically significant differences in the dexmedetomidine group.Conclusion: Two drugs were effective for controlled hypotension, but compared with esmolol, dexmedetomidine is more better in reducing blood loss ,and less requiring of fentanyl.But attention should be paid to the long emegence time and bradycardia.

Background: Host immune dysregulation participates in the prognosis of sepsis with high morbidity and mortality. Our study aimed to identify the roles of immuneassociated genes during sepsis progression and to predict sepsis survival up to 24 h at diagnosis, which may help plan future individualized treatments. Methods: GSE54514, GSE57065, and GSE95233 datasets were downloaded from the Gene Expression Omnibus (GEO) database for early identification of differentially expressed IRGs between sepsis patients and healthy controls. Candidate IRGs significantly associated with sepsis survival were obtained by univariate logistic regression analysis. Gene signatures of these IRGs were further selected by Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest Algorithm (RFA). The correlation between signature genes and prognosis was analyzed.Furthermore, signature IRGs were further validated by quantitative PCR (qPCR) on the whole blood of septic patients and an external COVID-19 dataset and candidate drug were predicted. Results: HLA.DPA1, IL18RAP, MMP9, RNASE3, S100P, and PTX3 were found significantly differentially expressed starting very early after sepsis infection and persisting for up to 5 days, and their formed IRG score had a satisfactory predictive value on sepsis outcome. Furthermore, our validation showed that these six IRGs were also significantly dysregulated in both an external COVID-19 dataset and sepsis patients. Finally, 10 potential compounds were predicted to have targeted these genes. Conclusion: Our study developed a prognostic modeling tool for sepsis survival based on IRG expression profiles, and has the capacity for early prediction of sepsis outcomes via monitoring the immunogenomic landscape, and possibly the individualized therapies for sepsis survival.

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Background: Host immune dysregulation participates in the prognosis of sepsis with high morbidity and mortality. The contribution of sepsis to alive or dead, and the early immunologic signature to which they are preventable, is unknown. Therefore, knowing the immunogenomic landscape in blood samples is of paramount importance. This study develops a machine learning model to learn signature IRGs associated with the dysregulation of the host immune in sepsis and to predict sepsis survival up to 24 h at diagnosis that may be useful for planning individualized therapies in the future.Methods: A total of 142 sepsis patients with corresponding clinical information were retrieved and analyzed from January 1, 2022, to March 31, 2022, as a secondary analysis of public data. The variables used for analysis included demographic characteristics, clinical conditions, and the differentially expressed immune-related genes (IRGs). The machine learning methods used included logistic regression, least absolute shrinkage, and selection operator (LASSO), random forest (RF). Prediction accuracy was randomly assigned to training, test, and validation sets. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized a calibration curve to explain the prediction model. Results: Our study cohort included 142 sepsis patients (mean [SD] age, 61.8 [15.8] years; 86 [60.6%] men; 104 [73.2%] survivor) downloaded from GEO database.The prognostic model based on IRGs and SOFA scores at diagnosis performed well in sepsis survival estimations (area under the curve, 0.842; 95% CI, 0.704-0.875). This model included a total of six survival-associated IRGs. Patients assigned to the high-risk group had worse survival than patients from the low-risk group (27 deaths [38%] vs 11 deaths [15.0%]; P < 0 .001). The cell adhesion molecules (CAMs), chemokine signaling, and antigen processing and presentation pathways were the associated pathways for survival (P < .001).Conclusions: This cohort study developed a prognostic modeling tool for sepsis survival based on IRG expression profiles, and has the capacity for early prediction of sepsis outcomes via monitoring the immunogenomic landscape, and also possibly the individualized therapies for sepsis survival.