Is acupuncture safe in the ICU? A systematic review and meta-analysis

Background and purposeThe safety of interventions for critically ill patients is a crucial issue. In recent years, several studies have treated critically ill patients with acupuncture. However, the safety of acupuncture in this setting remains to be systematically measured.MethodsIn May 2022, the electronic databases of PubMed and the Cochrane Library were searched for studies comparing acupuncture interventions to control interventions in critically ill patients. Study outcomes examined the incidence of severe adverse events (AEs), minor AEs, adverse reactions, ICU stays, and 28-day mortality.ResultsA total of 31 articles were analyzed, and no serious AEs related to acupuncture treatment were identified. No significant differences were found between the groups in the meta-analysis of minor AEs (risk ratio [RR] 5.69 [0.34, 96.60], P = 0.23, I2 = 76%). A reduced risk in the incidence of adverse reactions following acupuncture intervention was evidenced (RR 0.33 [0.22, 0.50], P = 0.00001, I2 = 44%). The patients in the acupuncture arm spent significantly less time in the intensive care unit (ICU) (Mean difference −1.45 [−11.94, −10.97], P = 0.00001, I2 = 56%) and also exhibited lower 28-day mortality rates (odds ratio 0.61 [0.48, 0.78], P = 0.0001, I2 = 0%).ConclusionThere is no evidence to indicate a higher risk of severe or minor AEs in patients who receive acupuncture. Acupuncture demonstrated favorable results in both ICU stay and 28-day mortality measurements, in addition to presenting with fewer adverse reactions compared to routine ICU care. However, the low certainty of the evidence resulting from a high risk of bias in the included studies merits substantial consideration, and further research is still warranted.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=142131, identifier: CRD42020142131

Applying Sequential Pattern Mining to Generate Block for Scheduling Problems

The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems

Applying Sequential Pattern Mining to Generate Block for Scheduling Problems

The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems

Applying Sequential Pattern Mining to Generate Block for Scheduling Problems

The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems

Applying Sequential Pattern Mining to Generate Block for Scheduling Problems

The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems

Applying Sequential Pattern Mining to Generate Block for Scheduling Problems

The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems

Applying Sequential Pattern Mining to Generate Block for Scheduling Problems

The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems

Erythropoietin improves skeletal muscle microcirculation and tissue bioenergetics in a mouse sepsis model

Introduction: The relationship between oxygen delivery and consumption in sepsis is impaired, suggesting a microcirculatory perfusion defect. Recombinant human erythropoietin (rHuEPO) regulates erythropoiesis and also exerts complex actions promoting the maintenance of homeostasis of the organism under stress. The objective of this study was to test the hypothesis that rHuEPO could improve skeletal muscle capillary perfusion and tissue oxygenation in sepsis. Methods: Septic mice in three experiments received rHu-EPO 400 U/kg subcutaneously 18 hours after cecal ligation and perforation (CLP). The first experiment measured the acute effects of rHuEPO on hemodynamics, blood counts, and arterial lactate level. The next two sets of experiments used intravital microscopy to observe capillary perfusion and nicotinamide adenine dinucleotide (NADH) fluorescence post-CLP after treatment with rHuEPO every 10 minutes for 40 minutes and at 6 hours. Perfused capillary density during a three-minute observation period and NADH fluorescence were measured. Results: rHuEPO did not have any effects on blood pressure, lactate level, or blood cell numbers. CLP mice demonstrated a 22% decrease in perfused capillary density compared to the sham group (28.5 versus 36.6 capillaries per millimeter; p \u3c 0.001). Treatment of CLP mice with rHuEPO resulted in an immediate and significant increase in perfused capillaries in the CLP group at all time points compared to baseline from 28.5 to 33.6 capillaries per millimeter at 40 minutes; p \u3c 0.001. A significant increase in baseline NADH, suggesting tissue hypoxia, was noted in the CLP mice compared to the sham group (48.3 versus 43.9 fluorescence units [FU]; p = 0.03) and improved with rHuEPO from 48.3 to 44.4 FU at 40 minutes (p = 0.02). Six hours after treatment with rHuEPO, CLP mice demonstrated a higher mean perfused capillary density (39.4 versus 31.7 capillaries per millimeter; p \u3c 0.001) and a lower mean NADH fluorescence as compared to CLP+normal saline mice (49.4 versus 52.7 FU; p = 0.03). Conclusion: rHuEPO produced an immediate increase in capillary perfusion and decrease in NADH fluorescence in skeletal muscle. Thus, it appears that rHuEPO improves tissue bioenergetics, which is sustained for at least six hours in this murine sepsis model

Causal Mediation Analysis for Difference-in-Difference Design and Panel Data

Advantages of panel data, i.e., difference in difference (DID) design data, are a large sample size and easy availability. Therefore, panel data are widely used in epidemiology and in all social science fields. The literatures on causal inferences of panel data setting or DID design are growing, but no theory or mediation analysis method has been proposed for such settings. In this study, we propose a methodology for conducting causal mediation analysis in DID design and panel data setting. We provide formal counterfactual definitions for controlled direct effect and natural direct and indirect effect in panel data setting and DID design, including the identification and required assumptions. We also demonstrate that, under the assumptions of linearity and additivity, controlled direct effects can be estimated by contrasting marginal and conditional DID estimators whereas natural indirect effects can be estimated by calculating the product of the exposure-mediator DID estimator and the mediator-outcome DID estimator. A panel regression-based approach is also proposed. The proposed method is then used to investigate mechanisms of the effects of the Covid 19 pandemic on the mental health status of the population. The results revealed that mobility restrictions mediated approximately 45 % of the causal effect of Covid 19 on mental health status

IAPP aggregation and cellular toxicity are inhibited by 1,2,3,4,6-penta-O-galloyl-β-D-glucose

The polyphenol, 1,2,3,4,6-penta-O-galloyl-β-D-glucose (PGG) has been found to exhibit a host of positive pharmacologic activities, including anti-cancer and anti-diabetic. Little is known about the mode of action of PGG in yielding these positive activities. We show here that PGG is a potent inhibitor of IAPP (islet amyloid polypeptide, amylin) aggregation. Preventing the initial aggregation event of IAPP is one strategy for slowing, and possibly preventing, the toxic effects of IAPP oligomeric intermediates. Equal molar ratios of PGG to IAPP substantially reduced the ability of IAPP to bind thioflavin T. Atomic force microscopy revealed that PGG prevented amyloid-based fiber formation under rigorous conditions conducive to forming IAPP aggregates. PGG was also found to protect PC12 rat cells from toxic IAPP. PGG was compared to the known amyloid inhibitors (and structural relatives); tannic acid and gallic acid. In every test, PGG was far superior to tannic and gallic acids at inhibiting amyloid aggregation. These results indicate that PGG is a potent inhibitor of IAPP amyloid aggregation and a potential lead molecule for development of an amyloid inhibiting therapeutic