BACKGROUND AND PURPOSEPungent constituents of ginger (Zingiber officinale) have beneficial effects on inflammatory pain and arthritic swelling. However, the molecular basis for these pharmacological properties is only partially understood. Here, we investigated the molecular target of 1-dehydro-[10]-gingerdione (D10G), one of the pungent constituents of ginger, that mediates its suppression of NF-kB-regulated expression of inflammatory genes linked to toll-like receptor (TLR)-mediated innate immunity. EXPERIMENTAL APPROACHRAW 264.7 macrophages or primary macrophages-derived from bone marrows of C57BL/6 or C3H/HeJ mice were stimulated with the TLR4 agonist LPS in the presence of D10G. Catalytic activity of inhibitory kB (IkB) kinase b (IKKb) was determined by a kinase assay and immunoblot analysis, and the expression of inflammatory genes by RT-PCR analysis and a promoter-dependent reporter assay. KEY RESULTSD10G directly inhibited the catalytic activity of cell-free IKKb. Moreover, D10G irreversibly inhibited cytoplasmic IKKb-catalysed IkBa phosphorylation in macrophages activated by TLR agonists or TNF-a, and also IKKb vector-elicited NF-kB transcriptional activity in these cells. These effects of D10G were abolished by substitution of the Cys 179 with Ala in the activation loop of IKKb, indicating a direct interacting site of D10G. This mechanism was shown to mediate D10G-induced disruption of NF-kB activation in LPS-stimulated macrophages and the suppression of NF-kB-regulated gene expression of inducible NOS, COX-2 and IL-6. CONCLUSION AND IMPLICATIONSThis study demonstrates that IKKb is a molecular target of D10G involved in the suppression of NF-kB-regulated gene expression in LPS-activated macrophages; this suggests D10G has therapeutic potential in NF-kB-associated inflammation and autoimmune disorders. AbbreviationsAP-1, activating protein 1; D10G, 1-dehydro-[10]-gingerdione; IkB, inhibitory kB; IKKb, IkB kinase b; iNOS, inducible NOS; PTN, parthenolide; TNFSF11, receptor activator of NF-kB ligand; SEAP, secretory alkaline phosphatase; TLR, toll-like receptor
Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal intubation and incision, using data for the period from between the start of anesthesia induction and immediately before tracheal intubation obtained from an anesthesia monitor, a drug administration infusion pump, an anesthesia machine, and from patients' demographics, together with preexisting disease information from electronic health records. Among 222 patients, 126 developed postinduction hypotension. The random-forest model showed the best performance, with an area under the receiver operating characteristic curve of 0.842 (95% confidence interval [CI]: 0.736-0.948). This was higher than that for the Naïve Bayes (0.778; 95% CI: 0.65-0.898), logistic regression (0.756; 95% CI: 0.630-0.881), and artificial-neural-network (0.760; 95% CI: 0.640-0.880) models. The most important features affecting the accuracy of machine-learning prediction were a patient's lowest systolic blood pressure, lowest mean blood pressure, and mean systolic blood pressure before tracheal intubation. We found that machine-learning models using data obtained from various anesthesia machines between the start of anesthesia induction and immediately before tracheal intubation can predict hypotension occurring during the period between tracheal intubation and incision.
The purpose of this study was to evaluate the reliability and validity of a Korean version of the 20-item COVID-19 phobia tool, which was developed through a translation-reverse translation process. These data were collected from 226 persons using a self-reported questionnaire. Exploratory and confirmatory factor analyses were used to test construct validity. Finally, for 19 out of 20 items, the item-level convergence and differential validity were confirmed. In addition, the reliability and validity of the tool as a whole has been verified. For the subscales, Cronbach’s α was 0.90 for psychological, 0.87 for psychosomatic, 0.86 for economic, and 0.87 for social. Appropriate reliability was confirmed. Correlations between the COVID-19 phobia tool and fear of COVID-19 confirmed validity. The Korean version of the COVID-19 phobia tool is an appropriate scale for measuring the fear of COVID-19 and relevant psychological characteristics. Therefore, future studies in areas such as health and nursing could use this tool as required.
Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.
Anesthesia induction is associated with frequent blood pressure fluctuation such as hypotension and hypertension. If it is possible to precisely predict blood pressure a few minutes ahead, anesthesiologists can proactively give anesthetic management before patients develop hemodynamic problem. The objective of this study is to develop a real-time model for predicting 3-min-ahead blood pressure from the start of anesthesia induction to surgical incision. We used only vital signs and anesthesia-related data obtained during anesthesia-induction phase and designed a bidirectional recurrent neural network followed by fully connected layers. We conducted experiments on our collected data of 102 patients, and obtained mean absolute errors between 8.2 mmHg and 11.1 mmHg and standard deviation between 8.7 mmHg and 12.7 mmHg. The average elapsed time for prediction of a batch of 100 unseen data was about 26.56 milliseconds. We believe that this study shows feasibility of real-time prediction of future blood pressures, and the performance will be improved by collecting more data and finding better model structures.
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