Effect of paratracheal pressure on the effectiveness of mask ventilation in obese anesthetized patients: a randomized, cross-over study.

Paratracheal pressure has been recently suggested to compress and occlude the upper esophagus at the lower left paratracheal region to prevent gastric regurgitation alternative to cricoid pressure. It also prevents gastric insufflation. The aim of this randomized cross-over study was to investigate the effectiveness of paratracheal pressure on mask ventilation in obese anesthetized paralyzed patients. After the induction of anesthesia, two-handed mask ventilation was initiated in a volume-controlled mode with a tidal volume of 8 mL kg‒1 based on ideal body weight (IBW), a respiratory rate of 12 breaths min- 1, and positive end-expiratory pressure of 10 cmH2O. Expiratory tidal volume and peak inspiratory pressure were recorded alternately with or without the application of 30 Newtons (approximately 3.06 kg) paratracheal pressure during a total of 16 successive breaths over 80 s. Association of patient characteristics with the effectiveness of paratracheal pressure on mask ventilation, defined as the difference in expiratory tidal volume between the presence or absence of paratracheal pressure were evaluated. In 48 obese anesthetized paralyzed patients, expiratory tidal volume was significantly higher with the application of paratracheal pressure than without paratracheal pressure [496.8 (74.1) mL kg- 1 of IBW vs. 403.8 (58.4) mL kg- 1 of IBW, respectively; P < 0.001]. Peak inspiratory pressure was also significantly higher with the application of paratracheal pressure compared to that with no paratracheal pressure [21.4 (1.2) cmH2O vs. 18.9 (1.6) cmH2O, respectively; P < 0.001]. No significant association was observed between patient characteristics and the effectiveness of paratracheal pressure on mask ventilation. Hypoxemia did not occur in any of the patients during mask ventilation with or without paratracheal pressure. The application of paratracheal pressure significantly increased both the expiratory tidal volume and peak inspiratory pressure during face-mask ventilation with a volume-controlled mode in obese anesthetized paralyzed patients. Gastric insufflation was not evaluated in this study during mask ventilation with or without paratracheal pressure.

Skin wound healing effects of (+)-syringaresinol from ginseng berry.

Background: Ginseng has been used as a traditional medicine and functional cosmetic ingredients for many years. Recent studies have focused on the potential biological effects of the ginseng berry and its ingredients. (+)-Syringaresinol (SYR) is enriched in ginseng berry and its beneficial effects on the skin have been recently reported. However, little is known about the its effects on the wound healing process of skin. Methods: Here, we evaluated the skin wound healing effect of (+)-SYR using the human fibroblast Hs68 cell and ex vivo pig and human skin tissue model. Scratch wound test and hydrogen peroxide (HPO) induce chemical wound model were employed. Results: (+)-SYR promoted the migration and proliferation of Hs68 cells without significant cytotoxicity at the tested concentrations. Especially, in ex vivo pig and human skin tissue, HPO-induced chemical wound was recovered almost completely by (+)-SYR. In line with the finding in Hs68, the protein expression levels of TGF-ß and PCNA, a proliferation marker were increased, demonstrating the beneficial effects of (+)-SYR on skin wound repair. Conclusion: Collectively, we demonstrated that (+)-SYR from ginseng berry, can enhance the wound healing effect by accelerating cell proliferation and skin regeneration, suggesting the potential utility of (+)-SYR for skin wound repair.

Comparison of the effect of sevoflurane and propofol on the optic nerve sheath diameter in patients undergoing middle ear surgery.

PURPOSE: During middle ear surgery, the patient's head is turned away from the surgical site, which may increase the intracranial pressure. Anesthetics also affect the intracranial pressure. The optic nerve sheath diameter (ONSD) measured using ultrasonography is a reliable marker for estimating the intracranial pressure. This aim of this study was to investigate the effect of sevoflurane and propofol on the ONSD in patients undergoing middle ear surgery. METHODS: Fifty-eight adult patients were randomized into sevoflurane group (n = 29) or propofol group (n = 29). The ONSD was measured using ultrasound after anesthesia induction before head rotation (T0), and at the end of surgery (T1). The occurrence and severity of postoperative nausea and vomiting (PONV) were assessed 1 h after the surgery. RESULTS: The ONSD was significantly increased from T0 to T1 in the sevoflurane group [4.3 (0.5) mm vs. 4.9 (0.6) mm, respectively; P < 0.001] and the propofol group [4.2 (0.3) mm vs. 4.8 (0.5) mm, respectively; P < 0.001]. No significant difference was observed in the ONSD at T0 (P = 0.267) and T1 (P = 0.384) between the two groups. The change in the ONSD from T0 to T1 was not significantly different between the sevoflurane and propofol groups [0.6 (0.4) mm vs. 0.6 (0.3) mm, respectively; P = 0.972]. The occurrence and severity of PONV was not significantly different between the sevoflurane and propofol groups (18% vs. 0%, respectively; P = 0.053). CONCLUSION: The ONSD was significantly increased during middle ear surgery. No significant difference was observed in the amount of ONSD increase between the sevoflurane and propofol groups.

Machine-learning based prediction models for assessing skin irritation and corrosion potential of liquid chemicals using physicochemical properties by XGBoost.

Skin irritation test is an essential part of the safety assessment of chemicals. Recently, computational models to predict the skin irritation draw attention as alternatives to animal testing. We developed prediction models on skin irritation/corrosion of liquid chemicals using machine learning algorithms, with 34 physicochemical descriptors calculated from the structure. The training and test dataset of 545 liquid chemicals with reliable in vivo skin hazard classifications based on UN Globally Harmonized System [category 1 (corrosive, Cat 1), 2 (irritant, Cat 2), 3 (mild irritant, Cat 3), and no category (nonirritant, NC)] were collected from public databases. After the curation of input data through removal and correlation analysis, every model was constructed to predict skin hazard classification for liquid chemicals with 22 physicochemical descriptors. Seven machine learning algorithms [Logistic regression, Naïve Bayes, k-nearest neighbor, Support vector machine, Random Forest, Extreme gradient boosting (XGB), and Neural net] were applied to ternary and binary classification of skin hazard. XGB model demonstrated the highest accuracy (0.73-0.81), sensitivity (0.71-0.92), and positive predictive value (0.65-0.81). The contribution of physicochemical descriptors to the classification was analyzed using Shapley Additive exPlanations plot to provide an insight into the skin irritation of chemicals. Supplementary Information: The online version contains supplementary material available at 10.1007/s43188-022-00168-8.

In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method.

As an alternative to in vivo Draize rabbit eye irritation test, this study aimed to construct an in silico model to predict the complete United Nations (UN) Globally Harmonized System (GHS) for classification and labeling of chemicals for eye irritation category [eye damage (Category 1), irritating to eye (Category 2) and nonirritating (No category)] of liquid chemicals with Integrated approaches to testing and assessment (IATA)-like two-stage random forest approach. Liquid chemicals (n = 219) with 34 physicochemical descriptors and quality in vivo data were collected with no missing values. Seven machine learning algorithms (Naive Bayes, Logistic Regression, First Large Margin, Neural Net, Random Forest (RF), Gradient Boosted Tree, and Support Vector Machine) were examined for the ternary categorization of eye irritation potential at a single run through 10-fold cross-validation. RF, which performed best, was further improved by applying the 'Bottom-up approach' concept of IATA, namely, separating No category first, and discriminating Category 1 from 2, thereafter. The best performing training dataset achieved an overall accuracy of 73% and the correct prediction for Category 1, 2, and No category was 80%, 50%, and 77%, respectively for the test dataset. This prediction model was further validated with an external dataset of 28 chemicals, for which an overall accuracy of 71% was achieved.