Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. Methods: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. Results: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029–1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). Conclusions: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Prolonged skin inflammation is caused by disrupted skin barrier resulting in chronic inflammatory diseases such as atopic dermatitis. As a potent natural product with anti-inflammatory property, Aster glehni (A. glehni) is a traditional edible herb and has been used to treat diabetes or colitis-associated colon cancer. In present study, we figured out an additional effect of A. glehni ethanol extract (AGE) in pro-inflammatory cytokines-stimulated human keratinocytes. Mixture of tumor necrosis factor-alpha (TNF-α) and interferongamma (IFN-γ) was used to induce inflammatory responses in the HaCaT keratinocytes. AGE suppressed activation of ERK mitogen-activated protein kinase, nuclear factor (NF)-κB, and signal transducer and activator of transcription 1 and 3 (STAT1 and STAT3). The treatment of AGE inhibited mRNA expressions of proinflammatory cytokines in TNF-α and IFN-γ-stimulated HaCaT cells. Also, AGE induced up-regulated expressions of skin barrier molecules like filaggrin, loricrin, or ZO-1. We evaluated the effects of AGE on protein or mRNA expression levels using western blot or qRT-PCR, respectively. Taken together, these results suggest that the treatment of AGE exerts anti-inflammatory effect on keratinocytes through suppressing inflammatory signaling pathways and up-regulating skin molecules in HaCaT keratinocytes.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Prolonged skin inflammation is caused by disrupted skin barrier resulting in chronic inflammatory diseases such as atopic dermatitis. As a potent natural product with anti-inflammatory property, Aster glehni (A. glehni) is a traditional edible herb and has been used to treat diabetes or colitis-associated colon cancer. In present study, we figured out an additional effect of A. glehni ethanol extract (AGE) in pro-inflammatory cytokines-stimulated human keratinocytes. Mixture of tumor necrosis factor-alpha (TNF-α) and interferongamma (IFN-γ) was used to induce inflammatory responses in the HaCaT keratinocytes. AGE suppressed activation of ERK mitogen-activated protein kinase, nuclear factor (NF)-κB, and signal transducer and activator of transcription 1 and 3 (STAT1 and STAT3). The treatment of AGE inhibited mRNA expressions of proinflammatory cytokines in TNF-α and IFN-γ-stimulated HaCaT cells. Also, AGE induced up-regulated expressions of skin barrier molecules like filaggrin, loricrin, or ZO-1. We evaluated the effects of AGE on protein or mRNA expression levels using western blot or qRT-PCR, respectively. Taken together, these results suggest that the treatment of AGE exerts anti-inflammatory effect on keratinocytes through suppressing inflammatory signaling pathways and up-regulating skin molecules in HaCaT keratinocytes.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Prolonged skin inflammation is caused by disrupted skin barrier resulting in chronic inflammatory diseases such as atopic dermatitis. As a potent natural product with anti-inflammatory property, Aster glehni (A. glehni) is a traditional edible herb and has been used to treat diabetes or colitis-associated colon cancer. In present study, we figured out an additional effect of A. glehni ethanol extract (AGE) in pro-inflammatory cytokines-stimulated human keratinocytes. Mixture of tumor necrosis factor-alpha (TNF-α) and interferongamma (IFN-γ) was used to induce inflammatory responses in the HaCaT keratinocytes. AGE suppressed activation of ERK mitogen-activated protein kinase, nuclear factor (NF)-κB, and signal transducer and activator of transcription 1 and 3 (STAT1 and STAT3). The treatment of AGE inhibited mRNA expressions of proinflammatory cytokines in TNF-α and IFN-γ-stimulated HaCaT cells. Also, AGE induced up-regulated expressions of skin barrier molecules like filaggrin, loricrin, or ZO-1. We evaluated the effects of AGE on protein or mRNA expression levels using western blot or qRT-PCR, respectively. Taken together, these results suggest that the treatment of AGE exerts anti-inflammatory effect on keratinocytes through suppressing inflammatory signaling pathways and up-regulating skin molecules in HaCaT keratinocytes.