Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.

Background: The use of acellular dermal matrix (ADM) in breast reconstruction can inhibit capsular contracture, increasing the success rate of surgery. Adipose-derived stem cells (ADSCs) can effectively suppress foreign body reaction, which is a major cause of capsular contracture. This study aimed to elucidate the synergistic effects of combining ADSCs with ADM on capsule formation, utilizing a rat model. Methods: The study utilized 12 rats, equally divided into two experimental groups. Group A received silicone implants covered with ADM, while Group B was implanted with silicone prostheses wrapped in ADM, pre-seeded with ADSCs. Capsule formation was assessed through visual examination, histological analysis, and reverse transcription-polymerase chain reaction (RT-PCR) at 4 and 8 weeks post-implantation. Results: At 4 weeks, the mean capsular thickness was 177.16 μm in Group A and 170.76 μm in Group B; at 8 weeks, it was 196.69 μm in Group A and 176.10 μm in Group B. Statistical analysis showed no significant difference in capsule thickness between the groups (P>0.05). Histological findings indicated that Group A had more inflammatory cells and collagen fibers and reduced angiogenesis. RT-PCR showed that angiogenesis-promoting gene expression in Group B was 14% higher at 4 weeks and 156% higher at 8 weeks compared to Group A. Conclusion Although no statistically significant reduction in capsule thickness was observed, ADSC-seeded implants showed histological features associated with reduced inflammation and enhanced angiogenesis, suggesting potential benefits in capsule formation management.

Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.

Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.

Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.

Successful liberation from mechanical ventilation is one of the most crucial processes in critical care because it is the first step by which a respiratory failure patient begins to transition out of the intensive care unit and return to their own life. Therefore, when devising appropriate strategies for removing mechanical ventilation, it is essential to consider not only the individual experiences of healthcare professionals, but also scientific and systematic approaches. Recently, numerous studies have investigated methods and tools for identifying when mechanically ventilated patients are ready to breathe on their own. The Korean Society of Critical Care Medicine therefore provides these recommendations to clinicians about liberation from the ventilator. Methods: Meta-analyses and comprehensive syntheses were used to thoroughly review, compile, and summarize the complete body of relevant evidence. All studies were meticulously assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method, and the outcomes were presented succinctly as evidence profiles. Those evidence syntheses were discussed by a multidisciplinary committee of experts in mechanical ventilation, who then developed and approved recommendations. Results: Recommendations for nine PICO (population, intervention, comparator, and outcome) questions about ventilator liberation are presented in this document. This guideline includes seven conditional recommendations, one expert consensus recommendation, and one conditional deferred recommendation. Conclusions: We developed these clinical guidelines for mechanical ventilation liberation to provide meaningful recommendations. These guidelines reflect the best treatment for patients seeking liberation from mechanical ventilation.

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.

Successful liberation from mechanical ventilation is one of the most crucial processes in critical care because it is the first step by which a respiratory failure patient begins to transition out of the intensive care unit and return to their own life. Therefore, when devising appropriate strategies for removing mechanical ventilation, it is essential to consider not only the individual experiences of healthcare professionals, but also scientific and systematic approaches. Recently, numerous studies have investigated methods and tools for identifying when mechanically ventilated patients are ready to breathe on their own. The Korean Society of Critical Care Medicine therefore provides these recommendations to clinicians about liberation from the ventilator. Methods: Meta-analyses and comprehensive syntheses were used to thoroughly review, compile, and summarize the complete body of relevant evidence. All studies were meticulously assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method, and the outcomes were presented succinctly as evidence profiles. Those evidence syntheses were discussed by a multidisciplinary committee of experts in mechanical ventilation, who then developed and approved recommendations. Results: Recommendations for nine PICO (population, intervention, comparator, and outcome) questions about ventilator liberation are presented in this document. This guideline includes seven conditional recommendations, one expert consensus recommendation, and one conditional deferred recommendation. Conclusions: We developed these clinical guidelines for mechanical ventilation liberation to provide meaningful recommendations. These guidelines reflect the best treatment for patients seeking liberation from mechanical ventilation.

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.

Successful liberation from mechanical ventilation is one of the most crucial processes in critical care because it is the first step by which a respiratory failure patient begins to transition out of the intensive care unit and return to their own life. Therefore, when devising appropriate strategies for removing mechanical ventilation, it is essential to consider not only the individual experiences of healthcare professionals, but also scientific and systematic approaches. Recently, numerous studies have investigated methods and tools for identifying when mechanically ventilated patients are ready to breathe on their own. The Korean Society of Critical Care Medicine therefore provides these recommendations to clinicians about liberation from the ventilator. Methods: Meta-analyses and comprehensive syntheses were used to thoroughly review, compile, and summarize the complete body of relevant evidence. All studies were meticulously assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method, and the outcomes were presented succinctly as evidence profiles. Those evidence syntheses were discussed by a multidisciplinary committee of experts in mechanical ventilation, who then developed and approved recommendations. Results: Recommendations for nine PICO (population, intervention, comparator, and outcome) questions about ventilator liberation are presented in this document. This guideline includes seven conditional recommendations, one expert consensus recommendation, and one conditional deferred recommendation. Conclusions: We developed these clinical guidelines for mechanical ventilation liberation to provide meaningful recommendations. These guidelines reflect the best treatment for patients seeking liberation from mechanical ventilation.