Background: The utility of the QuantiFERON-Monitor (QFM, Qiagen), a tool developed to assess general immune function, remains insufficiently explored in critically ill patients with acute respiratory failure (ARF). Therefore, we used the QFM to evaluate the immune function of patients with ARF at intensive care unit (ICU) admission and monitored QFM changes based on disease severity and clinical outcome correlations. Methods: We evaluated the immune function of 99 patients with ARF in an ICU setting.The QFM was evaluated upon ICU admission, day 7 post-ICU admission, and discharge.Their results were compared with those of five healthy controls. Results: The QFM levels at ICU admission were significantly lower in patients with ARF than in healthy controls (median IUs/mL: 5.5 vs. 465.0, respectively). The QFM levels in patients with coronavirus disease 2019 or pneumonia (9.2 and 7.9 IUs/mL, respectively) were higher than those in patients with acute respiratory distress syndrome or septic shock (4.9 and 3.6 IUs/mL, respectively). On day 7, the QFM levels increased to 8.3 IUs/ mL and reached 16.7 IUs/mL at discharge. At ICU admission, patients requiring ventilator support had lower QFM levels than those requiring nasal prong or high-flow nasal cannula support. Those who died in the ICU had significantly lower QFM levels (4.0 IUs/mL) at ICU admission than those who survived (5.8 IUs/mL). Conclusions Reduced QFM levels among patients with severe ARF reflect impaired cellular immune responses and suggest that QFM may serve as a practical tool for early risk stratification and immune monitoring in ICU settings.

In light of the burgeoning successes of cancer immunotherapy, glioblastoma (GBM) remains refractory due to an immunosuppressive microenvironment originating from its molecular heterogeneity. Thus, identifying promising therapeutic targets for treating GBM and discovering methodologies to effectively regulate them is still a tremendous challenge. Here we describe photodynamic protein tyrosine phosphatase 1B (PTP1B) proteolysis mediated by a proteolysis-targeting chimera (PROTAC) nanoassembly. The PTP1B-targeting PROTAC is conjugated with a photosensitizer via a cathepsin B (Cat B)-cleavable peptide, which spontaneously forms nanoassemblies due to intermolecular π-π stacking interactions. In GBM models, PROTAC nanoassemblies significantly accumulate in the tumor region across the disrupted blood-brain barrier (BBB), triggering a burst release of the photosensitizer and active PROTAC by Cat B-mediated enzymatic cleavage. Upon laser irradiation, photodynamic therapy (PDT) synergizes with PROTAC-mediated PTP1B proteolysis to induce potent immunogenic cell death (ICD) in tumor cells. Subsequently, persistent PTP1B degradation by nanoassemblies in Cat B-overexpressed intratumoral T cells downregulates exhaustion markers, reinvigorating their functionality. These sequential processes of photodynamic PTP1B proteolysis ultimately augment T cell-mediated antitumor immunity as well as protective immunity, completely eradicating the primary GBM and preventing its recurrence. Overall, our findings underscore the therapeutic potential of combining PDT with PROTAC activity for GBM immunotherapy.

Background/Aims: Cardiovascular disease is the leading cause of death worldwide. This study aimed to investigate the recent nationwide trends in major dietary risk factors for dyslipidemia and atherosclerosis. Methods: We estimated age-standardized mean intakes of fresh fruits, fresh vegetables, whole grains, dietary fiber, and sugar-sweetened beverages (SSBs); and mean percentage of energy intake from protein, total fat, saturated fat, and polyunsaturated fat using nationally representative samples from the Korean National Health Examination and Nutrition Survey 2013–2022. To assess overall diet quality, we calculated mean Korean Healthy Eating Index (KHEI) (range 0–100, higher scores indicating greater diet quality). Results: In 2013–2022, there were overall decreasing trends in age-standardized mean KHEI score and intakes of fresh fruits and vegetables, whole grains, and dietary fiber; and overall increasing trends in mean intakes of SSBs, protein, and dietary fat among both male and female. The KHEI score increased in older adults aged ≥ 60 years, whereas it decreased among younger adults. Throughout the study period, younger adults tended to have lower intakes of fresh fruits, fresh vegetables, and whole grains; higher intakes of SSBs, protein, and dietary fat; and lower KHEI score. The mean KHEI score was lower in male (vs. female) and lower (vs. higher) income groups. Conclusions Our data suggest that, from 2013 to 2022, there was a trend toward an unhealthy diet in Korean adults. Our findings also suggest dietary inequalities among age, sex, and income groups, suggesting the need for more intense interventions targeting the vulnerable populations.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Background/Aims: Cardiovascular disease is the leading cause of death worldwide. This study aimed to investigate the recent nationwide trends in major dietary risk factors for dyslipidemia and atherosclerosis. Methods: We estimated age-standardized mean intakes of fresh fruits, fresh vegetables, whole grains, dietary fiber, and sugar-sweetened beverages (SSBs); and mean percentage of energy intake from protein, total fat, saturated fat, and polyunsaturated fat using nationally representative samples from the Korean National Health Examination and Nutrition Survey 2013–2022. To assess overall diet quality, we calculated mean Korean Healthy Eating Index (KHEI) (range 0–100, higher scores indicating greater diet quality). Results: In 2013–2022, there were overall decreasing trends in age-standardized mean KHEI score and intakes of fresh fruits and vegetables, whole grains, and dietary fiber; and overall increasing trends in mean intakes of SSBs, protein, and dietary fat among both male and female. The KHEI score increased in older adults aged ≥ 60 years, whereas it decreased among younger adults. Throughout the study period, younger adults tended to have lower intakes of fresh fruits, fresh vegetables, and whole grains; higher intakes of SSBs, protein, and dietary fat; and lower KHEI score. The mean KHEI score was lower in male (vs. female) and lower (vs. higher) income groups. Conclusions Our data suggest that, from 2013 to 2022, there was a trend toward an unhealthy diet in Korean adults. Our findings also suggest dietary inequalities among age, sex, and income groups, suggesting the need for more intense interventions targeting the vulnerable populations.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Background/Aims: Cardiovascular disease is the leading cause of death worldwide. This study aimed to investigate the recent nationwide trends in major dietary risk factors for dyslipidemia and atherosclerosis. Methods: We estimated age-standardized mean intakes of fresh fruits, fresh vegetables, whole grains, dietary fiber, and sugar-sweetened beverages (SSBs); and mean percentage of energy intake from protein, total fat, saturated fat, and polyunsaturated fat using nationally representative samples from the Korean National Health Examination and Nutrition Survey 2013–2022. To assess overall diet quality, we calculated mean Korean Healthy Eating Index (KHEI) (range 0–100, higher scores indicating greater diet quality). Results: In 2013–2022, there were overall decreasing trends in age-standardized mean KHEI score and intakes of fresh fruits and vegetables, whole grains, and dietary fiber; and overall increasing trends in mean intakes of SSBs, protein, and dietary fat among both male and female. The KHEI score increased in older adults aged ≥ 60 years, whereas it decreased among younger adults. Throughout the study period, younger adults tended to have lower intakes of fresh fruits, fresh vegetables, and whole grains; higher intakes of SSBs, protein, and dietary fat; and lower KHEI score. The mean KHEI score was lower in male (vs. female) and lower (vs. higher) income groups. Conclusions Our data suggest that, from 2013 to 2022, there was a trend toward an unhealthy diet in Korean adults. Our findings also suggest dietary inequalities among age, sex, and income groups, suggesting the need for more intense interventions targeting the vulnerable populations.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.