Conservation Potential Trough In Vitro Regeneration of Two Threatened Medicinal Plants Ungernia sewertzowii and U. victoris.

Ungernia sewertzowii (US) and U. victoris (UV) are medicinal plants and sources of biologically active compounds for pharmaceutical needs. The leaves of US contain 0.29-0.81% sum of alkaloids with a predominance of lycorine, which is 0.04-0.46% in leaves and 0.15-0.38% in bulbs. Lycorine is used to treat acute and chronic bronchitis. The leaves of UV contain 0.27-0.71% sum of alkaloids with a predominance of galanthamine-0.13-1.15%. Galanthamine is used to treat mild-to-moderate dementia (Alzheimer's disease). The natural populations of US and UV are in danger as sources of income for local people. To resolve this problem, two protocols for microclonal propagation were developed to replace natural raw materials with in vitro regenerated plants. Callusogenesis of US and UV was induced on Murashige and Skoog (MS) nutrient media with 2.4D (0.5 mg/L) in combination with BAP (0.5 mg/L), Kin (0.5 mg/L), or Zea (0.5 mg/L). Direct (for US) and indirect (for US and UN) organogenesis were observed on MS with BAP (0.5 mg/L) or Kin (0.5 mg/L) in combination with IAA (0.5 mg/L) or NAA (0.5 mg/L). Direct organogenesis resulted in 3-5 bulbs of US on one explant; indirect organogenesis resulted in up to 100-150 bulbs of US and UV on one explant within 6 months, or five to six subcultures after transferring the callus to the nutrient medium. The tissue cultures of US and UV were characterized by very low data on antioxidant activity based on IC50 values for DPPH and ABTS radical scavenging activities, whereas in vitro regenerated plants (leaves and bulbs) had higher data. We concluded that in vitro regenerated plants are valuable sources of lycorine and galanthamine, which allow the protection of the natural populations of these two species from extinction.

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.

Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors.

BACKGROUND AND OBJECTIVES: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. METHODS: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. RESULTS: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. CONCLUSIONS: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department.

OBJECTIVE: Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department. METHODS: Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board. DISCUSSION: This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments. Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.

Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy.

BACKGROUND: Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear. OBJECTIVE: This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy. STUDY DESIGN: This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05. RESULTS: Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively. CONCLUSION: The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.

A questionnaire survey on the diagnosis and treatment of Fabry nephropathy in clinical practice.

BACKGROUND: Fabry nephropathy is characterized by a deficiency of lysosomal alpha-galactosidase A, which results in proteinuria and kidney disease. The ineffectiveness of enzyme replacement therapy (ERT) for severe kidney failure highlights the need for early detection and meaningful markers. However, because the diagnosis and treatment of Fabry disease can vary according to the expertise of physicians, we evaluated the opinions of Korean specialists. METHODS: A questionnaire regarding the management of Fabry nephropathy was emailed to healthcare providers with the experience or ability to treat individuals with Fabry nephropathy. RESULTS: Of the 70 experts who responded to the survey, 43 were nephrologists, and 64.3% of the respondents reported having treated patients with Fabry disease. Pediatricians are treating primarily patients with classic types of the disease, while nephrologists and cardiologists are treating more patients with variant types. Only 40.7% of non-nephrologists agreed that a kidney biopsy was required at the time of diagnosis, compared with 81.4% of nephrologists. Thirty-eight of 70 respondents (54.3%) reported measuring globotriaosylsphingosine (lyso-Gb3) as a biomarker. The most common period to measure lyso-Gb3 was at the time of diagnosis, followed by after ERT, before ERT, and at screening. For the stage at which ERT should begin, microalbuminuria and proteinuria were chosen by 51.8% and 28.6% of respondents, respectively. CONCLUSION: Nephrologists are more likely to treat variant Fabry disease rather than classic cases, and they agree that ERT should be initiated early in Fabry nephropathy, using lyso-Gb3 as a biomarker.

Association between focused cardiac ultrasound and time to furosemide administration in acute heart failure.

BACKGROUND: Heart failure (HF) is a global health burden, and its management in the emergency department (ED) is important. This study aimed to evaluate the association between focused cardiac ultrasound (FoCUS) and early administration of diuretics in patients with acute HF admitted to the ED. METHODS: This retrospective observational study was conducted at a tertiary academic hospital. Patients with acute HF patients who were admitted to the ED and receiving intravenous medication between January 2018 and December 2019 were enrolled. The main exposure was a FoCUS examination performed within 2 h of ED triage. The primary outcome was the time to furosemide administration. RESULTS: Of 1154 patients with acute HF, 787 were included in the study, with 116 of them having undergone FoCUS. The time to furosemide was significantly shorter in the FoCUS group (median time (q1-q3), 112 min; range, 65-163 min) compared to the non-FoCUS group (median time, 131 min; range, 71-229 min). In the multivariable logistic regression analysis adjusting for age, sex, chief complaint, mode of arrival, triage level, shock status, and desaturation at triage, early administration of furosemide within 2 h from triage was significantly higher in the FoCUS group (adjusted odds ratio, 1.63; 95% confidence intervals, 1.04-2.55) than in the non-FoCUS group. CONCLUSIONS: Early administration of intravenous furosemide was associated with FoCUS examination in patients with acute HF admitted to the ED. An early screening protocol could be useful for improving levels in clinical practice at EDs.

Hypernatremia is associated with poor long-term neurological outcomes in out-of-hospital cardiac arrest survivors.

BACKGROUND: Brain oedema after cardiac arrest is strongly associated with poor neurological outcomes. Excessive sodium supplementation may increase serum osmolarity and facilitate brain oedema development in cardiac arrest survivors. We aimed to investigate the association of serum sodium levels with long-term neurological outcomes in out-of-hospital cardiac arrest (OHCA) survivors. METHODS: This retrospective observational study used a multicentre prospective cohort registry of OHCA survivors collected between December 2013 and February 2018. We analyzed the association of serum sodium levels at the return of spontaneous circulation (ROSC) (Sodium 0H) and at 24 h after ROSC (Sodium 24H) with 1-year neurological outcomes in OHCA survivors. Patients with 1-year cerebral performance categories (CPC) 1 and 2 were included in the good outcome group while those with CPC 3, 4, and 5 were included in the poor outcome group. RESULTS: Among 277 patients, 84 (30.3%) and 193 (69.7%) were in the good and poor outcome groups, respectively. Compared with the good outcome group, the poor outcome group showed significantly higher Sodium 24H levels (140 mEq/L vs. 137.4 mEq/L, p < 0.001). Increased serum sodium levels per 1 mEq/L increased the risk of poor 1-year CPC by 13% (adjusted odds ratio = 1.13; 95% CI, 1.04⎼1.23; p = 0.004). CONCLUSIONS: Relatively high Sodium 24H levels showed a strong and independent association with poor long-term neurological outcomes in OHCA survivors. These findings may be applied in therapeutic strategies for improving neurological outcomes in OHCA survivors.

Aggressiveness of care in the last days of life in the emergency department of a tertiary hospital in Korea.

BACKGROUND: High-quality end-of-life (EOL) care requires both comfort care and the maintenance of dignity. However, delivering EOL in the emergency department (ED) is often challenging. Therefore, we aimed to investigate characteristics of EOL care for dying patients in the ED. METHODS: We conducted a retrospective cohort study of patients who died of disease in the ED at a tertiary hospital in Korea between January 2018 and December 2020. We examined medical care within the last 24 h of life and advance care planning (ACP) status. RESULTS: Of all 222 disease-related mortalities, 140 (63.1%) were men, while 141 (63.5%) had cancer. The median age was 74 years. As for critical care, 61 (27.5%) patients received cardiopulmonary resuscitation, while 80 (36.0%) received mechanical ventilation. The absence of serious illness (p = 0.011) and the lack of an advance statement (p < 0.001) were both independently associated with the receipt of more critical care. Only 70 (31.5%) patients received comfort care through opioids. Younger patients (< 75 years) (p = 0.002) and those who completed life-sustaining treatment legal forms (p = 0.001) received more comfort care. While EOL discussions were initiated in 150 (67.6%) cases, the palliative care team was involved only in 29 (13.1%). CONCLUSIONS: Patients in the ED underwent more aggressive care and less comfort care in a state of imminent death. To ensure better EOL care, physicians should minimize redundant evaluations and promptly introduce ACP.

Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG.

BACKGROUND: We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF). METHODS: This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG. RESULTS: We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913-0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively. CONCLUSIONS: An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance.

Noninvasive Versus Invasive Brain Temperature Measurement During Targeted Temperature Management: A Preclinical Study in a Swine Cardiac Arrest Model.

We aimed to evaluate correlation and agreement between noninvasive brain temperature (TBN) and invasive brain temperature (TBI) measurement during targeted temperature management (TTM) in a swine cardiac arrest model. Defibrillation attempts were provided after 5 minutes of ventricular fibrillation and 12 minutes of cardiopulmonary resuscitation in five pigs. After return of spontaneous circulation, TTM was provided with induction and maintenance phases with a target temperature of 33°C for 6 hours and a rewarming phase with a rewarming rate of 1°C/h for 4 hours. TBN and TBI were measured using a double sensor method and an intracranial catheter, respectively. Pulmonary artery temperature (TP), esophageal temperature (TE), and rectal temperature (TR) were measured. Primary outcomes were correlation and agreement between TBN and TBI and secondary outcomes were correlation and agreement among TBN and other temperatures. The Pearson correlation coefficient (PCC) between TBN and TBI was 0.95 (p < 0.001) during the whole TTM phases. PCCs between TBN and TBI during the induction, maintenance, and rewarming phases were 0.91 (p < 0.001), 0.88 (p < 0.001), and 0.94 (p < 0.001) and 95% limits of agreement (LoAs) between TBN and TBI were (-0.27°C to 0.78°C), (-0.18°C to 0.54°C), and (-0.93°C to 0.88°C), respectively. Correlation between TBN and TBI during the maintenance phase was higher than correlation between TBN and TE (PCC = 0.74, p < 0.001) or TP (PCC = 0.81, p < 0.001). The 95% LoAs were narrowest between TBN and TP in the induction phase (-0.58 to 0.11), between TBN and TBI in the maintenance phase (-0.54 to 0.18), and between TBN and TR in the rewarming phase (-0.96 to 0.84). Noninvasive brain temperature showed good correlation with invasive brain temperature during TTM in a swine cardiac arrest model. Correlation was highest during the rewarming phase and lowest during the maintenance phase. Agreement between the two measurements was not clinically acceptable.

An artificial intelligence electrocardiogram analysis for detecting cardiomyopathy in the peripartum period.

BACKGROUND: Peripartum cardiomyopathy (PPCM) is a fatal maternal complication, with left ventricular systolic dysfunction (LVSD; Left ventricular ejection fraction 45% or less) occurring at the end of pregnancy or in the months following delivery. The scarcity of screening tools for PPCM leads to a delayed diagnosis and increases its mortality and morbidity. We aim to evaluate an electrocardiogram (ECG)-deep learning model (DLM) for detecting cardiomyopathy in the peripartum period. METHODS: For the DLM development and internal performance test for detecting LVSD, we obtained a dataset of 122,733 ECG-echocardiography pairs from 58,530 male and female patients from two community hospitals. For the DLM external validation, this study included 271 ECG-echocardiography pairs (157 unique pregnant and postpartum period women) examined in the Ajou University Medical Center (AUMC) between January 2007 and May 2020. All included cases underwent an ECG within two weeks before or after the day of transthoracic echocardiography, which was performed within a month before delivery, or within five months after delivery. Based on the diagnostic criteria of PPCM, we analyzed the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to evaluate the model effectiveness. RESULTS: The ECG-based DLM detected PPCM with an AUROC of 0.877. Moreover, its sensitivity, specificity, PPV, and NPV for the detection of PPCM were 0.877, 0.833, 0.809, 0.352, and 0.975, respectively. CONCLUSIONS: An ECG-based DLM non-invasively and effectively detects cardiomyopathies occurring in the peripartum period and could be an ideal screening tool for PPCM.

MUL1-RING recruits the substrate, p53-TAD as a complex with UBE2D2-UB conjugate.

The RING domain of MUL1 (RINGMUL1 ) alone mediates ubiquitylation of the p53-transactivation domain (TADp53 ). To elucidate the mechanism underlying the simultaneous recruitment of UBE2D2 and the substrate TADp53 by RINGMUL1 , we determined the complex structure of RINGMUL1 :UBE2D2 and studied the interaction between RINGMUL1 and TADp53 in the presence of UBE2D2-UB thioester (UBE2D2~UB) mimetics. The RINGMUL1 -binding induced the closed conformation of UBE2D2S22R/C85S -UBK48R oxyester (UBE2D2RS -UBR OE ), and strongly accelerated its hydrolysis, which was suppressed by the additional N77A-mutation of UBE2D2. Interestingly, UBE2D2S22R/N77A/C85S -UBK48R oxyester (UBE2D2RAS -UBR OE ) already formed a closed conformation in the absence of RINGMUL1 . Although TADp53 exhibited weak binding for RINGMUL1 or UBE2D2 alone, its binding affinity was enhanced and even further for RINGMUL1 :UBE2D2 and RINGMUL1 :UBE2D2RAS -UBR OE , respectively. The recognition of TADp53 by RINGMUL1 as a complex with UBE2D2~UB is related to the multivalency of the binding events and underlies the ability of RINGMUL1 to ubiquitylate the intrinsically disordered protein, TADp53 .

Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism.

Aims: Although overt hyperthyroidism adversely affects a patient's prognosis, thyroid function tests (TFTs) are not routinely conducted. Furthermore, vague symptoms of hyperthyroidism often lead to hyperthyroidism being overlooked. An electrocardiogram (ECG) is a commonly used screening test, and the association between thyroid function and ECG is well known. However, it is difficult for clinicians to detect hyperthyroidism through subtle ECG changes. For early detection of hyperthyroidism, we aimed to develop and validate an electrocardiographic biomarker based on a deep learning model (DLM) for detecting hyperthyroidism. Methods and results: This multicentre retrospective cohort study included patients who underwent ECG and TFTs within 24 h. For model development and internal validation, we obtained 174 331 ECGs from 113 194 patients. We extracted 48 648 ECGs from 33 478 patients from another hospital for external validation. Using 500 Hz raw ECG, we developed a DLM with 12-lead, 6-lead (limb leads, precordial leads), and single-lead (lead I) ECGs to detect overt hyperthyroidism. We calculated the model's performance on the internal and external validation sets using the area under the receiver operating characteristic curve (AUC). The AUC of the DLM using a 12-lead ECG was 0.926 (0.913-0.94) for internal validation and 0.883(0.855-0.911) for external validation. The AUC of DLMs using six and a single-lead were in the range of 0.889-0.906 for internal validation and 0.847-0.882 for external validation. Conclusion: We developed a DLM using ECG for non-invasive screening of overt hyperthyroidism. We expect this model to contribute to the early diagnosis of diseases and improve patient prognosis.

Antioxidant, Whitening, Antiwrinkle, and Anti-Inflammatory Effect of Ajuga spectabilis Nakai Extract.

Since ancient times, plants have been a good source of natural antioxidants. Plants remove active oxygen through antioxidants and contain various active ingredients. These active ingredients of plants are used to alleviate skin aging and chronic diseases. Ajuga spectabilis Nakai (AS) is a perennial plant, is endemic to Korea, and has the characteristics of alpine plants. The aim of this study was to assure the possibility of using AS as a functional natural and cosmetic material. For this, we carried out biologically activated material characteristic evaluations about antioxidant, wrinkle reduction, and anti-inflammatory effects using AS extract. To carry out this experiment, we extracted AS extract from AS water extract (AS-W) and AS 70% ethanol extract (AS-E). AS-E showed the highest DPPH activity and tyrosinase inhibitory activity. After, the measurement of metalloprotease (MMP)-1 inhibition effect showed the AS-W and AS-E activation at the concentration of 100 µg/mL. In addition, at the same concentration, from the result of the measurement of the biosynthesis quantity of pro-collagen type-1 we knew that its excellent effect appeared in AS-E (CCD-986sk). The inhibition of NO production in AS-W and AS-E was confirmed in LPS-induced mouse macrophage RAW264.7 cells. On cell viability, it was judged that AS-E had no toxicity because it showed a high cell viability at a high concentration, and it was used for the anti-inflammatory activity. Inhibition of NO production worked only in AS-E; inflammatory cytokine TNF-α and IL-6 were suppressed in a concentration-dependent manner in AS-E. AS is believed to be used as a natural cosmetic material because it has been proven to have antioxidant, whitening, wrinkle-improving, and anti-inflammatory effects. Therefore, the results indicate that AS extract can play an important role as a functional natural material and a cosmetic material for whitening, wrinkle reduction, and anti-inflammatory effect.

Mechanism of protein tyrosine phosphatase 1B inhibition by theaflavanoside IV isolated from methanolic extract of tea (Camellia sinensis) seed shells.

Camellia sinensis (tea) seeds have been identified as potential sources of nutraceutical compounds. In this study, caffeine and theaflavanoside IV were annotated as the most abundant phytochemicals in the seed shells of C. sinensis. Both compound displayed potent inhibitions against protein tyrosine phosphatase 1B (PTP1B) with IC50 values of 37.9 ± 3.5 and 8.7 ± 1.1 µM, respectively. In the kinetic study, caffeine inhibited PTP1B with mixed type I mode, which prefers to bind to free enzyme. Theaflavanoside IV showed competitive and reversible simple slow-binding inhibition [k3 = 0.1 µM-1·min-1, k4 = 0.002 min-1, Kiapp = 0.0002 µM]. This is the first report on PTP1B-inhibitory activity of these compounds and their action mechanisms. These results suggest their potential in the development of antidiabetic agents.

High cholesterol concentrations as well as low cholesterol concentrations are associated with mortality at 28 days in sepsis: a retrospective cohort study.

BACKGROUND: Low serum cholesterol is known to be associated with poor prognosis in sepsis patients. On the other hand, there have been few studies on the association between high serum cholesterol, one of the major risk factors for cardiovascular adverse events, and prognosis of sepsis patients. We investigated the relationship between the serum total cholesterol concentration and outcome of sepsis patients. METHODS: We conducted a multicenter retrospective cohort study at the emergency departments (EDs) of three urban tertiary teaching hospitals. Patients were divided into three groups according to the initial serum total cholesterol concentration: low cholesterol (cholesterol <120 mg/dL), normal cholesterol (cholesterol 120-200 mg/dL), and high cholesterol (cholesterol >200 mg/dL). Multivariable Cox proportional hazard regression model was used to identify the independent association between the serum total cholesterol concentrations and mortality at 28 days. RESULTS: A total of 4,512 patients were included in the final analysis. The mortality at 28 days of the low, normal, and high cholesterol groups were 24.1%, 14.5%, and 20.5%, respectively (P<0.001). Both the low and high cholesterol groups had a higher risk of death than the normal cholesterol group (low cholesterol group [hazard ratio (HR), 1.46; 95% confidence interval (CIs), 1.25-1.71] and high cholesterol group (HR, 1.57; 95% CI, 1.14-2.16). CONCLUSIONS: Both low and high serum total cholesterol concentrations were associated with higher mortality at 28 days in sepsis patients.

Myostatin/Appendicular Skeletal Muscle Mass (ASM) Ratio, Not Myostatin, Is Associated with Low Handgrip Strength in Community-Dwelling Older Women.

BACKGROUND/AIMS: Elevated levels of serum myostatin have been proposed as a biomarker for sarcopenia. Recent studies have shown that elevated level of serum myostatin was associated with physical fitness and performance. This study aimed to examine the significance of myostatin in the association between muscle mass and physical performance in the elderly. METHODS: This cross-sectional study is based on the Korean Frailty and Aging Cohort study involving 1053 people aged 70 years or over. Anthropometric, physical performance, and laboratory data were collected. RESULTS: The mean age of the participants was 75.8 years, and 50.7% of them were female. Serum myostatin levels in men (3.7 ± 1.2 vs. 3.2 ± 1.1 ng/mL, p < 0.001) were higher compared with that in women. Serum myostatin level was associated with appendicular skeletal muscle mass (ASM) index and eGFR by cystatin C. Serum myostatin/ASM ratio was associated with handgrip strength in women. CONCLUSION: Higher serum myostatin levels were related with higher muscle mass and better physical performances in the elderly. Serum myostatin/ASM ratio may be a predictor for physical performance rather than myostatin.