Search

Abstract Recently, methane has been considered a next-generation carbon feedstock due to its abundance and it is main component of shale gas and biogas. Methylomonas sp. DH-1 has been evaluated as a promising industrial bio-catalyst candidate. Succinate is considered one of the top building block chemicals in the agricultural, food, and pharmaceutical industries. In this study, succinate production by Methylomonas sp. DH-1 was improved by combining adaptive laboratory evolution (ALE) technology with genetic engineering in the chromosome of Methylomonas sp. DH-1, such as deletion of bypass pathway genes (succinate dehydrogenase and succinate semialdehyde dehydrogenase) or overexpression of genes related with succinate production (citrate synthase, pyruvate carboxylase and phosphoenolpyruvate carboxylase). Through ALE, the maximum consumption rate of substrate gases (methane and oxygen) and the duration maintaining high substrate gas consumption rates was enhanced compared to those of the parental strain. Based on the improved methane consumption, cell growth (OD600) increased more than twice, and the succinate titer increased by ~ 48% from 218 to 323 mg/L. To prevent unwanted succinate consumption, the succinate semialdehyde dehydrogenase gene was deleted from the genome. The first enzyme of TCA cycle (citrate synthase) was overexpressed. Pyruvate carboxylase and phosphoenolpyruvate carboxylase, which produce oxaloacetate, a substrate for citrate synthase, were also overproduced by a newly identified strong promoter. The new strong promoter was screened from RNA sequencing data. When these modifications were combined in one strain, the maximum titer (702 mg/L) was successfully improved by more than three times. This study demonstrates that successful enhancement of succinic acid production can be achieved in methanotrophs through additional genetic engineering following adaptive laboratory evolution.

ABSTRACT One of the current promising solutions to address problems of CO2 emissions is the development of biological carbon fixation strategies. This strategy leads to higher chemical biosynthetic productivity through improved biological CO2 fixation. In a previous study, we introduced the Calvin-Benson Bassham (CBB) genes of the photosynthetic bacterium Cereibacter sphaeroides into Escherichia coli to develop a strain capable of endogenous CO2 recycling by heterologous expression of the CBB genes. However, the heterologous expression of recombinant proteins in E. coli is often hampered by inclusion bodies (IB), which are protein aggregates. Various factors contribute to IB formation, including host cell metabolism, protein synthesis, transformation machinery, and target protein properties. In this study, we investigated the influence of environmental conditions, particularly culture temperature, on IB formation and CO2 recycling in the CBB strain. As a result, by reducing the culture temperature from 37°C to 30°C, a significant suppression of IB formation was achieved, resulting in a remarkable decrease in CO2 release by approximately 5.76 times. In addition, interestingly, an enhancement in the accumulation of pyruvate by approximately 2.3 times was observed at the same time. These results demonstrate the simultaneous improvements in CO2 recycling and the synthesis of organic acids achieved through temperature control. Based on the findings of this study, we believe that temperature control would be a promising approach to increasing chemical biosynthetic productivity as well as biological CO2 fixation activity in integrated autotrophic biorefinery strategies. IMPORTANCE In a previous study, we successfully engineered Escherichia coli capable of endogenous CO2 recycling through the heterologous expression of the Calvin-Benson Bassham genes. Establishing an efficient gene expression environment for recombinant strains is crucial, on par with the importance of metabolic engineering design. Therefore, the primary objective of this study was to further mitigate greenhouse gas emissions by investigating the effects of culture temperature on the formation of inclusion bodies (IB) and CO2 fixation activity in the engineered bacterial strain. The findings demonstrate that lowering the culture temperature effectively suppresses IB formation, enhances CO2 recycling, and concurrently increases the accumulation of organic acids. This temperature control approach, without adding or modifying compounds, is both convenient and efficient for enhancing CO2 recycling. As such, additional optimization of various environmental parameters holds promise for further enhancing the performance of recombinant strains efficiently.

Hereditary transthyretin (ATTRv) amyloidosis is a rare and complex genetic disorder that can lead to life-threatening cardiac amyloidosis and rapid disease progression. Early diagnosis and treatment with disease-modifying drugs can improve patient outcomes; however, heart transplantation may be necessary in some patients. We present the unique case of a 65-year-old Korean woman diagnosed with ATTRv amyloidosis after experiencing progressive neurological symptoms, followed by heart failure. Despite the absence of significant symptoms of heart failure, subsequent screening revealed cardiac amyloid infiltration, which caused left ventricular hypertrophy and rapid disease progression. The patient underwent successful heart transplantation, and subsequent genetic testing revealed a pathogenic variant, NM_000371.3:c.425T>C (p.Val142Ala), which affects both the nerves and heart and has not been previously reported in Korea. Our report underscores the potential benefits of heart transplantation in managing advanced ATTRv amyloidosis and emphasizes the need for continued research on the genetic heterogeneity of the disease. Clinicians should consider ATTRv amyloidosis in the differential diagnosis of patients presenting with neurological symptoms and heart failure, particularly in those with a family history of the disease.

Abstract Background Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). Methods This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. Results During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882–0.920) and 0.863 (0.846–0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877–0.936) and 0.899 (95% CI, 0.872–0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845–0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p

Abstract Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.

Abstract Background In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 h from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days. Results We used 32,294 ECGs of 10,461 patients and 4483 ECGs of 4483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 h were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P

Industrial demand for capture and utilization using microorganisms to reduce CO2, a major cause of global warming, is significantly increasing. Rhodobacter sphaeroides is a suitable strain for the process of converting CO2 into high-value materials because it can accept CO2 and has various metabolic pathways. However, it has been mainly studied for heterotrophic growth that uses sugars and organic acids as carbon sources, not autotrophic growth. Here, we report that the regulation of reactive oxygen species is critical for growth when using CO2 as a sole carbon source in R. sphaeroides. In general, the growth rate is much slower under autotrophic conditions compared to heterotrophic conditions. To improve this, we performed random mutagenesis using N-methyl-N’-nitro-N-nitrosoguanidine (NTG). As a result, we selected the YR-1 strain with a maximum specific growth rate (μ) 1.44 day–1 in the early growth phase, which has a 110% faster growth rate compared to the wild-type. Based on the transcriptome analysis, it was confirmed that the growth was more sensitive to reactive oxygen species under autotrophic conditions. In the YR-1 mutant, the endogenous contents of H2O2 levels and oxidative damage were reduced by 33.3 and 42.7% in the cells, respectively. Furthermore, we measured that concentrations of carotenoids, which are important antioxidants. The total carotenoid is produced 9.63 g/L in the YR-1 mutant, suggesting that the production is 1.7-fold higher than wild-type. Taken together, our observations indicate that controlling ROS promotes cell growth and carotenoid production under autotrophic conditions.

Abstract Introduction The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. Methods and Results This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12‐lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. Conclusion The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning–based algorithm, combining a multilayer perceptron and convolutional neural network, for detecting significant AS using ECGs. Methods and Results This retrospective cohort study included adult patients who had undergone both ECG and echocardiography. A deep learning–based algorithm was developed using 39 371 ECGs. Internal validation of the algorithm was performed with 6453 ECGs from one hospital, and external validation was performed with 10 865 ECGs from another hospital. The end point was significant AS (beyond moderate). We used demographic information, features, and 500‐Hz, 12‐lead ECG raw data as predictive variables. In addition, we identified which region had the most significant effect on the decision‐making of the algorithm using a sensitivity map. During internal and external validation, the areas under the receiver operating characteristic curve of the deep learning–based algorithm using 12‐lead ECG for detecting significant AS were 0.884 (95% CI, 0.880–0.887) and 0.861 (95% CI, 0.858–0.863), respectively; those using a single‐lead ECG signal were 0.845 (95% CI, 0.841–0.848) and 0.821 (95% CI, 0.816–0.825), respectively. The sensitivity map showed the algorithm focused on the T wave of the precordial lead to determine the presence of significant AS. Conclusions The deep learning–based algorithm demonstrated high accuracy for significant AS detection using both 12‐lead and single‐lead ECGs.

Numerous clinical trials of drug candidates for Alzheimer’s disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

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.

Global warming is currently accelerating due to an increase in greenhouse gas emissions by industrialization. Microbial electrosynthesis (MES) using electroactive autotrophic microorganisms has recently been reported as a method to reduce carbon dioxide, the main culprit of greenhouse gas. However, there are still few cases of application of MES, and the molecular mechanisms are largely unknown. To investigate the growth characteristics in MES, we carried out growth tests according to reducing power sources in Rhodobacter sphaeroides. The growth rate was significantly lower when electrons were directly supplied to cells, compared to when hydrogen was supplied. Through a transcriptome analysis, we found that the expression of reactive oxygen species (ROS)-related genes was meaningfully higher in MES than in normal photoautotrophic conditions. Similarly, endogenous contents of H2O2 were higher and peroxidase activities were lower in MES. The exogenous application of ascorbic acid, a representative biological antioxidant, promotes cell growth by decreasing ROS levels, confirming the inhibitory effects of ROS on MES. Taken together, our observations suggest that reduction of ROS by increasing antioxidant activities is important for enhancing the cell growth and production of CO2-converting substances such as carotenoids in MES in R. sphaeroides

Microbial electrosynthesis (MES) is a promising technology platform for the production of chemicals and fuels from CO2 and external conducting materials (i.e., electrodes). In this system, electroactive microorganisms, called electrotrophs, serve as biocatalysts for cathodic reaction. While several CO2-fixing microorganisms can reduce CO2 to a variety of organic compounds by utilizing electricity as reducing energy, direct extracellular electron uptake is indispensable to achieve highly energy-efficient reaction. In the work reported here, Rhodobacter sphaeroides, a CO2-fixing chemoautotroph and a potential electroactive bacterium, was adopted to perform a cathodic CO2 reduction reaction via MES. To promote direct electron uptake, the graphite felt cathode was modified with a combination of chitosan and carbodiimide compound. Robust biofilm formation promoted by amide functionality between R. sphaeroides and a graphite felt cathode showed significantly higher faradaic efficiency (98.0%) for coulomb to biomass and succinic acid production than those of the bare (34%) and chitosan-modified graphite cathode (77.8%), respectively. The results suggest that cathode modification using a chitosan/carbodiimide composite may facilitate electron utilization by improving direct contact between an electrode and R. sphaeroides.

ObjectivesMajor public high-throughput functional genomic data repositories, including the Gene Expression Omnibus (GEO) and ArrayExpress have rapidly expanded. As a result, a large number of diverse high-throughput functional genomic data retrieval systems have been developed. However, high-throughput functional genomic data retrieval remains challenging.MethodsWe developed Gene Expression data Explore (GEE), the first powerful, flexible web and mobile search application for searching whole-genome epigenetic data and microarray data in public databases, such as GEO and ArrayExpress.ResultsGEE provides an elaborate, convenient interface of query generation competences not available via various high-throughput functional genomic data retrieval systems, including GEO, ArrayExpress, and Atlas. In particular, GEE provides a suitable query generator using eVOC, the Experimental Factor Ontology (EFO), which is well represented with a variety of high-throughput functional genomic data experimental conditions. In addition, GEE provides an experimental design query constructor (EDQC), which provides elaborate retrieval filter conditions when the user designs real experiments.ConclusionsThe web version of GEE is available at http://www.snubi.org/software/gee, and its app version is available from the Apple App Store.

High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.

In the recent climate change regime, industrial demand for renewable materials to replace petroleum-derived polymers continues to rise. Of particular interest is polyhydroxybutyrate (PHB) as a substitute for polypropylene. Accumulating evidence indicates that PHB is highly produced as a carbon storage material in various microorganisms. The effects of growth conditions on PHB production have been widely studied in chemolithotrophs, particularly in Rhodobacter. However, the results on PHB production in Rhodobacter have been somewhat inconsistent due to different strains and experimental conditions, and it is currently unclear how diverse environmental factors are linked with PHB production. Here, we report optimized growth conditions for PHB production and show that the growth conditions are closely related to reactive oxygen species (ROS) regulation. PHB accumulates in cells up to approximately 50% at the highest level under dark-aerobic conditions as opposed to light aerobic/anaerobic conditions. According to the time-course, PHB contents increased at 48 h and then gradually decreased. When observing the effect of temperature and medium composition on PHB production, 30 °C and a carbon/nitrogen ratio of 9:1 or more were found to be most effective. Among PHB biosynthetic genes, PhaA and PhaB are highly correlated with PHB production, whereas PhaC and PhaZ showed little change in overall expression levels. We found that, while the amount of hydrogen peroxide in cells under dark conditions was relatively low compared to the light conditions, peroxidase activities and expression levels of antioxidant-related genes were high. These observations suggest optimal culture conditions for growth and PHB production and the importance of ROS-scavenging signaling with regard to PHB production.

Haematococcus pluvialis accumulates astaxanthin, which is a high-value antioxidant, during the red cyst stage of its lifecycle. The development of a rigid cell wall in the cysts hinders the recovery of astaxanthin. We investigated morphological changes and cell disruption of mature H. pluvialis cyst cells while using high-pressure homogenization for astaxanthin extraction. When treated with French-press-cell (pressure, 10,000−30,000 psi; passage, 1−3), the intact cyst cells were significantly broken or fully ruptured, releasing cytoplasmic components, thereby facilitating the separation of astaxanthin by ethyl acetate. Fluorescence microscopy observations using three different fluorescent dyes revealed that a greater degree of cell breakage caused greater external dispersion of astaxanthin, chlorophyll, lipids, proteins, and carbohydrates. The mechanical treatment resulted in a high cell disruption rate of up to 91% based on microscopic cell typing and Coulter methods. After the ethyl acetate extraction, the astaxanthin concentration significantly increased by 15.2 mg/L in proportion to the increase in cell disruption rate, which indicates that cell disruption is a critical factor for solvent-based astaxanthin recovery. Furthermore, this study recommends a synergistic combination of the fast instrumental particle-volume-distribution analysis and microscope-based morphologic phenotyping for the development of practical H. pluvialis biorefinery processes that co-produce various biological products, including lipids, proteins, carbohydrates, chlorophyll, and astaxanthin.

We aimed to identify the clinical and echocardiographic determinants of symptoms and their prognostic implications in patients with moderately dysfunctional bicuspid aortic valves (BAVs).Among 1,019 subjects in the BAV registry treated in a single tertiary care center, the records of 127 patients (85 men, age 58±13 years) with moderately dysfunctional BAVs were comprehensively reviewed. The patients were divided into two groups based on symptom status: asymptomatic (n = 80) vs. symptomatic (n = 47). The primary end-point was defined as a composite of aortic valve surgery, hospitalization for heart failure, and any cause of death.The symptomatic group had a higher proportion of females, hypertension, aortic stenosis, and aortopathy than did the asymptomatic group. The symptomatic group showed lower e' (5.5±1.7 vs. 6.5±2.2 cm/s, p = 0.003), higher E/e' (13.3 ± 4.9 vs. 10.9±3.7, p = 0.002), and larger left atrial volume index (29.9±11.4 vs. 24.6±9.1 ml/m2, p = 0.006) than did the asymptomatic group. In multivariate logistic regression analysis, female gender (odds ratio [OR] 2.84, 95% confidence interval [CI] 1.10-7.36, p = 0.031), hypertension (OR 3.07, 95% CI 1.20-7.82, p = 0.019), moderate aortic stenosis (OR 5.33 5.78, 95% CI 1.99-16.83, p = 0.001), E/e' >15 (OR 3.82, 95% CI 1.03-11.19, p = 0.015), and aortopathy (OR 2.76, 95% CI 1.07-7.10, p = 0.035) were independently correlated with symptom status. The symptomatic group showed a significantly lower rate of event-free survival during the 8-year follow-up period (54±9% vs. 68±10%, p = 0.001).In patients with moderately dysfunctional BAVs, the presence of moderate aortic stenosis, aortopathy, and diastolic dysfunction determines symptom status, along with female gender and hypertension. Symptom status was associated with clinical outcomes.

This work studied Gram-positive and weak electricigen Corynebacterium glutamicum for its ability to transfer electrons and to produce bioelectricity in microbial fuel cells (MFCs). The electrochemical and liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) results revealed that C. glutamicum had the potential to mediate electron transfer to an electrode by emitting its own extracellular electron shuttles such as flavins. To enhance the current collection from C. glutamicum, a carbon cloth anode was modified with ferrocene-branched chitosan hydrogel (redox-hydrogel). The maximum current density of the ferrocene-branched chitosan redox hydrogel anode with C. glutamicum was drastically increased to 120 µA cm−2 relative to a bare carbon cloth electrode with C. glutamicum (261 nA cm−2). The power density and polarization curves for the MFC operation with the redox-hydrogel-modified anode showed that C. glutamicum effectively generated bioelectricity by means of the redox-hydrogel anode. The results suggest that, in such an electro-fermentation process, ferrocene-branched chitosan hydrogel grafted onto an anode surface would also facilitate both electron transfer from C. glutamicum to the anode and bioelectricity generation.

Although intensive statin therapy is recommended for high risk patients, evidence of its benefit in patients with stable coronary artery disease (CAD) and very low low-density lipoprotein-cholesterol (LDL-C) has been very rare. In this study, we investigated whether higher statin intensity reduces cardiovascular risks in this population.In this retrospective study, a total of 5234 patients with stable CAD were screened at three tertiary hospitals in Korea; 449 patients (mean age: 65 years, male: 69%) with LDL-C

Despite substantial premarket efforts, a significant portion of approved drugs has been withdrawn from the market for safety reasons. The deleterious impact of nonsynonymous substitutions predicted by the SIFT algorithm on structure and function of drug-related proteins was evaluated for 2504 personal genomes. Both withdrawn (n = 154) and precautionary (Beers criteria (n = 90), and US FDA pharmacogenomic biomarkers (n = 96)) drugs showed significantly lower genomic deleteriousness scores (P < 0.001) compared to others (n = 752). Furthermore, the rates of drug withdrawals and precautions correlated significantly with the deleteriousness scores of the drugs (P < 0.01); this trend was confirmed for all drugs included in the withdrawal and precaution lists by the United Nations, European Medicines Agency, DrugBank, Beers criteria, and US FDA. Our findings suggest that the person-to-person genome sequence variability is a strong independent predictor of drug withdrawals and precautions. We propose novel measures of drug safety based on personal genome sequence analysis.