Enhancing microbial CO2 electrocatalysis for multicarbon reduction in a wet amine-based catholyte

Microbial CO2 electroreduction (mCO2ER) offers a promising approach for producing high-value multicarbon reductants from CO2 by combining CO2 fixing microorganisms with conducting materials (i. e., cathodes). However, the solubility and availability of CO2 in an aqueous electrolyte pose significant limitations in this system. This study demonstrates the efficient production of long-chain multicarbon reductants, specifically carotenoids (~C40 ), within a wet amine-based catholyte medium during mCO2ER. Optimizing the concentration of the biocompatible CO2 absorbent, monoethanolamine (MEA), led to enhanced CO2 fixation in the electroautotroph bacteria. Molecular biological analyses revealed that MEA in the catholyte medium redirected the carbon flux towards carotenoid biosynthesis during mCO2ER. The faradaic efficiency of mCO2ER with MEA for carotenoid production was 4.5-fold higher than that of the control condition. These results suggest the mass transport bottleneck in bioelectrochemical systems could be effectively addressed by MEA-assissted mCO2ER, enabling highly efficient production of valuable products from CO2 .

Increase CO2 recycling of Escherichia coli containing CBB genes by enhancing solubility of multiple expressed proteins from an operon through temperature reduction

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.

Do ASD and ADHD Have Distinct Executive Function Deficits? A Systematic Review and Meta-Analysis of Direct Comparison Studies

OBJECTIVE

To evaluate if children and adolescents with a diagnosis of ASD or ADHD have distinct executive function (EF) profiles.

METHODS

Peer-reviewed articles comparing ASD, ADHD, and typically developing individuals under 19 years of age were identified. The domains evaluated were: working memory, response inhibition, planning, cognitive flexibility, attention, processing speed, and visuospatial abilities.

RESULTS

Fifty-eight articles met inclusion criteria. Analyses were performed on 45 performance metrics from 24 individual tasks. No differences in EF were found between individuals diagnosed with ASD and ADHD. Individuals diagnosed with ASD and ADHD exhibited worse performance in attention, flexibility, visuospatial abilities, working memory, processing speed, and response inhibition than typically developing individuals. Groups did not differ in planning abilities.

CONCLUSION

Children and adolescents with ASD and ADHD have similar EF profiles. Further research is needed to determine if comorbidity accounts for the commonality in executive dysfunction between each disorder.

A rare pathogenic variant identified in a heart transplant recipient with hereditary transthyretin amyloidosis: a case report

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.

Beneficial Role of Multi-Disciplinary Treatment for Anaplastic Thyroid Cancer with Initial Distant Metastasis

PURPOSE/OBJECTIVE(S)

Anaplastic thyroid cancer (ATC) is a rare, highly aggressive tumor, with median survival around 5 months. Approximately half of the ATC patients presents with distant metastases at diagnosis, showing even more devastating prognosis, yet no outcome analysis had been reported. In this study, we aim to evaluate the clinical outcome of M1 ATC patients, and to define the group of patients who would benefit from local treatment based on multi-disciplinary approach.

MATERIALS/METHODS

A total of 133 histology-confirmed ATC patients underwent protocol-based multidisciplinary treatment including surgery and chemoradiotherapy (CRT) between May 2016 and January 2022. Patients received intensity-modulated radiotherapy of 30 fractions concurrently with paclitaxel on days 1, 8 and 15 every 4 weeks, and lenvatinib was added upon progression. After 18 fractions of CRT, interim response analysis using modified RECIST was conducted for adaptive treatment planning. We reviewed 58 patients with distant metastasis at diagnosis (stage IVC). Overall survival (OS) and progression-free survival (PFS) were measured from the day of diagnosis.

RESULTS

Most common metastatic site was lung (91.4%), followed by bone (31.0%) and brain (5.2%). Lenvatinib was added for 35 patients after any sign of progression. Fourteen patients received upfront surgery (16 debulking and 5 total) followed by adjuvant CRT in 16 patients. Thirty-one patients received upfront CRT with 2 patients receiving total resection after sufficient down-staging. Six (10%) patients could not complete radiotherapy but continued receiving systemic treatment. The median follow-up was 5.9 months. The median and 1-year OS were 6.2 months and 20.5%, and PFS were 3.7 months and 3.5%. Total RT dose over 60 Gy significantly improved median OS (7.5 vs 4.1 months, p = 0.012) and median PFS (4.4 vs 3.0, p = 0.010). Patients with less than 10 initial metastatic tumors showed better median OS (9.1 vs 4.6 months, p = 0.002) but not PFS (5.1 vs 3.6, p = 0.485). At interim analysis, early response (CR, PR and SD) of primary tumor was not associated with survival, while progression of distant metastases showed significantly worse median OS (9.8 vs 4.6 months, p = 0.001). More than 10 metastatic tumors (HR 2.73, 95% CI 1.32-5.66) and stable metastasis at interim analysis (HR 2.39, 95% CI 1.04-5.48) remained as significant factor in the multivariable cox regression analysis. Median OS and PFS of patients with less than 10 metastases showing no progression at interim analysis were 9.1 months, and 5.1 months.

CONCLUSION

Local treatment combined with chemotherapy for M1 ATC patients showed outcome comparable to those of non-metastatic ATC results. Active local treatment should be considered especially for patients with less than 10 metastases, and patients without distant progression in early response evaluation.

Korean Society of Heart Failure Guidelines for the Management of Heart Failure: Management of the Underlying Etiologies and Comorbidities of Heart Failure

Most patients with heart failure (HF) have multiple comorbidities, which impact their quality of life, aggravate HF, and increase mortality. Cardiovascular comorbidities include systemic and pulmonary hypertension, ischemic and valvular heart diseases, and atrial fibrillation. Non-cardiovascular comorbidities include diabetes mellitus (DM), chronic kidney and pulmonary diseases, iron deficiency and anemia, and sleep apnea. In patients with HF with hypertension and left ventricular hypertrophy, renin-angiotensin system inhibitors combined with calcium channel blockers and/or diuretics is an effective treatment regimen. Measurement of pulmonary vascular resistance via right heart catheterization is recommended for patients with HF considered suitable for implantation of mechanical circulatory support devices or as heart transplantation candidates. Coronary angiography remains the gold standard for the diagnosis and reperfusion in patients with HF and angina pectoris refractory to antianginal medications. In patients with HF and atrial fibrillation, long-term anticoagulants are recommended according to the CHA2DS2-VASc scores. Valvular heart diseases should be treated medically and/or surgically. In patients with HF and DM, metformin is relatively safer; thiazolidinediones cause fluid retention and should be avoided in patients with HF and dyspnea. In renal insufficiency, both volume status and cardiac performance are important for therapy guidance. In patients with HF and pulmonary disease, beta-blockers are underused, which may be related to increased mortality. In patients with HF and anemia, iron supplementation can help improve symptoms. In obstructive sleep apnea, continuous positive airway pressure therapy helps avoid severe nocturnal hypoxia. Appropriate management of comorbidities is important for improving clinical outcomes in patients with HF.

Korean Society of Heart Failure Guidelines for the Management of Heart Failure: Management of the Underlying Etiologies and Comorbidities of Heart Failure

Most patients with heart failure (HF) have multiple comorbidities, which impact their quality of life, aggravate HF, and increase mortality. Cardiovascular comorbidities include systemic and pulmonary hypertension, ischemic and valvular heart diseases, and atrial fibrillation. Non-cardiovascular comorbidities include diabetes mellitus (DM), chronic kidney and pulmonary diseases, iron deficiency and anemia, and sleep apnea. In patients with HF with hypertension and left ventricular hypertrophy, renin-angiotensin system inhibitors combined with calcium channel blockers and/or diuretics is an effective treatment regimen. Measurement of pulmonary vascular resistance via right heart catheterization is recommended for patients with HF considered suitable for implantation of mechanical circulatory support devices or as heart transplantation candidates. Coronary angiography remains the gold standard for the diagnosis and reperfusion in patients with HF and angina pectoris refractory to antianginal medications. In patients with HF and atrial fibrillation, long-term anticoagulants are recommended according to the CHA2DS2-VASc scores. Valvular heart diseases should be treated medically and/or surgically. In patients with HF and DM, metformin is relatively safer; thiazolidinediones cause fluid retention and should be avoided in patients with HF and dyspnea. In renal insufficiency, both volume status and cardiac performance are important for therapy guidance. In patients with HF and pulmonary disease, beta-blockers are underused, which may be related to increased mortality. In patients with HF and anemia, iron supplementation can help improve symptoms. In obstructive sleep apnea, continuous positive airway pressure therapy helps avoid severe nocturnal hypoxia. Appropriate management of comorbidities is important for improving clinical outcomes in patients with HF.

Simultaneous enhancement of lipid biosynthesis and solvent extraction of Chlorella using aminoclay nanoparticles

Magnesium aminoclay nanoparticles (MgANs) exert opposing effects on photosynthetic microalgae by promoting carbon dioxide (CO₂) uptake and inducing oxidative stress. This study explored the potential application of MgAN in the production of algal lipids under high CO₂ concentrations. The impact of MgAN (0.05-1.0 g/L) on cell growth, lipid accumulation, and solvent extractability varied among three tested oleaginous Chlorella strains (N113, KR-1, and M082). Among them, only KR-1 exhibited significant improvement in both total lipid content (379.4 mg/g cell) and hexane lipid extraction efficiency (54.5%) in the presence of MgAN compared to those of controls (320.3 mg/g cell and 46.1%, respectively). This improvement was attributed to the increased biosynthesis of triacylglycerols and a thinner cell wall based on thin-layer chromatography and electronic microscopy, respectively. These findings suggest that using MgAN with robust algal strains can enhance the efficiency of cost-intensive extraction processes while simultaneously increasing the algal lipid content.

Efficacy of Silkworm Pupae Extract on Muscle Strength and Mass in Middle-Aged and Older Individuals: A Randomized, Double-Blind, Placebo-Controlled Trial

OBJECTIVES

We investigated the efficacy and safety of silkworm pupae extract (SWP) consumption for 12 weeks on muscle mass and strength in middle-aged and older individuals with relatively low skeletal muscle mass who do regular low-intensity exercise.

DESIGN

A randomized double-blinded placebo-controlled trial.

PARTICIPANTS

The study was conducted with 54 participants with relatively low skeletal muscle mass (SMM) (64.4 ± 6.1 years; body mass index, 23.8 ± 2.4 kg/m2).

INTERVENTION AND MEASUREMENTS

Participants were randomly assigned to one of two groups: 1000 mg of SWP/day plus regular exercise (SWP group, n=27) or placebo plus regular exercise (placebo group, n=27). All participants were required to engage in 30-60 minutes/day of walking for ≥3 days/week for 12 weeks. The primary outcome was knee extension/flexion strength (Nm), measured at the velocity of 60°/s. Secondary outcomes included body composition, biomarkers (creatine kinase and creatinine), handgrip strength, and quality of life questionnaire.

RESULTS

Both the intention-to-treat (ITT) and per-protocol (PP) analyses revealed no significant impact of SWP on knee strength compared to the placebo group over 12 weeks. On the other hand, the SWP group had significantly greater increases in right-handgrip strength by 1.94 kg (95% CI: 0.08-3.79; p = 0.041) and left-handgrip strength by 1.83 kg (0.25-3.41; p = 0.024) compared to the placebo group in the ITT population, after 12 weeks. Moreover, in the PP population, the SWP group revealed an even greater increase in right-handgrip strength by 2.07 kg (0.15-3. 98; p = 0.035) and left-handgrip strength by 2.21 kg (0.60-3.83; p = 0.008) for the 12-week period. However, this study resulted in a failure to detect significant differences in the body composition, biomarkers, quality of life questionnaire, physical activity, and caloric intake between the groups. None of the participants in the SWP group experienced any significant adverse events. In the placebo group, two participants experienced urticaria and allergic side effects, leading to their withdrawal from the study and two exhibited elevated levels of liver enzyme and increased diastolic blood pressure, respectively at 12 weeks.

CONCLUSION

SWP, in addition to low-intensity exercise, may enhance handgrip strengths in middle-aged and older adults with relatively lower SMM. Future studies need to use a large sample size over longer periods to validate our findings. This trial was registered at clinicaltrials.gov as NCT04994054.

Valorization of CO2 to β-farnesene in Rhodobacter sphaeroides

The valorization of CO₂ into valuable products is a sustainable strategy to help overcome the climate crisis. In particular, biological conversion is attractive as it can produce long-chain hydrocarbons such as terpenoids. This study reports the high yield of β-farnesene production from CO₂ by expressing heterologous β-farnesene synthase (FS) into Rhodobacter sphaeroides. To increase the expression of FS, a strong active promoter and a ribosome binding site (RBS) were engineered. Moreover, β-farnesene production was improved further through the supply of exogenous antioxidants and additional nutrients. Finally, β-farnesene was produced from CO₂ at a titer of 44.53 mg/L and yield of 234.08 mg/g, values that were correspondingly 23 times and 46 times higher than those from the initial production of β-farnesene. Altogether, the results here suggest that the autotrophic production of β-farnesene can provide a starting point for achieving a circular carbon economy.

Biohydrogen production and purification: Focusing on bioelectrochemical systems

Innovative technologies on green hydrogen production become significant as the hydrogen economy has grown globally. Biohydrogen is one of green hydrogen production methods, and microbial electrochemical cells (MECs) can be key to biohydrogen provision. However, MECs are immature for biohydrogen technology due to several limitations including extracellular electron transfer (EET) engineering. Fundamental understanding of EET also needs more works to accelerate MEC commercialization. Interestingly, studies on biohydrogen gas purification are limited although biohydrogen gas mixture requires complex purification for use. To facilitate an MEC-based biohydrogen technology as the green hydrogen supply this review discussed EET kinetics, engineering of EET and direct interspecies electron transfer associated with hydrogen yield and the application of advanced molecular biology for improving EET kinetics. Finally, this article reviewed biohydrogen purification technologies to better understand purification and use appropriate for biohydrogen, focusing on membrane separation.

Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography

Purpose

Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance.

Methods

This retrospective cohort study included two hospitals. We included 115,361 patients who had at least one ECG taken with an estimated glomerular filtration rate measurement within 30 min of the index ECG. A DLM was developed using 96,549 ECGs of 55,222 patients. The internal validation included 22,949 ECGs of 22,949 patients. Furthermore, we conducted an external validation with 37,190 ECGs of 37,190 patients from another hospital. The endpoint was to detect a moderate to severe RI (estimated glomerular filtration rate < 45 ml/min/1.73m²).

Results

The area under the receiver operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting RI during the internal and external validation was 0.858 (95% confidence interval 0.851–0.866) and 0.906 (0.900–0.912), respectively. In the initial evaluation of 25,536 individuals without RI patients whose DLM was defined as having a higher risk had a significantly higher chance of developing RI than those in the low-risk group (17.2% vs. 2.4%, p < 0.001). The sensitivity map indicated that the DLM focused on the QRS complex and T-wave for detecting RI.

Conclusion

The DLM demonstrated high performance for RI detection and prediction using 12-, 6-, single-lead ECGs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11255-022-03165-w.

Enhanced CO2 electroconversion of Rhodobacter sphaeroides by cobalt-phosphate complex assisted water oxidation

CO₂ can be a next generation feedstock for electricity-driven bioproduction due to its abundance and availability. Microbial electrosynthesis (MES), a promising technique for CO₂ electroconversion, provides an attractive route for the production of valuable products from CO₂, but issues surrounding efficiency and reasonable productivity should be resolved. Improving the anode performance for water oxidation under neutral pH is one of the most important aspects to advance current MES. Here, we introduce cobalt-phosphate (Co-Pi) assisted water oxidation at the counter electrode (i.e., anode) to upgrade the MES performance at pH 7.0. We show that CO₂ can be converted by photochemoautotrophic bacterium, Rhodobacter sphaeroides into organic acids and carotenoids in the MES reactor. Planktonic cells of R. sphareroides in the Co-Pi anode equipped MES reactor was ca. 1.5-fold higher than in the control condition (w/o Co-Pi). The faradaic efficiency of the Co-Pi anode equipped MES reactor was remarkably higher (58.3%) than that of the bare anode (27.8%). While the system can improve the CO₂ electroconversion nonetheless there are some further optimizations are necessary.

Regulation of Reactive Oxygen Species Promotes Growth and Carotenoid Production Under Autotrophic Conditions in Rhodobacter sphaeroides

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.

Paradigm shift in algal biomass refinery and its challenges

Microalgae have been studied and tested for over 70 years. However, biodiesel, the prime target of the algal industry, has suffered from low competitiveness and current steps toward banning the internal combustion engine all over the world. Meanwhile, interest in reducing CO₂ emissions has grown as the world has witnessed disasters caused by global warming. In this situation, in order to maximize the benefits of the microalgal industry and surmount current limitations, new breakthroughs are being sought. First, drop-in fuel, mandatory for the aviation and maritime industries, has been discussed as a new product. Second, methods to secure stable and feasible outdoor cultivation focusing on CO₂ sequestration were investigated. Lastly, the need for an integrated refinery process to simultaneously produce multiple products has been discussed. While the merits of microalgae industry remain valid, further investigations into these new frontiers would put algal industry at the core of future bio-based economy.

Improving hydrogen production by pH adjustment in pressurized gas fermentation

Gas fermentation utilizes syngas converted from biomass or waste as feedstock. A bubble column reactor for pressurizing was designed to increase the mass transfer rate between gas and liquid, and reduce energy consumption by medium agitation. Thermococcus onnurineus, a hydrogenic CO-oxidizer, was cultured initially under ambient pressure with the initial inlet gas composition; 60% CO and 40% N₂. The maximum H₂ productivity was 363 mmol/l/h, without pH adjustment. When additional pressure was applied, the pH rapidly declined; this may be attributed to the increased CO₂ solubility under pressure. By controlling pH, H₂ productivity increased up to 450 mmol/l/h; which is comparable to the previously reported H₂ productivity in a continuous stirred tank reactor. The results may suggest energy saving potentials of bubble column reactors in gas fermentation. This finding may be applied to other gas fermentation processes, as syngas itself contains CO₂ and many microbial processes also release CO₂.

Cell disruption and astaxanthin extraction from Haematococcus pluvialis: Recent advances

The green microalga Haematococcus pluvialis is an excellent source of astaxanthin, a powerful antioxidant widely used in cosmetics, aquaculture, health foods, and pharmaceuticals. This review explores recent developments in cell disruption and astaxanthin extraction techniques applied using H. pluvialis as a model species for large-scale algal biorefinery. Notably, this alga develops a unique cyst-like cell with a rigid three-layered cell wall during astaxanthin accumulation (∼4% of dry weight) under stress. The thick (∼2 µm), acetolysis-resistant cell wall forms the strongest barrier to astaxanthin extraction. Various physical, chemical, and biological cell disruption methods were discussed and compared based on theoretical mechanisms, biomass status (wet, dry, and live), cell-disruption efficacy, astaxanthin extractability, cost, scalability, synergistic combinations, and impact on the stress-sensitive astaxanthin content. The challenges and future prospects of the downstream processes for the sustainable and economic development of advanced H. pluvialis biorefineries are also outlined.

Deep-learning model for screening sepsis using electrocardiography

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 < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018).

Conclusions

The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.

Detection and classification of arrhythmia using an explainable deep learning model

BACKGROUND

Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data.

METHODS

In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets.

RESULTS

During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12‑lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925-0.991.

CONCLUSION

Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice.

A lysosomal enigma CLN5 and its significance in understanding neuronal ceroid lipofuscinosis

Neuronal Ceroid Lipofuscinosis (NCL), also known as Batten disease, is an incurable childhood brain disease. The thirteen forms of NCL are caused by mutations in thirteen CLN genes. Mutations in one CLN gene, CLN5, cause variant late-infantile NCL, with an age of onset between 4 and 7 years. The CLN5 protein is ubiquitously expressed in the majority of tissues studied and in the brain, CLN5 shows both neuronal and glial cell expression. Mutations in CLN5 are associated with the accumulation of autofluorescent storage material in lysosomes, the recycling units of the cell, in the brain and peripheral tissues. CLN5 resides in the lysosome and its function is still elusive. Initial studies suggested CLN5 was a transmembrane protein, which was later revealed to be processed into a soluble form. Multiple glycosylation sites have been reported, which may dictate its localisation and function. CLN5 interacts with several CLN proteins, and other lysosomal proteins, making it an important candidate to understand lysosomal biology. The existing knowledge on CLN5 biology stems from studies using several model organisms, including mice, sheep, cattle, dogs, social amoeba and cell cultures. Each model organism has its advantages and limitations, making it crucial to adopt a combinatorial approach, using both human cells and model organisms, to understand CLN5 pathologies and design drug therapies. In this comprehensive review, we have summarised and critiqued existing literature on CLN5 and have discussed the missing pieces of the puzzle that need to be addressed to develop an efficient therapy for CLN5 Batten disease.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

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.

Seed dormancy and germination of Asarum sieboldii, a disjunct relict species in East Asia

Disjunct species in the same genus are a common feature of the flora in the temperate forests of East Asia and eastern North America. This study aimed to evaluate whether the kind of seed dormancy was maintained after species in the genus Asarum (Aristolochiaceae) were separated from their common ancestor. We classified the seed dormancy of Asarum sieboldii, an East Asian species, based on a phenology study and experiments in controlled temperature conditions, and then compared it to that of the previously studied A. canadense, an eastern North American species. The underdeveloped embryo of A. sieboldii grew and germinated (radicle emergence) in autumn but shoot emergence did not occur until the following spring. The seeds of A. sieboldii had deep simple epicotyl morphophysiological dormancy because the seeds with emerged radicle required a relatively long period of cold stratification to break epicotyl dormancy and produce a shoot. Although the seed of A. sieboldii had weaker radicle dormancy and stronger epicotyl dormancy compared to A. canadense, the kind of seed dormancy was the same for the two species. The trait of seed dormancy was inherited from a shared common ancestor and maintained in populations well after the two species (or their ancestors) separated. However, quantitative differences in temperature requirements for radicle and shoot emergence suggest the possibility of adaptation to the environment.

Artificial intelligence to diagnose paroxysmal supraventricular tachycardia using electrocardiography during normal sinus rhythm

Aims

Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study.

Methods and results

This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948-0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT.

Conclusion

The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.

Associations between Chewing Difficulty, Subjective Cognitive Decline, and Related Functional Difficulties among Older People without Dementia: Focus on Body Mass Index

OBJECTIVE

This study aimed to determine whether chewing difficulty is associated with subjective cognitive decline (SCD) and related functional difficulties by body mass index.

DESIGN

A population-based cross-sectional study.

SETTING AND PARTICIPANTS

A nationwide sample of 54,004 individuals aged ≥65 years from the 2018 Korea Community Health Survey.

MEASUREMENTS

SCD and SCD-related functional difficulties were measured using the cognitive decline module of the Behavioral Risk Factor Surveillance System. Chewing difficulty was assessed based on a self-report questionnaire from an oral health-related behaviors interview survey. BMI was calculated from objective values by measuring height and weight through a physical meter.

RESULTS

Among the 54,004 individuals, the prevalence of SCD in underweight, overweight, and obesity group was 33.6% (n = 806), 30.3% (n = 9,691), and 28.7% (n=5,632) respectively. Chewing difficulty was associated with SCD and SCD-related functional difficulties. This association was more pronounced in underweight (BMI: <18.5 kg/m2) people [underweight: (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.48-1.92); normal weight: OR = 1.13, 95% CI 1.04-1.22; obese: OR = 1.15, 95% CI 1.05-1.27]. Similar trends were demonstrated for SCD-related functional difficulties (underweight: OR = 1.53, 95% CI 1.17-2.01; normal weight: OR = 1.36, 95% CI 1.15-1.63; obese: OR = 1.50, 95% CI 1.22-1.86).

CONCLUSIONS

Chewing difficulty was associated with SCD and SCD-related functional difficulties in older people. Our results suggest that underweight status may play roles in the associations between chewing difficulty and SCD and SCD-related functional difficulties.

Prevalence of Anemia and Its Association with Frailty, Physical Function and Cognition in Community-Dwelling Older Adults: Findings from the HOPE Study

OBJECTIVES

The prevalence of anemia and its impact on frailty and physical function amongst the multiethnic older populations in the Southeast Asian (SEA) countries are often not well studied. Singapore, a nation comprised of multiethnic communities, is one of the most rapidly aging population globally. We aim to evaluate the prevalence of anemia and its impact on frailty, and physical function in Healthy Older People Everyday (HOPE)- an epidemiologic population-based study on community-dwelling older adults in Singapore.

DESIGN

Cross-sectional study.

SETTING

Community.

PARTICIPANTS

480 adults ≥ 65 years old.

MEASUREMENTS

Data were collected from interviewers-administered questionnaires on socio-demographics, FRAIL scale, Mini-Mental State Examination, EQ-5D, Barthel Index, and Lawton index. Hemoglobin concentration and physical assessments, including anthropometry, grip strength, timed up-and-go (TUG) were measured.

RESULTS

The overall prevalence of anemia was 15.2% (73 out of 480). The Indian ethnic group had the highest prevalence of anemia (32%, OR=3.02; 95%CI= 1.23-7.41) with the lowest hemoglobin concentration compared to the overall population (13.0±1.3g/L and 13.5±1.4g/L, p=0.02). Hemoglobin levels and anemia were significantly associated with frailty (OR=2.28; 95% CI=1.02-5.10), low grip strength (OR=1.79; 95% CI=1.01-3.03), ≥ one IADL impairment (OR=2.35; 95% CI=1.39-3.97). Each 1 g/dL increase in hemoglobin was associated with a 6% decrease in frailty odds after adjusting for potential covariates (OR = 0.94, 95% CI: 0.90-0.99). There was a significant difference in the mean TUG between the non-anemic (11.0±3.4 seconds) and anemic (12.3±6.0 seconds, p=0.01) counterparts, but no difference in the number of falls.

CONCLUSION

In our multiethnic Asian population, anemia was adversely associated with frailty, decreased muscle strength, and IADL impairment. Health policies on anemia screening should be employed to avoid or potentially delay or reverse these adverse outcomes associated with anemia. Recognition, evaluation, and treatment of anemia amongst this vulnerable population is warranted.

Recent developments and key barriers to microbial CO2 electrobiorefinery

The electrochemical conversion of CO₂ can include renewable surplus electricity storage and CO₂ utilisation. This review focuses on the microbial CO₂ electrobiorefinery based on microbial electrosynthesis (MES) which merges electrochemical and microbial conversion to produce biofuels and higher-value chemicals. In this review, recent developments are discussed about bioelectrochemical conversion of CO₂ into biofuels and chemicals in MES via microbial CO₂-fixation and electricity utilisation reactions. In addition, this review examines technical approaches to overcome the current limitations of MES including the following: engineering of the biocathode, application of electron mediators, and reactor optimisation, among others. An in-depth discussion of strategies for the CO₂ electrobiorefinery is presented, including the integration of the biocathode with inorganic catalysts, screening of novel electroactive microorganisms, and metabolic engineering to improve target productivity from CO₂.

Photoautotrophic hydrogen production of Rhodobacter sphaeroides in a microbial electrosynthesis cell

Conventional photoheterotrophic H₂ production by purple sulfur bacteria requires additional organic substrates as the carbon and energy sources. This study examined the novel photoautotrophic H₂ production of Rhodobacter sphaeroides with concomitant CO₂ uptake in microbial electrosynthesis (MES). Under an applied potential of -0.9 V vs. Ag/AgCl to the cathode, Rhodobacter sphaeroides produced hydrogen with CO₂ as the sole carbon source under illumination. The initial planktonic cells decreased rapidly in suspension, whereas biomass formation on the cathode surface increased gradually during MES operation. The electron and carbon flow under photoautotrophic conditions in MES were estimated. Glutamate, as the nitrogen source, enhanced hydrogen production significantly (328 mL/L/day) compared to NH₄Cl (67 mL/L/day) during seven days of operation. The photoautotrophic condition with 6000 lx presented CO₂ consumption and simultaneous biomass formation on the cathode electrode. MES-driven electron and proton transfer enabled the simultaneous production of hydrogen and CO₂ uptake_.

Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features

Aims

Although heart failure with preserved ejection fraction (HFpEF) is a rapidly emerging global health problem, an adequate tool to screen it reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance.

Methods and results

This retrospective cohort study included two hospitals. 34 103 patients who underwent echocardiography and ECG within 1 week and indicated normal left ventricular systolic function were included in this study. A DLM based on an ensemble neural network was developed using 32 671 ECGs of 20 169 patients. The internal validation included 1979 ECGs of 1979 patients. Furthermore, we conducted an external validation with 11 955 ECGs of 11 955 patients from another hospital. The endpoint was to detect HFpEF. During the internal and external validation, the area under the receiver operating characteristic curves of a DLM using 12-lead ECG for detecting HFpEF were 0.866 (95% confidence interval 0.850-0.883) and 0.869 (0.860-0.877), respectively. In the 1412 individuals without HFpEF at initial echocardiography, patients whose DLM was defined as having a higher risk had a significantly higher chance of developing HFpEF than those in the low-risk group (33.6% vs. 8.4%, P < 0.001). Sensitivity map showed that the DLM focused on the QRS complex and T-wave.

Conclusion

The DLM demonstrated high performance for HFpEF detection using not only a 12-lead ECG but also 6- single-lead ECG. These results suggest that HFpEF can be screened using conventional ECG devices and diverse life-type ECG machines employing the DLM, thereby preventing disease progression.

Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram

INTRODUCTION

Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practice owing to their lack of interpretability. We developed an explainable DLM to detect AF using ECG and validated its performance using diverse formats of ECG.

METHODS

We conducted a retrospective study. The Sejong ECG dataset comprising 128,399 ECGs was used to develop and internally validated the explainable DLM. DLM was developed with two feature modules, which could describe the reason for DLM decisions. DLM was external validated using data from 21,837, 10,605, and 8528 ECGs from PTB-XL, Chapman, and PhysioNet non-restricted datasets, respectively. The predictor variables were digitally stored ECGs, and the endpoints were AFs.

RESULTS

During internal and external validation of the DLM, the area under the receiver operating characteristic curves (AUCs) of the DLM using a 12‑lead ECG in detecting AF were 0.997-0.999. The AUCs of the DLM with VAE using a 6‑lead and single‑lead ECG were 0.990-0.999. The AUCs of explainability about features such as rhythm irregularity and absence of P-wave were 0.961-0.993 and 0.983-0.993, respectively.

CONCLUSIONS

Our DLM successfully detected AF using diverse ECGs and described the reason for this decision. The results indicated that an explainable artificial intelligence methodology could be adopted to the DLM using ECG and enhance the transparency of the DLM for its application in clinical practice.

Therapeutic Drug Level Monitoring of Teicoplanin in Korean Pediatric Patients with Normal versus Impaired Renal Function

BACKGROUND

Teicoplanin is used to treat serious gram-positive infections. Optimal teicoplanin trough levels are considered to be ≥ 10 μg/mL. Despite its wide use in various clinical settings, data on teicoplanin trough level in pediatric patients are limited. Therefore, the aim of this study was to investigate the therapeutic drug level monitoring of teicoplanin in Korean pediatric patients, including those with impaired renal function.

METHODS

A retrospective study was performed in pediatric patients (age ≤ 18 years old) who received teicoplanin from September 2014 to April 2018. The regimen included a loading dose of 10 mg/kg/dose at 12 hours' interval three times in a row, and a maintenance dose of 10 mg/kg/dose commenced at 24 hours of interval after the loading dose, with a maximum of 400 mg/dose, respectively. The first therapeutic drug levels were measured. Distribution and characteristics of trough levels in patients with decreased renal function and those with bacteremia were also assessed.

RESULTS

A total of 187 trough levels were collected from 143 patients. Hematologic and oncologic diseases were the most common underlying diseases (83.2%, n = 119). One hundred eighty trough levels were first measured, and their median value was 16.2 μg/mL (range, 2.3-100 μg/mL) and the median interval between initial teicoplanin injection and 1st trough level was 96.5 hours (range 47.6-179.3 hours). Lower steady-state levels were observed in younger age group (median, 13.5 vs. 18.0 μg/mL, P = 0.038). Median trough levels were higher in patients with decreased renal functions (P < 0.001). In addition, among eight with gram-positive bacteremia, seven of them had a favorable outcome.

CONCLUSION

This study provides additive information on trough level monitoring of teicoplanin in children with impaired renal function and treatment effect in patients with gram-positive bacteremia. Careful monitoring for steady state trough levels of teicoplanin is warranted.

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

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.

Enhancement of astaxanthin production by Haematococcus pluvialis using magnesium aminoclay nanoparticles

Improving the content and production of high-value ketocarotenoid pigments is critical for the commercialization of microalgal biorefineries. This study reported the use of magnesium aminoclay (MgAC) nanoparticles for enhancement of astaxanthin production by Haematococcus pluvialis in photoautotrophic cultures. Addition of 1.0 g/L MgAC significantly promoted cellular astaxanthin biosynthesis (302 ± 69 pg/cell), presumably by inducing tolerable oxidative stress, corresponding to a 13.7-fold higher production compared to that in the MgAC-untreated control (22 ± 2 pg/cell). The lipid content and cell size of H. pluvialis improved by 13.6- and 2.1-fold, respectively, compared to that of the control. Despite reduced cell numbers, the overall astaxanthin production (10.3 ± 0.4 mg/L) improved by 40% compared to the control (7.3 ± 0.6 mg/L), owing to improved biomass production. However, an MgAC dosage above 1.0 g/L inhibited biomass production by inducing electrostatic cell wall destabilization and aggregation. Therefore, MgAC-induced stimulation of algae varies widely based on their morphological and physiological characteristics.

Risk of nosocomial transmission of coronavirus disease 2019: an experience in a general ward setting in Hong Kong

BACKGROUND

Coronavirus disease 2019 (COVID-19) was first reported in Wuhan in December 2019 and has rapidly spread across different cities within and outside China. Hong Kong started to prepare for COVID-19 on 31st December 2019 and infection control measures in public hospitals were tightened to limit nosocomial transmission within healthcare facilities. However, the recommendations on the transmission-based precautions required for COVID-19 in hospital settings vary from droplet and contact precautions, to contact and airborne precautions with placement of patients in airborne infection isolation rooms.

AIM

To describe an outbreak investigation of a patient with COVID-19 who was nursed in an open cubicle of a general ward before the diagnosis was made.

METHOD

Contacts were identified and risk categorized as 'close' or 'casual' for decisions on quarantine and/or medical surveillance. Respiratory specimens were collected from contacts who developed fever, and/or respiratory symptoms during the surveillance period and were tested for SARS-CoV-2.

FINDINGS

A total of 71 staff and 49 patients were identified from contact tracing, seven staff and 10 patients fulfilled the criteria of 'close contact'. At the end of 28-day surveillance, 76 tests were performed on 52 contacts and all were negative, including all patient close contacts and six of the seven staff close contacts. The remaining contacts were asymptomatic throughout the surveillance period.

CONCLUSION

Our findings suggest that SARS-CoV-2 is not spread by an airborne route, and nosocomial transmissions can be prevented through vigilant basic infection control measures, including wearing of surgical masks, hand and environmental hygiene.

A new aortoiliac calcification scoring system to predict grade C anastomotic leak following rectal cancer surgery

BACKGROUND

Aortoiliac calcification may be a surrogate marker of decreased visceral perfusion causing anastomotic leak (AL). The aim of this study was to evaluate the predictive role of aortoiliac calcification for AL after rectal cancer surgery.

METHODS

We enrolled patients with primary rectal cancer who had restorative resection at our institution between January 2013 and December 2015. An aortoiliac calcification score was calculated as the sum of calcification scores at the infrarenal aorta (0: no, 1: ≤ 3 cm, 2: > 3 cm) and the common iliac arteries (0: no, 1: unilateral, 2: bilateral). AL was classified into three grades: grade A, requiring no intervention; grade B, requiring therapeutic intervention without re-laparotomy; and grade C, requiring re-laparotomy. Clinicopathological characteristics were analyzed to identify risk factors for AL.

RESULTS

There were 583 patients. Three-hundred forty-five (59.2%) had an aortoiliac calcification score ≥ 3, and 37 (6.3%) patients experienced AL, in 30 cases (5.1%) grade C AL. Patients with an aortoiliac calcification score ≥ 3 had a higher incidence of grade C AL (6.7% vs. 2.9%, p = 0.045). Multivariate logistic regression analysis revealed that an aortoiliac calcification score ≥ 3 was an independent risk factor for grade C AL (odds ratio = 2.669, 95% confidence interval 1.066-6.686, p = 0.036).

CONCLUSIONS

Aortoiliac calcification may be considered a risk factor for grade C AL after rectal cancer surgery.

Development and Validation of a Novel Warfarin Dosing Algorithm for Korean Patients With VKORC1 1173C

BACKGROUND

Differences in the performance of suggested warfarin dosing algorithms among different ethnicities and genotypes have been reported; this necessitates the development of an algorithm with enhanced performance for specific population groups. Previous warfarin dosing algorithms underestimated warfarin doses in VKORC1 1173C carriers. We aimed to develop and validate a new warfarin dosing algorithm for Korean patients with VKORC1 1173C.

METHODS

A total of 109 patients carrying VKORC1 1173CT (N=105) or 1173CC (N=4) were included in this study. Multiple regression analysis was performed to deduce a new dosing algorithm. Following literature searches for genotype-guided warfarin dosing algorithms, 21 algorithms were selected and evaluated using the correlation coefficient (ρ) of actual dose and estimated dose, mean error, and root mean square error.

RESULTS

The developed algorithm is as follows: maintenance dose (mg/week)=exp [3.223-0.009×(age)+0.577×(body surface area [BSA])+0.178×(sex)-0.481×(CYP2C9 genotype)+0.227×(VKORC1 genotype)]. Integrated variables explained 44% of the variance in the maintenance dose. The predicted and actual doses showed moderate correlation (ρ=0.641) with the best performance with a mean error of -1.30 mg/week. The proportion of underestimated groups was 17%, which was lower than with the other algorithms.

CONCLUSIONS

This is the first study to develop and validate a warfarin dosing algorithm based on data from VKORC1 1173C carriers; it showed superior predictive performance compared with previously published algorithms.

Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022-0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP⁺⁺), and further improved the ethnicity-specific validations (0.759 ± 0.066-0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

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.

A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease

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.

Stacking Fault Energy Analyses of Additively Manufactured Stainless Steel 316L and CrCoNi Medium Entropy Alloy Using In Situ Neutron Diffraction

Stacking fault energies (SFE) were determined in additively manufactured (AM) stainless steel (SS 316 L) and equiatomic CrCoNi medium-entropy alloys. AM specimens were fabricated via directed energy deposition and tensile loaded at room temperature. In situ neutron diffraction was performed to obtain a number of faulting-embedded diffraction peaks simultaneously from a set of (hkl) grains during deformation. The peak profiles diffracted from imperfect crystal structures were analyzed to correlate stacking fault probabilities and mean-square lattice strains to the SFE. The result shows that averaged SFEs are 32.8 mJ/m2 for the AM SS 316 L and 15.1 mJ/m2 for the AM CrCoNi alloys. Meanwhile, during deformation, the SFE varies from 46 to 21 mJ/m2 (AM SS 316 L) and 24 to 11 mJ/m2 (AM CrCoNi) from initial to stabilized stages, respectively. The transient SFEs are attributed to the deformation activity changes from dislocation slip to twinning as straining. The twinning deformation substructure and atomic stacking faults were confirmed by electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The significant variance of the SFE suggests the critical twinning stress as 830 ± 25 MPa for the AM SS 316 L and 790 ± 40 MPa for AM CrCoNi, respectively.

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

P799 The echocardiographic and clinical characteristics of Fabry patients without overt left ventricular hypertrophy but progressing despite on enzyme replacement therapy

Background

Fabry disease (FD) is X-linked genetic disorder caused by the deficiency or absent activity of lysosomal α- galactosidase. When the heart is involved, progressive left ventricular hypertrophy (LVH) is the key feature. Although, LVH is not present in all subjects, some of them progress with LVH despite enzyme replacement therapy (ERT). The present study aimed to evaluate the characteristics of patient of FD without overt LVH, but progresses with their left ventricular mass index (LVMI).

Method : This study includes subjects carrying genetic mutations for FD without LVH (n = 12) in a single tertiary center. ‘Decreasing LVMI group (Group1)’ was defined when pre-ERT LVMI minus last LVMI value obtained by echocardiography was positive, and when negative, defined as ‘Increasing LVMI group (Group 2)’. The baseline characteristics and echocardiographic parameters including global strain was analyzed.

Result : Total 6 patients were classified as Group 1 and 2 each. The median age at diagnosis for male was 21 vs 27 for each group, and 21 vs 31 for female. The median follow-up duration was 4.0 vs 5.7 yr. The antibody formation for replaced enzyme was 3 for Group 2 and any family member who developed overt LVH was much 1 vs 3 for group 1 vs 2. Initial 3 plane GLS was worse and showed trend to drop in Group 2.

Conclusion : In the Fabry patients get worse with their LVMI but still not developed overt LVH despite enzyme replacement therapy, there are some clinical and echo-parameter difference. These findings could suggest insufficient enzyme replacement therapy.

Group 1 (n = 6) Group 2 (n = 6) Female/Male 4/2 3/3 Age of at Dx (Female) (median) 21 28 Age of at Dx (Male) (median) 21 31 F/U duration (median) 4.0 yrs 5.7 yrs Antibody formation 0 3 Any Overt LVH family Genotype c.56T &gt; C c.40-11T &gt; A, 782_delG, c.658C &gt; T Initial LVMI (Median) 98.2g/m2 70.9g/m2 Initial GLS (3P) -19 -17.8 Continuously elevated LysoGb3 2 4 Proteinuria &gt;300mg 1 3 (1 preparing HD)