Development of method using language processing techniques for extracting information on drug-health food product interactions

AIMS

Health food products (HFPs) are foods and products related to maintaining and promoting health. HFPs may sometimes cause unforeseen adverse health effects by interacting with drugs. Considering the importance of information on the interactions between HFPs and drugs, this study aimed to establish a workflow to extract information on Drug-HFP Interactions (DHIs) from open resources.

METHODS

First, Information on drugs, enzymes, their interactions, and known DHIs was collected from multiple public databases and literature sources. Next, a network consisted of enzymes, HFP, and drugs was constructed, assuming enzymes as candidates for hubs in Drug-HFP interactions (Method 1). Furthermore, we developed methods to analyze the biomedical context of each drug and HFP to predict potential DHIs out of the DHIs obtained in Method 1 by applying BioWordVec, a widely used biomedical terminology quantifier (Method 2-1 and 2-2).

RESULTS

44,965 DHIs (30% known) were identified in Method 1, including 38 metabolic enzymes, 157 HFPs, and 1256 drugs. Method 2-1 selected 7401 DHIs (17% known) from the DHIs of Method 1, while Method 2-2 chose 2819 DHIs (30% known). Based on the different assumptions in these methods where Method 2-1 specifically selects HFPs interacting with specific enzymes and Method 2-2 specifically selects HFPs with similar function with drugs, the propsed methods resulted in extracting a wide variety of DHIs.

CONCLUSIONS

By integrating the results of language processing techniques with those of the network analysis, a workflow to efficiently extract unknown and known DHIs was constructed.

Development of a Machine Learning Model for Classifying Cooking Recipes According to Dietary Styles

To complement classical methods for identifying Japanese, Chinese, and Western dietary styles, this study aimed to develop a machine learning model. This study utilized 604 features from 8183 cooking recipes based on a Japanese recipe site. The data were randomly divided into training, validation, and test sets for each dietary style at a 60:20:20 ratio. Six machine learning models were developed in this study to effectively classify cooking recipes according to dietary styles. The evaluation indicators were above 0.8 for all models in each dietary style. The top ten features were extracted from each model, and the features common to three or more models were employed as the best predictive features. Five well-predicted features were indicated for the following seasonings: soy sauce, miso (fermented soy beans), and mirin (sweet cooking rice wine) in the Japanese diet; oyster sauce and doubanjiang (chili bean sauce) in the Chinese diet; and olive oil in the Western diet. Predictions by broth were indicated in each diet, such as dashi in the Japanese diet, chicken soup in the Chinese diet, and consommé in the Western diet. The prediction model suggested that seasonings and broths could be used to predict dietary styles.

Role of LL-37 in Oral Bacterial DNA Accumulation in Dental Plaque

Dental plaque, a highly structured polymicrobial biofilm, persistently forms in the oral cavity and is a common problem affecting oral health. The role of oral defense factors in either collaborating or disrupting host-microbiome interactions remains insufficiently elucidated. This study aims to explore the role of LL-37, a critical antimicrobial peptide in the oral cavity, in dental plaque formation. Through immunostaining dental plaque specimens, we observed that LL-37 and DNA colocalized in the samples, appearing as condensed clusters. In vitro experiments revealed that LL-37 binds rapidly to oral bacterial DNA, forming high molecular weight, DNase-resistant complexes. This interaction results in LL-37 losing its inherent antibacterial activity. Further, upon the addition of LL-37, we observed a visible increase in the precipitation of bacterial DNA. We also discovered a significant correlation between the levels of the DNA-LL-37 complex and LL-37 within dental plaque specimens, demonstrating the ubiquity of the complex within the biofilm. By using immunostaining on dental plaque specimens, we could determine that the DNA-LL-37 complex was present as condensed clusters and small bacterial cell-like structures. This suggests that LL-37 immediately associates with the released bacterial DNA to form complexes that subsequently diffuse. We also demonstrated that the complexes exhibited similar Toll-like receptor 9-stimulating activities across different bacterial species, including Porphyromonas gingivalis, Fusobacterium nucleatum, Prevotella intermedia, and Streptococcus salivarius. However, these complexes prompted dissimilar activities, such as the production of IL-1β in monocytic cells via both NLRP3 pathway-dependent and pathway-independent mechanisms. This study, therefore, reveals the adverse role of LL-37 in dental plaque, where it binds bacterial DNA to form complexes that may precipitate to behave like an extracellular matrix. Furthermore, the unveiled stimulating properties and species-dependent activities of the oral bacterial DNA-LL-37 complexes enrich our understanding of dental plaque pathogenicity and periodontal innate immune responses.

Extracting the latent needs of dementia patients and caregivers from transcribed interviews in japanese: an initial assessment of the availability of morpheme selection as input data with Z-scores in machine learning

BACKGROUND

Given the increasing number of dementia patients worldwide, a new method was developed for machine learning models to identify the 'latent needs' of patients and caregivers to facilitate patient/public involvement in societal decision making.

METHODS

Japanese transcribed interviews with 53 dementia patients and caregivers were used. A new morpheme selection method using Z-scores was developed to identify trends in describing the latent needs. F-measures with and without the new method were compared using three machine learning models.

RESULTS

The F-measures with the new method were higher for the support vector machine (SVM) (F-measure of 0.81 with the new method and F-measure of 0.79 without the new method for patients) and Naive Bayes (F-measure of 0.69 with the new method and F-measure of 0.67 without the new method for caregivers and F-measure of 0.75 with the new method and F-measure of 0.73 without the new method for patients).

CONCLUSION

A new scheme based on Z-score adaptation for machine learning models was developed to predict the latent needs of dementia patients and their caregivers by extracting data from interviews in Japanese. However, this study alone cannot be used to assign significance to the adaptation of the new method because of no enough size of sample dataset. Such pre-selection with Z-score adaptation from text data in machine learning models should be considered with more modified suitable methods in the near future.

Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables

Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-related health data, and a combination of both. Machine learning (ML) models, particularly the random forest algorithm, demonstrated robust consistency across health, nutrition, and mixed categories in predicting death from CVD. Shapley additive explanation (SHAP) values showed age, systolic blood pressure, and several other health factors as crucial variables, while fiber, calcium, and vitamin E, among others, were significant nutritional variables. Our research emphasizes the importance of comprehensive health evaluation and dietary intake in predicting CVD mortality. The inclusion of nutrition variables improved the performance of our models, underscoring the utility of dietary intake in ML-based data analysis. Further investigation using large datasets with recurring dietary recalls is necessary to enhance the effectiveness and interpretability of such models.

Ramen Consumption and Gut Microbiota Diversity in Japanese Women: Cross-Sectional Data from the NEXIS Cohort Study

A cross-sectional study involving 224 healthy Japanese adult females explored the relationship between ramen intake, gut microbiota diversity, and blood biochemistry. Using a stepwise regression model, ramen intake was inversely associated with gut microbiome alpha diversity after adjusting for related factors, including diets, Age, BMI, and stool habits (β = -0.018; r = -0.15 for Shannon index). The intake group of ramen was inversely associated with dietary nutrients and dietary fiber compared with the no-intake group of ramen. Sugar intake, Dorea as a short-chain fatty acid (SCFA)-producing gut microbiota, and γ-glutamyl transferase as a liver function marker were directly associated with ramen intake after adjustment for related factors including diets, gut microbiota, and blood chemistry using a stepwise logistic regression model, whereas Dorea is inconsistently less abundant in the ramen group. In conclusion, the increased ramen was associated with decreased gut bacterial diversity accompanying a perturbation of Dorea through the dietary nutrients, gut microbiota, and blood chemistry, while the methodological limitations existed in a cross-sectional study. People with frequent ramen eating habits need to take measures to consume various nutrients to maintain and improve their health, and dietary management can be applied to the dietary feature in ramen consumption.

Different Recognition of Protein Features Depending on Deep Learning Models: A Case Study of Aromatic Decarboxylase UbiD

The number of unannotated protein sequences is explosively increasing due to genome sequence technology. A more comprehensive understanding of protein functions for protein annotation requires the discovery of new features that cannot be captured from conventional methods. Deep learning can extract important features from input data and predict protein functions based on the features. Here, protein feature vectors generated by 3 deep learning models are analyzed using Integrated Gradients to explore important features of amino acid sites. As a case study, prediction and feature extraction models for UbiD enzymes were built using these models. The important amino acid residues extracted from the models were different from secondary structures, conserved regions and active sites of known UbiD information. Interestingly, the different amino acid residues within UbiD sequences were regarded as important factors depending on the type of models and sequences. The Transformer models focused on more specific regions than the other models. These results suggest that each deep learning model understands protein features with different aspects from existing knowledge and has the potential to discover new laws of protein functions. This study will help to extract new protein features for the other protein annotations.

Prediction of Metabolic Flux Distribution by Flux Sampling: As a Case Study, Acetate Production from Glucose in Escherichia coli

Omics data was acquired, and the development and research of metabolic simulation and analysis methods using them were also actively carried out. However, it was a laborious task to acquire such data each time the medium composition, culture conditions, and target organism changed. Therefore, in this study, we aimed to extract and estimate important variables and necessary numbers for predicting metabolic flux distribution as the state of cell metabolism by flux sampling using a genome-scale metabolic model (GSM) and its analysis. Acetic acid production from glucose in Escherichia coli with GSM iJO1366 was used as a case study. Flux sampling obtained by OptGP using 1000 pattern constraints on substrate, product, and growth fluxes produced a wider sample than the default case. The analysis also suggested that the fluxes of iron ions, O₂, CO₂, and NH₄⁺, were important for predicting the metabolic flux distribution. Additionally, the comparison with the literature value of ¹³C-MFA using CO₂ emission flux as an example of an important flux suggested that the important flux obtained by this method was valid for the prediction of flux distribution. In this way, the method of this research was useful for extracting variables that were important for predicting flux distribution, and as a result, the possibility of contributing to the reduction of measurement variables in experiments was suggested.

Predicting Overweight and Obesity Status Among Malaysian Working Adults With Machine Learning or Logistic Regression: Retrospective Comparison Study

BACKGROUND

Overweight or obesity is a primary health concern that leads to a significant burden of noncommunicable disease and threatens national productivity and economic growth. Given the complexity of the etiology of overweight or obesity, machine learning (ML) algorithms offer a promising alternative approach in disentangling interdependent factors for predicting overweight or obesity status.

OBJECTIVE

This study examined the performance of 3 ML algorithms in comparison with logistic regression (LR) to predict overweight or obesity status among working adults in Malaysia.

METHODS

Using data from 16,860 participants (mean age 34.2, SD 9.0 years; n=6904, 41% male; n=7048, 41.8% with overweight or obesity) in the Malaysia's Healthiest Workplace by AIA Vitality 2019 survey, predictor variables, including sociodemographic characteristics, job characteristics, health and weight perceptions, and lifestyle-related factors, were modeled using the extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms, as well as LR, to predict overweight or obesity status based on a BMI cutoff of 25 kg/m².

RESULTS

The area under the receiver operating characteristic curve was 0.81 (95% CI 0.79-0.82), 0.80 (95% CI 0.79-0.81), 0.80 (95% CI 0.78-0.81), and 0.78 (95% CI 0.77-0.80) for the XGBoost, RF, SVM, and LR models, respectively. Weight satisfaction was the top predictor, and ethnicity, age, and gender were also consistent predictor variables of overweight or obesity status in all models.

CONCLUSIONS

Based on multi-domain online workplace survey data, this study produced predictive models that identified overweight or obesity status with moderate to high accuracy. The performance of both ML-based and logistic regression models were comparable when predicting obesity among working adults in Malaysia.

Cardiovascular risk factors and underlying pathology and prevalence of lipid plaques in women with acute coronary syndromes in different age groups

Background

An incidence of cardiovascular events increases with age in women. The relationship between cardiovascular risk factors, and the underlying pathology and the prevalence of lipid plaques has not been systematically studied in different age groups in women presented with acute coronary syndromes (ACS).

Purpose

We investigated the underlying pathology and the prevalence of lipid plaques in culprit lesions by optical coherence tomography (OCT) in women with different risk factors.

Methods

A total of 382 women who underwent pre-intervention OCT imaging were included. The underlying pathology and the prevalence of lipid plaques in the culprit lesion was compared between women with and without cardiovascular risk factors (i.e. hypertension, smoking, hyperlipidemia, diabetes mellitus, family history and chronic kidney disease) in three different age groups (<60 yr, 60–70 yr, >70 yr).

Results

The relative prevalence of plaque erosion was higher in younger women (<60 yr) and decreased with age (from 51% to 28%, p<0.001). There was no significant difference in the prevalence of lipid plaques between women with and without risk factors, except a higher prevalence of lipid plaques in current smokers compared to non-smokers (79% vs. 63%, p=0.003). In women with hyperlipidemia, the prevalence of lipid plaques was modest in young ages (<60 yr), but increased steeply with age (p<0.001). The increasing age trend for lipid plaque was also observed in women with hypertension (p=0.03) and current smokers (p=0.01). In women with diabetes mellitus and family history, the prevalence of lipid plaques was high even in young ages (<60 yr) and did not increase with age.

Conclusion

The prevalence of plaque erosion was higher in younger women (<60 yr) and decreased with age. Current smokers had significantly higher prevalence of lipid plaque. Patients with diabetes and positive family history had a higher prevalence of lipid plaque at young age. The prevalence of lipid plaques increased with age particularly in women with hyperlipidemia and hypertension.

Funding Acknowledgement

Type of funding sources: Foundation.

Level of vascular inflammation is higher in acute coronary syndromes compared to chronic coronary disease

Background

Vascular inflammation has been recognized as one of the key factors in the pathogenesis of acute coronary syndromes (ACS). Peri-coronary adipose tissue (PCAT) attenuation by computed tomography angiography (CTA) has emerged as a marker specific for coronary artery inflammation. We examined the relationship between clinical presentation and coronary artery inflammation assessed by PCAT attenuation and coronary plaque characteristics.

Methods

Patients with ACS or stable angina pectoris (SAP) who underwent pre-intervention coronary CTA and optical coherence tomography (OCT) were enrolled. PCAT attenuation was measured around the culprit lesion and in the proximal 40 mm of all coronary arteries. PCAT attenuation and OCT findings were compared between patients with ACS versus SAP.

Results

Among 471 patients (ACS: 198, SAP: 273), PCAT attenuation was higher in ACS patients than in SAP patients both at the culprit plaque level (−67.5±9.6 Hounsfield unit [HU] vs. −71.5±11.0 HU, p<0.001) and the culprit vessel level (−68.3±7.7 HU vs. −71.1±7.9 HU, p<0.001). The mean PCAT attenuation of all 3 coronary arteries was also significantly higher in ACS patients than in SAP patients (−68.8±6.3 HU vs. −70.5±7.1 HU, p=0.007). After adjusting patient characteristics, not only thin-cap fibroatheroma (OR: 2.44; 95% CI: 1.63–3.65) and macrophages (OR: 2.07; 95% CI: 1.34–3.21) but also PCAT attenuation in the culprit plaque (OR: 1.04; 95% CI: 1.02–1.06) was associated with the clinical presentation of ACS.

Conclusions

PCAT attenuation at culprit plaque, culprit vessel, and pan-coronary levels was higher in ACS patients than in SAP patients. Vascular inflammation appears to play a crucial role in the development of ACS.

Funding Acknowledgement

Type of funding sources: None.

Comprehensive Machine Learning Prediction of Extensive Enzymatic Reactions

New enzyme functions exist within the increasing number of unannotated protein sequences. Novel enzyme discovery is necessary to expand the pathways that can be accessed by metabolic engineering for the biosynthesis of functional compounds. Accordingly, various machine learning models have been developed to predict enzymatic reactions. However, the ability to predict unknown reactions that are not included in the training data has not been clarified. In order to cover uncertain and unknown reactions, a wider range of reaction types must be demonstrated by the models. Here, we establish 16 expanded enzymatic reaction prediction models developed using various machine learning algorithms, including deep neural network. Improvements in prediction performances over that of our previous study indicate that the updated methods are more effective for the prediction of enzymatic reactions. Overall, the deep neural network model trained with combined substrate-enzyme-product information exhibits the highest prediction accuracy with Macro F₁ scores up to 0.966 and with robust prediction of unknown enzymatic reactions that are not included in the training data. This model can predict more extensive enzymatic reactions in comparison to previously reported models. This study will facilitate the discovery of new enzymes for the production of useful substances.

Mangrove crab intestine and habitat sediment microbiomes cooperatively work on carbon and nitrogen cycling

Mangrove ecosystems, where litter and organic components are degraded and converted into detrital materials, support rich coastal fisheries resources. Sesarmid (Grapsidae) crabs, which feed on mangrove litter, play a crucial role in material flow in carbon-rich and nitrogen-limited mangrove ecosystems; however, the process of assimilation and conversion into detritus has not been well studied. In this study, we performed microbiome analyses of intestinal bacteria from three species of mangrove crab and five sediment positions in the mud lobster mounds, including the crab burrow wall, to study the interactive roles of crabs and sediment in metabolism. Metagenome analysis revealed species-dependent intestinal profiles, especially in Neosarmatium smithi, while the sediment microbiome was similar in all positions, albeit with some regional dependency. The microbiome profiles of crab intestines and sediments were significantly different in the MDS analysis based on OTU similarity; however, 579 OTUs (about 70% of reads in the crab intestinal microbiome) were identical between the intestinal and sediment bacteria. In the phenotype prediction, cellulose degradation was observed in the crab intestine. Cellulase activity was detected in both crab intestine and sediment. This could be mainly ascribed to Demequinaceae, which was predominantly found in the crab intestines and burrow walls. Nitrogen fixation was also enriched in both the crab intestines and sediments, and was supported by the nitrogenase assay. Similar to earlier reports, sulfur-related families were highly enriched in the sediment, presumably degrading organic compounds as terminal electron acceptors under anaerobic conditions. These results suggest that mangrove crabs and habitat sediment both contribute to carbon and nitrogen cycling in the mangrove ecosystem via these two key reactions.

Pathway engineering for high-yield production of lutein in Escherichia coli

Lutein is an industrially important carotenoid pigment, which is essential for photoprotection and photosynthesis in plants. Lutein is crucial for maintaining human health due to its protective ability from ocular diseases. However, its pathway engineering research has scarcely been performed for microbial production using heterologous hosts, such as Escherichia coli, since the engineering of multiple genes is required. These genes, which include tricky key carotenoid biosynthesis genes typically derived from plants, encode two sorts of cyclases (lycopene ε- and β-cyclase) and cytochrome P450 CYP97C. In this study, upstream genes effective for the increase in carotenoid amounts, such as isopentenyl diphosphate isomerase (IDI) gene, were integrated into the E. coli JM101 (DE3) genome. The most efficient set of the key genes (MpLCYe, MpLCYb and MpCYP97C) was selected from among the corresponding genes derived from various plant (or bacterial) species using E. coli that had accumulated carotenoid substrates. Furthermore, to optimize the production of lutein in E. coli, we introduced several sorts of plasmids that contained some of the multiple genes into the genome-inserted strain and compared lutein productivity. Finally, we achieved 11 mg/l as lutein yield using a mini jar. Here, the high-yield production of lutein was successfully performed using E. coli through approaches of pathway engineering. The findings obtained here should be a base reference for substantial lutein production with microorganisms in the future.

History-Driven Genetic Modification Design Technique Using a Domain-Specific Lexical Model for the Acceleration of DBTL Cycles for Microbial Cell Factories

The development of microbes for conducting bioprocessing via synthetic biology involves design-build-test-learn (DBTL) cycles. To aid the designing step, we developed a computational technique that suggests next genetic modifications on the basis of relatedness to the user's design history of genetic modifications accumulated through former DBTL cycles conducted by the user. This technique, which comprehensively retrieves well-known designs related to the history, involves searching text for previous literature and then mining genes that frequently co-occur in the literature with those modified genes. We further developed a domain-specific lexical model that weights literature that is more related to the domain of metabolic engineering to emphasize genes modified for bioprocessing. Our technique made a suggestion by using a history of creating a Corynebacterium glutamicum strain producing shikimic acid that had 18 genetic modifications. Inspired by the suggestion, eight genes were considered by biologists for further modification, and modifying four of these genes proved experimentally efficient in increasing the production of shikimic acid. These results indicated that our proposed technique successfully utilized the former cycles to suggest relevant designs that biologists considered worth testing. Comprehensive retrieval of well-tested designs will help less-experienced researchers overcome the entry barrier as well as inspire experienced researchers to formulate design concepts that have been overlooked or suspended. This technique will aid DBTL cycles by feeding histories back to the next genetic design, thereby complementing the designing step.

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

A novel orexin antagonist from a natural plant was discovered using zebrafish behavioural analysis

OBJECTIVE

Phenotypic screening is one of the most practical approaches to the identification of mediators of behaviour, since it is difficult to model brain function in vitro, at a cellular level. We used a zebrafish (Danio rerio) behavioural assay to discover novel, natural, neuroactive compounds.

MATERIALS AND METHODS

A zebrafish behavioural assay was performed for seven natural compounds, obtained from plants. The behavioural profiles were compared to those of known psychoactive drugs. We characterised a natural compound exhibiting a behaviour profile similar to that of suvorexant, using in silico, in vitro and microarray expression analysis.

RESULTS

The behavioural analysis performed in this study classified central nervous system drugs according to their mechanism. Zebrafish treated with a natural compound, 8b-(4'-Hydroxytigloyloxy) costunolide (8b), showed behaviour profiles similar to those of zebrafish treated with suvorexant, a known orexin antagonist. This behavioural assay was validated using in silico and in vitro assays, which revealed that the new compound was a dual orexin receptor antagonist. In addition, transcriptome analysis suggested that 8b might regulate the nuclear factor-κB (NF-κB) related pathway.

CONCLUSIONS

We conclude that zebrafish phenotypic screening, combined with in silico assays and gene expression profiling, is a useful strategy to discover and characterize novel therapeutic compounds, including natural products.

Occurrence and clinical course of peri-stent contrast staining: comparison between second-generation drug-eluting stents and third generation drug-eluting stents

Background

Peri-stent contrast staining (PSS) has been reported to be associated with very late stent thrombosis.

The aims of this study was to compare the occurrence rate of PSS between second generation drug-eluting stents (2nd DES) and third generation drug-eluting stents (3rd DES), and to identify clinical characteristics associated with PSS.

Methods and results

This study comprised 1899 patients with 2493 de novo lesions treated with 2nd or 3rd DES from October 2015 to September 2018. Follow-up angiography was available for 1883 lesions (75.5%). There were 725 patients with 968 lesions treated with 2nd DES, and 716 patients with 915 lesions treated with 3rd DES. The occurrence of PSS, types of PSS, and VLST related to PSS were compared between 2nd and 3rd DES implantation. Mean follow-up period was 30±12 months. The occurrence rate of PSS and segmental type of PSS were similar between two groups (2nd DES vs. 3rd DES, 1.5% vs. 1.7%, p=0.73, 47% vs. 50%, p=0.85, and respectively). The VLST related to PSS occurred in only one case in 3rd DES group. (0% vs. 6.3%, p=0.33).

Conclusion

The occurrence rate of PSS and clinical course were similar between 2nd and 3rd DES.

Funding Acknowledgement

Type of funding source: None

Predictors for rapid progression of coronary calcification: an optical coherence tomography (OCT) study

Background

The natural progression of coronary calcification has not been systematically studied. In vivo coronary calcium can be evaluated by OCT.

Purpose

To identify the incidence and predictors for rapid progression of coronary calcification.

Methods

Patients with serial OCT imaging at baseline and at 6 months were selected from our database. Changes in calcium index during 6-month follow-up and predictors for progression of calcification were studied. Calcium index was defined as the product of the mean calcium arc and calcium length. Rapid progression of calcification was defined as an increase in calcium index above the median value.

Results

Among 187 patients who had serial OCT imaging, 235 calcified plaques were identified in 108 patients (57.8%) at baseline with median calcium index of 132.0 (Interquartile range 58.5–281.2). After 6 months, the calcium index increased in 95.3% of calcified plaques from 132.0 to 178.2 (p<0.001). In multivariable analysis, diabetes mellitus (DM), chronic kidney disease (CKD), lipid-rich plaque, and macrophages were found to be independent predictors for rapid progression of coronary calcification (table). Interestingly, rapid progression of calcification was associated with a significant reduction of inflammatory features (thin-cap fibroatheroma [TCFA]; baseline 21.2% vs. follow-up 11.9%, p=0.003, macrophages; baseline 74.6% vs. follow-up 61.0%, p=0.001).

Conclusions

This study demonstrated that DM, CKD, lipid-rich plaque, and macrophages were independent predictors for rapid progression of coronary calcification. High level of vascular inflammation may stimulate rapid progression of calcification.

Funding Acknowledgement

Type of funding source: None

Predictors of rapid plaque progression: an optical coherence tomography study

Background

Two patterns of plaque progression have been described: slow linear progression and rapid step-wise progression. The former will cause stable angina when the narrowing reaches a critical threshold, while the latter may lead to acute coronary syndromes or sudden cardiac death.

Purpose

The aim of the study was to identify morphologic predictors for rapid plaque progression.

Methods

Patients who had OCT imaging during the index procedure and follow-up angiography with a minimum of 6-month interval were selected. Non-culprit lesion was defined as a plaque with a diameter stenosis ≥30% on index angiogram. Lesion progression was defined as the decrease of angiographic minimum lumen diameter ≥0.4 mm at follow-up (mean, 7.1 months). Baseline morphological characteristics of the plaques with rapid progression were evaluated by OCT. In a subgroup with follow-up OCT imaging for plaques with progression, morphological changes from baseline to follow-up were assessed.

Results

Among 517 lesions, 50 lesions showed progression. These lesions had a significantly higher prevalence of lipid-rich plaque (76.0% vs. 50.5%), thin-cap fibroatheroma (TCFA) (20.0% vs. 5.8%), layered plaque (60.0% vs. 34.0%), macrophage accumulation (62.0% vs. 42.4%), microvessel (46.0% vs. 29.1%), plaque rupture (12.0% vs. 4.7%), and thrombus (6.0% vs. 1.1%), compared to those without progression. The multivariable analysis identified lipid-rich plaque [odds ratio (OR) 2.17, 95% confidence interval (CI) 1.02–4.62, p=0.045], TCFA (OR 5.85, 95% CI 2.01–17.03, p=0.001), and layered plaque (OR 2.19, 95% CI 1.03–4.17, p=0.040) as predictors of subsequent lesion progression. In a subgroup with follow-up OCT, a new layer was detected in 14/41 (34.1%) plaques.

Conclusions

Lipid-rich plaque, TCFA, and layered plaque were predictors of subsequent rapid plaque progression. A new layer, a signature of rapid progression through plaque disruption and healing, was detected in 1/3 of the cases.

Funding Acknowledgement

Type of funding source: None

Impact of left atrial appendage morphology on the silent cerebral infarction after cryoballoon ablation for atrial fibrillation

Aims

Cryoballoon ablation (CBA) is effective for atrial fibrillation (AF), however acute silent cerebral infarction (SCI) is not uncommon after CBA. This study aimed to clarify the relationship between the morphology of LAA and SCI.

Methods and results

From 2015 to 2019, 207 consecutive patients (65.1±11.4 years, 26.6% of female, 31.4% of persistent AF) who underwent CBA for AF at our hospital were enrolled. All patients were evaluated with computed tomography for LAA before CBA and divided into two groups as chicken wing (CW) group and non-CW group. SCI was evaluated with magnetic resonance imaging at the following day after CBA. As a result, 18.4% of patients have occurred SCI after CBA. Although the patients' background didn't differ between the two groups, the Hounsfield scale (LAA/Aorta ratio) was lower (non-CW vs. CW = 0.68±0.12 vs. 0.73±0.11, p=0.004), and LAA orifice size tended to be larger (16.9±4.6 vs. 15.8±4.1, p=0.053) in non-CW group. SCI significantly occurred in non-CW group (29.5% vs. 11.6%, p=0.003), especially in cauliflower shape of 45.0%. In multivariate analysis, non-CW (p=0.002, Odds ratio 3.2, 95% of CI 1.5–7.0), skipped DOAC before CBA (p=0.04, Odds ratio 6.1, 95% of CI 1.1–115.4), and touch-up ablation in left atrium (p=0.02, Odds ratio 2.8, 95% CI 1.2–6.5) were independent predictors of SCI.

Conclusions

SCI was not rare after CBA for AF. Morphology of LAA was strongly associated with SCI.

Funding Acknowledgement

Type of funding source: None

Circadian variations in pathogenesis of ST-segment elevation myocardial infarction

Background

Previous studies have reported a circadian variation in the onset of ST-segment elevation myocardial infarction (STEMI). However, underlying mechanisms for the circadian variation have not been fully elucidated.

Purpose

We investigated the relationship between onset of STEMI and the underlying pathology using optical coherence tomography (OCT).

Methods

Patients presenting with STEMI were selected from an international, multi-center, longitudinal registry study, which included patients who underwent OCT imaging of the culprit lesion at 11 institutions in 6 countries. Onset of MI was estimated using the time of OCT imaging. Patients were divided into 4 groups based on the estimated time of onset (00:00–05:59, 06:00–11:59, 12:00–17:59, or 18:00–23:59). Underlying pathologies of MI (plaque rupture, plaque erosion, and calcified plaque) were compared among the 4 groups.

Results

Among 648 patients, plaque rupture was diagnosed in 386 patients (59.6%), plaque erosion in 197 patients (30.4%), and calcified plaque in 65 patients (10.0%). A marked circadian variation was detected in the incidence of plaque rupture with a peak at 9:00, whereas it was not evident in plaque erosion or calcified plaque. The probability of plaque rupture increased in the periods of 06:00–11:59 (odds ratio: 2.13, 95% confidence interval: 1.30 to 3.49, p=0.002) and 12:00–17:59 (odds ratio: 2.10, 95% confidence interval: 1.23 to 3.58, p=0.005), compared to the period of 00:00–05:59. This circadian pattern was observed only during weekdays (p=0.013) and it was not evident during the weekend (p=0.742).

Conclusions

Plaque rupture occurred most frequently in the morning and the circadian variation was evident only during the weekdays. Acute MI caused by plaque rupture may be related to catecholamine surge.

Funding Acknowledgement

Type of funding source: None

Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

Exploration and Evaluation of Machine Learning-Based Models for Predicting Enzymatic Reactions

Unannotated gene sequences in databases are increasing due to sequencing advances. Therefore, computational methods to predict functions of unannotated genes are needed. Moreover, novel enzyme discovery for metabolic engineering applications further encourages annotation of sequences. Here, enzyme functions are predicted using two general approaches, each including several machine learning algorithms. First, Enzyme-models (E-models) predict Enzyme Commission (EC) numbers from amino acid sequence information. Second, Substrate-Enzyme models (SE-models) are built to predict substrates of enzymatic reactions together with EC numbers, and Substrate-Enzyme-Product models (SEP-models) are built to predict substrates, products, and EC numbers. While accuracy of E-models is not optimal, SE-models and SEP-models predict EC numbers and reactions with high accuracy using all tested machine learning-based methods. For example, a single Random Forests-based SEP-model predicts EC first digits with an Average AUC score of over 0.94. Various metrics indicate that the current strategy of combining sequence and chemical structure information is effective at improving enzyme reaction prediction.

P1912Incidence of silent cerebral thromboembolism in catheter ablation for atrial fibrillation under the use of DOAC: Comparison of cryoballoon versus radiofrequency ablation system

Background

Silent cerebral thromboembolism (CE) in catheter ablation for atrial fibrillation (AF) is not rare. Prior our study has reported lower incidence of silent CE in AF radiofrequency (RF) ablation using irrigated-tip catheter than conventional 4 or 8mm-tip catheter. In addition, the incidence of silent CE in AF ablation under direct oral anticoagulant (DOAC) was equivalent to continuous therapeutic warfarin. Recently pulmonary vein isolation (PVI) using cryoballoon (CB) has emerged as an alternative technique to RF ablation because some studies suggested that the efficacy for PVI by CB was equivalent to RF. However, incidence of silent CE in CB ablation under the use of DOAC is unknown.

Objective

We aimed to evaluate the incidence new silent CE in AF ablation using CB system compared with irrigated RF system under the use of DOAC.

Methods

322 consecutive patients with paroxysmal or persistent AF (155 using CB system, 167 using RF system) who underwent the first AF ablation were taking DOAC more than one month prior to the procedure. Throughout AF ablation procedure, heparin was administered to maintain activated clotting time (ACT) between 300 and 400 seconds. Head MRI was performed in all patients within 24 hours after the procedure.

Results

In 14 (9.0%) patients using CB and in 20 (12.0%) patients using RF, head MRI showed new embolic lesions without neurological symptom (P=0.469). Although the amount of heparin during the procedure in group CB was significantly less than in group RF (16134±3125 U vs. 18689±5222 U; P<0.0001), amount of heparin per hour in group CB was more than in group RF (8335±1955 U/h vs. 6143±1918 U/h; P<0.0001) because procedure time in group CB was shorter than in group RF (121.3±34.1 min vs. 189.5±47.4 min; P<0.0001). Mean ACT in both groups was maintained high level (3367±25.3 sec. vs. 338.6±23.5 sec.; P=0.479). In univariate analysis, minimum ACT during procedure, as before puncture of interatrial septum, were significantly correlated with the incidence of silent CE (P=0.027).

Conclusions

The incidence of silent CE in AF ablation using CB system was lower than RF system, although it was not a significant difference between different ablation techniques for AF. In AF ablation, the use of CB may be preferred rather than RF as ablation system in regard to risk reduction of thromboembolic complications.

P6402Ischemic and bleeding events during dual antiplatelet therapy after second-generation drug-eluting stent implantation in hemodialysis patients: a propensity score-matched analysis

Background

Dual-antiplatelet therapy (DAPT) after second-generation drug eluting stent (2-DES) implantation reduced the risk of stent thrombosis and subsequent ischemic events, with an increase in bleeding risk. Although chronic kidney disease patients have high ischemic and bleeding risk, little is known about both risks in hemodialysis patients after 2-DES implantation during DAPT.

Method

From July 2009 to March 2017, we retrospectively analyzed post-discharge major adverse cardiac and cerebrovascular events [MACCE: cardiac death, myocardial infarction, target vessel revascularization (TVR) and cerebral infarction] and bleeding events in 644 consecutive patients during DAPT after 2-DES implantation. We divided them into 2 groups [102 hemodialysis (HD) and 518 non-hemodialysis (Non-HD) patients, mean age, 71±10 years] after excluding 24 patients (lost to follow up and peritoneal dialysis). Follow-up period was 49±24 months. Median DAPT duration was 12 months. The primary endpoint was MACCE. The secondary endpoint was bleeding events according to the Bleeding Academic Research Consortium (BARC) type 2, 3, or 5. MACCE and bleeding events were compared between HD and Non-HD by using the propensity score-matching (PSM) method.

Results

Among the 620 eligible patients, the primary and secondary events occurred in 207 (33.3%) and 76 (12.3%) patients, respectively. The rates of unadjusted MACCE [HD vs Non-HD: 53.9% vs 29.3%; Hazard ratio (HR) 2.39, p<0.01] and bleeding events (HD vs Non-HD: 21.6% vs 10.4%; HR 2.50, p<0.01) were significantly higher in HD than Non-HD.

After 1-to-1 propensity score adjustment for baseline differences (hypertension, diabetes mellitus, low ejection fraction, low albumin, anemia, and high C-reactive protein), a total of 160 patients (80 HD vs 80 Non-HD) was created. The rate of MACCE [HD vs Non-HD: 52.5% vs 31.3%; adjusted HR 2.04, p<0.01] was significantly higher in HD than Non-HD. Regarding MACCE, cardiac death (HD vs Non-HD: 18.8% vs 8.8%; adjusted HR 2.65, p=0.03) and TVR (HD vs Non-HD: 15.0% vs 6.3%; adjusted HR 2.74, p=0.046) occurred significantly higher in HD. On the other hand, bleeding events did not exhibit significant differences though HD had a numerically higher event rate (HD vs Non-HD: 25.0% vs 16.3%; adjusted HR 1.68, p=0.15), indicating that the bleeding risk in HD would be strongly dependent on the patient's background.

Conclusions

As a result of PSM, HD was shown to contribute to ischemic risk rather than bleeding risk. Even in the 2-DES era, HD was an independent risk factor of cardiac death and TVR. Therefore, further study on the current regimen of DAPT would be necessary while balancing both ischemic and bleeding risk.

P3590Impact of right ventricular branch slow flow phenomenon post percutaneous coronary intervention for acute coronary syndrome to predict sustained right ventricular dysfunction

Background

Although Right Ventricular dysfunction (RVD) is one of the predictor of poor prognosis, it is believed that ischemic RVD after percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) restores quickly. Because right ventricular perfusion has more ischemic preconditioning compared with left ventricle, due to their complex perfusion system. However, little is known about the time courses of RVD after ACS-PCI andtheir prognosis. We evaluated the relationship between right ventricular branch slow flow phenomenon (RVB-SF) post ACS-PCI in right coronary artery (RCA) and RVD at 6–8 months follow-up.

Method

We retrospectively analyzed consecutive 82 patients who underwent PCI for ACS in proximal or mid portion of RCA from August 2011 to March 2018 in our institution. Finally, both baseline and follow-up data were obtained from 70 patients. We analyzed TIMI frame count (TFC) to confirm the presence of RVB-SF (TFC ≥40 frame) after PCI. We also analyzed right ventricular fractional area change (RVFAC) at baseline and follow-up using echocardiography to detect sustained RVD (RVFAC ≤35%).

Result

We divided the patients into two groups (RVB-SF: 36 patients, RVB non-SF: 34 patients). Patient clinical characteristics were similar in both groups (sex, age, risk factors, medication, onset to balloon time, left ventricular stroke volume, max creatine kinase). Baseline RVFAC and follow-up RVFAC was significantly smaller in RVB-SF than in RVB non-SF, respectively. (27.1±1.7% vs. 38.3±1.8%, 31.4±1.0% vs. 48.7±1.1%, P<0.0001). However, ΔRVFAC (follow-up RVFAC – baseline RVFAC) was similar between groups. The size of inferior vena cava and systolic pulmonary artery pressure at follow-up were similar in both groups (12.1±0.6 mm vs. 11.7±0.7 mm, P=0.67, 25.7±1.5 mmHg vs. 25.2±1.5 mmHg, P=0.82). In RVB non-SF, 10 patients (29.4%) were diagnosed clinical RVAMI. However, follow-up RVFAC were similar and preserved in both groups (RVAMI: 48.1±1.3%, non-RVAMI: 49.9±1.9%, P=0.85). In RVB-SF, 19 patients (52.7%) were diagnosed clinical RVAMI. Follow-up RVFAC did not improved significantly in both groups (RVAMI: 30.4±1.4% vs. non-RVAMI: 32.6±1.5%, P=0.70). Multivariate analysis showed RVB-SF was the only independent predictor of sustained RVD at 6–8 months follow-up after ACS-PCI.

Conclusion

RVB-SF findings after ACS-PCI for RCA could predict sustained RVD at mid-term follow-up, which may indicate future prolonged RVD.

Reconstruction of metabolic pathway for isobutanol production in Escherichia coli

Background

The microbial production of useful fuels and chemicals has been widely studied. In several cases, glucose is used as the raw material, and almost all microbes adopt the Embden–Meyerhof (EM) pathway to degrade glucose into compounds of interest. Recently, the Entner–Doudoroff (ED) pathway has been gaining attention as an alternative strategy for microbial production.

Results

In the present study, we attempted to apply the ED pathway for isobutanol production in Escherichia coli because of the complete redox balance involved. First, we generated ED pathway-dependent isobutanol-producing E. coli. Thereafter, the inactivation of the genes concerning organic acids as the byproducts was performed to improve the carbon flux to isobutanol from glucose. Finally, the expression of the genes concerning the ED pathway was modified.

Conclusions

The optimized isobutanol-producing E. coli produced 15.0 g/L of isobutanol as the final titer, and the yield from glucose was 0.37 g/g (g-glucose/g-isobutanol).

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1171-4) contains supplementary material, which is available to authorized users.

Transporter engineering in biomass utilization by yeast

Biomass resources are attractive carbon sources for bioproduction because of their sustainability. Many studies have been performed using biomass resources to produce sugars as carbon sources for cell factories. Expression of biomass hydrolyzing enzymes in cell factories is an important approach for constructing biomass-utilizing bioprocesses because external addition of these enzymes is expensive. In particular, yeasts have been extensively engineered to be cell factories that directly utilize biomass because of their manageable responses to many genetic engineering tools, such as gene expression, deletion and editing. Biomass utilizing bioprocesses have also been developed using these genetic engineering tools to construct metabolic pathways. However, sugar input and product output from these cells are critical factors for improving bioproduction along with biomass utilization and metabolic pathways. Transporters are key components for efficient input and output activities. In this review, we focus on transporter engineering in yeast to enhance bioproduction from biomass resources.

Acute Inflammatory Syndrome Paradoxically Induced by De Novo Purine Inhibitors Synthesis Before Renal Transplantation: A Case Report and Review of the Literature

BACKGROUND

Mycophenolate mofetil (MMF) and mizoribine (MZR) are increasingly used as immunosuppressive agents for organ transplantation and chronic inflammation. We report a patient with rheumatoid arthritis who had an acute inflammatory syndrome triggered by preoperative immunosuppression therapy with both MMF and MZR.

CASE REPORT

A 41-year-old woman with IgA nephropathy was referred to our department for living donor renal transplantation. She had rheumatoid arthritis that was adequately treated with prednisolone 5 mg once a day and salazosulfapyridine 2000 mg once a day. MMF 1000 mg twice a day was started for desensitization therapy. Three days later, the patient developed arthritis in the joints of her left hand and elevated inflammatory markers. On day 7, MMF was switched to MZR 150 mg 3 times a day. However, the symptoms extended to both shoulders and the joints of the right foot; MZR was discontinued. The arthritis and inflammatory markers improved. Two months later, the patient was rechallenged with MMF followed by MZR, resulting in a similar clinical course as previously. Tacrolimus (TAC) 3 mg twice a day and everolimus (EVL) 0.5 mg twice a day were introduced as alternative immunosuppressant therapies. No arthritis occurred. ABO-compatible living donor renal transplantation was successfully performed. The patient received TAC, EVL, prednisolone, rituximab, and basiliximab, and her postoperative course was uneventful without arthritis or rejection. At 9 months postoperatively, the serum creatinine was 0.79 mg/dL.

CONCLUSIONS

Acute inflammatory syndrome is an extremely rare complication triggered by preoperative immunosuppression therapy. If antimetabolites cannot be used in immunologically high-risk patients, transplantation becomes very difficult. Clinicians should keep in mind this paradoxical reaction.