Development of a Limosilactobacillus reuteri therapeutic delivery platform with reduced colonization potential

Bacterial biotherapeutic delivery vehicles have the potential to treat a variety of diseases. This approach obviates the need to purify the recombinant effector molecule, allows delivery of therapeutics in situ via oral or intranasal administration, and protects the effector molecule during gastrointestinal transit. Lactic acid bacteria have been broadly developed as therapeutic delivery vehicles though risks associated with the colonization of a genetically modified microorganism have so-far not been addressed. Here, we present an engineered Limosilactobacillus reuteri strain with reduced colonization potential. We applied a dual-recombineering scheme for efficient barcoding and generated mutants in genes encoding five previously characterized and four uncharacterized putative adhesins. Compared with the wild type, none of the mutants were reduced in their ability to survive gastrointestinal transit in mice. CmbA was identified as a key protein in L. reuteri adhesion to HT-29 and enteroid cells. The nonuple mutant, a single strain with all nine genes encoding adhesins inactivated, had reduced capacity to adhere to enteroid monolayers. The nonuple mutant producing murine IFN-β was equally effective as its wild-type counterpart in mitigating radiation toxicity in mice. Thus, this work established a novel therapeutic delivery platform that lays a foundation for its application in other microbial therapeutic delivery candidates and furthers the progress of the L. reuteri delivery system towards human use.IMPORTANCEOne major advantage to leverage gut microbes that have co-evolved with the vertebrate host is that evolution already has taken care of the difficult task to optimize survival within a complex ecosystem. The availability of the ecological niche will support colonization. However, long-term colonization of a recombinant microbe may not be desirable. Therefore, strategies need to be developed to overcome this potential safety concern. In this work, we developed a single strain in which we inactivated the encoding sortase, and eight genes encoding characterized/putative adhesins. Each individual mutant was characterized for growth and adhesion to epithelial cells. On enteroid cells, the nonuple mutant has a reduced adhesion potential compared with the wild-type strain. In a model of total-body irradiation, the nonuple strain engineered to release murine interferon-β performed comparable to a derivative of the wild-type strain that releases interferon-β. This work is an important step toward the application of recombinant L. reuteri in humans.

Predicting long-term mortality of patients with postoperative acute kidney injury following noncardiac general anesthesia surgery using machine learning

Background

This study addresses the gap in knowledge regarding the long-term mortality implications of postoperative acute kidney injury (PO-AKI) utilizing advanced machine learning techniques to predict outcomes more accurately than traditional statistical models.

Methods

A retrospective cohort study was conducted using data from seven institutions between March 2009 and December 2019. Machine learning models were developed to predict all-cause mortality of PO-AKI patients using 23 preoperative variables and one postoperative variable. Model performance was compared to a traditional statistical approach with Cox regression analysis. The concordance index was used as a predictive performance metric to compare prediction capabilities among different models.

Results

Among 199,403 patients, 2,105 developed PO-AKI. During a median follow-up of 144 months (interquartile range, 99.61-170.71 months), 472 in-hospital deaths occurred. Subjects with PO-AKI had a significantly lower survival rate than those without PO-AKI (p < 0.001). For predicting mortality, the XGBoost with an accelerated failure time model had the highest concordance index (0.7521), followed by random survival forest (0.7371), multivariable Cox regression model (0.7318), survival support vector machine (0.7304), and gradient boosting (0.7277).

Conclusion

XGBoost with an accelerated failure time model was developed in this study to predict long-term mortality associated with PO-AKI. Its performance was superior to conventional models. The application of machine learning techniques may offer a promising approach to predict mortality following PO-AKI more accurately, providing a basis for developing targeted interventions and clinical guidelines to improve patient outcomes.

The biphasic impact of apolipoprotein E ε4 allele on age-related hearing loss

Both the ε4 variant of the apolipoprotein E (APOE) gene and hearing loss are well-known risk factors for Alzheimer's disease. However, previous studies have produced inconsistent findings regarding the association between APOE genotypes and hearing levels, necessitating further investigation. The aim of this study was to investigate the relationship between APOE genotypes and hearing levels. This retrospective study analyzed clinical data from a clinical data warehouse of seven affiliated Catholic Medical Center hospitals. The study included 1,162 participants with records of APOE genotypes, audiometric tests, and cognitive function tests. In Generalized linear mixed model analysis, ε4 carriers exhibited lower pure tone audiometry thresholds with an estimate of -0.353 (SE = 0.126, p = 0.005). However, the interaction term for age and APOE ε4 had a coefficient of 0.577 (SE = 0.214 p = 0.006), suggesting that the APOE ε4 gene may accelerate hearing deterioration with age. Subgroup analysis based on an age cut-off of 75 revealed that ε4 carriers had better hearing at younger ages, but showed no significant difference at older ages. These results indicate that the ε4 allele may have a biphasic effect on hearing levels depending on age.

Development of a Big Data Platform for the Collection and Utilization of Korea Biobank Network Clinical Information

Biobanks serve as vital repositories for human biospecimens and clinical data, promoting biomedical and clinical research. The integration of electronic health records particularly enhances research opportunities in the era of genomics and personalized medicine, improving understanding of tumor development and disease progression. Based on the Korea Biobank Network Common Data Model, it is possible to expand data collection across various diseases. We have developed an innovative big data platform designed to efficiently collect large-scale clinical information within the KBN. By implementing the system structure, data quality management processes, and basic statistical preprocessing functionalities, we have collected data from 136,473 individuals from 2021 to 2023, demonstrating the platform's continuous and efficient data collection capabilities. Integration with hospital systems and robust quality management ensure the acquisition of high-quality data.

Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea

A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877. In survival analysis, XGBSE achieved an AUROC exceeding 0.9 for 2-9 year predictions, with a C-index of 0.878 across all diseases, outperforming Cox regression (C-index of 0.887). A high-performance prediction model for cardiovascular diseases using the XGBSE algorithm was successfully developed and is poised for real-world clinical application following external simplification and validation.

Development and Evaluation of a Data Quality Management System for the Korea Biobank Network

As the reliance on clinical epidemiological information from human specimens grows, so does the need for effective clinical information management systems, particularly for biobanks. Our study focuses on enhancing the Korea Biobank Network's (KBN) system with data quality verification features. By comparing the quality of data collected before and after these enhancements, we observed a notable improvement in data accuracy, with the error rate decreasing from 0.1198% to 0.0492%. This advancement underscores the importance of robust data quality management in supporting high-quality clinical research and sets a precedent for the development of clinical information management systems.

The E592K variant of SF3B1 creates unique RNA missplicing and associates with high-risk MDS without ring sideroblasts

ABSTRACT

Among the most common genetic alterations in myelodysplastic syndromes (MDS) are mutations in the spliceosome gene SF3B1. Such mutations induce specific RNA missplicing events, directly promote ring sideroblast (RS) formation, and generally associate with a more favorable prognosis. However, not all SF3B1 mutations are the same, and little is known about how distinct hotspots influence disease. Here, we report that the E592K variant of SF3B1 associates with high-risk disease features in MDS, including a lack of RS, increased myeloblasts, a distinct comutation pattern, and a lack of favorable survival seen with other SF3B1 mutations. Moreover, compared with other hot spot SF3B1 mutations, E592K induces a unique RNA missplicing pattern, retains an interaction with the splicing factor SUGP1, and preserves normal RNA splicing of the sideroblastic anemia genes TMEM14C and ABCB7. These data have implications for our understanding of the functional diversity of spliceosome mutations, as well as the pathobiology, classification, prognosis, and management of SF3B1-mutant MDS.

A male mouse model for metabolic dysfunction-associated steatotic liver disease and hepatocellular carcinoma

The lack of an appropriate preclinical model of metabolic dysfunction-associated steatotic liver disease (MASLD) that recapitulates the whole disease spectrum impedes exploration of disease pathophysiology and the development of effective treatment strategies. Here, we develop a mouse model (Streptozotocin with high-fat diet, STZ + HFD) that gradually develops fatty liver, metabolic dysfunction-associated steatohepatitis (MASH), hepatic fibrosis, and hepatocellular carcinoma (HCC) in the context of metabolic dysfunction. The hepatic transcriptomic features of STZ + HFD mice closely reflect those of patients with obesity accompanying type 2 diabetes mellitus, MASH, and MASLD-related HCC. Dietary changes and tirzepatide administration alleviate MASH, hepatic fibrosis, and hepatic tumorigenesis in STZ + HFD mice. In conclusion, a murine model recapitulating the main histopathologic, transcriptomic, and metabolic alterations observed in MASLD patients is successfully established.

Angiotensin Receptor Blockers and the Risk of Suspected Drug-Induced Liver Injury: A Retrospective Cohort Study Using Electronic Health Record-Based Common Data Model in South Korea

INTRODUCTION

Angiotensin receptor blockers are widely used antihypertensive drugs in South Korea. In 2021, the Korea Ministry of Food and Drug Safety acknowledged the need for national compensation for a drug-induced liver injury (DILI) after azilsartan use. However, little is known regarding the association between angiotensin receptor blockers and DILI.

OBJECTIVE

We conducted a retrospective cohort study in incident users of angiotensin receptor blockers from a common data model database (1 January, 2017-31 December, 2021) to compare the risk of DILI among specific angiotensin receptor blockers against valsartan.

METHODS

Patients were assigned to treatment groups at cohort entry based on prescribed angiotensin receptor blockers. Drug-induced liver injury was operationally defined using the International DILI Expert Working Group criteria. Cox regression analyses were conducted to derive hazard ratios and the inverse probability of treatment weighting method was applied. All analyses were performed using R.

RESULTS

In total, 229,881 angiotensin receptor blocker users from 20 university hospitals were included. Crude DILI incidence ranged from 15.6 to 82.8 per 1000 person-years in treatment groups, most were cholestatic and of mild severity. Overall, the risk of DILI was significantly lower in olmesartan users than in valsartan users (hazard ratio: 0.73 [95% confidence interval 0.55-0.96]). In monotherapy patients, the risk was significantly higher in azilsartan users than in valsartan users (hazard ratio: 6.55 [95% confidence interval 5.28-8.12]).

CONCLUSIONS

We found a significantly higher risk of suspected DILI in patients receiving azilsartan monotherapy compared with valsartan monotherapy. Our findings emphasize the utility of real-world evidence in advancing our understanding of adverse drug reactions in clinical practice.

A Review of Current Digital Mental Health Care Applications for Anxiety Symptoms and Future Prospects

OBJECTIVE

Since the impact of the coronavirus disease-2019 pandemic, the need for efficiency in medical services has become more urgent than ever. The digital treatment market is rapidly growing worldwide and digital therapeutics (DTx), a major part of the digital medical services, is also emerging as a new paradigm for treatment, with its industry growing rapidly as well. Increasing research is done on the effectiveness of mobile DTx in improving mental health conditions such as insomnia, panic, and depression.

METHODS

This review paper investigates 1) the functions and characteristics of mobile digital mental health care applications for the treatment of anxiety symptoms, 2) extracts common attributes of the applications, and 3) compares them with existing traditional treatment mechanisms.

RESULTS

Among the 20,000 mental health management applications that have been developed so far, 8 applications that are relatively widely used were selected and reviewed. Check-in, self-help tips, quick relief, journal, courses for practice are common features of the digital mental health care applications for anxiety and are also widely used feature in the cognitive behavioral therapy.

CONCLUSION

Based on this review, we have proposed the essential elements and directions for the development of a Korean digital mental health care applications for anxiety disorders.

Long-Term Prognostic Value of Infarct Transmurality Determined by Contrast-Enhanced Cardiac Magnetic Resonance after ST-Segment Elevation Myocardial Infarction

The long-term prognostic significance of maximal infarct transmurality evaluated by contrast-enhanced cardiac magnetic resonance (CE-CMR) in ST-segment elevation myocardial infarction (STEMI) patients has yet to be determined. This study aimed to see if maximal infarct transmurality has any additional long-term prognostic value over other CE-CMR predictors in STEMI patients, such as microvascular obstruction (MVO) and intramyocardial hemorrhage (IMH). The study included 112 consecutive patients who underwent CE-CMR after STEMI to assess established parameters of myocardial injury as well as the maximal infarct transmurality. The primary clinical endpoint was the occurrence of major adverse cardiac events (MACE), which included all-cause death, non-fatal reinfarction, and new heart failure hospitalization. The MACE occurred in 10 patients over a median follow-up of 7.9 years (IQR, 5.8 to 9.2 years) (2 deaths, 3 nonfatal MI, and 5 heart failure hospitalization). Patients with MACE had significantly higher rates of transmural extent of infarction, infarct size >5.4 percent, MVO, and IMH compared to patients without MACE. In stepwise multivariable Cox regression analysis, the transmural extent of infarction defined as 75 percent or more of infarct transmurality was an independent predictor of the MACE after correction for MVO and IMH (hazard ratio 8.7, 95% confidence intervals [CIs] 1.1-71; p=0.043). In revascularized STEMI patients, post-infarction CE-CMR-based maximal infarct transmurality is an independent long-term prognosticator. Adding maximal infarct transmurality to CE-CMR parameters like MVO and IMH could thus identify patients at high risk of long-term adverse outcomes in STEMI.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications

Background

Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa.

Methods

In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy.

Results

A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05).

Conclusions

DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

A Study to Evaluate the Effectiveness and Safety of Prephase Steroid Treatment before Remission Induction Chemotherapy in Patients with Pediatric Acute Lymphoblastic Leukemia Using Common Data Model-Based Real-World Data: A Retrospective Observational Study

Background

Rapid reduction of leukemic cells in the bone marrow during remission induction chemotherapy (RIC) can lead to significant complications such as tumor lysis syndrome (TLS). We investigated whether prephase steroid treatment before RIC could decrease TLS incidence and improve overall survival in pediatric patients with acute lymphoblastic leukemia (ALL).

Methods

Data were extracted from the Common Data Model databases in two tertiary-care hospitals in Seoul, South Korea. Patients were classified into the treated or untreated group if they had received RIC with prephase steroid treatment ≥7 days before RIC in 2012-2021 or not, respectively. Stabilized Inverse Probability of Treatment Weighting (sIPTW) was applied to ensure compatibility between the treated and untreated groups. The incidence of TLS within 14 days of starting RIC, overall survival (OS), and the incidence of adverse events of special interest were the primary endpoints. Multiple sensitivity analyses were performed.

Results

Baseline characteristics were effectively balanced between the treated (n=308.4) and untreated (n=246.6) groups after sIPTW. Prephase steroid treatment was associated with a significant 88% reduction in the risk of TLS (OR 0.12, 95% CI: 0.03-0.41). OS was numerically greater in the treated group than in the untreated group although the difference was not statistically significant (HR 0.64, 95% CI 0.25-1.64). The treated group experienced significantly elevated risks for hyperbilirubinemia and hyperglycemia. The reduction in TLS risk by prephase steroid treatment was maintained in all of the sensitivity analyses.

Conclusion

Prephase steroid treatment for ≥7 days before RIC in pediatric patients with ALL reduces the risk of TLS, while careful monitoring for toxicities is necessary. If adequately analyzed, real-world data can provide crucial effectiveness and safety information for proper management of pediatric patients with ALL, for whom prospective randomized studies may be difficult to perform for ethical and practical reasons.

Pacing-induced cardiomyopathy in patients with preserved ejection fraction undergoing permanent cardiac pacemaker placement

BACKGROUND

Chronic right-ventricular (RV) pacing can worsen heart failure in patients with a low ejection fraction (EF), but little is known about pacing-induced cardiomyopathy (PICM) in patients with preserved EF. We aimed to investigate risk factors of PICM in these patients during long-term follow-up.

METHODS

The prospective registry at Chosun University Hospital, South Korea, included de novo patients with preserved EF undergoing transvenous permanent pacemaker (PPM) implantation for atrioventricular blockage from 2017 to 2021. Patients with EF ≥ 50% and expected ventricular pacing ≥ 40% were included. Composite outcomes were cardiac death (pump failure), hospitalization because of heart failure, PICM, and biventricular pacing (BVP) upgrade.

RESULTS

A total of 168 patients (69 men, 76.3 ± 10.4 years) were included. During three years of follow-up, one patient died, 14 were hospitalized, 16 suffered PICM, and two underwent BVP upgrade. PICM were associated with reduced global longitudinal strain (GLS), prolonged paced QRS duration (pQRSd) and diastolic variables (E/e', LAVI). Cox regression analysis identified pQRSd (hazard ratio [HR], 1.111; 95% confidence interval [CI], 1.011-1.222; P = 0.03) and reduced GLS (HR, 1.569; 95% CI, 1.163-2.118; P = 0.003) as independent predictors of PICM. GLS showed high predictive accuracy for PICM, with an area under the curve of 0.84 (95% CI 0.779-0.894; P < 0.001) [GLS -12.0, 62.5% sensitivity, and 86.1% specificity].

CONCLUSION

RV pacing increased the risk of PICM in patients with preserved EF. Reduced GLS and prolonged pQRSd could help identify individuals at high risk of PICM even with preserved EF.

Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

Developing a Cost-Effectiveness Model of Digital Therapeutics for Smoking Cessation

The purpose of this research was to construct a Markov model of digital therapeutics to predict the lifetime costs and consequences that would be incurred by a hypothetical group of adult smokers in Korea who only made a single attempt to stop smoking. To determine the efficacy of DTx, we created an annual cycle Markov model. The result shows that the NRT strategy is determined as the dominant strategy. Digital therapeutics acts as a complement to pharmacotherapy and is a low-cost option.

Development of depression detection algorithm using text scripts of routine psychiatric interview

Background

A psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient's medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient's emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients.

Methods

Seventy-seven clinical patients [with depression (n = 60); without depression (n = 17)] with a prior psychiatric diagnosis history participated in this study. The study was conducted with 24 male and 53 female subjects with the mean age of 33.8 (± 3.0). Psychiatrists conducted a conversational interview with each patient that lasted at least 30 min. All interviews with the subjects between August 2021 and November 2022 were recorded and transcribed into text scripts, and a text emotion recognition module was used to indicate the subject's representative emotions of each sentence. A machine learning algorithm discriminates patients with depression and those without depression based on text scripts.

Results

A machine learning model classified text scripts from depressive patients with non-depressive ones with an acceptable accuracy rate (AUC of 0.85). The distribution of emotions (surprise, fear, anger, love, sadness, disgust, neutral, and happiness) was significantly different between patients with depression and those without depression (p < 0.001), and the most contributing emotion in classifying the two groups was disgust (p < 0.001).

Conclusion

This is a qualitative and retrospective study to develop a tool to detect depression against patients without depression based on the text scripts of psychiatric interview, suggesting a novel and practical approach to understand the emotional characteristics of depression patients and to use them to detect the diagnosis of depression based on machine learning methods. This model could assist psychiatrists in clinical settings who conduct routine conversations with patients using text transcripts of the interviews.

Investigation of hepatic adverse events due to quetiapine by using the common data model

PURPOSE

Quetiapine is a drug used to treat schizophrenia, bipolar disorder, and major depressive disorder. However, it can cause mild or severe hepatic adverse events and rarely fatal liver damage. This study was aimed at investigating hepatic toxicity caused by quetiapine use by analyzing the information captured from hospital electronic health records by using the Observational Medical Outcomes Partnership common data model (CDM).

METHODS

This was a retrospective observational study involving a nested case-control method. A CDM based on an electronic health record database from five hospitals between January 2009 and May 2020 was used. We analyzed the status of quetiapine use, adverse events, and hepatic impairment.

RESULTS

The numbers of patients with non-serious and severe hepatic adverse reactions were 2566 (5.05%) and 835 (1.64%) out of 50 766 patients, respectively. After adjusting for covariates, the odds ratio of hepatic adverse events was 2.35 (95% CI: 2.03-2.72), and the odds ratio of severe hepatic adverse events was 1.76 (95% CI: 1.16-2.66).

CONCLUSION

Our findings suggest that quetiapine should be cautiously used, and hepatic function should be monitored in patients using quetiapine because it can cause mild or severe hepatic adverse events, complications, and in rare cases, fatal liver damage.

Healthcare data quality assessment for improving the quality of the Korea Biobank Network

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution's characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution's IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

Bioluminescent monitoring of recombinant lactic acid bacteria and their products

IMPORTANCE

Lactic acid bacteria constitute a genetically diverse group of microorganisms with significant roles in the food industry, biotechnology, agriculture, and medicine. A core understanding of bacterial physiology in diverse environments is crucial to select and develop bacteria for industrial and medical applications. However, there is a lack of versatile tools to track (recombinant) protein production in lactic acid bacteria. In this study, we adapted a peptide-based bioluminescent tagging system that is functional across multiple genera and species. This system enables tracking of tagged proteins both in vitro and in situ, while it also can be used to enumerate recombinant bacteria from the mouse gastrointestinal tract with accuracy comparable to that of conventional plate counts. Our work expands the lactic acid bacteria genetic toolbox and will facilitate researchers in industry and academia with opportunities to monitor microbes and proteins under different physiologically relevant conditions.

Association between HbA1c and hearing loss: a tertiary care center-based study

The purpose of this study was to investigate the correlation between glycated hemoglobin (HbA1c) levels and hearing loss (HL) using data from a tertiary hospital. Our hypothesis regarding the relationship between HL and HbA1c levels was that elevated HbA1c levels are associated with an increased risk of HL. We retrospectively reviewed the medical charts of patients diagnosed with sensorineural HL or diabetes between 2006 and 2021 at the Catholic Medical Center (CMC). Data were collected from the CMC's Clinical Data Warehouse. Participants were selected from patients who were prescribed pure-tone audiometry and an HbA1c blood test. The survey was completed for 5287 participants. The better ear pure-tone audiometry (PTA) for air conduction thresholds at 500, 1000, 2000, and 4000 Hz was calculated. Sensorineural HL was defined as a better ear PTA of 25 dB or higher. We used the HbA1c level as a diagnostic criterion for diabetes. The following criteria were used to define the HbA1c level: normal, HbA1c level below 5.6%; prediabetes, level between 5.6 and 6.4%; and diabetes, level of 6.5% or more. Among 5287 participants, 1129 were categorized as normal, 2119 as prediabetic, and 2039 as diabetic. The diabetic group was significantly older (p < 0.05). The PTA also significantly deteriorated in the diabetes group (p < 0.05). We analyzed the effects of age, sex, and HbA1c level on frequency-specific hearing using multiple regression. The hearing thresholds at all frequencies deteriorated significantly with increasing age and HbA1c level (p < 0.05). A case-control study was also performed to facilitate a comprehensive comparison between distinct groups. The participants were categorized into two groups: a case (PTA > 25 dB) and control group (PTA ≤ 25 dB), based on their PTA threshold of four frequencies. After adjusting for age and sex, we found no significant odds ratio (OR) of HL between the prediabetes group and the normal group. Notably, the OR of HL was significantly higher in the diabetes group with each PTA threshold and frequency. The 6.3% HbA1c level cutoff value was determined by analyzing the receiver operating characteristic curve for predicting hearing impairment > 25 dB. Diabetes was associated with hearing loss in all frequency ranges, particularly at high frequencies. Screening for HL is strongly recommended for patients with elevated HbA1c levels.

Peripheral Neuron-Organoid Interaction Induces Colonic Epithelial Differentiation via Non-Synaptic Substance P Secretion

Background and Objectives

The colonic epithelial layer is a complex structure consisting of multiple cell types that regulate various aspects of colonic physiology, yet the mechanisms underlying epithelial cell differentiation during development remain unclear. Organoids have emerged as a promising model for investigating organogenesis, but achieving organ-like cell configurations within colonic organoids is challenging. Here, we investigated the biological significance of peripheral neurons in the formation of colonic organoids.

Methods and Results

Colonic organoids were co-cultured with human embryonic stem cell (hESC)-derived peripheral neurons, resulting in the morphological maturation of columnar epithelial cells, as well as the presence of enterochromaffin cells. Substance P released from immature peripheral neurons played a critical role in the development of colonic epithelial cells. These findings highlight the vital role of inter-organ interactions in organoid development and provide insights into colonic epithelial cell differentiation mechanisms.

Conclusions

Our results suggest that the peripheral nervous system may have a significant role in the development of colonic epithelial cells, which could have important implications for future studies of organogenesis and disease modeling.

Development and Application of an Active Pharmacovigilance Framework Based on Electronic Healthcare Records from Multiple Centers in Korea

INTRODUCTION

With the availability of retrospective pharmacovigilance data, the common data model (CDM) has been identified as an efficient approach towards anonymized multicenter analysis; however, the establishment of a suitable model for individual medical systems and applications supporting their analysis is a challenge.

OBJECTIVE

The aim of this study was to construct a specialized Korean CDM (K-CDM) for pharmacovigilance systems based on a clinical scenario to detect adverse drug reactions (ADRs).

METHODS

De-identified patient records (n = 5,402,129) from 13 institutions were converted to the K-CDM. From 2005 to 2017, 37,698,535 visits, 39,910,849 conditions, 259,594,727 drug exposures, and 30,176,929 procedures were recorded. The K-CDM, which comprises three layers, is compatible with existing models and is potentially adaptable to extended clinical research. Local codes for electronic medical records (EMRs), including diagnosis, drug prescriptions, and procedures, were mapped using standard vocabulary. Distributed queries based on clinical scenarios were developed and applied to K-CDM through decentralized or distributed networks.

RESULTS

Meta-analysis of drug relative risk ratios from ten institutions revealed that non-steroidal anti-inflammatory drugs (NSAIDs) increased the risk of gastrointestinal hemorrhage by twofold compared with aspirin, and non-vitamin K anticoagulants decreased cerebrovascular bleeding risk by 0.18-fold compared with warfarin.

CONCLUSION

These results are similar to those from previous studies and are conducive for new research, thereby demonstrating the feasibility of K-CDM for pharmacovigilance. However, the low quality of original EMR data, incomplete mapping, and heterogeneity between institutions reduced the validity of the analysis, thus necessitating continuous calibration among researchers, clinicians, and the government.