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

Among the most common genetic alterations in the 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 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 co-mutation pattern, and a lack of the favorable survival seen with other SF3B1 mutations. Moreover, compared to other hotspot 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.

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.

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.

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.

Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet

This study aimed to develop a convolutional neural network (CNN) using the EfficientNet algorithm for the automated classification of acute appendicitis, acute diverticulitis, and normal appendix and to evaluate its diagnostic performance. We retrospectively enrolled 715 patients who underwent contrast-enhanced abdominopelvic computed tomography (CT). Of these, 246 patients had acute appendicitis, 254 had acute diverticulitis, and 215 had normal appendix. Training, validation, and test data were obtained from 4,078 CT images (1,959 acute appendicitis, 823 acute diverticulitis, and 1,296 normal appendix cases) using both single and serial (RGB [red, green, blue]) image methods. We augmented the training dataset to avoid training disturbances caused by unbalanced CT datasets. For classification of the normal appendix, the RGB serial image method showed a slightly higher sensitivity (89.66 vs. 87.89%; p = 0.244), accuracy (93.62% vs. 92.35%), and specificity (95.47% vs. 94.43%) than did the single image method. For the classification of acute diverticulitis, the RGB serial image method also yielded a slightly higher sensitivity (83.35 vs. 80.44%; p = 0.019), accuracy (93.48% vs. 92.15%), and specificity (96.04% vs. 95.12%) than the single image method. Moreover, the mean areas under the receiver operating characteristic curve (AUCs) were significantly higher for acute appendicitis (0.951 vs. 0.937; p < 0.0001), acute diverticulitis (0.972 vs. 0.963; p = 0.0025), and normal appendix (0.979 vs. 0.972; p = 0.0101) with the RGB serial image method than those obtained by the single method for each condition. Thus, acute appendicitis, acute diverticulitis, and normal appendix could be accurately distinguished on CT images by our model, particularly when using the RGB serial image method.

Data Quality Assessment for Observational Medical Outcomes Partnership Common Data Model of Multi-Center

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.

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

Among the most common genetic alterations in the myelodysplastic syndromes (MDS) are mutations in the spliceosome gene SF3B1. Such mutations induce specific RNA missplicing events, directly promote ring sideroblast (RS) formation, generally associate with more favorable prognosis, and serve as a predictive biomarker of response to luspatercept. However, not all SF3B1 mutations are the same, and 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 co-mutation pattern, and decreased survival. Moreover, in contrast to canonical 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 expand our knowledge of the functional diversity of spliceosome mutations, and they suggest that patients with E592K should be approached differently from low-risk, luspatercept-responsive MDS patients with ring sideroblasts and canonical SF3B1 mutations.

Abstract 1831: A long-acting and CD122-enhanced IL-2 analog, HM16390, synergizes with immune checkpoint inhibitor by remodeling an immune cell profile in tumor microenvironment

Immune checkpoint inhibitors (CPI) are widely used for cancer immunotherapy. However, the response to CPIs is dependent on phenotype of tumor microenvironment (TME). A cold tumor known as immune-excluded or -desert has shown poor response to CPIs due to an absence of effector T cells in TME. IL-2 analog, which is immune stimulator and able to recruit cancer-fighting cells into TME, may be promising therapeutic partner for overcoming a limitation of CPIs. Previously, HM16390, a long-acting CD-122-enhanced IL-2 analog, exerted dose-dependent anti-tumor activity in low immunogenic B16F10 melanoma mice. Here, we further investigated the immune cells composition in TME following HM16390 treatment and synergistic anti-tumor effect after combination with CPI. B16F10 mice were sacrificed at days 1, 3, and 8 following single subcutaneous administration of HM16390 or 5 consecutive daily administrations of aldesleukin. According to the result, HM16390 transiently increased peripheral cytokines (IFNγ and TNFα) more than aldesleukin. In line with this, we observed that the tumor-infiltrating NK/Treg ratio was significantly increased approximately 19 by treatment of HM16390 while showing 4.2 in aldesleukin treated group at Day 3. Furthermore, tumor-infiltrating CD8+/Treg ratio was also upregulated approximately 74 by treatment of HM16390 while only showing 6.1 in aldesleukin treated group at Day 8. Significantly increased pro-inflammatory molecules such as GrzB and IFNγ were also observed in HM16390 treated group compared to aldesleukin (p&lt;0.01 and p&lt;0.001, respectively). Next, we investigated synergistic anti-tumor effects in combination with CPI. B16F10 mice were repeatedly given HM16390 once a week or aldesleukin 5 consecutive days per week with or without mouse anti-PD1. After four weeks treatment, 25% of B16F10 mice showed complete response by treatment of HM16390, and significantly increased up to 88% by combination with anti-PD1. However, none of the mice showed complete response by treatment of aldesleukin/anti-PD1 combo. In conclusion, HM16390 effectively induced tumor growth inhibition through the activation and tumor infiltration of cytotoxic lymphocytes. This favorable immune alteration in tumor elicited a TME remodeling in which CPI could sufficiently respond.

Citation Format: Jaehyuk Choi, Jinyoung Kim, Yu Yon Kim, Seongju Jeong, Sungmin Bae, Daejin Kim, In Young Choi. A long-acting and CD122-enhanced IL-2 analog, HM16390, synergizes with immune checkpoint inhibitor by remodeling an immune cell profile in tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1831.

Abstract 1814: A long-acting and CD122-enhanced IL-2 analog, HM16390, shows a potent and durable anti-tumor effect in both syngeneic B16F10 or CT26 mouse models

Although recombinant IL-2 (aldesleukin) was approved for the treatment of metastatic renal cell carcinoma and melanoma, its suboptimal ligand binding affinity required high-dose administrations, resulting in dose-limiting toxicity such as vascular leak syndrome. To overcome the limitation of the current IL-2 therapy, a number of pharmaceutical companies have developed CD25 binding attenuated IL-2 muteins via various platform technology. These physical changes, however, accompanied a decrease in CD122-mediated signaling which was associated with cytotoxic lymphocytes expansion, leading to unsatisfied clinical consequences. In this context, we developed HM16390, a long-acting IL-2 analog with enhanced CD122 binding affinity to elicit potent anti-tumor efficacy. Furthermore, optimal binding affinity to CD25 was incorporated to prevent from unwanted toxicity. The pharmacokinetics in mice after single subcutaneous administration of HM16390 supported an extended duration of action (t1/2= 24.5 hr, bioavailability= 43.0%). To further investigate anti-tumor effect on various immunological conditions, syngeneic tumor mouse models, which are representing highly immunogenic colon cancer (CT26) and poorly immunogenic melanoma (B1F10), were chosen. After two weeks treatment in CT26 mice, complete response (CR) was observed in 89% and 100% of the mice by once weekly treatment of HM16390 at 2.1 and 8.5 mg/kg respectively, while only 22% of the mice showed CR by 5 consecutive day treatment per week of aldesleukin at 3.0 mg/kg. The cured mice had no evidence of relapse after tumor re-challenges at days 50 and 152. This immunological memory response against the CT26 tumor was demonstrated by significant increase in tumor-specific memory T cells in lymphoid organ and blood. Next, after four weeks treatment in B16F10 mice, once weekly treatment of HM16390 was well tolerated up to 42 mg/kg, and led to significant and dose-dependent retardation in tumor growth (vs syngeneic tumor vehicle) from 62.9% to 96.4% as well as dose-dependently prolonged median overall survival from 15 to 38 days (14 days in syngeneic tumor vehicle) at the dose range of 0.34 to 42 mg/kg. Of note, at doses ≥17 mg/kg, CR was observed in up to 22% of the mice. To sum up with the results, HM16390 has a dose-dependent and potent anti-tumor efficacy in murine models with the broad range of the immunogenic condition via CD122-enhanced IL-2 agonism.

Citation Format: Jinyoung Kim, Jaehyuk Choi, Yu Yon Kim, Seongju Jeong, Sang Hyun Park, Sungmin Bae, Daejin Kim, In Young Choi. A long-acting and CD122-enhanced IL-2 analog, HM16390, shows a potent and durable anti-tumor effect in both syngeneic B16F10 or CT26 mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1814.

Quality improvement of outpatient clinical chemistry tests through a novel middleware-laboratory information system solution

OBJECTIVES

Rapid and accurate laboratory tests are essential to support clinical decision-making. Despite the various efforts to control quality in the laboratory, our outpatient chemistry turnaround time (TAT) has deteriorated since 2018. Moreover, these difficulties have accelerated further due to the COVID-19 pandemic. Therefore, we aimed to improve laboratory work efficiency by identifying and eliminating the causes of reduced laboratory work efficiency.

DESIGN & METHODS

We surveyed to identify tasks that reduce work efficiency. Based on our survey, a new-concept of work assistance middleware linked to laboratory information system (LIS) was developed. The middleware supports test end-time prediction, automatic real-time TAT monitoring, and urgent test requests so that medical technologists can focus on their chemistry tests. The developed middleware was used for 6 months in laboratory and outpatient clinics, and its effectiveness was evaluated.

RESULTS

The median TAT for outpatient chemistry tests was reduced by 6.6 min, from 72.4 min to 65.8 min. And not only did the maximum TAT for the sample decrease from 353 min to 214 min, but the proportion of samples exceeding the TAT target (120 min) also decreased by 77%; from 2.00% in 2010 (1,905 out of 94,989 samples) to 0.46% in 2021 (453 out of 98,117 samples). 2,199 samples were urgently requested through middleware, and they were processed about 15% faster than other samples, effectively performing urgent tests. The test end-time prediction showed an error of 8.6 min in the evaluation using the MAE (Mean Absolute Error) index.

CONCLUSIONS

Through this study, the quality and efficiency of the laboratory were improved, and while reducing the workload of medical staff, it contributed to enhancing patient safety and satisfaction.

HM15912, a Novel Long-Acting Glucagon-Like Peptide-2 Analog, Improves Intestinal Growth and Absorption Capacity in a Male Rat Model of Short Bowel Syndrome

Extensive bowel resection caused by various diseases that affect the intestines, such as Crohn's disease, volvulus, and cancer, leads to short bowel syndrome (SBS). Teduglutide is the only approved glucagon-like peptide-2 (GLP-2) drug for SBS; however, it requires daily administration. A novel GLP-2 analog with a prolonged duration of action to reduce dosing frequency and promote a greater efficacy may provide patients with a better quality of life. In the present study, the sustained exposure of HM15912 was characterized in normal male rats. The efficacy of HM15912 on intestinal growth and absorption capacity was also evaluated in normal male mice, rats, and SBS rats. HM15912 exhibited a remarkably extended half-life (42.3 hours) compared with teduglutide (0.6 hours) in rats. Despite somewhat lower in vitro potency on GLP-2 receptor than human GLP-2 or teduglutide, this longer-lasting mode of action promotes HM15912 to be more effective in terms of small intestinal growth than existing GLP-2 analogs even with a less frequent dosing interval of as little as once a week in rodents, including SBS rats. Furthermore, the small intestinal weight was approximately doubled, and the D-xylose absorption was significantly increased after pre-treatment of existing GLP-2 analogs on the market or under clinical development followed by HM15912 in rodents. These results indicate that HM15912 possesses a significant small bowel trophic effect driven by continuously increased exposure, supporting that HM15912 may be a novel treatment option with greater efficacy and the longest dosing interval among existing GLP-2 analogs for SBS with intestinal failure. SIGNIFICANCE STATEMENT: HM15912, a novel long-acting glucagon-like peptide-2 (GLP-2) analog, has a significant small bowel hypertrophic effect in rodents with a reduced frequency of administration compared to the existing GLP-2 analogs on the market or currently under clinical development. This study supports the possibility that HM15912 could be administered much less frequently than other long-acting GLP-2 analogs for patients with short bowel syndrome.

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

We sought to establish an unsupervised algorithm with a three-dimensional (3D) variational autoencoder model (VAE) for the detection of abnormal extraocular muscles in small datasets of orbital computed tomography (CT) images. 334 CT images of normal orbits and 96 of abnormal orbits diagnosed as thyroid eye disease were used for training and validation; 24 normal and 11 abnormal orbits were used for the test. A 3D VAE was developed and trained. All images were preprocessed to emphasize extraocular muscles and to suppress background noise (e.g., high signal intensity from bones). The optimal cut-off value was identified through receiver operating characteristic (ROC) curve analysis. The ability of the model to detect muscles of abnormal size was assessed by visualization. The model achieved a sensitivity of 79.2%, specificity of 72.7%, accuracy of 77.1%, F1-score of 0.667, and AUROC of 0.801. Abnormal CT images correctly identified by the model showed differences in the reconstruction of extraocular muscles. The proposed model showed potential to detect abnormalities in extraocular muscles using a small dataset, similar to the diagnostic approach used by physicians. Unsupervised learning could serve as an alternative detection method for medical imaging studies in which annotation is difficult or impossible to perform.

Persistence or regression of prediabetes and coronary artery calcification among adults without diabetes

OBJECTIVE

The effect of changes in glycemic status on subclinical atherosclerosis is uncertain. We assessed the association of persistence, regression, or progression of prediabetes with coronary artery calcium score (CACS) as a measure of subclinical atherosclerosis.

DESIGN

A cross-sectional study, comprising 126 765 adults, and longitudinal sub-study, comprising 40 622 adults (with baseline and at least 1 follow-up computed tomography scan to assess changes in CACS), were undertaken.

METHODS

Changes in glycemic status over 1.5 years (interquartile range, 1.0-2.0) before the first CACS assessment were categorized according to 6 groups: persistent normoglycemia (reference), normoglycemia to prediabetes, normoglycemia to diabetes, prediabetes to normoglycemia, persistent prediabetes, and prediabetes to diabetes. Logistic regression was used to calculate the odds ratios (ORs) and 95% CIs for prevalent coronary artery calcification (CAC). Mixed models with random intercepts and random slopes were used to estimate 5-year CAC progression rates.

RESULTS

Mean (SD) age was 41.3 (7.0) years (74.7% male) (n = 126 765). Multivariable-adjusted OR for prevalent CAC was 1.13 (95% CI, 1.08-1.18) for persistent prediabetes, 1.05 (0.98-1.12) for regression to normoglycemia, and 1.46 (95% CI, 1.27-1.67) for progression from prediabetes to diabetes, compared with persistent normoglycemia. Coronary artery calcification progression increased significantly in all prediabetes groups. Multivariable-adjusted ratio of 5-year CAC progression rates was 1.19 (95% CI, 1.16-1.22) (persistent prediabetes), 1.11 (1.07-1.14) (regression to normoglycemia), and 1.63 (95% CI, 1.26-2.10) (progression from prediabetes to diabetes).

CONCLUSIONS

Unfavorable changes in glycemic status, including persistence of prediabetes or progression to diabetes from prediabetes, were associated with increased risk of CAC.

Initial Experience with Left Bundle Branch Area Pacing in Patients with Atrioventricular Block and Impaired LV Function

Chronic right ventricular (RV) pacing can exacerbate heart failure in patients with a low left ventricular ejection fraction (LVEF). Left bundle branch area pacing (LBBAP) has emerged as a novel physiological pacing technique; however, information remains limited on its use among patients with a low EF. This study investigated the safety and short-term clinical outcomes of LBBAP among patients with impaired left ventricular (LV) function. This retrospective analysis of pacemakers at Chosun University Hospital, South Korea, included all patients with impaired LV function (EF<50%) who underwent pacemaker implantation for atrioventricular blockage from 2019-2022. Clinical characteristics, 12-lead electrocardiography findings, echocardiography findings, and laboratory parameters were evaluated. Composite outcomes were defined as all-cause mortality, cardiac death, and hospitalization due to heart failure during the 6-month follow-up. Altogether 57 patients (25 men; mean age, 77.4±10.8 y; LVEF, 41.5±3.8%) were divided into LBBAP (n=16), biventricular pacing (BVP; n=16), and conventional RV pacing (RVP; n=25) groups. In the LBBAP group, the mean paced QRS duration (pQRSd) was narrower (119.5±14.7 vs. 140.2±14.3 vs. 163.2±13.9; p<0.001) and cardiac troponin I level was elevated post-pacing (1.14±1.29 vs. 0.20±0.29 vs. 0.24±0.51, p=0.001). Lead parameters were stable. One patient was hospitalized, and four died (one patient each from heart failure admission, myocardial infarction, unexplained death, and pneumonia in RVP vs. one from intracerebral hemorrhage in BVP) during the follow-up period. In conclusion, LBBAP is feasible in patients with impaired LV function without acute or significant complications and provides a remarkably narrower pQRSd with a stable pacing threshold.

Engineering Skeletal Muscle Grafts with PAX7::GFP-Sorted Human Pluripotent Stem Cell-Derived Myogenic Progenitors on Fibrin Microfiber Bundles for Tissue Regeneration

Tissue engineering strategies that combine human pluripotent stem cell-derived myogenic progenitors (hPDMs) with advanced biomaterials provide promising tools for engineering 3D skeletal muscle grafts to model tissue development in vitro and promote muscle regeneration in vivo. We recently demonstrated (i) the potential for obtaining large numbers of hPDMs using a combination of two small molecules without the overexpression of transgenes and (ii) the application of electrospun fibrin microfiber bundles for functional skeletal muscle restoration following volumetric muscle loss. In this study, we aimed to demonstrate that the biophysical cues provided by the fibrin microfiber bundles induce hPDMs to form engineered human skeletal muscle grafts containing multinucleated myotubes that express desmin and myosin heavy chains and that these grafts could promote regeneration following skeletal muscle injuries. We tested a genetic PAX7 reporter line (PAX7::GFP) to sort for more homogenous populations of hPDMs. RNA sequencing and gene set enrichment analyses confirmed that PAX7::GFP-sorted hPDMs exhibited high expression of myogenic genes. We tested engineered human skeletal muscle grafts derived from PAX7::GFP-sorted hPDMs within in vivo skeletal muscle defects by assessing myogenesis, engraftment and immunogenicity using immunohistochemical staining. The PAX7::GFP-sorted groups had moderately high vascular infiltration and more implanted cell association with embryonic myosin heavy chain (eMHC) regions, suggesting they induced pro-regenerative microenvironments. These findings demonstrated the promise for the use of PAX7::GFP-sorted hPDMs on fibrin microfiber bundles and provided some insights for improving the cell-biomaterial system to stimulate more robust in vivo skeletal muscle regeneration.

Prediabetes diagnosis is associated with the progression of coronary artery calcification: The Kangbuk Samsung Health Study

AIM

To investigate the associations between prediabetes defined by different diagnostic criteria and coronary artery calcification (CAC) and its progression over time.

MATERIALS AND METHODS

This cross-sectional study included 146 436 Korean adults without diabetes who underwent CAC estimation computed tomography (CT) during health examinations from 2011 to 2019. We used multinomial logistic regression models. The longitudinal study comprised 41 100 participants with at least one follow-up cardiac CT and annual CAC progression rates and ratios were estimated. Prediabetes was categorized into three groups: isolated glucose prediabetes (fasting blood glucose [FBG] 100-125 mg/dl, HbA1c < 5.7%), isolated HbA1c prediabetes (FBG < 100 mg/dl, HbA1c 5.7%-6.4%) and prediabetes meeting both FBG and HbA1c criteria (FBG 100-125 mg/dl, HbA1c 5.7%-6.4%).

RESULTS

After adjusting for covariates, the prevalence ratios (95% CI) for CAC scores of more than 100 comparing isolated glucose prediabetes, isolated HbA1c prediabetes and prediabetes fulfilling both criteria with those of normoglycaemia were 1.12 (0.99-1.26), 1.24 (1.11-1.39) and 1.31 (1.18-1.45), respectively. The multivariable-adjusted ratio (CIs) of annual CAC progression rates comparing the corresponding groups with the normoglycaemia group were 1.031 (1.023-1.039), 1.025 (1.019-1.032) and 1.054 (1.047-1.062), respectively.

CONCLUSIONS

CAC risk and CAC progression were consistently highest in individuals meeting both glucose and HbA1c criteria, while all three prediabetes types showed a significantly increased risk of CAC progression. Atherosclerosis risk reduction management is necessary for prediabetes, especially in patients meeting both criteria.

A novel glucagon analog with an extended half-life, HM15136, normalizes glucose levels in rodent models of congenital hyperinsulinism

Congenital hyperinsulinism (CHI) is a rare genetic condition characterized by uncontrolled insulin secretion, resulting in hypoglycemia. Although glucagon has lately been regarded as a therapeutic option for CHI, its use is severely hampered by its poor solubility and stability at physiological pH, as well as its short duration of action. To address these constraints, we developed HM15136, a novel long-acting glucagon analog composed of a glucagon analog conjugated to the Fc fragment of human immunoglobulin G4 via a polyethylene glycol linker. In this study, we established that HM15136 was more soluble than natural glucagon (≥ 150 mg/mL vs 0.03 mg/mL). Next, we confirmed that HM15136 activated glucagon receptor in vitro and induced glycogenolysis and gluconeogenesis in rat primary hepatocytes. Pharmacokinetics (PK)/Pharmacodynamics (PD) analysis of HM15136 shows that HM15136 has a markedly longer half-life (36 h vs. < 5 min) and increased bioavailability (90%) compared to native glucagon in mice. Further, HM15136 could effectively reverse acute hypoglycemia induced by insulin challenge, and multiple doses of HM15136 could sustain increased blood glucose levels in CHI rats. In conclusion, our findings indicate that HM15136 promotes sustained elevation of blood glucose, demonstrating the potential for development as a once-weekly therapy for CHI.

Study of the factors influencing the use of MyData platform based on personal health record data sharing system

Background

The application of telemedicine and electronic health (eHealth) technology has grown in importance during the COVID-19 pandemic, and a new approach in personal data management and processing MyData, has emerged. Data portability and informational self-determination are fundamental concepts of MyData. This study analysed the factors that influence acceptance of the MyData platform, which, reflects the right to self-determine personal data.

Methods

The study involved participants having experience using the MyData platform, and the key factors of the unified theory of acceptance and use of technology were used in the research model (performance expectancy, effort expectancy, social influence, facilitation condition and behavioural intention to use). The questionnaire comprided 27 items, and system usage log data were used to confirm that behavioural intention to use affected actual use behaviour through structural equation modeling.

Results

In total, 1153 participants completed the survey. The goodness of fit in the structural equation model indices indicates that the data fit the research model well. Performance expectancy, social influence, and facilitating conditions had direct effects on behavioural intention to use. We used system usage log data to confirm that behavioural intention to use positively affected actual use behaviour. The impact of the main factors in the unified theory of acceptance and use of technology was not moderated by age or gender, except for performance expectancy.

Conclusions

This study is the first to examine the factors influencing the use of the MyData platform based on the personal health record data sharing system in Korea. In addition, the study confirmed the use behaviour of the MyData platform utilising the system’s actual usage log for each function and analysing the effect of the intention of use on actual use. Our study serves as a significant foundation for the acceptance of data portability and sharing concepts. It also lays the foundation for expanding the data economy and ecosystem in the pandemic era.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01929-z.

The Importance of Metabolic Syndrome Status for the Risk of Non-Viral Hepatocellular Carcinoma: A Nationwide Population-Based Study

The positive association between metabolic syndrome (MetS) and hepatocellular carcinoma (HCC) has been suggested. However, no studies have yet looked at how the risk of developing HCC varies with changes in MetS status. Therefore, we aimed to investigate the association between changes in MetS and subsequent HCC development. Data were obtained from the Korean National Health Insurance Service. In this study, 5,975,308 individuals who participated in health screenings both in 2009-2010 and 2011-2012 were included. Individuals with preexisting viral hepatitis, liver cirrhosis, or cancer diagnoses were excluded. Subjects were divided into four groups according to change in MetS status during the 2-year interval screening (from 2009 to 2011): sustained non-MetS, transition to MetS, transition to non-MetS, and sustained MetS. Cox regression analysis was used to examine the hazard ratios of HCC. The subjects were followed through December 31, 2018. During a median of 7.3 years of follow-up, 25,880 incident HCCs were identified. Compared to the sustained non-MetS group, age, sex, smoking, alcohol, regular exercise, and body mass index-adjusted hazard ratios (95% confidence interval) for HCC development were 1.01 (0.97-1.05) for the transition to MetS group, 1.05 (1.003-1.09) for the transition to non-MetS group, and 1.07 (1.03-1.10) for the sustained MetS group. Stratified analyses according to age, sex, smoking, alcohol intake, exercise, diabetes mellitus, hypertension, dyslipidemia, and chronic kidney disease showed similar results. A significantly increased HCC risk was observed in the sustained MetS and transition to non-MetS groups. The baseline status of MetS was associated with the risk of HCC development. Strategies to improve MetS, especially targeting insulin resistance, might prevent HCC development.

Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model

BACKGROUND

Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outcome Partnership Common Data Model (OMOP CDM), the information can contribute to establish multicenter observational cancer studies. To the best of our knowledge, there have been no studies on OMOP CDM transformation and natural language processing (NLP) for thyroid cancer to date.

OBJECTIVE

We aimed to demonstrate the applicability of the OMOP CDM oncology extension module for thyroid cancer diagnosis and cancer stage information by processing free-text medical reports.

METHODS

Thyroid cancer diagnosis and stage-related modifiers were extracted with rule-based NLP from 63,795 thyroid cancer pathology reports and 56,239 Iodine whole-body scan reports from three medical institutions in the Observational Health Data Sciences and Informatics data network. The data were converted into the OMOP CDM v6.0 according to the OMOP CDM oncology extension module. The cancer staging group was derived and populated using the transformed CDM data.

RESULTS

The extracted thyroid cancer data were completely converted into the OMOP CDM. The distributions of histopathological types of thyroid cancer were approximately 95.3 to 98.8% of papillary carcinoma, 0.9 to 3.7% of follicular carcinoma, 0.04 to 0.54% of adenocarcinoma, 0.17 to 0.81% of medullary carcinoma, and 0 to 0.3% of anaplastic carcinoma. Regarding cancer staging, stage-I thyroid cancer accounted for 55 to 64% of the cases, while stage III accounted for 24 to 26% of the cases. Stage-II and -IV thyroid cancers were detected at a low rate of 2 to 6%.

CONCLUSION

As a first study on OMOP CDM transformation and NLP for thyroid cancer, this study will help other institutions to standardize thyroid cancer-specific data for retrospective observational research and participate in multicenter studies.

Plantamajoside Attenuates Neointima Formation via Upregulation of Tissue Inhibitor of Metalloproteinases in Balloon-Injured Rats

The abnormal change of vascular smooth muscle cell (VSMC) behavior is an important cellular event leading to neointimal hyperplasia in atherosclerosis and restenosis. Plantamajoside (PMS), a phenylethanoid glycoside compound of the Plantago asiatica, has been reported to have anti-inflammatory, antioxidative, and anticancer activities. In this study, the protective effects of PMS against intimal hyperplasia and the mechanisms underlying the regulation of VSMC behavior were investigated. MTT and BrdU assays were performed to evaluate the cytotoxicity and cell proliferative activity of PMS, respectively. Rat aortic VSMC migrations after treatment with the determined concentration of PMS (50 and 150 μM) were evaluated using wound healing and Boyden chamber assays. The inhibitory effects of PMS on intimal hyperplasia were evaluated in balloon-injured (BI) rat carotid artery. PMS suppressed the proliferation in platelet-derived growth factor-BB-induced VSMC, as confirmed from the decrease in cyclin-dependent kinase (CDK)-2, CDK-4, cyclin D1, and proliferating cell nuclear antigen levels. PMS also inhibited VSMC migration, consistent with the downregulated expression and zymolytic activities of matrix metalloproteinase (MMP)2, MMP9, and MMP13. PMS specifically regulated MMP expression through p38 mitogen-activated protein kinase and focal adhesion kinase pathways. Tissue inhibitor of metalloproteinase (TIMP)1 and TIMP2 levels were upregulated via Smad1. TIMPs inhibited the conversion of pro-MMPs to active MMPs. PMS significantly inhibited neointimal formation in BI rat carotid arteries. In conclusion, PMS inhibits VSMC proliferation and migration by upregulating TIMP1 and TIMP2 expression. Therefore, PMS could be a potential therapeutic agent for vascular atherosclerosis and restenosis treatment.

Human pluripotent stem cell-derived myogenic progenitors undergo maturation to quiescent satellite cells upon engraftment

Human pluripotent stem cell (hPSC)-derived myogenic progenitor cell (MPC) transplantation is a promising therapeutic approach for a variety of degenerative muscle disorders. Here, using an MPC-specific fluorescent reporter system (PAX7::GFP), we demonstrate that hPSC-derived MPCs can contribute to the regeneration of myofibers in mice following local injury and in mice deficient of dystrophin (mdx). We also demonstrate that a subset of PAX7::GFP MPCs engraft within the basal lamina of regenerated myofibers, adopt a quiescent state, and contribute to regeneration upon reinjury and in mdx mouse models. This subset of PAX7::GFP MPCs undergo a maturation process and remodel their molecular characteristics to resemble those of late-stage fetal MPCs/adult satellite cells following in vivo engraftment. These in-vivo-matured PAX7::GFP MPCs retain a cell-autonomous ability to regenerate and can repopulate in the niche of secondary recipient mice, providing a proof of principle for future hPSC-based cell therapy for muscle disorders.

Second primary malignancy risk in thyroid cancer and matched patients with and without radioiodine therapy analysis from the observational health data sciences and informatics

PURPOSE

Risk of second primary malignancy (SPM) after radioiodine (RAI) therapy has been continuously debated. The aim of this study is to identify the risk of SPM in thyroid cancer (TC) patients with RAI compared with TC patients without RAI from matched cohort.

METHODS

Retrospective propensity-matched cohorts were constructed across 4 hospitals in South Korea via the Observational Health Data Science and Informatics (OHDSI), and electrical health records were converted to data of common data model. TC patients who received RAI therapy constituted the target group, whereas TC patients without RAI therapy constituted the comparative group with 1:1 propensity score matching. Hazard ratio (HR) by Cox proportional hazard model was used to estimate the risk of SPM, and meta-analysis was performed to pool the HRs.

RESULTS

Among a total of 24,318 patients, 5,374 patients from each group were analyzed (mean age 48.9 and 49.2, women 79.4% and 79.5% for target and comparative group, respectively). All hazard ratios of SPM in TC patients with RAI therapy were ≤ 1 based on 95% confidence interval(CI) from full or subgroup analyses according to thyroid cancer stage, time-at-risk period, SPM subtype (hematologic or non-hematologic), and initial age (< 30 years or ≥ 30 years). The HR within the target group was not significantly higher (< 1) in patients who received over 3.7 GBq of I-131 compared with patients who received less than 3.7 GBq of I-131 based on 95% CI.

CONCLUSION

There was no significant difference of the SPM risk between TC patients treated with I-131 and propensity-matched TC patients without I-131 therapy.

Development and evaluation of smartphone usage management system for preventing problematic smartphone use

Objective

The increased use of smartphones has led to several problems, including excessive smartphone use and the decreased self-ability to control smartphone use. To prevent these problems, the MindsCare app was developed as a method of self-management and intervention based on an evaluation of smartphone usage. We designed the MindsCare app to manage smartphone usage and prevent problematic smartphone use by providing personalized interventions.

Methods

We recruited 342 Korean participants over the age of 20 and asked them to use MindsCare for 13 weeks. Subsequently, we evaluated the changes in average smartphone usage time and the usability of the app. We designed a usability evaluation questionnaire based on the Technology Acceptance Model and conducted factor and reliability analyses on the participants’ responses. In the eighth week of the study, participants responded to a survey on the usability of the app. We ultimately collected data from 190 participants.

Results

The average score for the usability of the system was 3.61 on a five-point Likert scale, and approximately 58% of the participants responded positively to the evaluation items. In addition, our analysis of MindsCare data revealed a significant reduction in average smartphone use time in the eighth week compared to the baseline (t = 3.47, p = 0.001). Structural equation model analysis revealed that effort expectancy and performance expectancy had a positive relation with behavior intention for the app.

Conclusions

Through this study, we confirmed the MindsCare app's smartphone usage time reduction effect and proved its good usability. As a result, MindsCare may contribute to achieving users’ goals of reducing problematic smartphone use.

Altered connectivity in the right inferior frontal gyrus associated with self-control in adolescents exhibiting problematic smartphone use: A fMRI study

BACKGROUND

With the continued spread of smartphones and development of the internet, the potential negative effects arising from problematic smartphone use (PSU) in adolescents are being reported on an increasing basis. This study aimed to investigate whether altered resting-state functional connectivity (rsFC) is related to the psychological factors underlying PSU in adolescents.

METHODS

Resting-state functional magnetic resonance images were acquired from 47 adolescents with PSU and 46 healthy control adolescents (the CON group). Seed-based functional connectivity analyses were then performed to compare the two groups with respect to rsFC in the right inferior frontal gyrus, associated with various forms of self-control, and rsFC in the left inferior frontal gyrus.

RESULTS

Compared to the CON group, the PSU group exhibited a reduction in rsFC between the right inferior frontal gyrus and limbic areas, including the bilateral parahippocampal gyrus, the left amygdala, and the right hippocampus. In addition, a reduction in fronto-limbic rsFC was associated with the severity of PSU, the degree of self-control, and the amount of time the subjects used their smartphones.

CONCLUSION

Adolescents with PSU exhibited reduced levels of fronto-limbic functional connectivity; this mechanism is involved in salience attribution and self-control, attributes that are critical to the clinical manifestation of substance and behavioral addictions. Our data provide clear evidence for alterations in brain connectivity with respect to self-control in PSU.

Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image

BACKGROUND

Early detection of asbestosis is important; hence, quick and accurate diagnostic tools are essential. This study aimed to develop an algorithm that combines lung segmentation and deep learning models that can be utilized as a clinical decision support system (CDSS) for diagnosing patients with asbestosis in segmented computed tomography (CT) images.

METHODS

We accurately segmented the lungs in CT images of patients examined at Seoul St. Mary's Hospital using a threshold-based method. Lungs with asbestosis and normal lungs were classified by applying the segmented image to the long-term recurrent convolutional network deep learning model. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 score from the test data.

RESULTS

The algorithm developed using the DenseNet201pre-trained model showed excellent performance, with a sensitivity of 0.962, specificity of 0.975, accuracy of 0.970, AUROC of 0.968, and F1 score of 0.961.

CONCLUSIONS

We developed an algorithm with significantly better diagnostic accuracy than a radiologist (0.970 vs. 0.73-0.79). Our developed algorithm is expected to be an excellent support tool if used as a CDSS to diagnose asbestosis using CT images.

Association between the use of allopurinol and risk of increased thyroid-stimulating hormone level

Allopurinol is the first-line agent for patients with gout, including those with moderate-to-severe chronic kidney disease. However, increased thyroid-stimulating hormone (TSH) levels are observed in patients with long-term allopurinol treatment. This large-scale, nested case-control, retrospective observational study analysed the association between allopurinol use and increased TSH levels. A common data model based on an electronic medical record database of 19,200,973 patients from seven hospitals between January 1997 and September 2020 was used. Individuals aged > 19 years in South Korea with at least one record of a blood TSH test were included. Data of 59,307 cases with TSH levels > 4.5 mIU/L and 236,508 controls matched for sex, age (± 5), and cohort registration date (± 30 days) were analysed. An association between the risk of increased TSH and allopurinol use in participants from five hospitals was observed. A meta-analysis (I² = 0) showed that the OR was 1.51 (95% confidence interval: 1.32-1.72) in both the fixed and random effects models. The allopurinol intake group demonstrated that increased TSH did not significantly affect free thyroxine and thyroxine levels. After the index date, some diseases were likely to occur in patients with subclinical hypothyroidism and hypothyroidism. Allopurinol administration may induce subclinical hypothyroidism.

Association between obesity and the risk of gastric cancer in premenopausal and postmenopausal women: A nationwide cohort study

BACKGROUND AND AIM

Obesity was suggested to increase the incidence of gastric cancer (GC) in women, but results from previous studies were inconsistent. We investigated the relationship between obesity and the risk of GC according to menopausal status.

METHODS

We enrolled 1 418 180 premenopausal and 4 854 187 postmenopausal women aged ≥ 40 years using the Korean National Health Insurance System Cohort (2009-2014). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for GC incidence according to body mass index (BMI) and waist circumference (WC) using the Cox proportional hazards models.

RESULTS

During the mean follow-up period of 7.2 years, 42 441 women were newly diagnosed with GC. Compared with the group with BMI 18.5-22.9 kg/m² , the adjusted HRs (95% CIs) for GC in the groups with BMI < 18.5, 23-24.9, 25-29.9, and ≥ 30 kg/m² were 1.12 (0.95-1.33), 0.96 (0.89-1.04), 1.02 (0.94-1.11), and 0.99 (0.83-1.18), respectively, for premenopausal women and 1.07 (1.00-1.14), 1.01 (0.99-1.04), 1.03 (1.00-1.05), and 1.11 (1.10-1.16), respectively, for postmenopausal women. Compared with the group with WC 65-74.9 cm, the adjusted HRs (95% CIs) for GC for the groups with WC < 65, 75-84.9, 85-94.9, and ≥ 90 cm were 1.00 (0.88-1.15), 1.03 (0.96-1.11), 1.10 (0.99-1.22), and 1.02 (0.81-1.27), respectively, for premenopausal women and 1.01 (0.94-1.09), 1.01 (0.99-1.04), 1.09 (1.06-1.13), and 1.14 (1.09-1.19), respectively, for postmenopausal women.

CONCLUSIONS

We found a positive association between the highest BMI and WC category and risk of GC in postmenopausal women; however, such an association was not evident in premenopausal women.

Machine learning prediction of dropping out of outpatients with alcohol use disorders

BACKGROUND

Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes.

METHODS

A total of 839 patients were selected out of 2,206 patients admitted for AUD in three hospitals under the Catholic Central Medical Center in Korea. We implemented six ML models-logistic regression, support vector machine, k-nearest neighbor, random forest, neural network, and AdaBoost-and compared the prediction performances thereof.

RESULTS

Among the six models, AdaBoost was selected as the final model for recommended use owing to its area under the receiver operating characteristic curve (AUROC) of 0.72. The four variables affecting the prediction based on feature importance were the length of hospitalization, age, residential area, and diabetes.

CONCLUSION

An ML algorithm was developed herein to predict the risk of patients with AUD in Korea discontinuing outpatient treatment. By testing and validating various machine learning models, we determined the best performing model, AdaBoost, as the final model for recommended use. Using this model, clinicians can manage patients with high risks of discontinuing treatment and establish patient-specific treatment strategies. Therefore, our model can potentially enable patients with AUD to successfully complete their treatments by identifying them before they can drop out.

Safety Evaluation of Bifidobacterium breve IDCC4401 Isolated from Infant Feces for Use as a Commercial Probiotic

Previously, our research group isolated Bifidobacterium breve IDCC4401 from infant feces as a potential probiotic. For this study, we evaluated the safety of B. breve IDCC4401 using genomic and phenotypic analyses. Whole genome sequencing was performed to identify genomic characteristics and investigate the potential presence of genes encoding virulence, antibiotic resistance, and mobile genetic elements. Phenotypic analyses including antibiotic susceptibility, enzyme activity, production of biogenic amines (BAs), and proportion of D-/L-lactate were evaluated using E-test, API ZYM test, high-performance liquid chromatography (HPLC), and D-/L-lactic acid assay respectively. The genome of B. breve IDCC4401 consists of 2,426,499 bp with a GC content of 58.70% and 2,016 coding regions. Confirmation of the genome as B. breve was provided by its 98.93% similarity with B. breve DSM20213. Furthermore, B. breve IDCC4401 genes encoding virulence and antibiotic resistance were not identified. Although B. breve IDCC4401 showed antibiotic resistance against vancomycin, we confirmed that this was an intrinsic feature since the antibiotic resistance gene was not present. B. breve IDCC4401 showed leucine arylamidase, cystine arylamidase, α-galactosidase, β-galactosidase, and α-glucosidase activities, whereas it did not show production of harmful enzymes such as β-glucosidase and β-glucuronidase. In addition, B. breve IDCC4401 did not produce any tyramine, histamine, putrescine, cadaverine, or 2-phenethylamine, which are frequently detected BAs during fermentation. B. breve IDCC4401 produced 95.08% of L-lactate and 4.92% of Dlactate. Therefore, our findings demonstrate the safety of B. breve IDCC 4401 as a potential probiotic for use in the food industry.

Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI

Occupation ratio and fatty infiltration are important parameters for evaluating patients with rotator cuff tears. We analyzed the occupation ratio using a deep-learning framework and studied the fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. To calculate the amount of fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. The mean Dice similarity coefficient, accuracy, sensitivity, specificity, and relative area difference for the segmented lesion, measuring the similarity of clinician assessment and that of a deep neural network, were 0.97, 99.84, 96.89, 99.92, and 0.07, respectively, for the supraspinatus fossa and 0.94, 99.89, 93.34, 99.95, and 2.03, respectively, for the supraspinatus muscle. The fatty infiltration measure using the Otsu thresholding method significantly differed among the Goutallier grades (Grade 0; 0.06, Grade 1; 4.68, Grade 2; 20.10, Grade 3; 42.86, Grade 4; 55.79, p < 0.0001). The occupation ratio and fatty infiltration using Otsu thresholding demonstrated a moderate negative correlation (ρ = - 0.75, p < 0.0001). This study included 240 randomly selected patients who underwent shoulder magnetic resonance imaging (MRI) from January 2015 to December 2016. We used a fully convolutional deep-learning algorithm to quantitatively detect the fossa and muscle regions by measuring the occupation ratio of the supraspinatus muscle. Fatty infiltration was objectively evaluated using the Otsu thresholding method. The proposed convolutional neural network exhibited fast and accurate segmentation of the supraspinatus muscle and fossa from shoulder MRI, allowing automatic calculation of the occupation ratio. Quantitative evaluation using a modified Otsu thresholding method can be used to calculate the proportion of fatty infiltration in the supraspinatus muscle. We expect that this will improve the efficiency and objectivity of diagnoses by quantifying the index used for shoulder MRI.

Behavioral Intention to Use a Smartphone Usage Management Application Between a Non-Problematic Smartphone Use Group and a Problematic Use Group

Despite the many advantages of smartphone in daily life, there are significant concerns regarding their problematic use. Therefore, several smartphone usage management applications have been developed to prevent problematic smartphone use. The purpose of this study is to investigate the factors of users' behavioral intention to use smartphone usage management applications. Participants were divided into a smartphone use control group and a problematic use group to find significant intergroup path differences. The research model of this study is fundamentally based on the Technology Acceptance Model and Expectation-Confirmation Theory. Based on this theorem, models were modified to best suit the case of problematic smartphone use intervention by smartphone application. We conducted online surveys on 511 randomly selected smartphone users aged 20-60 in South Korea, in 2018. The Smartphone Addiction Proneness Scale was used to measure participants' smartphone dependency. Descriptive statistics were used for the demographic analysis and collected data were analyzed using IBM SPSS Statistics 24.0 and Amos 24.0. We found that in both non-problematic smartphone use group and problematic smartphone use group, facilitating factors and perceived security positively affect the intentions of users to use the application. One distinct difference between the groups was that the latter attributed a lower importance to perceived security than the former. Some of our highlighted unique points are envisioned to provide intensive insights for broadening knowledge about technology acceptance in the field of e-Addictology.

The Correlation Between Caffeine Intake and Brain Health in Cognitively Normal Healthy Older Adults

Objectives

Caffeine is associated with brain health, and it is suggested to lower the risk of neurological diseases. This cross-sectional study aimed to investigate the association between caffeine intake and concentration of brain metabolites in healthy older adults.

Methods

Caffeine intake was determined in 60 cognitively normal, healthy older adults aged between 60–85 years (61.9% women) using a 7-day food record (7D) that was collected seven days prior to their Magnetic Resonance scan. The 7D was entered in the Nutrition Data System for Research (NDS-R version 2012) for nutrient analysis. Brain metabolites [N-acetylaspartate, creatine, total choline, glutamate + glutamine, and myo-inositol (mI)] were measured using a 1H magnetic resonance spectroscopic imaging method at 3T. All metabolites were quantified using LCModel analysis software and concentrations are shown as a ratio to creatine. Measurements were presented as mean ± standard deviation and n(%). We assessed the relationship between caffeine intake and brain metabolite concentrations by multiple linear regression, adjusting for age and sex. Statistical analyses were performed using SPSS (v25, IBM) with significance of P ≤ 0.05.

Results

Participants had a mean age of 69.3 ± 7.3 years. Mean caffeine intake among the participants was 152.17 ± 133.79mg/d (range: 0–635.98mg/d). There was a significant correlation between caffeine and mI (β = 0.443; P = 0.001). The remaining metabolites were not correlated with caffeine intake.

Conclusions

Higher caffeine intake in older adults was associated with higher brain mI concentrations. The metabolic link between brain mI concentrations and neurological diseases is still unclear, thus, future studies are necessary regarding the mechanism for the impact of caffeine on brain metabolism.

Funding Sources

This study is supported by funding from National Dairy Council (IYC). The Hoglund Brain Imaging Center is supported by grants from the NIH (C76 HF00201, P30 HD002528, S10 RR29577, UL1 TR000001, and P30AG035982) and the Hoglund Family Foundation.