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Background: The exogenous delivery of miRNA to mimic and restore miRNA-34a activity in various cancer models holds significant promise in cancer treatment. Nevertheless, its effectiveness is often impeded by challenges, including a short half-life, propensity for off-target accumulation, susceptibility to inactivation by blood-based enzymes, concerns regarding patient safety, and the substantial cost associated with scaling up. As a means of overcoming these barriers, we propose the development of miRNA-loaded Tat-A86 nanoparticles by virtue of Tat-A86's ability to shield the loaded agent from external environmental factors, reducing degradation and inactivation, while enhancing circulation time and targeted accumulation., Results: Genetically engineered Tat-A86, featuring 16 copies of the interleukin-4 receptor (IL-4R)-binding peptide (AP1), Tat for tumor penetration, and an elastin-like polypeptide (ELP) for presenting target ligands and ensuring stability, served as the basis for this delivery system. Comparative groups, including Tat-E60 and A86, were employed to discern differences in binding and penetration. The designed ELP-based nanoparticle Tat-A86 effectively condensed miRNA, forming stable nanocomplexes under physiological conditions. The miRNA/Tat-A86 formulation bound specifically to tumor cells and facilitated stable miRNA delivery into them, effectively inhibiting tumor growth. The efficacy of miRNA/Tat-A86 was further evaluated using three-dimensional spheroids of lewis lung carcinoma (LLC) as in vitro model and LLC tumor-bearing mice as an in vivo model. It was found that miRNA/Tat-A86 facilitates effective cell killing by markedly improving miRNA penetration, leading to a substantial reduction in the size of LLC spheroids. Compared to other controls, Tat-A86 demonstrated superior efficacy in suppressing the growth of 3D cellular aggregates. Moreover, at equivalent doses, miRNA-34a delivered by Tat-A86 inhibited the growth of LLC cells in allograft mice., Conclusions: Overall, these studies demonstrate that Tat-A86 nanoparticles can deliver miRNA systemically, overcoming the basic hurdles impeding miRNA delivery by facilitating both miRNA uptake and stability, ultimately leading to improved therapeutic effects., (© 2024. The Author(s).)

Background: The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics., Methods: Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia., Results: 5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43])., Conclusions: The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years., (© 2024. The Author(s).)

Background: Postoperative delirium is a common complication that is distressing. This study aimed to demonstrate a prediction model for delirium., Methods: Among 203,374undergoing non-cardiac surgery between January 2011 and June 2019 at Samsung Medical Center, 2,865 (1.4%) were diagnosed with postoperative delirium. After comparing performances of machine learning algorithms, we chose variables for a prediction model based on an extreme gradient boosting algorithm. Using the top five variables, we generated a prediction model for delirium and conducted an external validation. The Kaplan-Meier and Cox survival analyses were used to analyse the difference of delirium occurrence in patients classified as a prediction model., Results: The top five variables selected for the postoperative delirium prediction model were age, operation duration, physical status classification, male sex, and surgical risk. An optimal probability threshold in this model was estimated to be 0.02. The area under the receiver operating characteristic (AUROC) curve was 0.870 with a 95% confidence interval of 0.855-0.885, and the sensitivity and specificity of the model were 0.76 and 0.84, respectively. In an external validation, the AUROC was 0.867 (0.845-0.877). In the survival analysis, delirium occurred more frequently in the group of patients predicted as delirium using an internal validation dataset (p < 0.001)., Conclusion: Based on machine learning techniques, we analyzed a prediction model of delirium in patients who underwent non-cardiac surgery. Screening for delirium based on the prediction model could improve postoperative care. The working model is provided online and is available for further verification among other populations., Trial Registration: KCT 0006363., (© 2023. The Author(s).)

Background: Acute appendicitis often presents with vague abdominal pain, which fosters diagnostic challenges to clinicians regarding early detection and proper intervention. This is even more problematic with individuals with severe psychiatric disorders who have reduced sensitivity to pain due to long-term or excessive medication use or disturbed bodily sensation perceptions. This study aimed to determine whether psychiatric disorder, psychotropic prescription, and treatment compliance increase the risks of complicated acute appendicitis., Methods: The diagnosis records of acute appendicitis from four university hospitals in Korea were investigated from 2002 to 2020. A total of 47,500 acute appendicitis-affected participants were divided into groups with complicated and uncomplicated appendicitis to determine whether any of the groups had more cases of psychiatric disorder diagnoses. Further, the ratio of complicated compared to uncomplicated appendicitis in the mentally ill group was calculated regarding psychotropic dose, prescription duration, and treatment compliance., Results: After adjusting for age and sex, presence of psychotic disorder (odds ratio [OR]: 1.951; 95% confidence interval [CI]: 1.218-3.125), and bipolar disorder (OR: 2.323; 95% CI: 1.194-4.520) was associated with a higher risk of having complicated appendicitis compared with absence of psychiatric disorders. Patients who are taking high-daily-dose antipsychotics, regardless of prescription duration, show high complicated appendicitis risks; High-dose antipsychotics for < 1 year (OR: 1.896, 95% CI: 1.077-3.338), high-dose antipsychotics for 1-5 years (OR: 1.930, 95% CI: 1.144-3.256). Poor psychiatric outpatient compliance was associated with a high risk of complicated appendicitis (OR: 1.664, 95% CI: 1.014-2.732)., Conclusions: This study revealed a close relationship in the possibility of complicated appendicitis in patients with severe psychiatric disorders, including psychotic and bipolar disorders. The effect on complicated appendicitis was more remarkable by the psychiatric disease entity itself than by psychotropic prescription patterns. Good treatment compliance and regular visit may reduce the morbidity of complicated appendicitis in patients with psychiatric disorders., (© 2022. The Author(s).)

Background: Methylphenidate (MPH) is the most frequently prescribed medication for the treatment of attention deficit hyperactivity disorder (ADHD). However, the safety of its long-term use remain unclear. In particular, real-world evidence of long-term MPH treatment regarding the risk of depression, conduct disorders, and psychotic disorders in children and adolescents is needed. This study aimed to compare the risks of depression, conduct disorder, and psychotic disorder between long- and short-term MPH treatments in children and adolescents., Methods: This population-based cohort study used a nationwide claims database of all patients with ADHD in South Korea. Patients aged less than 18 years who were prescribed MPH were included in the study. Long- and short-term MPH were defined as > 1 year, and < 1 year, respectively. Overall, the risk of developing depressive disorder, conduct disorder and oppositional defiant disorder (ODD), and psychotic disorder were investigated. A 1:2 propensity score matching was used to balance the cohorts, and the Cox proportional hazards model was used to evaluate the safety of MPH., Results: We identified 1309 long-term and 2199 short-term MPH users. Long-term MPH use was associated with a significantly lower risk of depressive (hazard ratio [HR], 0.70 [95% confidence interval [CI] 0.55-0.88]) and conduct disorders and ODD (HR, 0.52 [95% CI 0.38-0.73]) than short-term MPH use. Psychotic disorder was not significantly associated with long-term MPH use (hazard ratio [HR], 0.83 [95% confidence interval [CI] 0.52-1.32])., Conclusions: Our findings suggest that long-term MPH use may be associated with a decreased risk of depression, conduct disorders and ODD. Moreover, the long-term use of MPH does not increase the risk of psychotic disorders. Long-term MPH administration may be considered as a favourable treatment strategy for children and adolescents with ADHD regarding depressive, conduct, and psychotic disorders., (© 2022. The Author(s).)

Background: Statin treatment increases the risk of new-onset diabetes mellitus (NODM); however, data directly comparing the risk of NODM among individual statins is limited. We compared the risk of NODM between patients using pitavastatin and atorvastatin or rosuvastatin using reliable, large-scale data., Methods: Data of electronic health records from ten hospitals converted to the Observational Medical Outcomes Partnership Common Data Model (n = 14,605,368 patients) were used to identify new users of pitavastatin, atorvastatin, or rosuvastatin (atorvastatin + rosuvastatin) for ≥ 180 days without a previous history of diabetes or HbA1c level ≥ 5.7%. We conducted a cohort study using Cox regression analysis to examine the hazard ratio (HR) of NODM after propensity score matching (PSM) and then performed an aggregate meta-analysis of the HR., Results: After 1:2 PSM, 10,238 new pitavastatin users (15,998 person-years of follow-up) and 18,605 atorvastatin + rosuvastatin users (33,477 person-years of follow-up) were pooled from 10 databases. The meta-analysis of the HRs demonstrated that pitavastatin resulted in a significantly reduced risk of NODM than atorvastatin + rosuvastatin (HR 0.72; 95% CI 0.59-0.87). In sub-analysis, pitavastatin was associated with a lower risk of NODM than atorvastatin or rosuvastatin after 1:1 PSM (HR 0.69; CI 0.54-0.88 and HR 0.74; CI 0.55-0.99, respectively). A consistently low risk of NODM in pitavastatin users was observed when compared with low-to-moderate-intensity atorvastatin + rosuvastatin users (HR 0.78; CI 0.62-0.98)., Conclusions: In this retrospective, multicenter active-comparator, new-user, cohort study, pitavastatin reduced the risk of NODM compared with atorvastatin or rosuvastatin., (© 2022. The Author(s).)

Background: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients., Methods: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date., Results: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations., Conclusions: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use., (© 2022. The Author(s).)

Background: This study aimed to evaluate incidence risk and adverse clinical outcomes in COVID-19 disease among short-term users of acid-suppressants in South Korea., Methods: This retrospective cohort study, conducted using a nationwide claims database for South Korea, used data from patients with COVID-19 tested between January 1 and May 15, 2020. Patients aged over 18 years and prescribed proton pump inhibitors (PPI) or histamine-2 receptor antagonist (H 2 RA) for more than 7 days were identified. Primary outcome was COVID-19 while secondary outcomes were all-cause mortality, hospitalization with respiratory disease, or intensive respiratory intervention. Large-scale propensity scores were used to match patients, while the Cox proportional hazard model was utilized to evaluate any association between exposure and outcome(s). The risk estimates were calibrated by using 123 negative control outcomes., Results: We identified 26,166 PPI users and 62,117 H 2 RA users. After propensity score matching, compared to H 2 RA use, PPI use was not significantly associated with lower risk of COVID-19 (calibrated hazard ratio [HR], 0.81 [95% confidence interval (CI), 0.30-2.19]); moreover, PPI use was not associated with adverse clinical outcomes in COVID-19, namely, hospitalization with respiratory disease (calibrated HR, 0.88 [95% CI, 0.72-1.08]), intensive respiratory interventions (calibrated HR, 0.92 [95% CI, 0.46-1.82]), except for all-cause mortality (calibrated HR, 0.54 [95% CI, 0.31-0.95])., Conclusions: In this study, we found that the PPI user was not associated with risk of COVID-19 compared to H 2 RA users. There was no significant relationship between severe clinical outcomes of COVID-19 and exposure to PPI compared with H 2 RA, except for all-cause mortality., (© 2022. The Author(s).)

Background: To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets., Methods: Five previously published prognostic models (ATRIA, CHADS 2 , CHADS 2 VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites., Results: The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57-0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation., Conclusion: This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.

Background: The successful deliveries of siRNA depend on their stabilities under physiological conditions because greater in vivo stability enhances cellular uptake and enables endosomal escape. Viral-based systems appears as most efficient approaches for gene delivery but often compromised in terms of biocompatibility, patient safety and high cost scale up process. Here we describe a novel platform of gene delivery by elastin-like polypeptide (ELP) based targeting biopolymers., Results: For better tumor targeting and membrane penetrating characteristics, we designed various chimeric ELP-based carriers containing a cell penetrating peptide (Tat), single or multiple copies of AP1 an IL-4 receptor targeting peptide along with coding sequence of ELP and referred as Tat-A 1 E 28 or Tat-A 4 V 48 . These targeted polypeptides were further analyzed for its ability to deliver siRNA (Luciferase gene) in tumor cells in comparison with non-targeted controls (Tat-E 28 or E 28 ). The positively charged amino acids of these polypeptides enabled them to readily complex with negatively charged nucleic acids. The complexation of nucleic acid with respective polypeptides facilitated its transfection efficiency as well as stability. The targeted polypeptides (Tat-A 1 E 28 or Tat-A 4 V 48 ) selectively delivered siRNA into tumor cells in a receptor-specific fashion, achieved endosomal and lysosomal escape, and released gene into cytosol. The target specific delivery of siRNA by Tat-A 1 E 28 or Tat-A 4 V 48 was further validated in murine breast carcinoma 4T1 allograft mice model., Conclusion: The designed delivery systems efficiently delivered siRNA to the target site of action thereby inducing significant gene silencing activity. The study shows Tat and AP1 functionalized ELPs constitute a novel gene delivery system with potential therapeutic applications.

Background: Copy number variations (CNVs) are increasingly recognized as significant disease susceptibility markers in many complex disorders including cancer. The availability of a large number of chromosomal copy number profiles in both malignant and normal tissues in cancer patients presents an opportunity to characterize not only somatic alterations but also germline CNVs, which may confer increased risk for cancer., Results: We explored the germline CNVs in five cancer cohorts from the Cancer Genome Atlas (TCGA) consisting of 351 brain, 336 breast, 342 colorectal, 370 renal, and 314 ovarian cancers, genotyped on Affymetrix SNP6.0 arrays. Comparing these to ~3000 normal controls from another study, our case-control association study revealed 39 genomic loci (9 brain, 3 breast, 4 colorectal, 11 renal, and 12 ovarian cancers) as potential candidates of tumor susceptibility loci. Many of these loci are new and in some cases are associated with a substantial increase in disease risk. The majority of the observed loci do not overlap with coding sequences; however, several observed genomic loci overlap with known cancer genes including RET in brain cancers, ERBB2 in renal cell carcinomas, and DCC in ovarian cancers, all of which have not been previously associated with germline changes in cancer., Conclusions: This large-scale genome-wide association study for CNVs across multiple cancer types identified several novel rare germline CNVs as cancer predisposing genomic loci. These loci can potentially serve as clinically useful markers conferring increased cancer risk.

Background: Vitamin D deficiencies and increases in urinary albumin excretion (UAE) are both important and potentially related health problems; however, the nature of their relationship has not been established in normoalbuminuric subjects., Methods: We obtained data from 14,594 normoalbuminuric Korean adults who underwent voluntary health screenings. We used a generalized additive model to examine the threshold level for relationship between serum 25-hydroxyvitamin D [25(OH)D] and urinary-albumin creatinine ratio (UACR) levels. We conducted multivariate logistic regression for high-normal UAE (UACR, 10-29 mg/g), according to various categories of vitamin D status., Results: The generalized additive model confirmed a non-linear relationship between serum 25(OH)D and UACR levels, and the threshold concentration of 25(OH)D was 8.0 ng/mL after multivariate adjustment. Comparing subjects who fell into the lowest category of serum 25(OH)D levels with subjects who were in the reference range (the highest category), we observed that the multivariate adjusted odds ratio (OR) for high-normal UAE was significantly increased, regardless of the criteria used to categorize vitamin D levels: OR of the 1st quartile over the 4th quartile, 1.20 (95% CI, 1.04-1.39); OR of the 1.0-4.9th percentile over the 50-100th percentile, 1.56 (95% CI, 1.25-1.93); and OR of vitamin D deficiency group over vitamin D sufficiency group, 1.28 (95% CI, 1.08-1.52)., Conclusions: We demonstrated that there was an inverse relationship between serum 25(OH)D less than 8.0 ng/mL and UACR in normoalbuminuric subjects, suggesting that severe vitamin D deficiency could cause an increase in UAE in subjects with normoalbuminuria.

Background: Congenital muscular torticollis (CMT) is characterized by thickening and/or tightness of the unilateral sternocleidomastoid muscle (SCM), ending up with torticollis. Our aim was to identify differentially expressed genes (DEGs) and novel protein interaction network modules of CMT, and to discover the relationship between gene expressions and clinical severity of CMT., Results: Twenty-eight sternocleidomastoid muscles (SCMs) from 23 subjects with CMT and 5 SCMs without CMT were allocated for microarray, MRI, or immunohistochemical studies. We first identified 269 genes as the DEGs in CMT. Gene ontology enrichment analysis revealed that the main function of the DEGs is for extracellular region part during developmental processes. Five CMT-related protein network modules were identified, which showed that the important pathway is fibrosis related with collagen and elastin fibrillogenesis with an evidence of DNA repair mechanism. Interestingly, the expression levels of the 8 DEGs called CMT signature genes whose mRNA expression was double-confirmed by quantitative real time PCR showed good correlation with the severity of CMT which was measured with the pre-operational MRI images (R2 ranging from 0.82 to 0.21). Moreover, the protein expressions of ELN, ASPN and CHD3 which were identified from the CMT-related protein network modules demonstrated the differential expression between the CMT and normal SCM., Conclusions: We here provided an integrative analysis of CMT from gene expression to clinical significance, which showed good correlation with clinical severity of CMT. Furthermore, the CMT-related protein network modules were identified, which provided more in-depth understanding of pathophysiology of CMT.

Background: Microarray analyses based on differentially expressed genes (DEGs) have been widely used to distinguish samples across different cellular conditions. However, studies based on DEGs have not been able to clearly determine significant differences between samples of pathophysiologically similar HIV-1 stages, e.g., between acute and chronic progressive (or AIDS) or between uninfected and clinically latent stages. We here suggest a novel approach to allow such discrimination based on stage-specific genetic features of HIV-1 infection. Our approach is based on co-expression changes of genes known to interact. The method can identify a genetic signature for a single sample as contrasted with existing protein-protein-based analyses with correlational designs., Methods: Our approach distinguishes each sample using differentially co-expressed interacting protein pairs (DEPs) based on co-expression scores of individual interacting pairs within a sample. The co-expression score has positive value if two genes in a sample are simultaneously up-regulated or down-regulated. And the score has higher absolute value if expression-changing ratios are similar between the two genes. We compared characteristics of DEPs with that of DEGs by evaluating their usefulness in separation of HIV-1 stage. And we identified DEP-based network-modules and their gene-ontology enrichment to find out the HIV-1 stage-specific gene signature., Results: Based on the DEP approach, we observed clear separation among samples from distinct HIV-1 stages using clustering and principal component analyses. Moreover, the discrimination power of DEPs on the samples (70-100% accuracy) was much higher than that of DEGs (35-45%) using several well-known classifiers. DEP-based network analysis also revealed the HIV-1 stage-specific network modules; the main biological processes were related to "translation," "RNA splicing," "mRNA, RNA, and nucleic acid transport," and "DNA metabolism." Through the HIV-1 stage-related modules, changing stage-specific patterns of protein interactions could be observed., Conclusions: DEP-based method discriminated the HIV-1 infection stages clearly, and revealed a HIV-1 stage-specific gene signature. The proposed DEP-based method might complement existing DEG-based approaches in various microarray expression analyses.