Transcriptome-wide RNA 5-methylcytosine profiles of human iPSCs and iPSC-derived cardiomyocytes.

Cardiac regenerative therapy has recently progressed by reprogramming somatic cells into induced pluripotent stem cells (iPSCs) and advanced by large-scale differentiation-derived cardiomyocytes (hiPSC-CMs). However, repairing damaged cardiac tissues with hiPSC-CMs remains limited due to immune rejection, cardiac arrhythmias, and concerns over tumor formation after hiPSC-CM transplantation. Despite efforts in profiling epigenomic changes during cardiac differentiation, regulatory mechanisms underlying 5-methylcytosine (mC) deposition in RNA mC epitranscriptomic landscape during hiPSC-to-cardiomyocyte differentiation remain unclear.

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth.

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals.

Loss of fragile WWOX gene leads to senescence escape and genome instability.

Induction of DNA damage response (DDR) to ensure accurate duplication of genetic information is crucial for maintaining genome integrity during DNA replication. Cellular senescence is a DDR mechanism that prevents the proliferation of cells with damaged DNA to avoid mitotic anomalies and inheritance of the damage over cell generations. Human WWOX gene resides within a common fragile site FRA16D that is preferentially prone to form breaks on metaphase chromosome upon replication stress.

HLA-E /HLA-G /HLA-II Human iPSC-Derived Cardiomyocytes Exhibit Low Immunogenicity for Heart Regeneration.

Although human pluripotent stem cells (hPSCs)-derived cardiomyocytes (hPSC-CMs) can remuscularize infarcted hearts and restore post-infarct cardiac function, post-transplant rejection resulting from human leukocyte antigen (HLA) mismatching is an enormous obstacle. It is crucial to identify hypoimmunogenic hPSCs for allogeneic cell therapy. This study is conducted to demonstrate the immune privilege of HLA-E /HLA-G /HLA-II human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs).

Enrichment of mRNA and Bisulfite-mRNA Library Preparation for Next-Generation Sequencing.

RNA post-transcriptional modifications in various types of RNA transcripts are associated with diverse RNA regulation in eukaryotic cells. Aberrant RNA 5-methylcytosine modifications and the dysregulated expression of RNA methyltransferases have been shown to be associated with various diseases, including cancers. Transcriptome-wide bisulfite-sequencing was developed to characterize the positions and the quantitative cytosine methylation levels in the bisulfite-converted RNA at the base-pair resolution.

Accelerating item factor analysis on GPU with Python package xifa.

Item parameter estimation is a crucial step when conducting item factor analysis (IFA). From the view of frequentist estimation, marginal maximum likelihood (MML) seems to be the gold standard. However, fitting a high-dimensional IFA model by MML is still a challenging task.

Usability and effectiveness of adherence monitoring of a mobile app designed to monitor and improve adherence to event-driven and daily HIV pre-exposure prophylaxis among men who have sex with men in Taiwan.

Objective: The UPrEPU mobile app is a self-monitoring system to enable men who have sex with men to optimize their pre-exposure prophylaxis adherence for HIV prevention. The app was designed to accommodate a rather complicated event-driven dosing schedule. We aim to evaluate the usability of the UPrEPU app and its effectiveness in improving adherence monitoring.

Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature.

Our study's main purpose is to emphasise the significance of medical knowledge of pathophysiology before machine learning. We investigated whether combining domain knowledge with machine learning results might increase accuracy and minimise the number of bio-features used to detect obstructive sleep apnea (OSA). The present study analysed data on 36 self-reported symptoms and 24 clinical features obtained from 3,495 patients receiving polysomnography at a regional hospital and a medical centre.

Mobile App (UPrEPU) to Monitor Adherence to Pre-exposure Prophylaxis in Men Who Have Sex With Men: Protocol for a User-Centered Approach to Mobile App Design and Development.

Background: Daily and on-demand pre-exposure prophylaxis (PrEP) has been well demonstrated to effectively prevent HIV acquisition for men who have sex with men (MSM). More than half of the MSM PrEP users in Taiwan prefer on-demand PrEP; however, on-demand PrEP involves a complicated dosing regimen because it requires precoital and postcoital dosing and sex events are hard to anticipate. Although there are a growing number of mobile apps designed to improve access to HIV prevention services and HIV medication adherence, few mobile apps focus on adherence to PrEP or are designed to accommodate a complicated, on-demand PrEP dosing schedule.

Penalized Least Squares for Structural Equation Modeling with Ordinal Responses.

Statistical modeling with sparsity has become an active research topic in the fields of statistics and machine learning. Because the true sparsity pattern of a model is generally unknown aforehand, it is often explored by a sparse estimation procedure, like least absolute shrinkage and selection operator (lasso). In this study, a penalized least squares (PLS) method for structural equation modeling (SEM) with ordinal data is developed.

Epigenetic silencing of AATK in acinar to ductal metaplasia in murine model of pancreatic cancer.

Background: Cancer subtype switching, which involves unclear cancer cell origin, cell fate decision, and transdifferentiation of cells within a confined tumor microenvironment, remains a major problem in pancreatic cancer (PDA).

Results: By analyzing PDA subtypes in The Cancer Genome Atlas, we identified that epigenetic silencing of apoptosis-associated tyrosine kinase (AATK) inversely was correlated with mRNA expression and was enriched in the quasi-mesenchymal cancer subtype. By comparing early mouse pancreatic lesions, the non-invasive regions showed AATK co-expression in cells with acinar-to-ductal metaplasia, nuclear VAV1 localization, and cell cycle suppression; but the invasive lesions conversely revealed diminished AATK expression in those with poorly differentiated histology, cytosolic VAV1 localization, and co-expression of p63 and HNF1α.

Pancreatic stellate cells activated by mutant KRAS-mediated PAI-1 upregulation foster pancreatic cancer progression via IL-8.

The dense fibrotic stroma enveloping pancreatic tumors is a major cause of drug resistance. Pancreatic stellate cells (PSCs) in the stroma can be activated to induce intra-tumor fibrosis and worsen patient survival; however, the molecular basics for the regulation of PSC activation remains unclear. The coculture system was used to study cancer cell-PSC interactions.

Postselection Inference in Structural Equation Modeling.

Most statistical inference methods were established under the assumption that the fitted model is known in advance. In practice, however, researchers often obtain their final model by some data-driven selection process. The selection process makes the finally fitted model random, and it also influences the sampling distribution of the estimator.

A penalized likelihood method for multi-group structural equation modelling.

In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis.

Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling.

Model selection is a popular strategy in structural equation modeling (SEM). To select an "optimal" model, many selection criteria have been proposed. In this study, we derive the asymptotics of several popular selection procedures in SEM, including AIC, BIC, the RMSEA, and a two-stage rule for the RMSEA (RMSEA-2S).

A Penalized Likelihood Method for Structural Equation Modeling.

A penalized likelihood (PL) method for structural equation modeling (SEM) was proposed as a methodology for exploring the underlying relations among both observed and latent variables. Compared to the usual likelihood method, PL includes a penalty term to control the complexity of the hypothesized model. When the penalty level is appropriately chosen, the PL can yield an SEM model that balances the model goodness-of-fit and model complexity.

Pharmacological strategies to target oncogenic KRAS signaling in pancreatic cancer.

The clear importance of mutated KRAS as a therapeutic target has driven the investigation of multiple approaches to inhibit oncogenic KRAS signaling at different molecular levels. However, no KRAS-targeted therapy has reached the clinic to date, which underlies the intrinsic difficulty in developing effective, direct inhibitors of KRAS. Thus, this article provides an overview of the history and recent progress in the development of pharmacological strategies to target oncogenic KRAS with small molecule agents.

Suppression of Tumor Growth and Muscle Wasting in a Transgenic Mouse Model of Pancreatic Cancer by the Novel Histone Deacetylase Inhibitor AR-42.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. This study was aimed at evaluating the efficacy of AR-42 (formerly OSU-HDAC42), a novel histone deacetylase (HDAC) inhibitor currently in clinical trials, in suppressing tumor growth and/or cancer-induced muscle wasting in murine models of PDAC.

Experimental Design: The in vitro antiproliferative activity of AR-42 was evaluated in six human pancreatic cancer cell lines (AsPC-1, COLO-357, PANC-1, MiaPaCa-2, BxPC-3, SW1990).

Preoperative assessment of labial bone perforation for virtual immediate implant surgery in the maxillary esthetic zone: A computer simulation study.

Background: In this computer simulation study, the authors investigated the frequency distribution of labial bone perforation (LBP) between various sagittal root position (SRP) classes with respect to the anterior maxillary osseous housing and evaluated the associated factors correlated with a higher risk of LBP when performing a virtual immediate implant surgery in the esthetic zone.

Methods: The authors analyzed cone-beam computed tomography (CBCT) images from 285 qualified study participants (1,449 teeth) to determine the probability of LBP when associated with selected variables, such as tooth type, SRP class, and morphologic parameters. The authors examined associated factors and analyzed the adjusted odds ratios by means of multiple logistic regression analysis.

Risk of lingual plate perforation for virtual immediate implant placement in the posterior mandible: A computer simulation study.

Background: This study sought to determine which factors are correlated to a higher risk of lingual plate perforation (LPP) when placing a virtual implant in the area of the anticipated extraction site of the posterior mandible.

Methods: Computed tomographic images of 300 patients (1,279 teeth) were analyzed in regard to the shape of the mandible (convergent, parallel, or undercut type), dimensional parameters of lingual concavity (angle, height, depth) and its relation to the inferior alveolar canal (zones A, B, C), distance from root apex to inferior alveolar canal, and probability of LPP. The odds ratio of variables was determined by multiple logistic regression modeling.

Risk assessment of inferior alveolar nerve injury for immediate implant placement in the posterior mandible: a virtual implant placement study.

Objectives: To investigate the prevalence and morphological parameters of lingual concavity, and whether these factors are related to a higher risk of inferior alveolar nerve (IAN) injury when performing an immediate implant surgery in posterior mandible region.

Methods: The CBCT images from 237 subjects (1008 teeth) were analysed the shape of the mandibles (C, P, U type), dimensional parameters of lingual concavity (angle, height, depth), and its relation to inferior alveolar canal (IAC) (A, B, C zone), RAC (distance from root apex to IAC) and probability of IAN injury. Multiple logistic regression modelling to determine the odds ratio of variables that made an important contribution to the probability of IAN injury and to adjust for confounding variables.

Remote cerebellar hemorrhage after cervical spinal surgery.

Remote cerebellar hemorrhage (RCH) is an unpredictable and rare complication of spinal surgery. We report five cases of RCH following cervical spinal surgery, and summarize another seven similar cases from the literature. Dural opening with cerebrospinal fluid (CSF) hypovolemia seems to be an important factor contributing to RCH following cervical spinal surgery.