Machine Learning for 1-Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry.

Background: In regions with limited donor availability, optimizing efficiency in lung transplant decision-making is crucial. Preoperative prediction of 1-year graft failure can enhance candidate selection and clinical decision-making.

Methods: We utilized data from the Korean Organ Transplantation Registry to develop and validate a deep learning-based model for predicting 1-year graft failure after lung transplantation.

Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry.

Optimizing lung transplant candidate selection is crucial for maximizing resource efficiency and improving patient outcomes. Using data from the International Society for Heart and Lung Transplantation (ISHLT) registry (29,364 patients), we developed a deep learning model to predict 1-year survival after lung transplantation. Initially, 25 pretransplant factors were identified, and their importance was assessed using SHapley Additive exPlanations values.

Pan-TRK positive uterine sarcoma in immunohistochemistry without neurotrophic tyrosine receptor kinase gene fusions: A case report.

Background: The classification of uterine sarcomas is based on distinctive morphological and immunophenotypic characteristics, increasingly supported by molecular genetic diagnostics. Data on neurotrophic tyrosine receptor kinase () gene fusion-positive uterine sarcoma, potentially aggressive and morphologically similar to fibrosarcoma, are limited due to its recent recognition. Pan-TRK immunohistochemistry (IHC) analysis serves as an effective screening tool with high sensitivity and specificity for -fusion malignancies.

Systematic Selection of High-Affinity ssDNA Sequences to Carbon Nanotubes.

Single-walled carbon nanotubes (SWCNTs) have gained significant interest for their potential in biomedicine and nanoelectronics. The functionalization of SWCNTs with single-stranded DNA (ssDNA) enables the precise control of SWCNT alignment and the development of optical and electronic biosensors. This study addresses the current gaps in the field by employing high-throughput systematic selection, enriching high-affinity ssDNA sequences from a vast random library.

3D graph contrastive learning for molecular property prediction.

Motivation: Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist.

METTL3 regulates alternative splicing of cell cycle-related genes via crosstalk between mRNA mA modifications and splicing factors.

N-methyladenosine (mA) modification in RNA affects various aspects of RNA metabolism and regulates gene expression. This modification is modulated by many regulatory proteins, such as mA methyltransferases (writers), mA demethylases (erasers), and mA-binding proteins (readers). Previous studies have suggested that alterations in mA regulatory proteins induce genome-wide alternative splicing in many cancer cells.

Therapeutic Effects of Amnion-Conjugated Chitosan-Alginate Membranes on Diabetic Wounds in an Induced Diabetic Swine Model: An In Vitro and In Vivo Study.

 Chitosan (CS) is a well-known antimicrobial dressing material. Moreover, widely used amniotic membranes contain growth factors beneficial for wound healing. Herein, we created a novel amnion-conjugated CS-alginate membrane dressing and tested its wound healing potency in a diabetic swine model.

The feasibility of granzyme B levels using an amperometric immunosensor for lung cancer detection.

Background: Low-dose computed tomography (LDCT) has improved the early detection of lung cancer. However, LDCT scans present several disadvantages, including the abundance of false-positive results, which lead to a high socioeconomic cost, psychological burden, and repeated exposure to radiation. Therefore, the identification of complementary biomarkers is needed to select high-risk individuals for LDCT.

Freeze-dried bovine amniotic membrane as a cell delivery scaffold in a porcine model of radiation-induced chronic wounds.

Background: Locoregional stem cell delivery is very important for increasing the efficiency of cell therapy. Amnisite BA (Amnisite) is a freeze-dried amniotic membrane harvested from bovine placenta. The objective of this study was to investigate the retention of cells of the stromal vascular fraction (SVF) on Amnisite and to determine the effects of cell-loaded Amnisite in a porcine radiation-induced chronic wound model.

Development and Validation of a Next-Generation Sequencing-Based Multigene Assay to Predict the Prognosis of Estrogen Receptor-Positive, HER2-Negative Breast Cancer.

Purpose: Multigene assays provide useful prognostic information regarding hormone receptor (HR)-positive breast cancer. Next-generation sequencing (NGS)-based platforms have numerous advantages including reproducibility and adaptability in local laboratories. This study aimed to develop and validate an NGS-based multigene assay to predict the distant recurrence risk.

Meningothelial hamartoma of the scalp.

Meningothelial hamartoma is a benign tumor composed of ectopic meningothelial elements in the dermis and subcutaneous tissue. It mainly occurs in the scalp; however, the incidence is extremely low. The origin of meningothelial hamartoma has not been elucidated; nevertheless, it has been theorized that it derives from ectopic meningothelial rests displaced during embryologic development.

Clinicopathological Characteristics of Mutation and Amplification in Korean Patients with Breast Cancers.

The frequency of mutation and amplification was various and their clinical significances have not been clarified in Korean patients with invasive breast carcinoma (IBC). The study aimed to investigate the clinical and prognostic significances of mutation and amplification in IBC patients. DNA was isolated from paired normal and tumoral tissues in 128 IBC patients and the mutation and expression of gene were analyzed.

TZAP Mutation Leads to Poor Prognosis of Patients with Breast Cancer .

ZBTB48 is a telomere-associated factor that has been renamed as telomeric zinc finger-associated protein (TZAP). It binds preferentially to long telomeres, competing with telomeric repeat factors 1 and 2. We analyzed the TZAP mutation in 128 breast carcinomas (BCs).

Comprehensive ensemble in QSAR prediction for drug discovery.

Background: Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions.

Role of Leukotriene B Receptor-2 in Mast Cells in Allergic Airway Inflammation.

Mast cells are effector cells in the immune system that play an important role in the allergic airway inflammation. Recently, it was reported that BLT2, a low-affinity leukotriene (LT) B receptor, plays a pivotal role in the pathogenesis of allergic airway inflammation through its action in mast cells. We observed that highly elevated expression levels of BLT2 are critical for the pathogenesis leading to allergic airway inflammation, and that if BLT2 expression is downregulated by siBLT2-mediated knockdown, allergic inflammation is dramatically alleviated.

Clinical and Prognostic Value of Human Mammary Tumor Virus in Korean Patients with Breast Carcinoma.

Background: Human mammary tumor virus (HMTV) is 90-95% homologous to mouse mammary tumor virus, one of the causal agents of murine mammary tumors. Although HMTV has been frequently detected in human breast cancers, its clinical and prognostic value remains unknown.

Methods: In the present study, we analyzed HMTV infection using polymerase chain reaction (PCR) in 128 breast cancers.

Mediatory roles of leukotriene B receptors in LPS-induced endotoxic shock.

Sepsis, a systemic inflammatory response syndrome caused by infection, is the most common disease in patients treated in intensive care units. Endotoxic shock, the most critical form of sepsis, is caused by gram-negative bacterial infection. However, the detailed mechanism of endotoxic shock remains unclear.

DNA Steganalysis Using Deep Recurrent Neural Networks.

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography.

Establishment of an Uncomplicated Radiation-delayed Wound Healing Model Using Irradiation in Pigs.

Introduction: Radiation-delayed wounds require diverse therapeutic strategies to achieve effective healing. However, the development of novel therapies with a radiation-delayed wound healing model is hindered by the lack of standardized animal models.

Objective: In this study, the authors propose and verify a procedure to establish a radiation-delayed wound healing model in pigs.

Changes in Lesional and Non-lesional Skin Microbiome During Treatment of Atopic Dermatitis.

The aim of this study was to evaluate changes in the skin surface microbiome in patients with atopic dermatitis during treatment. The effect of narrowband ultraviolet B phototherapy was also studied to determine the influence of exposure to ultraviolet. A total of 18 patients with atopic dermatitis were included in the study.

End-to-End Representation Learning for Chemical-Chemical Interaction Prediction.

Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a learning-based approach. In other drug analyses, deep learning has been actively used in recent years.

LncRNAnet: long non-coding RNA identification using deep learning.

Motivation: Long non-coding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs, but they play completely different roles, thus providing novel insights for biological studies. The development of next-generation sequencing has helped in the discovery of lncRNA transcripts.