High- organic-inorganic hybrid dielectric material for flexible thin-film transistors and printed logic circuits.

A new photopolymerizable organic-inorganic (O-I) hybrid sol-gel material, AUP@SiO-184, has been synthesized and utilized as a gate dielectric in flexible organic thin-film transistors (OTFTs). The previously reported three-arm alkoxy-functionalized silane amphiphilic polymer has yielded stable O-I hybrid materials comprising uniformly dispersed nanoparticles in the sol state. In this study, a photosensitizer was introduced, facilitating curing effects under ultraviolet light.

Modulation of PRL-1 within placental MSCs instigates the transition between EMT and MET subsequent to hepatic fibrosis.

Background/aims: Epithelial-to-mesenchymal transition (EMT) plays a crucial role in hepatic fibrogenesis and liver repair in chronic liver disease. Our research highlights the antifibrotic potential of placenta-derived mesenchymal stem cells (PD-MSCs) and the role of phosphatase of regenerating liver-1 (PRL-1) in promoting liver regeneration.

Methods: We evaluated the efficacy of PD-MSCs overexpressing PRL-1 (PD-MSCsPRL-1) in a bile duct ligation (BDL)-induced rat injury model, focusing on their ability to regulate EMT.

Dexmedetomidine-associated hypothermia in critical trauma: A case report and literature analysis.

Rationale: Hypothermia, defined as a core body temperature below 35°C, is a common and serious complication in severe trauma patients, often worsened by hemorrhage and medical interventions. Dexmedetomidine, an α2-adrenergic agonist used for sedation in intensive care units, has known thermoregulatory effects; however, its association with hypothermia in trauma patients remains insufficiently explored.

Patient Concerns: A 40-year-old male with severe polytrauma from a motor vehicle accident presented in distress, with hypotension, tachycardia, and a baseline temperature of 35.

Clinical implementation of next-generation sequencing testing and genomically-matched therapy: a real-world data in a tertiary hospital.

Next-generation sequencing (NGS) cancer profiling has gained traction in routine clinical practice in South Korea. Here, we evaluated the use of NGS testing and genomically-matched therapies for patients with advanced solid tumors in a real-world clinical practice. We analyzed results from NGS cancer panel tests (SNUBH pan-cancer version 2) ordered from June 2019 to June 2020.

A comprehensive multicenter analysis of clinical, molecular, and imaging characteristics and outcomes of H3 K27-altered diffuse midline glioma in adults.

Objective: The objective was to comprehensively investigate the clinical, molecular, and imaging characteristics and outcomes of H3 K27-altered diffuse midline glioma (DMG) in adults.

Methods: Retrospective chart and imaging reviews were performed in 111 adult patients with H3 K27-altered DMG from two tertiary institutions. Clinical, molecular, imaging, and survival characteristics were analyzed.

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials.

A Prospective, Single-Cohort, Open, Multi-Center, Observational Study of Sublingual Fentanyl for Breakthrough Cancer Pain: Effectiveness, Safety, and Tolerability in Korean Cancer Patients.

Purpose: Fentanyl, a highly lipophilic opioid, was developed as a sublingual fentanyl tablet (SFT) for the management of breakthrough cancer pain (BTcP), and its efficacy and safety were confirmed in a randomized, controlled study. We investigated the effectiveness and safety of SFT administered to alleviate BTcP in a real-world setting.

Materials And Methods: In this prospective, open, single-cohort study, conducted in 13 referral hospitals in South Korea, opioid-tolerant cancer patients receiving around-the-clock opioids for persistent cancer pain were enrolled if the individual had BTcP ≥ 1 episode/day during the preceding week.

Characterization of tumor-infiltrating lymphocytes and their spatial distribution in triple-negative breast cancer.

Background: The tumor immune microenvironment, particularly tumor-infiltrating lymphocytes (TILs), plays a critical role in disease progression and treatment response in triple-negative breast cancers (TNBCs). This study was aimed to characterize the composition of TILs and investigate their clinicopathological and prognostic significance with a special focus on the spatial distribution of TILs in TNBCs.

Methods: We analyzed TNBC samples through PanCancer Immune Profiling using NanoString nCounter assays to identify immune-related genes that are expressed differentially in relation to TIL levels and evaluated protein expression of selected markers through immunohistochemical staining on tissue microarrays.

Deep learning-driven macroscopic AI segmentation model for brain tumor detection via digital pathology: Foundations for terahertz imaging-based AI diagnostics.

We used deep learning methods to develop an AI model capable of autonomously delineating cancerous regions in digital pathology images (H&E-stained images). By using a transgenic brain tumor model derived from the TS13-64 brain tumor cell line, we digitized a total of 187 H&E-stained images and annotated the cancerous regions in these images to compile a dataset. A deep learning approach was executed through DEEP:PHI, which abstracts Python coding complexities, thereby simplifying the execution of AI training protocols for users.

Efficacy and safety of everolimus for patients with focal cortical dysplasia type 2.

Objective: This study aimed to evaluate the effectiveness and safety of everolimus in treating seizures associated with focal cortical dysplasia type 2 (FCD 2).

Methods: A prospective, crossover, placebo-controlled clinical trial (ClinicalTrials.gov: NCT03198949) enrolled patients aged 4-40 years with pathologically confirmed FCD 2 and a history of ≥3 seizures per month for two out of the 3 months prior to screening.

Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.

Background: Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnout, yet research on the introduction of digital technologies in this field remains limited. The combination of continuous and objective wearable sensor data acquired from patients with deep learning techniques holds the potential to overcome the limitations of traditional psychiatric assessments and support clinical decision-making.

Association of clozapine with structural and resting-state functional abnormalities of the hippocampus in chronic schizophrenia.

Introduction: Abnormalities in the hippocampus have been extensively reported in schizophrenia research. However, inconsistent findings exist, and how structural and functional abnormalities of the hippocampus are associated with clinical symptoms in schizophrenia, especially concerning clozapine treatment, remains uncertain.

Methods: We recruited 52 patients with schizophrenia, each with an illness duration of at least 5 years, and categorized them based on clozapine treatment.

Predicting 30-day mortality in severely injured elderly patients with trauma in Korea using machine learning algorithms: a retrospective study.

Purpose: The number of elderly patients with trauma is increasing; therefore, precise models are necessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict 30-day mortality in severely injured elderly patients with trauma and to compare the predictive performance of various machine learning models.

Methods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visited the regional trauma center at Chungbuk National University Hospital between 2016 and 2022.

Identifying clinical and proteomic markers for early diagnosis and prognosis prediction of major psychiatric disorders.

Background: To clarify if blood proteins can predict disease progression among individuals at clinical high-risk of severe mental illness (CHR-SMI), we developed a statistical model incorporating clinical and blood protein markers to distinguish the transition group (who developed severe mental illness) (CHR-SMI-T) and from non-transition group (CHR-SMI-NT) at baseline.

Methods: Ninety individuals (74 at CHR-SMI: 16 patients) were monitored for ≤4 years and were the focus of predictive models. Three predictive models (1 [100 clinical variables], 2 [158 peptides], and 3 [100 clinical variables +158 peptides]) were evaluated using area under the receiver operating characteristic (AUROC) values.

Phase 2 trial of avelumab in combination with gemcitabine in advanced leiomyosarcoma as a second-line treatment (EAGLES, Korean Cancer Study Group UN18-09).

Background: In this single-arm, multicenter, phase 2 trial, the authors evaluated the efficacy and safety of avelumab plus gemcitabine in patients with leiomyosarcoma (LMS) who failed on first-line chemotherapy.

Methods: Patients with advanced LMS received avelumab 10 mg/kg on days 1 and 15 (for up to 24 months) plus gemcitabine 1000 mg/m on days 1, 8, and 15 of a 28-day cycle until they developed disease progression or intolerable toxicity. The primary end point was the objective response rate (ORR).

Clinicians' Prediction of Survival Is Most Useful for Palliative Care Referral.

Background: Timely palliative transition in patients with advanced cancer is essential for their improved quality of life and overall survival (OS). Most prognostic models have been developed focusing on weeks' survival. The current study aimed to compare the accuracies of several indicators, such as the Karnofsky Performance Scale (KPS), Clinicians' Prediction of Survival (CPS), and Edmonton Symptom Assessment System (ESAS), for predicting the survival of patients.

Self-powered strain sensing devices with wireless transmission: DIW-printed conductive hydrogel electrodes featuring stretchable and self-healing properties.

With rapid advancements in health and human-computer interaction, wearable electronic skins (e-skins) designed for application on the human body provide a platform for real-time detection of physiological signals. Wearable strain sensors, integral functional units within e-skins, can be integrated with Internet of Things (IoT) technology to broaden the applications for human body monitoring. A significant challenge lies in the reliance of most existing wearable strain sensors on rigid external power supplies, limiting their practical flexibility.

Modeling the Determinants of Subjective Well-Being in Schizophrenia.

Background: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achieve subjective well-being (SWB). We aimed to identify the determinants of SWB and their interrelationships in schizophrenia.

Methods: Data were obtained from 637 patients with schizophrenia enrolled in multicenter, open-label, non-comparative clinical trials.

Highly Sensitive Dual-Mode Touch Sensing Device Using Poly(vinylidene fluoride)/Graphene Nitride for Wearable E-Skin: Patterning, High-Temperature Annealing, and Remote Data Transmission.

In recent years, significant advancements in printed electronics and flexible materials have catalyzed the development of electronic skins for wearable applications. However, the low glass transition temperature of flexible substrates poses a challenge as it is incompatible with the high-temperature annealing required for electrode fabrication, thereby limiting the performance of flexible electronic devices. In this study, we address these limitations by proposing a novel flexible device manufacturing process that combines adhesive printing patterning with a transfer printing technology.

Genetic Diagnosis in Neonatal Encephalopathy With Hypoxic Brain Damage Using Targeted Gene Panel Sequencing.

Background And Purpose: Neonatal encephalopathy (NE) is a neurological syndrome that presents with severe neurological impairments and complications. Hypoxic-ischemic encephalopathy is a major contributor to poor outcomes, being responsible for 50%-80% of admissions to neonatal intensive care units. However, some cases of NE accompanied by hypoxic brain damage cannot be solely attributed to hypoxia-ischemia.