Association Between Plasma Granzyme B Levels, Organ Failure, and 28-Day Mortality Prediction in Patients with Sepsis.

: Sepsis is basically an inflammatory disease that involves the host's immune response. Granzyme B, a cytotoxic protease, has garnered attention for its involvement in modulating immune responses. This study aimed to elucidate the clinical implications of granzyme B in critically ill patients with sepsis, focusing on plasma granzyme B levels as a potential prognostic marker.

Clinical implication of electrocardiogram change in patients experiencing lung transplantation with end stage lung disease.

Introduction: End-stage lung disease causes cardiac remodeling and induces electrocardiogram (ECG) changes. On the other way, whether lung transplantation (LTx) in end-stage lung disease patients are associated with ECG change is unknown. The object of this study was to investigate ECG changes before and after LTx in end-stage lung disease patients and whether these changes had clinical significance.

Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker.

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate. The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management. We aimed to investigate the potential of three-dimensional label-free CD8 + T cell morphology as a biomarker for sepsis.

Prognostic value of baseline and early treatment response of neutrophil-lymphocyte ratio, C-reactive protein, and lactate dehydrogenase in non-small cell lung cancer patients undergoing immunotherapy.

Background: Not all non-small cell lung cancer (NSCLC) patients will benefit from immune checkpoint therapy and use of these medications carry serious autoimmune adverse effects. Therefore, biomarkers are needed to better identify patients who will benefit from its use. Here, the correlation of overall survival (OS) with baseline and early treatment period serum biomarker responses was evaluated in patients with NSCLC undergoing immunotherapy.

Galactomannan Titer as a Diagnostic Marker of Invasive Pulmonary Aspergillosis in Lung Transplant Recipients: A Single-Center Retrospective Cohort Study.

Invasive pulmonary aspergillosis (IPA) can occur in immunocompromised patients, and an early detection and intensive treatment are crucial. We sought to determine the potential of Aspergillus galactomannan antigen titer (AGT) in serum and bronchoalveolar lavage fluid (BALF) and serum titers of beta-D-glucan (BDG) to predict IPA in lung transplantation recipients, as opposed to pneumonia unrelated to IPA. We retrospectively reviewed the medical records of 192 lung transplant recipients.

Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.

Background: Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have been proposed to demonstrate this association. This systematic review aimed to examine the analytical tools by considering original articles that utilized statistical and machine learning methods for detecting ADRs.

Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation.

Background: Privacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing.

Objective: Using machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility.

Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study.

Background: Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues.

Evaluation of the Privacy Risks of Personal Health Identifiers and Quasi-Identifiers in a Distributed Research Network: Development and Validation Study.

Background: Privacy should be protected in medical data that include patient information. A distributed research network (DRN) is one of the challenges in privacy protection and in the encouragement of multi-institutional clinical research. A DRN standardizes multi-institutional data into a common structure and terminology called a common data model (CDM), and it only shares analysis results.

Weight-Based Framework for Predictive Modeling of Multiple Databases With Noniterative Communication Without Data Sharing: Privacy-Protecting Analytic Method for Multi-Institutional Studies.

Background: Securing the representativeness of study populations is crucial in biomedical research to ensure high generalizability. In this regard, using multi-institutional data have advantages in medicine. However, combining data physically is difficult as the confidential nature of biomedical data causes privacy issues.

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

Background: Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened.

Developing a Mobile App for Monitoring Medical Record Changes Using Blockchain: Development and Usability Study.

Background: Although we are living in an era of transparency, medical documents are often still difficult to access. Blockchain technology allows records to be both immutable and transparent.

Objective: Using blockchain technology, the aim of this study was to develop a medical document monitoring system that informs patients of changes to their medical documents.

Developing a Mobile App for Patients to Monitor Medical Record Changes Using Blockchain.

According to recent revisions to medical laws in Korea, changes to electronic medical records are to be documented. To do so, however, a transparent system with which to store original documents and changes thereto is needed. The transparency and immutability of blockchain records are the key characters of blockchain technology.

Risk of Primary Spontaneous Pneumothorax According to Chest Configuration.

Background: We compared the chest configurations of patients with primary spontaneous pneumothorax (PSP) and age-sex-matched controls to determine the presence of chest wall deformities in patients with PSP.

Methods: We retrospectively enrolled 166 male patients with PSP (age, 18-19 years) and 85 age-sex-matched controls without PSP, who simultaneously underwent chest computed tomography (CT) and radiography at one of two institutes. After correcting for height, the following thoracic parameters were comparatively evaluated between the two groups: maximal internal transverse () and anteroposterior () diameters of the chest, maximal internal lung height (), Haller index (/), and /Height, /, /Height, /, and /Height ratios.