Using the Regression Slope of Training Loss to Optimize Chest X-ray Generation in Deep Convolutional Generative Adversarial Networks.

Diffusion models, variational autoencoders, and generative adversarial networks (GANs) are three common types of generative artificial intelligence models for image generation. Among these, GANs are the most frequently used for medical image generation and are often employed for data augmentation in various studies. However, due to the adversarial nature of GANs, where the generator and discriminator compete against each other, the training process can sometimes end with the model unable to generate meaningful images or even producing noise.

Direct View of Gate-Tunable Miniband Dispersion in Graphene Superlattices Near the Magic Twist Angle.

Superlattices from twisted graphene mono- and bilayer systems give rise to on-demand many-body states such as Mott insulators and unconventional superconductors. These phenomena are ascribed to a combination of flat bands and strong Coulomb interactions. However, a comprehensive understanding is lacking because the low-energy band structure strongly changes when an electric field is applied to vary the electron filling.

Revolutionizing Epithelial Differentiability Analysis in Small Airway-on-a-Chip Models Using Label-Free Imaging and Computational Techniques.

Organ-on-a-chip (OOC) devices mimic human organs, which can be used for many different applications, including drug development, environmental toxicology, disease models, and physiological assessment. Image data acquisition and analysis from these chips are crucial for advancing research in the field. In this study, we propose a label-free morphology imaging platform compatible with the small airway-on-a-chip system.

iGTP: Learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.

Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein-protein interactions (PPI) between different biological states.

Depression, Anxiety, and Pain Predict Quality of Life in Patients with Differentiated Thyroid Cancer Postradiotherapy Ablation in Taiwan: A 48-Week Follow-Up Study.

Despite the generally good prognosis of differentiated thyroid cancer (DTC), impairments in health-related quality of life (HRQoL) remain a major concern in these patients. This study examined the patterns and predictors of change in mental and physical HRQoL in DTC survivors following radiotherapy ablation. Two hundred patients with DTC who received radiotherapy ablation in southern Taiwan between 2015 and 2018 were interviewed using the Taiwan version of the 36-item Short-form Health Survey (SF-36), the Taiwanese Depression Questionnaire (TDQ), and the Hamilton Rating Scale for Anxiety (HAM-A) at baseline and after 24 and 48 weeks of treatment.

Cross-kingdom pathogen detection via duplex universal PCR and high-resolution melt.

Infectious diseases caused by pathogenic bacteria and fungi continuously pose a significant threat worldwide. The occurrence of polymicrobial infections, including polybacterial, polyfungal or bacteria-fungal co-infections further complicates diagnosis and treatment. Current diagnostic methods, heavily reliant on culture methods, are slow and often inefficient.

Multiplex digital profiling of DNA methylation heterogeneity for sensitive and cost-effective cancer detection in low-volume liquid biopsies.

Molecular alterations in cancerous tissues exhibit intercellular genetic and epigenetic heterogeneity, complicating the performance of diagnostic assays, particularly for early cancer detection. Conventional liquid biopsy methods have limited sensitivity and/or ability to assess epigenetic heterogeneity of rare epiallelic variants cost-effectively. We report an approach, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which leverages a digital microfluidic platform that incorporates a ratiometric fluorescence multiplex detection scheme and precise digital high-resolution melt analysis to enable low-cost, parallelized analysis of heterogeneous methylation patterns on a molecule-by-molecule basis for the detection of cancer in liquid biopsies.

Immunomodulation by the combination of statin and matrix-bound nanovesicle enhances optic nerve regeneration.

Modulating inflammation is critical to enhance nerve regeneration after injury. However, clinically applicable regenerative therapies that modulate inflammation have not yet been established. Here, we demonstrate synergistic effects of the combination of an HMG-CoA reductase inhibitor, statin/fluvastatin and critical components of the extracellular matrix, Matrix-Bound Nanovesicles (MBV) to enhance axon regeneration and neuroprotection after mouse optic nerve injury.

The two-stage detection-after-segmentation model improves the accuracy of identifying subdiaphragmatic lesions.

Chest X-rays (CXRs) are primarily used to detect lung lesions. While the abdominal portion of CXRs can sometimes reveal critical conditions, research in this area is limited. To address this, we introduce a two-stage architecture that separates the abdominal region from the CXR and detects abdominal lesions using a specialized dataset.

A 5-Year, Multi-Institutional Mentorship Program in Radiation Oncology: The Society for Women in Radiation Oncology Experience.

Purpose: Mentorship in the field of radiation oncology (RO) promotes career development and satisfaction. Many individuals, however, do not have access to mentorship or are unsatisfied with their mentorship experience, potentially because of insufficient gender-concordant mentorship opportunities. To address this, the Society for Women in Radiation Oncology (SWRO) created the SWRO Mentorship Program for women, gender minorities, and those with intersecting marginalized identities at all stages of training for physicians and medical physicists.

Induction of Ca signaling and cytotoxic responses of human lung fibroblasts upon an antihistamine drug oxatomide treatment and evaluating the protective effects of Ca chelating.

Background: Oxatomide, an antihistamine drug of the diphenylmethylpiperazine family, has anti-inflammatory effects in airway disease. Because oxatomide was shown to cause diverse physiological responses in several cell models, the impact of oxatomide on Ca signaling and its related physiological effects has not been explored in IMR-90 human fetal lung fibroblasts.

Objectives: This study assessed the effect of oxatomide on cell viability and intracellular free Ca concentrations ([Ca]) and examined whether oxatomide-induced cytotoxicity through Ca signaling in IMR-90 cells.

Exploiting β-Lactams-Induced Lysis and DNA Fragmentation for Rapid Molecular Antimicrobial Susceptibility Testing of Neisseria Gonorrhoeae via Dual-Digital PCR.

The evolution of antimicrobial resistance (AMR) presents substantial challenges to global medical health systems. Neisseria gonorrhoeae (N. gonorrhoeae), in particular, has developed resistance to all currently available antimicrobials.

scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics.

Background: The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challenges.

Results: We present scEMB, a transformer-based deep learning model developed to capture context-aware gene embeddings from large-scale single-cell transcriptomics data.

Lifestyle and risk factors associated with elevated prostate-specific antigen levels in rural men: implications for health counseling.

Background: The use of prostate-specific antigen (PSA) for early detection of prostate cancer (PCa) is common but controversial. In rural areas, PSA is widely used for screening because it is convenient and early-stage PCa often shows no symptoms. Studies suggest that PSA levels are linked to factors like unhealthy lifestyles, obesity, lack of exercise, inflammation, and aging.

Optimizing Object Detection Algorithms for Congenital Heart Diseases in Echocardiography: Exploring Bounding Box Sizes and Data Augmentation Techniques.

Background: Congenital heart diseases (CHDs), particularly atrial and ventricular septal defects, pose significant health risks and common challenges in detection via echocardiography. Doctors often employ the cardiac structural information during the diagnostic process. However, prior CHD research has not determined the influence of including cardiac structural information during the labeling process and the application of data augmentation techniques.

Clinical Nomogram Model for Pre-Operative Prediction of Microvascular Invasion of Hepatocellular Carcinoma before Hepatectomy.

: Microvascular invasion (MVI) significantly impacts recurrence and survival rates after liver resection in hepatocellular carcinoma (HCC). Pre-operative prediction of MVI is crucial in determining the treatment strategy. This study aims to develop a nomogram model to predict the probability of MVI based on clinical features in HCC patients.

Enhanced CRISPR/Cas-Based Immunoassay through Magnetic Proximity Extension and Detection.

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas-associated systems have recently emerged as a focal point for developing next-generation molecular diagnosis, particularly for nucleic acid detection. However, the detection of proteins is equally critical across diverse applications in biology, medicine, and the food industry, especially for diagnosing and prognosing diseases like cancer, Alzheimer's and cardiovascular conditions. Despite recent efforts to adapt CRISPR/Cas systems for protein detection with immunoassays, these methods typically achieved sensitivity only in the femtomolar to picomolar range, underscoring the need for enhanced detection capabilities.

Association of antipsychotic formulations with sudden cardiac death in patients with schizophrenia: A nationwide population-based case-control study.

The aim of this study was to clarify the association between various antipsychotic formulations-oral-daily antipsychotics (OAPs), long-acting injectable antipsychotics (LAIs), and their combination-and the risk of sudden cardiac death (SCD). We conducted a nationwide population-based case-control study using data from 2011 to 2020 from the National Health Insurance Research Database and multiple-cause-of-death data from Taiwan. The study included patients with a new diagnosis of schizophrenia who were followed for SCD occurrence until 2020.

Correction: Comparing Vision-Capable Models, GPT-4 and Gemini, With GPT-3.5 on Taiwan's Pulmonologist Exam.

[This corrects the article DOI: 10.7759/cureus.67641.

Clinical Features and Vaccination Effects among Children with Post-Acute Sequelae of COVID-19 in Taiwan.

Background: Post-acute sequelae of SARS-CoV-2 infection (PASC) affects patients after recovering from acute coronavirus disease 2019 (COVID-19). This study investigates the impact of SARS-CoV-2 vaccination on PASC symptoms in children in Taiwan during the Omicron pandemic.

Methods: We enrolled children under 18 years with PASC symptoms persisting for more than 4 weeks.

Estimation equation of limb lean soft tissue mass in Asian athletes using bioelectrical impedance analysis.

Background: The lean soft tissue mass (LSTM) of the limbs is approximately 63% of total skeletal muscle mass. For athletes, measurement of limb LSTM is the basis for rapid estimation of skeletal muscle mass. This study aimed to establish the estimation equation of LSTM in Asian athletes using bioelectrical impedance analysis (BIA).

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, and Artificial Intelligence to Interrogate the Metabolic Evolution of Glioblastoma.

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models.