The complete mitogenome dataset of the heptageniid mayfly (Ephemeroptera: Heptageniidae: Ecdyonurinae) from South Korea.

The Heptageniidae family stands out as one of the most abundant and widespread among mayflies, distinguished by its unique mitochondrial characteristics in intergenic spacers and duplication of transfer RNA (tRNA) genes. In this dataset, we present the complete mitochondrial genome sequence of (Navás, 1912), a member of the Heptageniidae family of mayflies. The total length of the mitogenome was determined to be 15,362 base pairs, encompassing 13 protein-coding genes (PCGs), 23 tRNA genes, and two ribosomal RNA genes.

Effect of the Chemical Structure of a Self-Assembled Monolayer on the Gas-Sensing Behavior of SnO Nanowires.

In this study, detailed investigations of the selective sensing capability of semiconducting metal oxide (SMO)-based gas sensors with self-assembled monolayer (SAM) functionalization were conducted. The selective gas-sensing behavior was improved by employing a simple and straightforward postmodification technique using functional SAM molecules. The chemical structure of the SAM molecules promoted interaction between the gas and SAM molecules, providing a gas selective sensing of SnO nanowires (NWs).

Efficient Isolation of Cellulose Nanocrystals from Seaweed Waste via a Radiation Process and Their Conversion to Porous Nanocarbon for Energy Storage System.

This article presents an efficient method for isolating cellulose nanocrystals (CNcs) from seaweed waste using a combination of electron beam (E-beam) irradiation and acid hydrolysis. This approach not only reduces the chemical consumption and processing time, but also improves the crystallinity and yield of the CNcs. The isolated CNcs were then thermally annealed at 800 and 1000 °C to produce porous nanocarbon materials, which were characterized using scanning electron microscopy, X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy to assess their structural and chemical properties.

Sustained BMP-2 delivery via alginate microbeads and polydopamine-coated 3D-Printed PCL/β-TCP scaffold enhances bone regeneration in long bone segmental defects.

Background/objective: Repair of long bone defects remains a major challenge in clinical practice, necessitating the use of bone grafts, growth factors, and mechanical stability. Hence, a combination therapy involving a 3D-printed polycaprolactone (PCL)/β-tricalcium phosphate (β-TCP) scaffold coated with polydopamine (PDA) and alginate microbeads (AM) for sustained delivery of bone morphogenetic protein-2 (BMP-2) was investigated to treat long bone segmental defects.

Methods: Several in vitro analyses were performed to evaluate the scaffold osteogenic effects in vitro such as PDA surface modification, namely, hydrophilicity and cell adhesion; cytotoxicity and BMP-2 release kinetics using CCK-8 assay and ELISA, respectively; osteogenic differentiation in canine adipose-derived mesenchymal stem cells (Ad-MSCs); formation of mineralized nodules using ALP staining and ARS staining; and mRNA expression of osteogenic differentiation markers using RT-qPCR.

Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients.

We aimed to develop efficient data labeling strategies for ground truth segmentation in lower-leg magnetic resonance imaging (MRI) of patients with Charcot-Marie-Tooth disease (CMT) and to develop an automated muscle segmentation model using different labeling approaches. The impact of using unlabeled data on model performance was further examined. Using axial T1-weighted MRIs of 120 patients with CMT (60 each with mild and severe intramuscular fat infiltration), we compared the performance of segmentation models obtained using several different labeling strategies.

Prognostic Role of Pre-Treatment Body Composition Parameters in Patients Undergoing First-Line Immunotherapy for Metastatic Renal Cell Carcinoma.

Purpose: We investigated the relationship between body mass index (BMI), radiological body composition, and survival outcomes in patients with metastatic renal cell carcinoma (mRCC) underwent first-line immune checkpoint inhibitor (ICI)-based therapy.

Methods: Analyzing data from 102 patients treated between November 2019 and March 2023, pre-treatment computed tomography (CT) scans assessed fat and muscle areas. BMI and body composition indices were examined, including skeletal muscle index, subcutaneous fat index (SFI), visceral fat index, and total fat index.

Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging.

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson's disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression.

Comprehensive deep learning-based assessment of living liver donor CT angiography: from vascular segmentation to volumetric analysis.

Background: Precise preoperative assessment of liver vasculature and volume in living donor liver transplantation is essential for donor safety and recipient surgery. Traditional manual segmentation methods are being supplemented by deep learning (DL) models, which may offer more consistent and efficient volumetric evaluations.

Methods: This study analyzed living liver donors from Samsung Medical Center using preoperative CT angiography data between April 2022 and February 2023.

3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.

Background: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP).

Materials And Methods: Living liver donors who underwent MRCP using the gradient and spin echo technique followed by three-dimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for the deep learning process.

CoFeO on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode.

In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFeO (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response of 19.

Selective Spin Dewetting for Perovskite Solar Modules Fabricated on Engineered Au/ITO Substrates.

We introduce a novel method for fabricating perovskite solar modules using selective spin-coating on various Au/ITO patterned substrates. These patterns were engineered for two purposes: (1) to enhance selectivity of monolayers primarily self-assembling on the Au electrode, and (2) to enable seamless interconnection between cells through direct contact of the top electrode and the hydrophobic Au connection electrode. Utilizing SAMs-treated Au/ITO, we achieved sequential selective deposition of the electron transport layer (ETL) and the perovskite layer on the hydrophilic amino-terminated ITO, while the hole transport layer (HTL) was deposited on the hydrophobic CH-terminated Au connection electrodes.

nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer.

Rationale And Objectives: To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer.

Materials And Methods: This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas).

Peptide Nucleic Acids Containing Cationic/Amino-Alkyl Modified Bases Promote Enhanced Hybridization Kinetics and Thermodynamics with Single-Strand DNA.

Peptide nucleic acids (PNAs) are antisense molecules with excellent polynucleotide hybridization properties; they are resistant to nuclease degradation but often have poor cell permeability leading to moderate cellular activity and limited clinical results. The addition of cationic substitutions (positive charges) to PNA molecules greatly increases cell permeability. In this report, we describe the synthesis and polynucleotide hybridization properties of a novel cationic/amino-alkyl nucleotide base-modified PNA (OPNA).

Modeling Long-term Spike Frequency Adaptation in SA-I Afferent Neurons Using an Izhikevich-based Biological Neuron Model.

To develop a biomimetic artificial tactile sensing system capable of detecting sustained mechanical touch, we propose a novel biological neuron model (BNM) for slowly adapting type I (SA-I) afferent neurons. The proposed BNM is designed by modifying the Izhikevich model to incorporate long-term spike frequency adaptation. Adjusting the parameters renders the Izhikevich model describing various neuronal firing patterns.

Multi-scale vascularization strategy for 3D-bioprinted tissue using coaxial core-shell pre-set extrusion bioprinting and biochemical factors.

Three-dimensional bioprinting is a key technology in bioartificial organ production. However, production of bioartificial organs has significant limitations because it is hard to build vascular structures, especially capillaries, in printed tissue owing to its low resolution. As the vascular structure plays a critical role in delivering oxygen and nutrients to cells and removing metabolic waste, building vascular channels in bioprinted tissue is essential for bioartificial organ production.

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges.

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities.

Layer-dependent stability of 2D mica nanosheets.

We report on the layer-dependent stability of muscovite-type two-dimensional (2D) mica nanosheets (KAlSiO(OH)). First-principles calculations on mica nanosheets with different layer thicknesses (n = 1, 2, and 3) reveal their layer-dependent stability; odd-numbered 2D mica nanosheets are more stable than even-numbered ones, and the preferable stability of odd-numbered layers originates from electronic effects. A core-shielding model is proposed with a reasonable assumption, successfully proving the instability of the even-numbered mica nanosheets.

Combined Ultraviolet Ozone and Thermally Activated Formamidinium Iodide Solution to Fabricate Large Grain FAPbIBrCl Films.

In this report, we report the fabrication of a large grain and high crystallinity perovskite film by combined ultraviolet-ozone (UVO) and thermal treatment of formamidinium iodide solution during the fabrication of formamidinium lead halide (FAPbIBrCl) films by a two-step deposition method. In this process, lead halide films were treated with UVO-treated FAI at different times. In addition, we have observed that hot-casting of UVO-assisted FAI nucleates the α-FAPbI phase in as-prepared films.

Fat Loss in Patients with Metastatic Clear Cell Renal Cell Carcinoma Treated with Immune Checkpoint Inhibitors.

The purpose of this study was to determine the prognostic impact of fat loss after immune checkpoint inhibitor (ICI) treatment in patients with metastatic clear cell renal cell carcinoma (ccRCC). Data from 60 patients treated with ICI therapy for metastatic ccRCC were retrospectively analyzed. Changes in cross-sectional areas of subcutaneous fat (SF) between the pre-treatment and post-treatment abdominal computed tomography (CT) images were expressed as percentages and were divided by the interval between the CT scans to calculate ΔSF (%/month).

Diagnostic Tools for Alzheimer's Disease: A Narrative Review Based on Our Own Research Experience.

Alzheimer's disease (AD), one of the most representative neurodegenerative diseases, has diverse neurobiological and pathophysiological mechanisms. Treatment strategies targeting a single mechanism have repeated faced failures because the mechanism of neuronal cell death is very complex that is not fully understood yet. Since complex mechanisms exist to explain AD, a variety of diagnostic biomarkers for diagnosing AD are required.

Lithium Superionic Conduction in BH -Substituted Thiophosphate Solid Electrolytes.

Compared with conventional liquid electrolytes, solid electrolytes can better improve the safety properties and achieve high-energy-density Li-ion batteries. Sulfide-based solid electrolytes have attracted significant attention owing to their high ionic conductivities, which are comparable to those of their liquid counterparts. Among them, Li thiophosphates, including Li-argyrodites, are widely studied.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.