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Development of a porcine multicellular endometrial organoid model for in vitro embryo attachment studies
No full textBackground: Embryo implantation is a complex process regulated by interactions between endometrial epithelial and stromal cells. The endometrium plays a critical role in this process, providing a supportive environment for embryo attachment. However, conventional 2D cell culture models fail to fully replicate the complex 3D structure and cellular interactions of the endometrium. To overcome these limitations, 3D organoid models have been developed to better mimic the in vivo endometrial environment.
Methods: In this study, a multicellular uterine organoid model was developed using porcine endometrial epithelial cells (pEECs) and porcine endometrial stromal cells (pESCs) to evaluate the effects of the endometrial environment on embryo implantation. First, single-cell endometrial organoids (pEOs) were formed by culturing pEECs in Matrigel, and their basic cellular characteristics were assessed. Then, a multicellular uterine organoid model was established by combining pEOs with pESCs. Finally, porcine embryos were co-cultured with this model to examine its effect on embryo attachment.
Results: The multicellular uterine organoid model facilitated embryo attachment, demonstrating that the 3D structure and cellular interactions of the endometrium play a significant role in embryo implantation. The presence of both epithelial and stromal cells contributed to a more physiologically relevant environment that supported embryo adhesion.
Conclusions: This study demonstrates that a multicellular uterine organoid model can serve as a useful in vitro system for porcine embryo implantation research. This model may contribute to a better understanding of embryo development and implantation mechanisms, with potential applications in regenerative medicine and biotechnology.N
Toxic effects of sterigmatocystin on porcine oocyte maturation and subsequent embryo development
No full textSterigmatocystin (STE), a precursor of aflatoxin B1, is one of the mycotoxins that easily contaminates feed. Although previous studies have suggested the toxic effects of aflatoxin B1 on oocyte maturation, little attention has been given to the effects of STE. Therefore, we investigated the effects of STE on porcine oocyte maturation. In this study, porcine oocytes were subjected to in vitro maturation supplemented with various concentrations of STE (0, 5, 10, and 25 mu M). The results showed that the cumulus cell expansion indexes of all STE-treated groups were significantly decreased compared to the control group, with 10 mu M significantly decreasing the transcript expression of cumulus expansion-related genes. Regarding nuclear maturation, metaphase II rates in all STEtreated groups were significantly lower than in the control group, with 10 mu M significantly decreasing the transcript expression of oocyte competence-, mitogen-activated protein kinase-, and maturation-promoting factor-related genes. While cleavage rates showed no significant differences, the blastocyst formation rates significantly declined in groups treated with more than 10 mu M of STE. Based on these findings, the 10 mu M STE group was selected for subsequent experiments. STE supplementation significantly increased reactive oxygen species levels and decreased glutathione levels in oocytes compared to the control group. Furthermore, STE significantly decreased mitochondrial quantity and membrane potential, while increasing the percentage of Y-H2AX-positive oocytes. The number of LC3-positive dots and Annexin-V-positive oocytes was also significantly higher in the STE-treated group than in the control group. In conclusion, STE impairs porcine oocyte maturation and subsequent embryo development by inducing oxidative stress, mitochondrial dysfunction, DNA damage, excessive autophagy, and early apoptosis.N
Dual-Enhanced Nanohybrids for Synergistic Photothermal and Photodynamic Therapy in Cancer Treatment with Immune Checkpoint Inhibitors
No full textThis study presents a nanohybrid that simultaneously improves both photothermal (PT) and photodynamic (PD) effects for cancer therapy. The conjugated polymer nanoparticle (CPN) comprises of p-type conjugated polymer as a photosensitizer, charge donor, and PT agent, n-type conjugated polymer as a charge acceptor and PD agent, and Au nanoparticles (NPs) as a PT agent. This nanohybrid is assembled through a film dispersion process using a hydrophobically modified phospholipid, producing a high yield of uniform hybrid NPs in a short timeframe, and displays exceptional photothermal and photodynamic effects, when activated at a single near-infrared wavelength. Photophysical analysis indicates that the inclusion of Au NPs enhances nonradiative exciton relaxation, while the incorporation of a n-type conjugated polymer boosts photoinduced charge transfer and potentially contributes to the charge-recombination mediated triplet-state formation for an enhanced generation of reactive oxygen species. During phototherapy, the nanohybrid demonstrates the most effective suppression of primary tumor growth and significantly boosts anti-tumor immune responses owing to its simultaneous photothermal and photodynamic effects. Furthermore, when combined with immune checkpoint inhibitors, nanohybrid treatment minimizes tumor sizes while maximizing survival rates in mice. Thus, the nanohybrid represents a promising nanoplatform for combination phototherapy in cancer treatment.N
A sustainable strategy to reduce net methane emissions from thermokarst lakes by electrochemical methane partial oxidation
No full textGlobal warming increases methane emissions from Arctic permafrost, which in turn reaccelerates global warming, creating a vicious cycle. Addressing this issue requires innovative solutions, such as electro-assisted methane partial oxidation (EMPO), which can provide an on-site facility for sustainable methane emission reduction in permafrost. In this study, a Co singe-atom catalyst was synthesized as an oxygen reduction reaction (ORR) catalyst that can be practically applied to stand-alone EMPO systems. To address performance degradation and cold weather freezing due to flooding of the electrodes, the hydrophobic polytetrafluoroethylene was mixed into a catalyst layer to regulate the microenvironment near cathodes. Furthermore, hydrophobic cathodes offer a pathway for nonpolar gases to increase the local concentration of methane. The enhanced local methane concentration, combined with an efficient ORR catalyst, yields 8 mmol g(cat)(-1) formic acid at a low potential bias. Remarkably, the EMPO system exhibits a consistent production even with air and is highly stable. This leads to possibility of on-site facility for methane conversion without external energy at thermokarst lakes.Y
Untangling the potential of non-entangled bottlebrush block copolymers as separator coating materials for high-rate and long-life sodium metal batteries
No full textSodium (Na) metal batteries are of great interest as next-generation battery systems due to the high energy density, natural abundance, and cost advantages of Na metal. However, Na metal systems face significant challenges, primarily due to uncontrolled Na electrodeposition and unstable electrolyte-electrode interphases, leading to collateral cell failures. Herein, we introduce a bottlebrush block copolymer (BBP), consisting of oligomeric poly(ethylene oxide) (PEO) and polystyrene blocks, as a coating material for conventional glass fiber (GF) separators. The BBP exhibits a controlled hexagonal cylindrical morphology and non-entangled topology, which significantly enhances ion-conducting properties and mechanical robustness through well-defined microphase separation. Additionally, the non-entangled topology of the BBP, facilitated by its bottlebrush structure, promotes effective PEO-Na+ coordination, thereby effectively regulating Na+ flux at the electrolyte-electrode interphases. This uniform Na+ flux achieved by the BBP coating ultimately leads to dendrite-free Na deposition and stable electrode interphases, resulting in high-rate and long-lasting Na metal batteries. This study highlights the potential of non-entangled bottlebrush-structured polymers as viable separator coating materials for Na metal battery systems.N
Engineering IgG antibodies for intracellular targeting and drug delivery
No full textEnabling immunoglobulin G (IgG)-format antibodies to autonomously internalize and localize in the cytosol of targeted cells-referred to as cytosol-penetrating antibodies (cytotransmab, CT)-is challenging yet highly promising. A primary barrier to cytosolic access for CT is limited endosomal escape. Herein, we developed a second-generation (2G) CT, named in2CT4.1, featuring an endosomal acidic pH-responsive endosomal escape motif (R-W/E motif) with Arg-Trp pairs and a Glu patch in the CH3 and CL domains of IgG1/kappa antibody. This motif selectively destabilizes endosomal membranes at endosomal acidic pH to facilitate cytosolic access while remaining inactive at neutral pH. The 2G CT, in2CT4.1, achieves efficient cytosolic localization at nanomolar concentrations, demonstrating approximately 3-fold higher endosomal escape efficiency compared to the firstgeneration CT. The potential of 2G CT is validated by engineering a cytosolic alpha-tubulin-targeting CT via an alpha-tubulin-specific variable domain in in2CT4.1. Additionally, the 2G CT effectively delivers the catalytic domain of diphtheria toxin to the cytosol of epidermal growth factor receptor-overexpressing tumor cells, resulting in near-complete suppression of tumor growth in a xenograft mouse model. These results establish 2G CT as a versatile platform for targeting cytosolic proteins and delivering therapeutic payloads, with broad potential in targeted cancer therapy and other applications.N
Assessing statistical literacy in medical students and doctors: a single-centre, cross-sectional survey in South Korea
No full textObjective Healthcare professionals must possess statistical literacy to provide evidence-based care and engage patients in decision-making. However, there have been concerns about healthcare professionals' inadequate understanding of health statistics. As an initial step in addressing the issue, we assessed the statistical literacy of medical students and doctors in South Korea by evaluating their comprehension of four statistical concepts: (a) single-event probability, (b) relative risk reduction, (c) positive predictive value and (d) 5-year survival rate. Design Cross-sectional survey study. Setting The survey was conducted from October 2018 to January 2019 in one medical school and its affiliated teaching hospital in Seoul, South Korea. Participants 303 medical students from all six grades and 291 doctors from various specialties. Primary and secondary outcome measures The primary outcome measure was the correct answer rate for each question. The secondary outcome measure was the mean number of correct answers across the four statistical literacy questions, calculated for each individual. Results The correct answer rates for basic numeracy questions were close to 100%. Regarding statistical literacy, 95.5% and 83.2% of the participants accurately understood single-event probability and relative risk reduction, respectively. However, only 49.3% and 49.2% of the participants accurately understood the positive predictive value and 5-year survival rate, respectively. The correct answer rates for the question about the 5-year survival rate differed significantly between students (40.9%) and doctors (57.7%) (p<0.001). There were no statistically significant differences in the correct answer rates for other questions, regardless of the student's grade level or the doctor's specialty. Conclusions Medical students and doctors have weaker statistical literacy than their basic numeracy. Therefore, it is essential to implement medical education and professional development programmes that focus on improving their statistical literacy. These programmes should specifically address measures of medical test accuracy and the distinction between a 5-year survival rate and mortality.Y
Plasma metabolite based clustering of breast cancer survivors and identification of dietary and health related characteristics: an application of unsupervised machine learning
No full textBACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, chi(2) test, and Fisher's exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION: The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.Y
Degradation path prediction of lithium-ion batteries under dynamic operating sequences
No full textReliable battery management requires the degradation of lithium-ion batteries (LIBs) under variable usage patterns to be accurately and continuously monitored and predicted. However, the chemically entangled internal states and the nonlinear accumulation of degradation mechanisms pose challenges to establishing these management processes. Here we present our comprehensive analysis of the degradation path for different operating sequences. The analysis is based on a dataset we constructed using measurements from 72 commercial battery cells operated according to 24 dynamic operating sequences and by employing a periodic diagnostic protocol to quantify the kinetic degradation at various states of charge. By incorporating the path-dependent characteristics of battery degradation into deep learning approaches, we developed a framework capable of predicting future health states from the state at a single time-point without historical information. Our predictive framework achieves test average percent errors of 0.76% and 0.81% for the degradation paths and capacity trajectories, respectively. The proposed battery management schemes offer high prediction reliability and accuracy for dynamic operation and are anticipated to be useful for extending the operational lifetime of LIBs.Y
Improved unsupervised 3D lung lesion detection and localization by fusing global and local features: Validation in 3D low-dose computed tomography
No full textUnsupervised anomaly detection (UAD) is crucial in low-dose computed tomography (LDCT). Recent AI technologies, leveraging global features, have enabled effective UAD with minimal training data of normal patients. However, this approach, devoid of utilizing local features, exhibits vulnerability in detecting deep lesions within the lungs. In other words, while the conventional use of global features can achieve high specificity, it often comes with limited sensitivity. Developing a UAD AI model with high sensitivity is essential to prevent false negatives, especially in screening patients with diseases demonstrating high mortality rates. We have successfully pioneered a new LDCT UAD AI model that leverages local features, achieving a previously unattainable increase in sensitivity compared to global methods (17.5% improvement). Furthermore, by integrating this approach with conventional global-based techniques, we have successfully consolidated the advantages of each model-high sensitivity from the local model and high specificity from the global model-into a single, unified, trained model (17.6% and 33.5% improvement, respectively). Without the need for additional training, we anticipate achieving significant diagnostic efficacy in various LDCT applications, where both high sensitivity and specificity are essential, using our fixed model. Code is available at https: //github.com/kskim-phd/Fusion-UADL.Y