Accelerating diabetic wound healing by ROS-scavenging lipid nanoparticle-mRNA formulation.

Current treatment options for diabetic wounds face challenges due to low efficacy, as well as potential side effects and the necessity for repetitive treatments. To address these issues, we report a formulation utilizing trisulfide-derived lipid nanoparticle (TS LNP)-mRNA therapy to accelerate diabetic wound healing by repairing and reprogramming the microenvironment of the wounds. A library of reactive oxygen species (ROS)-responsive TS LNPs was designed and developed to encapsulate interleukin-4 (IL4) mRNA. TS2-IL4 LNP-mRNA effectively scavenges excess ROS at the wound site and induces the expression of IL4 in macrophages, promoting the polarization from the proinflammatory M1 to the anti-inflammatory M2 phenotype at the wound site. In a diabetic wound model of db/db mice, treatment with this formulation significantly accelerates wound healing by enhancing the formation of an intact epidermis, angiogenesis, and myofibroblasts. Overall, this TS LNP-mRNA platform not only provides a safe, effective, and convenient therapeutic strategy for diabetic wound healing but also holds great potential for clinical translation in both acute and chronic wound care.

Mitochondrial tRNA pseudouridylation governs erythropoiesis.

Pseudouridine is the most prevalent RNA modification, and its aberrant function is implicated in various human diseases. However, the specific impact of pseudouridylation on hematopoiesis remains poorly understood. In this study, we investigated the role of tRNA pseudouridylation in erythropoiesis and its association with mitochondrial myopathy, lactic acidosis, and sideroblastic anemia syndrome (MLASA) pathogenesis. By utilizing patient-specific induced pluripotent stem cells (iPSCs) carrying a genetic PUS1 mutation and a corresponding mutant mouse model, we demonstrated impaired erythropoiesis in MLASA iPSCs and anemia in the MLASA mouse model. Both MLASA iPSCs and mouse erythroblasts exhibited compromised mitochondrial function and impaired protein synthesis. Mechanistically, we revealed that PUS1 deficiency resulted in reduced mitochondrial tRNA levels due to pseudouridylation loss, leading to aberrant mitochondrial translation. Screening of mitochondrial supplements aimed at enhancing respiration or heme synthesis showed limited effect in promoting erythroid differentiation. Interestingly, the mTOR inhibitor rapamycin facilitated erythroid differentiation in MLASA-iPSCs by suppressing mTOR signaling and protein synthesis, and consistent results were observed in the MLASA mouse model. Importantly, rapamycin treatment effectively ameliorated anemia phenotypes in the MLASA patient. Our findings provide novel insights into the crucial role of mitochondrial tRNA pseudouridylation in governing erythropoiesis and present potential therapeutic strategies for anemia patients facing challenges related to protein translation.

Sulfur-Vacancy Engineering Accelerates Rapid Surface Reconstruction in Ni-Co Bimetal Sulfide Nanosheet for Urea Oxidation Electrocatalysis.

Developing a highly efficient catalyst for electrocatalytic urea oxidation reaction (UOR) is not only beneficial for the degradation of urea pollutants in wastewater but also provides a benign route for hydrogen production. Herein, a sulfur-vacancy (Sv) engineering is proposed to accelerate the formation of metal (oxy)hydroxide on the surface of Ni-Co bimetal sulfide nanosheet arrays on nickel foam (Sv-CoNiS@NF) for boosting the urea oxidation electrocatalysis. As a result, the obtained Sv-CoNiS@NF demonstrates an outstanding electrocatalytic UOR performance, which requires a low potential of only 1.397 V versus the reversible hydrogen electrode to achieve the current density of 100 mA cm-2. The ex situ Raman spectra and density functional theory calculations reveal the key roles of the Sv site and Co9S8 in promoting the electrocatalytic UOR performance. This work provides a new strategy for accelerating the transformation of electrocatalysts to active metallic (oxy)hydroxide for urea electrolysis via engineering the surface vacancies.

hROS-Responsive Behavior for Long-Term Stability of Cellulosic Gold Nanoclusters.

Understanding the gold core-ligand interaction in gold nanoclusters (GNCs) is essential for the on-demand tailoring of their photoluminescence properties and long-term stability. Here, inspired by the suckers arranged directionally on the tentacles of octopus, a series of GNCs with regulating ligand structures are grown and stabilized on the cellulose nanocrystals (CNCs). The carboxylated CNCs providing an electron-rich environment to promote the luminescence of GNCs and stabilize it within a long-term of 1 year through anchoring and diluting effects, and the highest quantum yields reaches 31.02% in ultrapure water. Interestingly, this bionic preparation strategy is generally applicable to various ligands for tailoring on-demand hROS-responsive and nonresponsive GNCs to construct tunable-emission wavelength dual GNCs ratiometric probes. The results show that designing a specific ligand structure to inhibit the transformation of Au-Au to Au (I)-ligand in GNCs is crucial to regulate the hROS-responsive characteristics. As expected, the interfacial compatible dual GNCs ratiometric probe with a hROS limit of detection of 0.74 µmol L-1 can diagnose certain diseases through intracellular hROS imaging. This work provides important insights for understanding the gold core-ligand interaction in GNCs during the oxidation process triggered by intracellular hROS.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

Microneedle Patch Delivery of PLCG1-siRNA Efficient Enhanced Temozolomide Therapy for Glioblastoma.

The blood-brain barrier (BBB) and drug resistance present challenges for chemotherapy of glioblastoma (GBM). A microneedle (MN) patch with excellent biocompatibility and biodegradability was designed to bypass the BBB and release temozolomide (TMZ) and PLCG1-siRNA directly into the tumor site for synergistic treatment of GBM. The codelivery of TMZ and PLCG1-siRNA enhanced DNA damage and apoptosis. The potential mechanism behind this enhancement is to knockdown of PLCG1 expression, which positively regulates the expression of signal transducer and activator of transcription 3 genes, thereby preventing DNA repair and enhancing the sensitivity of GBM to TMZ. The MN patch enables long-term sustainable drug release through in situ implantation and increases local drug concentrations in diseased areas, significantly extending mouse survival time compared to other drug treatment groups. MN drug delivery provides a platform for the combination treatment of GBM and other central nervous system diseases.

Largemouth bass galectin, MsGal-9: Mediating various functions as a pattern recognition receptor and a potential damage-associated molecular pattern.

Galectins are lectins that bind to ß-galactose and are widely expressed in immune system tissues, playing pivotal roles in innate immunity through their conserved carbohydrate-recognition domains (CRDs). In this present investigation, a tandem-repeat galectin was discovered in the largemouth bass, Micropterus salmoides (designated as MsGal-9). The open reading frame of MsGal-9 encodes two CRDs, each containing two consensus motifs that are essential for ligand binding. MsGal-9 is expressed in various tissues of the largemouth bass, with particularly high expression levels in the liver and spleen. The full-length form of MsGal-9, as well as the N-terminal (MsGal-9-N) and C-terminal (MsGal-9-C) CRDs, were individually recombined. Their ability for nonself recognition was studied. The three recombinant proteins were able to bind to glucan (GLU), peptidoglycan (PGN), and lipopolysaccharide (LPS), with MsGal-9 displaying the highest binding activity. Furthermore, rMsGal-9-N exhibited higher binding activity towards GLU in comparison to rMsGal-9-C. Further investigations revealed that the full-length rMsGal-9 could significantly bind to Gram-positive bacteria, Gram-negative bacteria, and fungi, while rMsGal-9-C specifically bound to Escherichia coli. However, rMsGal-9-N did not exhibit significant binding activity towards any microbes. These findings indicate that MsGal-9 requires both CRDs to cooperate in order to fulfill its nonself recognition function. All three recombinant proteins demonstrated agglutination activity towards various microbes, with MsGal-9 and MsGal-9-N displaying a similar broad binding spectrum, while MsGal-9-C agglutinated three types of bacteria. Moreover, both MsGal-9 and MsGal-9-N were capable of coagulating largemouth bass red blood cells, whereas MsGal-9-C lacked this ability. However, MsGal-9-C played a significant role in enhancing the encapsulation of leukocytes in comparison to MsGal-9-N. All three proteins acted as potential damage-associated molecular patterns (DAMPs), inducing apoptosis in leukocytes.

Mechanisms of acid generation from ionic photoacid generators for extreme ultraviolet and electron beam lithography.

Photoacid generators (PAGs) are important components of chemically amplified resists. The properties of PAGs directly affect the sensitivity of photoresists, line edge roughness, and resolution. Understanding the photoacid generation process in extreme ultraviolet (EUV) and electron beam (EB) lithography is helpful for photoresist design. However, the microscopic mechanisms remain largely unclear and the large variety in the molecular structure of PAGs presents a challenge to overcome. In this work, we investigate the microscopic processes of photoacid production of ionic PAGs for EUV and EB lithography. The PAG dissociation pathway is found to depend on the molecular structure and conformations. The processes of photoacid production and by-product generation are also revealed. The results contribute to a better understanding of the photochemical reactions in EUV and EB lithography, providing insights into the molecular design of novel PAGs and photoresists.

Structural and functional alterations in cerebral small vessel disease: an ALE-based meta-analysis.

Cerebral small vessel disease (CSVD) is one of the most important causes of stroke and dementia. Although increasing studies have reported alterations of brain structural or neuronal functional activity exhibited in patients with CSVD, it is still unclear which alterations are reliable. Here, we performed a meta-analysis to establish which brain structural or neuronal functional activity changes in those studies were consistent. Activation likelihood estimation revealed that changes in neuronal functional activity in the left angular gyrus, bilateral anterior cingulate cortex/left medial prefrontal cortex, right rolandic operculum, and alterations of gray structure in the left insular cortex/superior temporal gyrus/claustrum were reliable in sporadic CSVD. Decreased neuronal functional activity in the caudate head, anterior cingulate cortex, and reduced gray matter volume in the insular cortex/superior temporal gyrus/claustrum were associated with CSVD-related cognitive impairment. Furthermore, unlike sporadic CSVD, the reliable alterations of neuronal functional activity in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy were concentrated in the left parahippocampal gyrus. The current study presents stable brain structural and neuronal functional abnormalities within the brain, which can help further understand the pathogenesis of CSVD and CSVD-cognitive impairment and provide an index to evaluate the effectiveness of treatment protocols. HIGHLIGHTS: • Default mode network and salience network are reliable networks affected in sporadic CSVD in resting-state.• Altered corticostriatal circuitry is associated with cognitive decline.• Decreased gray matter volume in the insular cortex is stable "remote effects" of sporadic CSVD.• The parahippocampal gyrus may be a reliable affected brain region in CADASIL.

Monolithic Titanium Alkoxide Networks for Lithium-Ion Conductive All-Solid-State Electrolytes.

Reticular chemistry provides opportunities to design solid-state electrolytes (SSEs) with modular tunability. However, SSEs based on modularly designed crystalline metal-organic frameworks (MOFs) often require liquid electrolytes for interfacial contact. Monolithic glassy MOFs can have liquid processability and uniform lithium conduction, which is promising for the reticular design of SSE without liquid electrolytes. Here, we develop a generalizable strategy for the modular design of noncrystalline SSEs based on a bottom-up synthesis of glassy MOFs. We demonstrate such a strategy by linking polyethylene glycol (PEG) struts and nanosized titanium-oxo clusters into network structures termed titanium alkoxide networks (TANs). The modular design allows the incorporation of PEG linkers with different molecular weights, which give optimal chain flexibility for high ionic conductivity, and the reticular coordinative network provides a controlled degree of cross-linking that gives adequate mechanical strength. This research shows the power of reticular design in noncrystalline molecular framework materials for SSEs.

Study of CHF3/CH2F2 Adsorption Separation in TIFSIX-2-Cu-i.

Hydrofluorocarbons (HFCs) have important applications in different industries; however, they are environmentally unfriendly due to their high global warming potential (GWP). Hence, reclamation of used hydrofluorocarbons via energy-efficient adsorption-based separation will greatly contribute to reducing their impact on the environment. In particular, the separation of azeotropic refrigerants remains challenging, such as typical mixtures of CH2F2 (HFC-23) and CHF3 (HFC-32), due to a lack of adsorptive mechanisms. Metal-organic frameworks (MOFs) can provide a promising solution for the separation of CHF3-CH2F2 mixtures. In this study, the adsorption mechanism of CHF3-CH2F2 mixtures in TIFSIX-2-Cu-i was revealed at the microscopic level by combining static pure-component adsorption experiments, molecular simulations, and density-functional theory (DFT) calculations. The adsorption separation selectivity of CH2F2/CHF3 in TIFSIX-2-Cu-i is 3.17 at 3 bar under 308 K. The existence of similar TiF62- binding sites for CH2F2 or CHF3 was revealed in TIFSIX-2-Cu-i. Interactions between the fluorine atom of the framework and the hydrogen atom of the guest molecule were found to be responsible for determining the high adsorption separation selectivity of CH2F2/CHF3. This exploration is important for the design of highly selective adsorbents for the separation of azeotropic refrigerants.

Taming Two-Dimensional Polymerization by a Machine-Learning Discovered Crystallization Model.

Rapidly synthesizing high-quality two-dimensional covalent organic frameworks (2D COFs) is crucial to their practical applications. Here, we use a machine-learning approach that overcomes the challenges associated with bottom-up model derivation for the non-classical 2D COF crystallization processes. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, bond-forming rate constants, and common solution synthesis conditions for 2D COFs that grow by a nucleation-elongation mechanism. The results elucidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately described previously by classical, empirical models with assumptions invalid for 2D COF polymerization. By understanding the dynamic processes at play, the NEgen1 model reveals a simple strategy of gradually increasing monomer addition speed for growing large 2D COF crystals. This insight enables us to rapidly synthesize large COF-5 colloids, which could only be achieved previously by prolonged reaction times or by introducing chemical modulators. These results highlight the potential for systematically improving the crystal quality of 2D COFs, which has wide-reaching relevance for many of the applications for which 2D COFs are speculated to be valuable.

Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging.

BACKGROUND: Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep learning (DL) algorithm for detecting CVT using routine brain magnetic resonance imaging. METHODS: Routine brain magnetic resonance imaging, including T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images of patients suspected of CVT from April 2014 through December 2019 who were enrolled from a CVT registry, were collected. The images were divided into 2 data sets: a development set and a test set. Different DL algorithms were constructed in the development set using 5-fold cross-validation. Four radiologists with various levels of expertise independently read the images and performed diagnosis within the test set. The diagnostic performance on per-patient and per-segment diagnosis levels of the DL algorithms and radiologist's assessment were evaluated and compared. RESULTS: A total of 392 patients, including 294 patients with CVT (37±14 years, 151 women) and 98 patients without CVT (42±15 years, 65 women), were enrolled. Of these, 100 patients (50 CVT and 50 non-CVT) were randomly assigned to the test set, and the other 292 patients comprised the development set. In the test set, the optimal DL algorithm (multisequence multitask deep learning algorithm) achieved an area under the curve of 0.96, with a sensitivity of 96% (48/50) and a specificity of 88% (44/50) on per-patient diagnosis level, as well as a sensitivity of 88% (129/146) and a specificity of 80% (521/654) on per-segment diagnosis level. Compared with 4 radiologists, multisequence multitask deep learning algorithm showed higher sensitivity both on per-patient (all P<0.05) and per-segment diagnosis levels (all P<0.001). CONCLUSIONS: The CVT-detected DL algorithm herein improved diagnostic performance of routine brain magnetic resonance imaging, with high sensitivity and specificity, which provides a promising approach for detecting CVT.

Tuning Crystallinity and Stacking of Two-Dimensional Covalent Organic Frameworks through Side-Chain Interactions.

Two-dimensional covalent organic frameworks (2D COFs) form as layered 2D polymers whose sheets stack through high-surface-area, noncovalent interactions that can give rise to different interlayer arrangements. Manipulating the stacking of 2D COFs is crucial since it dictates the effective size and shape of the pores as well as the specific interactions between functional aromatic systems in adjacent layers, both of which will strongly influence the emergent properties of 2D COFs. However, principles for tuning layer stacking are not yet well understood, and many 2D COFs are disordered in the stacking direction. Here, we investigate effects of pendant chain length through a series of 2D imine-linked COFs functionalized with n-alkyloxy chains varying in length from one carbon (C1 COF) to 11 carbons (C11 COF). This series reveals previously unrecognized and unanticipated trends in both the stacking geometry and crystallinity. C1 COF adopts an averaged eclipsed geometry with no apparent offset between layers. In contrast, all subsequent chain lengths lead to some degree of unidirectional slip stacking. As pendant chain length is increased, trends show average layer offset increasing to a maximum of 2.07 Å in C5 COF and then decreasing as chain length is extended through C11 COF. Counterintuitively, shorter chains (C2-C4) give rise to lower yields of weakly crystalline materials, while longer chains (C6-C9) produce greater yields of highly crystalline materials, as confirmed by powder X-ray diffraction and scanning electron microscopy. Molecular dynamics simulations corroborate these observations, suggesting that long alkyl chains can interact favorably to promote the self-assembly of sheets. In situ proton NMR spectroscopy provides insights into the reaction equilibrium as well as the relationship between low COF yields and low crystallinity. These results provide fundamental insights into principles of supramolecular assembly in 2D COFs, demonstrating an opportunity for harnessing favorable side-chain interactions to produce highly crystalline materials.

SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.

Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biological data to identify the association between circRNA and disease can effectively reduce cost and save time. Considering the limitations of existing computational models, we propose a semi-supervised generative adversarial network (GAN) model SGANRDA for predicting circRNA-disease association. This model first fused the natural language features of the circRNA sequence and the features of disease semantics, circRNA and disease Gaussian interaction profile kernel, and then used all circRNA-disease pairs to pre-train the GAN network, and fine-tune the network parameters through labeled samples. Finally, the extreme learning machine classifier is employed to obtain the prediction result. Compared with the previous supervision model, SGANRDA innovatively introduced circRNA sequences and utilized all the information of circRNA-disease pairs during the pre-training process. This step can increase the information content of the feature to some extent and reduce the impact of too few known associations on the model performance. SGANRDA obtained AUC scores of 0.9411 and 0.9223 in leave-one-out cross-validation and 5-fold cross-validation, respectively. Prediction results on the benchmark dataset show that SGANRDA outperforms other existing models. In addition, 25 of the top 30 circRNA-disease pairs with the highest scores of SGANRDA in case studies were verified by recent literature. These experimental results demonstrate that SGANRDA is a useful model to predict the circRNA-disease association and can provide reliable candidates for biological experiments.

Photoredox/Cobalt-Catalyzed Cascade Oxidative Synthesis of 2,5-Disubstituted 1,3,4-Oxadiazoles under Oxidant-Free Conditions.

An efficient oxidant-free, photoredox-mediated cascade cyclization strategy for the synthesis of 1,3,4-oxadiazoles by using an organo acridinium photocatalyst and a cobaloxime catalyst has been developed. Various acylhydrazones have been transformed into the corresponding 1,3,4-oxadiazole products in up to 96% yield, and H2 is the only byproduct. Mechanistic experiments and density functional theory (DFT) calculation studies indicate carbon-centered radicals rather than oxygen-centered radicals as π-radicals produced by the oxidation of photoexcited Mes-Acr+* along with deprotonation, which is responsible for this transformation. The practical utility of this method is highlighted by the one-pot gram-scale synthesis starting directly from commercially available aldehydes and acylhydrazides.

Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning.

The construction of material prediction models using machine learning algorithms can aid in the polyimide structural design and screening of materials as well as accelerate the development of new materials. There is a lack of research on predicting the optical properties of polyimide materials and the interpretation of the structural features. Here, we collected 652 polyimide molecular structures and used seven popular machine learning algorithms to predict the glass transition temperature and cut-off wavelength of polyimide materials and extract key feature information of repeating unit structures. The results showed that the root mean square error of the glass transition temperature prediction model was 33.92 °C, and the correlation coefficient was 0.861. The root mean square error of the cut-off wavelength prediction model was 17.18 nm, and the correlation coefficient was 0.837. The elasticity of the molecular structure was also found to be the key factor affecting glass transition temperature, and the presence and location of heterogeneous atoms had a significant effect on the cut-off wavelengths. Finally, eight polyimide materials were synthesized to test the accuracy of the prediction models, and the experimental characterization values agreed with the predicted values. The results would contribute to the development of polyimide structural design and materials preparation for flexible display.

An Ultrathin Functional Layer Based on Porous Organic Cages for Selective Ion Sieving and Lithium-Sulfur Batteries.

Thin films with effective ion sieving ability are highly desired in energy storage and conversion devices, including batteries and fuel cells. However, it remains challenging to design and fabricate cost-effective and easy-to-process ultrathin films for this purpose. Here, we report a 300 nm-thick functional layer based on porous organic cages (POCs), a new class of porous molecular materials, for fast and selective ion transport. This solution processable material allows for the design of thin films with controllable thickness and tunable porosity by tailoring cage chemistry for selective ion separation. In the prototype, the functional layer assembled by CC3 can selectively sieve Li+ ions and efficiently suppress undesired polysulfides with minimal sacrifice for the system's total energy density. Separators modified with POC thin films enable batteries with good cycle performance and rate capability and offer an attractive path toward the development of future high-energy-density energy storage devices.

Evaluating the resource recovery efficiency of garbage removal and disposal in Taiwan.

All countries recognize the importance of enhancing circular resource utilization and promoting zero waste in order to achieve a circular economy. This study focuses on 22 counties (cities) in Taiwan and analyses the relationship between changes in resource recovery efficiency and productivity over an 8-year period (2011-2018) using the super efficiency directional distance function and total factor productivity. The results indicate that the average total efficiency of resource recovery in the 22 counties (cities) is 1.3951, with 17 counties (cities) exceeding 1. The average gross productivity of resource recovery is 1.0417, and only four counties have a gross productivity of less than 1. Despite having more administrative resources, municipalities directly under the central government have a slightly lower average total efficiency of resource recovery (1.1464) than non-municipalities (1.4885). To improve efficiency, the government should allocate resources rationally and upgrade technology. The study's findings can serve as a reference for the 22 counties (cities) in Taiwan and provide guidance for municipal governments worldwide in developing policies related to resource recovery.

Boron-Dipyrromethene-Based Fluorescent Emitters Enable High-Performance Narrowband Red Organic Light-Emitting Diodes.

Narrowband organic light-emitting diodes (OLEDs) are receiving significant attention and have demonstrated impressive performance in blue and green OLEDs. However, developing high-performance narrowband red OLEDs remains a highly desired yet challenging task. Herein, we have developed narrowband red fluorescent emitters by utilizing a boron-dipyrromethene (BODIPY) skeleton in combination with a methyl-shield strategy. These emitters exhibit small full-width at half-maxima (FWHM) ranging from 21 nm (0.068 eV) to 25 nm (0.081 eV) and high photoluminescence quantum yields (ΦPL ) ranging from 88.5 % to 99.0 % in toluene solution. Using BODIPY-based luminescent materials as emitters, high-performance narrowband red OLEDs have been assembled with external quantum efficiency as high as 18.3 % at 623 nm and 21.1 % at 604 nm. This work represents, to our knowledge, the first successful case of achieving NTSC pure-red OLEDs with the Commission Internationale de l'Éclairage (CIE) coordinates of [0.67, 0.33] based on conventional fluorescent emitters.