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The Hydroxycarboxylic acid receptor 2 (HCA2), also known as the niacin receptor or GPR109A, is a prototypical GPCR that plays a central role in the inhibition of lipolytic and atherogenic activities. Its activation also results in vasodilation that is linked to the side-effect of flushing associated with dyslipidemia drugs such as niacin. GPR109A continues to be a target for developing potential therapeutics in dyslipidemia with minimized flushing response. Here, we present cryo-EM structures of the GPR109A in complex with dyslipidemia drugs, niacin or acipimox, non-flushing agonists, MK6892 or GSK256073, and recently approved psoriasis drug, monomethyl fumarate (MMF). These structures elucidate the binding mechanism of agonists, molecular basis of receptor activation, and insights into biased signaling elicited by some of the agonists. The structural framework also allows us to engineer receptor mutants that exhibit G-protein signaling bias, and therefore, our study may help in structure-guided drug discovery efforts targeting this receptor. GPR109A is a prototypical GPCR and a key drug target for dyslipidemia. Here, the authors present cryo-EM structures of this receptor to elucidate agonist-binding and activation, and design receptor mutants with transducer-coupling-bias. [ABSTRACT FROM AUTHOR]

While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI diagnosis model under adversarial images generated by Generative Adversarial Network (GAN) models and to evaluate the effects on human experts when visually identifying potential adversarial images. Our GAN model makes intentional modifications to the diagnosis-sensitive contents of mammogram images in deep learning-based computer-aided diagnosis (CAD) of breast cancer. In our experiments the adversarial samples fool the AI-CAD model to output a wrong diagnosis on 69.1% of the cases that are initially correctly classified by the AI-CAD model. Five breast imaging radiologists visually identify 29%-71% of the adversarial samples. Our study suggests an imperative need for continuing research on medical AI model's safety issues and for developing potential defensive solutions against adversarial attacks. While active efforts are advancing medical AI model development and clinical translation, safety issues of medical AI models have emerged. Here, the authors investigate the effects on an AI model and on human experts of potential fake/adversarial images for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Clostridioides difficile toxin B (TcdB) is the key virulence factor accounting for C. difficile infection-associated symptoms. Effectively neutralizing different TcdB variants with a universal solution poses a significant challenge. Here we present the de novo design and characterization of pan-specific mini-protein binders against major TcdB subtypes. Our design successfully binds to the first receptor binding interface (RBI-1) of the varied TcdB subtypes, exhibiting affinities ranging from 20 pM to 10 nM. The cryo-electron microscopy (cryo-EM) structures of the mini protein binder in complex with TcdB1 and TcdB4 are consistent with the computational design models. The engineered and evolved variants of the mini-protein binder and chondroitin sulfate proteoglycan 4 (CSPG4), another natural receptor that binds to the second RBI (RBI-2) of TcdB, better neutralize major TcdB variants both in cells and in vivo, as demonstrated by the colon-loop assay using female mice. Our findings provide valuable starting points for the development of therapeutics targeting C. difficile infections (CDI). Xinchen Lv and colleagues design pan-specific protein mini-binders that neutralize Clostridioides difficile toxin B (TcdB) and solve TcdB-binder complex structures via cryo-electron microscopy. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC. Gastric cancer detection by endoscopy is intrusive and time-consuming, and early detection is key to improving survival. Here, the authors propose a metabolite-based model to enable early detection. [ABSTRACT FROM AUTHOR]

Neuroendocrine prostate cancer is a rapidly progressive and lethal disease characterized by early visceral metastasis, poor prognosis, and limited treatment options. Uncovering the oncogenic mechanisms could lead to the discovery of potential therapeutic avenues. Here, we demonstrate that the RNA-binding protein ELAVL3 is specifically upregulated in neuroendocrine prostate cancer and that overexpression of ELAVL3 alone is sufficient to induce the neuroendocrine phenotype in prostate adenocarcinoma. Mechanistically, ELAVL3 is transcriptionally regulated by MYCN and subsequently binds to and stabilizes MYCN and RICTOR mRNA. Moreover, ELAVL3 is shown to be released in extracellular vesicles and induce neuroendocrine differentiation of adenocarcinoma cells via an intercellular mechanism. Pharmacological inhibition of ELAVL3 with pyrvinium pamoate, an FDA-approved drug, effectively suppresses tumor growth, reduces metastatic risk, and improves survival in neuroendocrine prostate cancer mouse models. Our results identify ELAVL3 as a critical regulator of neuroendocrine differentiation in prostate cancer and propose a drug repurposing strategy for targeted therapies. Neuroendocrine prostate cancer (NEPC) is an aggressive subtype with limited effective therapeutic options. Here, the authors identify the RNA-binding protein ELAVL3 as a driver of differentiation into NEPC via a positive feedback loop with MYCN and demonstrate the use of the repurposed drug pyrvinium pamoate to target this axis using preclinical models of NEPC. [ABSTRACT FROM AUTHOR]

Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages 'neural implicit representations' that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems. Analysis of experimental data in condensed matter is often challenging due to system complexity and slow character of physical simulations. The authors propose a framework that combines machine learning with theoretical calculations to enable real-time analysis for electron, neutron, and x-ray spectroscopies. [ABSTRACT FROM AUTHOR]

The extreme 5′-end of the enterovirus RNA genome contains a conserved cloverleaf-like domain that recruits 3CD and PCBP proteins required for initiating genome replication. Here, we report the crystal structure at 1.9 Å resolution of this domain from the CVB3 genome in complex with an antibody chaperone. The RNA folds into an antiparallel H-type four-way junction comprising four subdomains with co-axially stacked sA-sD and sB-sC helices. Long-range interactions between a conserved A40 in the sC-loop and Py-Py helix within the sD subdomain organize near-parallel orientations of the sA-sB and sC-sD helices. Our NMR studies confirm that these long-range interactions occur in solution and without the chaperone. The phylogenetic analyses indicate that our crystal structure represents a conserved architecture of enteroviral cloverleaf-like domains, including the A40 and Py-Py interactions. The protein binding studies further suggest that the H-shape architecture provides a ready-made platform to recruit 3CD and PCBP2 for viral replication. A cloverleaf-like RNA domain within the enterovirus genome is essential for replication. Here, the authors determine the 1.9 Å resolution crystal structure of such RNA from coxsackievirus B3 – a model enterovirus to study many other human viruses. [ABSTRACT FROM AUTHOR]

Telomeres are specialized nucleoprotein structures at the ends of linear chromosomes. The progressive shortening of steady-state telomere length in normal human somatic cells is a promising biomarker for age-associated diseases. However, there remain substantial challenges in quantifying telomere length due to the lack of high-throughput method with nucleotide resolution for individual telomere. Here, we describe a workflow to capture telomeres using newly designed telobaits in human culture cell lines as well as clinical patient samples and measure their length accurately at nucleotide resolution using single-molecule real-time (SMRT) sequencing. Our results also reveal the extreme heterogeneity of telomeric variant sequences (TVSs) that are dispersed throughout the telomere repeat region. The presence of TVSs disrupts the continuity of the canonical (5'-TTAGGG-3')n telomere repeats, which affects the binding of shelterin complexes at the chromosomal ends and telomere protection. These findings may have profound implications in human aging and diseases. There exist challenges in quantifying the length of individual telomeres at single nucleotide resolution. Here the authors report a method to capture telomeres and accurately analyse their length in human cell lines and patient peripheral blood leukocyte samples using single-molecule real-time sequencing. [ABSTRACT FROM AUTHOR]

Chemotherapy and targeted therapy are the major treatments for gastric cancer (GC), but drug resistance limits its effectiveness. Here, we profile the proteome of 206 tumor tissues from patients with GC undergoing either chemotherapy or anti-HER2-based therapy. Proteome-based classification reveals four subtypes (G-I–G-IV) related to different clinical and molecular features. MSI-sig high GC patients benefit from docetaxel combination treatment, accompanied by anticancer immune response. Further study reveals patients with high T cell receptor signaling respond to anti-HER2-based therapy; while activation of extracellular matrix/PI3K-AKT pathway impair anti-tumor effect of trastuzumab. We observe CTSE functions as a cell intrinsic enhancer of chemosensitivity of docetaxel, whereas TKTL1 functions as an attenuator. Finally, we develop prognostic models with high accuracy to predict therapeutic response, further validated in an independent validation cohort. This study provides a rich resource for investigating the mechanisms and indicators of chemotherapy and targeted therapy in GC. The mechanisms of resistance to therapy in gastric cancer remain to be explored. Here, proteomic profiling of 206 tumour tissues from patients treated with chemotherapy and anti-HER2-based therapy results in the identification of four molecular subtypes and the development of prognostic models. [ABSTRACT FROM AUTHOR]

Cystic fibrosis is a monogenic lung disease caused by dysfunction of the cystic fibrosis transmembrane conductance regulator anion channel, resulting in significant morbidity and mortality. The progress in elucidating the role of CFTR using established animal and cell-based models led to the recent discovery of effective modulators for most individuals with CF. However, a subset of individuals with CF do not respond to these modulators and there is an urgent need to develop novel therapeutic strategies. In this study, we generate a panel of airway epithelial cells using induced pluripotent stem cells from individuals with common or rare CFTR variants representative of three distinct classes of CFTR dysfunction. To measure CFTR function we adapt two established in vitro assays for use in induced pluripotent stem cell-derived airway cells. In both a 3-D spheroid assay using forskolin-induced swelling as well as planar cultures composed of polarized mucociliary airway epithelial cells, we detect genotype-specific differences in CFTR baseline function and response to CFTR modulators. These results demonstrate the potential of the human induced pluripotent stem cell platform as a research tool to study CF and in particular accelerate therapeutic development for CF caused by rare variants. Hundreds of mutations in the gene CFTR lead to cystic fibrosis and represent a challenge to developing therapeutics. Here, authors demonstrate the ability of airway cells derived from human iPSCs to model genotype-specific CFTR function as well as pharmacologic rescue of disease causing mutations. [ABSTRACT FROM AUTHOR]

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database (http://www.qshgm.lbci.net/) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases. Microbes communicate with each other by Quorum sensing (QS) languages. Here the authors construct a QS database and the QS communication network to decipher intricate QSbased communications and form one of the key knowledge maps for human gut microbiota. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a "window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces. Reinforcement learning algorithms are emerging as powerful machine learning approaches. This paper introduces a novel machine-learning approach for learning in continuous action space and applies this strategy to the generation of high dimensional potential models for a wide variety of materials. [ABSTRACT FROM AUTHOR]

Increases in adhesive and invasive commensal bacteria, such as Escherichia coli, and subsequent disruption of the epithelial barrier is implicated in the pathogenesis of inflammatory bowel disease (IBD). However, the protective systems against such barrier disruption are not fully understood. Here, we show that secretion of luminal glycoprotein 2 (GP2) from pancreatic acinar cells is induced in a TNF–dependent manner in mice with chemically induced colitis. Fecal GP2 concentration is also increased in Crohn's diease patients. Furthermore, pancreas-specific GP2-deficient colitis mice have more severe intestinal inflammation and a larger mucosal E. coli population than do intact mice, indicating that digestive-tract GP2 binds commensal E. coli, preventing epithelial attachment and penetration. Thus, the pancreas–intestinal barrier axis and pancreatic GP2 are important as a first line of defense against adhesive and invasive commensal bacteria during intestinal inflammation. Glycoprotein-2 (GP-2) can protect the intestinal epithelial barrier from bacteria and is associated with protection against Crohn's disease. Here, the authors show pancreatic GP-2 is the source of the intestine's luminal GP-2 that binds bacteria and prevents them from attaching to the epithelium, also limiting pathology in a DSS colitis mouse model. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality. Identifying COVID-19 patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, the authors present a clinical risk scoring system trained on a large data set of patients from 69 healthcare institutions in multiple countries. [ABSTRACT FROM AUTHOR]

Asparaginyl-tRNA synthetase1 (NARS1) is a member of the ubiquitously expressed cytoplasmic Class IIa family of tRNA synthetases required for protein translation. Here, we identify biallelic missense and frameshift mutations in NARS1 in seven patients from three unrelated families with microcephaly and neurodevelopmental delay. Patient cells show reduced NARS1 protein, impaired NARS1 activity and impaired global protein synthesis. Cortical brain organoid modeling shows reduced proliferation of radial glial cells (RGCs), leading to smaller organoids characteristic of microcephaly. Single-cell analysis reveals altered constituents of both astrocytic and RGC lineages, suggesting a requirement for NARS1 in RGC proliferation. Our findings demonstrate that NARS1 is required to meet protein synthetic needs and to support RGC proliferation in human brain development.Asparaginyl-tRNA synthetase1 (NARS1) is required for protein synthesis. Here, the authors identify biallelic NARS1 mutations in individuals with microcephaly and neurodevelopmental delay. Cortical brain organoid modeling recapitulates microcephaly characteristics and scRNA-seq reveals a role for NARS1 in radial glial cell proliferation. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) was inconsistently associated with migraine and the mechanisms of BP-lowering medications in migraine prophylaxis are unknown. Leveraging large-scale summary statistics for migraine (Ncases/Ncontrols = 59,674/316,078) and BP (N = 757,601), we find positive genetic correlations of migraine with diastolic BP (DBP, rg = 0.11, P = 3.56 × 10−06) and systolic BP (SBP, rg = 0.06, P = 0.01), but not pulse pressure (PP, rg = −0.01, P = 0.75). Cross-trait meta-analysis reveals 14 shared loci (P ≤ 5 × 10−08), nine of which replicate (P < 0.05) in the UK Biobank. Five shared loci (ITGB5, SMG6, ADRA2B, ANKDD1B, and KIAA0040) are reinforced in gene-level analysis and highlight potential mechanisms involving vascular development, endothelial function and calcium homeostasis. Mendelian randomization reveals stronger instrumental estimates of DBP (OR [95% CI] = 1.20 [1.15–1.25]/10 mmHg; P = 5.57 × 10−25) on migraine than SBP (1.05 [1.03–1.07]/10 mmHg; P = 2.60 × 10−07) and a corresponding opposite effect for PP (0.92 [0.88–0.95]/10 mmHg; P = 3.65 × 10−07). These findings support a critical role of DBP in migraine susceptibility and shared biology underlying BP and migraine. The association between blood pressure (BP) and migraine is poorly understood. Here, the authors explore this relationship using summary-level GWAS data for BP and migraine. Cross-trait meta-analysis reveals shared loci between BP and migraine, while Mendelian randomization suggests that diastolic BP specifically plays a key role in migraine susceptibility. [ABSTRACT FROM AUTHOR]

Detection of viral genomes by the innate immune system elicits an antiviral gene program mediated by type I interferons (IFNs). While viral RNA and DNA species induce IFN via separate pathways, the mechanisms by which these pathways are differentially modulated are unknown. Here we show that the positive regulator of IFN in the RNA pathway, TRAF3 has an inhibitory function in the DNA pathway. Loss of TRAF3 coincides with increased expression of the alternative NF-κB-inducing molecule, NIK, which interacts with the DNA pathway adaptor, STING, to enhance IFN induction. Cells lacking NIK display defective IFN activation in the DNA pathway due to impaired STING signaling, and NIK-deficient mice are more susceptible to DNA virus infection. Mechanistically, NIK operates independently from alternative NF-κB signaling components and instead requires autophosphorylation and oligomerization to activate STING. Thus a previously undescribed pathway for NIK exists in activating IFN in the DNA pathway. [ABSTRACT FROM AUTHOR]

Functional characterization of disease-causing variants at risk loci has been a significant challenge. Here we report a high-throughput single-nucleotide polymorphisms sequencing (SNPs-seq) technology to simultaneously screen hundreds to thousands of SNPs for their allele-dependent protein-binding differences. This technology takes advantage of higher retention rate of protein-bound DNA oligos in protein purification column to quantitatively sequence these SNP-containing oligos. We apply this technology to test prostate cancer-risk loci and observe differential allelic protein binding in a significant number of selected SNPs. We also test a unique application of self-transcribing active regulatory region sequencing (STARR-seq) in characterizing allele-dependent transcriptional regulation and provide detailed functional analysis at two risk loci (RGS17 and ASCL2). Together, we introduce a powerful high-throughput pipeline for large-scale screening of functional SNPs at disease risk loci. [ABSTRACT FROM AUTHOR]

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs. [ABSTRACT FROM AUTHOR]

Providing insight into one's health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, we introduce the Gut Microbiome Health Index (GMHI), a biologically-interpretable mathematical formula for predicting the likelihood of disease independent of the clinical diagnosis. GMHI is formulated upon 50 microbial species associated with healthy gut ecosystems. These species are identified through a multi-study, integrative analysis on 4347 human stool metagenomes from 34 published studies across healthy and 12 different nonhealthy conditions, i.e., disease or abnormal bodyweight. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of disease presence (or absence) compared to α-diversity indices. Validation on 679 samples from 9 additional studies results in a balanced accuracy of 73.7% in distinguishing healthy from non-healthy groups. Our findings suggest that gut taxonomic signatures can predict health status, and highlight how data sharing efforts can provide broadly applicable discoveries. A biologically-interpretable and robust metric that provides insight into one's health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, the authors introduce a species-level index that predicts the likelihood of having a disease. [ABSTRACT FROM AUTHOR]

Signal transduction systems enable organisms to monitor their external environments and accordingly adjust the cellular processes. In mast cells, the second messenger Ap4A binds to the histidine triad nucleotide-binding protein 1 (HINT1), disrupts its interaction with the microphthalmia-associated transcription factor (MITF), and eventually activates the transcription of genes downstream of MITF in response to immunostimulation. How the HINT1 protein recognizes and is regulated by Ap4A remain unclear. Here, using eight crystal structures, biochemical experiments, negative stain electron microscopy, and cellular experiments, we report that Ap4A specifically polymerizes HINT1 in solution and in activated rat basophilic leukemia cells. The polymerization interface overlaps with the area on HINT1 for MITF interaction, suggesting a possible competitive mechanism to release MITF for transcriptional activation. The mechanism depends precisely on the length of the phosphodiester linkage of Ap4A. These results highlight a direct polymerization signaling mechanism by the second messenger. The second messenger Ap4A contributes to mast cell activation by interacting with HINT1 but the molecular mechanism is not fully understood. Here, the authors show that Ap4A regulates downstream signaling by polymerizing HINT1 and provide insights into how specificity over other second messengers is achieved. [ABSTRACT FROM AUTHOR]

This paper reports our experimental studies on the underlying mechanism responsible for electroluminescence spectral narrowing phenomenon in the cavity-based organic light-emitting diodes. It is found that the microcavity generates an emerging phenomenon: a magneto-photoluminescence signal in Poly(9,9-dioctylfluorene-alt-benzothiadiazole) polymer under photoexcitation, which is completely absent when microcavity is not used. This provides an evidence that microcavity leads to the formation of spatially extended states, functioning as the intermediate states prior to the formation of Frenkel excitons in organic materials. This is confirmed by the magneto-electroluminescence solely observed from the cavity-based light-emitting diodes under electrical injection. Furthermore, the narrowed electroluminescence output shows a linear polarization, concurrently occurred with magneto-electroluminescence. This indicates that the spatially extended sates become aligned towards forming coherent light-emitting excitons within the microcavity through optical resonance. Clearly, the spatially extended states present the necessary condition to realize electroluminescence spectral narrowing phenomenon towards lasing actions in cavity-based organic light-emitting diodes. Realizing electrically-pumped lasing actions in microcavity organic light-emitting diodes requires elucidating the origin of spectrally-narrowed electroluminescence amplification. Here, the authors show that the microcavity induces spatially extended states with polaron-pair like characteristics. [ABSTRACT FROM AUTHOR]