Science Bulletin

Japanese Encephalitis Virus (JEV) belongs to the Flavivirus family, and the RNA dependent RNA polymerase (RdRp) domain located at the C terminus of non structural protein 5 (NS5) regulates de novo viral genome synthesis. The conserved priming loop inserted in the thumb domain of RDRP initiates de novo genome synthesis. Interestingly, the reported replication process initiates with an interaction between NS5 (methyltransferase/RDRP) and NS3 (protease/helicase), and inhibiting this interaction directly correlates with the inhibition of viral replication. The function of the priming loop in the context of the NS5 NS3 interaction is not yet known. In this study, we studied the priming loop function in the NS5 NS3 interaction and viral replication. Using a structure based drug design approach, we screened the Maybridge compound library against the RdRp priming loop and identified 15 candidate compounds based on binding energy. Among them, DSHS00151 showed significant dose dependent inhibition of the NS5 NS3 interaction with an IC50 of 3.5 micromolar in the mammalian two hybrid assay, inhibited viral infectivity with an IC50 of 2.54 micromolar, and reduced viral RNA load with an IC50 of 2.9 micromolar, compared to CD10712. Molecular Dynamics (MD) simulations revealed that DSHS00151 exhibits stronger and more stable binding with the priming loop residues (790 to 812) compared to CD10712. The molecular mechanism suggested by the docking of the NS5 and NS3 proteins showed that the priming loop tends to rotate toward the NS3 protein to stabilize the complex, and compound binding restricts this loop rotation, thus compromising the stability of the NS5 and NS3 complex and affecting viral replication. Overall, our study validated the function of the NS5 priming loop as an allosteric site, and compounds binding to this allosteric site belong to the Non Nucleoside Reverse Transcriptase Inhibitors (NNRTI) class of inhibitors, which disrupt the NS5 and NS3 interaction and could be developed as novel anti JEV therapeutics.

Text-driven medical image segmentation demands unified models based on multi-modal learning, capable of processing diverse imaging modalities while providing spatially reliable prediction estimates for clinical practice. Nevertheless, existing vision-language model solutions suffer from poor domain generalization and exhibit limited capability for uncertainty estimation to model the cumulative unreliability of cross-modal alignment across network layers. In this paper, we propose UniMedSeg, a unified vision-language framework for multi-modal medical image segmentation built upon frozen pre-trained vision-language backbones. Our framework incorporates modality-aware adaptation, cross-layer uncertainty-guided encoding, and a frequency-spatial dual-prompt decoder, wherein the progressive uncertainty propagation mechanism of the encoder suppresses misleading cross-modal features, whereas the decoder disentangles anatomical structure and boundary details in the frequency domain under dual textual prompts. Auxiliary losses further sharpen boundary representations and calibrate pixel-wise uncertainty maps. Extensive experiments demonstrate that UniMedSeg preserves competitive in-domain segmentation performance, improves domain generalization, and yields clinically useful spatial risk estimation.

Purpose: To develop and evaluate a recurrent neural network that operates directly on undersampled golden-angle radial k-space, enabling low-latency cardiac cine reconstruction for interventional cardiac MRI (iCMR). Methods: KOSTAL (K-space Optimized Spatial Temporal Architecture for Low latency) combines a per-coil k-space convolutional gated recurrent unit (convGRU), which learns a mask-conditioned view-sharing rule over the accumulated k-space, with an image-space recurrent module. It was trained and tested on the open-access multi-coil OCMR dataset using 10 simulated golden-angle spokes per frame against a 1000-spoke reference. Ablations varied recurrent module architecture and placement; temporal behavior was assessed using a imaging-plane transition test. Generalization was evaluated without retraining in 23 pediatric patients prospectively scanned during free-breathing using a radial golden-angle bSSFP sequence with binned XD-GRASP and clinical breath-held cine serving as references. Results: At 10 spokes per frame, KOSTAL reached a median SSIM of 0.907 (gridding: 0.622) in under 34 ms of processing time per frame. Image quality (IQ) matched gridded results formed from 7-11 times more spokes. Recurrence in k-space rather than image space accounted for most of this gain (0.891 versus [≤]0.650). IQ recovered from an abrupt plane transition in 75 ms, versus 303 ms for 200-spoke gridding. KOSTAL had the best agreement with the binned reference and the highest long-axis CNR. KOSTAL accurately tracked left-ventricular blood-pool volume (end-diastolic volume bias -0.7 mL) with a shorter lag (71 ms) than iterative reconstruction (239-287 ms). Conclusion: Learned, mask-conditioned recurrence in k-space yields high image quality at a latency compatible with interventional guidance.

Experimental surgery is one of the most fundamental fields of biomedical research, recreating clinical conditions on living organisms similar to clinical patients, to facilitate the design of novel therapies. For modeling neurological and psychiatric disorders, the majority of operative techniques mandatorily include craniotomy for consecutive interactions with the open brain. The most wide-spread methodology involves stereotactic surgery in rodents, implementing a specific head holder, attached to the coordinate table, providing the precise animal head positioning according to the brain structures topology. Despite the high efficiency, such systems require complex instrumental skills and long procedure duration per animal even for simple implantation protocols. Hence, novel alternative model species could be a valuable supplementary tool for such manipulations. Therefore, the zebrafish (Danio rerio) fits perfectly, due to its low cost, easy husbandry, rapid development, optic transparency, and high genetic and physiological similarity to rodents and humans. Furthermore, the everted zebrafish telencephalon provides excellent accessibility to critical mammalian-like structures involved in emotion processing and cognition. However, conventional avian- or rodent-inspired craniotomies in adult zebrafish often trigger inadvertent disruption of the brain vessels network, resulting in high operative mortality. A novel, geometry-driven, non-lethal cranial window protocol preserving neurovascular integrity and ensuring 90-100% post-operative survival is presented to overcome above mentioned limitations. This rapid, simple, and high reproducible technique opens new avenues for precise region-specific in vivo neurosurgery, high-throughput psychopharmacological screening, and neurostimulation in a powerful non-mammalian model species

Accurate molecular property prediction requires effective molecular representations that can describe a molecule from multiple complementary perspectives. Existing deep learning approaches typically use SMILES strings, two-dimensional molecular graphs, or three-dimensional conformations as inputs. These representations capture different aspects of molecular information: SMILES encodes a sequential description, molecular graphs describe atom-bond connectivity, and 3D conformations provide spatial information from atomic coordinates. However, these modalities are often learned separately or merged with low effective fusion operations, which may not sufficiently capture interactions among sequential, topological, and geometric features. In this work, we propose Tri-Modality Cross-Attention (TMCA), a multimodal framework that integrates 1D SMILES, 2D molecular graphs, and 3D molecular conformations for molecular property prediction. TMCA uses pretrained encoders for the 1D and 3D branches, with a SMILES-based Transformer for the 1D branch and a recent conformation-aware pretrained model for the 3D branch. At the same time, a trainable 2D graph encoder is designed to support modality fusion. All the parameters from the pretrained models are frozen to make overall training more efficient. We evaluate TMCA on four datasets (i.e., BBBP, BACE, ClinTox, and HIV) from the MoleculeNet database for classification tasks and compare its performance with state-of-the-art competing approaches. Experimental results show that TMCA achieves the best overall performance, demonstrating the value of multi-modality integration for drug property prediction and the effectiveness of our cross-attention-based fusion strategy. The source code of TMCA is available at: https://github.com/Ay-Zhao/TMCA

Brain foundation models have shown promising advances in learning generalizable representations from functional magnetic resonance imaging (fMRI) data. However, existing models are typically tied to specific brain atlases and cannot be applied to fMRI data represented using different atlases without full retraining, thereby severely limiting their broader utility across neuroimaging studies. To address this bottleneck, we introduce AREION, atlas-agnostic representation learning of brain connectomes. AREION overcomes atlas dependency through two integrated innovations. First, a Brain Region Contextualizing technique maps brain regions and connectivity entries from diverse atlases into a unified representation space based on their spatial, functional, and morphological characteristics. Second, a Hierarchical Brain Transformer Encoder processes region-specific connectivity profiles as variable-length token sequences and jointly captures local functional structure and global brain functional organization, conditioned on brain regional context, without assuming a fixed number of regions. Together, these two techniques enable the learning of generalizable representations across brain atlases with different parcellation resolutions and connectivity dimensions. AREION is pretrained on a large-scale multi-atlas cohort of fMRI data via self-supervised modeling and evaluated across diverse downstream tasks. Extensive experiments show that it outperforms strong supervised baselines and fMRI foundation models while using substantially fewer parameters, demonstrating strong transferability to atlases unseen during pretraining, generalization to external datasets, and excellent interpretability at the brain regional level.

Intracranial volume (ICV) is often estimated as part of automated neuroimaging processing pipelines and commonly used to account for and characterize cranial size. FreeSurfer's estimated intracranial volume (eTIV) produces implausible longitudinal estimates in developmental samples when using versions prior to FreeSurfer 8. Across two different adolescent datasets, we document eTIV changes of up to 39% between 1.5 - 2 year sessions for FreeSurfer 7, with errors more pronounced on Siemens scanners and in female participants. These errors are markedly reduced in FreeSurfer 8 and AssemblyNet. However, the widespread use of FreeSurfer 7 raises concern about developmental neuroimaging studies that utilize eTIV without quality control.

While many cellular and viral mechanisms are associated with proviral latency, the positive transcription factor b (P-TEFb), a cellular co-factor of the nuclear factor kappa B and viral transactiator proteins, plays a critical role in low viral transcription levels in resting infected cells. P-TEFb, comprised of CDK9 and Cyclin T1 (CycT1), is absent in quiescent CD4+T cells that represent the bulk of the viral latent reservoir. In these cells, the CycT1 subunit is dephosphorylated and dissociated from CDK9, leading to its rapid proteasomal degradation. Based on the knowledge of CycT1 phosphorylation and proteolysis, we designed a mutant CycT1 protein [constitutively active (CA)-CycT1] that is resistant to protein degradation and is stably expressed in resting CD4+ T cells. Interestingly, the expression of CA-CycT1 significantly delayed the establishment of viral latency in CD4+ T cells. This is the first example of expressing a high level of CycT1 proteins in resting CD4+ T cells, which modulates viral latency and T cell functions. This finding presents a significant therapeutic potential for the development of effective therapies for latent viral infections.

HIV infection significantly elevated the odds of cardiovascular diseases, including myocardial fibrosis. We aimed to determine the difference of late gadolinium enhancement (LGE), extracellular volume (ECV), T1, and T2 using cardiac magnetic resonance imaging (CMR) in patients living with HIV (PLWH) and healthy controls. The databases Scopus, Web of Science, Embase, and PubMed were searched systematically using relevant keywords. STATA version 17 was used for meta-analysis using random effect and fixed effect models for heterogeneous and non-heterogeneous analyses. An overall number of 13 studies were included in the current analysis involving 1425 PLWH and 766 healthy controls. PLWH showed significantly higher LGE (OR: 5.47, 95% CI: 1.90-15.64, p<0.001) and ECV (mean difference: 1.46, 95% CI: 0.21-2.71, p=0.02) compared to controls. However, differences in Left Ventricular Ejection Fraction (LVEF) were not significant (mean difference: -1.41, 95% CI: -2.81- -0.01, p=0.05). There were no meaningful disparities in pro B-type natriuretic peptide (BNP) values (mean difference: 8.67, 95% CI: 0.04-17.29, p=0.05) or T2 values (mean difference: 0.91 ms, 95% CI: -1.04-2.86, p=0.36), but T1 values were significantly higher in PLWH (mean difference: 13.78 ms, 95% CI: 9.16-18.40, p<0.001). In meta regression, LGE was correlated with nadir CD4 (z=-3.41, p-value<0.001). In addition, ECV was correlated with white race (Z=-3.97, p-value<0.001), Hepatitis C (Z=-2.08, p-value=0.037), and smoking (Z=2.05, p-value=0.041). HIV is related to a significantly elevated rate of myocardial fibrosis, and factors such as lower nadir CD4, white race, hepatitis C, and smoking were correlated with hepatitis C. Keywords: Myocardial fibrosis, HIV, AIDS, Cardiovascular diseases, Heart failure

Drug-drug interactions (DDIs) represent a critical challenge in drug development and clinical practice, as they can lead to severe adverse effects, including toxicity and reduced therapeutic efficacy. Deep learning methods have shown promise in large-scale, rapid DDI prediction; however, current approaches suffer from significant limitations in providing mechanistic insights into these interactions. Here, we propose DualTopoDDI, a dual-topology-enhanced interpretable deep learning model for DDI prediction. We applied DualTopoDDI to predict 9.2 billion potential interactions among approved drugs, achieving 97.99% high-confidence predictions. The model demonstrates molecular-level interpretability, identifying key substructures responsible for drug actions in terms of both atom-centric and bond-centric views. DualTopoDDI also excels in elucidating particular DDI toxicity mechanisms, which is validated by its successful explanation of the controversial cardiac toxicity in two COVID-19 drug combination regimens at the time. Evaluations across 11 benchmark datasets demonstrates that DualTopoDDI not only achieves state-of-the-art performance but also showcases robust generalizability and good interpretability. Overall, DualTopoDDI offers a powerful, interpretable tool for understanding and predicting drug-drug interactions, providing critical insights for drug safety and design.

The teleost whole-genome duplication (TGD) contributed to functional diversification of opsins. Some TGD paralogs, including those of parapinopsin (PP), Vertebrate Ancient (VA) opsin, and long wavelength-sensitive (LWS) opsin, show different absorption spectra and/or expression patterns. However, our knowledge of detailed evolutionary processes and mechanisms by which TGD contributed to opsin diversification is still limited. Here, we report that TGD paralogs of parietopsin (PT1 and PT2) are retained in extant species. While most species, including the zebrafish, have only PT1, several characins, including the red piranha (Pygocentrus nattereri), retain both PT1 and PT2. The Mexican tetra (Astyanax mexicanus) and catfishes have only PT2. To assess the degree of functional diversification between PT1 and PT2, we characterized spectral properties and expression patterns. Maximum absorption spectra differ slightly among PTs. Those of red piranha PT1, PT2, Mexican tetra PT2, and Japanese catfish (Silurus asotus) PT2 were located at 517 nm, 528 nm, 517 nm, and ~535 nm, respectively. Fluorescence in situ hybridization showed that (1) piranha PT1 and PT2 are expressed in the same pineal cells, and (2) they are also co-expressed with PP1. Based on these findings, functional diversification of opsins after gene duplication is discussed.

BACKGROUND: While the systemic right and left ventricles have different functional prognoses, only a limited number of reports have addressed their myocardial architecture. This study analyzed myofiber orientation of human specimens with various congenital heart diseases using X-ray phase-contrast tomography. METHODS: Three specimens of each normal heart, ventricular septal defect, right atrial isomerism, left atrial isomerism, and ventricular inversion were examined. In reconstructed images of both ventricles, the center of the free wall was resliced parallel to the myocardial surface. A myofiber helical angle was identified in each slice and plotted from epicardium to endocardium. The plotted curves were compared between both ventricles. RESULTS: In all ventricles, the helical angle was changed from left-handed on epicardial slices to right-handed on endocardial slices. Whereas the right ventricle?s helical angle curves showed a plateau with or without a rapid rise on the endocardial side, the left ventricle?s curves had sustained increase with or without a plateau in the middle. The median wall-thickness?scaled helical angle change rates were 53{degrees} and 102{degrees} in the right and left ventricle, respectively, and their distribution of each specimen was higher in the left ventricle than in the right ventricle. CONCLUSIONS: Ventricular myofiber orientation has a consistent transmural helical transition independently of cardiac morphology and looping. Their transitions can have a plateau and rapid changes permitting the identification of layers within the ventricular wall. The left ventricle has more helical fibers and the right ventricle has more circumferential fibers, which may explain their difference of ventricular performance.

BACKGROUND: Because of the high mortality rate associated with fulminant myocarditis (FM), there is limited evidence regarding the prognostic efficacy of endomyocardial biopsy (EMB). In this study, we aimed to evaluate the influence of EMB in the early phase after admission on treatment and prognosis in FM using a large-scale nationwide registry in Japan. METHODS: Patients with FM who required catecholamine or mechanical circulatory support between 2012 and 2017 from 235 hospitals across Japan were included. The primary outcome of death or heart transplantation within 90 days according to performance of early EMB were determined using Kaplan-Meier and Cox regression analyses. Factors associated with performance of early EMB, and subsequent medical treatment were analyzed. RESULTS: The median age of 736 patients with clinical FM was 56 years, and 407 patients (55%) received EMB. Most EMB (89%) were performed during the early phase (within 5 days of admission) and 63% were performed on the first day of admission. The prognosis of FM patients who underwent EMB was more favorable than that of those who did not based on the landmark analysis (log-rank test, P=0.04). Factor analysis demonstrated that FM patients who underwent EMB had a lower left ventricular ejection fraction, higher incidence of cardiogenic shock on admission, and higher rate of early mechanical circulatory support, as well as early initiation of corticosteroids and guideline-directed medical therapy (GDMT) for heart failure. Absence of any GDMT was associated with a less favorable prognosis on multivariate analysis (hazard ratio, 4.34 [95% CI, 3.14?6.01]; P<0.0001). CONCLUSIONS: Although performing EMB in the early phase after admission depends on disease severity in FM patients, it is associated with early initiation of advanced medical treatment and may contribute to improving the prognosis.

The human subcortex integrates information from distributed cortical regions through functionally organized cortico-subcortical systems. Using the publicly available MICA-MICs multimodal MRI dataset of 50 healthy adults, we characterized the microstructural organization of functionally converging cortical regions. First, using resting-state fMRI., we identified cortical regions converging on subcortical targets, including thalamic, basal ganglia, and subcortical limbic structures. Then, using depth-resolved quantitative T1 profiles, we constructed a convergence-restricted microstructural profile covariance matrix, followed by diffusion-map embedding. The first microstructural gradient predominantly differentiated primary and association cortex, whereas the second gradient showed target-related differentiation beyond differences in primary versus association cortex. Cytoarchitectural analysis further revealed target-related differences in cortical composition. Together, these findings reveal complementary dimensions of cortico-subcortical functional organization, spanning broad cortical hierarchy, target-related microstructural differentiation, and cytoarchitectural composition.

BackgroundThe progression of abdominal aortic aneurysm (AAA) is closely associated with the phenotypic switch of vascular smooth muscle cells (VSMC). While members of the Serine/Arginine-rich splicing factor (SRs) superfamily are increasingly acknowledged for their pivotal role in modulating cell phenotype, the specific contribution of these factors to AAA development has remained underexplored. MethodsThrough integrated analyses the whole transcriptome of human and murine AAA tissues derived from clinical samples and animal models, we identified serine-arginine repeat matrix 4 (SRRM4) as a candidate gene regulating AAA formation. To investigate the role of SRRM4 in AAA formation, we used global and VSMC-specific SRRM4-knockout mice to construct AAA mouse models in two different ways. The impact of SRRM4 on AAA was further investigated in in vivo and in vitro models ResultsElevated levels of SRRM4 mRNA and protein were observed in VSMC from both human and murine AAA tissues. Targeted silencing of SRRM4 in VSMC (but not in endothelial or myeloid cells) significantly attenuated AAA formation in both AngII- and CaCl2-induced mouse models. Our results identify the RE1 silencing transcription factor (REST) as a critical target gene of SRRM4 in AAA, a master regulator of cellular phenotypic stability. We show that SRRM4 modulates VSMC phenotype through alternative splicing of the REST gene, resulting in a non-functional REST4 isoform. Furthermore, our study shows that REST directly represses STAT1, a mechanism by which SRRM4 influences VSMC phenotypic transition. Regarding clinical significance, inhibition of both STAT1 expression and function was shown to be beneficial in preventing AAA formation and slowing disease progression in mouse models. ConclusionsThis investigation uncovers a previously unrecognized role of SRRM4-mediated splicing of the REST gene in the progression of AAA and identifies a novel REST/STAT1 axis critical in the phenotypic modulation of VSMC. The findings of this study may provide insights for the development of innovative therapeutic strategies for AAA. What is New?O_LIOur study demonstrates that the RNA splicing factor SRRM4 (serine/arginine repetitive matrix 4) expression is increased in human and murine abdominal aortic aneurysm (AAA) tissues and unveils a previously undescribed role of SRRM4 in regulating AAA development. C_LIO_LISRRM4 promotes AAA development and progression through inducing splicing of REST (RE1 silencing transcription factor) gene and upregulating STAT1 (signal transducer and activator of transcription 1) expression, which result in extracellular matrix degradation, inflammation, and VSMC phenotypic switching. C_LIO_LIOur study provides proof-of-concept evidence indicating the therapeutic value of STAT1 inhibition in AAA prevention and treatment. C_LI What Are the Clinical Implications?O_LIOur study defining a novel mechanism underlying the SRRM4/REST/STAT1 axis is crucial for developing new therapeutic strategies for AAA. C_LIO_LIBased on these findings, we have initiated a prospective study (REST4-AAA; ClinicalTrials.gov Identifier: NCT07056491) to evaluate REST4 expression levels in serum of AAA patients and explore its potential as a serum biomarker for AAA prediction C_LIO_LIOur findings indicate that fludarabine, a clinically available agent reported to selectively suppress STAT1, effectively attenuates AAA progression in mice, suggesting its potential for repurposing as a therapeutic strategy for AAA. C_LI

Cellular diversity is required for the proper function of many tissues, including the retina, with its ~130 cell types.1-3 Previous lineage studies suggested that retinal progenitor cells (RPCs) generate cell diversity through biased cell divisions.4-9 However, the extent to which each progenitor cell contributes to diversity, and the overarching rules governing the lineages of the collection of progenitor cells, are unknown. While classic lineage tracing approaches including retroviral labeling4,6,10, dye injection11,12, and time-lapse microscopy13,14 were used to make the foundational insights regarding retinal lineages, they cannot be used to interrogate multigenerational lineages at scale. Here we describe the creation of an evolvable lineage tracing tool, "SCRIBBLE" for Sequential Combinatorial Recorder for Iterative Barcode-Based Lineage Evolution. SCRIBBLE uses prime editor to iteratively edit a barcode sequence while preserving the order of genesis of clonally related progeny. Through a series of rationally designed high-throughput screens and design optimizations, we identified novel sequences that support a high level of barcode editing and information content. Evaluation of SCRIBBLE in embryonic mouse retina recapitulated known lineages in retinal development, demonstrating that SCRIBBLE is capable of high-resolution lineage tracing in a complex tissue.

Patients with implantable cardioverter-defibrillators (ICDs) often are excluded from receiving CMR scans at 3.0T due to ICD-induced image artifacts. This work aims to transfer sequence protocols established for CMR imaging in the presence of ICDs at 1.5T to a first human application at 3.0T. All measurements were performed on a clinical 3T MRI scanner. Phantom measurements were used to establish and optimize wideband PSIR LGE imaging at 3.0T. Volunteer measurements with externally placed ICDs were used for optimization of CINE imaging and wideband PSIR imaging. Thirdly, patients with implanted ICD were examined with the optimized protocols. Human measurements showed that SA stacks obtained with spoiled GRE after contrast agent showed a markedly lower artifact burden than the conventional approach. For scar imaging, a wideband PSIR with a bandwidth of 3kHz and a frequency offset of +750Hz allowed a sufficient suppression of relevant artifacts in the majority of scans. No strong correlation between the image quality and the distance between the cardiac silhouette and the device was detected. The presented work shows the feasibility of clinical cardiac MRI at 3.0T including GRE-CINE and wideband PSIR-LGE for patients with ICDs.

Abstract Background The SYNTAX score (SS) quantifies the severity and complexity of coronary artery lesions and serves as an independent predictor of major adverse cardiovascular events and mortality in patients with acute coronary syndrome (ACS). However, few studies have examined the association between SS and in-hospital cardiac arrest (IHCA) in this population. Therefore, this study aims to elucidate the relationship between SS and the incidence of IHCA in patients with ACS. Methods This study consecutively enrolled 3,224 patients undergoing percutaneous coronary intervention (PCI). Logistic regression and restricted cubic spline (RCS) analyses were employed to examine the association between SS and the incidence of IHCA. Participants were stratified into three groups based on SS: Group T1 (SS ? 10, N = 1,097), Group T2 (10 < SS ? 18, N = 1,089), and Group T3 (SS > 18, N = 1,038). The primary endpoint was the incidence of IHCA. Results The overall incidence of IHCA was 4% (N=147). After adjusting for covariates, SS was significantly associated with the incidence of IHCA in ACS patients treated with PCI (OR = 1.0400; 95% confidence interval (CI) = 1.0200?1.0700; p < 0.001). Compared with the T1 group, the T3 group exhibited an increased risk of IHCA (OR = 2.1800; 95% CI = 1.2900?3.6900; p = 0.0040). Subgroup analyses revealed that, after covariate adjustment, patients with ST-segment elevation myocardial infarction (STEMI) had an increased risk of IHCA (OR = 1.0700; 95% CI = 1.0300?1.1100; p < 0.001). Similarly, both patients with diabetes mellitus (DM) (OR = 1.0400; 95% CI = 1.000?1.0700; p = 0.0270) and those without DM (OR = 1.0600; 95% CI = 1.0200?1.1000; p = 0.0020) showed an increased risk. Furthermore, elderly patients (?60 years) (OR = 1.0300; 95% CI = 1.0100?1.0700; p = 0.0210) and non-elderly patients (<60 years) (OR = 1.0500; 95% CI = 1.000?1.1000; p = 0.0470) had an increased risk, as did male patients (OR = 1.0500; 95% CI = 1.0200?1.0700; p = 0.0020). RCS analysis indicated a dose-response relationship between IHCA incidence and SS (nonlinear p-value = 0.906), with the risk of IHCA increasing when SS exceeded 23.975. Incorporating SS into the baseline risk model improved the predictive value for IHCA incidence in ACS patients undergoing PCI (Net reclassification improvement [NRI]: 0.3537 [0.1906?0.5167], p < 0.001; Integrated discriminant improvement [IDI]: 0.0104 [0.0025?0.0183], p = 0.0097). Conclusions Among patients with ACS undergoing PCI, SS is significantly associated with the incidence of IHCA. Consequently, SS may serve as an effective predictor of IHCA in this population.

Protein kinase CK2 (CK2), a ubiquitously expressed serine/threonine kinase, has emerged as a key host factor exploited by several viruses, including Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we investigate the role of CK2 subunits in SARS-CoV-2 replication and evaluate the antiviral potential and broad-spectrum anti-coronavirus activity of two CK2 inhibitors: the previously described ATP-competitive inhibitor CX-4945 and a newly developed, highly selective bivalent inhibitor, AB668. The siRNA-mediated knockdown of CK2 subunits significantly reduced SARS-CoV-2 replication, highlighting its essential role in the viral lifecycle. Co-immunoprecipitation assays revealed that the SARS-CoV-2 nucleoprotein interacts with CK2. Furthermore, bio-layer interferometry assays demonstrated that the nucleoprotein specifically binds to the tetrameric CK2 holoenzyme (CK22{beta}2) but not to individual CK2 or CK2{beta} subunits. Pharmacological inhibition of CK2 with AB668 exhibited potent antiviral activity, significantly reducing viral replication by targeting a pre-replicative or RNA-replicative step of the viral life cycle. Importantly, AB668 displayed broad-spectrum antiviral activity, effectively inhibiting both HCoV-OC43 ({beta}-CoV) and HCoV-229E (-CoV). In summary, our findings establish CK2 as a critical host factor for SARS-CoV-2 replication and identify AB668 as a promising candidate for broad-spectrum antiviral therapy. With its high selectivity, minimized off-target effects, and efficacy against multiple coronaviruses, AB668 represents a valuable chemobiological and therapeutic tool.

Protein--Protein Interactions (PPIs) are fundamental to cellular processes and represent major targets for therapeutic development. Recent advances in machine learning for protein modeling, particularly protein language models, have enabled powerful representations of protein sequences and monomeric structures. However, compared with the increasingly generalizable representations available for individual proteins, transferable representations explicitly designed to capture protein-complex and interface contexts remain less developed. Here, we developed GraPPI, a unified graph-based deep learning framework for learning transferable representations of protein--protein complexes. At its core, PPIencoder represents protein complexes as heterogeneous residual graphs and uses self-supervised masked-edge prediction to learn interaction-aware representations that integrate sequence, three-dimensional geometry, and physicochemical properties. We then evaluated the transferability of these pretrained representations across complementary PPI tasks spanning binding-mode classification, binding-affinity prediction, and mutation-associated affinity prediction. GraPPI accurately distinguished plausible binding interfaces from perturbed or decoy binding interfaces, achieving AUROC values above 0.92 on an independent benchmark. For protein--protein binding-affinity prediction, GraPPI achieved Pearson correlation coefficients of 0.690 and 0.684 on the S90 and S79 benchmarks, respectively, with performance competitive with or exceeding that of recently reported affinity-prediction methods. GraPPI further transferred effectively to mutation-associated binding-affinity prediction, achieving a Pearson correlation of 0.84 and a mean absolute error of 1.20 kcal/mol on an independent SKEMPI benchmark. Together, these results establish GraPPI as a generalizable framework for protein-complex representation learning, extending pretrained protein representations from individual proteins toward the interaction context in which protein function is realized.