We present a physical characterization of Tsinghua University-Ma Huateng Telescope for Survey (TMTS) J00063798+3104160 (J0006), a rapidly rotating, ultramassive white dwarf (WD) identified in high-cadence light curves from the TMTS. A coherent 23 minute periodicity is detected in TMTS, Transiting Exoplanet Survey Satellite, and Zwicky Transient Facility photometry. A time series of low-resolution spectra with the Keck-I 10 m telescope reveals broad, shallow hydrogen absorption features indicative of an extreme magnetic field and shows no evidence for radial-velocity variations. Atmospheric modeling yields a magnetic field strength of ∼250 MG, while Gaia astrometry and photometry imply a mass of 1.06 ± 0.01 M _⊙ . A significant infrared excess is detected in the Wide-field Infrared Survey Explorer W1 band and is well fitted by a 550 K blackbody, likely arising from residual material of a merger. We interpret the 23 minute photometric modulation as the rotation period of an isolated, massive WD formed likely through the merger of a double WD binary. With one of the shortest rotation periods known among candidate merger remnants and with constraints from a deep Einstein Probe X-ray nondetection, J0006 provides a rare and important observational window into the poorly explored intermediate stages of postmerger evolution.

The R2Pub, built by Beijing Planetarium, is a state-of-the-art 60 cm equatorial binocular telescope located at the Daocheng Site (with an altitude of 4700 m) of Yunnan Observatories in China. This paper provides an overview of the R2Pub telescope system, discusses its design and capabilities, and presents an evaluation of its performance for astronomical surveys. R2Pub is a prime-focus binocular system, with each tube covering a field of view of about 18 deg2. This system is designed to detect various transients in local universe, including variables, eclipsing binaries, supernovae, gamma-ray bursts afterglow, tidal disruption events, Active Galactic Nuclei, and other unknown transients, which are ideal targets for both time-domain astronomy research and science population. The entire R2Pub system has completed the construction and installation of all observatory infrastructure, including the dome, equatorial mount, optical tube, and associated components, and has now entered the commissioning phase. The high-altitude location, good seeing, and dark background sky light at Daocheng site ensure optimal observational conditions for time-domain astronomy. Performance testing during the commissioning phase has demonstrated that the R2Pub system can achieve a 5 sigma limiting magnitude of approximately 18.7 mag in the Pan-STARRS r ' band for 60 s exposures. The ongoing observations from R2Pub is expected to contribute significantly to the study of time-variable phenomena in the universe and greatly improve the public outreach in astronomy. In particular, the binocular telescope systems capable of simultaneous dual-band observations can obtain the instantaneous color information of transient sources, enabling more accurate characterization of their physical properties and evolution, and providing a significant advantage in rapid distinguishing different classes of variables and transients.

BackgroundColorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment.MethodsWe collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target.ResultsWe identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification.ConclusionsSCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

In this work, we present a detailed asteroseismological analysis of Wide Field Survey Telescope (WFST) J053009.62+594557.0, a newly discovered faint pulsating white dwarf by the WFST with a Gaia G magnitude of 19.13. Analysis of two nights of high-precision WFST g-band photometry reveals three significant pulsation frequencies with high signal-to-noise ratios. Follow-up P200/DBSP spectroscopy classifies the object as a DA white dwarf with T-eff = 11,609 +/- 605 K and M = 0.63 +/- 0.22 M-circle dot. To probe its internal structure, we construct asteroseismological models with the White Dwarf Evolution Code (WDEC). After exploring sufficient matching models, best-fitting solutions yield Teff = 11, 850 +/- 10 K and M = 0.600 +/- 0.005 M-circle dot, consistent with independent constraints from Gaia color-magnitude diagram, Gaia XP spectrum, P200 spectral fitting, SED fitting, and Gaia parallax. It is shown that the asteroseismological distance agrees with the Gaia parallax to 1.45%.

This study presents an investigation of nearly two dozen candidate Luminous Blue Variables (cLBVs) in the galaxies M31 and M33. Eight stars have been studied in detail, while additional 16 objects are briefly mentioned. Multi-epoch spectra of confirmed cLBVs from LAMOST and previous literature show broad hydrogen, He I lines, abundant Fe II and [Fe II] emission lines, and discernible spectral variability, consistent with the characteristics of known LBVs. Low outflow velocities inferred from P Cygni profiles are also incorporated into the classification criteria. Moreover, key stellar properties, including temperature and luminosity, are determined using the Spectral Energy Distribution fitting and spectral modeling. By comparison with stellar evolutionary tracks on the temperature-luminosity diagram, the initial masses are estimated to be in the range of approximately 32-60 M-circle dot. Except for J013401 and J013411, other stars are located within the typical LBV region between the S Doradus instability strip and their outburst phase. More importantly, our samples, except for the binary system, are all positioned in the LBV region rather than that of B[e]SGs in the near-infrared color-color diagram. Based on all available information, one of the eight sources is confirmed as an LBV, four stars are designated as high-probability cLBVs, and the remaining three stars await further photometric observations to secure their classification. Given the current scarcity of known cLBVs, our study has the potential to make a significant increase in the number of LBVs in M31 and M33.

White dwarfs (WDs), the evolutionary endpoints of most stars, can form through both single-star and binary channels. While single-star evolutionary models enable reliable WD age estimates, binary evolution introduces interactions that can accelerate WD formation and result in a variety of exotic WDs, which may exhibit strong magnetic fields, rapid rotation, or even serve as potential gravitational wave sources. Such systems offer valuable insights into magnetic field generation, angular momentum evolution, and compact object physics. Star clusters, with their approximately coeval populations, allow precise age determination of member WDs. If a WD's total age derived from single-star evolution exceeds that of its host cluster, it likely indicates a binary origin. In this study, we use Gaia 5D astrometry to identify 439 WD candidates in 117 open clusters, with 244 likely formed via binary evolution. We discuss the possibility of dynamical ejection for WDs meeting only 2D (proper motion space) membership criteria. Spectroscopic observations further reveal a subset with strong magnetic fields and rapid rotation, supporting their binary evolutionary origin.

Super-enhancer-associated long non-coding RNAs (lncRNAs) have been shown to play key roles in the occurrence and development of malignant tumors, including esophageal squamous cell carcinoma (ESCC), yet their precise molecular mechanisms remain elusive. ChIP-Seq, dual-luciferase reporter assays, HiChIP-Seq, and ChIP-qPCR were performed to investigate the transcriptional regulation mechanism of MIR205HG. The functions and downstream signal transduction mechanisms of MIR205HG in ESCC were explored by a series of in vitro and in vivo assays. Furthermore, comprehensive bioinformatics methods were used to analyze its correlation with ESCC patient survival. Here, we identified and characterized MIR205HG, a lncRNA driven by super-enhancer, as a crucial oncogene in ESCC. MIR205HG was up-regulated in SCCs and its high expression correlated with poor clinical outcomes. Both TP63 and KLF5, two important master transcription factors in ESCC, could simultaneously occupy the super-enhancer region at the MIR205HG locus to promote its transcription and overexpression. MIR205HG is essential for ESCC proliferation, migration, invasion, and the growth of xenograft tumors in vitro and in vivo. Mechanistically, MIR205HG directly bound PTBP3 and acted as a molecular scaffold to promote HIF-1α translation, leading to enhanced cellular glycolysis via up-regulating the expression of HK2 and LDHA. Moreover, survival and pseudotime analyses of ESCC scRNA-seq data revealed a significant positive correlation between MIR205HG/PTBP3 signaling and the stemness and malignancy of ESCC cells. Finally, we showed that specifically targeting MIR205HG-SE using a CRISPR interference method resulted in potent suppressive effects on ESCC malignant phenotypes. We identified a pivotal oncogenic super-enhancer-driven lncRNA, MIR205HG, which interacts with PTBP3 to promote glycolysis in ESCC. It may serve as a promising prognostic biomarker and therapeutic target for patients.

Observations reveal a pronounced deficit of white dwarfs (WDs) in open clusters (OCs) relative to theoretical expectations, suggesting that a significant fraction of WDs may have escaped from their parent clusters after formation. In this work, we perform a systematic search for escaped WD candidates from OCs by back-tracing the motions of WDs and star clusters from Gaia DR3 catalogs. We identify 476 candidate WDs with kinematics consistent with having escaped from one of 175 OCs. A control-field Monte Carlo (MC) test yields a contamination rate of 87.6

Cell-cell communication (CCC) is central to the organization, function, and plasticity of multicellular life. Advancing experimental technologies and growing insights into complex multicellular systems and disease microenvironments are driving the demand for experimentally validated CCCs that are systematically and manually curated across diverse tissues, phenotypes, and signaling modalities. Here, we present CCCdb (http://www.licpathway.net/cccdb/index.php), a comprehensive, manually curated database of experimentally validated CCCs for human and mouse. A total of 8467 entries were extracted from thousands of publications, each annotated with standardized information on cell types, tissues, and phenotypes. These entries cover 98 tissues, 1132 cell types, and 548 phenotypes, mediated through communication via direct contact, autocrine, paracrine, and endocrine signaling. CCCdb curates experiment-supported CCCs across cellular subtypes, tissue interfaces, and physiological or pathological states. To enhance accessibility and biological interpretability, CCCdb integrates a ReAct-based AI assistant that enables intelligent natural-language queries and intuitive navigation through biological information. We believe that CCCdb will serve as a foundational resource for elucidating the mechanisms by which cells maintain tissue homeostasis and drive disease progression.

Transcriptomic profiling of Traditional Chinese Medicine (TCM) perturbations is essential for elucidating the molecular mechanisms of therapeutic interventions. Although data from TCM treatment experiments are scattered across public repositories, a comprehensive, harmonized dataset remains unavailable due to heterogeneous experimental designs and inconsistent metadata. Here, we present a curated, harmonized resource comprising 362 human gene expression profiles derived from 27 TCMs and 137 TCM-derived ingredients spanning 26 human disease contexts, re-processed via a unified bioinformatics pipeline. This atlas captures TCM-induced genome-wide alterations in both protein-coding genes and long non-coding RNAs. We confirmed the dataset’s biological fidelity by validating the high reproducibility of the dataset, the enrichment of known pharmacological targets, and recapitulated the well-established therapeutic associations between TCM and disease treatment. This standardized dataset serves as a foundational resource for researchers to systematically investigate therapeutic mechanisms and predict clinical indications of TCM.

Epigenetic dysregulation mediated by long non-coding RNAs (lncRNAs), super-enhancers (SEs), and m⁶A modification remains incompletely characterized in esophageal squamous cell carcinoma (ESCC). ChIP-qPCR, dual-luciferase reporter assays and CRISPRi were performed to investigate the transcriptional regulation mechanism of H19 by super-enhancer. The functions and downstream signal transduction mechanisms of the H19 in ESCC were explored by a series of in vitro and in vivo assays, including RNA stability, RIP-qPCR, and MeRIP-qPCR. Furthermore, single-cell RNA sequencing (scRNA-seq) and pseudotime trajectory analysis were used to analyze its correlation with ESCC patient survival. We identified H19, a super-enhancer-driven lncRNA, as a critical oncogenic factor in ESCC, with its overexpression significantly correlating with poor patient prognosis. Mechanistically, the master transcription factor KLF5 binds the super-enhancer region of H19 locus to drive its transcriptional activation. Post-transcriptionally, H19 interacts with the m⁶A methylation reader protein IGF2BP2, which stabilizes H19 in an m⁶A-dependent manner mediated by METTL3. The resulting H19/IGF2BP2 complex upregulates the oncogenic Tweety family member 3 (TTYH3) by enhancing mRNA stability. Functional assays revealed that the H19/IGF2BP2-TTYH3 axis facilitates ESCC malignant progression, while the IGF2BP2 inhibitor CWI1-2 suppresses tumorigenesis. ScRNA-seq combined with pseudotime trajectory analysis further established a significant positive correlation between METTL3/IGF2BP2/TTYH3 expression and malignant phenotypes in ESCC. The H19/IGF2BP2-TTYH3 axis drives ESCC progression, providing valuable prognostic biomarkers and promising therapeutic targets for ESCC treatment.

Integrating causal variant effects with single-cell assay for transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) enables a more effective elucidation of the roles and impacts of genetic variations at the single-cell level. With the accumulation of genome-wide association studies and single-cell genomic data, there is an urgent need for comprehensive analysis and efficient exploration of these data to uncover the underlying biological processes. To address this, we developed scVMAP (https://bio.liclab.net/scvmap/), a user-friendly database aiming to provide trait-relevant cell populations at single-cell resolution. The current version of scVMAP has integrated 183 scATAC-seq datasets and 15 884 fine-mapping results, generating more than 32.1 billion trait-cell pairs, offering valuable resources for exploring the functional localization of single-cell variations. To enhance the understanding of how phenotypic associations are mapped to single-cell data, scVMAP provides a wealth of detailed information, including trait relevance scores (TRSs) for each cell, cell-type-specific differential gene and transcription factor (TF) activities, trait-relevant gene and TF interactions, and regulatory networks linking traits to cell types. Based on these comprehensive analytical results, scVMAP offers users convenient interfaces to search, browse, analyse, and visualize relationships between traits and cell populations at single-cell resolution.

Background: Acute myocardial infarction (AMI) remains the most lethal critical emergency worldwide. Although Angong Niuhuang Pill (ANP) is an established rescue medicine that has demonstrated outstanding therapeutic potential for cardiovascular diseases, its modern molecular mechanism has never been systematically elucidated because of its chemical complexity and unidentified targets. Methods: This study utilizes a multi-layer analytical pipeline of AI mining, network pharmacology, transcriptomics, and experimental confirmation. The components of ANP were comprehensively identified by UHPLC-Q Exactive Orbitrap HRMS. The TranSiGen algorithm was utilized to deeply mine the data and rank the components according to their relevance to AMI. The top 20 components were selected as prior weights and introduced into network pharmacology for analysis. Subsequently, a mouse model of AMI was established by ligating the left coronary artery. Cardiac function in the mice was evaluated by echocardiography and serum biochemical indicators. The pathological changes in the heart tissue were assessed by hematoxylin-eosin (H&E) and Masson staining. Cardiac transcriptome sequencing was performed, and pathway enrichment was analyzed by KEGG. The key pathways were verified by qPCR and immunofluorescence, achieving cross-validation between AI prediction and experimental findings. Results: The identification of ANP resulted in the detection of a total of 73 compounds, and the TranSiGen algorithm was employed to prioritize these compounds, yielding a ranked list of the top 20 candidates. Functional evaluation using echocardiography, serum biochemical markers, and histopathological examination demonstrated that ANP significantly ameliorated cardiac function in mice following myocardial infarction. Integration of network pharmacology and transcriptomic enrichment identified convergent axes of IL-17 signaling and mitochondrial quality control, which were subsequently experimentally validated as mechanisms by which ANP ameliorated cardiac injury. Experimental validation confirmed that ANP downregulated protein expression of IL-17A and TNF-alpha, normalized PINK1 and LC3-II/LC3-I marker profiles, with concomitant p62 reduction, thereby providing comprehensive molecular evidence at both transcriptional and translational levels to support the AI-driven predictions. Conclusions: This study identified IL-17 signaling and mitochondrial quality control as pathway axes associated with ANP-mediated cardioprotection against AMI, supported by AI-driven compound screening, transcriptome-network cross-validation, and experimental confirmation. This analytical framework may be adaptable to other complex TCM formulas for mechanism exploration and clinical translation.

Network pharmacology (NP) explores pharmacological mechanisms through biological networks. Multi-omics data enable multi-layer network construction under diverse conditions, requiring integration into NP analyses. We developed POINT, a novel NP platform enhanced by multi-omics biological networks, advanced algorithms, and knowledge graphs (KGs) featuring network-based and KG-based analytical functions. In the network-based analysis, users can perform NP studies flexibly using 1,158 multi-omics biological networks encompassing proteins, transcription factors, and non-coding RNAs across diverse cell line-, tissue- and disease-specific conditions. Network-based analysis-including random walk with restart (RWR), GSEA, and diffusion profile (DP) similarity algorithms-supports tasks such as target prediction, functional enrichment, and drug screening. We merged networks from experimental sources to generate a pre-integrated multi-layer human network for evaluation. RWR demonstrated superior performance with a 33.1 second-best algorithm, PageRank, in identifying known targets across 2,002 drugs. Additionally, multi-layer networks significantly improve the ability to identify FDA-approved drug-disease pairs compared to the single-layer network. For KG-based analysis, we compiled three high-quality KGs to construct POINT KG, which cross-references over 90 illustrated the platform's capabilities through two case studies. POINT bridges the gap between multi-omics networks and drug discovery; it is freely accessible at http://point.gene.ac/.

Myocardial ischemia/reperfusion injury (MI/RI) remains a major challenge in the treatment of acute myocardial infarction due to the lack of effective therapeutic options. While mesenchymal stromal cells (MSCs) and their derivates show promising potential for MI/RI therapy, their clinical application is hindered by low transplantation efficiency and insufficient yield. In this study, we engineered nanoscale artificial cell-derived vesicles (ACDVs) by extruding Ginsenoside Rg1-primed MSCs (Rg1-MSCs), resulting in Rg1-ACDVs. Rg1-ACDVs displayed superior therapeutic efficacy compared to non-primed ACDVs and extracellular vesicles derived from Rg1-MSCs (Rg1-EVs). Multi-omics analysis revealed that Rg1-ACDVs possess distinct molecular signatures associated with promoting cell cycle progression and reducing DNA damage. These findings were further validated experimentally, demonstrating that Rg1-ACDVs effectively reduce reactive oxygen species (ROS) accumulation and mitigate DNA damage both in vitro and in vivo. This study highlights the synergistic benefits of combining Ginsenoside Rg1 priming with nanoscale engineering and introduces Rg1-ACDVs as a scalable and innovative strategy, offering a promising approach for improving clinical outcomes in MI/RI therapy.

Detecting gaseous debris disks around white dwarfs offers a unique window into the ultimate fate of planetary systems and the composition of accreted planetary material. Here we present a systematic search for such disks through the Ca ii infrared triplet using the Dark Energy Spectroscopic Instrument (DESI) Early Data Release. From a parent sample of 2706 spectroscopically confirmed white dwarfs, we identify 22 candidate systems showing tentative emission-line features, which corresponds to a raw occurrence rate of 0.81%, more than 10 times higher than previous estimates. The detected emission lines are predominantly weak and require confirmation by follow-up observations. Three of these candidates also exhibit infrared excess in Wide-field Infrared Survey Explorer photometry, suggesting a possible coexistence of gas and dust. However, the high candidate rate indicates that most are likely false positives due to telluric residuals or unresolved binaries. This work demonstrates the potential of DESI spectra for blind searches of rare circumstellar phenomena. The recently released DESI DR1, with its substantially larger spectroscopic sample, will enable searches for more gaseous disks and provide better insights into their occurrence and nature.

BackgroundCombination of anti-PD-1 monoclonal antibody with chemotherapy has been widely used as a first-line treatment for metastatic esophageal squamous cell carcinoma (ESCC). However, the efficacy of this therapeutic combination as a neoadjuvant intervention for resectable ESCC remains inadequately explored. This study aims to evaluate the efficacy and safety of sintilimab in combination with chemotherapy as a neoadjuvant therapy for ESCC.MethodsIn this single-arm, phase II study, patients with histopathologically diagnosed resectable ESCC who had clinical cT1-3/N0-1M0 (stage II-III) were recruited. Sintilimab (200mg, iv, d1) in combined with chemotherapy (nab-paclitaxel 260 mg/m2, d1 and cisplatin 75 mg/m2, d1-3) were administered every 3 weeks for 2 cycles. The primary endpoint was pathological complete response (pCR).ResultsFrom November 2020 through November 2022, 29 patients were enrolled and 27 completed the two cycles of neoadjuvant therapy. A total of 21 patients underwent surgery. The pCR rate was 28.6% (6/21) and the major pathologic response (MPR) rate was 42.9% (9/21). The most common Grade 3 or 4 treatment-related adverse events were leukopenia (26.7%) and neutropenia (20%). No delays in surgical procedures or unexpected surgical complications attributable to the treatment were reported.ConclusionsThe combination of sintilimab and chemotherapy as a neoadjuvant regimen was tolerable and associated with favorable responses for ESCC patients. Given these favorable results, this regimen could serve as a viable alternative in the neoadjuvant treatment landscape for ESCC, with particular applicability to Chinese patient populations.Clinical trial registrationhttps://www.chictr.org.cn/, identifier ChiCTR2000040345.

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data from glioblastoma (GBM) and low-grade glioma (LGG) samples, we identified 55 distinct cell states via the EcoTyper framework, validated for stability and prognostic impact in an independent cohort. We constructed multi-omics datasets of 620 samples, integrating transcriptomic, copy number variation (CNV), somatic mutation (MUT), Microbe (MIC), EcoTyper result data. A scRNA-seq enhanced Self-Normalizing Network-based glioma prognosis model achieved a C-index of 0.822 (training) and 0.817 (test), with AUC values of 0.867, 0.876, and 0.844 at 1, 3, and 5 years in the training set, and 0.820, 0.947, and 0.936 in the test set. Gradient attribution analysis enhanced the interpretability of the model and identified key molecular markers. The classification into high- and low-risk groups was validated as an independent prognostic factor. HDAC inhibitors are proposed as potential treatments. This study demonstrates the potential of integrating scRNA-seq and multi-omics data for robust glioma prognosis and clinical decision-making support.

Network pharmacology plays a pivotal role in systems biology, bridging the gap between traditional Chinese medicine (TCM) theory and contemporary pharmacological research. Network pharmacology enables researchers to construct multilayered networks that systematically elucidate TCM’s multi-component, multi-target mechanisms of action. This review summarizes key databases commonly used in network pharmacology, including those focused on herbs, components, diseases, and dedicated platforms for network pharmacology analysis. Additionally, we explore the growing use of network pharmacology in TCM, citing literature from Web of Science, PubMed, and CNKI over the past two decades with keywords like “network pharmacology”, “TCM network pharmacology”, and “herb network pharmacology”. The application of network pharmacology in TCM is widespread, covering areas such as identifying the material basis of TCM efficacy, unraveling mechanisms of action, and evaluating toxicity, safety, and novel drug development. However, challenges remain, such as the lack of standardized data collection across databases and insufficient consideration of processed herbs in research. Questions also persist regarding the reliability of study outcomes. This review aims to offer valuable insights and reference points to guide future research in precision TCM network pharmacology.

The"one drug-multiple targets"paradigm has revolutionized therapeutic development for complex diseases by addressing the limitations of single-target approaches[1].However,elucidating multi-target synergism remains a major challenge.Network phar-macology(NP)enables polypharmacological investigations through biological networks[2].Recent advances have highlighted the utility of NP across diverse diseases through protein-protein interaction(PPI)networks.Although PPI networks are widely used in NP,the increasing recognition of intracellular regulatory ele-ments has expanded potential drug targets beyond proteins to include biomolecules such as transcription factors(TFs)and non-coding RNAs(ncRNAs)[3].These findings demonstrate the limita-tions of relying solely on PPI networks for target discovery and highlight the need for multi-layer networks that integrate biomo-lecules across diverse regulatory levels.