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2026 Year 13 Volume 5 Issue
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Abstract：BackgroundLung invasive mucinous adenocarcinoma (LIMA) is a rare, unique, and heterogeneous subtype of lung cancer whose patterns of lymph node (LN) metastasis are unknown, and a consensus on LN dissection (LND)has not been reached. This study aimed to evaluate LN metastasis patterns in LIMAs and establish optimal LND strategies.MethodsData about 19, 596 LNs from 1474 LIMA patients collected between January 2010 and December 2021 at 8 lung cancer research centers and tertiary hospitals across China, and data from 5304 LIMA patients between 2004 and 2021 in the SEER database were analysed. Metastasis probabilities were calculated for each LN station to con-struct a metastasis atlas. Statistical methods, including LOWESS fitting, restricted cubic spline, Kaplan-Meier, and logis-tic regression analyses, were employed to identify optimal LND strategies.ResultsCompared with non-mucinous adenocarcinoma patients, LIMA patients exhibited distinct clinicopathologi-cal features and a significantly lower probability of LN metastasis (4.20% vs. 7.19%, P < 0.05). Metastasis was most common in the peripheral and hilar/interlobar zones (especially stations 14 and 10), with minimal involvement in the lower zone (stations 8 and 9). A U-shaped relationship between the LN count and prognosis (including overall survival, relapse-free survival, and cancer-specific survival) was found, with 6–20 and 18 LNs as the optimal range and cut-off point, respectively. Excessive or insufficient dissection was linked to poorer outcomes. A predictive model (area under the receiver operating characteristic cure = 0.8367) revealed that patients with a probability ≥ 0.5 had a significantly greater proportion of patients with stage N1+ disease (including N1 and N2 patients) (68.09% vs.11.63%, P < 0.001) and worse overall survival [hazard ratio (HR) = 4.00, 95% CI 2.72–5.87, P < 0.001] and relapse-free survival (HR = 5.53, 95% CI 3.97–7.71, P < 0.001). The minimum numbers of LNs for the low- (probability < 0.1), medium-(probability 0.1–0.5), and high- (probability > 0.5) risk patients were 7, 14, and 17, respectively. For those with uncertain metastatic risk, dissecting 18 LNs may be the most appropriate and robust strategy.ConclusionsThis study systematically revealed the pattern of LIMA-specific LN metastasis and proposed a risk-stratified LND strategy. These recommendations balance the imperatives of accurate staging with the preservation of long-term patient prognosis, offering a practical guideline for surgical decision-making.Keywords：Lung invasive mucinous adenocarcinoma (LIMA);Lymph node dissection (LND);Metastasis atlas;N-staging;prognosis16|6|0Updated：2026-06-10 -
Abstract：BackgroundTenascin-C (TNC) is an extracellular matrix (ECM) protein involved in tissue damage and fibrosis. Chimeric antigen receptor (CAR) cell therapy is a novel therapeutic approach that has attracted increasing attention in recent years. Here, we engineered CAR-macrophages targeting TNC (TNC-CAR-Ms) and explored the underlying mechanism through which TNC-CAR-Ms treat liver fibrosis.MethodsThe role of TNC in liver fibrosis was studied in established Tnc knockout (KO) and littermate control mice. A TNC-targeted single-chain variable fragment (scFv) was designed to generate TNC-CAR-Ms and evaluate their bio-logical function. The phagocytosis and killing effects of TNC-CAR-Ms were tested in vitro, while the antifibrotic efficacy and safety of TNC-CAR-Ms were evaluated in vivo. The underlying mechanism through which TNC-CAR-Ms treat liver fibrosis was investigated by Western blotting, flow cytometry, and RNA sequencing.ResultsTNC expression was significantly upregulated in the liver and activated hepatic stellate cells (HSCs) in carbon tetrachloride (CCl4)-treated mice. Animal studies showed that Tnc KO protects mice from CCl4-induced liver damage and fibrosis. Upon demonstrating their ability to engulf and kill activated HSCs, we intravenously administered TNC-CAR-Ms to fibrotic mice and found that TNC-CAR-Ms significantly reduced liver fibrosis. Mechanistically, TNC-CAR-Ms specifically migrated to liver tissues, potently reduced TNC expression, and decreased the activity of the Toll-like receptor 4 (TLR4)/nuclear factor kappa-B (NF-κB) and integrin/focal adhesion kinase (FAK) signaling pathway. In addi-tion, TNC-CAR-Ms significantly modified the hepatic immune microenvironment, characterized mainly by an increase in the numbers of M2-polarized macrophages and CD8+ T cells in the liver. Finally, in CCl4-treated mice, the depletion of CD8+ T cells with an anti-CD8α antibody significantly impaired the antifibrotic effect of TNC-CAR-Ms.ConclusionsOur proof-of-concept study demonstrates the therapeutic potential of TNC-CAR-Ms in alleviating liver fibrosis and may inform the development of future therapeutic strategies for the treatment of a range of liver diseases with a fibrotic phenotype.Keywords：Liver fibrosis;Tenascin-C (TNC);Chimeric antigen receptor (CAR);macrophage;Hepatic stellate cells (HSCs)33|20|0Updated：2026-06-10 -
Dual-locked targeted alpha-emitter enhanced tumor immunotherapy via Diels–Alder reaction-based self-immolative molecular cage strategy AI Introduction
Abstract：BackgroundTargeted alpha therapy (TAT) has emerged as a promising strategy for cancer treatment by selectively delivering high linear energy transfer (LET) alpha-emitters to tumor cells while minimizing off-target toxicity. However, the clinical translation of alpha-emitters, particularly radium-223 (223Ra), remains challenging due to inefficient targeted delivery and uncontrolled release of recoil daughter products, leading to systemic toxicity.MethodsHerein, a dual-locked pretargeted strategy was developed integrating platinumIV (PtIV)-loaded hydrogel nanoparticles (HNPs) (HAQ@HNPs) and 223Ra-loaded HNPs (223Ra@HNPs) into an inverse electron demand Diels–Alder(IEDDA)-activated drug delivery system. In vitro cytotoxicity, ROS, and apoptosis, together with in vivo biodistribution,imaging, and therapeutic studies, were performed to evaluate the therapeutic efficacy and immune activation.ResultsThis caged dual-locked approach enables precise pretargeted accumulation at the tumor site, followed by rapid dissociation and controlled release of 223Ra and PtIV upon IEDDA-triggered activation, thereby ensuring high tumor specificity while minimizing systemic exposure. The synergistic combination of TAT and chemotherapy effectively disrupts redox homeostasis, induces immunogenic cell death (ICD), and elicits a robust antitumor immune response. Furthermore, when combined with programmed death-ligand 1 (PD-L1) blockade, this strategy significantly enhances systemic antitumor immunity, leading to robust inhibition of tumor growth and metastasis.ConclusionsThese findings underscore the potential of dual-locked pretargeted strategies to advance TAT by improving therapeutic efficacy and addressing the critical challenge of radionuclide leakage, paving the way for next-generation precision-targeted radiopharmaceuticals.Keywords：Targeted alpha therapy (TAT);Alpha-emitters;Radium-223 (223Ra);Bioorthogonal click chemistry19|14|0Updated：2026-06-10 -
Abstract：BackgroundDespite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.MethodsLiquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.ResultsA global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.ConclusionsAs an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.Keywords：Non-small cell lung cancer;Artificial intelligence;Liquid biopsy;Machine learning;Circulating tumor DNA;Minimal residual disease27|34|0Updated：2026-06-10
RESEARCH
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Abstract：Sepsis is a dysregulated host response to infection that frequently results in fatal multiple organ dysfunction. Despite advances in clinical identification and management, both its incidence and mortality have remained persistently high. Emerging evidence indicates that cell-free DNA (cfDNA), as a novel biomarker and molecular therapeutic target,holds promise for improving the clinical management of sepsis. cfDNA refers to DNA fragments present in body fluids, including naked DNA, membrane-coated DNA, nucleosomes, and neutrophil extracellular traps (NETs). cfDNA is released from host cells or pathogens into body fluids through pathways, such as NETosis, mitochondrial damage,cell necrosis, apoptosis, pyroptosis, and erythroblast enucleation. The released cfDNA triggers a strong inflammatory response by activating Toll-like receptor (TLR) 9, the absent in melanoma 2 (AIM2) inflammasome, and the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway. At the same time, cfDNA activates the coagulation cascade and inhibits anticoagulant and fibrinolytic systems through multiple mechanisms, resulting in microcirculatory disorders. These pathological effects are closely associated with sepsis-related organ dysfunction and poor prognosis. Elucidation of the release and pathological mechanisms of cfDNA provides a foundation for the development of targeted treatment strategies. Currently, molecular therapeutic approaches targeting cfDNA,including peptidylarginine deiminase (PAD) 4 inhibitors, pore-forming inhibitors, antioxidants, cfDNA scavengers,and deoxyribonucleases (DNases), have shown certain efficacy in treating sepsis and systemic inflammation. In terms of sepsis monitoring, compared with traditional markers, cfDNA exhibits extremely high timeliness and dynamic monitoring capability. cfDNA can simultaneously indicate the complex interplay among infection, host response,and organ damage, making it suitable for early diagnosis, prognosis assessment, treatment monitoring, organ function evaluation, and pathogen detection. Given its broad application prospects in the diagnosis and treatment of sepsis, this paper systematically elaborates on the mechanisms of cfDNA release and pathological effects in sepsis,reviews progress in cfDNA-targeted monitoring and therapeutic strategies, discusses technical challenges, and outlines potential future directions.Keywords：sepsis;Cell-free DNA (cfDNA);Liquid biopsy;cfDNA scavengers;Deoxyribonuclease (DNase)42|19|0Updated：2026-06-10 -
Abstract：The cytokine storm, a life-threatening systemic inflammatory syndrome, is the primary driver of multiorgan failure in different clinical situations, including severe infections, autoimmune diseases, chimeric antigen receptor (CAR)T cell immunotherapy for cancer, and genetic syndromes. This review focuses primarily on cytokine storms triggered by severe infections such as viral pneumonia and bacterial sepsis, and explores the underlying mechanisms of cytokine storms and potential therapeutic interventions. Cytokine storms are characterized primarily by the excessive release of proinflammatory cytokines, which are triggered by pathogen-associated molecular patterns (PAMPs),damage-associated molecular patterns (DAMPs), and PANoptosis, all of which activate immune signaling cascades. Amplification mechanisms involve positive feedback loops and the failure of negative feedback mechanisms, leading to uncontrolled inflammation. Like a pyrrhic victory, the excessive activation of the immune system eliminated invading pathogens but caused catastrophic damage due to multiple organ dysfunction syndrome (MODS), turning the life-saving response into a life-threatening war. Therapeutic strategies, including cytokine antagonists, Janus kinase (JAK) inhibitors, caspase inhibitors, glucocorticoids, and blood purification therapies, aim to interrupt the self-amplifying cycle of inflammation that propagates organ injury, thereby reducing MODS and mortality. Challenges include optimizing the treatment timing and patient stratification. Future research should focus on combination therapies and personalized medicine based on the heterogeneity of infections and sepsis. Advances in multiomics and targeted therapies provide new hope for managing infections and sepsis.Keywords：Cytokine storm;inflammatory response;Severe infection;sepsis;Multiple organ dysfunction syndrome36|14|0Updated：2026-06-10
REVIEW
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A methodological guideline for consciousness assessment via neural electrophysiological activity AI Introduction
Abstract：BackgroundPhysiological, pharmacological, and pathological alterations of consciousness provide critical windows into its neural substrates. Given the inherent complexity and multidimensionality of consciousness, defining quantitative, dynamic signatures of neural activity, and translating them into clinically applicable tools remains challenge. This study aimed to build an electroencephalography (EEG)-based methodological guideline for clinical consciousness assessment.MethodsEEG signals were systematically categorized across periodic and aperiodic activity, connectivity and network topology, spatiotemporal dynamics, self-organized criticality, and transcranial magnetic stimulation (TMS)-evoked responses. These biomarkers were mapped onto a conceptual framework of consciousness, comprising wakefulness and internal/external awareness, based on their validation across clinical conditions. The discriminative efficacy of various biomarkers was then evaluated across 4 independent datasets.ResultsIntegrated EEG features each captured distinct yet complementary dimensions of consciousness, supporting a unified neurophysiological architecture underlying diverse alterations of consciousness. Spectral power and peak frequency tracked the loss of consciousness during propofol anesthesia and sleep. Steeper aperiodic slopes, loss of frontoparietal connectivity, disrupted small-world organization, and reduced effective dimensionality were particularly effective in distinguishing minimally conscious state (MCS) from unresponsive wakefulness syndrome(UWS). Additionally, spatiotemporal patterns exhibited consciousness-specific alterations, with both pharmacological and pathological alterations influencing specific microstate dynamics.ConclusionsSynthesizing integrated neural dynamics and multidimensional consciousness, this guideline establishes both methodological and theoretical foundations for translating neurophysiological biomarkers into clinical applications. While this work advances both conceptual clarity and practical methodology, large-scale validation across expanded clinical cohorts, experimental models, and multimodal platforms is essential to fully establish causal linkages and translational utility.Keywords：Consciousness;electroencephalogram;Temporo-spatio-spectral analysis;Sleep;General anesthesia;disorders of consciousness16|16|0Updated：2026-06-10
METHODOLOGY
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