Acute kidney injury (AKI) is a clinical syndrome with high mortality, and its pathogenesis involves complex inflammatory regulatory mechanisms. As core components of the cytokine network, interleukins (ILs) exert pleiotropic effects in the development of AKI, participating in processes such as inflammation, fibrosis, tissue damage repair, and remote organ injury. Moreover, ILs influence the progression of AKI by mediating the crosstalk among renal resident cells, immune cells, and fibroblasts. Pro-inflammatory ILs primarily accelerate the progression of AKI by recruiting neutrophils and inducing renal cell apoptosis, whereas anti-inflammatory ILs alleviate AKI by inhibiting the release of inflammatory cytokines and enhancing regulatory T cell function. Dual-function ILs may either promote disease progression or facilitate tissue repair depending on their cellular origin or the specific pathological stage. In terms of therapeutic strategies, monoclonal antibodies targeting ILs and their receptors, as well as advancements in extracellular vesicle technology, have shown promising potential. Future research should focus on elucidating the specific signaling networks of ILs and their intercellular interactions in order to promote precision medicine approaches for AKI and to block the transition from AKI to chronic kidney disease (CKD).

Advancing targeted cancer immunotherapy is pivotal for overcoming distant metastasis and tumor relapse. The recent study in Nature Cancer by Sanlorenzo et al. demonstrates a breakthrough strategy combining systemic type I interferon with topical Toll-like receptor 7/8 agonists, where oral imiquimod primes plasmacytoid dendritic cells (DCs) to produce type I interferon, thereby sensitizing conventional DCs in tumors to local Toll-like receptor 7 activation. This approach triggers c-Jun-dependent IL-12 production and CCL2-mediated plasmacytoid DC recruitment, enabling localized and systemic tumor suppression. Importantly, the therapy synergizes with PD-1 blockade to prevent recurrence, representing a significant advance in DC-targeted cancer immunotherapy.

Opioid use disorders (OUDs) pose a substantial global health burden, with methadone maintenance treatment (MMT) widely adopted as an intervention; however, MMT is associated with immunosuppression, metabolic disturbances, and dysbiosis of the gut microbiota. Despite the potential of acupuncture in reducing methadone dosages and opioid addiction, the underlying biological mechanisms remain unclear. Therefore, we aimed to integrate clinical trial data with multi-omics analysis, including single-cell sequencing, transcriptomics, metabolomics, and metagenomics, to evaluate the effects of acupuncture in patients undergoing MMT. We collected peripheral blood mononuclear cells, plasma, and fecal samples from 48 MMT participants in a randomized, placebo-controlled trial. Participants were divided into acupuncture (n = 25) and sham-acupuncture (n = 23) groups. After 8 weeks of intervention, 84% of patients in the acupuncture group achieved ≥20% reduction in methadone dosage, compared to 39% in the sham-acupuncture group (P < 0.01). Our findings revealed that acupuncture may activate the defense response to viruses, with altered immune cell functions in classical monocytes correlating with clinical responses to reduced methadone doses. Acupuncture might ameliorate intestinal microbial disruptions caused by OUD by up-regulating Bilophila and modulating bile acid metabolism. Furthermore, acupuncture up-regulated galectin-9 (LGALS9)-mediated intercellular communication between classical monocytes and other immune subsets. To further validate the mechanistic link between bile acid metabolism and immune regulation, we conducted in vitro experiments using THP-1 monocytes treated with cholic acid. The results showed that bile acid exposure suppressed galectin-9 and IFN-γ expression, while low-dose bile acid (simulating acupuncture effects) partially reversed this effect. These findings support a bile acid–galectin–interferon axis that may be modulated by acupuncture in OUD. Collectively, our results provide clinical and mechanistic evidence supporting acupuncture as a potential adjunct therapy to mitigate the adverse effects of long-term opioid use.

Strain-stiffening hydrogels, which mimic the nonlinear mechanical behavior of biological tissues such as skin, arteries, and cartilage, hold transformative potential for biomedical applications. This study introduces immersion phase separation (IPS) 3-dimensional (3D) printing, an innovative technique that enables the one-step fabrication of strain-stiffening hydrogel scaffolds with intricate, hierarchical architectures. This technique addresses the long-standing challenge of balancing structural complexity and intrinsic mechanoresponsive behavior in traditional hydrogel fabrication methods. By leveraging dynamic hydrophobic interactions and solvent exchange kinetics, IPS 3D printing achieves multiscale control over pore architectures (5 to 200 μm) and anisotropic microchannels while preserving J-shaped stress–strain curves (fracture stress: ~0.7 MPa; elongation: >1,000%). The physically cross-linked network enables closed-loop recyclability (>95% material recovery) without performance degradation, while functional fillers (e.g., carbon nanotubes, copper, and hydroxyapatite) enhance properties such as electrical conductivity (2-orders-of-magnitude improvement) and real-time motion sensing capabilities. This platform facilitates the creation of patient-specific implants with tailored mechanical properties and paves the way for adaptive biohybrid devices that mimic the dynamic behavior of native tissues, holding promise for regenerative medicine, soft robotics, and advanced biomedical applications. IPS 3D printing uniquely resolves the trade-off between structural sophistication and functional biomimicry, establishing a paradigm for replicating dynamic biological tissues.

Causal inference is crucial in biological research, as it enables the understanding of complex relationships and dynamic processes that drive cellular behavior, development, and disease. Within this context, gene regulatory network (GRN) inference serves as a key approach for understanding the molecular mechanisms underlying cellular function. Despite substantial advancements, challenges persist in GRN inference, particularly in dynamic rewiring, inferring causality, and context specificity. To tackle these issues, we present single cell-specific causal network (SiCNet), a novel causal network construction method that utilizes single-cell gene expression profiles and a causal inference strategy to construct molecular regulatory networks at a single-cell level. Additionally, SiCNet utilizes cell-specific network information to construct network outdegree matrix (ODM), enhancing the performance of cell clustering. It also enables the construction of context-specific GRNs to identify key regulators of fate transitions for diverse processes such as cellular reprogramming and development. Furthermore, SiCNet can delineate the intricate dynamic regulatory processes involved in development, providing deep insights into the mechanisms governing cellular transitions and the gene regulation across developmental stages.

Cancer immunotherapy has greatly changed the therapeutic landscape for metastatic malignancies. Nevertheless, due to immune-related adverse events, drug resistance, and other factors, cancer immunotherapy remains largely untapped. Recent research has shown that the microbiota is crucial in shaping immune function and that its modulation can influence antitumor immunity. However, because of the intricate nature of the microbiome and immune system, a comprehensive mechanistic framework for understanding how the microbiota influences antitumor immune responses is still lacking. In this review, we summarize the mechanisms of the microbiota in antitumor immunity. We also comprehensively outline the methods for measuring the microbiota and their limitations. Additionally, we discuss the key challenges facing the targeting of the microbiota as a regulatory strategy for cancer immunotherapy.

There is remarkable demand for bio-based specialty resins such as benzoxazine thermosets, but they are brittle and difficult to process. This study reports the synthesis of a processable bio-based polybenzoxazine resin via the copolymerization of rigid and soft benzoxazine dimers synthesized from bio-based phenols. The polybenzoxazine copolymer demonstrated excellent thermoplasticity and a tunable toughness in the range of 9.0 to 24.1 MJ/m³. The incorporation of soft segments and dynamic ester bonds in the polybenzoxazine notably improved its thermoplasticity compared with traditional thermosetting benzoxazine resins. The polybenzoxazine copolymer also demonstrated a dielectric constant of 2.99 and a dielectric loss of 0.019 at 3 GHz, as well as a high breakdown voltage of 27.2 kV/mm. This research highlights the promising mechanical and thermal properties of the resulting bio-based resin, as well as its tunable dielectric properties, making it a competitive candidate for various high-performance applications in the polymer industry.

Apolipoprotein E (ApoE) has been implicated in neurodegenerative diseases; however, its function and underlying mechanisms in depression remain elusive. In this study, we employed chronic social defeat stress (CSDS) to establish a mouse model of depression and observed significantly reduced ApoE expression in the hippocampus. By leveraging ApoE knockout (ApoE^−/−) and knockdown (ApoE-KD) mouse models, we demonstrated that ApoE deficiency induced depression-like behaviors, which were closely associated with impaired GABAergic synaptic transmission and down-regulation of ApoE receptors and K⁺–Cl⁻ cotransporter 2 (KCC2). In addition, we found an interaction between KCC2 and the ApoE receptor low-density lipoprotein receptor (LDLR) through coimmunoprecipitation analysis. Moreover, overexpression of ApoE or targeted activation of GABAergic neurons in the hippocampus significantly reversed depression-like behaviors in both CSDS-exposed and ApoE-KD mice. Lastly, treatment with KCC2 activators, CLP290 and CLP257, restored the expression levels of KCC2 and the GABA_AR α1 subunit, significantly alleviating depression-like behaviors induced by CSDS or ApoE-KD. Together, our results elucidate the pivotal role of ApoE in the pathophysiology of depression and highlight the ApoE–KCC2 signaling pathway as a potential target for developing innovative antidepressant therapies.

Protein kinases are key mediators of cellular signaling and control cell functions through the phosphorylation of target proteins. They have become major targets for therapeutic agents aimed at treating human diseases, particularly cancer. Protein kinase inhibitors (PKIs) have emerged at the forefront of drug development, and their investigations continue to be intense, with several candidates undergoing clinical trials and persistent endeavors to identify new chemical scaffolds. The main focus is still on developing isoform-selective compounds, which are inhibitors designed to target certain protein kinases, specifically isoforms, for more precise treatment. The identification and advancement of versatile inhibitor scaffolds that more effectively target individual kinases is essential for minimizing off-target effects and resistance. This review highlights important progress in PKI therapy, emphasizing the expansion of treatments for cancer, inflammatory diseases, and neurodegenerative diseases. Future efforts should focus on improving the specificity of inhibitors via mechanistic insights, developing combination therapies, establishing novel strategies, such as CRISPR-Cas9 integration with artificial intelligence-driven drug design, and overcoming resistance to enhance clinical treatment outcomes. Clinical case stories show the challenges and possible opportunities in this quickly evolving area.

Background: No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. Methods: This study included 246 LANPC patients (training cohort, n = 117; external test cohort, n = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. Results: The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, |r|= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, |r|= 0.31 to 0.48), CD8 (84, |r|= 0.30 to 0.59), PD-L1 (73, |r|= 0.32 to 0.48), and CD163 (53, |r| = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, |r|= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, |r|= 0.44 to 0.64), CD45RO (65, |r|= 0.42 to 0.67), CD19 (35, |r|= 0.44 to 0.58), CD66b (61, |r| = 0.42 to 0.67), and FOXP3 (21, |r| = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. Conclusion: The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology–pathology correlation suggests a potential biological basis for the predictive models.