Boron neutron capture therapy (BNCT) is a targeted radiotherapy technique that enables dual targeting at the physico-biological level. Efficient neutron sources and specific boron carriers are essential for successful treatment. By exploiting the ¹⁰B(n,α)⁷Li nuclear reaction, the BNCT enables precise eradication of ¹⁰B-loaded tumor malignant cells while sparing adjacent healthy tissues, rendering it particularly advantageous for treating invasive or radioresistant recurrent tumors. Numerous clinical trials related to BNCT have focus on evaluating the safety and efficacy of treating glioblastoma, head and neck carcinoma, meningioma, malignant melanoma, and liver cancer. Preliminary studies have shown that BNCT treatment may extend the overall survival (OS) and improve the quality of life for patients with these cancers. Additionally, the scope of BNCT clinical trials has expanded to other tumor types, such as lung cancer, breast cancer, extramammary Paget’s disease, osteosarcoma, clear cell sarcoma, malignant peripheral nerve sheath tumor, angiosarcoma, thyroid cancer, recurrent chordoma, and gastrointestinal malignancies. This review provides a comprehensive overview of BNCT clinical trials, covering the therapeutic principles of BNCT and the current status and outcomes of clinical trials for various types of tumors. With advancements in neutron beam quality and the development of efficient, specific boron carriers, the therapeutic efficacy of BNCT is expected to be further enhanced, and its scope of application is anticipated to expand. Looking ahead, the BNCT is expected to be integrated with other tumor treatment modalities to augment local tumor control, thereby improving patients’ OS and quality of life.

Calculations of collisions involving excited electronic states play an important role in many high-energy environments, for example, in simulating thermal energy content and heat flux in flows around hypersonic reentry vehicles, and useful data are usually not available from either experiment or theory. We apply a deep learning framework—compatibilization by deep neural network—to automatically discover and fit a compatible potential energy matrix (CPEM) for singlet oxygen atom collisions with N₂ in the ¹A′ manifold of N₂O. The procedure yields not only a fit to the CPEM and its gradient but also analytic representations of the adiabatic potential energy surfaces and their gradients across a dense 13-state manifold; these potential energy surfaces are suitable for dynamics calculations of inelastic and reactive collisions across a broad range of collision energies extending above the dissociation threshold of N₂. We propose a new asymptotically extended formulation of the curvature-driven coherent switches with decay of mixing (κCSDM) semiclassical dynamics method that resolves the conflict between differing symmetries of the interacting atom–diatom system and the completely separated final states. We use the new dynamics method with the analytic representation of potential surface gradients to compute electronically nonadiabatic cross-sections for N₂(X ) + O(¹S) collisions, primarily producing N₂(X) + O(¹D), N₂(A) + O(³P), and NO(X) + N(²D). Our methods provide new capabilities for modeling electronic energy transfer under extreme conditions, with implications across chemistry, physics, and aerospace engineering.

Primary breast cancer (PBC) with axillary lymph node metastasis (ALNM+) is associated with distinct clinical outcomes, including reduced survival (The Cancer Genome Atlas/Foshan cohorts, P < 0.05) and an attenuated response to anti-programmed cell death protein 1 antibody/anti-programmed death-ligand 1 antibody (anti-PD-1/anti-PD-L1) therapy. Through ALNM-stratified single-cell RNA sequencing profiling, we identified 3 hallmark immune subsets in ALNM+ PBC: (a) proliferative MKI67+ T cells, (b) exhausted GZMA+ CD8+ T cells, and (c) CCL13/CXCL10/TOP2A+ macrophages. Cross-modal integration of metastasis–epithelial–mesenchymal transition (EMT) signatures with Mendelian colocalization analysis prioritized TFF3 as a central mechanistic regulator. We validated malignant-cell-specific TFF3 expression across pan-cancer single-cell profiles and in PBC lineages. Integration of Mendelian colocalization signatures with pan-cancer spatial atlases established the TFF3 oncogene as a regulator of spatial EMT programs. Radiogenomic modeling that incorporated machine-learning-derived computed tomography features identified a TFF3-based radiomics risk score. Spatial multi-omics analyses—including bulk RNA sequencing, proteomics, and spatial transcriptomics—established a correlation between TFF3 expression and both MAPK signaling activation and EMT markers. Functional validation demonstrated that TFF3 plays a dual role as an amplifier of the MAPK–EMT axis and a modulator of immune checkpoints. Critically, the prometastatic phenotype driven by TFF3 was rescued upon pharmacological inhibition of MAPK signaling, providing direct evidence of this mechanistic link. In vivo xenograft models confirmed that TFF3 knockdown suppressed metastasis. Pharmacogenomic screening identified 6-mercaptopurine as a novel TFF3 antagonist, which exhibited dose-dependent inhibition of the MAPK–EMT axis. Furthermore, the antimigratory effect of 6-mercaptopurine was reversed by TFF3 overexpression, confirming the functional specificity of this drug–target interaction. Notably, tumors with high TFF3 expression (TFF3hi) exhibited elevated resistance to PD-1 inhibitors but heightened sensitivity to MAPK inhibitors, suggesting a potential theranostic framework for ALNM stratification.

Drug-associated reward memory underlies both the development and relapse of addiction, yet its molecular basis remains poorly understood. Here, transcriptomic profiling and functional validation identified a novel long non-coding RNA (lncRNA), Gm44763, as a critical regulator of morphine-induced reward memory specifically in neurons of the medial prefrontal cortex (mPFC). Behavioral and molecular analyses demonstrated that Gm44763 functions as a sponge for miR-298-5p, thereby relieving the repression of the downstream target gene, eukaryotic translation initiation factor 4E (eIF4E), and modulating both the acquisition and retrieval of reward memory. Golgi staining and fiber photometry further revealed that Gm44763 normalized morphine-induced alterations in synaptic structure and neuronal excitability. miR-298-5p bidirectionally regulated morphine-induced reward memory and reversed both behavioral and neuronal effects mediated by Gm44763. Mechanistically, the downstream effector eIF4E modulates translation via its interaction with eIF4G, thereby contributing to morphine-induced memory regulation. This process can be effectively modulated by 4EGI-1, a selective inhibitor of the eIF4E/eIF4G interaction. In summary, this study characterized lncRNA expression profiles in the mPFC of mice with morphine-induced conditioned place preference. We identified and validated Gm44763 as a novel lncRNA regulator of morphine-induced reward memory and synaptic plasticity. We further delineate a previously uncharacterized Gm44763/miR-298-5p/eIF4E axis that may represent a novel regulatory pathway linking transcriptional and translational control to drug-associated memory formation.

Sepsis-induced cardiac dysfunction (SICD) is a major contributor to mortality in sepsis. Kinesin family member 13B (KIF13B) has been identified as a critical protective factor for metabolic disorder and cardiovascular disease; however, the role of KIF13B in SICD remains unknown. After introducing lipopolysaccharide or cecal ligation and puncture surgery to wild-type (WT) and global Kif13b knockout (Kif13b^−/−) mice combined with lipopolysaccharide-treated neonatal rat cardiomyocytes, we found that KIF13B expression levels were markedly down-regulated in septic hearts and cardiomyocytes. Kif13b deletion exacerbated SICD progress with reduced cardiac contractile function and resulted in increased mortality, accompanied by promoted lipid accumulation, fibrosis, and mitochondrial impairment. Mechanistically, the loss of KIF13B enhanced the lysosomal degradation of the lipid-droplet-associated protein perilipin 5 (PLIN5), thus disrupting the mitochondrial localization of PLIN5 and then impairing cardiac lipid homeostasis and proper mitochondrial function. Nevertheless, cardiac-directed AAV9-PLIN5 gene therapy sufficiently corrected cardiac dysfunction, inhibited lipid accumulation, and reduced oxidative stress in Kif13b^−/− mice with SICD. In summary, these findings provide a new insight into the molecular mechanism underlying the pathogenesis of SICD, highlighting the KIF13B/PLIN5 axis as a potential therapeutic target for the treatment of SICD.

Small inspection robots are highly desirable for inspecting complex machinery and detecting damage in confined spaces. However, common climbing robots that rely on vacuum suction or bioinspired dry adhesion often suffer from bulky sizes or slow locomotion speeds. Developing compact yet intelligent wall-climbing robots that mimic the agility and payload capacity of geckos remains an important challenge. In this work, we design a 20-g, 10-cm artificial intelligence (AI)-integrated robot capable of carrying a 70-g payload while climbing on vertical and inverted surfaces at a speed of 70 mm/s. Acoustic adhesion is generated by vibrating a flexible annular disk on smooth surfaces, where air is periodically absorbed and expelled, resulting in negative pressure. The thin air layer with negative pressure indicates anisotropic performance, characterized by strong normal adhesion and negligible tangential resistance, making it highly suitable for designing small, yet strong, climbing robots. The theoretical model and laser surface morphology measurements reveal the thickness-dependent adhesion of a thin air layer beneath the disk. A servo-spring system is designed to meet the stringent requirements of a thin air layer thickness, yielding robust normal adhesion. Resonance analysis and the use of proper spring material stiffness further enhance adhesion performance. Therefore, combining this innovative acoustic adhesion with optimized structural design, our robot achieves gecko-like mobility and payload capacity. Additionally, integrated AI techniques simplify robot control, allowing voice-commanded operation and autonomous task execution. We demonstrate the functions of these climbing robots through agile inspections in a 3-dimensional maze and retired aircraft engines. This work presents the design of small, strong, and agile climbing robots that utilize anisotropic acoustic adhesions, demonstrating agile mobility across gaps, right corners, and discontinuous curved surfaces. It offers potential solutions for in situ damage detection in aero-engines and other complex equipment cavities.

The performance of hard carbon anodes in sodium-ion batteries is restricted by competing mechanisms: excessive surfaces cause irreversible reactions lowering the initial coulombic efficiency, while insufficient active sites limit capacity. To mitigate this trade-off effect, a synergistic strategy of ultramicropore confinement and electronic-state modulation in lignin-derived hard carbon was created. Using sodium lignosulfonate, a common sulfonated polymer in paper-making waste, we developed N/S-codoped hard carbon microspheres (N-S@HDM) via preoxidation-induced cross-linking and optimized pyrolysis. Preoxidation inhibits graphitic alignment, creating an expanded interlayer spacing and a closed-pore-dominated structure (94.27% at 1,300 °C). This interconnected network (f_a = 0.85) enables ultramicropore confinement, thus shortening diffusion paths, boosting kinetics, and providing ample Na⁺ storage sites to reduce interfacial decomposition. Concurrent N/S doping optimizes electronic states by enhancing electron delocalization, lowers charge-transfer resistance, and generates high-density adsorption sites. The optimized N-S@HDM-1300 achieves an ultrahigh initial coulombic efficiency of 90.6% and a reversible capacity of 401.5 mAh g⁻¹ (0.03 A g⁻¹), with exceptional cyclability (95.0% retention after 500 cycles). This study pioneers a dual-regulation paradigm for biomass-derived carbon materials, coupling pore engineering and electronic optimization to advance sodium-ion battery anode design.

Impaired mitophagy and the accumulation of damaged mitochondria are key drivers of endothelial cell (EC) dysfunction in diabetic wounds. While mitochondrial transplantation (MT) has demonstrated therapeutic potential in such mitochondrial damage-related diseases, its application is still thwarted by elusive mechanisms and practical hurdles such as poor targeting specificity and low delivery efficiency. Here, we reveal that MT acts by reactivating mitophagy to selectively eliminate dysfunctional mitochondria, thereby restoring mitochondrial homeostasis and rescuing EC functionality. To exploit this discovery, we engineer a biomimetic MT strategy through coating EC-derived apoptotic vesicle membrane (AVM) onto the surface of isolated mitochondria. The resulting mitochondria–AVM complex (Mito-AVM) leverages homologous targeting and phosphatidylserine-mediated “eat-me” signaling, achieving a remarkable 150% increase in delivery efficiency to ECs in diabetic wounds. Furthermore, we construct a 3-aminophenylboric acid-modified hyaluronic acid/polyvinyl alcohol hydrogel for the diabetic wound microenvironment, enabling reactive oxygen species/glucose-triggered sustained release of encapsulated Mito-AVM at the wound site. In summary, our work elucidates a fundamental mechanism of MT and provides an efficient and targeted strategy for MT therapy, offering fresh perspectives for diabetic wound treatment.

Follicle-stimulating hormone (FSH) coordinates ovarian follicle development by aligning mitochondrial biogenesis with increased metabolic demand. Although FSH is known to stimulate glycolysis in granulosa cells (GCs), the mechanism by which glycolytic flux coupled to mitochondrial biogenesis remains unclear. Here, we demonstrate that histone lactylation functions as a lactate-sensitive epigenetic mediator linking FSH-driven metabolic alterations to mitochondrial biogenesis in GCs. Mechanistically, FSH increases intracellular lactate levels through glycolytic activation, thereby promoting P300/CBP-dependent lactylation of histone H4 at lysine 5 (H4K5la). H4K5la directly enhances HDAC4 expression, and HDAC4 subsequently deacetylates PGC-1α at lysine residues 329/330. Deacetylated PGC-1α cooperates with nuclear respiratory factors NRF1/2 to drive transcription of key mitochondrial regulators (TFAM, TFB1M, TFB2M), ultimately promoting mitochondrial biogenesis. Disruption of the H4K5la/HDAC4/PGC-1α axis markedly impaired mitochondrial biogenesis and follicular development, evidenced by reduced ovarian weight, smaller follicle size, decreased antral follicle number, and impaired GC proliferation and estradiol (E2) production in FSH-treated mice. These findings identify a metabolic–epigenetic regulatory pathway in which histone lactylation links glycolysis to mitochondrial adaptation, providing mechanistic insight into FSH-dependent reproductive physiology.

Evaluating chemical toxicity and its potential hazards to human health and the environment is essential in diverse fields, including medicine, industry, and agriculture. Multi-species acute toxicity prediction (MSATP) is critical in toxicity assessment. Traditional methods rely on exposing animals to a single high dose of a compound and observing its toxicity. However, with growing ethical concerns regarding animal testing, advancements in computational technology have positioned the artificial intelligence-based MSATP as an efficient alternative. Current research on MSATP commonly employs multi-task deep neural networks for modeling. However, the small size, high dimensionality, and sparsity of MSATP tabular data render them unsuitable for neural network approaches. To address this, we proposed a multi-task cascade forest framework for MSATP. This framework integrated feature enhancement through knowledge transfer, sample enhancement using a greedy search strategy with the covariance distance measure. The framework accommodated tasks of varying sizes in multi-task learning and was specifically designed for tabular data, achieving a 12% improvement in performance compared to current state-of-the-art methods (R² equals to 0.64, root mean square error equals to 0.57). In a single-view context, we conducted ablation experiments to validate the effectiveness of the data enhancement strategy and introduced external dataset experiments to assess the generalization capability of the proposed method for cross-species prediction. In a multi-view context, the feature fusion method and consensus ensemble were demonstrated to further enhance the model performance. Additionally, we analyzed feature importance vectors to provide interpretable insights into species toxicity correlations. Overall, this framework effectively addressed MSATP tasks and exhibited substantial potential for application in various toxicity prediction domains.