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The electrochemical nitrate reduction reaction (NO 3 RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d -states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO 3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO 3 RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V RHE and a yield rate of 6.25 mol h −1 g −1 at −0.6 V RHE . This study provides machine-learned design rules besides the d -band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations. Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia.

Engineering strong metal–support interactions (SMSI) is an effective strategy for tuning structures and performances of supported metal catalysts but induces poor exposure of active sites. Here, we demonstrate a strong metal–support interaction via a reverse route (SMSIR) by starting from the final morphology of SMSI (fully-encapsulated core–shell structure) to obtain the intermediate state with desirable exposure of metal sites. Using core–shell nanoparticles (NPs) as a building block, the Pd–FeO x NPs are transformed into a porous yolk–shell structure along with the formation of SMSIR upon treatment under a reductive atmosphere. The final structure, denoted as Pd–Fe 3 O 4 –H, exhibits excellent catalytic performance in semi-hydrogenation of acetylene with 100% conversion and 85.1% selectivity to ethylene at 80 °C. Detailed electron microscopic and spectroscopic experiments coupled with computational modeling demonstrate that the compelling performance stems from the SMSIR, favoring the formation of surface hydrogen on Pd instead of hydride. Strong metal–support interactions (SMSI) are effective in tuning the structures and catalytic performances of catalysts but limited by the poor exposure of active sites. Here, the authors develop a strategy to engineer SMSI via a reverse route, which is in favor of metal site exposure while embracing the SMSI.

The ketogenic diet (KD) is a high-fat, adequate-protein, and very-low-carbohydrate diet regimen that mimics the metabolism of the fasting state to induce the production of ketone bodies. The KD has long been established as a remarkably successful dietary approach for the treatment of intractable epilepsy and has increasingly garnered research attention rapidly in the past decade, subject to emerging evidence of the promising therapeutic potential of the KD for various diseases, besides epilepsy, from obesity to malignancies. In this review, we summarize the experimental and/or clinical evidence of the efficacy and safety of the KD in different diseases, and discuss the possible mechanisms of action based on recent advances in understanding the influence of the KD at the cellular and molecular levels. We emphasize that the KD may function through multiple mechanisms, which remain to be further elucidated. The challenges and future directions for the clinical implementation of the KD in the treatment of a spectrum of diseases have been discussed. We suggest that, with encouraging evidence of therapeutic effects and increasing insights into the mechanisms of action, randomized controlled trials should be conducted to elucidate a foundation for the clinical use of the KD.

Given that only a subset of patients with colorectal cancer (CRC) benefit from immune checkpoint therapy, efforts are ongoing to identify markers that predict immunotherapeutic response. Increasing evidence suggests that microbes influence the efficacy of cancer therapies. Fusobacterium nucleatum induces different immune responses in CRC with different microsatellite-instability (MSI) statuses. Here, we investigated the effect of F. nucleatum on anti-PD-L1 therapy in CRC. We found that high F. nucleatum levels correlate with improved therapeutic responses to PD-1 blockade in patients with CRC. Additionally, F. nucleatum enhanced the antitumor effects of PD-L1 blockade on CRC in mice and prolonged survival. Combining F. nucleatum supplementation with immunotherapy rescued the therapeutic effects of PD-L1 blockade. Furthermore, F. nucleatum induced PD-L1 expression by activating STING signaling and increased the accumulation of interferon-gamma (IFN-γ) + CD8 + tumor-infiltrating lymphocytes (TILs) during treatment with PD-L1 blockade, thereby augmenting tumor sensitivity to PD-L1 blockade. Finally, patient-derived organoid models demonstrated that increased F. nucleatum levels correlated with an improved therapeutic response to PD-L1 blockade. These findings suggest that F. nucleatum may modulate immune checkpoint therapy for CRC.

Fusobacterium nucleatum ( F. nucleatum ) promotes intestinal tumor growth and its relative abundance varies greatly among patients with CRC, suggesting the presence of unknown, individual-specific effectors in F. nucleatum -dependent carcinogenesis. Here, we identify that F. nucleatum is enriched preferentially in KRAS p.G12D mutant CRC tumor tissues and contributes to colorectal tumorigenesis in Villin-Cre/Kras G12D+/- mice. Additionally, Parabacteroides distasonis ( P. distasonis ) competes with F. nucleatum in the G12D mouse model and human CRC tissues with the KRAS mutation. Orally gavaged P. distasonis in mice alleviates the F. nucleatum -dependent CRC progression. F. nucleatum invades intestinal epithelial cells and binds to DHX15, a protein of RNA helicase family expressed on CRC tumor cells, mechanistically involving ERK/STAT3 signaling. Knock out of Dhx15 in Villin-Cre/Kras G12D+/- mice attenuates the CRC phenotype. These findings reveal that the oncogenic effect of F. nucleatum depends on somatic genetics and gut microbial ecology and indicate that personalized modulation of the gut microbiota may provide a more targeted strategy for CRC treatment. Several studies have shown that Fusobacterium nucleatum aggravates colorectal cancer (CRC) development and chemoresistance. Here the authors show that F. nucleatum is enriched preferentially in patients with KRAS p.G12D mutant CRC and that it promotes colorectal tumorigenesis in preclinical models by binding DHX15 on tumor cells.

Taming interfacial electronic effects on Pt nanoparticles modulated by their concomitants has emerged as an intriguing approach to optimize Pt catalytic performance. Here, we report Pt nanoparticles assembled on vacancy-abundant hexagonal boron nitride nanosheets and their use as a model catalyst to embrace an interfacial electronic effect on Pt induced by the nanosheets with N-vacancies and B-vacancies for superior CO oxidation catalysis. Experimental results indicate that strong interaction exists between Pt and the vacancies. Bader charge analysis shows that with Pt on B-vacancies, the nanosheets serve as a Lewis acid to accept electrons from Pt, and on the contrary, when Pt sits on N-vacancies, the nanosheets act as a Lewis base for donating electrons to Pt. The overall-electronic effect demonstrates an electron-rich feature of Pt after assembling on hexagonal boron nitride nanosheets. Such an interfacial electronic effect makes Pt favour the adsorption of O 2 , alleviating CO poisoning and promoting the catalysis. Tuning electronic properties of metallic catalysts is a useful way to improve their activity, however control over metal-support interactions is still challenging. Here the authors report a vacancy-induced interfacial electronic effect for Pt assembled on vacancy-abundant h -BN nanosheets leading to superior CO oxidation catalysis.

Background The management and prognosis of each type of anterior mediastinal mass differ substantially. Radical thymectomy is regarded as the preferred surgical approach for resectable thymic epithelial tumors (TETs), whereas chemotherapy is the recommended treatment for mediastinal lymphoma after confirming the histological diagnosis through needle biopsy, and surgical procedures should be avoided. Consequently, an accurate diagnosis of mediastinal lymphoma and TETs holds paramount importance in clinical treatment and prognosis for patients with thymic neoplasms. Methods Patients of TETs and mediastinal lymphomas with histopathological proof were included in the present study. The ADC histogram parameters were extracted from ADC maps. Clinical characteristics, radiological features and ADC histogram metrics (including ADCmin, ADCmax, and ADCmean; 5th, 10th, 25th, 50th, 75th, 90th and 95th percentiles of ADC values; skewness and kurtosis) were evaluated between two groups. Multivariate logistic regression was used to build a comprehensive diagnostic model. Receiver operator characteristics (ROC) curve analysis was subsequently carried out to evaluate diagnostic performance. A nomogram was developed to differentiate TETs and mediastinal lymphomas. Results A cohort of 130 consecutive patients, comprising 93 individuals with TETs and 37 with mediastinal lymphomas, was enrolled in the study. TETs comprised 57 low-risk thymomas (61.3%), 20 high-risk thymomas (21.5%), and 16 thymic carcinomas (17.2%); mediastinal lymphomas comprised 13 Hodgkin lymphoma (HL) (35.1%) and 24 non-Hodgkin lymphoma (NHL) (64.9%). It was found that patients with mediastinal lymphomas were significantly younger compared to those with TETs (38.11 ± 13.51 years vs. 53.66 ± 12.99 years, P  < 0.001). The rate of serum lactate dehydrogenase (LDH) elevation was markedly higher in the lymphoma group (54.1% vs. 2.2%, P  < 0.001). The maximal diameter of lesions and skewness were significantly larger in patients with mediastinal lymphoma, whereas the 25th -95th percentile of ADC values, ADCmax and ADCmean were significantly lower compared to patients with TETs (all P  < 0.05). ADC histogram parameters did not differ among TET subtypes (all P  > 0.05), whereas NHL had lower 10th -95th percentile of ADC values and ADCmean than HL (all P  < 0.05). The comprehensive diagnostic model was established based on forward stepwise regression, including age, serum LDH level and skewness, with higher AUC than skewness alone (0.914, 95%CI: 0.850–0.977 vs. 0.785, 95%CI: 0.701–0.869, P  < 0.01). The predictive C-index nomogram performance was 0.917 (95%CI: 0.915–0.918). Conclusion The comprehensive diagnostic model, integrating ADC histogram parameters and clinical characteristics, demonstrated significant potential in distinguishing between TETs and mediastinal lymphomas.

Background: Hemorrhagic stroke is a life-threatening cerebrovascular disease, and early identification is crucial for timely clinical intervention. Microwave imaging is non-ionizing, portable, and low-cost, and thus has potential for pre-hospital and bedside screening; however, existing methods often suffer from limited reconstruction resolution, scarce data, and suboptimal information utilization when only a single modality is used. Methods: We propose a dual-channel, multi-input multimodal deep neural network for hemorrhagic stroke recognition, which jointly exploits complementary features from microwave images and time-domain waveforms and performs feature-level cross-modal fusion. A high-fidelity microwave brain simulation dataset is constructed for model training, and multiple temporal encoding strategies are systematically evaluated. Results: The proposed multimodal model achieves improved accuracy and stability compared with single-modality baselines and conventional approaches, demonstrating the benefit of cross-modal feature fusion for microwave-based hemorrhage recognition. Conclusions: Multimodal learning can enhance discrimination and robustness in microwave-based hemorrhage recognition, supporting its potential use for rapid, non-ionizing pre-hospital and bedside assessment.

Microwave imaging is a promising non-ionizing technique for bedside follow-up of intracranial hemorrhage, but dynamic monitoring remains challenging under limited multistatic sampling because weak inter-frame changes can be obscured by measurement variability, model mismatch, and the high cost of frame-by-frame nonlinear inversion. To address this problem, this paper proposes a state-referenced truncated singular-value decomposition (SR-TSVD) framework for dynamic microwave monitoring of hemorrhagic evolution. The method maintains an internal gate state and reconstructs only the state-referenced increment at each monitoring instant. A row-whitened TSVD inversion is introduced to reduce channel dominance effects and improve robustness to route-dependent imbalance, while a residual-driven gate-refresh mechanism updates the internal state only when the current linearization background becomes insufficiently accurate. The proposed method was validated through two-dimensional numerical experiments and hardware phantom measurements. The numerical study examined different lesion evolution scenarios and analyzed the effects of antenna count, frequency diversity, and measurement noise. The hardware study showed that the method preserves the main dynamic evolution in a real measurement system and remains more stable than baseline linear methods under sparse array conditions. These results indicate that SR-TSVD provides an effective and computationally practical framework for repeated bedside microwave monitoring of intracranial hemorrhage.

Palmitoylation is a critical post-translational modification that involves the covalent binding of palmitic acid to cysteine residues within proteins. It is widely recognized that palmitoylation plays a significant role in regulating protein membrane localization, stability, and interactions. The kidney plays a key role in maintaining fluid homeostasis and excreting metabolic waste, and its normal function relies on the precise regulation of protein function. Emerging evidence reveals the crucial role of palmitoylation in renal physiological and pathological processes. However, the intricate pathways and molecular regulators in the kidney that are involved in palmitoylation remain insufficiently elucidated. This review summarizes the role and possible underlying physiological and pathological mechanism of palmitoylation in the kidney, including enzymes and inhibitors that regulate palmitoylation, the signaling pathways involved, target proteins involved in palmitoylation, and specific modification sites. Moreover, we focus on detection techniques and corresponding research strategies for palmitoylation. This review can also serve as a practical reference to improve the understanding of palmitoylation and the treatment of kidney-related diseases.