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Iron phthalocyanine (FePc) is a promising non-precious catalyst for the oxygen reduction reaction (ORR). Unfortunately, FePc with plane-symmetric FeN 4 site usually exhibits an unsatisfactory ORR activity due to its poor O 2 adsorption and activation. Here, we report an axial Fe–O coordination induced electronic localization strategy to improve its O 2 adsorption, activation and thus the ORR performance. Theoretical calculations indicate that the Fe–O coordination evokes the electronic localization among the axial direction of O–FeN 4 sites to enhance O 2 adsorption and activation. To realize this speculation, FePc is coordinated with an oxidized carbon. Synchrotron X-ray absorption and Mössbauer spectra validate Fe–O coordination between FePc and carbon. The obtained catalyst exhibits fast kinetics for O 2 adsorption and activation with an ultralow Tafel slope of 27.5 mV dec −1 and a remarkable half-wave potential of 0.90 V. This work offers a new strategy to regulate catalytic sites for better performance. Iron phthalocyanine with a 2D structure and symmetric electron distribution around Fe-N 4 active sites is not optimal for O 2 adsorption and activation. Here, the authors report an axial Fe–O coordination induced electronic localization strategy to enhance oxygen reduction reaction performance.

Background Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. Methods The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). Results The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p <0.05. Conclusion KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

When air-breathing aircraft is flying over a wide area, the upflow of the combustor central axis will change greatly, and the flow field parameters will fluctuate along the axis. Therefore, it is necessary to carry out a study on the influence of axis flow field parameter fluctuation on combustion chamber performance, so as to provide relevant theoretical support for the combustion chamber of air-breathing aircraft in wide-area flight design. Based on the N-S gas-phase control model, combined with combustion model, turbulence model, burning rate model and mass transfer model, a numerical simulation model of flow combustion in solid fuel scramjet combustor is established. Through this model, the influence of the upflow field parameter fluctuation of the central axis on the combustor performance is carried out. The results show that for a optimized combustor configuration, different inlet air flow rates will cause the Mach number of the combustor to oscillate along the flow direction. The greater the oscillation amplitude, the greater the total pressure loss. Too high or too low inlet air flow rate will both result in Mach number oscillation in the combustor. However, selecting a suitable inlet air flow rate can significantly reduce the Mach number oscillation of combustor flow. Therefore, for different combustor configurations, appropriate inlet air flow should be designed to reduce the Mach number oscillation of the flow field in the combustor, reducing the flow loss and improving the working performance of the combustor.

Background Sepsis is a leading cause of mortality among older adults, and atrial fibrillation (AF), as a common comorbidity, significantly increases the risk of adverse outcomes. This study aimed to investigate nationwide mortality trends, racial and regional disparities, and contributing factors in adults aged 55–84 years with sepsis-AF. Methods We analyzed CDC WONDER mortality data for adults aged 55–84 years (1999–2020), defining cases as deaths with sepsis (A40–A41) as the underlying cause and AF (I48) listed as a contributing condition anywhere on the certificate. Individuals aged 55–84 years with sepsis-AF noted on death certificates were included. Age-adjusted mortality rates (AAMR) were calculated based on the 2000 US standard population. Temporal trends in AAMR were assessed using Joinpoint regression, and analyses were stratified by race and geographic region. Results The overall AAMR increased significantly from 0.64 to 1.57 per 100,000 (145% rise; average annual percent change [AAPC] = 4.49%). Marked disparities were observed: males exhibited 30.7% higher mortality than females by 2020; Black individuals showed higher mean AAMR throughout the study period though rates converged statistically by 2020; and the South recorded the highest regional AAMR while the West showed the largest relative increase. AAMR surged across all groups in 2020, coinciding with the COVID-19 pandemic. Conclusions Mortality due to sepsis-AF among older adults aged 55–84 years in the US has exhibited a sustained upward trajectory, accompanied by significant demographic and regional disparities. Targeted public health interventions are essential to address these inequalities and mitigate mortality in high-risk populations.

Bone tumors, encompassing primary sarcomas such as osteosarcoma and secondary skeletal metastases from carcinomas, present a stubborn clinical problem. Their treatment is hampered by three interconnected barriers: the physical impediment of a mineralized matrix that restricts drug access, a profoundly immunosuppressive microenvironment that inactivates antitumor immunity, and the lack of endogenous tissue regeneration following therapeutic intervention. Conventional modalities-including systemic chemotherapy, surgical resection, and even modern immunotherapies-often yield disappointing results against these complex lesions. In this context, nanotechnology offers a fresh therapeutic perspective. Engineered nanoplatforms are designed to home in on bone lesions, disrupt local immunosuppressive networks, and re-establish immunosurveillance. This review critically examines how these integrated systems counteract immune resistance. We focus on platforms that achieve precise bone targeting, reprogram the local immune landscape, and, crucially, coordinate the timing of tumor clearance with the process of functional bone repair. By tackling the dual challenges of immune evasion and structural defects, these multifunctional agents mark a significant departure from conventional approaches, holding the potential to simultaneously eradicate tumors and restore skeletal integrity. Graphical abstract

Bovine mastitis poses significant challenges to the global dairy industry, leading to substantial economic losses and public health concerns. Staphylococcus aureus , a prevalent causative agent of bovine mastitis, depends on effective adhesion and biofilm formation to establish infections. Berberine (BER), a naturally occurring phytochemical, demonstrates broad-spectrum antibacterial activity but suffers from poor bioavailability. This study developed a composite berberine-carboxymethyl chitosan/sodium alginate hydrogel to address these limitations. The hydrogel was characterized using scanning electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction. In vitro assessments revealed that the BER hydrogel eradicated S. aureus biofilms (42% eradication at 156.26 μg/mL), inhibited bacterial adhesion, and reduced inflammatory cytokines (IL-6 and TNF-α) in S. aureus -infected MAC-T cells, with compliant biosafety biocompatibility (hemolysis rate <5%) and sustained drug release (100% over 6 h), though pH-dependent release kinetics necessitate microenvironment-specific formulation refinement. In conclusion, the BER hydrogel represents a potential therapeutic candidate for S. aureus -induced bovine mastitis.

BackgroundWhether pulmonary arterial hypertension (PAH)–targeted therapies, particularly endothelin receptor antagonists (ERAs), are associated with disproportionate sepsis reporting in real-world pharmacovigilance data remains insufficiently explored. The monocyte transcriptional states that characterize sepsis-related immune dysregulation and may provide biological context for such reporting signals are also incompletely defined.MethodsWe constructed an integrated, hypothesis-generating analytical framework incorporating: (i) FDA Adverse Event Reporting System (FAERS) disproportionality analysis coupled with XGBoost-based modeling for pharmacovigilance signal detection; (ii) single-cell RNA sequencing (scRNA-seq) analysis of peripheral blood mononuclear cells with intercellular communication inference; (iii) weighted gene co-expression network analysis (WGCNA) and cytoHubba-based topological prioritization, combined with an ensemble machine learning framework for diagnostic signature construction; and (iv) molecular docking and 100-nanosecond all-atom molecular dynamics (MD) simulation.ResultsFAERS analysis identified Maitentan and ambrisentan as PAH-targeted therapies with positive reporting signals for the MedDRA Preferred Term “Sepsis,” with adjusted reporting associations persisting after adjustment for available demographic variables. Sex-stratified analysis showed marked heterogeneity in reporting signals, although these findings may be influenced by the sex distribution of PAH populations and other unmeasured confounders. ScRNA-seq resolved seven monocyte subpopulations, among which the interferon-responsive Mono_IFN subset—marked by IFIT1, ISG15, and IFITM3 expression—occupied a signaling hub position within the IFN-γ communication network and expanded in sepsis-associated states. Systematic comparison of 112 integrated machine learning algorithm combinations based on cytoHubba-prioritized genes yielded a 29-gene diagnostic model with cross-cohort discrimination for sepsis. Transcriptional co-expression analysis nominated IFITM3, a marker of the interferon-responsive monocyte state, as a candidate node connecting the diagnostic signature with interferon-related immune dysregulation. Molecular docking and 100-nanosecond MD simulation suggested a structurally stable riociguat–IFITM3 interaction in silico . This finding remains exploratory and requires biochemical and functional validation.ConclusionThis integrated pharmacovigilance and transcriptomic study identifies sepsis-reporting signals associated with selected endothelin receptor antagonists and characterizes an IFITM3-associated interferon-responsive monocyte state in sepsis datasets. These findings should be interpreted as reporting associations and transcriptomic hypotheses rather than evidence of causal drug-induced sepsis. The predicted riociguat–IFITM3 interaction provides a computational hypothesis for future experimental validation.

Since its advent 120 years ago, the [2+n] coupling cyclization of ketene has been prevalently used for the synthesis of N - and O -heterocycles. In contrast, its vinylogous version, i.e., use of alkenyl ketene as 4 C synthon, remain elusive. We report herein that in the rare S N 1-type ring-opening of electron-deficient cyclopropene, the initially formed sp 2 -carbocation-containing zwitterionic intermediate undergoes facile 1,4-alkoxy migration to generate a functionalized alkenyl ketene. This electrophilic intermediate not only allows for challenging N -nucleophiles to be engaged in the conventional ketene [2+n] reactions (n = 1 ~ 3), also unveiled the vinylogous [4+n] cyclization mode, as exemplified by the [4 + 1] and formal [4 + 4] cyclization to construct pyrrolidinone and azocine frameworks. The protocol offers a unified entry to a distinct class of lactam scaffolds that exhibit anti-cancer potential, constituent key natural product scaffold and display interesting 1e- and 2e- reactivities. This work reveals a broader synthetic potential of the facile S N 1 type ring-opening of cyclopropene as “dehydro”-donor-acceptor cyclopropane (DDAC) substrate, and could have ramifications in ketene chemistry, N -heterocyclic chemistry and related medicinal research, as well as the donor-acceptor system chemistry. The reactivity of ketenes is limited to the conventional [2+n] mode where the ketene is used as a 2 C synthon. Here, the authors present a suite of reactivity of a vinylogous [4+n] cyclization mode of ketenes besides the conventional one, forming a variety of lactam type azacycles.

Open caissons are extensively employed in various underground engineering projects, necessitating real-time determination and prediction of their sinking states to ensure stable and secure sinking and prevent potential construction risks. Recognizing that the monitoring data of open caisson sinking is inherently streaming data updating in real-time, this study proposes an integrated RRCF-HMA-ERT streaming algorithm and establishes a practical engineering framework based on the algorithm. This framework gathers multi-constellation GNSS RTK and earth pressure monitoring data during open caisson sinking. The collected streaming data are then processed by the proposed algorithm in real-time, accurately determining the current sinking states and predicting future sinking states of the open caisson. Taking a large-scale open caisson project in Jiangsu Province, China as an example, a complete workflow for applying the constructed framework is illustrated, and the proposed algorithm and framework are validated. Results demonstrate that the algorithm and framework can accurately determine the sinking states (sinking speed, inclination, and horizontal deviation). When predicting various sinking states in real-time, the values of RMSE are all less than 2.1 and R 2 values exceed 0.93, indicating high prediction accuracy. The average calculation time for each analysis round is 0.07 s, showcasing the streaming algorithm’s speed and efficiency.

Submarine pipelines buried in marine mud are often affected by cathodic protection (CP) potential and sulfate-reducing bacteria (SRB). In this study, the slow strain rate tension (SSRT) test, hydrogen permeation test, and surface topography analysis were used to investigate the synergistic effect of SRB and CP potential on the stress corrosion cracking (SCC) behaviour of X100 pipeline steel. The results show that the hydrogen permeation current and SCC sensitivity of the specimens increased with decreasing CP potential, especially in the SRB-inoculated solution. The decrease in CP potential stimulated the hydrogen evolution reaction, and SRB increased the hydrogen permeation current density, promoting crack expansion. The SRB and CP potential had synergistic effects accelerating hydrogen permeation and SCC. However, as the potential decreased, the synergistic effect diminished due to the alkalization of the solution.