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The electrochemical CO 2 reduction to high-value-added chemicals is one of the most promising and challenging research in the energy conversion field. An efficient ECR catalyst based on a Cu-based conductive metal-organic framework (Cu-DBC) is dedicated to producing CH 4 with superior activity and selectivity, showing a Faradaic efficiency of CH 4 as high as ~80% and a large current density of −203 mA cm −2 at −0.9 V vs. RHE. The further investigation based on theoretical calculations and experimental results indicates the Cu-DBC with oxygen-coordinated Cu sites exhibits higher selectivity and activity over the other two crystalline ECR catalysts with nitrogen-coordinated Cu sites due to the lower energy barriers of Cu-O 4 sites during ECR process. This work unravels the strong dependence of ECR selectivity on the Cu site coordination environment in crystalline porous catalysts, and provides a platform for constructing highly selective ECR catalysts. Crystalline porous catalysts with single Cu sites are dedicated to exploring the dependence of CO 2 electroreduction selectivity on the coordination environment of catalytic sites. The conductive MOF Cu-DBC with oxygen-coordinated Cu sites shows a high Faradaic efficiency ~80% of CO 2 -to-CH 4 .

Rational design of robust photocatalytic systems to direct capture and in-situ convert diluted CO 2 from flue gas is a promising but challenging way to achieve carbon neutrality. Here, we report a new type of host-guest photocatalysts by integrating CO 2 -enriching ionic liquids and photoactive metal-organic frameworks PCN-250-Fe 2 M (M = Fe, Co, Ni, Zn, Mn) for artificial photosynthetic diluted CO 2 reduction in gas-solid phase. As a result, [Emim]BF 4 (39.3 wt%)@PCN-250-Fe 2 Co exhibits a record high CO 2 -to-CO reduction rate of 313.34 μmol g −1 h −1 under pure CO 2 atmosphere and 153.42 μmol g −1 h −1 under diluted CO 2 (15%) with about 100% selectivity. In scaled-up experiments with 1.0 g catalyst and natural sunlight irradiation, the concentration of pure and diluted CO 2 (15%) could be significantly decreased to below 85% and 10%, respectively, indicating its industrial application potential. Further experiments and theoretical calculations reveal that ionic liquids not only benefit CO 2 enrichment, but also form synergistic effect with Co 2+ sites in PCN-250-Fe 2 Co, resulting in a significant reduction in Gibbs energy barrier during the rate-determining step of CO 2 -to-CO conversion. Artificial photosynthetic diluted CO 2 reduction from fuel gas is promising but challenging for carbon neutrality. Here, the authors report a host-guest system by integrating CO 2 -enriching ionic liquids and photoactive metal-organic frameworks, greatly enhancing CO 2 -to-CO conversion efficiency.

HighlightsRepresentative methods for calculating the depth of discharge of different Zn anodes are introduced.Recent advances of aqueous Zn metal batteries with high Zn utilization are reviewed and categorized according to Zn anodes with different structures.The working mechanism of anode-free aqueous Zn metal batteries is introduced in detail, and different modification strategies for anode-free aqueous Zn metal batteries are summarized.Aqueous zinc metal batteries (AZMBs) are promising candidates for next-generation energy storage due to the excellent safety, environmental friendliness, natural abundance, high theoretical specific capacity, and low redox potential of zinc (Zn) metal. However, several issues such as dendrite formation, hydrogen evolution, corrosion, and passivation of Zn metal anodes cause irreversible loss of the active materials. To solve these issues, researchers often use large amounts of excess Zn to ensure a continuous supply of active materials for Zn anodes. This leads to the ultralow utilization of Zn anodes and squanders the high energy density of AZMBs. Herein, the design strategies for AZMBs with high Zn utilization are discussed in depth, from utilizing thinner Zn foils to constructing anode-free structures with theoretical Zn utilization of 100%, which provides comprehensive guidelines for further research. Representative methods for calculating the depth of discharge of Zn anodes with different structures are first summarized. The reasonable modification strategies of Zn foil anodes, current collectors with pre-deposited Zn, and anode-free aqueous Zn metal batteries (AF-AZMBs) to improve Zn utilization are then detailed. In particular, the working mechanism of AF-AZMBs is systematically introduced. Finally, the challenges and perspectives for constructing high-utilization Zn anodes are presented.

Objective This study aimed to describe the temporal trends and risk factors of kidney cancer (KC) burden from 1990 to 2021, evaluate its age, period, and cohort effects, and project the disease burden over the next 15 years. Methods Data were derived from the 2021 Global Burden of Disease (GBD) study. A joinpoint regression model was used to estimate the average annual percentage change (AAPC) in KC prevalence and mortality, while age-period-cohort analysis was applied to estimate age, period, and cohort effects. We extended the Bayesian age-period-cohort (BAPC) model to predict the disease burden of KC from 2022 to 2036. Results In 2021, the number of incident KC cases in China reached 65,799 (4.62 cases per 100,000 total population). Additionally, KC resulted in 24,867 deaths (1.75 deaths per 100,000 total population).The incidence rate of KC continued to rise from 1.38 per 100,000 in 1990 to 4.62 per 100,000 in 2021, with males consistently exceeding females in case numbers. Meanwhile, KC mortality rose from 0.77 per 100,000 in 1990 to 1.75 per 100,000 in 2021.Throughout the study period, the average annual percent changes (AAPC) in incidence and mortality were 3.92% and 2.61%, respectively. Males exhibited higher prevalence and mortality of KC. In the Age-Period-Cohort (APC) analysis, the risk of KC was observed to increase with advancing age in the age dimension. Period effects analysis revealed an overall downward trajectory in all age groups. Cohort-level analysis indicated that early birth cohorts had higher susceptibility, with those born before 1920–1925 exhibiting a higher risk profile that subsequently decreased over time. Smoking and high body mass index (BMI) were the primary risk factors for KC-related disability-adjusted life years (DALYs) and mortality, while the contribution of occupational exposure to trichloroethylene was relatively minor. By 2036, the age-standardized incidence and mortality of KC are projected to rise to 4.58 and 1.31 per 100,000, respectively. Conclusion To alleviate the disease burden of KC, comprehensive strategies are required, including risk factor prevention in primary care settings, KC screening for the elderly and high-risk populations, and access to high-quality medical services.

ObjectivesTo utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.MethodsA deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists’ reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.ResultsOf 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance.ConclusionsThe algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.Key Points• The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis.• The deep learning model improves diagnosis efficiency by shortening processing time.• The deep learning model can automatically calculate the volume of the lesions and whole lung.

Inflammatory bowel disease (IBD) is characterized by chronic and relapsing intestinal inflammation, which currently lacks safe and effective medicine. Some previous studies indicated that Astragaloside IV (AS-IV), a natural saponin extracted from the traditional Chinese medicine herb Ligusticum chuanxiong , alleviates the experimental colitis symptoms in vitro and in vivo . However, the mechanism of AS-IV on IBD remains unclear. Accumulating evidence suggests that M2-polarized intestinal macrophages play a pivotal role in IBD progression. Here, we found that AS-IV attenuated clinical activity of DSS-induced colitis that mimics human IBD and resulted in the phenotypic transition of macrophages from immature pro-inflammatory macrophages to mature pro-resolving macrophages. In vitro , the phenotype changes of macrophages were observed by qRT-PCR after bone marrow-derived macrophages (BMDMs) were induced to M1/M2 and incubated with AS-IV, respectively. In addition, AS-IV was effective in inhibiting pro-inflammatory macrophages and promoting the pro-resolving macrophages to ameliorate experimental colitis via the regulation of the STAT signaling pathway. Hence, we propose that AS-IV can ameliorate experimental colitis partially by modulating macrophage phenotype by remodeling the STAT signaling, which seems to have an essential function in the ability of AS-IV to alleviate the pathological progress of IBD.

Unmanned Aerial Vehicles (UAVs) offer significant advantages for construction site monitoring through flexible deployment and high-resolution imagery. However, existing vision-based detection methods face three critical technical gaps: (1) CNN-based detectors rely on local receptive fields with limited global context modeling, which is insufficient for disambiguating small construction objects from cluttered backgrounds; (2) transformer-based detectors capture global dependencies but incur quadratic computational complexity O ( n 2 ) , making them impractical for high-resolution UAV imagery; and (3) conventional multi-scale fusion strategies inadequately bridge the semantic gap between low-level spatial details and high-level semantic features, leading to degraded performance under extreme scale variations. To address these limitations, we propose CSM-DETR, a novel detection transformer specifically designed for UAV-based construction monitoring. Our framework adopts the MobileMamba as backbone to achieve linear computational complexity O ( n ) while capturing long-range spatial dependencies, and incorporates the Hierarchical Local-Aware Fusion (HLAF) mechanism for adaptive multi-scale feature aggregation. Furthermore, we propose three key innovations: (1) a Dual-Attention Spatial Integration (DASI) module enhancing multi-scale spatial feature representation through parallel local and global attention streams; (2) a Cross-Scale Deformable Fusion (CSDF) module enabling flexible cross-scale feature interaction through deformable sampling; and (3) a Scale-Aware Composite Loss (SAC Loss) providing scale-aware supervision for challenging small objects. We construct a comprehensive benchmark dataset named UAV-CSM47, containing 15,860 high-resolution aerial images with 47 construction-related object categories. Extensive experiments demonstrate that CSM-DETR achieves state-of-the-art performance with 91.8% mAP@0.5 and 73.6% mAP@0.5:0.95, outperforming YOLOv13-L by 3.3 percentage points and Co-DETR by 2.7 percentage points while maintaining competitive inference speed at 38 FPS on an NVIDIA RTX 3090 GPU. Ablation studies validate each component’s effectiveness, and cross-domain evaluation confirms strong generalization capability. The proposed system provides a practical solution for automated construction site monitoring with broad applications in safety supervision, progress tracking, and resource management.

Photocatalytic CO 2 reduction reaction has been developed as an effective strategy to convert CO 2 into reusable chemicals. However, the reduction products of this reaction are often of low utilization value. Herein, we effectively connect photocatalytic CO 2 reduction and amino carbonylation reactions in series to reconvert inexpensive photoreduction product CO into value-added and easily isolated fine chemicals. In this tandem transformation system, we synthesize an efficient photocatalyst, NNU-55-Ni, which is transformed into nanosheets (NNU-55-Ni-NS) in situ to improve the photocatalytic CO 2 -to-CO activity significantly. After that, CO serving as reactant is further reconverted into organic molecules through the coupled carbonylation reactions. Especially in the carbonylation reaction of diethyltoluamide synthesis, CO conversion reaches up to 85%. Meanwhile, this tandem transformation also provides a simple and low-cost method for the 13 C isotopically labeled organic molecules. This work represents an important and feasible pathway for the subsequent separation and application of CO 2 photoreduction product. A Ni-based MOF catalyst is reported to facilitate the photocatalytic reduction of CO2 to CO, a low-value product. In tandem, the as-produced CO is used as a reactant in the Pd-catalyzed carbonylation of aryl halides and other fine organic chemicals.

The hallmark of tumorigenesis is the successful circumvention of cell death regulation for achieving unlimited replication and immortality. Ferroptosis is a newly identified type of cell death dependent on lipid peroxidation which differs from classical programmed cell death in terms of morphology, physiology and biochemistry. The broad spectrum of injury and tumor tolerance are the main reasons for radiotherapy and chemotherapy failure. The effective rate of tumor immunotherapy as a new treatment method is less than 30%. Ferroptosis can be seen in radiotherapy, chemotherapy, and tumor immunotherapy; therefore, ferroptosis activation may be a potential strategy to overcome the drug resistance mechanism of traditional cancer treatments. In this review, the characteristics and causes of cell death by lipid peroxidation in ferroptosis are briefly described. In addition, the three metabolic regulations of ferroptosis and its crosstalk with classical signaling pathways are summarized. Collectively, these findings suggest the vital role of ferroptosis in immunotherapy based on the interaction of ferroptosis with tumor immunotherapy, chemotherapy and radiotherapy, thus, indicating the remarkable potential of ferroptosis in cancer treatment.