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In 2005, Shi defined I-regular S-posets and used this concept to characterize PP-pomonoids and po-cancellable pomonoids. In this paper, we continued the development of the homological classification of pomonoids by using the I-regularity of S-posets. First, we characterized pomonoids over which all I-regular S-posets have one of the properties around projectivity or injectivity, and many known results were generalized. Moreover, some possible conditions on pomonoids that describe when their diagonal posets are I-regular were found. Finally, some characterizations of pomonoids by the I-regularity of their Rees factor posets were given.

Microplastic pollution is a topic of increasing concern, especially since this issue was first addressed in soils. Results have so far been variable in terms of effects, suggesting that there is substantial context-dependency in microplastic effects in soil. To better define conditions that may affect microplastic-related impacts, we here examined effects as a function of microplastic shape and polymer type, and we tested if effects on soil properties and soil microbial activities change with incubation time. In our laboratory study, we evaluated twelve different secondary microplastics representing four microplastic shapes: fibers, films, foams and fragments; and eight polymer types: polyamide (PA), polycarbonate (PC), polyethylene (PE), polyester (PES), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyurethane (PU). We mixed the microplastics with a sandy soil (0.4% w/w) and incubated at 25°C for 31 days. Then, we collected soil samples on the 3rd, 11th, and 31st day, and measured soil pH, respiration and four enzyme activities (soil enzymatic activities). Our results showed that microplastics could affect soil pH, respiration and enzymatic activities depending on microplastic shape and polymer type, effects that were altered with incubation time. Soil pH increased with foams and fragments and overall decreased in the first days of incubation and then increased. Soil respiration increased with PE foams and was affected by the incubation time, declining over time. Overall, acid phosphatase activity was not affected by shape or polymer type. β-D-glucosidase activity decreased with foams, cellobiosidase activity decreased with fibers, films and foams while N-acetyl-β-glucosaminidase activities decreased with fibers and fragments. Enzymatic activities fluctuated during the incubation time, except N-acetyl-β-glucosaminidase, which showed a declining trend with incubation time. Enzymatic activities were negatively correlated with soil pH and this relationship was less strong when microplastics were added to the soil. Our study adds to the evidence that research should embrace the complexity and diversity of microplastics, highlighting the role of microplastic shape and polymer type in influencing effects; additionally, we show that incubation time is also a parameter to consider, as effects are dynamic even in the short term.

With the development of genome sequencing, the role of molecular targeted therapy in cancer is becoming increasingly important. However, genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients. Radiogenomics aims to correlate imaging characteristics with gene expression patterns, gene mutations, and other genome-related characteristics. Due to the noninvasive nature of medical imaging, the field of radiogenomics is rapidly developing and may serve as a substitute tool for genetic testing. In this article, we briefly summarise the current role of radiogenomics in predicting gene mutations in brain, lung, colorectal, breast, and kidney tumours. Key Points • The role of molecular targeted therapy in individual cancer-precision therapy is becoming increasingly important with the development of genetic testing. • Radiogenomics may provide accurate imaging biomarkers as a substitute for genetic testing. • While the field of radiogenomics holds great promise, there are still a number of limitations that need to be overcome.

Oxidative stress plays an important role in the development of aging-related diseases by accelerating the lipid peroxidation of polyunsaturated fatty acids in the cell membrane, resulting in the production of aldehydes, such as malondialdehyde and 4-hydroxy-2-nonenal (4-HNE) and other toxic substances. The compound 4-HNE forms adducts with DNA or proteins, disrupting many cell signaling pathways including the regulation of apoptosis signal transduction pathways. The binding of proteins to 4-HNE (4-HNE-protein) acts as an important marker of lipid peroxidation, and its increasing concentration in brain tissues and fluids because of aging, ultimately gives rise to some hallmark disorders, such as neurodegenerative diseases (Alzheimer’s and Parkinson’s diseases), ophthalmic diseases (dry eye, macular degeneration), hearing loss, and cancer. This review aims to describe the physiological origin of 4-HNE, elucidate its toxicity in aging-related diseases, and discuss the detoxifying effect of aldehyde dehydrogenase and glutathione in 4-HNE-driven aging-related diseases.

Background Gexia-Zhuyu Tang (GZT), a traditional Chinese medicine formula, is used to treat a variety of diseases. However, its roles in gastric cancer (GC) remain unclear. Objective The aim of this study was to explore the roles and underlying molecular mechanisms of modified GZT in GC. Methods The effects of modified GZT on GC were investigated by constructing mouse xenograft models with MFC cell line. The fecal samples from low-dose, high-dose, and without modified GZT treatment groups were collected for the 16S rRNA gene sequencing and fecal microbiota transplantation (FMT). Histopathological alterations of mice were evaluated using the hematoxylin–eosin (HE). Immunohistochemical (IHC) analysis with Ki67 and GSDMD was performed to measure tissue cell proliferation and pyroptosis, respectively. Proteins associated with pyroptosis, invasion, and metastasis were detected by Western blotting. Enzyme-linked immunosorbent assay (ELISA) was used to assess inflammation-related factors levels. Results Modified GZT inhibited GC tumor growth and reduced metastasis and invasion-related proteins expression levels, including CD147, VEGF, and MMP-9. Furthermore, it notably promoted caspase-1-dependent pyroptosis, as evidenced by a dose-dependent increase in TNF-α, IL-1β, IL-18, and LDH levels, along with elevated protein expression of NLRP3, ASC, and caspase-1. Additionally, modified GZT increased species abundance and diversity of the intestinal flora. FMT assay identified that modified GZT inhibited GC tumor progression through regulation of intestinal flora. Conclusions Modified GZT treatment may promote pyroptosis by modulating gut microbiota in GC. This study identifies a new potential approach for the GC clinical treatment.

Molecular biostatistical workflows in oncology often rely on predictive models that use multimodal data. Advances in deep learning and artificial intelligence technologies have enabled the multimodal fusion of large volumes of multimodal data. Here, we presented a decision level multimodal data fusion framework for integrating multiomics and pathological tissue slide images for prognosis prediction. Our approach established the spatial map of instances by connecting the neighboring nuclei in space and calculated the characteristic tensor via graph convolution layers for the input pathological tissue slide images. Global Average Pooling was applied to align and normalize the feature tensors from pathological images and the multiomics data, enabling seamless integration. We tested our proposed approach using Breast Invasive Carcinoma data and Non-Small Cell Lung Cancer data from the Cancer Genome Atlas, which contains paired whole-slide images, transcriptome data, genotype, epienetic, and survival information. In a 10-fold cross-validation, the comparison results demonstrated that the multimodal fusion paradigm improves outcome predictions from single modal data alone with the average C-index increasing from 0.61 to 0.52 to 0.75 and 0.67 for breast cancer and non-small cell lung cancer cohort, respectively. The proposed decision level multimodal data fusion framework is expected to provide insights and technical methodologies for the follow-up studies.

Photo‐thermal catalytic CO2 hydrogenation is currently extensively studied as one of the most promising approaches for the conversion of CO2 into value‐added chemicals under mild conditions; however, achieving desirable conversion efficiency and target product selectivity remains challenging. Herein, the fabrication of Ir‐CoO/Al2O3 catalysts derived from Ir/CoAl LDH composites is reported for photo‐thermal CO2 methanation, which consist of Ir‐CoO ensembles as active centers that are evenly anchored on amorphous Al2O3 nanosheets. A CH4 production rate of 128.9 mmol gcat⁻1 h⁻1  is achieved at 250 °C under ambient pressure and visible light irradiation, outperforming most reported metal‐based catalysts. Mechanism studies based on density functional theory (DFT) calculations and numerical simulations reveal that the CoO nanoparticles function as photocatalysts to donate electrons for Ir nanoparticles and meanwhile act as “nanoheaters” to effectively elevate the local temperature around Ir active sites, thus promoting the adsorption, activation, and conversion of reactant molecules. In situ diffuse reflectance infrared Fourier transform spectroscopy (in situ DRIFTS) demonstrates that illumination also efficiently boosts the conversion of formate intermediates. The mechanism of dual functions of photothermal semiconductors as photocatalysts for electron donation and as nano‐heaters for local temperature enhancement provides new insight in the exploration for efficient photo‐thermal catalysts. This work prepares Ir‐CoO/Al2O3 catalysts to realize the highly efficient photo‐thermal catalytic CO2 methanation under mild conditions. The CoO nanoparticles function as photocatalysts to donate electrons for Ir nanoparticles and meanwhile act as “nanoheaters” to effectively elevate the local temperature around Ir active sites, thus promoting the adsorption, activation, and conversion of reactant molecules.

Objective To explore the correlation between serum bilirubin, blood uric acid, and C-reactive protein (CRP) and the severity of chronic obstructive pulmonary disease (COPD). Methods: Patients with COPD who were admitted to our hospital between March 2020 and March 2023 were retrospectively studied. Based on whether their condition progressed to the acute exacerbation stage, they were divided into an exacerbation group (100 cases) and a stability group (100 cases). The clinical data from both groups were analysed to assess the correlations between serum bilirubin, blood uric acid, CRP, and the severity of COPD. Results: Univariate analysis indicated significant differences in the neutrophil-to-lymphocyte ratio ( t  = 5.678, P  < 0.05), α-hydroxybutyrate dehydrogenase ( t  = 5.862, P  < 0.05), total bilirubin ( t  = 4.341, P  < 0.05), direct bilirubin ( t  = 5.342, P  < 0.05), indirect bilirubin ( t  = 5.452, P  < 0.05), blood uric acid ( t  = 4.698, P  < 0.05), and CRP ( t  = 4.892, P  < 0.05) between the two groups. Multivariate analysis revealed that total bilirubin, blood uric acid, and CRP were positively correlated with exacerbations of COPD (regression coefficients were 0.413, 0.354, and 0.356, respectively; P  < 0.05). The evaluation of predictive value showed that the combined predictive value of these three indicators was the highest, with an AUC of 0.823 (95% CI: 0.754–0.911). Conclusion: Serum bilirubin, blood uric acid, and CRP levels are elevated in patients with acute exacerbations of COPD (AECOPD), showing good consistency in predicting the occurrence of AECOPD. The combined diagnostic value of these three indicators is greater than that of any single indicator, providing a reference for the early clinical prediction of AECOPD.

Wetlands play a key role in maintaining ecological balance and climate regulation. However, due to the complex and variable spectral characteristics of wetlands, there are no publicly available global 30-meter time-series wetland dynamic datasets at present. In this study, we present novel global 30 m annual wetland maps (GWL_FCS30D) using time-series Landsat imagery on the Google Earth Engine platform, covering the period of 2000–2022 and containing eight wetland subcategories. Specifically, we make full use of our prior globally distributed wetland training sample pool, and adopt the local adaptive classification and spatiotemporal consistency checking algorithm to generate annual wetland maps. The GWL_FCS30D maps were found to achieve an overall accuracy and Kappa coefficient of 86.95 ± 0.44% and 0.822, respectively, in 2020, and show great temporal variability in the United States and the European Union. We expect the dataset would provide vital support for wetland ecosystems protection and sustainable development.

The rapid advancement of the Internet of Things (IoT), artificial intelligence (AI), and personalized healthcare has catalyzed an unprecedented demand for flexible and wearable sensor technologies [...]