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Abstract Background The prevalence of age-related disorders, particularly in neurological and cardiovascular systems, is an increasing global health concern. Mesenchymal stem cell (MSC) therapy, particularly using human umbilical cord-derived MSCs (HUCMSCs), has shown promise in mitigating these disorders. This study investigates the effects of HUCMSCs on aging-related conditions in a senescence-accelerated mouse model (SAMP8), with a focus on DNA damage, gut microbiota alterations, and metabolic changes. Methods SAMP8 mice were treated with clinical-grade HUCMSCs via intraperitoneal injections. Behavioral and physical assessments were conducted to evaluate cognitive and motor functions. The Single-Strand Break Mapping at Nucleotide Genome Level (SSiNGLe) method was employed to assess DNA single-strand breaks (SSBs) across the genome, with particular attention to exonic regions and transcription start sites. Gut microbiota composition was analyzed using 16S rRNA sequencing, and carboxyl metabolomic profiling was performed to identify changes in circulating metabolites. Results HUCMSC treatment significantly improved motor coordination and reduced anxiety in SAMP8 mice. SSiNGLe analysis revealed a notable reduction in DNA SSBs in MSC-treated mice, especially in critical genomic regions, suggesting that HUCMSCs may mitigate age-related DNA damage. The functional annotation of the DNA breaktome indicated a potential link between reduced DNA damage and altered metabolic pathways. Additionally, beneficial alterations in gut microbiota were observed, including an increase in short-chain fatty acid (SCFA)-producing bacteria, which correlated with improved metabolic profiles. Conclusion The administration of HUCMSCs in SAMP8 mice not only reduces DNA damage but also induces favorable changes in gut microbiota and metabolism. The observed alterations in DNA break patterns, along with specific changes in microbiota and metabolic profiles, suggest that these could serve as potential biomarkers for evaluating the efficacy of HUCMSCs in treating age-related disorders. This highlights a promising avenue for the development of new therapeutic strategies that leverage these biomarkers, to enhance the effectiveness of HUCMSC-based treatments for aging-associated diseases.

IntroductionDifferent steaming durations dramatically alter the structure of Polygonatum cyrtonema polysaccharides (PCPs). This study aimed to compare characteristics of digestion, absorption, and fermentation by gut microbiota across four representative PCPs from different steaming durations (0, 4, 8, and 12 h), each with unique molecular weights and monosaccharide profiles.MethodsChemical composition of the four PCPs was analyzed. Digestibility was evaluated using an in vitro saliva-gastrointestinal digestion model. Absorption characteristics were assessed with a Caco-2 monolayer model, and impacts on gut microbiota composition and short chain fatty acid (SCFA) levels were analyzed using in vitro fermentation with human gut microbiota.ResultsLonger steaming durations altered the chemical profiles of PCPs, reducing carbohydrate content (84.87–49.58%) and increasing levels of uronic acid (13.99–19.61%), protein (1.07–5.43%), and polyphenols (0.05–2.75%). Four PCPs were unaffected by saliva digestion but showed enhanced gastrointestinal digestibility, with reducing sugar content rising from 4.06% (P0) to 38.5% (P12). The four PCPs showed varying absorption characteristics, with P0 having the highest permeability coefficient value of 9.59 × 10−8 cm/s. However, all PCPs exhibited poor permeability, favoring gut microbiota fermentation. The four PCPs altered gut microbiota composition and elevated SCFA production, but levels declined progressively with longer steaming durations. All PCPs significantly increased the abundance of Bacteroidota, Firmicutes, and Actinobacteriota, making them the dominant bacterial phyla. Additionally, all PCPs significantly increased the abundance of Bifidobacterium, Prevotella, and Faecalibacterium compared to the control group, which, along with Bacteroides, became the dominant microbiota. Increasing the steaming duration led to a reduction in Prevotella levels, with PCPs from raw rhizomes showing the highest relative abundance at 24.90%. PCPs from moderately steamed rhizomes (4 h) led to a significant rise in Faecalibacterium (7.73%) among four PCPs. P8 and P12, derived from extensively steamed rhizomes (≥8 h), exhibited similar gut microbiota compositions, with significantly higher relative abundances of Bacteroides (20.23–20.30%) and Bifidobacterium (21.05–21.51%) compared to P0 and P4.DiscussionThis research highlights the importance of adjusting steaming durations to maximize the probiotic potential of P. cyrtonema polysaccharides, enhancing their effectiveness in modulating gut microbiota and SCFA levels.

Low-carbon transformation of the economy is the inevitable orientation of socialism with Chinese characteristics to high-quality development in the new era, while the Chinese decentralized development model determines that the competition of local governments in China is an important factor influencing the green technological innovation on low-carbon transformation of the economy. How to achieve coordinated economic growth and ecological environment has become a prob-lem for local governments. Data from a Chinese provincial panel covering the years 2007–2019 is used to investigate the effects of heterogeneous local government competition (Comp), namely, economic, ecological and service competitions on economic low-carbon transition, and moderating effects of heterogeneous government competition and green technology innovation (GTECH) on the low-carbon economic transition (LCT). The results reveal that there are substantial disparities in the consequences of heterogeneous government competition on low-carbon economic transition (LCT). Among them, economic competition significantly dampens economic low-carbon transition (LCT), and ecological competition and service competition significantly boost economic low-carbon transi-tion (LCT). After performing robustness checks, these results continue to be strongly convincing. The study of moderating effects shows that economic competition can dampen the positive influence of green technology innovation (GTECH) to the economic low-carbon transition (LCT). However, ecological competition and service competition facilitate the promoting effect of green technology innovation on economic low-carbon transition (LCT). First published online 14 March 2024

ObjectiveThis study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis.MethodsThe Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field.ResultsA total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The “deep-learning algorithm” started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC.ConclusionsResearch related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.

Compared with traditional technological innovation modes, green technology innovation is more targeted for low carbon development and critical support for countries worldwide to combat climate change. The impact of green technology innovation on carbon emissions is considered in terms of fixed effect and mediating effect models through industrial structure upgrading. For this purpose, the sample dataset of 30 provincial administrative areas in China from 2008 to 2020 is employed. The results demonstrate that green technology innovation exerts significantly inhibitory effects on carbon emissions, whose conclusion still holds after removing municipalities and replacing the dependent variable. Industrial structure upgrading is vital for green technology innovation to diminish carbon emissions. There is significant regional heterogeneity in the effects of green technology innovation on carbon emissions, i.e., the direct and indirect impact of green technology innovation on carbon emission reduction is significant in the eastern-central area, but its effect is insignificant in the western region. Therefore, it is essential to realize carbon emission reduction by further bolstering green technology innovation and accelerating industrial structure upgrading to fulfill the synergy of technology and structure.

The digital economy has introduced far-reaching innovations in the fields of government governance, enterprise production, and social operation. How to motivate the economic development mode towards a low-carbon and greenway transformation through the digital economy is a major issue concerning the Chinese government. However, there is scarce evidence to interpret the role mechanism of the digital economy on carbon emission efficiency from the factor misallocation scenario. Taking a database from 30 provincial-level administrative regions for the period from 2011 to 2019 in China as an example, the paper examines the effect of the digital economy on carbon emission efficiency, as well as explores its role mechanism deeply in terms of factor misallocation (capital misallocation and labor misallocation). The results suggest that there is a significant potential for the digital economy to contribute to carbon emission efficiency, as well as this finding, is valid when considering both the endogeneity issue and a series of robustness checks. Also, the digital economy can significantly contribute to carbon efficiency in both southern and northern regions, but more strongly in the northern region. Besides, the digital economy can inhibit the factor misallocation (labor misallocation and capital misallocation) level which ultimately improves carbon emission efficiency. Finally, as a digital economy, it can positively impact carbon efficiency in the long run by mitigating factor misallocation (labor misallocation and capital misallocation).

Green economic growth is an unavoidable choice for China’s development model, while the government-led Chinese economic development system determines that local government competition may have an essential impact on green economic growth. For this purpose, this study employs data on Chinese 272 prefecture-level cities and the system generalized method of moments (SYS-GMM) model to investigate the impact of multi-dimensional local government competition (ecological competition, service competition, economic competition, and comprehensive competition) on green economic growth. The empirical results reveal that local government competition significantly influences green economic growth, in which economic competition significantly inhibits green economic growth, and ecological competition, service competition, and comprehensive competition positively influences green economic growth. The influence mechanism indicates that economic competition, ecological competition, service competition, and comprehensive competition significantly affect green economic growth through economic agglomeration and industrial structure upgrading, respectively. Moreover, the impact of multi-dimensional local government competition on green economic growth shows significant temporal and regional heterogeneity. Therefore, policymakers should further develop a multi-dimensional local government competition target system for local government officials and moderately enhance both ecological competition and service competition that is oriented to green economic growth.

Carbon emissions of animal husbandry have been gaining increasing attention due to their high share in global carbon emissions. In this regard, it is essential to assess the regional differences, dynamic evolution patterns, convergence characteristics, and the impact of livestock structure on carbon emissions of animal husbandry. Using data from 30 provincial administrative regions from 2000 to 2018 in China, this study employs the Thiel index method, kernel density analysis, and convergence analysis to quantify the impact of livestock structure on carbon emissions of animal husbandry. The statistical results reveal that carbon emissions of animal husbandry exhibit a rising and declining trend. Specifically, the carbon emissions of animal husbandry are highest in agricultural areas (with a declining trend), followed by agro-pastoral areas (with a declining trend), and the pastoral areas (with a rising trend). It is further revealed that there are no δ convergence and β convergence of carbon emissions of animal husbandry. Finally, essential and useful policy recommendations are put forward to inhibit carbon emissions of animal husbandry.

To rationally utilize digested slurry, and thereby promote the integrated development of planting and breeding in breeding industry, the water-fertilizer requirement analysis of typical winter wheat-summer corn rotation tillage model in Zhengzhou was completed based on the water/nutrient requirement of crops. The results showed that the water demand and effective rainfall of winter wheat during the whole growth period were 492 and 190 mm, and the application amount of digested slurry was 3090 m³. The N, P, and K provided were 3600, 197, and 1310 kg, respectively, which exceeded the normal nutrient requirement of wheat. During the whole growth period of summer corn, the water demand was 354 mm, the effective rainfall was 290 mm, and the application amount of digested slurry was 763 m³. The amount of N, P, and K provided was 890, 48.6, and 324 kg, respectively. The amount of N and K exceeded the normal nutrient demand of corn, while the P was insufficient. Therefore, digested slurry can provide full nutrient requirements in winter wheat planting season in Zhengzhou, and P fertilizer should be added when applying digested slurry in corn planting season. If long-term application, excessive nutrient loss may cause non-point source pollution.