Unraveling the role of tissue colonized microbiome in ovarian cancer progression.

BACKGROUND: Ovarian cancer (OC) is found to be the third most common gynecologic malignancy over the world, having the highest mortality rate among such tumors. Emerging studies underscore the presence of microorganisms within tumor tissues, with certain pathogens intricately linked to disease onset and progression. Disruption of the microbiome frequently precipitates disturbances in host metabolic and immune pathways, thereby fostering the development of cancer. METHODS: In this study, we initiated the investigation by conducting microbial reannotation on the RNA sequencing data derived from ovarian cancer tissues. Subsequently, a comprehensive array of analyses on tissue microbes was executed. These analyses encompassed the assessment of intergroup variations in microbial diversity, differential microbiological analysis, exploration of the association between host gene expression and microbial abundance, as well as an enrichment analysis of functional pathways linked to host genes associated with microbes. RESULTS: The analysis results revealed that Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes were the main components at phylum level in ovarian tissue. Notably, the microbial composition of ovarian cancer tissue significantly diverged from that of normal ovarian tissue e, exhibiting markedly lower alpha diversity and distinct beta diversity. Besides, pathogenic microorganisms Achromobacter xylosoxidans and Enterobacter hormaechei were enriched in cancer tissue. Host genes associated with these pathogens were enriched in key pathways including "JAK-STAT signaling pathway", "Transcriptional misregulation in cancer", and "Th1 and Th2 cell differentiation", suggesting their role in ovarian cancer progression through microbial dysbiosis and immune response interaction. CONCLUSION: Abundance of pathogenic microorganisms in ovarian cancer tissue could modulate the expression of host genes, consequently impacting cancer-related signaling pathways and fostering cancer progression.

Study on the Compositional Analysis, Extraction Process, and Hemostatic and Anti-Inflammatory Activities of Cirsium japonicum Fisch. ex DC.-Cirsium setosum (Willd.) MB Extracts.

Cirsium japonicum Fisch. ex DC. (CF) and Cirsium setosum (Willd.) MB (CS) are commonly used clinically to stop bleeding and eliminate carbuncles. Still, CF is mainly used for treating inflammation, while CS favors hemostasis. Therefore, the present study used UHPLC-MS to analyze the main chemical constituents in CF-CS extract. We optimized the extraction process using single-factor experiments and response surface methodology. Afterward, the hemostatic and anti-inflammatory effects of CF-CS extract were investigated by determining the clotting time in vitro, the bleeding time of rabbit trauma, and the induction of rabbit inflammation using xylene and lipopolysaccharide. The study of hemostatic and anti-inflammatory effects showed that the CF-CS, CF, and CS extract groups could significantly shorten the coagulation time and bleeding time of rabbits compared with the blank group (p < 0.01); compared with the model group, it could dramatically inhibit xylene-induced ear swelling in rabbits and the content of TNF-α, IL-6, and IL-1ß in the serum of rabbits (p < 0.01). The results showed that combined CF and CS synergistically increased efficacy. CF-CS solved the problem of the single hemostatic and anti-inflammatory efficacy of a single drug, which provided a new idea for the research and development of natural hemostatic and anti-inflammatory medicines.

Mechanism of action of Sambucus williamsii Hance var. miquelii in the treatment of osteoporosis analyzed by UHPLC-HRMS/MS combined network pharmacology and experimental validation.

Sambucus williamsii Hance var. miquelii(SWH) is a precious wild Chinese herb whose fruit, rhizome, leaves and root bark can be used as medicine. Sambucus Linn has pharmacological effects such as anti-osteoporosis, promoting fracture healing, anti-viral and anti-inflammatory. In this study, the main chemical components of the alcoholic extracts from SWH were rapidly identified by ultra-high performance liquid chromatography-quadrupole orbit trap high-resolution mass spectrometry (UHPLC- HRMS MS), and a total of 42 compounds were characterized from the alcoholic extracts of SWH. The results of network pharmacological validation showed that kaempferol, quercetin, luteolin, isorhamnetin and morroniside were the main active components, and KEGG enrichment demonstrated that SWH mainly affected the signaling pathways such as PI3K-Akt, TNF and FoxO by modulating the related targets such as AKT1, PIK3R1, EGFR, RELA SRC and PTGS2. The molecular docking results showed binding solid activity between the main active components of SWH and the targets. The network pharmacology was validated by establishing an animal model of osteoporosis (OP) in rats by gavage administration of vitamin A acid. The results of the pharmacological experiments showed that SWH could improve the degree of bone loss in the femur of osteoporotic rats, increase the number of trabeculae and decrease trabeculae porosity, up-regulate the Ca and P content in the serum of OP rats, down-regulate the scope of ALP and BGP in the serum, and promote the calcification of the bone matrix, and then exert the anti-OP efficacy. In this study, network pharmacology and pharmacological experiments verified the pharmacological mechanism of SWH in anti-OP rats. This provides a theoretical basis for the research and development of anti-OP drugs and a reference for the application of other traditional Chinese medicines in treating OP diseases.

Characterisation of aroma compounds, sensory characteristics, and bioactive components of a new type of huangjiu fermented with Chinese wild rice (Zizania latifolia).

Chinese wild rice (CWR) is a nutritious and healthy whole grain, worth developing. To develop and use its value, a new type of huangjiu was brewed with CWR, and the flavour characteristics, sensory quality, functional and bioactive components were evaluated. CWR (67 flavour substances) and glutinous rice (GR)-CWR huangjiu (62 flavour substances) had a better flavour than GR huangjiu (54 flavour substances), and the overall style of GR-CWR huangjiu was more skewed towards GR. The fruity, honey, caramel-like, herb and smoky aroma attributes of CWR huangjiu were higher than those of GR huangjiu (P < 0.05), while only the alcoholic was weaker (P < 0.05) due to the lower alcohol content. The huangjiu brewed using CWR had a better taste than that brewed using only GR. Furthermore, CWR huangjiu had the highest content of total dietary fiber (732.0 ± 15.2 mg/100 g), followed by GR-CWR (307.0 ± 8.5 mg/100 g), and GR (127.0 ± 2.3 mg/100 g). CWR huangjiu also had the highest total phenolic compounds (3.32 ± 0.05 mg/100 g/%vol) and total saponins (2.46 ± 0.03 mg/100 g/%vol) contents, followed by GR-CWR and GR. This study provides guidance for exploring further possibilities for CWR in the future.

Molecular analyses of exosome-derived miRNAs revealed reduced expression of miR-184-3p and decreased exosome concentration in patients with alveolar echinococcosis.

Both E. multilocularis and host-derived exosomes are involved in the pathogenic process of alveolar echinococcosis (AE). Exosomes secrete miRNAs that have regulatory roles in host-pathogen interactions in multiple ways. In the present study, we collected and purified supernatants of E. multilocularis cultures, as well as human plasma exosomes. High-throughput sequencing showed the identities of 45 exosomal miRNAs in E. multilocularis. The lengths of these miRNAs ranged from 19 to 25 nucleotides (nt), with the majority (n = 18) measuring 22 nt. Notably, emu-let-7-5p emerged as the most abundant among these miRNAs, with a detected count of 33,097 and also length of 22 nt. Nanoparticle tracking analysis (NTA) showed that the concentration of exosomes in the plasma of AE patients was lower compared to that in the healthy individuals. This result suggested that the concentration of plasma exosomes was able to distinguish AE patients from healthy individuals. Using qRT-PCR to assess the relative expression of 10 miRNAs of E. multilocularis, we showed that the expression of miR-184-3p was downregulated significantly in the exosomes of plasma from AE patients compared to that in the control group. In summary, this study indicates that AE induces a reduction in the concentration of human plasma exosomes, as well as downregulating miR-184-3p in infected individuals.

Construction and application of a QSRR approach for identifying flavonoids.

A quantitative structure retention relationship (QSRR) method was developed to identify flavonoid isomers auxiliary using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method based on the linear relationships between the Ln(k') values of flavonoids and their hydrogen bonding energy (XAH) and dissolution energy (ES). Chromatographic separation was achieved with a Hypersil GOLD C18 (100 mm × 2.1 mm, 1.9 µm) column and Agilent SB-C18 (2.1 ×50 mm, 1.8 µm) column on a Dionex Ultimate 3000 RSLC chromatograph. Compounds were eluted isocratically using a mobile phase containing 0.1% formic acid/water solution and methanol at a ratio of 55:45 (v/v). Mass spectrometry was performed in the negative and positive ionization modes on a Thermo Fisher Q Exactive Orbitrap mass spectrometer equipped with an electrospray ionization interface. The established QSRR model was Ln(k') = 5.6163 + 0.0469ES - 0.0984XAH, with a determination coefficient (R2) of 0.9981, adjusted determination coefficient (adjR2) of 0.9976, and corrected root mean square error of 0.0682. The determination coefficient of the leave-one-out (LOO) cross-validation (Q2LOO) was 0.9976, and the cross-verification root mean square error was 0.0754. Simulated samples containing 7 flavonoids were used to validate the feasibility of the method. The classical method (UHPLC-MS/MS combined the CD software and the mzCloud, mzVault and Chemspider databases) was used to identify the seven flavonoids in the simulated samples. This classic identification strategy cannot provide accurate identification results, which provided multiple identification results for each compound in the simulated samples. On the basis of the results, the 7 flavonoids were accurately identified by the established QSRR model, and the reference standards were used to validate it. The relative error of retention time(RE(tR)) between the model calculation and experimental results was less than 10%. This method effectively complements and improves the classical methods, that UHPLC-MS/MS combined the CD software and the mass spectra databases were used to identify flavonoids identification.

Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections.

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by abnormal immune responses to various, predominantly bacterial, infections. Different bacterial infections lead to substantial variation in disease manifestation and therapeutic strategies. However, the underlying cellular heterogeneity and mechanisms involved remain poorly understood. METHODS: Multiple bulk transcriptome datasets from septic patients with 12 types of bacterial infections were integrated to identify signature genes for each infection. Signature genes were mapped onto an integrated large single-cell RNA (scRNA) dataset from septic patients, to identify subsets of cells associated with different sepsis types, and multiple omics datasets were combined to reveal the underlying molecular mechanisms. In addition, an scRNA dataset and spatial transcriptome data were used to identify signaling pathways in sepsis-related cells. Finally, molecular screening, optimization, and de novo design were conducted to identify potential targeted drugs and compounds. RESULTS: We elucidated the cellular heterogeneity among septic patients with different bacterial infections. In Escherichia coli (E. coli) sepsis, 19 signature genes involved in epigenetic regulation and metabolism were identified, of which DRAM1 was demonstrated to promote autophagy and glycolysis in response to E. coli infection. DRAM1 upregulation was confirmed in an independent sepsis cohort. Further, we showed that DRAM1 could maintain survival of a pro-inflammatory monocyte subset, C10_ULK1, which induces systemic inflammation by interacting with other cell subsets via resistin and integrin signaling pathways in blood and kidney tissue, respectively. Finally, retapamulin was identified and optimized as a potential drug for treatment of E. coli sepsis targeting the signature gene, DRAM1, and inhibiting E. coli protein synthesis. Several other targeted drugs were also identified in other types of sepsis, including nystatin targeting C1QA in Neisseria sepsis and dalfopristin targeting CTSD in Streptococcus viridans sepsis. CONCLUSION: Our study provides a comprehensive overview of the cellular heterogeneity and underlying mechanisms in septic patients with various bacterial infections, providing insights to inform development of stratified targeted therapies for sepsis.

MVIRA: A model based on Missing Value Imputation and Reliability Assessment for mortality risk prediction.

BACKGROUND: Mortality risk prediction is to predict whether a patient has the risk of death based on relevant diagnosis and treatment data. How to accurately predict patient mortality risk based on electronic health records (EHR) is currently a hot research topic in the healthcare field. In actual medical datasets, there are often many missing values, which can seriously interfere with the effect of model prediction. However, when missing values are interpolated, most existing methods do not take into account the fidelity or confidence of the interpolated values. Misestimation of missing variables can lead to modeling difficulties and performance degradation, while the reliability of the model may be compromised in clinical environments. MATERIALS AND METHODS: We propose a model based on Missing Value Imputation and Reliability Assessment for mortality risk prediction (MVIRA). The model uses a combination of variational autoencoder and recurrent neural networks to complete the interpolation of missing values and enhance the characterization ability of EHR data, thus improving the performance of mortality risk prediction. In addition, we also introduce the Monte Carlo Dropout method to calculate the uncertainty of the model prediction results and thus achieve the reliability assessment of the model. RESULTS: We perform performance validation of the model on the public datasets MIMIC-III and MIMIC-IV. The proposed model showed improved performance compared with competitive models in terms of overall specialties. CONCLUSION: The proposed model can effectively improve the accuracy of mortality risk prediction, and can help medical institutions assess the condition of patients.

Shortest path core-selection incentive for federated learning platform with medical applications.

As the main technology to solve data islands and mine data value, federated learning has been widely researched and applied, and more and more federated learning platforms are emerging. The federated learning platform organizes users, devices and data to collaborate in a crowdsourcing manner and complete specific federated learning tasks. This paper designs the shortest path core-selection incentive mechanism by combining the VCG auction mechanism and the core concept of cooperative games. This mechanism solves the problems of overpayment, false-name attack, and deviation from the core of the VCG mechanism, and saves the expenditure of the federated learning task issuer. It adopts the VCG-nearest principle in the core selection, so that the federated learning task participants get rewards as close as possible to the outcome of VCG mechanism. This mechanism can guarantee four economic attributes: incentive compatibility, individual rationality, alliance rationality, and maximization of social efficiency. Medical experimental results illustrate the effectiveness of the mechanism.

EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records.

Privacy concerns often arise as the key bottleneck for the sharing of data between consumers and data holders, particularly for sensitive data such as Electronic Health Records (EHR). This impedes the application of data analytics and ML-based innovations with tremendous potential. One promising approach for such privacy concerns is to instead use synthetic data. We propose a generative modeling framework, EHR-Safe, for generating highly realistic and privacy-preserving synthetic EHR data. EHR-Safe is based on a two-stage model that consists of sequential encoder-decoder networks and generative adversarial networks. Our innovations focus on the key challenging aspects of real-world EHR data: heterogeneity, sparsity, coexistence of numerical and categorical features with distinct characteristics, and time-varying features with highly-varying sequence lengths. Under numerous evaluations, we demonstrate that the fidelity of EHR-Safe is almost-identical with real data (<3% accuracy difference for the models trained on them) while yielding almost-ideal performance in practical privacy metrics.

Ultra-broad bandwidth low-dispersion mirror with smooth dispersion and high laser damage resistance.

Low-dispersion mirrors (LDMs), which require a broad bandwidth, low dispersion, and high damage threshold, are essential optics in ultra-intense and ultra-short laser devices. Bragg mirrors and chirped LDMs do not satisfy these requirements simultaneously. We propose a novel LDM (NLDM) based on the hump-like structure and quarter wavelength optical thickness (QWOT) structure to achieve a broad bandwidth, smooth dispersion, and high robustness. The spectral and dispersion characteristics of the two structures compensate for each other, which makes up for the deficiency that the dispersion bandwidth of the sinusoidal modulation structure cannot be broadened. Based on this structure, the LDM can achieve a design bandwidth of 240 nm and support the transmission of sub-11-fs pulses. The accuracy of the NLDM is experimentally evaluated. The structure shows the potential for broad-spectrum laser damage performance due to the low electric field intensity. The NLDM improves the mirror performance and paves the way for a new generation of ultra-intense and ultra-short laser devices.

400nm ultra-broadband gratings for near-single-cycle 100 Petawatt lasers.

Compressing high-energy laser pulses to a single-cycle and realizing the "λ3 laser concept", where λ is the wavelength of the laser, will break the current limitation of super-scale projects and contribute to the future 100-petawatt and even Exawatt lasers. Here, we have realized ultra-broadband gold gratings, core optics in the chirped pulse amplification, in the 750-1150 nm spectral range with a > 90% -1 order diffraction efficiency for near single-cycle pulse stretching and compression. The grating is also compatible with azimuthal angles from -15° to 15°, making it possible to design a three-dimensional compressor. In developing and manufacturing processes, a crucial grating profile with large base width and sharp ridge is carefully optimized and controlled to dramatically broaden the high diffraction efficiency bandwidth from the current 100-200 nm to over 400 nm. This work has removed a key obstacle to achieving the near single-cycle 100-PW lasers in the future.

Antagonistic Activity of Volatile Organic Compounds Produced by Acid-Tolerant Pseudomonas protegens CLP-6 as Biological Fumigants To Control Tobacco Bacterial Wilt Caused by Ralstonia solanacearum.

Tobacco bacterial wilt, which is caused by Ralstonia solanacearum, is a devastating soilborne disease of tobacco worldwide and is widespread in the continuously acidic fields of southern China. Here, the fumigation activity under different pH conditions, component identification, and bioactivity of the volatile organic compounds (VOCs) produced by an acid-tolerant strain, Pseudomonas protegens CLP-6, were investigated. There was a wide antimicrobial spectrum of the VOCs against phytopathogens, including four bacteria, eight fungi, and two oomycetes. The antagonistic activity of the VOCs against R. solanacearum was proportionally correlated with the concentration of the inoculum, amount, culture time, and culture pH for CLP-6. The number of gene copies of R. solanacearum was significantly inhibited by VOCs produced at pH 5.5 in vivo. The control effect of VOCs emitted at pH 5.5 was 78.91% for tobacco bacterial wilt, which was >3-fold greater than that at pH 7.0. Finally, the main volatile compounds were identified by solid-phase microextraction (SPME)-gas chromatography-mass spectroscopy (GC-MS) as S-methyl thioacetate, methyl thiocyanate, methyl disulfide, 1-decene, 2-ethylhexanol, 1,4-undecadiene, 1-undecene, 1,3-benzothiazole, and 2,5-dimethylpyrazine, and the inhibition rates of 1,3-benzothiazole, 2-ethylhexanolmethyl thiocyanate, dimethyl disulfide, and S-methyl thioacetate were 100%, 100%, 88.91%, 67.64%, and 53.29%, respectively. S-Methyl thioacetate was detected only at pH 5.5. In summary, VOCs produced by P. protegens CLP-6 had strong antagonistic activities against phytopathogens, especially R. solanacearum, under acidic conditions and could be used to develop a safe and additive fumigant against R. solanacearum on tobacco and even other Solanaceae crop bacterial wilt diseases in acidic fields. IMPORTANCE VOCs produced by beneficial bacteria penetrate the rhizosphere to inhibit the growth of plant-pathogenic microorganisms; thus, they have the potential to be used as biological agents in controlling plant diseases. Tobacco bacterial wilt, which is caused by the acidophilic pathogen R. solanacearum, is a major bacterial disease in southern China and is prevalent in acidic soil. In this study, we discovered that the VOCs produced by P. protegens CLP-6 had excellent inhibitory effects on important plant pathogens. Moreover, two of the VOCs, namely, 1,3-benzothiazole and 2-ethylhexanol, had excellent inhibitory effect on R. solanacearum, and another VOC substance, methyl thiocyanate, was produced only at pH 5.5. The VOCs produced by the acid-tolerant strain P. protegens CLP-6 may have potential as environment-friendly microbial fumigant agents for bacterial wilt of tobacco or even other Solanaceae crops in acidic soils in China.

Rapid and sensitive detection of quizalofop-p-ethyl by gold nanoparticle-based lateral flow immunoassay in agriproducts and environmental samples.

Quizalofop-p-ethyl is a widely used herbicide that poses a threat to human health and environmental safety. In this study, anti-quizalofop-p-ethyl monoclonal antibodies (mAbs) were prepared and used to develop a gold nanoparticle-based lateral flow immunoassay (AuNP-LFIA) for the detection of quizalofop-p-ethyl in agriproducts and environmental samples. Four hybridoma cell lines were obtained, among which 5B6D10E11 secreted mAb with the highest sensitivity, with a 50 % inhibition concentration of 4.57 ng/mL in the indirect competitive enzyme-linked immunosorbent assay. After optimization, the AuNP-LFIA strip based on the mAb (5B6D10E11) showed a visual detection limit of 10 ng/mL, and the results could be directly determined by the naked eye within 8 min. The cross-reactivity of the AuNP-LFIA for analogs of quizalofop-p-ethyl was negligible except for quizalofop-p-acid. The established AuNP-LFIA was proven to be accurate and precise based on the recovery test. Furthermore, the detection results of AuNP-LFIA were consistent with those of ultra-high-performance liquid chromatography tandem mass spectrometry.

Advances in the pharmacological treatment of hepatic alveolar echinococcosis: From laboratory to clinic.

Hepatic alveolar echinococcosis (HAE) is a zoonotic parasitic disease caused by the larvae of Echinococcus multilocularis. Because of its characteristics of diffuse infiltration and growth similar to tumors, the disability rate and mortality rate are high among patients. Although surgery (including hepatectomy, liver transplantation, and autologous liver transplantation) is the first choice for the treatment of hepatic alveolar echinococcosis in clinic, drug treatment still plays an important and irreplaceable role in patients with end-stage echinococcosis, including patients with multiple organ metastasis, patients with inferior vena cava invasion, or patients with surgical contraindications, etc. However, Albendazole is the only recommended clinical drug which could exhibit a parasitostatic rather than a parasitocidal effect. Novel drugs are needed but few investment was made in the field because the rarity of the cases. Drug repurposing might be a solution. In this review, FDA-approved drugs that have a potential curative effect on hepatic alveolar echinococcosis in animal models are summarized. Further, nano drug delivery systems boosting the therapeutic effect on hepatic alveolar echinococcosis are also reviewed. Taken together, these might contribute to the development of novel strategy for advanced hepatic alveolar echinococcosis.

Cebpb Regulates Skeletal Stem Cell Osteogenic Differentiation and Fracture Healing via the WNT/ß-Catenin Pathway.

Fracture is the most common traumatic organ injury, and fracture nonunion is a critical clinical challenge. The research on the mechanisms of skeletal stem cell (SSC) differentiation and fracture healing may help develop new treatment strategies and improve the prognosis of patients at high risk of nonunion. Bioinformatic analysis of scRNA-seq data of mouse SSCs and mouse osteoprogenitors was applied to discover major transcription factors for the regulation of SSC differentiation. FACS was used to isolate SSCs prospectively. The expression of Cebpb, osteogenesis-related genes (Runx2, Sp7, and Bglap2), and markers for Notch, Hedgehog, MAPK, BMP2/SMAD, and WNT/ß-catenin signaling pathways (Hes1, Gli1, p-Erk1/2, p-Smad1/5/9, and ß-catenin) were detected in SSCs with qPCR or western blot, respectively. Alkaline phosphatase assay and alizarin red S staining were used to illustrate the osteogenic differentiation ability of SSCs in vitro. A WNT inhibitor, IWR-1, was further used to explore the mechanism of WNT signaling in the differentiation of SSCs. Micro-CT, mechanical testing, and immunohistochemistry of osteogenic and chondrogenic proteins (Sp7 and Col2α1) were used to demonstrate the capacity of Cebpb knockdown in promoting fracture healing in a monocortical defect model. We found that Cebpb was the crucial transcription factor regulating SSC differentiation. Inhibiting Cebpb in SSCs enhanced the expression of active ß-catenin to promote the expression of WNT target genes, thus facilitating the osteogenic differentiation of SSCs. Bone mass, mechanical properties, and osteogenic protein expression were also increased in the Cebpb inhibition group compared to the group without Cebpb inhibition. Collectively, our results proved that Cebpb knockdown promotes SSC osteogenic differentiation and fracture healing via the WNT/ß-catenin signaling pathway.

High serum levels of ferritin may predict poor survival and walking ability for patients with hip fractures: a propensity score matching study.

Aim: To identify the relationship between ferritin and outcomes for patients with hip fractures. Patients & methods: All patients with hip fractures presenting between May 2017 and January 2021 were included. Univariate and multivariate analyses were performed to determine the risk factors for 1-year survival. Propensity score matching (PSM) was performed for groups divided by ferritin levels. Results: A total of 165 patients were included of whom 28 died during the first year after surgery. Ferritin levels differed significantly between groups divided by 1-year survival. High ferritin (≥308.5 ng/ml) was related to poor 1-year survival and 6-month and 1-year independent walking rate. Conclusion: High ferritin (≥308.5 ng/ml) may predict poor survival and free-walking abilities after surgery for patients with hip fractures.

Land Resource Use Classification Using Deep Learning in Ecological Remote Sensing Images.

Aiming at the problems that the traditional remote sensing image classification methods cannot effectively integrate a variety of deep learning features and poor classification performance, a land resource use classification method based on a convolutional neural network (CNN) in ecological remote sensing images is proposed. In this study, a seven-layer convolution neural network is constructed, and then the two fully connected layer features of the improved CNN network training output are fused with the fifth layer pooled layer features after dimensionality reduction by principal component analysis (PCA), so as to obtain an effective remote sensing image feature of land resources based on deep learning. Further, the classification of land resources remote sensing images is completed based on a support vector machine classifier. The remote sensing images of Pingshuo mining area in Shanxi Province are used to analyze the proposed method. The results show that the edge of the recognized image is clear, the classification accuracy, misclassification rate, and kappa coefficient are 0.9472, 0.0528, and 0.9435, respectively, and the model has excellent overall performance and good classification effect.