DDID: a comprehensive resource for visualization and analysis of diet-drug interactions.

Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.

Identification of HSP90B1 in pan-cancer hallmarks to aid development of a potential therapeutic target.

Heat shock proteins play crucial roles in various biochemical processes, encompassing protein folding and translocation. HSP90B1, a conserved member of the heat shock protein family, growing evidences have demonstrated that it might be closely associated with cancer development. In the present study, we employed multi-omics analyses and cohort validations to explore the dynamic expression of HSP90B1 in pan-cancer and comprehensively evaluate HSP90B1 as a novel biomarker that hold promise for precision cancer diagnostics and therapeutics. The results suggest HSP90B1 was highly expressed in various kinds of tumors, often correlating with a poor prognosis. Notably, methylation of HSP90B1 emerged as a protective factor in several cancer types. In immune infiltration analysis, the expression of HSP90B1 in most tumors showed a negative association with CD8 + T cells. HSP90B1 expression was positively correlated with microsatellite instability and tumor mutational burden. HSP90B1 expression was also discovered to be positively correlated with tumor metabolism, cell cycle-related pathways and the expression of immune checkpoint genes. The expression of HSP90B1 was mainly negatively correlated with immunostimulatory genes and positively correlated with immunosuppressive genes, as well as strongly correlated with chemokines and their receptor genes. In addition, the HSP90B1 inhibitor PU-WS13 demonstrated significant efficacy in suppressing cancer cell proliferation in both leukemic and solid tumor cells, and remarkably reduced the expression of the cancer cell surface immune checkpoint PD-L1. The single-cell RNA sequencing analysis further highlighted that HSP90B1 was significantly higher in tumor cells compared to surrounding cells, revealing a potential target therapeutic window. Taken together, HSP90B1 emerges as a promising avenue for breakthroughs in cancer diagnosis, prognosis and therapy. This study provides a rationale for HSP90B1 targeted cancer diagnosis and therapy in future.

Point spread function modeling for photoacoustic tomography - I: three-dimensional detection geometries.

Photoacoustic computed tomography (PACT) has been under intensive investigation as a promising noninvasive biomedical imaging modality. Various acoustic detector arrays have been developed to achieve enhanced imaging performance. In this paper, we study the effect of the detection geometry on image quality through point spread function (PSF) modeling based on back-projection image reconstruction. Three commonly-used three-dimensional detection geometries, namely, spherical, cylindrical, and planar detector arrays, are investigated. The effect of detector bandwidth and aperture on PSF in these detection geometries is also studied. This work provides a performance evaluation tool for acoustic detector arrays used in PACT and can be helpful in the design and selection of detector arrays in practical imaging applications.

Point spread function modeling for photoacoustic tomography - II: two-dimensional detection geometries.

Point spread function (PSF) modeling is important for the characterization of the imaging performance of a photoacoustic computed tomography (PACT) system. This work aims to study the degradation mechanism of PSF in PACT and investigate the impact of the shape of detection geometry on PSF. PSF modeling of three typical two-dimensional detection geometries, including circular, curved, and linear detector arrays, is presented. Based on the non-ideal detection geometries, the effect of detector bandwidth and detector aperture on PSF is also investigated. Moreover, PSFs of each geometry with typical detector bandwidths and typical detector aperture sizes are presented. Experiments are conducted to validate the results. The proposed PSF modeling approach and corresponding results can help predict and interpret the quality of photoacoustic images produced by a practical PACT system. It is beneficial for the design of detector arrays for enhanced imaging performance.

Blind multi-Poissonian image deconvolution with sparse log-step gradient prior.

Blind image deconvolution plays a very important role in the fields such as astronomical observation and fluorescence microscopy imaging, in which the noise follows Poisson distribution. However, due to the ill-posedness, it is a very challenging task to reach a satisfactory result from a single blurred image especially when the power of the Poisson noise is at a high level. Therefore, in this paper, we try to achieve high-quality restoration results with multi-blurred images which are contaminated by Poisson noise. Firstly, we design a novel sparse log-step gradient prior which adopts a mixture of logarithm and step functions to regularize the image gradients and combine it with the Poisson distribution to formulate the blind multi-image deconvolution problem. Secondly, we incorporate the methods of variable splitting and Lagrange multiplier to convert the original problem into sub-problems, then we alternately solve them to achieve the estimation of all the blur kernels of corresponding blurred images. Besides, we also design a non-blind multi-image deconvolution algorithm which is based on the log-step gradient prior to reach the final restored image. Experimental results on both synthetic and real-world blurred images show that the proposed prior is very capable of suppressing negative artifacts caused by ill-posedness. The algorithm can achieve restored image of very high quality which is competitive with some state-of-the-art methods.

On the imaging depth limit of photoacoustic tomography in the visible and first near-infrared windows.

It is well known that photoacoustic tomography (PAT) can circumvent the photon scattering problem in optical imaging and achieve high-contrast and high-resolution imaging at centimeter depths. However, after two decades of development, the long-standing question of the imaging depth limit of PAT in biological tissues remains unclear. Here we propose a numerical framework for evaluating the imaging depth limit of PAT in the visible and the first near-infrared windows. The established framework simulates the physical process of PAT and consists of seven modules, including tissue modelling, photon transportation, photon to ultrasound conversion, sound field propagation, signal reception, image reconstruction, and imaging depth evaluation. The framework can simulate the imaging depth limits in general tissues, such as the human breast, the human abdomen-liver tissues, and the rodent whole body and provide accurate evaluation results. The study elucidates the fundamental imaging depth limit of PAT in biological tissues and can provide useful guidance for practical experiments.

Sub-Nyquist sampling-based high-frequency photoacoustic computed tomography.

High-frequency (greater than 30 MHz) photoacoustic computed tomography (PACT) provides the opportunity to reveal finer details of biological tissues with high spatial resolution. To record photoacoustic signals above 30 MHz, sampling rates higher than 60 MHz are required according to the Nyquist sampling criterion. However, the highest sampling rates supported by existing PACT systems are typically within the range of 40-60 MHz. Herein, we propose a novel PACT imaging method based on sub-Nyquist sampling. The results of numerical simulation, phantom experiment, and in vivo experiment demonstrate that the proposed imaging method can achieve high-frequency PACT imaging with a relatively low sampling rate. An axial resolution of 22 µm is achieved with a 30-MHz transducer and a 41.67-MHz sampling rate. To the best of our knowledge, this is the highest axial resolution ever achieved in PACT based on a sampling rate of not greater than 60 MHz. This work is expected to provide a practical way for high-frequency PACT imaging with limited sampling rates.

Laser-driven electron emission and expansion for the generation and guidance of giant EMPs.

High-power laser interacting with matter generates intense electromagnetic pulses (EMPs), which are closely associated with laser and target parameters. In this study, EMPs induced by picosecond (ps) laser coupling with solid targets are recorded at the XG-III laser facility. Gold wire targets produce more intense EMPs with a maximum EMP value of 608 kV/m compared to some planar targets. EMP propagation in the normal direction is highly coincident with the expansion of detected hot electrons, which is verified by the particle-in-cell simulations. This work is expected to pave, to our knowledge, a new avenue for directional guidance of laser-driven EMPs.

Polystyrene nano-plastics impede skeletal muscle development and induce lipid accumulation via the PPARγ/LXRß pathway in vivo and in vitro in mice.

Nano-plastics (NPs) have emerged as a significant environmental pollutant, widely existing in water environment, and pose a serious threat to health and safety with the intake of animals. Skeletal muscle, a vital organ for complex life activities and functional demands, has received limited attention regarding the effects of NPs. In this study, the effects of polystyrene NPs (PS-NPs) on skeletal muscle development were studied by oral administration of different sizes (1 mg/kg) of PS-NPs in mice. The findings revealed that PS-NPs resulted in skeletal muscle damage and significantly hindered muscle differentiation, exhibiting an inverse correlation with PS-NPs particle size. Morphological analysis demonstrated PS-NPs caused partial disruption of muscle fibers, increased spacing between fibers, and lipid accumulation. RT-qPCR and western blots analyses indicated that PS-NPs exposure downregulated the expression of myogenic differentiation-related factors (Myod, Myog and Myh2), activated PPARγ/LXRß pathway, and upregulated the expressions of lipid differentiation-related factors (SREBP1C, SCD-1, FAS, ACC1, CD36/FAT, ADIPOQ, C/EBPα and UCP-1). In vitro experiments, C2C12 cells were used to confirm cellular penetration of PS-NPs (0, 100, 200, 400 µg/mL) through cell membranes along with activation of PPARγ expression. Furthermore, to verify LXRß as a key signaling molecule, silencing RNA transfection experiments were conducted, resulting in no increase in the expressions of PPARγ, LXRß, SREBP1C, FAS, CD36/FAT, ADIPOQ, C/EBPα and UCP-1 even after exposure to PS-NPs. However, the expressions of SCD-1and ACC1 remained unaffected. The present study evidenced that exposure to PS-NPs induced lipid accumulation via the PPARγ/LXRß pathway thereby influencing skeletal muscle development.

Spatial and channel attention-based conditional Wasserstein GAN for direct and rapid image reconstruction in ultrasound computed tomography.

Ultrasound computed tomography (USCT) is an emerging technology that offers a noninvasive and radiation-free imaging approach with high sensitivity, making it promising for the early detection and diagnosis of breast cancer. The speed-of-sound (SOS) parameter plays a crucial role in distinguishing between benign masses and breast cancer. However, traditional SOS reconstruction methods face challenges in achieving a balance between resolution and computational efficiency, which hinders their clinical applications due to high computational complexity and long reconstruction times. In this paper, we propose a novel and efficient approach for direct SOS image reconstruction based on an improved conditional generative adversarial network. The generator directly reconstructs SOS images from time-of-flight information, eliminating the need for intermediate steps. Residual spatial-channel attention blocks are integrated into the generator to adaptively determine the relevance of arrival time from the transducer pair corresponding to each pixel in the SOS image. An ablation study verified the effectiveness of this module. Qualitative and quantitative evaluation results on breast phantom datasets demonstrate that this method is capable of rapidly reconstructing high-quality SOS images, achieving better generation results and image quality. Therefore, we believe that the proposed algorithm represents a new direction in the research area of USCT SOS reconstruction.

[Efficiency Characteristics and Evolution Patterns of Urban Carbon Metabolism of Production-Living-Ecological Space in Beijing-Tianjin-Hebei Region].

This study explored the carbon metabolism efficiency of a production-living-ecological space system, which is of great significance for regional factor integration and spatial optimization. In this study, the material flow analysis method was introduced to establish a framework for evaluating the carbon metabolism efficiency of the production-living-ecological space system, and the super-efficiency DEA model and Malmquist index were used to empirically analyze the spatio-temporal distribution, dynamic change, and evolution patterns of the carbon metabolism efficiency of production-living-ecological space in the Beijing-Tianjin-Hebei Region, China, from 2000 to 2020 on the basis of the urban metabolic perspective. The results showed that:① the carbon metabolism efficiency of the production-living-ecological space showed a fluctuating growth trend, indicating the significant spatial differentiation of carbon metabolism efficiency in each city. There was a low overall carbon metabolism efficiency level, with a distribution pattern of being high in the middle and low in the north and south. ② The Malmquist index showed that the Total Factor Productivity (TFP) of carbon metabolism efficiency was greater than 1, and both the Technical Change (TC) and Pure Efficiency Change (PEC) were less than 1, in which the TFP showed an increasing trend, whereas there was no significant contribution of technological progress or pure technical efficiency to carbon metabolism efficiency. The total factor productivity of more than 50% of the cities showed an improving trend, only 38.46% of which made technological progress in improving carbon metabolism efficiency, and more than half of the urban pure technical efficiency showed a decreasing trend, in which the technical efficiency change and scale efficiency change were greater than 1 in most cities. ③ There were different types of carbon efficiency characteristics in each city, and according to the movement rules of the corresponding points in the quartile map, the evolution patterns of tourism industry efficiency were classified into stable, reciprocating, progressive, and abrupt. Therefore, local governments should adopt differentiated strategies to reasonably allocate spatial resources of production-living-ecological space and improve the technical level and scale efficiency, so as to improve the efficiency of urban carbon metabolism.

Changes of uterocervical angle and cervical length in early and mid-pregnancy and their value in predicting spontaneous preterm birth.

Objective: To explore the feasibility of transvaginal ultrasound measurement of uterocervical angle (UCA) and cervical length (CL) in early and mid-pregnancy and evaluate their combined prediction of spontaneous preterm birth (sPTB) in singleton pregnancies. Patients and Methods: This retrospective study comprised 274 pregnant women who underwent transvaginal ultrasound measurement of CL in mid-pregnancy (15-23+6 weeks); in 75 among them, CL also had been measured in early-pregnancy (<14 weeks). These 274 pregnant women were further divided into a preterm group (n = 149, <37 weeks gestation) and a control group (n = 125, >37 weeks gestation) according to delivery before or after 37 weeks, respectively. In the preterm group, 35 patients delivered before 34 weeks and the remaining 114 delivered between 34 and 37 weeks. Results: The optimal threshold of CL to predict preterm birth risk in women with <37 weeks gestation was 3.38 cm, and the optimal threshold of the UCA to predict preterm birth risk in the same group of women was 96°. The optimal threshold of CL to predict preterm birth risk in women with <34 weeks gestation was 2.54 cm, while that of the UCA in the same group of patients was 106°. The area under the curve for predicting preterm birth by combining the UCA and CL measurements was greater than that by using the UCA or CL alone (p < 0.01). The sensitivity and specificity for predicting preterm birth at <34 weeks gestation was 71.7% and 86.4%, respectively; and the sensitivity and specificity for predicting preterm birth at <37 weeks gestation was 87.6% and 80.6%, respectively. The difference between the two groups in CL and UCA were not significant in early pregnancy (p > 0.01), but only in mid-pregnancy (p < 0.01). There was a negative correlation between UCA and gestational week at delivery (r = -0.361, p < 0.001) and a positive correlation between CL and gestational week at delivery (r = 0.346, p < 0.001) in mid-pregnancy. The proportion of deliveries at <34 weeks was highest when the UCA was >105°, and the proportion of deliveries between 35 and 37 weeks was highest when the UCA was between 95° and 105°. The proportion of deliveries at <34 weeks was highest when the CL was <2.5 cm. Conclusion: The combination of UCA and CL has a better ability to predict preterm birth than either measurement alone. A more obtuse UCA or a shorter CL is associated with an earlier spontaneous preterm birth. The UCA increases from early to mid-pregnancy, while the CL decreases from early to mid-pregnancy.

Image restoration for ring-array photoacoustic tomography system based on blind spatially rotational deconvolution.

Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.

CircORC2 promoted proliferation and inhibited the sensitivity of osteosarcoma cell lines to cisplatin by regulating the miR-485-3p/TRIM2 axis.

Resistance to chemotherapy leads to poor prognosis for osteosarcoma (OS) patients. However, due to the high metastasis of tumor and the decrease in sensitivity of tumor cells to cisplatin (DDP), the 5-year survival rate of OS patients is still unsatisfactory. This study explored a mechanism for improving the sensitivity of OS cells to DDP. A DDP-resistant OS cell model was established, and we have found that circORC2 and TRIM2 were upregulated in DDP-resistant OS cells, but miR-485-3p was downregulated. The cell viability and proliferation of the OS cells decreased gradually with the increase of DDP dose, but a gradual increase in apoptosis was noted. CircORC2 promoted OS cell proliferation and DDP resistance and upregulated TRIM2 expression by targeting miR-485-3p. Functionally, circORC2 downregulated miR-485-3p to promote OS cell proliferation and inhibit DDP sensitivity. Additionally, it promoted cell proliferation and inhibited the sensitivity of DDP by regulating the miR-485-3p/TRIM2 axis. In conclusion, circORC2 promoted cell proliferation and inhibited the DDP sensitivity in OS cells via the miR-485-3p/TRIM2 axis. These findings indicated the role of circORC2 in regulating the sensitivity of OS cells to DDP.

Mechanical rheological model on the assessment of elasticity and viscosity in tissue inflammation: A systematic review.

Understanding the extent of inflammation is crucial for early disease detection, monitoring disease progression, and evaluating treatment responses. Over the past decade, researchers have demonstrated the need to understand the extent of inflammation through qualitative or quantitative characterization of tissue viscoelasticity using different techniques. In this scientific review, an examination of research on the association between elasticity and Viscosity in diseases, particularly as tissue inflammation progresses, is conducted. A review of utilizing mechanical rheological models to characterize quantitative viscoelastic parameters of normal and inflamed tissues is also undertaken. Based on inclusion and exclusion criteria, we identified 14 full-text studies suitable for review out of 290 articles published from January 2000 to January 2024. We used PRISMA guidelines for the systematic review. In the review, three studies demonstrated the criterion used by the researchers in identifying the best rheological model. Eleven studies showed the clinical application of the rheological model in quantifying the viscoelastic properties of normal and pathological tissue. The review quantified viscoelastic parameters for normal and pathological tissue across various soft tissues. It evaluated the effectiveness of each viscoelastic property in distinguishing between normal and pathological tissue stiffness. Furthermore, the review outlined additional viscoelastic-related parameters for researchers to consider in future stiffness classification studies.

Teacher-student guided knowledge distillation for unsupervised convolutional neural network-based speckle tracking in ultrasound strain elastography.

Accurate and efficient motion estimation is a crucial component of real-time ultrasound elastography (USE). However, obtaining radiofrequency ultrasound (RF) data in clinical practice can be challenging. In contrast, although B-mode (BM) data is readily available, elastographic data derived from BM data results in sub-optimal elastographic images. Furthermore, existing conventional ultrasound devices (e.g., portable devices) cannot provide elastography modes, which has become a significant obstacle to the widespread use of traditional ultrasound devices. To address the challenges above, we developed a teacher-student guided knowledge distillation for an unsupervised convolutional neural network (TSGUPWC-Net) to improve the accuracy of BM motion estimation by employing a well-established convolutional neural network (CNN) named modified pyramid warping and cost volume network (MPWC-Net). A pre-trained teacher model based on RF is utilized to guide the training of a student model using BM data. Innovations outlined below include employing spatial attention transfer at intermediate layers to enhance the guidance effect of the model. The loss function consists of smoothness of the displacement field, knowledge distillation loss, and intermediate layer loss. We evaluated our method on simulated data, phantoms, and in vivo ultrasound data. The results indicate that our method has higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values in axial strain estimation than the model trained on BM. The model is unsupervised and requires no ground truth labels during training, making it highly promising for motion estimation applications.

Decreased plasma miR-140-3p is associated with coronary artery disease.

Background: Although many circulating miRNAs (c-miRNAs) are associated with coronary artery disease (CAD), they are far from being the biomarker for CAD diagnosis or risk prediction. Therefore, novel c-miRNAs discovery and validation are still required, especially evaluating their prediction capacity. Objectives: Identify novel CAD-related c-miRNAs and evaluate its risk prediction capacity for CAD. Methods: miRNAs associated with CAD were preliminarily investigated in three paired samples representing pre-CAD stage and CAD stage of three female individuals using the Applied Biosystems miRNA TaqMan® Low-Density Array (TLDA). Then, the candidate miRNAs were further verified in an independent case-control study including 129 CAD patients and 76 controls, and their potential practical value in prediction for CAD was evaluated using a machine learning (ML) algorithm. The accuracy of classification and prediction was assessed with the area under the receiver operating characteristic curve (AUC). Results: TLDA analysis shows that miR-140-3p decreased significantly in CAD-stage (FC = -3.01, P = 0.007). Further study shows that miR-140-3p was significantly lower in CAD group [1.26 (0.68, 2.01)] than in control group [2.07 (1.19, 3.21)] (P < 0.001) and independently associated with CAD (P < 0.001). The addition of miR-140-3p to the variables including smoking history, HDL-c, and APOA1 improved the accuracy of classification by logistic regression and of prediction for CAD by ML models. The ML models built with miR-140-3p and HDL-c, respectively, had a similar prediction accuracy. The feature importance of miR-140-3p and HDL-c in the ML models was also similar. Decision curve analysis showed that miR-140-3p and HDL-c had almost identical net benefits. Conclusion: Reduced levels of miR-140-3p is linked to CAD, and it is possible to use the plasma level of miR-140-3p as a means of evaluating the risk of CAD.

Elaboration and validation of a novelty nomogram for the prognostication of anxiety susceptibility in individuals suffering from low back pain.

Low back pain (LBP) constitutes a distressing emotional ordeal and serves as a potent catalyst for adverse emotional states, notably anxiety. We dedicated to discerning methodologies for identifying patients who are predisposed to heightened levels of anxiety and pain. A self-assessment questionnaire was administered to patients afflicted with LBP. The pain scores were subjected to analysis in conjunction with anxiety scores, and a clustering procedure was executed using the scientific k-means methodology. Subsequently, six machine learning algorithms, including Logistics Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB), were employed. Next, five pertinent variables were identified, namely Age, Course, Body Mass Index (BMI), Education, and Marital status. Furthermore, a LR model was utilized to construct a nomogram, which was subsequently subjected to assessment for discrimination, calibration, and evaluation of its clinical utility. As a result, 599 questionnaires were valid (effective rate: 99 %). The correlation analysis revealed a significant association between anxiety and pain scores (r = 0.31, P < 0.001). LBP patients could be divided into two clusters, Cluster1 had higher pain scores (P < 0.05) and SAS scores (P < 0.001). The proposed nomogram demonstrated an area under the receiver operating characteristics curve (ROC) of 0.841 (95 %CI: 0.804-0.878) and 0.800 (95 %CI: 0.733-0.867) in the training and test groups, respectively. Briefly, the established nomogram has demonstrated remarkable proficiency in discerning individuals afflicted with LBP who are at a heightened risk of experiencing anxiety.

Influences of shallow groundwater depth on N2O diffusion along the soil profile of summer maize fields in North China Plain.

The emissions of nitrous oxide (N2O) from agricultural fields are a significant contribution to global warming. Understanding the mechanisms of N2O emissions from agricultural fields is essential for the development of N2O emission mitigation strategies. Currently, there are extensive studies on N2O emissions on the surface of agricultural soils, while studies on N2O fluxes at the interface between the saturated and unsaturated zones (ISU) are limited. Uncertainties exist regarding N2O emissions from the soil-shallow groundwater systems in agricultural fields. In this study, a three-year lysimeter experiment (2019-2020, 2022) was conducted to simulate the soil-shallow groundwater systems under four controlled shallow groundwater depth (SGD) (i.e., SGD = 40, 70, 110, and 150 cm) conditions in North China Plain (NCP). Weekly continuous monitoring of N2O emissions from soil surface, N2O concentration in the shallow groundwater and the upper 10 cm of pores at the ISU, and nitrogen cycling-related parameters in the soil and groundwater was conducted. The results showed that soil surface N2O emissions increased with decreased shallow groundwater depth, and the highest emissions of 96.44 kg ha-1 and 104.32 kg ha-1 were observed at G2 (SGD = 40 cm) in 2020 and 2022. During the observation period of one maize growing season, shallow groundwater acted as a sink for the unsaturated zone when the groundwater depth was 40 cm, 70 cm, and 110 cm. However, when SGD was 150 cm, shallow groundwater became a source for the unsaturated zone. After fertilization, the groundwater in all treatment plots behaved as a sink for the unsaturated zone, and the diffusion intensity decreased with increasing SGD. The results would provide a theoretical basis for cropland water management to reduce N2O emissions.