HCL: A Hierarchical Contrastive Learning Framework for Zero-Shot Relation Extraction.

Zero-shot relation extraction (ZSRE) is shown to become more significant in the current information extraction system, which aims at predicting relation classes that lack annotations or have just never appeared during training. Previous works focus on projecting sentences with their corresponding relation descriptions to an intermediate semantic space and searching the nearest semantic for predicting unseen classes. Though these methods can achieve sound performance, they only obtain inferior semantic information via a trivial distance metric and neglect the interaction in the instance representations. We are thus motivated to tackle these issues and propose a hierarchical contrastive learning (HCL) framework for ZSRE including projection-level and instance-level modules. Specifically, the projection-level component replaces the distance score function by contrastive loss to connect the input sentence with the relation semantic space. And the instance-level component integrates the external knowledge from sentence entities to establish new contrastive pairs for efficiently learning representations from mutual information. The experimental results on three well-known datasets demonstrate that our model surpasses the existing SOTA by at most 18.97% improvement on the F1 score when unseen classes are 15 . Moreover, our model can achieve more competitive performance alone with the increasing number of unseen classes.

Unusual case of retroperitoneal hematoma and duodenal ulcerative bleeding after nephrectomy: Case report.

RATIONALE: Retroperitoneal hematomas are relatively common in patients undergoing nephrectomy. Herein, we report an unusual case involving a giant retroperitoneal hematoma and subsequent duodenal ulcerative bleeding following a radical nephrectomy. PATIENT CONCERNS: A 77-year-old woman was admitted to our hospital for lower back pain, and she had severe right hydronephrosis and a urinary tract infection. DIAGNOSES: The patient was diagnosed and confirmed as high-grade urothelial carcinoma. INTERVENTIONS: After ineffective conservative treatments, a right radical nephrectomy and ureteral stump resection were performed. The patient received proton pump inhibitors to prevent stress ulcer formation and bleeding. On the first day post-surgery, she had normal gastrointestinal (GI) endoscopy findings. On the second day post-surgery, abdominal computed tomography revealed a retroperitoneal hematoma. Notably, 14 days post-surgery, massive GI bleeding occurred, and GI endoscopy identified an almost perforated ulcer in the bulbar and descending duodenum. OUTCOMES: The patient died on day 15 after surgery. LESSONS: Duodenal ulceration and bleeding might occur following a retroperitoneal hematoma in patients treated with nephrectomy. Timely intervention may prevent duodenal ulcers and complications, and thus could be a promising life-saving intercession.

IoUformer: Pseudo-IoU prediction with transformer for visual tracking.

Siamese tracking has witnessed tremendous progress in tracking paradigm. However, its default box estimation pipeline still faces a crucial inconsistency issue, namely, the bounding box decided by its classification score is not always best overlapped with the ground truth, thus harming performance. To this end, we explore a novel simple tracking paradigm based on the intersection over union (IoU) value prediction. To first bypass this inconsistency issue, we propose a concise target state predictor termed IoUformer, which instead of default box estimation pipeline directly predicts the IoU values related to tracking performance metrics. In detail, it extends the long-range dependency modeling ability of transformer to jointly grasp target-aware interactions between target template and search region, and search sub-region interactions, thus neatly unifying global semantic interaction and target state prediction. Thanks to this joint strength, IoUformer can predict reliable IoU values near-linear with the ground truth, which paves a safe way for our new IoU-based siamese tracking paradigm. Since it is non-trivial to explore this paradigm with pleased efficacy and portability, we offer the respective network components and two alternative localization ways. Experimental results show that our IoUformer-based tracker achieves promising results with less training data. For its applicability, it still serves as a refinement module to consistently boost existing advanced trackers.

Quantifying the effects of plant density on soybean lodging resistance and growth dynamics in maize-soybean strip intercropping.

Shading-induced soybean stem lodging is a prevalent concern in the maize (Zea mays L.)-soybean (Glycine max L. Merr.) strip intercropping system, leading to a substantial decline in yield. Nevertheless, the associations between soybean growth, stem lodging, and yield formation in this scenario remain unclear. To investigate this, the logistic and beta growth models were utilized to analyze the growth process of soybean organs (stems, leaves, branches, and pods) and the accumulation of carbohydrates (lignin, cellulose, and sucrose) at three planting densities (8.5, 10, and 12.5 plants m-2) in both strip intercropping and skip strip monoculture systems. The results indicate that shading stress caused by maize in the intercropping system reduced lignin and cellulose accumulation in soybean stems, thus decelerating soybean organ growth compared to monoculture. Furthermore, intercropped soybean at higher planting density (PD3) exhibited a 28% reduction in the maximum dry matter growth rate (cm) and a 11% decrease in the time taken to reach the maximum dry matter growth rate (te) compared to the lower planting density (PD1). Additionally, a 29% decrease in the maximum accumulation rate (cmax) of sucrose, lignin, and cellulose was observed, along with a 13% decrease in the continuous accumulation time (tc) of these carbohydrates in intercropped soybean at PD3. Interspecific and intraspecific shading stress led to a preferential allocation of assimilates into soybean stems, enhancing plant height during the initial stage, while at later stages, a greater proportion of sucrose was allocated to leaves. Consequently, this hindered the conversion of sucrose into lignin and cellulose within the stems, ultimately resulting in a reduction in the lodging resistance index (LRI). Overall, this study provides valuable insights into the effects of shading stress on soybean growth and yield. It also emphasizes how optimizing planting density in intercropping systems can effectively alleviate shading stress and enhance crop productivity.

Sparse Low-Rank Multi-View Subspace Clustering With Consensus Anchors and Unified Bipartite Graph.

Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to reduce the complexity cost. However, due to the sampling operation being performed on each individual view independently and not considering the distribution of samples in all views, the produced anchors are usually slightly distinguishable, failing to characterize the whole data. Moreover, it is necessary to fuse multiple separated graphs into one, which leads to the final clustering performance heavily subject to the fusion algorithm adopted. What is worse, existing MVSC methods generate dense bipartite graphs, where each sample is associated with all anchor candidates. We argue that this dense-connected mechanism will fail to capture the essential local structures and degrade the discrimination of samples belonging to the respective near anchor clusters. To alleviate these issues, we devise a clustering framework named SL-CAUBG. Specifically, we do not utilize sampling strategy but optimize to generate the consensus anchors within all views so as to explore the information between different views. Based on the consensus anchors, we skip the fusion stage and directly construct the unified bipartite graph across views. Most importantly, l1 norm and Laplacian-rank constraints employed on the unified bipartite graph make it capture both local and global structures simultaneously. l1 norm helps eliminate the scatters between anchors and samples by constructing sparse links and guarantees our graph to be with clear anchor-sample affinity relationship. Laplacian-rank helps extract the global characteristics by measuring the connectivity of unified bipartite graph. To deal with the nondifferentiable objective function caused by l1 norm, we adopt an iterative re-weighted method and the Newton's method. To handle the nonconvex Laplacian-rank, we equivalently transform it as a convex trace constraint. We also devise a four-step alternate method with linear complexity to solve the resultant problem. Substantial experiments show the superiority of our SL-CAUBG.

Non-coding RNAs in renal cell carcinoma: Implications for drug resistance.

Renal cell carcinoma (RCC) represents a malignant tumor of the urinary system. Individuals with early-stage RCC could be cured by surgical treatment, but a considerable number of cases of advanced RCC progress to drug resistance. Recently, numerous reports have demonstrated that a variety of non-coding RNAs (ncRNAs) contribute to tumor occurrence and development. ncRNAs can act as oncogenic or tumor suppressor genes to regulate proliferation, migration, drug resistance and other processes in RCC cells through a variety of signaling pathways. Considering the lack of treatment options for advanced RCC after drug resistance, ncRNAs may be a good choice as biomarkers of drug resistance in RCC and targets to overcome drug resistance. In this review, we discussed the effects of ncRNAs on drug resistance in RCC and the great potential of ncRNAs as a biomarker of or a new therapeutic method in RCC.

SiamDF: Tracking training data-free siamese tracker.

Much progress has been made in siamese tracking, primarily benefiting from increasing huge training data. However, very little attention has been really paid to the role of huge training data in learning an effective siamese tracker. In this study, we undertake an in-depth analysis of this issue from a novel optimization perspective, and observe that training data is particularly adept at background suppression, thereby refining target representation. Inspired by this insight, we present a data-free siamese tracking algorithm named SiamDF, which requires only a pre-trained backbone and no further fine-tuning on additional training data. Particularly, to suppress background distractors, we separately improve two branches of siamese tracking by retaining the pure target region as target input with the removal of template background, and by exploring an efficient inverse transformation to maintain the constant aspect ratio of target state in search region. Besides, we further promote the center displacement prediction of the entire backbone by eliminating its spatial stride deviations caused by convolution-like quantification operations. Our experimental results on several popular benchmarks demonstrate that SiamDF, free from both offline fine-tuning and online update, achieves impressive performance compared to well-established unsupervised and supervised tracking methods.

Spatiotemporally resolved metabolomics and isotope tracing reveal CNS drug targets.

Deconvolution of potential drug targets of the central nervous system (CNS) is particularly challenging because of the complicated structure and function of the brain. Here, a spatiotemporally resolved metabolomics and isotope tracing strategy was proposed and demonstrated to be powerful for deconvoluting and localizing potential targets of CNS drugs by using ambient mass spectrometry imaging. This strategy can map various substances including exogenous drugs, isotopically labeled metabolites, and various types of endogenous metabolites in the brain tissue sections to illustrate their microregional distribution pattern in the brain and locate drug action-related metabolic nodes and pathways. The strategy revealed that the sedative-hypnotic drug candidate YZG-331 was prominently distributed in the pineal gland and entered the thalamus and hypothalamus in relatively small amounts, and can increase glutamate decarboxylase activity to elevate γ-aminobutyric acid (GABA) levels in the hypothalamus, agonize organic cation transporter 3 to release extracellular histamine into peripheral circulation. These findings emphasize the promising capability of spatiotemporally resolved metabolomics and isotope tracing to help elucidate the multiple targets and the mechanisms of action of CNS drugs.

Berberine is a potential alternative for metformin with good regulatory effect on lipids in treating metabolic diseases.

Metformin (MTF) and berberine (BBR) share several therapeutic benefits in treating metabolic-related disorders. However, as the two agents have very different chemical structure and bioavailability in oral route, the goal of this study is to learn their characteristics in treating metabolic disorders. The therapeutic efficacy of BBR and MTF was systemically investigated in the high fat diet feeding hamsters and/or ApoE(-/-) mice; in parallel, gut microbiota related mechanisms were studied for both agents. We discovered that, although both two drugs had almost identical effects on reducing fatty liver, inflammation and atherosclerosis, BBR appeared to be superior over MTF in alleviating hyperlipidemia and obesity, but MTF was more effective than BBR for the control of blood glucose. Association analysis revealed that the modulation of intestinal microenvironment played a crucial role in the pharmacodynamics of both drugs, in which their respective superiority on the regulation of gut microbiota composition and intestinal bile acids might contribute to their own merits on lowering glucose or lipids. This study shows that BBR may be a good alternative for MTF in treating diabetic patients, especially for those complicated with dyslipidemia and obesity.

Facile synthesis of lipase-loaded starch nanoparticles as recyclable biocatalyst in Pickering interfacial systems.

To develop recyclable biocatalyst used in Pickering interfacial systems, the pH-responsive monomer [2-(dimethylamine)ethyl methacrylate] (DMAEMA) was grafted onto the maize starch molecule via free radical polymerization. Subsequently, combined with the gelatinization-ethanol precipitation and lipase (Candida rugosa) absorption process, an enzyme-loaded starch nanoparticle with DMAEMA grafting (D-SNP@CRL) was tailor-made, showing a nanometer size and regular sphere. X-ray photoelectron spectroscopy and confocal laser scanning microscopy confirmed a concentration-induced enzyme distribution within D-SNP@CRL, thereof the outside-to-inside enzyme distribution was proved to be optimum in achieving the highest catalytic efficiency. Benefited from the tunable wettability and size of D-SNP@CRL under pH variation, the generated Pickering emulsion could be readily applied as the recyclable microreactors for the n-butanol/vinyl acetate transesterification. This catalysis exhibited both highly catalytic activity and good recyclability, making the enzyme-loaded starch particle a promising green and sustainable biocatalyst in the Pickering interfacial system.

circ_0001274 Competitively Binds miR-143-3p to Upregulate VWF Expression to Improve Acute Traumatic Coagulopathy.

Accumulating evidence has noted the circRNA-microRNA- (circRNA-miRNA-) mRNA competing endogenous RNA (ceRNA) regulatory network in disease development and progression. The current study explored the ceRNA network in acute traumatic coagulopathy (ATC). Potential ATC-related genes were screened, and upstream miRNAs and circRNAs of VWF (the candidate target) were assayed through database searching and high-throughput sequencing technology. circ_0001274/miR-143-3p/VWF ceRNA regulatory network was constructed and validated. The expression of circ_0001274/miR-143-3p/VWF was determined in the peripheral blood samples from ATC patients and ATC mouse models. Online database and circRNA sequencing analysis results identified VWF as a key gene in ATC as supported by assays and that VWF was lowly expressed in ATC patients and mice. Further experiments demonstrated that miR-143-3p could target and inhibit VWF, and circ_0001274 could competitively sponge miR-143-3p. Functionally, circ_0001274 could competitively sequester miR-143-3p to upregulate VWF expression, potentially improving ATC. Our study highlights the critical role of circ_0001274/miR-143-3p/VWF axis in improving ATC.

Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association.

Recently, advances in detection and re-identification techniques have significantly boosted tracking-by-detection-based multi-pedestrian tracking (MPT) methods and made MPT a great success in most easy scenes. Several very recent works point out that the two-step scheme of first detection and then tracking is problematic and propose using the bounding box regression head of an object detector to realize data association. In this tracking-by-regression paradigm, the regressor directly predicts each pedestrian's location in the current frame according to its previous position. However, when the scene is crowded and pedestrians are close to each other, the small and partially occluded targets are easily missed. In this paper, we follow this pattern and design a hierarchical association strategy to obtain better performance in crowded scenes. To be specific, at the first association, the regressor is used to estimate the positions of obvious pedestrians. At the second association, we employ a history-aware mask to filter out the already occupied regions implicitly and look carefully at the remaining regions to find out the ignored pedestrians during the first association. We integrate the hierarchical association in a learning framework and directly infer the occluded and small pedestrians in an end-to-end way. We conduct extensive pedestrian tracking experiments on three public pedestrian tracking benchmarks from less crowded to crowded scenes, demonstrating the proposed strategy's effectiveness in crowded scenes.

Synthesis and antibacterial activity of novel 2­fluoro ketolide antibiotics with 11,12­quinoylalkyl side chains.

A series of novel 2­fluoro ketolide antibiotics with 11,12­quinoylalkyl side chains derived from telithromycin and cethromycin were designed and synthesized. The corresponding targets 2a-o were tested for their in vitro activities against a series of macrolide-sensitive and macrolide-resistant pathogens. Some of them showed a similar antibacterial spectrum and comparable or slightly better activity to telithromycin. Among them, compounds 2g and 2k, displayed excellent activities against macrolide-sensitive and macrolide-resistant pathogens.

Designed Synthesis of Compartmented Bienzyme Biocatalysts Based on Core-Shell Zeolitic Imidazole Framework Nanostructures.

For complex cascade biocatalysis, multienzyme compartmentalization helps to optimize substrate transport channels and promote the orderly and tunable progress of step reactions. Herein, a simple and general synthesis strategy is proposed for the construction of a multienzyme biocatalyst by compartmentalizing glucose oxidase and horseradish peroxidase (GOx and HRP) within core-shell zeolite imidazole frameworks (ZIF)-8@ZIF-8 nanostructures. Owing to the combined effects of biomimetic mineralization and the fine regulation of the ZIF-8 growth process, the uniform shell encloses the seed (core) surface by epitaxial growth, and the bienzyme system is accurately localized in a controlled manner. The versatility of this strategy is also reflected in ZIF-67. Meanwhile, with the ability to covalently bind divalent metal ions, lithocholic acid (LCA) is used as a competitive ligand to improve the pore structure of the ZIF from a single micropore to a hierarchical micro/mesopore network, which greatly increases mass transfer efficiency. Furthermore, the multienzyme cascade reaction is exemplified by the oxidation of o-phenylenediamine (OPD). The findings show that the bienzyme assembly strategy significantly affects the biocatalytic efficiency mainly by influencing the utilization efficiency of the intermediate (Hydrogen peroxide, H2 O2 ) between the step reactions. This study sheds new light on facile synthetic routes to constructing in vitro multienzyme biocatalysts.

Target recognition initiated self-dissociation based DNA nanomachine for sensitive and accurate MicroRNA (miRNA) detection.

As a valuable biomarker for various tumor, sensitive and reliable quantitative determination of microRNA (miRNA) is crucial for both disease diagnosis and cancer treatment. Herein, we depict a novel simple and sensitive miRNA detection approach by exploiting an elegantly designed target recognition initiated self-dissociation based DNA nanomachine. In this nanomachine, target recognition assists the formation of active DNAzyme secondary conformation, and the active DNAzyme generates a nicking site in H probe, initiating the self-assembly of H probe. With the reflexed sequences as primer, dual signal recycles are formed under the cooperation of DNA polymerase and Nb.BbvCI. Eventually, the method exhibits a high sensitivity with the limit of detection as low as 12 fM. In addition, the method is also demonstrated with a high selectivity that can distinguish one mismatched base pair. We believe the established approach can be a robust tool for the early-diagnosis of a variety of cancers and also in anticancer drug development.

Discriminative Geometric-Structure-Based Deep Hashing for Large-Scale Image Retrieval.

Deep hashing reaps the benefits of deep learning and hashing technology, and has become the mainstream of large-scale image retrieval. It generally encodes image into hash code with feature similarity preserving, that is, geometric-structure preservation, and achieves promising retrieval results. In this article, we find that existing geometric-structure preservation manner inadequately ensures feature discrimination, while improving feature discrimination of hash code essentially determines hash learning retrieval performance. This fact principally spurs us to propose a discriminative geometric-structure-based deep hashing method (DGDH), which investigates three novel loss terms based on class centers to induce the so-called discriminative geometrical structure. In detail, the margin-aware center loss assembles samples in the same class to the corresponding class centers for intraclass compactness, then a linear classifier based on class center serves to boost interclass separability, and the radius loss further puts different class centers on a hypersphere to tentatively reduce quantization errors. An efficient alternate optimization algorithm with guaranteed desirable convergence is proposed to optimize DGDH. We theoretically analyze the robustness and generalization of the proposed method. The experiments on five popular benchmark datasets demonstrate superior image retrieval performance of the proposed DGDH over several state of the arts.

Screening and Preclinical Evaluation of Novel Radiolabeled Anti-Fibroblast Activation Protein-α Recombinant Antibodies.

Background: Fibroblast activation protein-α (FAPα) is selectively overexpressed in tumor-associated fibroblasts in more than 90% of epithelial tumors, and may be a good target for anticancer treatment, for example, using an anti-FAPα recombinant antibody (rAb) labeled with radionuclides. In the present report, the radiolabeling and preclinical evaluation of novel anti-FAPα rAbs were investigated. Materials and Methods: Two novel anti-FAPα VHHs (AMS002-1 and AMS002-2) with high binding affinity to FAPα were selected from an antibody phage library. The anti-FAPα VHHs were then fused with the Fc fragment of human IgG4 to create two VHH-Fc rAbs. The VHH-Fc rAbs were radiolabeled with 89Zr and 177Lu. The radiolabeled products were evaluated by radioligand-binding assays using FAPα-expressing cells. The biodistribution and tumor-targeting properties were investigated by small-animal PET/CT. AMS002-1-Fc, which showed promising tumor-targeting properties in 89Zr-microPET imaging, was radiolabeled with 177Lu for efficacy study on HT1080 tumor-bearing mice and monitored with SPECT/CT imaging. Results: The two VHH-Fc rAbs with good affinity with KD values in low nanomolar range were identified. Both PET/CT imaging with 89Zr-AMS002-1-Fc rAb and SPECT/CT imaging with 177Lu-AMS002-1-Fc rAb demonstrated highest tumor uptakes at 72 h p.i. and long tumor retention in the preclinical models. Furthermore, ex vivo biodistribution analysis revealed high tumor uptake of 89Zr-AMS002-1-Fc at 48 h p.i. with the value of 6.91% ± 2.08% ID/g. Finally, radioimmunotherapy with 177Lu-AMS002-1-Fc rAb delayed the tumor growth without significant weight loss in mice with HT1080 xenografts. The tumor size of untreated control group was 2.59 times larger compared with the treatment group with 177Lu-AMS002-1-Fc at day 29. Conclusion: 89Zr/177Lu-AMS002-1-Fc represent a pair of promising radiopharmaceuticals for theranostics on FAPα-expressing tumors.

Generic Structure Extraction with Bi-Level Optimization for Graph Structure Learning.

Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph structure, improve the robustness of GNN merely from a local view by considering the local information related to each edge and indiscriminately applying the mechanism across edges, which may suffer from the local structure heterogeneity of the graph (i.e., the uneven distribution of inter-class connections over nodes). To overcome the drawbacks, we extract the graph structure as a learnable parameter and jointly learn the structure and common parameters of GNN from the global view. Excitingly, the common parameters contain the global information for nodes features mapping, which is also crucial for structure optimization (i.e., optimizing the structure relies on global mapping information). Mathematically, we apply a generic structure extractor to abstract the graph structure and transform GNNs in the form of learning structure and common parameters. Then, we model the learning process as a novel bi-level optimization, i.e., Generic Structure Extraction with Bi-level Optimization for Graph Structure Learning (GSEBO), which optimizes GNN parameters in the upper level to obtain the global mapping information and graph structure is optimized in the lower level with the global information learned from the upper level. We instantiate the proposed GSEBO on classical GNNs and compare it with the state-of-the-art GSL methods. Extensive experiments validate the effectiveness of the proposed GSEBO on four real-world datasets.

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed t-SNE of Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue.

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body.

Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.