A spatiotemporal atlas of cholestatic injury and repair in mice.

Cholestatic liver injuries, characterized by regional damage around the bile ductular region, lack curative therapies and cause considerable mortality. Here we generated a high-definition spatiotemporal atlas of gene expression during cholestatic injury and repair in mice by integrating spatial enhanced resolution omics sequencing and single-cell transcriptomics. Spatiotemporal analyses revealed a key role of cholangiocyte-driven signaling correlating with the periportal damage-repair response. Cholangiocytes express genes related to recruitment and differentiation of lipid-associated macrophages, which generate feedback signals enhancing ductular reaction. Moreover, cholangiocytes express high TGFß in association with the conversion of liver progenitor-like cells into cholangiocytes during injury and the dampened proliferation of periportal hepatocytes during recovery. Notably, Atoh8 restricts hepatocyte proliferation during 3,5-diethoxycarbonyl-1,4-dihydro-collidin damage and is quickly downregulated after injury withdrawal, allowing hepatocytes to respond to growth signals. Our findings lay a keystone for in-depth studies of cellular dynamics and molecular mechanisms of cholestatic injuries, which may further develop into therapies for cholangiopathies.

Challenging anticoagulation therapy for multiple primary malignant tumors combined with thrombosis: A case report and review of literature.

BACKGROUND: Venous thromboembolism significantly contributes to patient deterioration and mortality. Management of its etiology and anticoagulation treatment is intricate, necessitating a comprehensive consideration of various factors, including the bleeding risk, dosage, specific anticoagulant medications, and duration of therapy. Herein, a case of lower extremity thrombosis with multiple primary malignant tumors and high risk of bleeding was reviewed to summarize the shortcomings of treatment and prudent anticoagulation experience. CASE SUMMARY: An 83-year-old female patient was admitted to the hospital due to a 2-wk history of left lower extremity edema that had worsened over 2 d. Considering her medical history and relevant post-admission investigations, it was determined that the development of left lower extremity venous thrombosis and pulmonary embolism in this case could be attributed to a combination of factors, including multiple primary malignant tumors, iliac venous compression syndrome, previous novel coronavirus infection, and inadequate treatment for prior thrombotic events. However, the selection of appropriate anticoagulant medications, determination of optimal drug dosages, and establishment of an appropriate duration of anticoagulation therapy were important because of concurrent thrombocytopenia, decreased quantitative fibrinogen levels, and renal insufficiency. CONCLUSION: Anticoagulant prophylaxis should be promptly initiated in cases of high-risk thrombosis. Individualized anticoagulation therapy is required for complex thrombosis.

Substantial viral diversity in bats and rodents from East Africa: insights into evolution, recombination, and cocirculation.

BACKGROUND: Zoonotic viruses cause substantial public health and socioeconomic problems worldwide. Understanding how viruses evolve and spread within and among wildlife species is a critical step when aiming for proactive identification of viral threats to prevent future pandemics. Despite the many proposed factors influencing viral diversity, the genomic diversity and structure of viral communities in East Africa are largely unknown. RESULTS: Using 38.3 Tb of metatranscriptomic data obtained via ultradeep sequencing, we screened vertebrate-associated viromes from 844 bats and 250 rodents from Kenya and Uganda collected from the wild. The 251 vertebrate-associated viral genomes of bats (212) and rodents (39) revealed the vast diversity, host-related variability, and high geographic specificity of viruses in East Africa. Among the surveyed viral families, Coronaviridae and Circoviridae showed low host specificity, high conservation of replication-associated proteins, high divergence among viral entry proteins, and frequent recombination. Despite major dispersal limitations, recurrent mutations, cocirculation, and occasional gene flow contribute to the high local diversity of viral genomes. CONCLUSIONS: The present study not only shows the landscape of bat and rodent viromes in this zoonotic hotspot but also reveals genomic signatures driven by the evolution and dispersal of the viral community, laying solid groundwork for future proactive surveillance of emerging zoonotic pathogens in wildlife. Video Abstract.

Peripapillary atrophy area predicts the decrease of macular choroidal thickness in young adults during myopia progression.

OBJECTIVE: This study aimed to investigate the influence of peripapillary atrophy (PPA) area and axial elongation on the longitudinal changes in macular choroidal thickness (ChT) in young individuals with myopia. METHODS AND ANALYSIS: In this longitudinal investigation, 431 eyes-342 categorised as non-high myopia (non-HM) and 89 as HM-were examined for 2 years. Participants were examined with swept-source optical coherence tomography. The macular ChT, PPA area and axial length (AL) were measured at baseline and follow-up visits. Multiple regression analysis was performed to identify factors associated with ChT changes. The areas under the receiver operating characteristic curves were analysed to ascertain the predictive capacity of the PPA area and axial elongation for the reduction in macular ChT. RESULTS: Initial measurements revealed that the average macular ChT was 240.35±56.15 µm in the non-HM group and 198.43±50.27 µm in the HM group (p<0.001). It was observed that the HM group experienced a significantly greater reduction in average macular ChT (-7.35±11.70 µm) than the non-HM group (-1.85±16.95 µm, p=0.004). Multivariate regression analysis showed that a greater reduction of ChT was associated with baseline PPA area (ß=-26.646, p<0.001) and the change in AL (ß=-35.230, p<0.001). The combination of the baseline PPA area with the change in AL was found to be effective in predicting the decrease in macular ChT, with an area under the curve of 0.741 (95% CI 0.694 to 0.787). CONCLUSION: Over 2 years, eyes with HM exhibit a more significant decrease in ChT than those without HM. Combining the baseline PPA area with the change in AL could be used to predict the decrease of macular ChT.

Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing.

Precision medicine's emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the vehicle routing problem (VRP) assembler: an approach that transforms this task into a vehicle routing problem, an optimization formulation solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google's OR-Tools, achieving a haplotype-resolved local assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to Hifiasm with phasing accuracy approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

TIN2-mediated reduction of mitophagy induces RPE senescence under high glucose.

The telomere-associated protein TIN2 localizes to both telomeres and mitochondria. Nevertheless, the impact of TIN2 on retinal pigment epithelial (RPE) cells in diabetic retinopathy (DR) remains unclear. This research aims to examine the role of TIN2 in the senescence of RPE and its potential as a therapeutic target. Western blotting and immunofluorescence staining were utilized to identify TIN2 expression and mitophagy. RT-qPCR was employed to identify senescent associated secretory phenotype (SASP) in ARPE-19 cells infected with TIN2 overexpression. To examine mitochondria and the cellular senescence of RPE, TEM, SA-ß-gal staining, and cell cycle analysis were used. The impact of TIN2 was examined using OCT and immunohistochemistry in mice. DHE staining and ZO-1 immunofluorescence were applied to detect RPE oxidative stress and tight junctions. Our research revealed that increased mitochondria-localized TIN2 aggravated the cellular senescence of RPE cells both in vivo and in vitro under hyperglycemia. TIN2 overexpression stimulated the mTOR signaling pathway in ARPE-19 cells and exacerbated the inhibition of mitophagy levels under high glucose, which can be remedied through the mTOR inhibitor, rapamycin. Knockdown of TIN2 significantly reduced senescence and mitochondrial oxidative stress in ARPE-19 cells under high glucose and restored retinal thickness and RPE cell tight junctions in DR mice. Our study indicates that increased mitochondria-localized TIN2 induced cellular senescence in RPE via compromised mitophagy and activated mTOR signaling. These results propose that targeting TIN2 could potentially serve as a therapeutic strategy in the treatment of DR.

Multimodal cell atlas of the ageing human skeletal muscle.

Muscle atrophy and functional decline (sarcopenia) are common manifestations of frailty and are critical contributors to morbidity and mortality in older people1. Deciphering the molecular mechanisms underlying sarcopenia has major implications for understanding human ageing2. Yet, progress has been slow, partly due to the difficulties of characterizing skeletal muscle niche heterogeneity (whereby myofibres are the most abundant) and obtaining well-characterized human samples3,4. Here we generate a single-cell/single-nucleus transcriptomic and chromatin accessibility map of human limb skeletal muscles encompassing over 387,000 cells/nuclei from individuals aged 15 to 99 years with distinct fitness and frailty levels. We describe how cell populations change during ageing, including the emergence of new populations in older people, and the cell-specific and multicellular network features (at the transcriptomic and epigenetic levels) associated with these changes. On the basis of cross-comparison with genetic data, we also identify key elements of chromatin architecture that mark susceptibility to sarcopenia. Our study provides a basis for identifying targets in the skeletal muscle that are amenable to medical, pharmacological and lifestyle interventions in late life.

Consistent signatures in the human gut microbiome of old- and young-onset colorectal cancer.

The incidence of young-onset colorectal cancer (yCRC) has been increasing in recent decades, but little is known about the gut microbiome of these patients. Most studies have focused on old-onset CRC (oCRC), and it remains unclear whether CRC signatures derived from old patients are valid in young patients. To address this, we assembled the largest yCRC gut metagenomes to date from two independent cohorts and found that the CRC microbiome had limited association with age across adulthood. Differential analysis revealed that well-known CRC-associated taxa, such as Clostridium symbiosum, Peptostreptococcus stomatis, Parvimonas micra and Hungatella hathewayi were significantly enriched (false discovery rate <0.05) in both old- and young-onset patients. Similar strain-level patterns of Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were observed for oCRC and yCRC. Almost all oCRC-associated metagenomic pathways had directionally concordant changes in young patients. Importantly, CRC-associated virulence factors (fadA, bft) were enriched in both oCRC and yCRC compared to their respective controls. Moreover, the microbiome-based classification model had similar predication accuracy for CRC status in old- and young-onset patients, underscoring the consistency of microbial signatures across different age groups.

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution.

Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires in situ annotation and labeling of cells. Effective spatial visualization of these data demands appropriate colorization of numerous cell types. However, current colorization frameworks often inadequately account for the spatial relationships between cell types. This results in perceptual ambiguity in neighboring cells of biological distinct types, particularly in complex environments such as brain or tumor. To address this, we introduce Spaco, a potent tool for spatially aware colorization. Spaco utilizes the Degree of Interlacement metric to construct a weighted graph that evaluates the spatial relationships among different cell types, refining color assignments. Furthermore, Spaco incorporates an adaptive palette selection approach to amplify chromatic distinctions. When benchmarked on four diverse datasets, Spaco outperforms existing solutions, capturing complex spatial relationships and boosting visual clarity. Spaco ensures broad accessibility by accommodating color vision deficiency and offering open-accessible code in both Python and R.

2D Ferromagnetic M3 GeTe2 (M = Ni/Fe) for Boosting Intermediates Adsorption toward Faster Water Oxidation.

In this work, 2D ferromagnetic M3 GeTe2 (MGT, M = Ni/Fe) nanosheets with rich atomic Te vacancies (2D-MGTv ) are demonstrated as efficient OER electrocatalyst via a general mechanical exfoliation strategy. X-ray absorption spectra (XAS) and scanning transmission electron microscope (STEM) results validate the dominant presence of metal-O moieties and rich Te vacancies, respectively. The formed Te vacancies are active for the adsorption of OH* and O* species while the metal-O moieties promote the O* and OOH* adsorption, contributing synergistically to the faster oxygen evolution kinetics. Consequently, 2D-Ni3 GeTe2v exhibits superior OER activity with only 370 mV overpotential to reach the current density of 100 mA cm-2 and turnover frequency (TOF) value of 101.6 s-1 at the overpotential of 200 mV in alkaline media. Furthermore, a 2D-Ni3 GeTe2v -based anion-exchange membrane (AEM) water electrolysis cell (1 cm2 ) delivers a current density of 1.02 and 1.32 A cm-2 at the voltage of 3 V feeding with 0.1 and 1 m KOH solution, respectively. The demonstrated metal-O coordination with abundant atomic vacancies for ferromagnetic M3 GeTe2 and the easily extended preparation strategy would enlighten the rational design and fabrication of other ferromagnetic materials for wider electrocatalytic applications.

Spatially resolved single-cell atlas of ascidian endostyle provides insight into the origin of vertebrate pharyngeal organs.

The pharyngeal endoderm, an innovation of deuterostome ancestors, contributes to pharyngeal development by influencing the patterning and differentiation of pharyngeal structures in vertebrates; however, the evolutionary origin of the pharyngeal organs in vertebrates is largely unknown. The endostyle, a distinct pharyngeal organ exclusively present in basal chordates, represents a good model for understanding pharyngeal organ origins. Using Stereo-seq and single-cell RNA sequencing, we constructed aspatially resolved single-cell atlas for the endostyle of the ascidian Styela clava. We determined the cell composition of the hemolymphoid region, which illuminates a mixed ancestral structure for the blood and lymphoid system. In addition, we discovered a cluster of hair cell-like cells in zone 3, which has transcriptomic similarity with the hair cells of the vertebrate acoustico-lateralis system. These findings reshape our understanding of the pharynx of the basal chordate and provide insights into the evolutionary origin of multiplexed pharyngeal organs.

Two-stage adversarial learning based unsupervised domain adaptation for retinal OCT segmentation.

BACKGROUND: Deep learning based optical coherence tomography (OCT) segmentation methods have achieved excellent results, allowing quantitative analysis of large-scale data. However, OCT images are often acquired by different devices or under different imaging protocols, which leads to serious domain shift problem. This in turn results in performance degradation of segmentation models. PURPOSE: Aiming at the domain shift problem, we propose a two-stage adversarial learning based network (TSANet) that accomplishes unsupervised cross-domain OCT segmentation. METHODS: In the first stage, a Fourier transform based approach is adopted to reduce image style differences from the image level. Then, adversarial learning networks, including a segmenter and a discriminator, are designed to achieve inter-domain consistency in the segmentation output. In the second stage, pseudo labels of selected unlabeled target domain training data are used to fine-tune the segmenter, which further improves its generalization capability. The proposed method was tested on cross-domain datasets for choroid or retinoschisis segmentation tasks. For choroid segmentation, the model was trained on 400 images and validated on 100 images from the source domain, and then trained on 1320 unlabeled images and tested on 330 images from target domain I, and also trained on 400 unlabeled images and tested on 200 images from target domain II. For retinoschisis segmentation, the model was trained on 1284 images and validated on 312 images from the source domain, and then trained on 1024 unlabeled images and tested on 200 images from the target domain. RESULTS: The proposed method achieved significantly improved results over that without domain adaptation, with improvement of 8.34%, 55.82% and 3.53% in intersection over union (IoU) respectively for the three test sets. The performance is better than some state-of-the-art domain adaptation methods. CONCLUSIONS: The proposed TSANet, with image level adaptation, feature level adaptation and pseudo-label based fine-tuning, achieved excellent cross-domain generalization. This alleviates the burden of obtaining additional manual labels when adapting the deep learning model to new OCT data.

COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection.

Existing approaches towards anomaly detection (AD) often rely on a substantial amount of anomaly-free data to train representation and density models. However, large anomaly-free datasets may not always be available before the inference stage; in which case an anomaly detection model must be trained with only a handful of normal samples, a.k.a. few-shot anomaly detection (FSAD). In this paper, we propose a novel methodology to address the challenge of FSAD which incorporates two important techniques. Firstly, we employ a model pre-trained on a large source dataset to initialize model weights. Secondly, to ameliorate the covariate shift between source and target domains, we adopt contrastive training to fine-tune on the few-shot target domain data. To learn suitable representations for the downstream AD task, we additionally incorporate cross-instance positive pairs to encourage a tight cluster of the normal samples, and negative pairs for better separation between normal and synthesized negative samples. We evaluate few-shot anomaly detection on 3 controlled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness of the proposed method.

SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics.

The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for subsequent personalized analysis. The execution time for the entire analysis is ∼148 min with 1 GB reads 1 × 1 cm chip test data, 1.8 times faster than with an unoptimized workflow.

BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis.

As genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results. Availability & Implementation: The source code of the BatchEval Pipeline is available at https://github.com/STOmics/BatchEval.

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images.

In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets, outperforming other methods. The improved capability of capturing single-cell gene expression profiles results in a deeper understanding of the contribution of single-cell phenotypes to tissue biology. Availability & Implementation: The source code of STCellbin is available at https://github.com/STOmics/STCellbin.

Refined image quality assessment for color fundus photography based on deep learning.

Purpose: Color fundus photography is widely used in clinical and screening settings for eye diseases. Poor image quality greatly affects the reliability of further evaluation and diagnosis. In this study, we developed an automated assessment module for color fundus photography image quality assessment using deep learning. Methods: A total of 55,931 color fundus photography images from multiple centers in Shanghai and the public database were collected and annotated as training, validation, and testing data sets. The pre-diagnosis image quality assessment module based on the multi-task deep neural network was designed. The detailed criterion of color fundus photography image quality including three subcategories with three levels of grading was applied to improve precision and objectivity. The auxiliary tasks such as the localization of the optic nerve head and macula, the classification of laterality, and the field of view were also included to assist the quality assessment. Finally, we validated our module internally and externally by evaluating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and quadratic weighted Kappa. Results: The "Location" subcategory achieved area under the receiver operating characteristic curves of 0.991, 0.920, and 0.946 for the three grades, respectively. The "Clarity" subcategory achieved area under the receiver operating characteristic curves of 0.980, 0.917, and 0.954 for the three grades, respectively. The "Artifact" subcategory achieved area under the receiver operating characteristic curves of 0.976, 0.952, and 0.986 for the three grades, respectively. The accuracy and Kappa of overall quality reach 88.15% and 89.70%, respectively, on the internal set. These two indicators on the external set were 86.63% and 88.55%, respectively, which were very close to that of the internal set. Conclusions: This work showed that our deep module was able to evaluate the color fundus photography image quality using more detailed three subcategories with three grade criteria. The promising results on both internal and external validation indicated the strength and generalizability of our module.

EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics.

BACKGROUND: The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality. FINDINGS: We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb. CONCLUSIONS: Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.

Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder.

BACKGROUND: Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance. RESULTS: To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy. CONCLUSIONS: Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.

Prevalence of Gastrointestinal Parasites in Zoo Animals and Phylogenetic Characterization of Toxascaris leonina (Linstow, 1902) and Baylisascaris transfuga (Rudolphi, 1819) in Jiangsu Province, Eastern China.

The burden of gastrointestinal parasites in zoo animals has serious implications for their welfare and the health of veterinarians and visitors. Zhuyuwan Zoo is located in the eastern suburb of Yangzhou city in eastern China, in which over 40 species of zoo animals are kept. In order to understand the infection status of GI parasites in Zhuyuwan Zoo, a total of 104 fresh fecal samples collected randomly from birds (n = 19), primates (n = 19), and non-primate mammals (n = 66) were analyzed using the saturated saline flotation technique and nylon sifter elutriation and sieving method for eggs/oocysts, respectively. Two Ascaris species were molecularly characterized. The results showed that the overall prevalence of parasitic infection was 42.3% (44/104). The parasitic infection rate in birds, primates, and non-primate mammals were 26.3% (5/19), 31.6% (6/19), and 50.0% (33/66), respectively. A total of 11 species of parasites were identified, namely, Trichostrongylidae, Capillaria sp., Trichuris spp., Strongyloides spp., Amidostomum sp., Toxascaris leonina, Baylisascaris transfuga, Parascaris equorum, Paramphistomum spp., Fasciola spp., and Eimeria spp. Paramphistomum spp. eggs were first detected from the captive Père David's deer, and Fasciola spp. eggs were first reported from sika deer in zoo in China. A sequence analysis of ITS-2 and cox1 showed that the eggs isolated from the African lion (Panthera leo Linnaeus, 1758) were T. leonina, and the eggs from the brown bear (Ursus arctos Linnaeus, 1758) were B. transfuga. The public health threat posed by these potential zoonotic parasitic agents requires attention. These results lay a theoretical foundation for prevention and control of wild animal parasitic diseases at zoos in China.