The CD163/TWEAK/Fn14 axis: A potential therapeutic target for alleviating inflammatory bone loss.

Objective: Osteoclast (OC) over-activation is an important cause of bone loss that is strongly correlated with inflammation. Although the CD163/TWEAK/Fn14 axis has been implicated in several inflammatory pathologies, its contributions to inflammatory bone loss remain poorly understood. This study aimed to evaluate the interaction of the CD163/TWEAK/Fn14 axis with OC in inflammatory bone loss. Methods: To assess the role of CD163 in bone homeostasis, we characterized the bone phenotypes of CD163-deficient mice and their wild-type littermates. CD163 and TWEAK levels were evaluated in the bone marrow of mice with LPS-induced bone loss and individuals with rheumatoid arthritis (RA). Bone mass changes were assessed using uCT and histology following supplementation with recombinant mouse CD163 protein (rCD163) or blockade of TWEAK/Fn14 signaling in CD163-deficient mice and mice with LPS-induced bone loss. The impact of CD163/TWEAK on OC differentiation and bone resorption capacity was analyzed in vitro. Results: CD163 deficiency caused decreased bone mass and increased OC abundance. Lower CD163 expression and higher TWEAK expression were observed in the bone marrow of mice with LPS-induced bone loss and individuals with RA. TWEAK, mainly derived from CD68+ macrophages, was responsible for bone loss, and supplementing rCD163 or blocking TWEAK/Fn14 signaling contributed to rescue bone loss. TWEAK/Fn14 synergistically promoted RANKL-dependent OC differentiation and bone resorption capability through downstream mitogen-activated protein kinases (MAPK) signaling, while the pro-osteoclastic effect of TWEAK was suppressed by CD163. Conclusion: Our findings suggest that the CD163/TWEAK/Fn14 axis is a potential therapeutic target for inflammatory bone loss by regulating osteoclastogenesis.

Application of smart-responsive hydrogels in nucleic acid and nucleic acid-based target sensing: A review.

In recent years, nucleic acid-related sensing and detection have become essential in clinical diagnostics, treatment and genotyping, especially in connection with the Human Genome Project and the COVID-19 pandemic. Many traditional nucleic acid-related sensing strategies have been employed in analytical chemistry, including fluorescence, colorimetric and chemiluminescence methods. However, their key limitation is the lack of understanding of the interaction during analysis, particularly at the 3D matrix level close to biological tissue. To address this issue, smart-responsive hydrogels are increasingly used in biosensing due to their hydrophilic and biocompatible properties. By combining smart-responsive hydrogels with traditional nucleic acid-related sensing, biological microenvironments can be mimicked, and targets can be easily accessed and diffused, making them ideal for nucleic acid sensing. This review focuses on utilizing smart-responsive hydrogels for nucleic acid-related sensing and detection, including nucleic acid detection, other nucleic acid-based analyte detection and nucleic acid-related sensing platforms applying nucleic acid as sensing tools in hydrogels. Additionally, the analytical mechanisms of smart-responsive hydrogels with the combination of various detection platforms such as optical and electrochemical techniques are described. The limitations of using smart-responsive hydrogels in nucleic acid-related sensing and proposed possible solutions are also discussed. Lastly, the future challenge of smart-responsive hydrogels in nucleic acid-related sensing is explored. Smart-responsive hydrogels can be used as biomimetic materials to simulate the extracellular matrix, achieve biosensing, and exhibit great potential in nucleic acid-related sensing. They serve as a valuable complement to traditional detection and analytical methods.

The plausibility machine commonsense (PMC) dataset: A massively crowdsourced human-annotated dataset for studying plausibility in large language models.

Commonsense reasoning has emerged as a challenging problem in Artificial Intelligence (AI). However, one area of commonsense reasoning that has not received nearly as much attention in the AI research community is plausibility assessment, which focuses on determining the likelihood of commonsense statements. Human-annotated benchmarks are essential for advancing research in this nascent area, as they enable researchers to develop and evaluate AI models effectively. Because plausibility is a subjective concept, it is important to obtain nuanced annotations, rather than a binary label of 'plausible' or 'implausible'. Furthermore, it is also important to obtain multiple human annotations for a given statement, to ensure validity of the labels. In this data article, we describe the process of re-annotating an existing commonsense plausibility dataset (SemEval-2020 Task 4) using large-scale crowdsourcing on the Amazon Mechanical Turk platform. We obtain 10,000 unique annotations on a corpus of 2000 sentences (five independent annotations per sentence). Based on these labels, each was labelled as plausible, implausible, or ambiguous. Next, we prompted the GPT-3.5 and GPT-4 models developed by OpenAI. Sentences from the human-annotated files were fed into the models using custom prompt templates, and the models' generated labels were used to determine if they were aligned with those output by humans. The PMC-Dataset is meant to serve as a rich resource for analysing and comparing human and machine commonsense reasoning capabilities, specifically on plausibility. Researchers can utilise this dataset to train, fine-tune, and evaluate AI models on plausibility. Applications include: determining the likelihood of everyday events, assessing the realism of hypothetical scenarios, and distinguishing between plausible and implausible statements in commonsense text. Ultimately, we intend for the dataset to support ongoing AI research by offering a robust foundation for developing models that are better aligned with human commonsense reasoning.

Typical Morphological Characteristics of the Immunohistochemistrical Subtypes of Pituitary Microadenomas: A dual center study.

PURPOSE: This study aimed to investigate the relation of magnetic resonance image (MRI) features and immunohistochemistrical subtypes of pituitary microadenomas (PMAs) characterized by location and growth pattern. MATERIALS AND METHODS: A double-center, retrospective review of MRI characteristics was conducted in 57 PMA cases recorded from February 2014 to September 2023 and identified on the basis of 2017 WHO classification of pituitary gland tumors. The geometric center of the tumor was defined, and the possibility of PMA vertical or lateral growth pattern was evaluated according to ratio of maximum diameter between the X and Y axes. RESULTS: Among the PMAs, somatotroph adenomas (STAs) significantly frequented the lateral-anteroinferior portion of pituitary gland (P=0.036). Lactotroph adenomas (LTAs) showed significant locational preference for the lateral-posteroinferior portion (P=0.037), and gonadotroph adenomas (GTAs) were predominately located in the central-anteroinferior portion (P=0.022). Furthermore, the PMAs in the suprasellar portion exhibited vertical extension with statistical significance (P=0.0). CONCLUSION: In our cohort, the micro-STAs were predominately located in the lateral-anteroinferior portion of pituitary gland, the micro-LTAs in the lateral-posteroinferior portion, and the micro-GTAs in the central-anteroinferior portion. The growth pattern of the PMAs was highly correlated with their vertical position instead of their immunohistochemistrical subtypes. Therefore, MRI shows potential in differentiating partial PMA subgroups, especially the cases in silent groups.

CBD2: A functional biomarker database for colorectal cancer.

The rapidly evolving landscape of biomarkers for colorectal cancer (CRC) necessitates an integrative, updated repository. In response, we constructed the Colorectal Cancer Biomarker Database (CBD), which collected and displayed the curated biomedicine information for 870 CRC biomarkers in the previous study. Building on CBD, we have now developed CBD2, which includes information on 1569 newly reported biomarkers derived from different biological sources (DNA, RNA, protein, and others) and clinical applications (diagnosis, treatment, and prognosis). CBD2 also incorporates information on nonbiomarkers that have been identified as unsuitable for use as biomarkers in CRC. A key new feature of CBD2 is its network analysis function, by which users can investigate the visible and topological network between biomarkers and identify their relevant pathways. CBD2 also allows users to query a series of chemicals, drug combinations, or multiple targets, to enable multidrug, multitarget, multipathway analyses, toward facilitating the design of polypharmacological treatments for CRC. CBD2 is freely available at http://www.eyeseeworld.com/cbd.

Immunosuppressive tumor microenvironment and immunotherapy of hepatocellular carcinoma: current status and prospectives.

Hepatocellular carcinoma (HCC) is a major health concern worldwide, with limited therapeutic options and poor prognosis. In recent years, immunotherapies such as immune checkpoint inhibitors (ICIs) have made great progress in the systemic treatment of HCC. The combination treatments based on ICIs have been the major trend in this area. Recently, dual immune checkpoint blockade with durvalumab plus tremelimumab has also emerged as an effective treatment for advanced HCC. However, the majority of HCC patients obtain limited benefits. Understanding the immunological rationale and exploring novel ways to improve the efficacy of immunotherapy has drawn much attention. In this review, we summarize the latest progress in this area, the ongoing clinical trials of immune-based combination therapies, as well as novel immunotherapy strategies such as chimeric antigen receptor T cells, personalized neoantigen vaccines, oncolytic viruses, and bispecific antibodies.

A noise audit of human-labeled benchmarks for machine commonsense reasoning.

With the advent of large language models, evaluating and benchmarking these systems on important AI problems has taken on newfound importance. Such benchmarking typically involves comparing the predictions of a system against human labels (or a single 'ground-truth'). However, much recent work in psychology has suggested that most tasks involving significant human judgment can have non-trivial degrees of noise. In his book, Kahneman suggests that noise may be a much more significant component of inaccuracy compared to bias, which has been studied more extensively in the AI community. This article proposes a detailed noise audit of human-labeled benchmarks in machine commonsense reasoning, an important current area of AI research. We conduct noise audits under two important experimental conditions: one in a smaller-scale but higher-quality labeling setting, and another in a larger-scale, more realistic online crowdsourced setting. Using Kahneman's framework of noise, our results consistently show non-trivial amounts of level, pattern, and system noise, even in the higher-quality setting, with comparable results in the crowdsourced setting. We find that noise can significantly influence the performance estimates that we obtain of commonsense reasoning systems, even if the 'system' is a human; in some cases, by almost 10 percent. Labeling noise also affects performance estimates of systems like ChatGPT by more than 4 percent. Our results suggest that the default practice in the AI community of assuming and using a 'single' ground-truth, even on problems requiring seemingly straightforward human judgment, may warrant empirical and methodological re-visiting.

Association between baseline hemoglobin level and early neurological deterioration after intravenous thrombolysis in patients with acute ischemic stroke. / åŸºçº¿è¡€çº¢è›‹ç™½æ°´å¹³å¯¹æ€¥æ€§ç¼ºè¡€æ€§è„‘å’ä¸­æ‚£è€ é™è„‰æº¶æ “åŽæ—©æœŸç¥žç»åŠŸèƒ½æ¶åŒ–çš„å½±å“.

OBJECTIVES: To investigate the association between baseline hemoglobin level and early neurologic deterioration (END) after intravenous thrombolysis in patients with acute ischemic stroke (AIS). METHODS: Data of AIS patients who received intravenous thrombolytic therapy at multiple hospitals across the country between January 2017 and July 2020 were collected from the online database Acute Stroke Patients for Stroke Management Quality Evaluation (CASE-Ⅱ, NCT04487340). Binary logistic regression analysis was used to study the factors affecting the occurrence of END after intravenous thrombolytic therapy, and the correlation between baseline hemoglobin level and END was investigated by limiting cubic spline curve analysis. RESULTS: A total of 8162 patients were included. Patients with END had lower baseline hemoglobin levels (136 and 140 g/L, P<0.01) and higher rates of anemia (24.2% and 16.9%, P<0.01) compared with non-END patients. Binary logistic regression analysis showed that baseline hemoglobin level (OR=0.995, 95%CI: 0.991-0.999, P<0.05) and anemia (OR=1.238, 95%CI: 1.055-1.454, P<0.01) were independently correlated with the occurrence of END after intravenous thrombolysis in AIS patients. Restricted cubic spline regression showed that there was a U-shaped relationship between hemoglobin level and the risk of END after intravenous thrombolysis in AIS patients (P<0.01), although this relationship was only significant in male patients (P<0.05) and not in female patients (P>0.05). CONCLUSIONS: There is a correlation between baseline hemoglobin level and the risk of END in AIS patients after intravenous thrombolysis, especially in male patients, in whom both lower and higher hemoglobin level may increase the risk of END.

Unlatching the Additional Zinc Storage Ability of Vanadium Nitride Nanocrystallites.

Vanadium-based materials, due to their diverse valence states and open-framework lattice, are promising cathodes for aqueous zinc ion batteries (AZIBs), but encounters the major challenges of in situ electrochemical activation process, potent polarity of the aqueous electrolyte and periodic expansion/contraction for efficient Zn2+ storage. Herein, architecting vanadium nitride (VN) nanosheets over titanium-based hollow nanoarrays skeletal host (denoted VNTONC) can simultaneously modulate address those challenges by creating multiple interfaces and maintaining the (1 1 1) phase of VN, which optimizes the Zn2+ storage and the stability of VN. Benefiting from the modulated crystalline thermodynamics during the electrochemical activation of VN, two outcomes are achieved; I) the cathode transforms into a nanocrystalline structure with increased active sites and higher conductivity and; II) a significant portion of the (1 1 1) crystal facets is retained in the process leading to the additional Zn2+ storage capacity. As a result, the as-prepared VNTONC electrode demonstrates remarkable discharge capacities of 802.5 and 331.8 mAh g-1 @ 0.5 and 6.0 A g-1, respectively, due to the enhanced kinetics as validated by theoretical calculations. The assembled VNTONC||Zn flexible ZIB demonstrates excellent Zn storage properties up to 405.6 mAh g-1, and remarkable robustness against extreme operating conditions.

Uptake of ox-LDL by binding to LRP6 mediates oxidative stress-induced BMSCs senescence promoting obesity-related bone loss.

Obesity has long been thought to be a main cause of hyperlipidemia. As a systemic disease, the impact of obesity on organs, tissues and cells is almost entirely negative. However, the relationship between obesity and bone loss is highly controversial. On the one hand, obesity has long been thought to have a positive effect on bone due to increased mechanical loading on the skeleton, conducive to increasing bone mass to accommodate the extra weight. On the other hand, obesity-related metabolic oxidative modification of low-density lipoprotein (LDL) in vivo causes a gradual increase of oxidized LDL (ox-LDL) in the bone marrow microenvironment. We have reported that low-density lipoprotein receptor-related protein 6 (LRP6) acts as a receptor of ox-LDL and mediates the bone marrow stromal cells (BMSCs) uptake of ox-LDL. We detected elevated serum ox-LDL in obese mice. We found that ox-LDL uptake by LRP6 led to an increase of intracellular reactive oxygen species (ROS) in BMSCs, and N-acetyl-L-cysteine (NAC) alleviated the cellular senescence and impairment of osteogenesis induced by ox-LDL. Moreover, LRP6 is a co-receptor of Wnt signaling. We found that LRP6 preferentially binds to ox-LDL rather than dickkopf-related protein 1 (DKK1), both inhibiting Wnt signaling and promoting BMSCs senescence. Mesoderm development LRP chaperone (MESD) overexpression inhibits ox-LDL binding to LRP6, attenuating oxidative stress and BMSCs senescence, eventually rescuing bone phenotype.

Melatonin, a potentially effective drug for the treatment of infected bone nonunion.

Osteomyelitis (OM), characterized by heterogeneity and complexity in treatment, has a high risk of infection recurrence which may cause limb disability. Management of chronic inactive osteomyelitis (CIOM) without typical inflammatory symptoms is a great challenge for orthopedic surgeons. On the basis of data analysis of 1091 OM cases, we reported that latent osteogenic decline in CIOM patients was the main cause of secondary surgery. Our research shows that impairment of osteoblasts capacity in CIOM patients is associated with ferroptosis of osteoblasts caused by internalization of Staphylococcus aureus. Further studies show that melatonin could alleviate ferroptosis of osteoblasts in infected states through Nox4/ROS/P38 axis and protect the osteogenic ability of CIOM patients. Knockout of NADPH oxidase 4 (Nox4) in vivo could effectively relieve ferroptosis of osteoblasts in the state of infection and promote osteogenesis. Through a large number of clinical data analyses combined with molecular experiments, this study clarified that occult osteogenic disorders in CIOM patients were related to ferroptosis of osteoblasts. We revealed that melatonin might be a potential therapeutic drug for CIOM patients and provided a new insight for the treatment of OM.

Ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry combined with network pharmacology for analysis of potential quality markers of three processed products of Qingpi.

Qingpi, a well-known traditional Chinese medicine for qi-regulating and commonly processed into three types of pieces, has been widely used in the clinical application of liver disease for thousands of years. In this study, an ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry approach along with multivariate statistical analysis was developed to assess and characterize the differentiations of three processed products and confirm the potential quality markers of Qingpi. In addition, a systematic analysis combined with network pharmacology and molecular docking was performed to clarify the potential mechanism of Qingpi for the treatment of liver disease. As a result, 18 components were identified and an integrated network of Qingpi-Components-Target-Pathway-Liver Disease was constructed. Eight compounds were finally screened out as the potential quality markers acting on ten main targets and pathways of liver disease. Molecular docking analysis results indicated that the quality markers had a good binding activity with the targets. Overall, this work preliminarily identified the potential quality markers of three processed products of Qingpi, and predicted its targets in the prevention and treatment of liver disease, which can provide supporting information for further study of the pharmacodynamic substances and mechanisms of Qingpi.

In vitro characterization of alternative l-threonate and d-erythronate catabolic pathways.

l-threonate is the metabolite of vitamin C, while d-erythronate is the metabolite of N-acetyl-d-glucosamine, the nutritional supplement for joint health. They are widely distributed in the environment and human biofluids. Nevertheless, the catabolisms of l-threonate and d-erythronate are sparsely reported. Here we explored the functional diversity of an acid sugar kinase family (Pfam families PF07005-PF17042), and discovered a novel 2-oxo-tetronate kinase. The conserved genome neighborhood of the 2-oxo-tetronate kinase encodes members of class-II fructose-bisphosphate aldolase family (F_bP_aldolase, PF01116) and a dehydrogenase family (PF03446-PF14833). Instructed by this analysis, we experimentally verified that these enzymes are capable of degrading l-threonate into dihydroxyacetone phosphate (DHAP) in Arthrobacter sp. ZBG10, Clostridium scindens ATCC 35704, and Pseudonocardia dioxanivorans ATCC 55486. Meanwhile, a convergent catabolic pathway for d-erythronate was characterized in P. dioxanivorans ATCC 55486. Moreover, the phylogenetic distribution analysis indicates that the biological range of the identified l-threonate and d-erythronate catabolic pathways appears to extend mostly to members of the Actinomycetota, Cyanobacteriota, Bacillota, Pseudomonadota, and Bacteroidota phyla.

Optimal weak measurement scheme for chiral molecular detection based on photonic spin Hall effect.

In this paper, we propose a precision method to measure the chiroptical signal of Artemisinin solutions using the photonic spin Hall effect (PSHE) on the Ce:YIG-YIG-SiO2 structure as a probe. The effects of transmission distance, incident angles, applied magnetic fields of different directions, and beam waist of light on the weak measurement system are analytically investigated through simulations. It is found that decreasing the beam waist of the incident spot, increasing the incident angle, increasing the transmission distance, and adding a longitudinal magnetic field is conducive to enhancing the amplification transverse shift of PSHE, thus the measurement sensitivity is greatly improved. Based on the optimal weak measurement scheme, the detection limit for concentration measurement of artemisinin based on optical rotatory (OR) was reduced to 0.05 mg/ml. The measurement precision of the OR angle has been improved to 10-7rad.

Quantifying confidence shifts in a BERT-based question answering system evaluated on perturbed instances.

Recent work on transformer-based neural networks has led to impressive advances on multiple-choice natural language processing (NLP) problems, such as Question Answering (QA) and abductive reasoning. Despite these advances, there is limited work still on systematically evaluating such models in ambiguous situations where (for example) no correct answer exists for a given prompt among the provided set of choices. Such ambiguous situations are not infrequent in real world applications. We design and conduct an experimental study of this phenomenon using three probes that aim to 'confuse' the model by perturbing QA instances in a consistent and well-defined manner. Using a detailed set of results based on an established transformer-based multiple-choice QA system on two established benchmark datasets, we show that the model's confidence in its results is very different from that of an expected model that is 'agnostic' to all choices that are incorrect. Our results suggest that high performance on idealized QA instances should not be used to infer or extrapolate similarly high performance on more ambiguous instances. Auxiliary results suggest that the model may not be able to distinguish between these two situations with sufficient certainty. Stronger testing protocols and benchmarking may hence be necessary before such models are deployed in front-facing systems or ambiguous decision making with significant human impact.

Case Report: A novel de novo variant of COL1A1 in fetal genetic osteogenesis imperfecta.

Objective: Osteogenesis imperfecta (OI) is a rare genetic disorder. Clinical severity is heterogeneous. The purpose of this study was to investigate the genetic characteristics of a fetus with OI by whole exome sequencing (WES) and identify the cause of the disease. Methods: In this study, a fetus with osteogenic dysplasia was referred to our hospital. DNA was extracted from the aborted fetal tissue and peripheral blood of the parents. To identify the pathogenic genes, we conducted the trio-WES using DNA. A de novo variant in the COL1A1 gene is suspected to be the cause of the OI phenotype. We used Sanger sequencing for validation and various bioinformatics methods (such as SIFT, PolyPhen2, Mutation Taster, conservative analysis, SWISS Model, glycosylation site prediction, and I-Mutant 2.0) for analysis. Results: Both WES and Sanger sequencing identified a novel de novo variant of COL1A1 (c. 1309G>A, p. Gly437Ser) in a fetus with OI. Bioinformatic analysis showed that the affected residue, p. Gly437, was highly conserved in multiple species and predicted that the variant was deleterious and may have an impact on protein function. This variant is present in highly conserved glycine residues of Gly-X-Y sequence repeats of the triple helical region of the collagen type I α chain, which may be the cause of OI. Conclusion: This study revealed that the c.1309G>A (p. Gly437Ser) variant in the COL1A1 gene may be the genetic cause of fetal OI in this case. The discovery of this variant enriched the variation spectrum of OI. WES improves the accurate diagnosis of fetal OI, and doctors can provide patients with appropriate genetic counseling.

Bone-derived PDGF-BB drives brain vascular calcification in male mice.

Brain vascular calcification is a prevalent age-related condition often accompanying neurodegenerative and neuroinflammatory diseases. The pathogenesis of large-vessel calcifications in peripheral tissue is well studied, but microvascular calcification in the brain remains poorly understood. Here, we report that elevated platelet-derived growth factor BB (PDGF-BB) from bone preosteoclasts contributed to cerebrovascular calcification in male mice. Aged male mice had higher serum PDGF-BB levels and a higher incidence of brain calcification compared with young mice, mainly in the thalamus. Transgenic mice with preosteoclast-specific Pdgfb overexpression exhibited elevated serum PDGF-BB levels and recapitulated age-associated thalamic calcification. Conversely, mice with preosteoclast-specific Pdgfb deletion displayed diminished age-associated thalamic calcification. In an ex vivo cerebral microvascular culture system, PDGF-BB dose-dependently promoted vascular calcification. Analysis of osteogenic gene array and single-cell RNA-Seq (scRNA-Seq) revealed that PDGF-BB upregulated multiple osteogenic differentiation genes and the phosphate transporter Slc20a1 in cerebral microvessels. Mechanistically, PDGF-BB stimulated the phosphorylation of its receptor PDGFRß (p-PDGFRß) and ERK (p-ERK), leading to the activation of RUNX2. This activation, in turn, induced the transcription of osteoblast differentiation genes in PCs and upregulated Slc20a1 in astrocytes. Thus, bone-derived PDGF-BB induced brain vascular calcification by activating the p-PDGFRß/p-ERK/RUNX2 signaling cascade in cerebrovascular cells.

TG-CSR: A human-labeled dataset grounded in nine formal commonsense categories.

Machine Common Sense Reasoning is the subfield of Artificial Intelligence that aims to enable machines to behave or make decisions similarly to humans in everyday and ordinary situations. To measure progress, benchmarks in the form of question-answering datasets have been developed and published in the community to evaluate machine commonsense models, including large language models. We describe the individual label data produced by six human annotators originally used in computing ground truth for the Theoretically-Grounded Commonsense Reasoning (TG-CSR) benchmark's composing datasets. According to a set of instructions, annotators were provided with spreadsheets containing the original TG-CSR prompts and asked to insert labels in specific spreadsheet cells during annotation sessions. TG-CSR data is organized in JSON files, individual raw label data in a spreadsheet file, and individual normalized label data in JSONL files. The release of individual labels can enable the analysis of the labeling process itself, including studies of noise and consistency across annotators.

High-fat diet promotes tumor growth in the patient-derived orthotopic xenograft (PDOX) mouse model of ER positive endometrial cancer.

Endometrial cancer, one of the common gynecological malignancies, is affected by several influencing factors. This study established a unique patient-derived orthotopic xenograft (PDOX) nude mouse model for the study of influencing factors in ER positive endometrial cancer. The aim of this study was to demonstrate that a high-fat diet can affect the growth of ER positive endometrial cancer PDOX model tumors. The tumor tissues were expanded by subcutaneous transplantation in nude mice, and then the subcutaneous tumor tissues were orthotopically implanted into the nude mouse uterus to establish the PDOX model. After modeling, they were divided into high-fat diet group and normal diet group for 8 weeks of feeding, which showed that high-fat diet significantly promoted tumor growth (P < 0.001) and increased the protein expression level of ERα in tumor tissues. This study demonstrates that PDOX models of endometrial cancer can embody the role of dietary influences on tumor growth and that this model has the potential for preclinical studies of cancer promoting factors.