Phenome-wide association study in 25,639 pregnant Chinese women reveals loci associated with maternal comorbidities and child health.

Phenome-wide association studies (PheWAS) have been less focused on maternal diseases and maternal-newborn comorbidities, especially in the Chinese population. To enhance our understanding of the genetic basis of these related diseases, we conducted a PheWAS on 25,639 pregnant women and 14,151 newborns in the Chinese Han population using ultra-low-coverage whole-genome sequence (ulcWGS). We identified 2,883 maternal trait-associated SNPs associated with 26 phenotypes, among which 99.5% were near established genome-wide association study (GWAS) loci. Further refinement delineated these SNPs to 442 unique trait-associated loci (TALs) predicated on linkage disequilibrium R2 > 0.8, revealing that 75.6% demonstrated pleiotropy and 50.9% were located in genes implicated in analogous phenotypes. Notably, we discovered 21 maternal SNPs associated with 35 neonatal phenotypes, including two SNPs associated with identical complications in both mothers and children. These findings underscore the importance of integrating ulcWGS data to enrich the discoveries derived from traditional PheWAS approaches.

Regional Analysis of the Immune Microenvironment in Human Endometrium.

PROBLEM: Endometrial immune cells are essential for maintaining homeostasis and the endometrial receptivity to embryo implantation. Understanding regional variations in endometrial immune cell populations is crucial for comprehending normal endometrial function and the pathophysiology of endometrial disorders. Despite previous studies focusing on the overall immune cell composition and function in the endometrium, regional variations in premenopausal women remain unclear. METHOD OF STUDY: Endometrial biopsies were obtained from four regions (anterior, posterior, left lateral, and right lateral) of premenopausal women undergoing hysteroscopy with no abnormalities. A 15-color human endometrial immune cell-focused flow cytometry panel was used for analysis. High-dimensional flow cytometry combined with a clustering algorithm was employed to unravel the complexity of endometrial immune cells. Additionally, multiplex immunofluorescent was performed for further validation. RESULTS: Our findings revealed no significant variation in the distribution and abundance of immune cells across different regions under normal conditions during the proliferative phase. Each region harbored similar immune cell subtypes, indicating a consistent immune microenvironment. However, when comparing normal regions to areas with focal hemorrhage, significant differences were observed. An increase in CD8+ T cells highlights the impact of localized abnormalities on the immune microenvironment. CONCLUSIONS: Our study demonstrates that the endometrial immune cell landscape is consistent across different anatomical regions during the proliferative phase in premenopausal women. This finding has important implications for understanding normal endometrial function and the pathophysiology of endometrial disorders.

Electrocatalytic Aromatic Alcohols Splitting to Aldehydes and H2 Gas.

Selective electrocatalytic transformation of alcohols to aldehydes offers an efficient and environmentally friendly platform for the simultaneous production of fine chemicals and pure hydrogen gas. However, traditional alcohol oxidation reactions (AORs) in aqueous electrolyte unavoidably face competitive reactions (e.g., water oxidation and overoxidations reactions) for the presence of active oxygen species from water oxidation, causing an unwanted decrease in final efficiency and selectivity. Here, we developed an integrated all-solid proton generator-transfer electrolyzer to trigger the pure alcohol splitting reaction (ASR). In this splitting process, only O-H and C-H bonds can be cleaved at the proton generator (Pt nanoparticles), thereby completely avoiding all competitive reactions involving oxygen active species to give a > 99% selectivity to aldehydes. The as-generated protons are transported to the cathode by a three-dimensional (3D) conducting network (assemblies of ionomers and carbon spheres) for efficient hydrogen production. Unlike the poor selectivity (<22%) and durability (<3 h) of a conventional AOR electrolyzer, this ASR electrolyzer could be continuously operated at a low cell voltage of 1.2 V for at least 10 days to give a high Faradaic efficiency of 80-93% for aldehyde production.

Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma?

BACKGROUND AND PURPOSE: Computed tomography (CT) and biopsy may be insufficient for preoperative evaluation of the grade and outcome of patients with chondrosarcoma. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting histologic grade and prognosis in chondrosarcoma (CS). METHODS: A multicenter 211 (training cohort/ test cohort, 127/84) CS patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting the grade. Kaplan-Meier survival analysis was used to assess the association of the model-predicted grade with recurrence-free survival (RFS). Model performance was evaluated with the area under the receiver operating characteristic curve (AUC) and the Harrell's concordance index (C-index). RESULTS: The DLRM (AUC, 0.879; 95 % confidence interval [CI], 0.802-0.956) outperformed (z = 2.773, P=0.006) the RS (AUC, 0.715;95 % CI, 0.606-0.825) in predicting grade in the test cohort. RFS showed significant differences (log-rank test, P<0.05) between low-grade and high-grade patients stratified by DLRM. The DLRM achieved a higher C-index (0.805; 95 % CI, 0.694-0.916) than the RS (0.692, 95 % CI, 0.540-0.844) did in predicting RFS for CS patients in the test cohort. CONCLUSION: The DLRM can accurately predict the histologic grade and prognosis in CS.

Physicochemical properties and in vitro release of formononetin nano-particles by ultrasonic probe-assisted precipitation in four polar organic solvents.

Although formononetin has a considerable biological activity, its therapeutic use is limited by its low solubility. Formononetin was dissolved in ethanol, methanol, N, N-dimethylformamide (DMF), and dimethyl sulfoxide (DMSO) in this investigation, the antisolvent precipitation procedure with the assistance of an external ultrasonic probe was used to manufacture the formononetin nano-particles. The ideal parameters for response surface BBD optimization are as follows: feed volume flow rate of 6 mL/min; ultrasonic power of 860 W; and liquid-liquid ratio of 1:12.5. The formononetin nano-particles have a smaller particle diameter than raw sample; the lowest size can be as small as (329 ± 1.99) nm, which is 45 times smaller than raw. An in vitro digestion test using a solution that simulated intestinal solution revealed that the release rate of the nano-particle was 1.75 times than that of the raw formononetin. The formononetin nano-particles generated by the aforementioned four solvents have the following order of diameter: ethanol > methanol > DMF > DMSO. This study provided a technical reference for the functional food components in deep processing.

Aminophenylboronic acid-modified nitrogen-doped graphene quantum dots and their applications in lysine sensing based on interplaying fluorescent mechanisms.

Using nitrogen-doped graphene quantum dots (N-GQDs) and 3-aminophenylboronic acid (APBA), a novel fluorescence nanosensor was developed. This nanosensor exhibits high selectivity and sensitivity for lysine detection. Its sensing mechanism involves the suppression of electron transfer from APBA to the N-GQDs unit, thereby inhibiting photoinduced electron transfer and initiating internal charge transfer. At an optimal pH of 7, the protonated α-amine and ε-amine groups of lysine interact with the amide and boronic acid moieties, respectively. This interaction results in a redshift of fluorescence, substantially enhancing the response signal. A linear response was observed within a concentration range 0.40-3.01 µM, with the detection limit being 0.005 µM. A similar linear range was also achieved for the determination of lysine in human serum. Density functional theory calculations correlating molecular orbits and geometries support UV-vis and fluorescence findings. Additionally, the nanosensor was successfully applied to detect lysine in living cells and real samples, including milk and honey. For practical application, we construct a lysine-specific sensing platform using a commercial chip (TCS34725) that collects red, blue, and green signals, thereby facilitating the convenient use of the nanosensor. Overall, this study offers new perspectives on the development and application of fluorescent nanosensors for detecting individual amino acids.

Electron Divergence of Cuδ- and Pdδ+ in Cu3Pd Alloy-Based Heterojunctions Boosts Concerted C≡C Bond Binding and the Volmer Step for Alkynol Semihydrogenation.

Electrocatalytic semihydrogenation of alkynols presents a sustainable alternative to conventional thermal methodologies for the high-value production of alkenols. The design of efficient catalysts with superior catalytic and energy efficiency for semihydrogenation poses a significant challenge. Here, we present the application of an electron-divergent Cu3Pd alloy-based heterojunction in promoting the electrocatalytic semihydrogenation of alkynols to alkenols using water as the proton source. The tunable electron divergence of Cuδ- and Pdδ+, modulated by rectifying contact with nitrogen-rich carbons, enables the concerted binding of active H species from the Volmer step of water dissociation and the C≡C bond of alkynols on Pdδ+ sites. Simultaneously, the pronounced electron divergence of Cu3Pd facilitates the universal adsorption of OH species from the Volmer step and alkynols on the Cuδ- sites. The electron-divergent dual-center substantially boosts water dissociation and inhibition of completing hydrogen evolution to give a turnover frequency of 2412 h-1, outperforming the reported electrocatalysts' value of 7.3. Moreover, the continuous production of alkenols at industrial-related current density (-200 mA cm-2) over the efficient and durable Cu3Pd-based electrolyzer could achieve a cathodic energy efficiency of 45 mol kW·h-1, 1.7 times the bench-marked reactors, promising great potential for sustainable industrial synthesis.

DNA methylation signatures provide novel diagnostic biomarkers and predict responses of immune therapy for breast cancer.

Breast cancer (BRCA) is one of the most common malignant tumors affecting women worldwide. DNA methylation modifications can influence oncogenic pathways and provide potential diagnostic and therapeutic targets for precision oncology. In this study, we used non-parametric permutation tests to identify differentially methylated positions (DMPs) between paired tumor and normal BRCA tissue samples from the Cancer Genome Atlas (TCGA) database. Then, we applied non-negative matrix factorization (NMF) to the DMPs to derive eight distinct DNA methylation signatures. Among them, signatures Hyper-S3 and Hypo-S4 signatures were associated with later tumor stages, while Hyper-S1 and Hypo-S3 exhibited higher methylation levels in earlier stages. Signature Hyper-S3 displayed an effect on overall survival. We further validated the four stage-associated signatures using an independent BRCA DNA methylation dataset from peripheral blood samples. Results demonstrated that 24 commonly hypomethylated sites in Hypo-S4 showed lower methylation in BRCA patients compared to healthy individuals, suggesting its potential as an early diagnostic biomarker. Furthermore, we found that methylation of 23 probes from four stage-related signatures exhibited predictive power for immune therapy response. Notably, methylation levels of all three probes from the Hypo-S4 and activity of the Hypo-S4 demonstrated highly positive relevance to PD-L1 gene expression, implying their significant predictive values for immunotherapy outcomes. GO and KEGG pathway enrichment analysis revealed that genes with these 23 immunotherapy-related methylation probes are mainly involved in glycan degradation or protein deglycosylation. These methylation signatures and probes may serve as novel epigenetic biomarkers for predicting tumor immunotherapy response. Our findings provide new insights into precision oncology approaches for BRCA.

From bits to green: Unraveling the digital economy's influence on natural resource efficiency.

This study explores the impact of the digital economy (DE) on natural resource efficiency (NRE) across 275 Chinese cities between 2011 and 2021. Through a comprehensive empirical analysis, we find that the DE significantly positively affects NRE. A key moderating factor in this relationship is green technological innovation (GTI), focusing on the quality rather than the quantity of green technology. Our study also uncovers regional variations of moderating effect. Additionally, we identify several mechanisms through which the DE contributes to enhanced NRE, including the transformation of industrial structure and improvements in green total factor productivity. A detailed heterogeneity analysis shows that the DE's impact on NRE varies according to city-specific factors such as natural resource endowment, city size, environmental regulations, and administrative levels. These findings provide a more nuanced understanding of how the DE influences NRE at the urban level, contributing to the broader discourse on sustainable development in the digital age. Our research offers policy recommendations and potential pathways for cities to leverage the DE for greater natural resource efficiency.

Materials Advances in Photocatalytic Solar Hydrogen Production: Integrating Systems and Economics for a Sustainable Future.

Photocatalytic solar hydrogen generation, encompassing both overall water splitting and organic reforming, presents a promising avenue for green hydrogen production. This technology holds the potential for reduced capital costs in comparison to competing methods like photovoltaic-electrocatalysis and photoelectrocatalysis, owing to its simplicity and fewer auxiliary components. However, the current solar-to-hydrogen efficiency of photocatalytic solar hydrogen production has predominantly remained low at ≈1-2% or lower, mainly due to curtailed access to the entire solar spectrum, thus impeding practical application of photocatalytic solar hydrogen production. This review offers an integrated, multidisciplinary perspective on photocatalytic solar hydrogen production. Specifically, the review presents the existing approaches in photocatalyst and system designs aimed at significantly boosting the solar-to-hydrogen efficiency, while also considering factors of cost and scalability of each approach. In-depth discussions extending beyond the efficacy of material and system design strategies are particularly vital to identify potential hurdles in translating photocatalysis research to large-scale applications. Ultimately, this review aims to provide understanding and perspective of feasible pathways for commercializing photocatalytic solar hydrogen production technology, considering both engineering and economic standpoints.

Case report: Lymph node metastasis of pelvic alveolar rhabdomyosarcoma diagnosed by fine needle aspiration cytology.

Rhabdomyosarcoma (RMS) is a common soft tissue malignant tumor, especially in young patients. Alveolar rhabdomyosarcoma (ARMS) is a subtype of RMS that is prevalent in adolescents. This malignant tumor usually develops in the extremities and can also involve the trunk, perineum, and pelvis. Now, we report a rare case of pelvic lymph node metastatic alveolar RMS in a young patient, which was determined by fine needle aspiration cytology (FNAC). To the best of our knowledge, this is the first case in which the definite diagnosis of ARMS was initially made by FNAC.

Realization of monolayer ZrTe5 topological insulators with wide band gaps.

Two-dimensional topological insulators hosting the quantum spin Hall effect have application potential in dissipationless electronics. To observe the quantum spin Hall effect at elevated temperatures, a wide band gap is indispensable to efficiently suppress bulk conduction. Yet, most candidate materials exhibit narrow or even negative band gaps. Here, via elegant control of van der Waals epitaxy, we have successfully grown monolayer ZrTe5 on a bilayer graphene/SiC substrate. The epitaxial ZrTe5 monolayer crystalizes in two allotrope isomers with different intralayer alignments of ZrTe3 prisms. Our scanning tunneling microscopy/spectroscopy characterization unveils an intrinsic full band gap as large as 254 meV and one-dimensional edge states localized along the periphery of the ZrTe5 monolayer. First-principles calculations further confirm that the large band gap originates from strong spin-orbit coupling, and the edge states are topologically nontrivial. These findings thus provide a highly desirable material platform for the exploration of the high-temperature quantum spin Hall effect.

Dual Starvations Induce Pyroptosis for Orthotopic Pancreatic Cancer Therapy through Simultaneous Deprivation of Glucose and Glutamine.

Pancreatic cancer is a highly fatal disease, and existing treatment methods are ineffective, so it is urgent to develop new effective treatment strategies. The high dependence of pancreatic cancer cells on glucose and glutamine suggests that disrupting this dependency could serve as an alternative strategy for pancreatic cancer therapy. We identified the vital genes glucose transporter 1 (GLUT1) and alanine-serine-cysteine transporter 2 (ASCT2) through bioinformatics analysis, which regulate glucose and glutamine metabolism in pancreatic cancer, respectively. Human serum albumin nanoparticles (HSA NPs) for delivery of GLUT1 and ASCT2 inhibitors, BAY-876/V-9302@HSA NPs, were prepared by a self-assembly process. This nanodrug inhibits glucose and glutamine uptake of pancreatic cancer cells through the released BAY-876 and V-9302, leading to nutrition deprivation and oxidative stress. The inhibition of glutamine leads to the inhibition of the synthesis of the glutathione, which further aggravates oxidative stress. Both of them lead to a significant increase in reactive oxygen species, activating caspase 1 and GSDMD and finally inducing pyroptosis. This study provides a new effective strategy for orthotopic pancreatic cancer treatment by dual starvation-induced pyroptosis. The study for screening metabolic targets using bioinformatics analysis followed by constructing nanodrugs loaded with inhibitors will inspire future targeted metabolic therapy for pancreatic cancer.

Silver Recovery from Crystalline Silicon Photovoltaic Solar Cells Using Continuous Stirred-Tank Reactors.

Silver can be recycled from the end-of-life crystalline silicon photovoltaic (PV), yet the recycling and its technology scale-up are still at an early stage especially in continuously operations e.g., continoursely stirred tank reactors (CSTR). Here, the silver recovery from the solar cells is technically understood and optimized in the CSTR system from the point of view of silver recovery efficiency, through integrating experimental and numerical investigations. Specifically, based on the experiments, a kinetics model is developed and scanning electron microscopy surface morphology is characterized; and a computational fluid dynamics-discrete element method (CFD-DEM) particle-scale model is integrated with the kinetics model and validated against the fluid-flow pattern and silver leaching performance results from lab measurements. The validated CFD-DEM model is then applied to understand the particle-scale behavior of silver leaching in the CSTR system in terms of hydrodynamics and AgNO3 distribution under different impeller speeds. The simulation results show that the silver leaching performance is improved in an improved CSTR design with a lower impeller position and doubled impeller layers. This work reveals the effectiveness and underlying hydrodynamics of silver leaching in CSTR systems and lays a foundation for improving silver recovery in PV recycling.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response.

BACKGROUND: Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes. METHODS: We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts. RESULTS: We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment. CONCLUSIONS: Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma.

OBJECTIVE: The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome. METHODS: A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC. RESULTS: Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001). CONCLUSIONS: The CT-based RN performed well in predicting both the histologic grade and outcome of CS.

Antisolvent precipitation for the synergistic preparation of ultrafine particles of nobiletin under ultrasonication-homogenization and evaluation of the inhibitory effects of α-glucosidase and porcine pancreatic lipase in vitro.

To further enhance the application of nobiletin (an important active ingredient in Citrus fruits), we used ultrasonic homogenization-assisted antisolvent precipitation to create ultrafine particles of nobiletin (UPN). DMSO was used as the solvent, and deionized water was used as the antisolvent. When ultrasonication (670 W) and homogenization (16000 r/min) were synergistic, the solution concentration was 57 mg/mL, and the minimum particle size of UPN was 521.02 nm. The UPN samples outperformed the RN samples in terms of the inhibition of porcine pancreatic lipase, which was inhibited (by 500 mg/mL) by 68.41 % in the raw sample, 90.34 % in the ultrafine sample, and 83.59 % in the positive control, according to the data. Fourier transform infrared spectroscopy analysis revealed no chemical changes in the samples before or after preparation. However, the crystallinity of the processed ultrafine nobiletin particles decreased. Thus, this work offers significant relevance for applications in the realm of food chemistry and indirectly illustrates the expanded application potential of nobiletin.

CDSKNNXMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure.

BACKGROUND: Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency. METHODS: We propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure. RESULTS: We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods. CONCLUSIONS: The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.

The dysfunction of CD8+ T cells triggered by endometriotic stromal cells promotes the immune survival of endometriosis.

Endometriosis is defined as an oestrogen-dependent and inflammatory gynaecological disease of which the pathogenesis remains unclear. This study aimed to investigate the cellular heterogeneity and reveal the effect of CD8+ T cells on the progress of endometriosis. Three ovarian endometriosis patients were collected, and single-cell RNA sequencing (scRNA-seq) progressed and delineated the cellular landscape of endometriosis containing five cell clusters. The endometrial cells (EMCs) were the major component, of which the mesenchymal cells were preponderant and characterized with increased inflammation and oestrogen synthesis in endometriosis. The proportion of T cells, mainly CD8+ T cells rather than CD4+, was reduced in endometriotic lesions, and the cytokines and cytotoxicity of ectopic T cells were depressed. CD8+ T cells depressed the proliferation of ESCs through inhibiting CDK1/CCNB1 pathway to arrest the cell cycle and triggered inflammation through activating STAT1 pathway. Correspondingly, the coculture with ESCs resulted in the dysfunction of CD8+ T cells through upregulating STAT1/PDCD1 pathway and glycolysis-promoted metabolism reprogramming. The endometriotic lesions were larger in nude mouse models with T-cell deficiency than the normal mouse models. The inhibition of T cells via CD90.2 or CD8A antibody increased the endometriotic lesions in mouse models, and the supplement of T cells to nude mouse models diminished the lesion sizes. In conclusion, this study revealed the global cellular variation of endometriosis among which the cellular count and physiology of EMCs and T cells were significantly changed. The depressed cytotoxicity and aberrant metabolism of CD8+ T cells were induced by ESCs with the activation of STAT1/PDCD1 pathway resulting in immune survival to promote endometriosis.