Enhanced Replenishment of Active Lattice Oxygen Using Chiral Copper Oxide.

Effective catalytic performance of the transition metal oxide is attributed to high specific surface areas, abundant surface oxygen atoms, and balanced valence ratios. Although the chirality of the transition metal has attracted attention, most studies have focused on optical application. A few chiral transition metal oxides were used as electrocatalysts and photocatalysts. The influence of the chiral catalysts on the thermal catalysis process has been less explored. In this study, Mn-loaded chiral (M/l-CuO and M/d-CuO) and achiral CuO (M/a-CuO) were synthesized and compared in the catalytic oxidization of toluene. Spectrally analyzed Mn was well-dispersed on both chiral and achiral CuO. l-CuO and d-CuO showed nanoflower-like chirality. The angles between each (001) plane of CuO were the source of chirality. The toluene turnover frequency (TOF) of the samples was in the order of Mn/d-CuO (5.6 × 10-5 s-1) > Mn/l-CuO (4.4 × 10-5 s-1) > Mn/a-CuO (3.2 × 10-5 s-1) at 240 °C, consistent with the order of the oxygen replenishment rate. The as-prepared catalysts had similar ratios of lattice oxygen/surface adsorbed oxygen, Mn3+/Mn4+, and Cu+/Cu2+. A higher TOF was attributed to chirality, which increased the lattice oxygen replenishment speed from the gaseous phase to the solid surface. Our study indicates gas-solid catalysis from a structure-activity viewpoint.

Clinical remission of moderate generalized myasthenia gravis through exclusive use of Buzhong Yiqi decoction: A case report.

BACKGROUND: Myasthenia gravis (MG) is a rare autoimmune neuromuscular disorder with significant morbidity and mortality. Traditional Chinese medicine (TCM) offers an alternative approach to standard pharmacological and surgical interventions, which are often associated with adverse side effects. This case report details the clinical remission of a 50-year-old male with moderate generalized MG following exclusive treatment with a modified Buzhong Yiqi decoction (BYD), a TCM formula, without the use of immunosuppressive agents. CASE SUMMARY: The patient presented with diplopia, bilateral ptosis, weakness in chewing, limb weakness, and other symptoms indicative of spleen and stomach qi deficiency. Modified BYD was prescribed, focusing on strengthening the spleen, nourishing qi and blood, and enhancing immune response. The treatment included ingredients such as Radix Astragali, Angelica sinensis, Atractylodes macrocephala, and others, aiming to restore balance and improve the patient's condition. After two weeks of TCM treatment, the patient showed significant improvement in symptoms of myasthenia. By the second month, all clinical symptoms had disappeared. The patient continued to receive the TCM regimen until the thirtieth month of treatment. At the time of writing this report, the patient has no clinical symptoms and has experienced no relapse. Notably, no obvious adverse effects were reported throughout the treatment. CONCLUSION: The success of this case suggests that TCM may serve as an independent treatment option for moderate MG, offering a steroid-free alternative, which would be particularly valuable for patients who are intolerant of or refuse steroid therapy, potentially with significant clinical implications. However it needs a randomized clinical trial comparing TCM to conventional Western medicine treatment to validate it.

Promising preclinical models for lung cancer research-lung cancer organoids: a narrative review.

Background and Objective: Traditional cell line models are the commonly used preclinical models for lung cancer research. However, cell lines cannot recapitulate the complex tumor heterogeneity and cannot mimic the microenvironment of human cancer. Recently, 3D multicellular in vitro self-assembled models called "organoids" have been developed at a fast pace in the field of research, which can mimic the actual primary tumor. At present, several studies have reported on protocols of lung cancer organoids (LCOs) generation, and using LCOs can provide novel insight into the basic and translational research of lung cancer. However, the establishment of the LCO models remains challenging due to the complexity of lung cancer and the immaturity of organoid technology, so it is necessary to understand the influences of different methodologies on LCO generation and review the applications and limitations of LCO models. Methods: In this review, we searched the literature in the recent ten years in the field of LCOs. Key Content and Findings: We summarized the methodology, the problems, and the solutions in the LCOs generation, its application and limitations, as well as proposing future challenges and perspectives. Conclusions: Currently, LCOs are successfully generated via exploring the methodology by the researchers. Though there are still challenges in clinical application, LCOs are applied in some cancer studies including investigation of anti-cancer treatment response in vitro, modeling tumor immune microenvironment, and construction of organ chips, which are forging a promising path towards precision medicine.

Ag/AgBr-oxygen enriched g-C3N4 for efficient photocatalytic degradation of trimethylamine.

In this study, Ag/AgBr-O-gCN samples with ternary Z-type heterojunctions were prepared by in situ photoreduction using water as the reducing agent for generating Ag/AgBr active species and oxygen doping. The experimental results indicated that Ag/AgBr-O-gCN degraded trimethylamine by nearly 100% in half an hour and maintained 90% of its original activity after five cycles. The kinetic constant of Ag/AgBr-O-gCN was excellent at 0.0928 min-1, 3.8 times that of gCN, 2.3 times that of Ag/AgBr-gCN, and 1.9 times that of O-gCN. Unlike Ag/AgBr-gCN photoreduced by methanol, gCN was used as an electron donor in the aqueous solution during the photoreduction process, and oxidation sites between the gCN skeleton and Ag/AgBr were formed for constructing the heterojunction system. The Z-type heterojunction system was established by introducing a suitable size of Ag nanoparticles as the recombination center to keep indirect contact between gCN and AgBr. This effectively reduced the electron-hole recombination rate and caused activity enhancement. This study offers a novel idea for the construction of a ternary heterojunction.

A relatively rare traditional Chinese medicine pattern of primary Sjögren syndrome: A case report.

RATIONALE: This report presents a unique case of a patient diagnosed with Primary Sjögren's syndrome and a relatively rare traditional Chinese medicine pattern, known as the combined cold and heat pattern and cold-dampness syndrome. The patient's condition was successfully managed using Chinese herbal medicine, specifically the modified Da-Chai-Hu decoction and Linggui Zhugan decoction. PATIENT CONCERNS: A 56-year-old woman had chronic dry eye and mouth for over 10 years. She was initially managed with traditional Chinese herbal medicine (TCHM) prescriptions, including the Zengye decoction, but the therapeutic effects were unsatisfactory. As the disease progressed, she was diagnosed with an anxiety disorder due to symptoms of vexation and insomnia. Treatment with alprazolam and venlafaxine failed to alleviate these symptoms. Recently, her general condition gradually worsened, with symptoms including a bitter taste in her mouth, dizziness, hot flashes, chills, poor appetite, chest discomfort, and constipation. DIAGNOSES: After a series of examinations, including a Schirmer test and labial gland biopsy, she was diagnosed with Sjögren's syndrome. INTERVENTIONS: Despite regular treatment with pilocarpine, sodium hyaluronate eye drops, venlafaxine, and alprazolam, the dry mouth symptoms intensified. Consequently, she sought further intervention through the TCHM. OUTCOMES: After 8 weeks of treatment with the modified Da-Chai-Hu decoction and Linggui Zhugan decoction, she reported a significant improvement in her dryness-related symptoms and sleep quality. LESSONS: This case report demonstrates that TCHM can effectively treat Primary Sjögren's syndrome, and should be considered for broader applications. Furthermore, this underscores the importance of tailoring treatment formulas to patients by identifying their specific syndrome differentiation in a clinical setting.

The Modified Neo-Bioscore System for Staging Breast Cancer Treated with Neoadjuvant Therapy Based on Prognostic Significance of HER2-Low Expression.

Background: Recently, the classification of HER2 status evolves from binary to ternary, and HER2-low expression may exhibit prognostic significance. We aimed to investigate whether HER2-low tumor is distinct from HER2-zero or HER2-positive tumors, and then to develop a modified staging system (mNeo-Bioscore) that incorporates HER2-low status into Neo-Bioscore. Patients and Methods: This cohort study was conducted using data from the prospective database on breast cancer patients between January 2014 and February 2019. Results: Among 259 patients enrolled in the study, the HER2-low tumor exhibited significantly lower histological grade, pathological staging and Ki-67 level than the other two groups. HER2-low patients and HER2-positive patients receiving concurrent HER2-directed therapy may have similar LRFS (p = 0.531) and OS (p = 0.853), while HER2-zero peers may have significantly worse LRFS (p = 0.006) and OS (p = 0.017). In particular, a similar trend was also found in the patients without pathological complete response after surgery. Incorporation of HER2-low status made improvement in fit: 5-year OS rate estimates ranged from 33.33% to 100% for mNeo-Bioscore vs 61.36% to 100% for Neo-Bioscore. Conclusions: This study demonstrated that HER2-low tumor may exhibit prognostic significance. The innovative mNeo-Bioscore, based on a new classification of HER2 status, may serve as a prognostic staging system superior to Neo-Bioscore.

Chronic Obstructive Pulmonary Disease and Osteoporosis: A Two-Sample Mendelian Randomization Analysis.

Background: The incidence of chronic obstructive pulmonary disease (COPD) and osteoporosis are rising worldwide. Observational studies showed that COPD is associated with increased risk of osteoporosis. We performed the Mendelian randomization (MR) study to genetically investigate the causality of COPD on osteoporosis. Methods: We explored the causal effect of COPD on osteoporosis by the MR analysis. A total of 108 genetic loci single nucleotide polymorphisms were related to COPD. The primary approach in this MR analysis was the inverse variance-weighted (IVW) method. Simple median, weighted median, MR Egger and penalized weighted median analyzed the sensitivity. Results: The study found that genetically predicted COPD is causally linked to an increased risk of osteoporosis, as evidenced by both the fixed-effect IVW model and random-effect IVW model (odds ratio [OR], 1.010; 95% CI, 1.001-1.019, P=0.021; OR, 1.010; 95% CI, 1.001-1.020, P=0.039). This association was also observed in other methods, including the simple median, weighted median, Penalised weighted median, MR-Egger method, and MR Egger (bootstrap) method. The results of the IVW and MR-Egger analyses showed no heterogeneity (Q=131.374, P=0.061 and Q=128.895, P=0.069, respectively). Additionally, MR-Egger regression did not reveal any pleiotropic influence through genetic variants (intercept, -0.004; P=0.101). Lastly, the leave-one-out sensitivity analysis did not identify any individual SNP that significantly influenced the association between COPD and osteoporosis. Conclusion: The Mendelian randomization analysis showed a significant detrimental effect of COPD on the risk of osteoporosis.

Molecular imprinting-based triple-emission ratiometric fluorescence sensor with aggregation-induced emission effect for visual detection of doxycycline.

The development of high-performance sensors for doxycycline (DOX) detection is necessary because its residue accumulation will cause serious harm to human health and the environment. Here, a novel tri-emission ratiometric fluorescence sensor was proposed by using "post-mixing" strategy of different emissions fluorescence molecularly imprinted polymers with salicylamide as dummy template (DMIPs). BSA was chosen as assistant functional monomer, and also acted as sensitizers for the aggregation-induced emission (AIE) effect of DOX. The blue-emitting carbon dots and the red-emitting CdTe quantum dots were separately introduced into DMIPs as the response signals. Upon DOX recognition within 2 min, blue and red fluorescence of the tri-emission DMIPs sensor were quenched while green fluorescence of DOX was enhanced, resulting in a wide range of color variations observed over bluish violet-rosered-light pink-orange-yellow-green with a detection limit of 0.061 µM. The sensor possessed highly selective recognition and was successfully applied to detect DOX in complicated real samples. Moreover, with the fluorescent color collection and data processing, the smartphone-assisted visual detection of the sensors showed satisfied sensitivity with low detection limit. This work provides great potential applications for rapid and visual detection of antibiotics in complex substrates.

Successful treatment of Bowen's Disease in the nipple-areola complex with a combination of photodynamic therapy and cryotherapy-A case report.

Photodynamic therapy (PDT) is a non-invasive, effective treatment for superficial skin conditions, offering superior cosmetic outcomes compared with traditional therapies. Bowen's disease (BD) of the nipple-areola complex (NAC) is rare and thus, lacks a standardized treatment approach. This report details the case of a 48-year-old woman who was successfully treated for BD of the NAC using PDT. Over a follow-up period of 30 months, there was no evidence of disease recurrence, underscoring the potential of PDT as a viable treatment option for this rare manifestation of BD.

SC-GAN: Structure-completion generative adversarial network for synthetic CT generation from MR images with truncated anatomy.

Creating synthetic CT (sCT) from magnetic resonance (MR) images enables MR-based treatment planning in radiation therapy. However, the MR images used for MR-guided adaptive planning are often truncated in the boundary regions due to the limited field of view and the need for sequence optimization. Consequently, the sCT generated from these truncated MR images lacks complete anatomic information, leading to dose calculation error for MR-based adaptive planning. We propose a novel structure-completion generative adversarial network (SC-GAN) to generate sCT with full anatomic details from the truncated MR images. To enable anatomy compensation, we expand input channels of the CT generator by including a body mask and introduce a truncation loss between sCT and real CT. The body mask for each patient was automatically created from the simulation CT scans and transformed to daily MR images by rigid registration as another input for our SC-GAN in addition to the MR images. The truncation loss was constructed by implementing either an auto-segmentor or an edge detector to penalize the difference in body outlines between sCT and real CT. The experimental results show that our SC-GAN achieved much improved accuracy of sCT generation in both truncated and untruncated regions compared to the original cycleGAN and conditional GAN methods.

Automatic end-to-end VMAT treatment planning for rectal cancers.

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. METHODS: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. RESULTS: In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. CONCLUSIONS: This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.

A Comprehensively Prognostic and Immunological Analysis of PARP11 in Pan-cancer.

Poly (ADP ribose) polymerase family member 11(PARP11) has important immune regulatory functions in viral infection and tumor immune response. Particularly, PARP11 showed protumor activities in multiple preclinical murine models. However, no systematic pan-cancer analysis has been conducted to explore PARP11 function. In this study we used multiple databases to assess PARP11 expression, which associations with clinical outcomes, immune checkpoint factors, prognostic significance, genomic characteristics, and immunological aspects. The analysis revealed varying expression levels of PARP11 across different cancer types and a significant correlation between its expression and immune cell infiltration. Insights from the CellMiner database suggest a strong link between PARP11 expression and sensitivity to anticancer drugs, highlighting its potential as a therapeutic target. Moreover, PARP11 expression correlates with patient survival during anti-PD1 and anti-CTLA4 treatments, suggested that PARP11 would be a predictor of immune checkpoint inhibitor (ICI) treatment. In summary, PARP11 would be a potential immunoregulatory target and a diagnosis and prognosis marker for certain types of cancers. The detailed mechanisms of PARP11 in tumor immune responses need to be further investigated.

Deep learning-based automatic segmentation of cardiac substructures for lung cancers.

PURPOSE: Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation models for cardiac substructures. MATERIALS AND METHODS: Nineteen cardiac substructures (whole heart, 4 heart chambers, 6 great vessels, 4 valves, and 4 coronary arteries) in 100 patients treated for non-small cell lung cancer were manually delineated by two radiation oncologists. The valves and coronary arteries were delineated as planning risk volumes. An nnU-Net auto-segmentation model was trained, validated, and tested on this dataset with a split ratio of 75:5:20. The auto-segmented contours were evaluated by comparing them with manually drawn contours in terms of Dice similarity coefficient (DSC) and dose metrics extracted from clinical plans. An independent dataset of 42 patients was used for subjective evaluation of the auto-segmentation model by 4 physicians. RESULTS: The average DSCs were 0.95 (+/- 0.01) for the whole heart, 0.91 (+/- 0.02) for 4 chambers, 0.86 (+/- 0.09) for 6 great vessels, 0.81 (+/- 0.09) for 4 valves, and 0.60 (+/- 0.14) for 4 coronary arteries. The average absolute errors in mean/max doses to all substructures were 1.04 (+/- 1.99) Gy and 2.20 (+/- 4.37) Gy. The subjective evaluation revealed that 94% of the auto-segmented contours were clinically acceptable. CONCLUSION: We demonstrated the effectiveness of our nnU-Net model for delineating cardiac substructures, including coronary arteries. Our results indicate that this model has promise for studies regarding radiation dose to cardiac substructures.

Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers.

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices.

Unsupervised domain adaptation for cross-patient seizure classification.

Objective. Epileptic seizure is a chronic neurological disease affecting millions of patients. Electroencephalogram (EEG) is the gold standard in epileptic seizure classification. However, its low signal-to-noise ratio, strong non-stationarity, and large individual difference nature make it difficult to directly extend the seizure classification model from one patient to another. This paper considers multi-source unsupervised domain adaptation for cross-patient EEG-based seizure classification, i.e. there are multiple source patients with labeled EEG data, which are used to label the EEG trials of a new patient.Approach. We propose an source domain selection (SDS)-global domain adaptation (GDA)-target agent subdomain adaptation (TASA) approach, which includes SDS to filter out dissimilar source domains, GDA to align the overall distributions of the selected source domains and the target domain, and TASA to identify the most similar source domain to the target domain so that its labels can be utilized.Main results. Experiments on two public seizure datasets demonstrated that SDS-GDA-TASA outperformed 13 existing approaches in unsupervised cross-patient seizure classification.Significance. Our approach could save clinicians plenty of time in labeling EEG data for epilepsy patients, greatly increasing the efficiency of seizure diagnostics.

Front-End Replication Dynamic Window (FRDW) for Online Motor Imagery Classification.

Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed; front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022.

A transformer-based hierarchical registration framework for multimodality deformable image registration.

Deformable image registration (DIR) between daily and reference images is fundamentally important for adaptive radiotherapy. In the last decade, deep learning-based image registration methods have been developed with faster computation time and improved robustness compared to traditional methods. However, the registration performance is often degraded in extra-cranial sites with large volume containing multiple anatomic regions, such as Computed Tomography (CT)/Magnetic Resonance (MR) images used in head and neck (HN) radiotherapy. In this study, we developed a hierarchical deformable image registration (DIR) framework, Patch-based Registration Network (Patch-RegNet), to improve the accuracy and speed of CT-MR and MR-MR registration for head-and-neck MR-Linac treatments. Patch-RegNet includes three steps: a whole volume global registration, a patch-based local registration, and a patch-based deformable registration. Following a whole-volume rigid registration, the input images were divided into overlapping patches. Then a patch-based rigid registration was applied to achieve accurate local alignment for subsequent DIR. We developed a ViT-Morph model, a combination of a convolutional neural network (CNN) and the Vision Transformer (ViT), for the patch-based DIR. A modality independent neighborhood descriptor was adopted in our model as the similarity metric to account for both inter-modality and intra-modality registration. The CT-MR and MR-MR DIR models were trained with 242 CT-MR and 213 MR-MR image pairs from 36 patients, respectively, and both tested with 24 image pairs (CT-MR and MR-MR) from 6 other patients. The registration performance was evaluated with 7 manually contoured organs (brainstem, spinal cord, mandible, left/right parotids, left/right submandibular glands) by comparing with the traditional registration methods in Monaco treatment planning system and the popular deep learning-based DIR framework, Voxelmorph. Evaluation results show that our method outperformed VoxelMorph by 6 % for CT-MR registration, and 4 % for MR-MR registration based on DSC measurements. Our hierarchical registration framework has been demonstrated achieving significantly improved DIR accuracy of both CT-MR and MR-MR registration for head-and-neck MR-guided adaptive radiotherapy.

Metal-Controlled Switchable Regioselective Synthesis of Substituted Imidazoles and Pyrroles via Ring Opening/Cyclocondensation with 2H-Azirines.

Catalytic selective annulation of 2H-azirines constitutes a general and modular strategy for the generation of molecular complexity. By using Pd-catalyzed ring opening/heterocyclization associated with direct cleavage of C-N and C-C bonds under appropriate conditions, the formation of imidazoles is presented. Alternatively, the silver-catalyzed radical [3 + 2] cycloannulation of 2H-azirines and 1,3-dicarbonyl compounds provides highly functionalized pyrrole derivatives. Both aliphatic cyclic and acyclic diketones are tolerated with good regioselectivity. Moreover, a radical capture experiment was carried out to determine the proposed mechanism, providing support for a facile radical process.

Influenza A virus inhibits TET2 expression by endoribonuclease PA-X to attenuate type I interferon signaling and promote viral replication.

Influenza A virus (IAV) expresses several accessory proteins to limit host anti-viral restriction factors to facilitate viral replication. The Ten-Eleven Translocation 2 (TET2) is a methylcytosine dioxygenase that promotes DNA demethylation by catalyzing the oxidation of 5-methylcytosine (5mC) into 5-hydroxymethylcytosine (5hmC), which plays a vital role in hematopoiesis and immunity. Here we report that TET2 is a host restriction factor that limits IAV replication. But IAV endoribonuclease PA-X is able to remove the replication restriction by binding to TET2 mRNA and driving TET2 mRNA degradation to reduce TET2 expression during infection. Genetic inactivation of TET2 markedly enhances IAV replication in vitro and in vivo. Mechanistically, we found that TET2 regulates demethylation and transcription of STAT1 and some interferon-stimulated genes (ISGs), including ISG15, ISG20, and IFIT5, so the loss of TET2 greatly impairs type I Interferon signaling. Furthermore, we confirmed that TET2-mediated demethylation of the STAT1 gene is critical for interferon anti-viral activity. Our study demonstrates that the host TET2 is essential to the innate immune response against IAV infection.