The role of the CXCR6/CXCL16 axis in the pathogenesis of fibrotic disease.

CXC chemokine receptor 6 (CXCR6), a seven-transmembrane domain G-protein-coupled receptor, plays a pivotal regulatory role in inflammation and tissue damage through its interaction with CXC chemokine ligand 16 (CXCL16). This axis is implicated in the pathogenesis of various fibrotic diseases and correlates with clinical parameters that indicate disease severity, activity, and prognosis in organ fibrosis, including afflictions of the liver, kidney, lung, cardiovascular system, skin, and intestines. Soluble CXCL16 (sCXCL16) serves as a chemokine, facilitating the migration and recruitment of CXCR6-expressing cells, while membrane-bound CXCL16 (mCXCL16) functions as a transmembrane protein with adhesion properties, facilitating intercellular interactions by binding to CXCR6. The CXCR6/CXCL16 axis is established to regulate the cycle of damage and repair during chronic inflammation, either through modulating immune cell-mediated intercellular communication or by independently influencing fibroblast homing, proliferation, and activation, with each pathway potentially culminating in the onset and progression of fibrotic diseases. However, clinically exploiting the targeting of the CXCR6/CXCL16 axis requires further elucidation of the intricate chemokine interactions within fibrosis pathogenesis. This review explores the biology of CXCR6/CXCL16, its multifaceted effects contributing to fibrosis in various organs, and the prospective clinical implications of these insights.

Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study.

BACKGROUND: Due to the complexity and numerous comorbidities associated with Crohn's disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care. AIM: To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR. METHODS: A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models. RESULTS: Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model. CONCLUSION: Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.

A correction method for radial distortion and nonlinear response of infrared cameras.

The key feature of non-contact temperature measurement provided by infrared (IR) cameras underpins their versatility. However, the accuracy of temperature measurements with IR cameras depends on imaging quality due to their non-contact nature, such as the lens, body temperature, and measurement environment. This paper addresses the correction of radial distortion and nonlinear response issues in IR cameras. To address radial distortion, we have designed a passive checkerboard calibration board specifically for infrared cameras. This board is used to calibrate the IR camera and derive the necessary camera parameters. Subsequently, these parameters are applied during the actual measurement process to rectify radial distortion effectively. Building on the radial distortion correction method mentioned above, we propose a multi-point segmented calibration approach that considers different temperature ranges and imaging regions. This method alleviates the issue of reduced temperature measurement accuracy due to variations in camera responses by computing gain and offset coefficient matrices for each temperature range. Experimental results demonstrate the effectiveness of the calibration board in correcting radial distortion in IR cameras, with a mean reprojection error of less than 0.16 pixels. Regarding the nonlinear response problem, the introduced method significantly reduces the relative error in temperature measurement. In the verification phase, spanning from 100 to 500 °C, the average relative error in temperature measurement decreases by 0.49% from 1.61% before and after correction, which highlights a substantial improvement in temperature measurement accuracy. This work gives a useful reference to improve the imaging quality and temperature measurement accuracy using infrared cameras.

Adsorption-desorption behavior of florpyrauxifen-benzyl on three microplastics in aqueous environment as well as its mechanism and various influencing factors.

Microplastics (MPs) and pesticides are two categories contaminants with proposed negative impacts to aqueous ecosystems, and adsorption of pesticides on MPs may result in their long-range transport and compound combination effects. Florpyrauxifen-benzyl, a novel pyridine-2-carboxylate auxin herbicide has been widely used to control weeds in paddy field, but the insights of which are extremely limited. Therefore, adsorption and desorption behaviors of florpyrauxifen-benzyl on polyvinyl chloride (PVC), polyethylene (PE) and disposable face masks (DFMs) in five water environment were investigated. The impacts of various environmental factors on adsorption capacity were evaluated, as well as adsorption mechanisms. The results revealed significant variations in adsorption capacity of florpyrauxifen-benzyl on three MPs, with approximately order of DFMs > PE > PVC. The discrepancy can be attributed to differences in structural and physicochemical properties, as evidenced by various characterization analysis. The kinetics and isotherm of florpyrauxifen-benzyl on three MPs were suitable for different models, wherein physical force predominantly governed adsorption process. Thermodynamic analysis revealed that both high and low temperatures weakened PE and DFMs adsorption, whereas temperature exhibited negligible impact on PVC adsorption. The adsorption capacity was significantly influenced by most environmental factors, particularly pH, cations and coexisting herbicide. This study provides valuable insights into the fate of florpyrauxifen-benzyl in presence of MPs, suggesting that PVC, PE and DFMs can serve as carriers of florpyrauxifen-benzyl in aquatic environment.

The relation between effortful control and executive function training in preschoolers.

In recent years, the question of whether executive function (EF) is malleable has been widely documented. Despite using the same training tasks, transfer effects remain uncertain. Researchers suggested that the inconsistency might be attributed to individual differences in temperamental traits. In the current study, we investigated how effortful control, a temperamental trait, would affect EF training outcomes in children. Based on parent rating, 79 6-year-old preschoolers were identified as having higher or lower effort control and were assigned to three conditions: working memory (WM) training, inhibitory control (IC) training, and a business-as-usual control group. Children completed assessments at baseline, 1 week after intervention (posttest), and 3 months after intervention (follow-up). As compared with the control group, the WM and IC training groups showed improvement in both trained tasks and nontrained measures. At baseline, children with higher effortful control scores showed greater WM capacity and better IC. Furthermore, effortful control was positively correlated with training gain in both training groups, with children with higher effortful control benefitting more through training. In the WM training group, effortful control was positively correlated with near transfer on WM outcomes both immediately and longitudinally. At posttest, the WM and IC training groups showed a positive correlation between effortful control and fluid intelligence performance. Our results underscore the importance of individual differences in training benefits, in particular the role of effortful control, and further illustrate the potential avenues for designing more effective individualized cognitive training programs to foster learning and optimize children's development.

Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15.

To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural predictions by using both traditional sequence alignments and Foldseek-based structure alignments, ranks structural predictions through multiple complementary metrics, and refines the structural predictions via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. MULTICOM_qa ranked 3rd among 26 CASP15 server predictors and MULTICOM_human ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first predictions submitted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 predictions submitted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the Foldseek Structure Alignment-based Multimer structure Generation (FSAMG) method outperforms the widely used sequence alignment-based multimer structure generation.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Hydrolysis of propyrisulfuron in water: Kinetics, influence of 34 environmental factors, transformation products identification, mechanisms and toxicities.

Propyrisulfuron is a novel sulfonylurea herbicide used for controlling annual grass and broad-leaved weeds in fields, but its fates and behaviors in environment are still unknown, which are of utmost importance for environmental protection. To reduce its potential environmental risks in agricultural production, the hydrolysis kinetics, influence of 34 environmental factors including 12 microplastics (MPs), disposable face masks (DFMs) and its different parts, 6 fertilizers, 5 ions, 3 surfactants, a co-existed herbicide of florpyrauxifen-benzy, humic acid and biochar, and the effect of MPs and DFMs on its hydrolysis mechanisms were systematically investigated. The main hydrolysis products (HPs), possible mechanisms, toxicities and potential risks to aquatic organisms were studied. Propyrisulfuron hydrolysis was an acid catalytic pyrolysis, endothermic and spontaneous process driven by the reduction of activation enthalpy, and followed the first-order kinetics. All environmental factors can accelerate propyrisulfuron hydrolysis to varying degrees except humic acid, and different hydrolysis mechanisms occurred in the presence of MPs and DFMs. In addition, 10 possible HPs and 7 possible mechanisms were identified and proposed. ECOSAR prediction and ecotoxicity testing showed that acute toxicity of propyrisulfuron and its HPs for aquatic organisms were low, but may have high chronic toxicity and pose a potential threat to aquatic ecosystems. The investigations are significantly important for elucidating the environmental fates and behaviors of propyrisulfuron, assessing the risks in environmental protection, and further providing guidance for scientific application in agro-ecosystem.

Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15.

Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively.

Transformation of colitis and colorectal cancer: a tale of gut microbiota.

Intestinal inflammation modifies host physiology to promote the occurrence of colorectal cancer (CRC), as seen in colitis-associated CRC. Gut microbiota is crucial in cancer progression, primarily by inducing intestinal chronic inflammatory microenvironment, leading to DNA damage, chromosomal mutation, and alterations in specific metabolite production. Therefore, there is an increasing interest in microbiota-based prevention and treatment strategies, such as probiotics, prebiotics, microbiota-derived metabolites, and fecal microbiota transplantation. This review aims to provide valuable insights into the potential correlations between gut microbiota and colitis-associated CRC, as well as the promising microbiota-based strategies for colitis-associated CRC.

Degradation of a New Herbicide Florpyrauxifen-Benzyl in Water: Kinetics, Various Influencing Factors and Its Reaction Mechanisms.

Florpyrauxifen-benzyl is a novel herbicide used to control weeds in paddy fields. To clarify and evaluate its hydrolytic behavior and safety in water environments, its hydrolytic characteristics were investigated under varying temperatures, pH values, initial mass concentrations and water types, as well as the effects of 40 environmental factors such as microplastics (MPs) and disposable face masks (DFMs). Meanwhile, hydrolytic products were identified by UPLC-QTOF-MS/MS, and its hydrolytic pathways were proposed. The effects of MPs and DFMs on hydrolytic products and pathways were also investigated. The results showed that hydrolysis of florpyrauxifen-benzyl was a spontaneous process driven by endothermic, base catalysis and activation entropy increase and conformed to the first-order kinetics. The temperature had an obvious effect on hydrolysis rate under alkaline condition, the hydrolysis reaction conformed to Arrhenius formula, and activation enthalpy, activation entropy, and Gibbs free energy were negatively correlated with temperature. Most of environmental factors promoted hydrolysis of florpyrauxifen-benzyl, especially the cetyltrimethyl ammonium bromide (CTAB). The hydrolysis mechanism was ester hydrolysis reaction with a main product of florpyrauxifen. The MPs and DFMs did not affect the hydrolytic mechanisms but the hydrolysis rate. The results are crucial for illustrating and assessing the environmental fate and risks of florpyrauxifen-benzyl.

Enhancing AlphaFold-Multimer-based Protein Complex Structure Prediction with MULTICOM in CASP15.

AlphaFold-Multimer has emerged as the state-of-the-art tool for predicting the quaternary structure of protein complexes (assemblies or multimers) since its release in 2021. To further enhance the AlphaFold-Multimer-based complex structure prediction, we developed a new quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine the outputs generated by AlphaFold2-Multimer. Specifically, MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural models by using both traditional sequence alignments and new Foldseek-based structure alignments, ranks structural models through multiple complementary metrics, and refines the structural models via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. Our server (MULTICOM_qa) ranked 3rd among 26 CASP15 server predictors and our human predictor (MULTICOM_human) ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first models predicted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 models predicted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the novel Foldseek Structure Alignment-based Model Generation (FSAMG) method based on AlphaFold-Multimer outperforms the widely used sequence alignment-based model generation. The source code of MULTICOM is available at: https://github.com/BioinfoMachineLearning/MULTICOM3.

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph transformer.

MOTIVATION: The state-of-art protein structure prediction methods such as AlphaFold are being widely used to predict structures of uncharacterized proteins in biomedical research. There is a significant need to further improve the quality and nativeness of the predicted structures to enhance their usability. In this work, we develop ATOMRefine, a deep learning-based, end-to-end, all-atom protein structural model refinement method. It uses a SE(3)-equivariant graph transformer network to directly refine protein atomic coordinates in a predicted tertiary structure represented as a molecular graph. RESULTS: The method is first trained and tested on the structural models in AlphaFoldDB whose experimental structures are known, and then blindly tested on 69 CASP14 regular targets and 7 CASP14 refinement targets. ATOMRefine improves the quality of both backbone atoms and all-atom conformation of the initial structural models generated by AlphaFold. It also performs better than two state-of-the-art refinement methods in multiple evaluation metrics including an all-atom model quality score-the MolProbity score based on the analysis of all-atom contacts, bond length, atom clashes, torsion angles, and side-chain rotamers. As ATOMRefine can refine a protein structure quickly, it provides a viable, fast solution for improving protein geometry and fixing structural errors of predicted structures through direct coordinate refinement. AVAILABILITY AND IMPLEMENTATION: The source code of ATOMRefine is available in the GitHub repository (https://github.com/BioinfoMachineLearning/ATOMRefine). All the required data for training and testing are available at https://doi.org/10.5281/zenodo.6944368.

Application of molecular imprinting technology based on new nanomaterials in adsorption and detection of fluoroquinolones.

Irrational use of fluoroquinolones (FQs) can lead to allergic reactions, adverse reactions to the heart and damage of the liver; thus, it is of great significance to establish rapid, sensitive and accurate detection methods for FQs. Molecularly imprinted polymers (MIPs) with specific structures synthesized by molecular imprinting technology (MIT) are widely used for the detection of FQs due to their high specificity, high sensitivity and stable performance. Recently, new functional nanomaterials with different morphologies and sizes, which can provide rich sites for surface chemical reactions, have attracted more and more attention of the researchers. Thus, the application status and development prospects of MIT based on new nanomaterials in the adsorption and detection of FQs were summarized in this study, providing a theoretical basis and technical guarantee for the development of new and efficient food safety analysis strategies based on MIPs.

How does digital infrastructure affect residents' healthcare expenditures? Evidence from Chinese microdata.

Healthcare expenditure is only one of the heavy burdens that families face in developing countries. Current research mainly focuses on analyzing the effects of financial policy. There is a lack of studies that examine the understanding and assessment of the impact of digital infrastructure on this issue. In this study, we used the Broadband China policy as a quasi-natural experiment to explore the impact of digital infrastructure on residents' healthcare expenditures in China. Using the differences-in-differences (DID) model and micro-survey data, we found that digital infrastructure has a positive impact on reducing the burden of healthcare expenditure in China. Our findings indicate that residents in cities can save up to 18.8% on healthcare expenses following large-scale digital infrastructure construction. Through mechanism analysis, we found that digital infrastructure reduces residents' healthcare expenditures by improving both commercial insurance availability and the healthcare efficiency of residents. In addition, the effects of digital infrastructure on reducing healthcare expenditure are more pronounced among middle-aged individuals, those with low levels of education, and those with low incomes, which indicates this digital construction wave helps bridge the social gap between the poor and the rich. This study provides compelling evidence of the positive impact of digital society construction on social health and wellbeing.

Nodal promotes colorectal cancer survival and metastasis through regulating SCD1-mediated ferroptosis resistance.

Re-expression of an embryonic morphogen, Nodal, has been seen in several types of malignant tumours. By far, studies about Nodal's role in colorectal cancer (CRC) remain limited. Ferroptosis is essential for CRC progression, which is caused by cellular redox imbalance and characterized by lipid peroxidation. Herein, we observed that Nodal enhanced CRC cell's proliferative rate, motility, invasiveness, and epithelial-mesenchymal transition (EMT) in vivo and in vitro. Notably, Nodal overexpression induced monounsaturated fatty acids synthesis and increased the lipid unsaturation level. Nodal knockdown resulted in increased CRC cell lipid peroxidation. Stearoyl-coenzyme A desaturase 1 (SCD1) inhibition at least partially abolished the resistance of Nodal-overexpressing cells to RSL3-induced ferroptosis. Mechanistically, SCD1 was transcriptionally up-regulated by Smad2/3 pathway activation in response to Nodal overexpression. Significant Nodal and SCD1 up-regulation were observed in CRC tissues and were associated with CRC metastasis and poor clinical outcomes. Furthermore, bovine serum albumin nanoparticles/si-Nodal nanocomplexes targeting Nodal had anti-tumour effects on CRC progression and metastasis. This research elucidated the role of Nodal in CRC development and revealed a potential gene-based therapeutic strategy targeting Nodal for improving CRC treatment.

3D-equivariant graph neural networks for protein model quality assessment.

MOTIVATION: Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. RESULTS: We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method-AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method-AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/BioinfoMachineLearning/EnQA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Review of research on migration, distribution, biological effects, and analytical methods of microfibers in the environment.

Microplastics have been proven to be one of the critical environmental pollution issues. Moreover, microfibers, the most prominent form of microplastics in the environment, have likewise attracted the attention of various countries. With the increase in global population and industrialization, the production and use of fibers continue to increase yearly. As a result, a large number of microfibers are formed. If fiber products are not used or handled correctly, it will cause direct/indirect severe microfiber environmental pollution. Microfibers will be further broken into smaller fiber fragments when they enter the natural environment. Presently, researchers have conducted extensive research in the identification of microfibers, laying the foundation for further resourcefulness research. This work used bibliometric analysis to review the microfiber contamination researches systematically. First, the primary sources of microfibers and the influencing factors are analyzed. We aim to summarize the influence of the clothing fiber preparation and care processes on microfiber formation. Then, this work elaborated on the migration in/between water, atmosphere, and terrestrial environments. We also discussed the effects of microfiber on ecosystems. Finally, microfibers' current and foreseeable effective treatment, disposal, and resource utilization methods were explained. This paper will provide a structured reference for future microfiber research.

Measurements of emissivity and temperature for polarized long-wavelength infrared light from tungsten under simulating tokamak conditions.

Tungsten is regarded as the baseline first wall material in tokamaks. This work provides a polarized method for measuring the emissivity and temperature of the tungsten using an infrared camera and a polarizer under simulating tokamak conditions. In the experiment, a polarizer with an adjustable polarization direction is set up in front of an infrared camera. A rotatable fixture is used to fix the sample and change the angle between the surface and the normal. The sample is rotated from 0° to 80°, and the polarized emissivity first increases and then decreases with increasing rotation angle. The uncertainty in emissivity resulting from this polarized method and non-polarized method is analyzed. To compare the effects of the polarized method and the non-polarized method, the rotation angle is adjusted to 0°, and a fitting model is used to describe the relationship between emissivity and temperature. Errors between the calculated temperature and measured temperature are used as a scale, and the polarized method improves the accuracy of temperature measurement. This polarized method provides a technical way to measure the emissivity and temperature in a tokamak and can be applied in other similar applications.