[Study on Multi-Bands Recognition for Varieties of Mutton by Using Hyperspectral Technologies].

This paper focused on the research on identifying and classifying for mutton varieties of Tan-han hybrid sheep,Yanchi Tan-sheep and small-tailed sheep in Ningxia by using visible/ near-infrared (400~1 000 nm). Near infrared (900~1 700 nm) hyperspectral technologies, baseline and SG convolution smoothing spectra pretreatment methods were applied respectively according to the characteristics of different spectrum bands; the characteristic wavelengths were extracted by using successive projection algorithm (SPA);then combined with linear discriminant analysis (LDA) and radial basis kernel function of support vector machine (RBFSVM) model were applied to identify the different mutton varieties under characteristic wavelengths and full-wave bands. Results showed that there were good effects for mutton varieties identification in different hyperspectral bands, among which Baseline-Fullwave-RBFSVM and the same models under 12 characteristic wavelengths obtained accuracy of 100% and 98.75% in 400~1 000 nm respectively, and Baseline-Fullwave-RBFSVM and the same models under 7 characteristic wavelengths obtained accuracy of 96.25% and 87.80% in 900~1 700 nm respectively.The identification accuracy of RBFSVM nonlinear classification was higher than the LDA linear discriminant result, meanwhile the identification accuracy in 400~1 000 nm bands was better than in 1 000~1 700 nm bands, which explained that the differences of color and texture were more significant than the component contents among the 3 varieties mutton. Combined hyperspectral technologies with RBFSVM models can obtain a better recognition effect of mutton varieties.

Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging.

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449 nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426 µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119 µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.

Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer.

Rolling bearings are critical components of industrial equipment, and predicting their remaining useful life (RUL) is a challenging task. This article proposes a new end-to-end model (MDSCT) to achieve efficient and accurate prediction of bearing RUL. MDSCT uses the raw vibration signals collected by sensors for prediction, without relying on a large amount of prior knowledge. The feature extraction backbone of this model is composed of an MDSC attention module that integrates multiple parallel depth-wise separable convolutions and an efficient attention mechanism, combined with a tansformer encoder (PPSformer) optimized using patch embedding and probesparse self attention techniques. They are respectively used to capture local subtle features and global dependent features in degraded signals. This article also proposes an improved adaptive activation function AdaptH_Swish to enhance the model's ability to model nonlinear relationships. To comprehensively verify the comprehensive performance of the model, this paper conducted detailed ablation and comparative experiments using two standard datasets, PHM2012 and XJTU-SY. The experimental results not only confirm the rationality and efficiency of the model structure design, but also demonstrate significant advantages in three evaluation indicators compared to various existing methods, fully demonstrating the high generalization and strong robustness of the MDSCT model in bearing RUL prediction tasks.

Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton.

Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R2p) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat.

Dynamic changes in the water and volatile compounds of chicken breast during the frying process.

The influence of frying times (0, 2, 4, 6, 8, and 10 min) on the continuous changes in the water distribution and the concentrations of key volatile compounds in chicken breast during the frying process were studied. The fried chicken samples could be distinguished by PCA of E-nose and PLS-DA of GC-MS. A total of 40 volatile compounds were identified by GC-MS, and 28 compounds were verified to be the key compounds after further screening by OAVs. The T22 was increased first and then decreased, while the M22 and M23 in fried chicken were considerably decreased and increased with increasing frying time, respectively. The content of the water and the total peak area of LF-NMR in fried chicken samples during the frying process significantly decreased, and the water was transferred from high to low degrees of freedom. In addition, water content, T21, T22, M22 and L* value were positively correlated with most alcohols and aldehydes, and were negatively correlated with pyrazines, while a*, b*, M23 and all amino acids were positively correlated with pyrazines and were negatively correlated with most alcohols and aldehydes. The results may guide the production processes of fried chicken and help produce high-quality chicken products.

Research progress of chilled meat freshness detection based on nanozyme sensing systems.

It is important to develop rapid, accurate, and portable technologies for detecting the freshness of chilled meat to meet the current demands of meat industry. This report introduces freshness indicators for monitoring the freshness changes of chilled meat, and systematically analyzes the current status of existing detection technologies which focus on the feasibility of using nanozyme for meat freshness sensing detection. Furthermore, it examines the limitations and foresees the future development trends of utilizing current nanozyme sensing systems in evaluating chilled meat freshness. Harmful chemicals are produced by food spoilage degradation, including biogenic amines, volatile amines, hydrogen sulfide, and xanthine, which have become new freshness indicators to evaluate the freshness of chilled meat. The recognition mechanisms are clarified based on the special chemical reaction with nanozyme or directly inducting the enzyme-like catalytic activity of nanozyme.

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging.

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM.

The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.

Spectral reconstruction from RGB image to hyperspectral image: Take the detection of glutamic acid index in beef as an example.

The use of spectral reconstruction (SR) to recovery RGB images to full-scene hyperspectral image (HSI) is an important measure to achieve real-time and low-cost HSI applications. Taking the detection of glutamic acid index for 360 beef samples as an example, the feasibility of using 11 state-of-the-art reconstruction algorithms to achieve RGB to HSI in complex food systems was investigated. The multivariate correlation analysis was used to prove that RGB is a projection of three-channel comprehensive coverage wide-band information. The comprehensive quality attributes (PSNR-Params-FLOPS) was proposed to determine the optimal reconstruction model (MST++, MST, MIRNet, and MPRNet). Moreover, SSIM values and t-SNE were introduced to evaluate the consistency of the reconstruction results. Finally, Lightweight Transformer was used to establish the detection models of Raw-HSI, RGB and SR-HSI for the prediction of glutamic acid index for beef. The results showed that the MST++ model exhibited the best performance in SR, with RMSE, PSNR, and SSIM values of 0.015, 36.70, and 0.9253, respectively. Meanwhile, the prediction effect of MST++ (R2P = 0.8422 and RPD = 2.46) reconstructed was close to the Raw-HSI (R2P = 0.8526 and RPD = 2.69). The results provide practical application scenarios and detailed analysis ideas for RGB-to-HSI.

Dynamic changes in the water distribution and key aroma compounds of roasted chicken during roasting.

The effects of roasting times (0, 2, 4, 6, 8, 10, 12, and 14 min) on the dynamic changes of the water distribution and key aroma compounds in roasted chicken during the electric roasting process were studied. In total, 36 volatile compounds were further determined by GC-MS and 11 compounds, including 1-octen-3-ol, 1-heptanol, hexanal, decanal, (E)-2-octenal, acetic acid hexyl ester, nonanal, 2-pentylfuran, heptanal, (E, E)-2,4-decadienal and octanal, were confirmed as key aroma compounds. The relaxation time of T22 and T23 was increased first and then decreased, while the M22 and M23 in roasted chicken were decreased and increased with increasing roasting time, respectively. The fluidity of the water in the chicken during the roasting process was decreased, and the water with a high degree of freedom migrated to the water with a low degree of freedom. In addition, the L*, a*, b*, M23 and all amino acids were positively correlated with all the key aroma compounds, while T22, M22 and moisture content were negatively correlated with all the key aroma compounds.

Electrochemical detection of enrofloxacin in meat using bimetallic organic framework-derived NiCo2O4@NiO.

A novel electrochemical aptasensor based on a bimetallic organic frame-derived carbide nanostructure of Co and Ni (NiCo2O4@NiO) was prepared for rapid and sensitive enrofloxacin (ENR) detection of sheep and pork liver meats. The composite was fabricated by solvothermal and direct pyrolysis methods and dropped onto a modified electrode to improve the electron transfer efficiency. Furthermore, different techniques such as scanning electron microscopy and X-ray photoelectron spectroscopy were used to characterize the morphology and structure of the materials. Electrochemical impedance spectroscopy and cyclic voltammetry were used to evaluate the performance of the electrochemical sensor. As a result, the electrochemical aptasensor based on NiCo2O4@NiO exhibited excellent sensing performances for ENR with an extremely low detection limit of 1.67 × 10-2 pg mL-1 and a broad linear range of 5 × 10-2 to 5 × 104 pg mL-1, as well as great selectivity, excellent reproducibility, high stability and applicability. In addition, the relative standard deviation for real samples was in the range of 93.83 to 100.09% and 94.95 to 100.01% for sheep and pork liver. The results showed that the composite can be expected to greatly facilitate ENR detection and practical applications in harmful food due to the advantages of simple fabrication, controllable, large-area uniformity, environmental friendliness, and trace detection.

Combination of hyperspectral imaging and entropy weight method for the comprehensive assessment of antioxidant enzyme activity in Tan mutton.

The antioxidant enzymes play the crucial role in inhibiting mutton spoilage. In this study, visible near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with entropy weight method (EWM) was developed for the first time to evaluate the antioxidant properties of Tan mutton. The comprehensive index of antioxidant enzymes (AECI) consisting of peroxidase (49.34%), catalase (37.97%) and superoxidase (12.69%) was constructed by the EWM. Partial least squares regression, least squares support vector machine and artificial neural networks (ANN) were developed based on characteristic wavelengths extracted by successful projections algorithm, uninformative variable selection, iteratively retains informative variables (IRIV), regression coefficient and competitive adaptive reweighted sampling (CARS). The textural features (TF) were extracted by the gray level co-occurrence matrix and fused with the spectral data to establish models. Visualization of the changes in antioxidant enzyme activity was constructed from the optimal model. In addition, two-dimensional correlation spectra (2D-COS) with AECI as a perturbation variable was used to identify spectral features, revealing chemical bond changes order under the characteristic peaks at 612-799-473-708-559 nm. The results showed that the IRIV-CARS-TF-ANN model performed the best, with prediction set coefficient of determination (RP2) of 0.8813, which improved 2.12%, 1.11% and 2.77% over the RP2 of full band, IRIV and IRIV-CARS, respectively. It was suggested that fusion data of HSI may effectively predict the activity of antioxidant enzymes in Tan mutton.

A new sensor for the rapid electrochemical detection of ractopamine in meats with high sensitivity.

In this research, g-C3N4/Cu@CoO/NC, which contained graphitic phase carbon nitride (g-C3N4) with a binary nanostructure and Cu@CoO/NC with a bimetallic MOF precursor, was constructed by a low-temperature pyrolysis process. The g-C3N4/Cu@CoO/NC was characterised by several techniques, including X-ray diffraction, scanning electron microscope, transmission electron microscope and X-ray photoelectron spectroscopy. Further, it was used to prepare an electrochemical sensor for the detection of ractopamine (RAC) in meat samples. The sensor showed excellent electrochemical oxidation characteristics for RAC detection, with a wide linear range (0.005 µmol/L to 32.73 µmol/L) and low detection limit (1.53 nmol/L). Meanwhile, the reproducibility, stability and interference of the g-C3N4/Cu@CoO/NC/GCE sensor were found to be excellent. Besides, the g-C3N4/Cu@CoO/NC/GCE sensor was well-used for the detection of RAC in pork, pig liver and lamb samples with recovery rates ranging from 96.5 % to 102.2 %.

Evaluation of key aroma compounds and protein secondary structure in the roasted Tan mutton during the traditional charcoal process.

The traditional charcoal technique was used to determine the changes in the key aroma compounds of Tan mutton during the roasting process. The results showed that the samples at the different roasting time were distinguished using GC-MS in combination with PLS-DA. A total of 26 volatile compounds were identified, among which 14 compounds, including (E)-2-octenal, 1-heptanol, hexanal, 1-hexanol, heptanal, 1-octen-3-ol, 1-pentanol, (E)-2-nonenal, octanal, 2-undecenal, nonanal, pentanal, 2-pentylfuran and 2-methypyrazine, were confirmed as key aroma compounds through the odor activity values (OAV) and aroma recombination experiments. The OAV and contribution rate of the 14 key aroma compounds were maintained at high levels, and nonanal had the highest OAV (322.34) and contribution rate (27.74%) in the samples after roasting for 10 min. The content of α-helix significantly decreased (P < 0.05), while the ß-sheet content significantly increased (P < 0.05) during the roasting process. The content of random coils significantly increased in the samples roasted for 0-8 min (P < 0.05), and then no obvious change was observed. At the same time, ß-turn content had no obvious change. Correlation analysis showed that the 14 key aroma compounds were all positively correlated with the content of α-helix and negatively correlated with the contents of ß-sheet and random coil, and also positively correlated with the content of ß-turn, except hexanal and 2-methypyrazine. The results are helpful to promoting the industrialization of roasted Tan mutton.

Water distribution and key aroma compounds in the process of beef roasting.

The key aroma compounds and water distribution of the beef at different roasting times (0, 3, 6, 9, 12, 15, and 18 min) were identified and analyzed. The results showed that the L * value increased considerably before peaking and then decreased. On average, a * values decreased significantly first and then kept stable, while b * values increased first and then decreased. A total of 47 odorants were identified in all samples, including 14 alcohols, 18 aldehydes, 6 ketones, 1 ester, 3 acids, 4 heterocyclic compounds, and 1 other compound. Among them, 11 key aroma compounds were selected and aldehydes and alcohols predominantly contributed to the key aroma compounds. The fluidity of the water in the beef during the roasting process was decreased, and the water with a high degree of freedom migrated to the water with a low degree of freedom. The correlation analysis showed that water content and L * were negatively correlated with key aroma compounds of the samples, while M 21 was positively correlated with key aroma compounds.

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef.

Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near-infrared hyperspectral imaging (NIR-HSI) combined with two-dimensional correlation spectroscopy (2D-COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D-COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH2 in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136-1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D-COS analysis, and provided excellent results (R2C of 0.8141, R2P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D-COS combined with NIR-HSI could be used as an effective method to monitor Ala content in beef.

Identification of Near Geographical Origin of Wolfberries by a Combination of Hyperspectral Imaging and Multi-Task Residual Fully Convolutional Network.

Ningxia wolfberry is the only wolfberry product with medicinal value in China. However, the nutritional elements, active ingredients, and economic value of the wolfberry vary considerably among different origins in Ningxia. It is difficult to determine the origin of wolfberry by traditional methods due to the same variety, similar origins, and external characteristics. In the study, we have for the first time used a multi-task residual fully convolutional network (MRes-FCN) under Bayesian optimized architecture for imaging from visible-near-infrared (Vis-NIR, 400-1000 nm) and near-infrared (NIR-1700 nm) hyperspectral imaging (HSI) technology to establish a classification model for near geographic origin of Ningxia wolfberries (Zhongning, Guyuan, Tongxin, and Huinong). The denoising auto-encoder (DAE) was used to generate augmented data, then principal component analysis (PCA) was combined with gray level co-occurrence matrix (GLCM) to extract the texture features. Finally, three datasets (HSI, DAE, and texture) were added to the multi-task model. The reshaped data were up-sampled using transposed convolution. After data-sparse processing, the backbone network was imported to train the model. The results showed that the MRes-FCN model exhibited excellent performance, with the accuracies of the full spectrum and optimum characteristic spectrum of 95.54% and 96.43%, respectively. This study has demonstrated that the MRes-FCN model based on Bayesian optimization and DAE data augmentation strategy may be used to identify the near geographical origin of wolfberries.

Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat.

Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectral and textural data of hyperspectral imaging (900-1700 nm) was proposed in this paper. Firstly, spectral information was obtained and preprocessed. Then, the spectral features were extracted by variable combination population analysis-genetic algorithm (VCPA-GA) and interval variable iterative space shrinking analysis (IVISSA). Subsequently, the prediction models of partial least squares regression (PLSR) and least-squares support vector machines (LSSVMs) were established and compared. The model constructed with SNVD-VCPA-GA-PLSR achieved better performance. To improve the prediction results of the models, the textural features were extracted using a gray-level co-occurrence matrix (GLCM) and fused with spectral features. The optimized model achieved good results, with Rc of 0.8716, RMSEC of 0.0793 g/100 g, RPDc of 2.398, and Rp of 0.8121 with RMSEP of 0.1481 g/100 g and RPDp of 1.756. Finally, the spatial distribution of the C18:0 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C18:0 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.

Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat.

The feasibility of combining spectral and textural information from hyperspectral imaging to improve the prediction of the C16:0 and C18:1 n9 contents for lamb was explored. 29 and 22 optimal wavelengths were selected for the C16:0 and C18:1 n9 contents, respectively, by conducting the variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) algorithm. To extract the textural features of images, a gray-level co-occurrence matrix (GLCM) analysis was implemented in the first principal component image. The least squares support vector machine (LSSVM) model and the partial least squares regression (PLSR) model were developed to predict the C16:0 and C18:1 n9 contents from the spectra and the fusion data. The distribution map was visualized using the best model with the imaging process. The results showed that the combination of the spectral and textural information of hyperspectral imaging coupled with the VCPA-IRIV algorithm had strong potential for the prediction and visualization of the C16:0 and C18:1 n9 contents of lamb.

The diagnostic and prognostic value of interleukin-10 in cerebrospinal fluid for central nervous system lymphoma: a meta-analysis.

Central nervous system lymphoma (CNSL) presents diagnostic and prognostic challenges. The aim of this meta-analysis was to evaluate the diagnostic and prognostic value of interleukin (IL)-10 in cerebrospinal fluid (CSF) for CNSL comprehensively. PubMed and Cochrane Library databases were searched through September 2016. Four studies with 212 CNSL patients and 262 control patients were included. The pooled sensitivity and specificity of CSF IL-10 for diagnosing CNSL were 81% (95% CI: 66-91%) and 97% (95% CI: 83-100%), respectively. The summary receiver operating characteristic (SROC) curve indicated that the area under the curve was 0.95 (0.93-0.97). The ROC curve based on extracted individual data showed that the optimal cutoff value was 6.88 pg/ml. Moreover, elevated CSF IL-10 was found to be associated with shorter progression-free survival (hazard ratio: 2.89, 95% CI: 1.13-7.41, p = .027). In conclusion, our meta-analysis showed that CSF IL-10 is an effective diagnostic and prognostic biomarker for CNSL.