Epstein-Barr virus infection with non-tumor-associated Anti-N-Methyl-D-Aspartate receptor encephalitis: a case report and review of literature.

To study a case of a middle-aged male with a non-tumor-associated Epstein-Barr virus (EBV) infection associated with Anti-N-methyl-D-aspartate receptor encephalitis (NMDARE), to explore the role of EBV in the pathogenesis of anti-NMDARE. The patient was diagnosed with "Anti-NMDARE, EBV infection" by using Cerebrospinal fluid (CSF) autoimmune encephalitis profile, and Metagenomics Next-Generation Sequencing (mNGS) pathogenic microbial assays, we discuss the relationship between EBV and NMDARE by reviewed literature. EBV infection may trigger and enhance anti-NMDARE, and the higher the titer of NMDAR antibody, the more severe the clinical presentation.

Plasma nesfatin-1 level is associated with severity of depression in Chinese depressive patients.

BACKGROUND: Nesfatin-1 plays a role in the regulation of emotional states like depression. The aim of this study was to investigate the plasma nesfatin-1levels in Chinese patients with depression and healthy subjects, and to determine the possible association between the plasma nesfatin-1 level and the severity of depression. METHODS: A total of 103 depressive patients and 32 healthy subjects were assessed. According to HAMD-17scores, 51, 18, and 34 patients were enrolled in the mild depression, moderate depression, and severe depression groups, respectively. Plasma nesfatin-1 levels were determined by the ELISA method. Differences between groups were compared and associations between plasma nesfatin-1 and other variables were analyzed. RESULTS: The plasma nesfatin-1 was significantly positively correlated with HAMD-17 score (r = 0.651). Compared with healthy controls (8.11 ± 3.31 ng/mL), the plasma nesfatin-1 level significantly increased in patients with mild depression (11.17 ± 3.58 ng/mL), with moderate depression (16.33 ± 8.78 ng/mL), and with severe depression (27.65 ± 8.26 ng/mL) respectively. Plasma nesfatin-1 level (Odds ratio [OR] = 1.269) was an independent indicator for severe depression by multivariate logistic regression analysis. CONCLUSION: The plasma nesfatin-1 level is positively correlated with the severity of depression. Plasma nesfatin-1 level may be a potential indicator for depression severity.

Measuring the Moisture Content in Maize Kernel Based on Hyperspctral Image of Embryo Region.

Maize is among the most important economic corps in China while moisture content is a critical parameterin the process of storage and breeding. To measure the moisture content in maize kernel, a near-infrared hyperspectral imaging system has been built to acquire reflectance images from maize kernel samples in the spectral region between 1 000 and 2 500 nm. Near-infrared hyperspectral information of full surface and embryo of maize kernel were firstly extracted based on band ratio coupled with a simple thresholding method and the spectra analysis between moisture content in maize kernel and embryo was performed. The characteristic bands were then selected with the help of Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA) and Successive Projection Algorithm (SPA). Finally, these selected variables were used as the inputs to build Partial Least Square (PLS) models for determining the moisture content of maize kernel. In this study, a significant relation, which the spectral reflectance decreases as moisture content increase, between moisture content and spectral of embryo in maize kernel was observed. For the investigated independent test samples, all the proposed regression models, namely CARS-PLS, GA-PLS and SPA-PLS, achieved a good performance by using the information of embryo region. The correlation coefficient (Rp) and Root Mean Squared Error of Prediction (RMSEP) and number of characteristic wavelength for the prediction set were 0.931 2, 0.315 3, 9 and 0.917 6, 0.336 9, 14 and 0.922 7, 0.336 6, 16 for CARS-PLS, GA-PLS and SPA-PLS models, respectively. And, compared with models obtained by full surface spectral information, less characteristic wavelengths is used for development of CARS-PLS, GA-PLS and SPA-PLS models, while similar results were obtained. Comprehensively analyzing to both model accuracy and model complexity, SPA-PLS model by using embryo region information achieved the best result. Wavelengths at 1 197，1 322 and 1 495 nm were applied to extracted the information of embryo region, and the bands at 1 322, 1 342, 1 367, 1 949, 2 070 and 2 496 nm were used to establish the SPA-PLS model. These results demonstrated that near-infrared hyperspectral information from embryo region is more effective for determination of moisture nondestructive in maize kernel.

[Near-infrared spectra combining with CARS and SPA algorithms to screen the variables and samples for quantitatively determining the soluble solids content in strawberry].

In using spectroscopy to quantitatively or qualitatively analyze the quality of fruit, how to obtain a simple and effective correction model is very critical for the application and maintenance of the developed model. Strawberry as the research object, this research mainly focused on selecting the key variables and characteristic samples for quantitatively determining the soluble solids content. Competitive adaptive reweighted sampling (CARS) algorithm was firstly proposed to select the spectra variables. Then, Samples of correction set were selected by successive projections algorithm (SPA), and 98 characteristic samples were obtained. Next, based on the selected variables and characteristic samples, the second variable selection was performed by using SPA method. 25 key variables were obtained. In order to verify the performance of the proposed CARS algorithm, variable selection algorithms including Monte Carlo-uninformative variable elimination (MC-UVE) and SPA were used as the comparison algorithms. Results showed that CARS algorithm could eliminate uninformative variables and remove the collinearity information at the same time. Similarly, in order to assess the performance of the proposed SPA algorithm for selecting the characteristic samples, SPA algorithm was compared with classical Kennard-Stone algorithm Results showed that SPA algorithm could be used for selection of the characteristic samples in the calibration set. Finally, PLS and MLR model for quantitatively predicting the SSC (soluble solids content) in the strawberry were proposed based on the variables/samples subset (25/98), respectively. Results show that models built by using the 0.59% and 65.33% information of original variables and samples could obtain better performance than using the ones obtained by using all information of the original variables and samples. MLR model was the best with R(pre)2 = 0.9097, RMSEP=0.3484 and RPD = 3.3278.

[Near-infrared hyperspectral imaging combined with CARS algorithm to quantitatively determine soluble solids content in "Ya" pear].

The present study proposed competitive adaptive reweighted sampling (CARS) algorithm to be used to select the key variables from near-infrared hyperspectral imaging data of "Ya" pear. The performance of the developed model was evaluated in terms of the coefficient of determination(r2), and the root mean square error of prediction (RMSEP) and the ratio (RPD) of standard deviation of the validation set to standard error of prediction were used to evaluate the performance of proposed model in the prediction process. The selected key variables were used to build the PLS model, called CARS-PLS model. Comparing results obtained from CARS-PLS model and results obtained from full spectra PLS, it was found that the better results (r(2)pre = 0. 908 2, RMSEP=0. 312 0 and RPD=3. 300 5) were obtained by CARS-PLS model based on only 15. 6% information of full spectra. Moreover, performance of CARS-PLS model was also compared with PLS models built by using variables got by Monte Carlo-uninformative variable elimination (MC-UVE) and genetic algorithms (GA) method. The result found that CARS variable selection algorithm not only can remove the uninformative variables in spectra, but also can reduce the collinear variables from informative variables. Therefore, this method can be used to select the key variables of near-infrared hyperspectral imaging data. This study showed that near-infrared hyperspectral imaging technology combined with CARS-PLS model can quantitatively predict the soluble solids content (SSC) in "Ya" pear. The results presented from this study can provide a reference for predicting other fruits quality by using the near-infrared hyperspectral imaging.

[Application of characteristic NIR variables selection in portable detection of soluble solids content of apple by near infrared spectroscopy].

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including unin- formative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were pro- posed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS- SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.

[Principles and applications of hyperspectral imaging technique in quality and safety inspection of fruits and vegetables].

The quality and safety of fruits and vegetables are the most concerns of consumers. Chemical analytical methods are traditional inspection methods which are time-consuming and labor intensive destructive inspection techniques. With the rapid development of imaging technique and spectral technique, hyperspectral imaging technique has been widely used in the nondestructive inspection of quality and safety of fruits and vegetables. Hyperspectral imaging integrates the advantages of traditional imaging and spectroscopy. It can obtain both spatial and spectral information of inspected objects. Therefore, it can be used in either external quality inspection as traditional imaging system, or internal quality or safety inspection as spectroscopy. In recent years, many research papers about the nondestructive inspection of quality and safety of fruits and vegetables by using hyperspectral imaging have been published, and in order to introduce the principles of nondestructive inspection and track the latest research development of hyperspectral imaging in the nondestructive inspection of quality and safety of fruits and vegetables, this paper reviews the principles, developments and applications of hyperspectral imaging in the external quality, internal quality and safety inspection of fruits and vegetables. Additionally, the basic components, analytical methods, future trends and challenges are also reported or discussed in this paper.

[Characteristic wavelengths selection of soluble solids content of pear based on NIR spectral and LS-SVM].

To improve the precision and robustness of the NIR model of the soluble solid content (SSC) on pear. The total number of 160 pears was for the calibration (n=120) and prediction (n=40). Different spectral pretreatment methods, including standard normal variate (SNV) and multiplicative scatter correction (MSC) were used before further analysis. A combination of genetic algorithm (GA) and successive projections algorithm (SPA) was proposed to select most effective wavelengths after uninformative variable elimination (UVE) from original spectra, SNV pretreated spectra and MSC pretreated spectra respectively. The selected variables were used as the inputs of least squares-support vector machine (LS-SVM) model to build models for de- termining the SSC of pear. The results indicated that LS-SVM model built using SNVE-UVE-GA-SPA on 30 characteristic wavelengths selected from full-spectrum which had 3112 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.956, 0.271 for SSC. The model is reliable and the predicted result is effective. The method can meet the requirement of quick measuring SSC of pear and might be important for the development of portable instruments and online monitoring.

[Detection of slight bruises on apples based on hyperspectral imaging and MNF transform].

Bruising is one of the major defects occurring on apple surface inevitably during postharvest handling and processing stage. To detect slight bruises on apples fast and efficiently, a novel bruises detection algorithm based on hyperspectral imaging and minimum noise fraction transform is proposed. First, the hyperspectral images in the visible and near-infrared (400 approximately 1 000 nm) ranges are acquired, and MNF transform based on full ranges could obtain better detection performance compared to PCA transform; Second, five wavebands (560, 660, 720, 820 and 960 nm) are selected as the effective wavebands based on the coefficient curve of I-RELIEF method conducted on spectra extracted from intact and bruise surface; Third, the bruises detection algorithm is developed based on the effective wavebands and MNF transform method. For the investigated 40 sound samples and 40 different time stage bruise samples, the results with a 97. 1% overall detection rate are got. The recognition results indicate that the proposed methods and the effective wavelengths selected in this paper are feasible and efficient. This research lays a foundation for the development of multispectral imaging system based on MNF transform for slight bruises detection on apples.

[Effectively predicting soluble solids content in apple based on hyperspectral imaging].

It is very important to extract effective wavelengths for quantitative analysis of fruit internal quality based on hyperspectral imaging. In the present study, genetic algorithm (GA), successive projections algorithm (SPA) and GA-SPA combining algorithm were used for extracting effective wavelengths from 400-1 000 nm hyperspectral images of Yantai "Fuji" apples, respectively. Based on the effective wavelengths selected by GA, SPA and GA-SPA, different models were built and compared for predicting soluble solids content (SSC) of apple using partial least squares (PLS), least squared support vector machine (LS-SVM) and multiple linear regression (MLR), respectively. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Among all the models, the SPA-MLR achieved the best results, where Rp(2), RMSEP and RPD were 0.950 1, 0.308 7 and 4.476 6 respectively. Results showed that SPA can be effectively used for selecting the effective wavelengths from hyperspectral data. And, SPA-MLR is an optimal modeling method for prediction of apple SSC. Furthermore, less effective wavelengths and simple and easily-interpreted MLR model show that the SPA-MLR model has a great potential for online detection of apple SSC and development of a portable instrument.

[Application of hyperspectral fluorescence image technology in detection of early rotten oranges].

Rottenness is most prevalent and devastating disease that threats citrus fruit. Automatic detection of early rottenness can enhance the competitiveness and profitability of the citrus industry. However, there is no efficient automatic detection technology at this time that could detect this disease. The navel orange was selected as research objective. Hyperspectral fluorescence imaging was used to detect early rottenness in orange. Optimum index factor (OIF) method was applied to identify the optimal band combination. 100% detection rate was achieved based on the optimal bands ratio image and two threshold values. The research showed that the proposed method can effectively overcome the affect from florescence effect because stem damage area and stem also can produce florescence under ultraviolet light. This study will lay a foundation for developing multispectral detection system used in on-line detection of early rottenness fruit.

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas.

Background: Cuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown. Methods: In this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB). Results: Two distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients' prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity-related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB. Conclusion: Collectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients' therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.

Deciphering a Novel Necroptosis-Related miRNA Signature for Predicting the Prognosis of Clear Cell Renal Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common histological and devastating subtype of renal cell carcinoma. Necroptosis is a form of programmed cell death that causes prominent inflammatory responses. miRNAs play a significant role in cancer progression through necroptosis. However, the prognostic value of necroptosis-related miRNAs remains ambiguous. In this study, 39 necroptosis-related miRNAs (NRMs) were extracted and 17 differentially expressed NRMs between normal and tumor samples were identified using data form The Cancer Genome Atlas (TCGA). After applying univariate Cox proportional hazard regression analysis and LASSO Cox regression model, six necroptosis-related miRNA signatures were identified in the training cohort and their expression levels were verified by qRT-PCR. Using the expression levels of these miRNAs, all patients were divided into the high- and low-risk groups. Patients in the high-risk group showed poor overall survival (P < 0.0001). Time-dependent ROC curves confirmed the good performance of our signature. The results were verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression models demonstrated that the risk score was an independent prognostic factor. Additionally, a predictive nomogram with good performance was constructed to enhance the implementation of the constructed signature in a clinical setting. We then employed miRBD, miRTarBase, and TargetScan to predict the target genes of six necroptosis-related miRNAs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that 392 potential target genes were enriched in cell proliferation-related biological processes. Six miRNAs and 59 differentially expressed target genes were used to construct an miRNA-mRNA interaction network, and 11 hub genes were selected for survival and tumor infiltration analysis. Drug sensitivity analysis revealed potential drugs that may contribute to cancer management. Hence, necroptosis-related genes play an important role in cancer biology. We developed, for the first time, a necroptosis-related miRNA signature to predict ccRCC prognosis.

[Advance on application of hyperspectral imaging to nondestructive detection of agricultural products external quality].

Hyperspectral imaging is an emerging technique that integrates conventional imaging and spectroscopy to obtain both spatial and spectral information from a studied object simultaneously. The images data can reflect the external features, surface defects and contamination. The spectra data can analyze physical structure and chemical composition in studied object. Therefore, hyperspectral imaging technology causes more and more attention, and has become a hot research topic recently. In order to track the latest research developments at home and abroad, application of hyperspectral reflectance and fluorescence imaging techniques to nondestructive detection of agricultural products external quality was reviewed, which would provide reference for application of hyperspectral imaging to agriculture.

Effect of acceptance and commitment therapy on rehabilitation patients with spinal cord injury.

This study aimed to explore the differences between the effectiveness of using a combination of rehabilitation and acceptance commitment therapy (ACT), and rehabilitation therapy alone for the treatment of spinal cord injury (SCI). The newly admitted patients with spinal cord injury whose post-traumatic stress disorder (PTSD) score was higher than 38 points were randomly categorized into the treatment group and control group, with 30 patients in each group. One group underwent ACT and rehabilitation treatment, while the other underwent rehabilitation treatment only. PTSD and functional independence measure (FIM) scores were evaluated. Changes in scores were compared between the two groups before, one month, two months, and three months after treatment. The total PTSD score in SCI patients who were treated with ACT was significantly different before and after treatment (P < 0.05). Total FIM scores were also significantly different before and after treatment (P < 0.05). The FIM score in the treatment group was significantly higher than that in the control group after 2 and 3 months of treatment (P < 0.05). The combination of rehabilitation therapy and ACT could immediately reduce stress levels and significantly improve impaired function, lifelong self-care ability, and the impact of rehabilitation therapy.

[Quantitative structure-cytotoxicity relationship of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives].

Both AM1 semi-empirical quantum chemistry method and HF/3-21g* ab initio method were employed to get related parameters or descriptors, particularly, the parameters of the solvation energy delta G with polarizable continuum model, for 42 anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives with known cytotoxicity. With parameters of quantum chemical calculation and traditional ones, 2 multiple linear regression models were obtained. The better regression equation has a high correlation coefficient (r = 0.938) and a low standard deviation (s = 0.125) and the squared correlation coefficient Q2 of the cross-validation is 0.799 (literaure: 0.740) by leave-one-out method. The results have certain significance for the design of new anti-HIV-1 drugs with lower cytotoxicity.

Phylogenetic Analysis and Development of Molecular Tool for Detection of Diaporthe citri Causing Melanose Disease of Citrus.

Melanose disease caused by Diaporthe citri is considered as one of the most important and destructive diseases of citrus worldwide. In this study, isolates from melanose samples were obtained and analyzed. Firstly, the internal transcribed spacer (ITS) sequences were used to measure Diaporthe-like boundary species. Then, a subset of thirty-eight representatives were selected to perform the phylogenetic analysis with combined sequences of ITS, beta-tubulin gene (TUB), translation elongation factor 1-α gene (TEF), calmodulin gene (CAL), and histone-3 gene (HIS). As a result, these representative isolates were identified belonging to D. citri, D. citriasiana, D. discoidispora, D. eres, D. sojae, and D. unshiuensis. Among these species, the D. citri was the predominant species that could be isolated at highest rate from different melanose diseased tissues. The morphological characteristics of representative isolates of D. citri were investigated on different media. Finally, a molecular tool based on the novel species-specific primer pair TUBDcitri-F1/TUBD-R1, which was designed from TUB gene, was developed to detect D. citri efficiently. A polymerase chain reaction (PCR) amplicon of 217 bp could be specifically amplified with the developed molecular tool. The sensitivity of the novel species-specific detection was upon to 10 pg of D. citri genomic DNA in a reaction. Therefore, the D. citri could be unequivocally identified from closely related Diaporthe species by using this simple PCR approach.

Clinical and Basic Research on Renshen Yangrong Decoction.

Renshen Yangrong Decoction has been used to treat asthenic disease symptoms, such as exhaustion, and qi and blood deficiency diseases. It not only promotes hematopoietic function and improve immune functions, but also alleviates coronary heart diseases, diabetic complications, malignant tumor, and brain injury. It has satisfactory curative effect on sleep disorders and fatigue. Herein, we provide an overview of fundamental research on Renshen Yangrong Decoction focusing on its hematopoietic and immune functions and the status of clinical research with regard to the above-mentioned diseases in recent years.

Correlation Analysis Between Attentional Bias and Somatic Symptoms in Depressive Disorders.

Objective: To investigate the relationship between attentional bias and the severity of depression as assessed by the TORAWARE state and physical symptoms. Methods: We enrolled 55 patients with depression and 60 healthy people. The Hamilton Depression Scale (HAMD-24), Somatic Self-Rating Scale (SSS), and the Chinese version of the Self-Rating Scale for the TORAWARE State of Neurosis (SSTN) were selected to assess the severity of psychological symptoms. Dot-probe tasks were used to detect attentional bias. We then analyzed the correlation of attentional bias with the total scores on the symptom scales. Results: The negative attentional bias and negative disengaging index scores were both greater than 0 (t = 3.15 and 2.78, respectively; all P < 0.01). The negative attention bias score was positively correlated with the SSTN and negative disengaging index scores (r = 0.29 and 0.53, respectively; all P < 0.05). SSTN score was positively correlated with the total HAMD and SSS scores (r = 0.34 and 0.38, respectively; all P < 0.05). Conclusion: There is no direct correlation between negative attentional bias and depression. It may be through the intermediate mechanism of TORAWARE state to influence symptoms.