Studies have shown that the proposed strategy can relatively accurately predict the recurrence outcome within three years, which is helpful for physicians to evaluate individual patients before surgery.Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians’ clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F Hepatocellular carcinoma (HCC) accounts for 85%-90% of the main pathological types of primary liver cancer –3. It isAccurate preoperative prediction of recurrence can help doctors assess the necessity and risk of surgery, so that they can design rational clinical decisions. Early (1-2 years after surgery)  and longThe rise of artificial intelligence (AI) technology has brought new strategies for the prediction of HCC recurrence, especially novel data processing methods represented by machine learning and radiomics. Studies have shown that patients’ preoperative imaging, personal information and clinical manifestations are closely related to prognosis , 17. BecNevertheless, there is still room for improvement in the current related work. For example, the lesion area adopted to extract features in most studies needs to be manually segmented from the original image, which brings great challenges to improving work efficiency and reducing costs. In addition, the features of the input model are often not concise and efficient, which will lead to a decrease in accuracy. It is necessary to explore efficient feature representations and achieve automatic and accurate predictions.This study aimed to develop an excellent predictive strategy for recurrence-free survival (RFS) outcomes in patients with HCC within 3 years after surgery. A 3-dimension deep learning framework was applied to automate lesion segmentation. Seven feature representation methods were compared to explore the most superior feature combinations, including clinical baseline indicators, radiomics features during arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), and combination of clinical data with radiomics features during each phase. Four novel Boosting ensemble learning models were selected for prediction of recurrence outcome. This work has the following highlights:● Deep learning was employed for automatic segmentation of regions of interest (ROI), which avoided the drawbacks of manual delineation.● Seven feature representations were explored to find the best model input.● The study compared novel Boosting ensemble learning methods to select the model with best performance, which may be applicable in the future.The workflow of this study is shown in HCC patients who underwent partial hepatectomy in Qingdao University Affiliated Hospital from January 2014 to December 2018 were followed after surgery regularly. The inclusion criteria were as follows: 1. The pathological diagnosis was HCC; 2. The first treatment was partial hepatectomy; 3. Enhanced CT examination was performed within 1 month before surgery, and all periods were completed; 4. The patient’s personal information and relevant clinical data were complete; 5. It has been confirmed that whether the recurrence occurred within 36 months after surgery. The following were the exclusion criteria: 1. Patients who have received chemotherapy, interventional therapy, targeted therapy, etc. before partial hepatectomy; 2. Patients with a history of other tumors; 3. Patients whose tumors have metastasized; 4. Imaging and clinical data were incomplete; 5. The follow-up data were incomplete or the recurrence outcome within 3 years couldn’t be judged. Additionally, all patients included in the study underwent radical hepatectomy. The criteria for radical hepatectomy were: (1) no residual tumor was found at the margin of resection, which was negative; (2) no tumor was found in the remaining liver; (3) tumor markers returned to normal within two months after surgery. Ultimately, 105 patients were selected for the study. RFS period is defined as the time from the date of liver resection to the date of recurrence and within 3 years after surgery is within 36 months from the date of liver resection.It must be emphasized that the principles of the Declaration of Helsinki were followed and the study was approved by the hospital ethics committee (ethics number: 20001-01). All patients signed an informed consent certificate before surgery.The scanning equipment for the detection was the German CT  and the American Discovery CT . The scanning method was a three-level contrast-enhanced scan of the upper abdomen, and the scanning range was from the top of the liver to the lower edge of the two kidneys. During the scanning process, the voltage, current, scanning layer thickness, layer spacing, and pixel matrix size were set to 120 kV, 200-350 mA, 5 mm, 5 mm, and 512 × 512, respectively. Workers administered iohexol and 350 mg/m1 of iodine through a peripheral vein at a flow rate of 3.0 ml/s and a dose of 1.5 ml/kg under the action of a pressure syringe. Finally, AP, PVP, and DP images were obtained for the study.Generally, studies mostly segment lesions manually, which reduces work efficiency. Based on the previous manual annotation, we built a 3D U-Net deep learning model for automatic and accurate segmentation of lesions.This work adopted the supervised learning to automatically segment the ROIs, so manual annotation was required before model training. Two physicians with extensive experience in radiology were selected for this task, one of whom delineated the tumor area of each slice with the help of 3D Slicer  software without knowing any patient’s baseline data, and the other one was responsible for checking the annotation results. Once there was a dispute, return to discuss and re-mark if necessary. All CT images for the three periods were delineated and formed into volumes of interest (VOIs).Considering that some slices in CT images do not contain ROIs, this will increase the computational complexity. Slices without lesions were cropped according to the annotated images and the remainders were studied. Moreover, we normalized the image format to 256×256×48 for better input to the model. In order to expand the amount of data, data augmentation operations were performed on the divided training set, including but not limited to image flipping, rotation, cropping, scaling, and blurring , 22.CT images have 3D structures, and the traditional method convert them into 2D slices and then send into the 2D segmentation model, which results in the loss of spatial information. In this study, a 3D convolutional neural network (3D U-Net) was constructed to segment lesions directly, which comprehensively preserved the spatial information between slices , 24.Similar to the classic U-Net, the 3D U-Net also consists of Encoder and Decoder, each of which contains four sub-modules. In the Encoder, each sub-module contains two 3 × 3 × 3 convolutional layers, and each convolutional layer is connected to an activation function. After completing the convolution operation, max-pooling with a stride of 2 is performed on each dimension. In Decoder, each sub-module contains an upsampling process (deconvolution operation) with a stride of 2, and then two 3 × 3 × 3 convolutional layers and activation functions are added in turn. It must be emphasized that the padding in the convolutional layer of this module is set to 1, which makes the convolution operation not change the size of the image. Changes in image size are completely controlled by pooling and upsampling. Additionally, the last sub-module of the Decoder consists of a 1 × 1 × 1 convolutional layer, which reduces the number of output feature maps. Batch normalization (BN) was introduced before each activation function.This work aims to segment liver tumors from other tissues, where the input channel of the model was set to 256 × 256 × 48, and the activation function adopted ReLU. After the construction was completed, the total parameters and the trainable parameters of the neural network reached 4,122,466 and 4,117,570, respectively.Feature extraction is an essential part of radiomics analysis. In this study, we performed radiomic feature extraction for segmented liver tumors. Using the Pyradiomics 3.0.1 library in Python, a total of 788 dimensional features including Shape, Firstorder, GLCM, GLRLM, GLSZM, and GLDM were extracted. Each type of features was performed 9 transformations including Original, Wavelet-LLH, Wavelet-LHL, Wavelet-LHH, Wavelet-HLL, Wavelet-HLH, Wavelet-HHL, Wavelet-HHH, and Wavelet-LLL. Among them, “Wavelet-XXX” represents the wavelet transform, followed by the corresponding basis function type.Due to the high dimension of the extracted features, it is easy to cause “dimensionality disaster” and affect the model performance. Therefore, selecting features with large contributions can reduce the dimension as much as possible without affecting the comprehensiveness of the features. This work employed the Least Absolute Shrinkage Selector Operator (Lasso) algorithm to select the extracted features and ranked the contribution of each feature. By constructing a penalty function, Lasso can compress the coefficients of variables and make some regression coefficients 0, so as to achieve the purpose of variable selection. In addition, Lasso can also filter variables and reduce the complexity of the model. The variable screening here refers to not putting all the variables into the model for fitting, but selectively putting the variables into the model to get better performance parameters. Complexity adjustment refers to controlling the complexity of the model through a series of parameters to avoid overfitting. The optimal model was fit and the value of the penalty parameter α was determined based on the sklearn library in Python. For the dimensionality-reduced features, correlation coefficients and cluster heatmaps, as well as the coefficient distribution of each feature are visualized to better interpret the radiomics features.This study collected clinical baseline data of HCC patients in addition to CT images, such as personal information and clinical indicators. The gender and age of patients were collected as personal information data. Clinical indicators here were mainly tumor markers and liver function indicators, including alpha-fetoprotein (AFP), hepatitis B surface antigen (HBsAg), albumin (ALB), the total bilirubin (T- BIL), alanine aminotransferase (ALT) and aspartate aminotransferase (AST), etc. It should be noted that positive and negative results were obtained for AFP and HBsAg, while other liver function indicators were represented as specific test results.A total of seven feature representations, including selected radiomics features during AP, PVP, and DP, clinical baseline features, and their combined features, were input into the recurrence prediction models. The Boosting ensemble learning algorithms were adopted to predict the RFS outcome within 3 years.Gradient Boosting Decision Tree (GBDT) is a classic ensemble algorithm in machine learning. Its main idea is to employ weak classifiers (decision trees) to iteratively train to obtain the optimal model, which has the advantages of good training effect and not easy overfitting. LightGBM (Light Gradient Boosting Machine) is a framework for implementing the GBDT algorithm. It supports efficient parallel training and has faster training speed, lower memory consumption, better accuracy, support for distributed and fast processing of massive data, etc. . CurrentA leaf-wise algorithm with a depth limit is adopted in LightGBM. This strategy finds the leaf with the largest split gain from all the current leaves each time, and then splits and loops, which reduce more errors and get better accuracy under the same number of splits. Moreover, the Gradient-based One-Side Sampling (GOSS) operation is proposed to reduce computation and improve accuracy. This method does not calculate the gradient through the sample points used, but calculates the gradient by partial sampling. The Exclusive Feature Bundling (EFB) is also proposed to bundle some features together to reduce the feature dimension, thereby reducing the time-consuming to find the best fork. This study implemented the LightGBM algorithm based on the sklearn library in Python to perform the binary classification task, that is, recurrence or not within 3 years.d (d is the depth of the tree). One of the advantages of this tree is that the prediction performance is better, and this structure can also weaken the shortcomings of easy fitting in decision trees to a certain extent. When Catboost completes training, it stores the leaf node value of each tree into a vector. When predicting, it can quickly retrieve the corresponding leaf node value by judging which leaf node it is in, so it can improve the prediction efficiency and enhance the model performance. This work selected it for predicting HCC recurrence.Categorical Boosting (CatBoost), as a novel ensemble learning algorithm, has been applied to some medical data processing tasks, but has not been used to predict HCC recurrence , 30. CatXGBoost has been widely used in the field of medical data analysis since it was proposed in 2014 , 32. In We also employed GBDT as the baseline model for comparison. It is an ensemble learning algorithm based on decision trees that iterates over new learners through gradient descent. In this paper, the classification task was performed, and the Classification And Regression Tree (CART) was selected.For the analysis of clinical baseline data, the differences involved in this study were compared using student t-test or Mann-Whitney U-test, where the criterion of significant difference was set at P<0.05. Mean ± 95% confidence interval (CI) was calculated as results for continuous variables. To reflect the criticality of certain variables, the univariate Kaplan-Meier curve was introduced for survival analysis.1 score, receiver operating characteristic curve (ROC) and corresponding AUC were introduced as performance evaluation criteria for the ensemble learning models. It should be emphasized that the classification threshold was set to 0.5.We calculated the mean Intersection overUnion (mIoU), accuracy (Acc), Kappa and Dice coefficients of 3D U-Net to reflect the segmentation effect. Additionally, Acc, recall, precision (Prec), F® Tesla V100 (32 GB memory) with acceleration support of the compute unified device architecture (CUDA). All work was carried out in the Windows 10 operating system, and the programming language, deep learning framework and key libraries included Python 3.7, Pytorch, Pyradiomics, sklearn, VTK, etc.The image data during the three scanning periods were randomly divided into training set, validation set and test set according to the ratio of 8:1:1. The segmentation model was trained on the training set and validation set, and the test set was employed to demonstrate the performance. All lesions segmented by the model during three periods were acquired and their radiomic features were extracted. For the Lasso regression algorithm, the study obtained the best α value through 10-fold cross-validation to select key features. Considering the small sample size, this study selected the 5-fold cross-validation method to determine the features representation and predict the recurrence outcome, and calculated the mean value of five experiments and the corresponding 95% CI as the results. The relevant computing equipment for this experiment was configured with a CPU AMD Ryzen 7 5800H (16 GB memory) and a GPU NVIDIADuring follow-up, 52 patients (49.52%) were found to have recurrence within 3 years after surgery, of which 46 (88.46%) were male and 6 (11.54%) were female; 24 (46.15%) were aged 60 years or older and 28 (53.85%) were younger than 60 years old; 34 (65.38%) were AFP positive, and 18 (34.62%) were negative; 51 (98.08%) were HBsAg positive, and 1 (1.92%) were negative. 53 patients (50.48%) were found to have no recurrence within 3 years after surgery, of which 41 (77.36%) were male and 12 (22.64%) were female; 25 (47.17%) were aged 60 years or order and 28 (52.83%) were younger than 60 years old; 30 (56.60%) were AFP positive, and 23 (43.40%) were negative; 45 (84.91%) were HBsAg positive, and 8 (15.09%) were negative. Based on this, a univariate Cox proportional hazards model was established to judge the influence of different factors on RFS, and the related results were represented by the Kaplan-Meier curves Figure 2-3, 2, respectively. After the model iterated for 500 epochs, it fully converged . At this point, we stopped the training and saved the parameters. The performance on the test set was excellent, with mIoU of 0.8874, Acc of 0.9915, Kappa of 0.8738 and Dice coefficient of 0.9360, which indicates that the deep learning model has strong generalization ability for segmenting liver lesions. To visually compare the segmentation effects, this paper presents 3D reconstruction visualization images of the upper abdomen based on CT scans, manually annotated tumors, and deep learning-segmented tumors , outperforming CatBoost (AUC=0.8084), XGBoost (AUC=0.7441) and GBDT (AUC=0.7343) when inputting radiomics features during AP and clinical baseline indicators. Previous studies have confirmed the state-of-the-art of the XGBoost algorithm in the HCC prognosis prediction task . XGBoostTo address this issue, we adopted LightGBM for predicting recurrence. Based on XGBoost, LightGBM employs histogram algorithm to solve the problem of excessive number of split points. This method takes up less memory and reduces computation time. Secondly, it introduces the GOSS algorithm, which extracts according to the weight information of the samples to reduce a large number of samples with small gradients, and at the same time does not change the distribution of the dataset too much. Moreover, LightGBM also proposes the EFB mode, which reduces dimensionality by bundling features. Therefore, LightGBM can improve the model accuracy while reducing the computational effort , which lIt should be emphasized that this study aimed to predict the postoperative recurrence risk of patients only through preoperative factors, including preoperative imaging examination and clinical indicators detection. Because only in this way can it help the doctor’s clinical decision-making. Although postoperative pathological examinations, such as microvascular invasion (MVI) are very meaningful for recurrence prediction , they weThere are some other studies to predict the recurrence of HCC after surgery. Shen et al.  used theIt is undeniable that the present study still has some shortcomings. For example, the small sample size from a single center challenges the applicability of the models. This work only focuses on the prediction of recurrence outcomes within 3 years, and further follow-up is required to predict at different times in the future. Moreover, the proposed method has not been tested in real clinical practice, which needs to be validated in the future. Zeng et al.  developeThis study aims to help physicians to evaluate the effectiveness of surgery and thus facilitate rational clinical decision-making. An ensemble learning strategy based on efficient feature representation was proposed for the recurrence outcome in HCC patients within three years after surgery. The 3D U-Net was used to automatically segment the lesions. Radiomics features during AP and clinical baseline features were selected as input and four ensemble models were trained. The results showed that LighGBM outperformed other ensemble algorithms, suggesting that it may be a novel model for predicting recurrence. In the future, the dataset will be expanded for early and late recurrence prediction and external clinical validation will be performed to validate the applicability of the method. When the generalization ability of the method is successfully verified, the relevant software (or web program) will be designed and applied to clinical practice.dongjiahong@mail.tsinghua.edu.cn.The data analyzed in this study is subject to the following licenses/restrictions: Since the dataset involved in this study involves patient privacy and has signed a non-disclosure agreement, it cannot be made public. Requests to access these datasets should be directed to Jiahong Dong, Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.LW: Study concept, image preprocessing, experimental design, data analysis, writing of manuscript. MW: Experimental design, editing the manuscript, and data collection. CZ: Data analysis and data collection. RL: Data collection. SB: Experimental design. SY and JD: Study concept and funding. All authors contributed to the article and approved the submitted version.This work was supported by National Natural Science Foundation of China ; CAMS Innovation Fund for Medical Sciences (grant number: 2019-I2M-5-056).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We sequenced the complete genome of the pseudorabies virus (PRV) FJ epidemic strain, and we studied the characteristics and the differences compared with the classical Chinese strain and that of other countries. Third-generation sequencing and second-generation sequencing technology were used to construct, sequence, and annotate an efficient, accurate PRV library. The complete FJ genome was 143,703 bp, the G+C content was 73.67%, and it encoded a total of 70 genes. The genetic evolution of the complete genome and some key gene sequences of the FJ strain and PRV reference strains were analyzed by the maximum likelihood (ML) method of MEGA 7.0 software. According to the ML tree based on the full-length genome sequences, PRV FJ strain was assigned to the branch of genotype II, and it showed a close evolutionary relationship with PRV epidemic variants isolated in China after 2011. The gB, gC, gD, gH, gL, gM, gN, TK, gI, and PK genes of the FJ strain were assigned to the same branch with other Chinese epidemic mutants; its gG gene was assigned to the same branch with the classic Chinese Fa and Ea strains; and its gE gene was assigned to a relatively independent branch. Potential recombination events were predicted by the RDP4 software, which showed that the predicted recombination sites were between 1694 and 1936 bp, 101,113 and 102,660 bp, and 107,964 and 111,481 bp in the non-coding region. This result broke the previously reported general rule that pseudorabies virus recombination events occur in the gene coding region. The major backbone strain of the recombination event was HLJ8 and the minor backbone strain was Ea. Our results allowed us to track and to grasp the recent molecular epidemiological changes of PRV. They also provide background materials for the development of new PRV vaccines, and they lay a foundation for further study of PRV. Pseudorabies (PR), also known as Aujeszky’s disease (AD), is an acute infectious disease caused by the pseudorabies virus (PRV) . The disThe PRV virus is a member of the Herpesviridae, Alphaherpesvirinae subfamily, varicella virus genus . In termIn the 1970s, the PRV Bartha-k61 vaccine strain was imported into China, and pseudorabies was well controlled for a time . HoweverRecently, pseudorabies broke out at an intensive pig farm in Fujian province, China. Some suckling piglets and weaned piglets had fever, lethargy, neurological symptoms, and they died. Sows gave birth to stillbirths and weak fetuses. The staff immunized pigs with PRV vaccine by the method of nasal drops six months ago. The incidence rate was approximately 23%, and the death rate of piglets was over 14% on the pig farm. We isolated and identified a PRV strain from 35 brain and tonsil samples of suckling piglets brought from the farm, and we named it the PRV FJ strain. After many rounds of virus multiplication and plaque purification in cell culture flasks, fluid virus samples were collected and frozen in a cryogenic refrigerator at −80 °C.g for 30 min according to the manufacturer’s instructions. After centrifugation, the concentrated liquid on the filter membrane was carefully aspirated with a 2–200 μL range pipette .The PRV FJ strain was inoculated into a full monolayer of BHK-21 cells. After the cytopathic effect (CPE) reached 80−90%, the cell culture flask was placed in a cryogenic refrigerator at −80 °C. After freezing and thawing three times, the cell culture flask was separately packed into a 50 mL aseptic centrifuge tube, and the supernatant was centrifuged at 4 °C and 3500 rpm for 15 min. After the supernatant was sterilized and filtered by a 0.45 μm filter membrane , it was transferred to a 15 mL ultrafilter tube with a maximum cut-off of 100 kD . The tube was centrifuged at 4000× (1)200 μL of the 10% SDS solution, and 15 μL of the 10 mg/mL RNase A were added in the Eppendorf tubes and incubated at 60 °C for 30 min in the metal bath .(2)100 μL of the 10 mg/mL Proteinase K was added and incubated in a metal bath at 56 °C for 30 min.(3)2O was added to make up the concentrated viral solution to 400 μL, then 600 μL of phenol: chloroform: isoamyl alcohol = 25:24:1 DNA extraction reagent was added, the Eppendorf tubes were carefully inverted and mixed, then the Eppendorf tubes were stood for 5 min to make the liquid stratified.The ddH(4)These Eppendorf tubes were centrifuged for 5 min at 12,000 r/min with a microcentrifuge , and they took the supernatant to avoid aspirating to the impurities in the middle layer.(5)Repeat steps (3) and (4).(6)An equal volume of isopropanol was added, mixed lightly, and precipitated for 1 h in a refrigerator at −20 °C.(7)These Eppendorf tubes were centrifuged at 12,000 r/min for 5 min with the microcentrifuge to discard the supernatant, 800 μL of anhydrous ice ethanol was added, then 1/10 volume of 3 mol/L NaAc was added, washed with light mixing, and left for 5 min.(8)These Eppendorf tubes were centrifuged at 12,000 r/min for 5 min with the microcentrifuge at 4 °C to discard the supernatant, the precipitate was placed in a biosafety cabinet, the exhaust air was turned on and blown until there was no smell of alcohol.(9)The precipitate was carefully dissolved in 100μL TE solution.DNA of the PRV FJ strain was extracted using the phenol–chloroform method:Then the extracted DNA samples were stored in a refrigerator at −20 °C for further use.The extracted PRV FJ DNA samples were sent to Wuhan BaiYi biotechnology company for complete genome sequencing. After the samples were qualified, the database was built with the PRV HLJ8 strain  as the reference sequence. Third- and second-generation high-throughput sequencing was carried out using a PacBio RS II sequencing system and a MGISEQ-2000 sequencing system, respectively. For the PacBio RS II system, the Sequel Binding Kit 2.1, the Sequel Sequencing Kit 2.1, and the Sequel SMRT Cell 1mv2  were used for sequencing. The data were processed with the SMRT LINK 6.0 software. The read quality value in the original data was filtered. Based on the complete genome sequencing using the PacBio equipment, the obtained sequence was corrected using the MGISEQ-2000 s-generation sequencing platform. Finally, the complete PRV FJ genome sequence was assembled and annotated.https://www.megasoftware.net, accessed on 25 October 2021).Fifteen PRV genome sequences uploaded to NCBI  were comThe genome alignments from the 15 PRV reference strains and the FJ strain were analyzed with the Recombination Detection Program 4 (RDP4) software to screen for potential recombination events. Seven algorithms, including RDP, BootScan, GENECONV, Maxchi, SiScan, Chimera, and 3Seq were employed .http://www.ncbi.nim.nih.gov/genbank, accessed on 18 October 2021) under the accession numbers of MW286330.The complete PRV FJ genome sequence was deposited in the GenBank database . Compared with the Bartha strain, the FJ strain had low homology in UL56, UL51, UL27, UL36, UL41, UL28.5, UL21, and LLT genes. Except for the HB1201 strain, the other Chinese reference strains and the MY-1 strain also showed homology differences in the above regions. The other country’s Becker, Kolchis, and Kaplan reference strains only showed significant homology differences in UL27, UL36, UL21, and US1 gene regions. The homology difference between HB1201 and Bartha was the greatest, and there were large base deletions in the UL56, UL27, UL21, UL36, LLT, US1, US3, and IE180 gene regions. In summary, after homology comparison with the Bartha strain, the regions with lower homology between the FJ strain and the other reference strains were mainly distributed in the non-coding region  method. All the strains were classified into two major branches. The other country’s Bartha, Becker, Kolchis, and Kaplan strains were located in the European and the North American genotype (genotype I) branch, while the Chinese strains and the MY-1 strain were located in the Asian genotype (genotype II) branch, which was consistent with the results of other reported genetic evolution analyses. The FJ strain was still located in the genotype II branch, close to the GD0304 strain branch, and it had the lowest genetic relationship with the ZJ01 strain in the genotype II branch .We selected the coding sequences of 12 genes related to immunogenicity and virulence of PRV, including TK, PK, gB, gC, gD, gG, gH, gL, gM, gN, gI and gE genes and analyzed them using the ML method of the MEGA 7.0 software. Except for gL, the phylogenetic trees of all genes produced the typical genotype I and genotype II branches, while the gL evolutionary tree showed that the Chinese epidemic mutant HeN1 strain belonged to the European and the North American genotype I. The Becker strain belonged to genotype II. All the above genes of the FJ strain were located in the large genotype II branch, and its gB, gC, gD, gH, gL, gM, gN, TK, gI, and PK genes were in the same branch as other Chinese mutants. Its gG gene was assigned to the same branch with the classical Chinese PRV Fa and Ea strains’ gG gene, while its gE gene was assigned to a relatively independent branch. All the genes of the MY-1 strain were located in the large branch of genotype II, except for TK, gL, gM, and gN; the other genes were located in a single branch compared with Chinese strains. The selected PRV FJ genes were far away from the other country’s strains, and they were very close to the Chinese mutants; and, the above-mentioned immunogenicity and virulence-related genes were not significantly different from the previous PRV variants .http://web.cbio.uct.ac.za/~darren/rdp.html, accessed on 14 October 2021); we predicted the recombination possibilities of the strain using Bootscan, LARD, 3seq, PhylPro, Maxchi, SiScan, and Chimaera algorithms. We detected several recombination signals for the FJ genome sequence  ; the HNBAmong the PRV genes we selected for phylogenetic tree analysis, gB, gD, gH, gL, and gK are necessary to ensure their replication, growth, and proliferation in cells . As PRV Through the detection of the complete genome recombination events of the PRV FJ strain, we found recombinant signals in the 1694–1936, 101,113–102,660, and 107,964–111,481 bp regions, indicating that recombination occurred in the corresponding regions of the HLJ8 and the Ea strains. According to the location of coding sequence annotations in However, gene recombination in the non-coding region may affect the ability of PRV to induce interferon-beta promoter activity and regulate viral messenger RNA (mRNA) ,31. The


Gastric cancer (GC) is one of the most common malignant tumors worldwide, and we hope to identify an economical but practical prognostic indicator. It has been reported that inflammatory indicators and tumor markers are associated with GC progression and are widely used to predict prognosis. However, existing prognostic models do not comprehensively analyze these predictors.This study retrospectively reviewed 893 consecutive patients who underwent curative gastrectomy from January 1, 2012, to December 31, 2015, in the Second Hospital of Anhui Medical University. Prognostic factors predicting overall survival (OS) were analyzed using univariate and multivariate Cox regression analyses. Nomograms including independent prognostic factors were plotted for predicting survival.p=0.001, p=0.016). The NLR-CA19-9 score (NCS) is constructed as the combination of the NLR and CA19-9. We defined NLR<2.46 and CA19-9≤37 U/ml as an NCS of 0, NLR≥2.46 or CA19-9>37 U/ml as an NCS 1, and NLR≥2.46 and CA19-9>37 U/ml as an NCS of 2. The results showed that higher NCS was significantly associated with worse clinicopathological characteristics and OS (p<0.05). Multivariate analyses revealed that the NCS was an independent prognostic factor for OS . Compared with traditional predictive indices, the NCS had the highest AUC for a 12-month survival, a 36-month survival, a 60-month survival, and OS . The nomogram had a higher Harrell’s C-index than the TNM stage alone (0.788 vs. 0.743).Ultimately, 425 patients were enrolled in this study. Multivariate analyses demonstrated that the neutrophil-to-lymphocyte ratio  and CA19-9 were independent prognostic factors for OS (The NCS provides more accurate predictions of the prognosis of GC patients, and its predictive value is significantly better than that of traditional inflammatory indicators or tumor markers. It is an effective complement to existing GC assessment systems. Gastric cancer (GC) is the fifth-most common malignancy in humans and ranks third in a tumor-related mortality according to the latest epidemiologic data . RadicalIn 1863, Virchow first discovered the relationship between inflammation and cancer ; subsequThe relationship between GC and tumor markers has basically been clarified, and a series of studies have explored the value of tumor markers in the diagnosis and prognosis of gastric cancer –20. At tHowever, these indicators remain controversial. For both inflammation indicators and tumor markers, the specificity and sensitivity in predicting the long-term outcome of GC patients alone are poor, and thus, there is an urgent need for a new, easy method to predict GC more accurately and perform targeted follow-up treatment and observation for patients who may have a poorer prognosis. Therefore, we attempted to combine inflammation indicators and tumor markers with a high predictive value to predict the prognosis of GC patients more accurately. In addition, we used prospective clinical data to investigate whether the new indicator could effectively predict postoperative outcomes of GC and compared its predictive value with other traditional indices.Between January 1, 2012, and December 31, 2015, a total of 893 consecutive patients admitted to the Second Hospital of Anhui Medical University were recruited for the trial. Patient eligibility criteria of this study included the following: (1) all patients who underwent gastrectomy with curative R0 resection; (2) postoperative pathology confirmed GC; (3) no active inflammatory, chronic infection, or autoimmune rheumatic diseases; and (4) no other malignancies. Patients who met the following criteria were excluded from this study: (1) serious complications or death that occurred within 15 days after operative, (2) treatment with neoadjuvant chemotherapy, (3) lack of inflammation and tumor marker data, and (4) acute complications such as perforation or bleeding. All patients, except for those with pTNM stage I, received 6–8 cycles of postoperative chemotherapy based on fluorouracil combined with platinum. This study was reviewed and approved by the Ethical Review Committee of the Second Hospital of Anhui Medical University, and the approved number is YX2021-138(f1). The patients were informed orally or in writing about the relevant matters of the study. All patients expressed their complete understanding of the study and signed an informed consent form.The collection of clinical indicators included basic demographic information , routine blood tests , and tumor-related information . All blood test data were collected 1 week before surgery. The NLR was calculated by dividing the neutrophil count by the lymphocyte count. The PLR was calculated by dividing the platelet count by the lymphocyte count. The LMR was calculated by dividing the lymphocyte count by the monocyte count. The clinical stage of GC was determined following the eighth American Joint Committee on Cancer (AJCC) guidelines. The optimal cutoff values of CEA (5 ng/ml) and CA19-9 (37 U/ml) were determined by the standards of our hospital.After surgery, all patients were followed by radiology and laboratory tests every 3 months for 2 years and every 6 months for 2–5 years. In addition, examinations, including chest radiography, endoscopy, or abdominal and pelvic CT, were performed at least once per year. The follow-up period lasted 5 years after surgery or to the date of death. The overall survival (OS) was defined as the time interval from surgery to the last follow-up or to death from any cause.p< 0.05 in the univariate analysis were subsequently included in a multivariate analysis. OS was assessed using the Kaplan-Meier method. Differences between the areas under the curve (AUCs) of each inflammatory and tumor marker were compared to determine the predictive value of each index for OS and the postoperative setting time points.Data are presented as mean ± SD for normally distributed data and median (interquartile range) for data not-normally distributed. Receiver operating characteristic (ROC) curves were generated. The optimal cutoff values of the NLR, PLR, and LMR were obtained through the ROC curves by calculating the Youden indices corresponding to different cutoff values of each inflammatory index in the ROC curve, and the corresponding cutoff values of the maximum value of the Youden index were used to divide the patients into two groups. A Cox proportional hazards regression model was used to identify the independent predictors associated with OS, and variables with a value of Models with independent prognostic factors were selected to plot the nomogram. The predictive values for survival were determined by Harrell’s C-index. In theory, the higher the C-index is the more precise the prognosis prediction. The decision curve analysis (DCA) plot can be used to represent the model with the greatest net benefits that had the highest clinical use, and it has been widely used to estimate whether the clinical use of diagnostic tests and prediction models would do more good than harm . In the p values less than 0.05 were considered statistically significant.All statistical analyses were performed using SPSS v.22.0 for Windows . The nomogram, DCA, and calibration curves were plotted with R Studio . Overall, 425 patients were included in the study Fig. . There wp=0.038, 0.009, <0.001, 0.016, 0.001, respectively)  , which combines the CA19-9 level and the NLR, was established. Based on this, the scoring standard of the comprehensive index was obtained, which ranged from 0 to 2. We defined NLR<2.46 and CA19-9≤37 U/ml as an NCS of 0, NLR≥2.46 or CA19-9>37 U/ml as an NCS 1, and NLR≥2.46 and CA19-9>37 U/ml as an NCS of 2. The NCS was 0 for 168 39.53%) patients, 1 for 175 (41.18%) patients, and 2 for 82 (19.29%) patients. The association between the NCS and the clinicopathological characteristics of patients with GC is demonstrated in Table 3% patienp<0.05)  for OS and 36- and 60-month survival, respectively, and the differences were all statistically significant p<0.0  was higher than that of the TNM stage alone , indicating a more accurate and robust performance estimate. In addition, the DCA of the novel nomogram revealed a superior net clinical benefit over the 8th AJCC TNM staging system alone and exhibited higher clinical use than the AJCC staging system in OS prediction  19-9 is one of the most common tumor markers of gastric cancer, and positivity is frequently related to tumor stage, poor prognosis, recurrence, and metastasis –20. Jingp<0.05). Moreover, a model including the NCS  was superior to that constructed without it  in predicting OS after radical gastrectomy. Importantly, the novel index in our model could be conveniently attained from routine laboratory inspection, which was advantageous because it avoids the potential unavailability of other predictors.Unfortunately, previous conclusions on these indicators have not always been consistent. Some scholars have insisted that there was no significant correlation between the preoperative NLR and survival time in GC patients –35. In aThe results of this study that the NCS, as a novel scoring system, has favorable sensitivity and specificity in predicting the long-term postoperative outcomes of GC patients and could provide more precise and informative prognostic value than other indicators. This new indicator may have vital use in predicting recurrence, therapeutic intervention, and surveillance strategies, especially when incorporating the TNM stage. For example, patients with a higher NCS could be strongly recommended to receive postoperative multimodal treatment, such as chemotherapy, immunotherapy, or targeted therapy and should receive a more rigorous follow-up schedule, with comprehensive medical examinations at least every 3 months for 5 years, such as gastrointestinal endoscopy, CT with contrast, and serum tumor markers, which may provide a survival benefit for GC patients.Several limitations remain in our present study. First, the main drawback of our study is its retrospective nature, and bias in the process of patient selection cannot be avoided. Second, because the specific time of tumor recurrence in some patients cannot be completely determined, we did not study the relationship between various indicators and postoperative progression-free survival (PFS). Third, the pathological stages of the patients in our study are relatively broad, so the conclusion may not apply to patients with a particular TNM stage. But we are still expanding the sample size to verify whether our conclusion are applicable to similar stages.In conclusion, to the best of our knowledge, the NCS is an independent prognostic factor for OS in GC patients and provides great value in predicting postoperative overall survival. It is an effective complement to existing GC assessment systems. Future studies should consider combining the NCS into the current TNM system to more specifically predict the prognosis of GC patients, who will be likely to benefit from a rigorous follow-up strategy.


P < 0.001 for all cases), HIT-6 , and FD  scores at the end of treatment showed a significant decrease in the 3 groups compared to the baseline. However, the differences in those variables between the 3 groups were not significant at the end of the study. The polyherbal formulation alone may improve the symptoms of migraine patients and other groups. This effect could be due to improving digestion and FD in migraine patients.A holistic concept based on traditional Persian medicine (TPM) describes a headache with a gastrointestinal (GI) origin (gastric-headache). Although the neurological manifestations of this headache are similar to those of other headaches, its etiology is different. Considering its simultaneous effects on the brain and GI system, a formulation was designed based on this concept. This study aimed to determine the safety and efficacy of the designed formulation on migraine headache (MH) associated with functional dyspepsia (FD). A total of 75 diagnosed cases of MH patients with concurrent FD were randomly divided equally into 3 groups: (i) the polyherbal formulation, sodium valproate (VPA), and amitriptyline group, (ii) VPA, amitriptyline, and polyherbal formulation placebo group, and (iii) the polyherbal formulation and VPA placebo group. The primary outcomes, including frequency, duration, and severity of MH attacks, were measured at baseline and weeks 4, 8, and 12. However, secondary outcomes, including the Headache Impact Test 6 (HIT-6) Questionnaire and Parkman's score, were evaluated at baseline and end of treatment. The frequency, duration, and severity of migraine ( The International Headache Society (IHS) has classified headaches into primary and secondary types. Primary headaches include migraine, tension-type, and cluster headaches . MigrainOn the other hand, functional dyspepsia (FD) is a relapsing and remitting disorder characterized by upper abdominal discomfort, early satiety, postprandial abdominal distension, nausea, vomiting, and anorexia, according to ROME III and Parkman's criteria , 6. The Interestingly, the prevalence of dyspepsia in Lankarani et al. study's was about 32.1% . MoreoveHelicobacter pylori infection [Genetic, hormonal, immune, neuronal, and environmental factors are implicated in susceptibility to MH , 23. In nfection , 26–28. nfection .Based on previous studies, the central nervous system (CNS) is linked to the gastrointestinal (GI) system in various ways. The CNS interacts with GI and the enteric nervous system (ENS) . This biSeveral preventive strategies are used for MH, including antiepileptic drugs, such as sodium valproate (VPA), tricyclic antidepressants, anti-inflammatory drugs, and beta-blockers , 33. HowSeveral studies have evaluated CAM use among patients with specific chronic diseases, including diabetes, cancer, IBS, gingivitis, and cardiovascular diseases, with evidence suggesting high CAM use among patients with chronic illnesses –38. A hoPhyllanthus emblica L. (Amla), Rosa damascena Mill L. (Damask rose), Coriandrum sativum (coriander), and Zingiber officinalis (ginger). Next, the formulations were powdered using a grinder and filtered through a 100-mesh stainless steel screen to obtain isodiametric particles. The final powder was used to prepare the capsules. The capsules were prepared by the School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran, containing 750 mg of the polyherbal formulation, including 150 mg of Damask rose petal and 150 mg of coriander seeds, 250 mg of ginger rhizomes, and 200 mg of dried amla fruit. The capsules were packaged in plastic boxes containing 90 capsules. The standardization of the polyherbal formulation was based on total phenol. Gallic acid in the hydroalcoholic extract constituted 229 mg/g of the crude extract. Also, we used 200-mg VPA tablets .The constituents of the polyherbal formulation consist of In the current study, we considered 2 types of placebo prepared by the School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran. The placebo tablets contained 60% microcrystalline cellulose, 20% lactose, 19.5% starch, and 0.5% magnesium stearate , similarThis study was conducted in 2 educational hospitals  affiliated with Mashhad University of Medical Sciences, Mashhad, Iran, from May 2017 to August 2018. After screening and selecting eligible subjects, the baseline information, demographic characteristics ), type of migraine (with/without aura), and family and personal history of migraine were documented. Three main aspects of MH  were recorded to assess the attacks.IRCT20170317033107N3).This study was approved by the Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.AC.REC.1350.58). All patients were informed about the study protocol, and signed informed consent was obtained before entering the study. This randomized clinical trial was registered on the Iranian registry of clinical trials website (β (ICHD-III β) [This randomized, double-blind clinical trial was performed on patients with MH associated with FD. The selected patients met the diagnostic criteria for MH according to the International Classification of Headache Disorders-IIID-III β)  and ROMED-III β) . A 12-itFurthermore, the Headache Impact Test 6 (HIT-6) was also performed for the patients at the beginning and end of the study. This questionnaire contains 6 questions, with a minimum score of 36 and a maximum score of 72. Higher scores indicate the more significant impact of headache on disabling pain experience, daily function, social function, vitality, cognitive function, and psychological distress .Noteworthy, all diagnoses are based on specific criteria by a neurologist and gastroenterologist. Additionally, to determine the effects of the interventions in this study, patients were advised not to change their medications (both for migraine and dyspepsia).To determine possible side effects, routine laboratory tests, including complete blood cell count (CBC), fasting blood sugar (FBS), triglyceride (TG), cholesterol, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total and direct bilirubin, were carried out. Since all tests were normal both before and after the interventions, the data are not shown.β criteria) [2 without the following criteria during the study: (1) bile reflux, cholelithiasis, or steatohepatitis, (2) uncontrolled hypertension, diabetes, GI cancer, or peptic ulcer, (3) pregnancy or lactation, (4) occurrence or diagnosis of any hepatic, renal, cardiac, pulmonary, systemic, or psychological diseases, (5) neurological diseases other than MH during the study, and (6) history of using nitrate-containing medicines. Based on the exclusion criteria, we aligned the study's patients using new migraine cases with FD. Therefore, the medications were considered the same for both groups.All Iranians of Persian ethnicity aged 18–55 years diagnosed with MH (based on the ICHD-III riteria)  and concThere were 3 intervention groups in this study that received treatment as follows: d how to use.Group 1: The polyherbal formulation capsule, 1,500 mg (2 capsules of 750 mg) 3 times a day + Depakine® tablet, 200 mg, twice a day + Amitriptyline tablet, 10 mg, once a day.Group 2: Depakine® tablet, 200 mg, twice a day + Amitriptyline, 10 mg, once a day + the polyherbal formulation placebo capsule 3 times a day.Group 3: The polyherbal formulation capsule, 1,500 mg (2 capsules of 750 mg) 3 times a day + Depakine® placebo tablet twice a day.The duration of the intervention was 12 weeks in this study. During this period, the patients were followed up to determine the frequency, duration, and severity of MH attacks in weeks 4, 8, and 12. HIT-6 was completed, and ROME III and Parkman's criteria were evaluated both at baseline (week 0) and end of week 12. The possible adverse effects of medications were also monitored during the intervention using laboratory tests. At the end of the clinical trial and 6 weeks after the intervention termination (week 18), the patient's satisfaction with the treatment, symptom relief, and relapse was surveyed using a Likert scale. A specialist collected all data using a handmade preformed checklist of criteria. Based on their medication compliance, all medications were delivered to patients every 15 to 30 days.The patients also received a dietary guide pamphlet on abstinence orders and behavioral advice, which are described below:Abstinence orders: Restrictions on some types of foods, including mucus-producing , flatulent, hard-to-digest, and heavy meals ; some dairy products according to TPM and current nutrition guidelines; and especially some high-FODMAP  foods  [shrooms) .Behavioral advice: Avoid drinking cold water with an empty stomach or after intensive physical activity and exercise [exercise , 50, avoexercise .The patients were required to complete a self-report checklist regarding their adherence to the guidelines, their abstinence, and the medicine used during the course of treatment. There are no definitive guidelines for establishing consensus regarding the current study and the cutoff. While some researchers have suggested that 51% agreement among respondents can indicate consensus regarding the cutoff, others have adopted more stringent levels. We defined consensus as 75% as the cutoff or more responses falling within a 2-point bracket on a response scale, according to Clyne et al. study's .Based on a pilot study on 15 patients with migraine-associated FD (indicating a 65% improvement in migraine attacks), as well as the sample size of similar studies  , we recrA total of 75 participants were randomly assigned a number using SPSS software from 1 to 75 and divided into 3 columns . The subjects were placed in one of the groups according to their order of entering the study. Noteworthy, this study was a double-blind clinical trial, and patients and researchers were unaware of the treatment groups.P values less than 0.05 were considered statistically significant.The final clinical and paraclinical data were analyzed using SPSS version 16. The characteristics of each group were described by descriptive methods, including central tendency and dispersion, according to the study's objectives and studied variables. The nonparametric Friedman's test was used to compare quantitative variables  in the 3 groups from week 0 to week 12. The nonparametric Kruskal–Wallis's test was also used to compare mean differences between the groups at the beginning and end of the study. Moreover, Spearman's nonparametric test was performed to investigate the correlations of frequency, duration, and severity of MH attacks with severe FD. In all calculations, A total of 75 MH patients with concurrent FD participated in this study and were randomly divided into 3 intervention groups. P < 0.001 for all cases). However, the intergroup analysis showed no significant difference between the groups regarding the frequency, duration, and severity of MH attacks  with the occurrence of migraine. The possible mechanisms include response to chronic inflammation and the presence of vasoactive peptide mediators (e.g., calcitonin gene-related peptide , substanBased on the evidence discussed in TPM and current phytopharmacological studies, the possible mechanisms contributing to the efficacy of the poly-herbal formulation in migraine treatment may be the increased tonicity of the gastroesophageal junction, improvement of the gastric emptying rate, reduction of gas production and decomposition in the gut, and prevention of gas effects on the brain. Overall, a decrease in FD symptoms can decrease the rate of MH attacks.β [β to ICHD-3 was a change to the diagnostic criteria of migraine with aura and migraine with typical aura, with the aim of better differentiating migraines from transient ischemic attacks [As a limitation of the current study, the study was scientifically and ethically approved in 2017, the final version of the ICHD was ICHD-3β , and we  attacks . HoweverBased on the results, we can conclude that using the polyherbal formulation and dietary abstinence may positively affect the frequency, duration, and severity of MH. Also, the polyherbal formulation can improve FD in combination with dietary abstinence and VPA. Therefore, the polyherbal formulation has remarkable effects on FD and can be used as an adjuvant therapy with VPA for MH . However


GAUT, CESA, PSK, CyclinB, CyclinA, CyclinD3 and cdc2 genes associated with plant regeneration were significantly down-regulated to 65%, 47%, 2%, 18.60%, 21.32%, 52% and 45% of young leaves, respectively. After enzymatic digestion, Aux/IAA expression was up-regulated compared with young leaves, and Aux/IAA expression was 3.53 times higher than that of young leaves. Compared with young leaves, these key genes were significantly changed after enzymatic digestion. These results indicate that the process of somatic enzymatic digestion process may affect the regeneration of heterozygous cells to a certain extent.Sugarcane somatic cell hybridization can break through the barrier of genetic incompatibility between distantly related species in traditional breeding. However, the molecular mechanisms of sugarcane protoplast regeneration and the conditions for protoplast preparation remain largely unknown. In this study, young sugarcane (ROC22) leaves were enzymatically digested, and the viability of protoplasts reached more than 90% after enzymatic digestion . Transcriptome sequencing was performed on young sugarcane leaves and protoplasts after enzymatic digestion to analyze the differences in gene expression in somatic cells before and after enzymatic digestion. A total of 117,411 unigenes and 43,460 differentially expressed genes were obtained, of which 21,123 were up-regulated and 22,337 down-regulated. The GO terms for the 43,460 differentially expressed genes (DEGs) were classified into three main categories: biological process, cellular component and molecular function, which related to developmental process, growth, cell proliferation, transcription regulator activity, signal transducer activity, antioxidant activity, oxidative stress, kinase activity, cell cycle, cell differentiation, plant hormone signal transduction, and so on. After enzymatic digestion of young sugarcane leaves, the expressions of  Somatic cell hybridization is a breeding method in which crop plants are fused by somatic cells to obtain regenerated plants. It is characterized by the ability to transfer genes from one species to another, which provides an effective means of breaking the barriers to hybridization before and after fertilization of distantly related and unrelated species in traditional breeding. . SomaticPlant somatic cell fusion through cell wall separation (enzymolysis) and passivation includes a series of processes, and every step is likely to damage cells ,11. The This study used sugarcane young leaves as materials for transcriptome sequencing to screen the differentially expressed genes related to plant regeneration, to study the molecular mechanism of hybrid cell regeneration through analyzing the key gene expression in the process of somatic cell enzymolysis and chimera cell regeneration, which may provide a reference for further studying the causes of heterozygous cell regeneration difficulties and the molecular regulation mechanism of heterozygous cell regeneration in the future, to improve the success rate of regeneration and achieve accurate regulation of heterozygous cell regeneration in sugarcane.The top part of the stem with sheaths of sugarcane at early elongation stage a was selWe identified 43,460 differentially expressed genes (DEGs) in protoplasts VS young leaves after enzymatic digestion with the set threshold at 0.05. A total of 43,460 genes were identified as DEGs by RNA-seq. Of them, 21,123 were up-regulated while 22,337 were down-regulated .TM were transformed into sequenced reads by base calling. Raw data are recorded in a FASTQ file, which contains sequence information (reads) and corresponding sequencing quality information.Trinity was used to assemble clean reads, and the assembly quality was shown in Quantitative analysis of differential genes expression in young sugarcane leaves before and after enzymatic digestion showed that 86,045 DEGs (76.49%) were common between these two assemblies, 11,197 DEGs (9.95%) unique to those in the sample before enzymatic digestion, and 15,248 DEGs (13.56%) unique to those in the sample after enzymatic digestion .To validate the RNA-seq results, ten genes were randomly selected for relative expression analyses by quantitative PCR (qRT-PCR). In all cases, the up-and-down trend of the fluorescence quantitative results of the selected DEGs was consistent with the RNA-seq results, indicating that the results were reliable. However, there were slight differences in the expression levels of these genes due to the differences in the sensitivity and accuracy of fluorescence quantification and RNA-seq . p-value (FDR) of <0.05. By similar expression kinetics, 370 DEGs were depicted by a heatmap analysis to show significant clusters of DEGs according to tissue specificity and time treatment. Similar expression profiles were observed for replicated samples, and specific clusters were denoted for the samples before and after enzymatic digestion of young sugarcane leaves, which reflected considerable difference between the treatments of young leaves and enzymatic digestion >2 and an adjusted digestion.Gene Ontology (GO) analysis was employed to identify the important functional groups of the total DEGs between the treatments of young leaves VS enzymatic digestion and visualize the transcript expression. The GO-term annotation suggested that a large proportion of the DEGs were attributed to developmental process, reproductive process, growth, cell proliferation, transcription regulator activity, signal transducer activity, antioxidant activity, oxidative stress, kinase activity, cell cycle, cell differentiation and so on. The DEGs related to these processes are closely related to the cell wall regeneration, cell division and differentiation of freshly isolated protoplasts during culture . KEGG metabolic pathway analysis showed that the 21,228 DEGs between young leaves and enzymatic digestion samples were enriched in 20 pathways , and theOf the 3856 DEGs unique in the young sugarcane leaves before and after enzymatic digestion of young leaves, 2266 transcripts were down-regulated and 1590 up-regulated, respectively . The mosGAUT; cellulose synthase gene, CESA), cell cycle-related genes , cell proliferation-related genes  and plant hormone signal transduction-related genes (Aux/IAA). After enzymolysis of the young sugarcane leaves, the expression levels of the following genes decreased gradually, and GAUT expression was only 65% of the original. The expression of CESA was only 47% of the original. Compared with the young leaves before enzymolysis, the expression level of PSK in protoplasts was very low, which was only 2% of that in the young leaves. After enzymolysis, Aux/IAA expression was up-regulated compared with that in the young leaves, and Aux/IAA expression was 3.53 times higher than that in the young leaves. Cyclin B expression was significantly down-regulated after enzymolysis, accounting for 18.60% of that in the young leaves only. The expression level of Cyclin A was significantly down-regulated, which was only 21.32% of that in the young leaves. The expression level of Cyclin D3 in protoplasts treated by enzymolysis was significantly lower than that in the young leaves (only 52%). The expression of CDC2 was similar to Cyclin D3, which was only 45% of that in the young leaves  were found among them. The expression levels of DEGs in hormone signal transduction pathway were significantly different between sugarcane young leaves and protoplasts after enzymatic digestion, and these key DEGs were significantly down-regulated after enzymatic digestion, suggesting that enzymatic digestion may have a certain degree of adverse influence on heterozygotic cell regeneration. Xu et al. (2021) identified these genes that are not only required for but also greatly promote protoplast regeneration, in particular, callus formation .Cellulose, hemicellulose and pectin are the three main components of plant primary cell walls. Galacturonic acid transferase  is the key enzyme catalyzing UDP-galacturonic acid to form homogalacturonan frame in pectin bio-synthesis ,26. CellPeptide signaling is an integral part of cell-to-cell communication which helps to relay the information responsible for coordinating cell proliferation and differentiation. Phytosulfokin Receptor (PSKR) is a transmembrane LRR-RLK family protein with a binding site for small signaling peptide, phytosulfokine (PSK). PSK signaling through PSKR promotes normal growth and development and also plays a role in defense responses . MatsubaAux/IAA is the initial response gene of growth hormone, which is the hub of the growth hormone signaling process . When grThe cell cycle proteins Cyclin A and Cyclin B form a complex with cell cycle-dependent protein kinase (CDK) to regulate cell cycle progression ,36. CyclThe expression of CyclinD3, a cell cycle protein, is highly dependent on cytokinin ,42. Cdc2The sugarcane variety ROC22 was used, and the young leaves were sampled at early elongation stage. The samples were quick-frozen in liquid nitrogen and sorted at −80 °C for further use.6/mL, 0.5 mL were taken and shaken well, then frozen in liquid nitrogen and stored at −80 °C. Two replicates were set.The separation of protoplast from young sugarcane leaves was done based on the method proposed by Song (2018)  with sliTotal RNA was extracted from the materials before and after enzymatic hydrolysis of young sugarcane leaves. Agarose gel electrophoresis was performed on the extracted RNA, and Agilent 2100 Bioanalyzer  was used to determine the total RNA concentration, RIN value, 28S/18S and fragment size of the samples. After the RNA was qualified, the total RNA was processed by enriching the mRNA with polyA tail by Oligod magnetic beads. The RNA obtained was segmented by interrupting buffer, and the random N6 primers were reversely transcribed, and then the two strands of cDNA were synthesized to form double-stranded DNA. The synthetic double-stranded DNA ends were smoothed and phosphorylated at the 5′ end to form a sticky end protruding a “A” at the 3′ end, and then connected with a bubbling-like connector protruding a “T” at the 3′ end. The ligands were amplified by PCR with specific primers. The PCR products were thermally denatured into single strands, and the single strand DNA was cycled with a bridge primer to obtain a single strand circular DNA library, which was then sequenced on Illumina platform.The full-length transcriptome sequencing was carried out, and a total of 26.48 Gb of data were obtained from RNA-seq using BGISEQ-500 platform. First, the sequencing joints were pruned, and then reads with unknown base N content greater than 5% and low-quality reads were removed . After clean reads were obtained in the following analysis, trinity was used for de novo assembly of clean reads, and tgicl was used for clustering de-redundancy of transcripts to obtain unigene. According to the assembly results, unigene was used, the clean reads of each sample were compared to unigene by Bowtie2 software , and then the gene expression level of each sample was calculated by RSEM. According to the method of Wang et al. (2010) , DEGseq p-value. Generally, the GO item at FDR 0.01 and the KEGG pathway at FDR 0.01 were considered to be significantly enriched.To define the function of DEGs, enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using the Phyper function in R software , followed by FDR correction for TM II Qrt-pcr was performed using TaKaRa’s TB GreenTM Premix Ex TaqTM II Kit. RT-PCR was performed according to the TB GreenTM Premix Ex TaqTM II kit from TaKaRa, and the amplification system was 20 μL and the amplification procedure was as follows: 95 °C 30 s, 1 cycle; quantitative analysis: 95 °C 5 s, 60 °C 30 s, 40 cycles; melt curve: 95 °C 5 s, 60 °C 1 min, 95 °C, 1 cycle; cooling: 95 °C 30 s, 1 cycle.According to the transcriptome data results, 10 genes were randomly selected from the differentially expressed genes for fluorescence quantitative PCR validation to determine the reliability of the RNA-seq results. Primer 3.0 was used to design the primers, and the primer sequences were shown in Excel 2010 was used for data calculation, and a SPSS19.0 statistical software package was used to conduct one-way analysis of variance and test the significant difference between groups.In our study, transcriptome data analysis revealed that the expression of regeneration-related genes such as cell wall synthesis, cell cycle regulation, cell proliferation, hormone signaling pathways, somatic embryogenesis genes, and oxidative stress genes were significantly different after enzymatic digestion of young sugarcane leaves compared to before enzymatic digestion. Key genes such as GAUT, CESA, CyclinA, CyclinB, CyclinD3, cdc2, and PSK were significantly down-regulated after enzymatic digestion and significantly changed after Aux/IAA enzymatic digestion compared to young leaves. These results suggest that the enzymatic digestion process may affect the genes of cell regeneration to some extent, which is the molecular basis of the difficulty of sugarcane protoplast regeneration. The impact hypothesis was modeled as follows :


Furthermore, the interaction between poor sleep quality and long sleep duration gave the highest risk of depression ; p < 0.001). Multinomial logistic regression revealed that the factors leading to a significant increase in the risk of poor sleep quality, with consideration to sleep duration, in the population were age, gender, marital status, education, wealth index, physical activity, chronic illness, season, and urban area (p < 0.05). Sleep quality was found to be associated with a higher risk of depression than sleep duration. The findings of this study may be beneficial to healthcare professionals who develop health promotion strategies for reducing the incidence of depression in communities.There is substantial evidence that a lack of sleep quality and duration can increase the risk of depression in adults. Still, few studies have compared sleep quality and duration to the risk of depression in Indonesia. Therefore, this study aimed to compare the prevalence and risk of depression associated with both sleep quality and duration and identified those factors associated with sleep quality with sleep duration. This study was a cross-sectional study, and the data were obtained from the 2014 Indonesian Family Life Survey, with a total sample comprised of 19,675 respondents aged older than 15 years old. A self-reported questionnaire was used to assess sleep quality and duration. Depression was assessed using the Center for Epidemiologic Studies Depression (CESD-10) questionnaire. Logistic regression was used to examine the risk of depression, and multinomial logistic regression was used to examine the risk of poor sleep quality with consideration to sleep duration. The prevalence of depression was the highest in the poor sleep quality and long sleep duration groups (48.5%). After all variables associated with depression were adjusted, poor sleep quality was identified as a factor leading to a higher risk of depression (OR = 4.2; 95% CI: 3.7–4.6;  Depression is a worldwide public health problem; it contributes considerably to the global illness burden and affects people from all communities. Depression can severely impair a person’s ability to manage their daily activities and can even lead to suicide . The preThe etiology of depression varies and is only partially understood, and is influenced by numerous factors. One study revealed that the occurrence of depression is related to sleep quality and sleep duration; lack of sleep can disrupt an individual’s internal biological clock in the suprachiasmatic nucleus of the hypothalamus and can thereby cause depression . Sleep qThe dual process model of the incidence of depression considered from the perspective of sleep–wake cycle regulation includes sleep quality and duration . There aBehavioral and health indicators can also affect sleep; for example, individuals who do not engage in sufficient physical activity are more likely to experience disturbed sleep  and evenEnvironmental factors can lead to depression, although these factors are changeable . ChangesNumerous studies have investigated the variables linked to depression, including sleep quality ,5,27 andThis was a cross-sectional study from secondary data of the fifth wave of the Indonesian Family Life Survey (IFLS 5) in 2014. This dataset comprised anonymous data that are available to researchers who comply with research and development (RAND) corporation guidelines. The IFLS survey used stratified the sampling for rural or urban locations in 13 of the 27 provinces. Each province had enumeration areas (EAs) drawn at random from a nationally representative sample. In 1993, 321 EAs were chosen at random from 13 provinces to participate in the IFLS. A total of 20 households were recruited for each urban EA and 30 households from the rural EA. This dataset included about 83% of Indonesians residing within 321 EAs .p > 0.05).The original sample comprised 34,635 respondents aged older than 15 years old. Responses from participants, who provided age, gender, marital status, educational level, wealth index, Body Mass Index (BMI), seasonal influence, light pollution, area of residence, physical activity level, chronic illness, sleep duration, sleep quality, and depression data, were included for further analysis. Respondents with 20% missing data were excluded [The Institutional Review Board (IRB) of the RAND Corporation and Universitas Gadjah Mada in Indonesia assessed and approved the surveys and methodology for the IFLS, and respondents over the age of 18 signed individual informed consent, and for those under 15–18 years old respondents, this was given by their guardian . This stSelf-reported sleep quality was measured through responses to the item “my sleep quality was…,” with the five possible answers being very poor, poor, fair, good, and very good. The responses were then placed into three categories indicating poor (very poor and poor), intermediate (fair), and good (very good and good) sleep quality .Sleep duration was measured with the items “What time did you wake up yesterday?” and “What time did you go to sleep yesterday?” Individuals with a sleep duration within the National Sleep Foundation (NSF) recommended range (8 to 10 h for teenagers (14–17 years), 7 to 9 h for young adults (18–24 years), and adults (26–64 years), and 7 to 8 h for older adults (≥65 years) were considered to have normal sleep duration . IndividDepression symptom was assessed through the 10 items of the Center for Epidemiological Studies Depression (CESD-10) questionnaire. This questionnaire is commonly used to assess depressive symptoms in adults . Each itAge, gender, marital status, education, and wealth index were the five sociodemographic characteristics considered in this study. Education is categorized by low (<12 years attainment) and high (≥12 years attainment). The wealth index is a household expenditure aggregate that includes information gathered at the individual and household levels, as well as multiple indicators of economic and noneconomic well-being, such as consumption, income, and assets . In this2), thin (17.0–18.4 kg/m2), normal (18.5–25.0 kg/m2), overweight (25.1–27.0 kg/m2), and obese (>27 kg/m2)), were used [For the lifestyle and health indicators, levels of physical activity were assessed through items from a modified short-form version of the International Physical Activity Questionnaire. The items were used to assess the type and duration of physical activity involved in all aspects of life, such as that involved in work, homelife, and exercise, and were subsequently classified as indicating low, moderate, or high levels of exercise . Chronicere used .−9 W/cm2/sr) was used to assess light pollution. Levels of light pollution were categorized using a light pollution map from: https://www.lightpollutionmap.info/, eight grades of exposure were identified for the included counties: 0.00–0.15 units, 0.15–0.25 units, 0.25–0.50 units, 0.50–1.50 units, 1.50–10.00 units, 10.00–50.00 units, 50.00–75.00 units, and ≥75.00 units [Three variables were used to assess environmental exposure. The first variable was seasonal factors, which were evaluated according to the province where the respondents lived, the month and year when the data were collected; these data were further divided into two categories: dry and rainy seasons. The second variable was light pollution, which was retrieved and tagged using the Quantum Geographical Information System (QGIS) open-source geographic information application based on the Visible Infrared Imaging Radiometer Suite (VIIRS) 2014 data observed for the sky radiance at night and the latitude and longitude of participants’ provinces. . The mea00 units . The thi00 units .Descriptive analyses of participant characteristics were presented in the form of numbers, percentages, and graphs. A bivariate analysis to identify associations between each variable and depression was conducted using chi-square tests. Multivariate logistic regression was used to analyze the risk of depression, and the adjusted model included the following variables: age, gender, educational level, marital status, wealth index, physical activity level, chronic illness, BMI, season, light pollution, and area of residence. Multinomial logistic regression analysis was applied to predict the risk of poor sleep quality with consideration of sleep duration. The percentage of the odds ratio (OR) was reported using a 95% confidence interval (CI). Statistical Product and Service Solutions (SPSS), version 24 , Windows, was used for all analyses.2, comprised 55.7%. For environmental exposure, the rainy season had the highest proportion (72.6%). The majority of respondents (35.7%) lived in a light pollution area, with pollution levels ranging from 1.5 to 10 units. Moreover, respondents living in urban areas made up 60.6%. The majority of the respondents (56.2%) reported intermediate sleep quality and normal sleep duration (57.4%). Additionally, age, marital status, wealth index, physical activity, chronic illness, BMI, season, light pollution, area of residence, sleep quality, and sleep duration were found to be significantly related to depression, but not gender and education.Overall, of the 19,657 respondents studied, 4429 (22.5%) had depression. p < 0.001) and intermediate sleep quality  had a higher risk of depression. Compared to normal sleep duration, respondents with long  and short sleep duration  had a higher risk of depression. Moreover, the interaction of sleep quality and sleep duration showed that the interaction of poor sleep quality and long sleep duration had the highest risk of depression  compared to the interaction of good sleep quality and normal sleep duration.After adjusting for all factors, The percentages for the prevalence of depression for each sleep quality group with consideration for sleep duration are presented in The risk factors for poor and intermediate sleep quality in the short- and long-sleep duration groups are presented in In this large, nationally representative population sample, individuals with poor sleep quality and long sleep duration had the highest prevalence of depression 48.5%; . This cup < 0.001; p < 0.001) and intermediate sleep quality had an OR of 1.4  than short sleep duration  that significantly in-creased the risk of poor sleep quality, with consideration to sleep duration.It is challenging to ascertain people with depressive symptoms from a community, even though family and community are the first lines of defense in assessing and preventing depression. From nursing, clinical, and public health perspectives, these findings may be important when screening for the risk of depression in adolescents and adults. Additionally, this study can be useful for planning health promotion strategies because sleep factors are modifiable through sleep improvement interventions, such as music relaxation , environ