A Precise Framework for Rice Leaf Disease Image-Text Retrieval Using FHTW-Net.

Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval. To address the challenge of matching diverse image categories with complex text descriptions during the retrieval process, we initially employed ViT and BERT to extract fine-grained image and text feature sequences enriched with contextual information. Subsequently, two-way mixed self-attention (TMS) was introduced to enhance both image and text feature sequences, with the aim of uncovering important semantic information in both modalities. Then, we developed false-negative elimination-hard negative mining (FNE-HNM) strategy to facilitate in-depth exploration of semantic connections between different modalities. This strategy aids in selecting challenging negative samples for elimination to constrain the model within the triplet loss function. Finally, we introduced warm-up bat algorithm (WBA) for learning rate optimization, which improves the model's convergence speed and accuracy. Experimental results demonstrated that FHTW-Net outperforms state-of-the-art models. In image-to-text retrieval, it achieved R@1, R@5, and R@10 accuracies of 83.5%, 92%, and 94%, respectively, while in text-to-image retrieval, it achieved accuracies of 82.5%, 98%, and 98.5%, respectively. FHTW-Net offers advanced technical support and algorithmic guidance for cross-modal retrieval of rice leaf diseases.

Mechanism of Nicotiflorin in San-Ye-Qing rhizome for anti-inflammatory effect in ulcerative colitis.

BACKGROUND: The incidence of ulcerative colitis (UC) is on the rise globally and the development of drugs targeting UC is urgent. Finding the target of action of natural products is important for drug discovery, elucidation of drug action mechanism, and disease mechanism. San-Ye-Qing (SYQ), is an ancient herbal medicine, but whether the powder of its rhizome has pharmacological effects against UC and its mechanism of action are not clear. PURPOSE: To evaluate the therapeutic effectiveness of rhizome powder of SYQ in treating UC, and conduct an isolation and characterization of the chemical constituents of the powder. Further, screen the most potent compounds among them and determine the potential mechanism for treating UC. METHODS: In vivo, the therapeutic effect of SYQ's rhizome powder on UC was assessed by mice's body weight, DAI score, colon length, tissue MPO activity, serum inflammatory markers, etc. Additionally, HPLC was used to isolate and identify the specific chemical components of SYQ's rhizome powder. Then, the most effective compounds and their therapeutic targets were analysed and screened in SYQ rhizome powder using network pharmacology, combined with CCK-8 assay, NO release assay and molecular docking assay, in conjunction with CETSA, DARTS, SPR and enzyme activity assay. Finally, the biological effects of the key compound on the targets were validated using Western blot and ELISA. RESULTS: In vivo, SYQ rhizome powder effectively restored mice's body weight, lowered DAI and pathological score, downregulated the expression of inflammatory biomarkers, and restored colon length, as well as the colonic epithelial and mucus barriers. Afterward, 9 compounds were isolated and identified from the powder of the rhizomes of SYQ by HPLC. Nicotiflorin is the primary compound in SYQ with the highest concentration. According to both CCK-8 and NO release tests, Nicotiflorin is also the most efficacious compound. Combined with network pharmacological prediction, molecular docking analysis, CETSA, DARTS, SPR and enzyme activity assay, Nicotiflorin may ultimately suppress inflammation by targeting p65 and inhibiting the NF-κB pathway, thereby attenuating the activation of NLRP3 inflammasome. To verify this conclusion, Western blot and ELISA experiments were conducted. CONCLUSIONS: Our results suggest that the extract from SYQ rhizomes has therapeutic properties for UC. Its active ingredient Nicotiflorin exerted potent anti-UC effects by binding to p65 and inhibiting the activation of NF-κB and NLRP3 inflammasomes.

Comprehensive Analysis of Immunobiological Effects and Prognostic Significance Related to Endoplasmic Reticulum Stress in Pancreatic Carcinoma.

Context: Pancreatic cancer (PC) has a poor response to the many treatments available for it, including surgery, chemotherapeutics, targeted therapy, and immunotherapy. It's crucial to investigate alternative methods of prognostic assessment and decision-making in choosing a therapy, making it necessary to explore its differentially expressed genes (DEGs). Objective: The study intended to assess the role of endoplasmic reticulum stress (ERS)-related genes (ERSRGs) in PC to create an effective, prognostic risk-prediction model and potential immunotherapy options. Design: The research team performed a genetic study. Setting: The study took place at the Affiliated Hospital of Nantong University in Nantong, Jiangsu, China. Outcome Measures: The research team: (1) performed molecular subtype identification and analysis, (2) developed a prognostic risk model, (3) evaluated the clinical features of the risk model, (4) identified the clinicopathological features affecting survival, (5) analyzed the potential immune roles in ERS, (6) constructed five gene signatures associated with ERS, (7) examined the association between different risk categories and sensitivity to GDSC drugs as a potential predictor of response to chemotherapy , and (8) identified the biological processes associated with different risk categories. Results: Significant differences existed in the survival prognosis of subtype C and subtype A or B (P < .001). The high-risk group with the lower TIDE score had a significantly better response to immunotherapy (P < .0057). The high-risk group had a significantly higher somatic mutation rate (P < .00017) and a worse survival prognosis (P < .001). Differences in mRNA expression existed for ERAP2 (P < .001), IGF2BP2 (P = .006), DSG3 (P = .001), MAPK10 (P = .006), and PRKCSH (P ≤ .015) in clinical samples. Conclusions: Through the analysis of ERS subtypes of pancreatic cancer, the study found that the infiltration abundance of stromal cells and immune cells can affected by ERS, thus changing the prognosis of patients. The predictive model provides reference values for clinical prognosis and immunotherapy for PC patients through its ability to evaluate patients' immune statuses. Clinical treatment can provide individualized guidance and can effectively predict the prognosis of PC patients.

[Corrigendum] FKBP11 protects intestinal epithelial cells against inflammation­induced apoptosis via the JNK­caspase pathway in Crohn's disease.

Subsequently to the publication of the above article, the authors realized that Fig. 4 in their paper had been assembled containing two erroneously placed gel slices; essentially, the GAPDH bands featured in Fig. 4A had also been included in Fig. 5, and the data for the FKBP11 bands in Fig. 4A had also been included to show the GRP78 bands in Fig. 4. The authors were able to revisit their original data and to correct the data that had been featured incorrectly in Fig. 4. The corrected version of Fig. 4, now showing the true data for the GRP78 protein bands in Fig. 4C and the correct GAPDH protein bands for Fig. 4A, is shown on the next page. Note that these errors did not significantly affect the results or the conclusions reported in this paper. All the authors agree to the publication of this Corrigendum, and are grateful to the Editor of Molecular Medicine Reports for allowing them the opportunity to correct this error. Moreover, the authors apologize to the readership for any inconvenience caused. [Molecular Medicine Reports 18: 4428­4438, 2018; DOI: 10.3892/mmr.2018.9485].

An algorithm for simplified hepatitis C virus treatment with non-specialist care based on nation-wide data from Taiwan.

BACKGROUND: Both European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases and the Infectious Diseases Society of America (AASLD-IDSA) guidelines recommend simplified hepatitis C virus (HCV) treatment with pan-genotypic sofosbuvir/velpatasvir or glecaprevir/pibrentasvir for eligible patients. This observational study used real-world data to assess these regimens' safety in eligible patients and develop an algorithm to identify patients suitable for simplified treatment by non-specialists. METHODS: 7,677 HCV-infected patients from Taiwan Hepatitis C Registry (TACR) who received at least one dose of sofosbuvir/velpatasvir or glecaprevir/pibrentasvir, and fulfilled the EASL/AASLD-IDSA criteria for simplified treatment were analyzed. Multivariate analysis was conducted on patient characteristics and safety data. RESULTS: Overall, 92.8% (7,128/7,677) of patients achieved sustained virological response and only 1.9% (146/7,677) experienced Grades 2-4 laboratory abnormalities in key liver function parameters (alanine aminotransferase, aspartate aminotransferase, and total bilirubin), with only 18 patients (0.23%) experiencing Grades 3-4 abnormalities. Age > 70 years old, presence of hepatocellular carcinoma, total bilirubin > 1.2 mg/dL, estimated glomerular filtration rate < 60 mL/min/1.73 m2, and Fibrosis-4 > 3.25 were associated with higher risks of Grades 2-4 abnormalities. Patients with any of these had an odds of 4.53 times than that of those without in developing Grades 2-4 abnormalities (p < 0.01). CONCLUSIONS: Real-world data from Taiwan confirmed that simplified HCV treatment for eligible patients with pan-genotypic regimens is effective and well tolerated. The TACR algorithm, developed based on this study's results, can further identify patients who can be safely managed by non-specialist care.

Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.

BACKGROUND/AIMS: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. METHODS: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. RESULTS: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. CONCLUSION: Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Association of ACP5 with the tumor immune microenvironment and its role in cell proliferation and metastasis in pancreatic cancer.

BACKGROUND: Tartrate-resistant acid phosphatase (ACP5) has been implicated in the progression of most malignant tumors, but its role in pancreatic cancer (PC) remained unclear. Thus, this study aimed to elucidate the role and function of ACP5 in PC progression. METHODS: The expression of ACP5 in PC samples was assessed via R programming, TNM plot, and Gene Expression Profiling Interactive Analysis (GEPIA). Western blotting and immunohistochemistry (IHC) were performed to detect ACP5 expression in cells and tissues. The correlation between ACP5 and methylation was analyzed using the University of ALabama at Birmingham Cancer data analysis Portal (UALCAN) and cBio Cancer Genomics Portal (cBioPortal). The Database for Annotation, Visualization and Integrated Discovery (DAVID) and Gene Set Enrichment Analysis (GSEA) were used for the enrichment of ACP5 in PC. Subsequently, Cell Counting Kit-8 (CCK8), clonogenic, and wound healing assays were used to investigate the role of ACP5 in PC. Finally, Tumor Immune Estimation Resource (TIMER) and R programming was utilized in evaluating the association between ACP5 and immune cell infiltration in PC. RESULTS: The analyses confirmed that ACP5 was highly expressed in PC samples. According to UALCAN and cBioPortal analysis, ACP5 expression, and methylation levels were negatively correlated in PC. The enrichment analysis also revealed that ACP5 was enriched in the proliferation and migration pathways. Meanwhile, ACP5 knockout reduced PC cell proliferation and migration and impaired the cells' independent viability. This gene also positively correlated with immune cell infiltration in PC, particularly regulatory T cells (Tregs). CONCLUSION: ACP5 is crucial for proliferation, migration, and immune cell infiltration in PC. Therefore, ACP5 may be a valuable target for future PC treatment.

HSSNet: A End-to-End Network for Detecting Tiny Targets of Apple Leaf Diseases in Complex Backgrounds.

Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel2) are easily overlooked. To address the problems of complex background interference, difficult detection of tiny targets and biased detection of prediction boxes that exist in standard detectors, in this paper, we constructed a tiny target dataset TTALDD-4 containing four types of diseases, which include Alternaria leaf spot, Frogeye leaf spot, Grey spot and Rust, and proposed the HSSNet detector based on the YOLOv7-tiny benchmark for professional detection of apple leaf disease tiny targets. Firstly, the H-SimAM attention mechanism is proposed to focus on the foreground lesions in the complex background of the image. Secondly, SP-BiFormer Block is proposed to enhance the ability of the model to perceive tiny targets of leaf diseases. Finally, we use the SIOU loss to improve the case of prediction box bias. The experimental results show that HSSNet achieves 85.04% mAP (mean average precision), 67.53% AR (average recall), and 83 FPS (frames per second). Compared with other standard detectors, HSSNet maintains high real-time detection speed with higher detection accuracy. This provides a reference for the automated control of apple leaf diseases.

An Accurate Classification of Rice Diseases Based on ICAI-V4.

Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network's ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network's feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method's strong performance and feasibility for rice disease classification in real-life scenarios.

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet.

Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf's edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet. First, a perceptual adaptive convolution module is proposed. It can effectively extract the disease's defining characteristics. Second, a location reinforcement attention mechanism is proposed at the neck of the network. It suppresses the interference of the soil backdrop and prevents extraneous information from accessing the network's feature fusion phase. Then, a proximity feature aggregation network with switchable atrous convolution and deconvolution is proposed by combining the mechanisms of secondary observation and feature consistency. The network solves the problem of disease interclass similarities. Finally, the experimental results show that PLPNet achieved 94.5% mean average precision with 50% thresholds (mAP50), 54.4% average recall (AR), and 25.45 frames per second (FPS) on a self-built dataset. The model is more accurate and specific for the detection of tomato leaf diseases than other popular detectors. Our proposed method may effectively improve conventional tomato leaf disease detection and provide modern tomato cultivation management with reference experience.

A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds.

In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLAM) is proposed. It inhibits the entry of disturbance information into the network's feature layer. Then, an SCM-Block containing MADM and CLAM is proposed. We utilized the block to construct a new backbone network to increase the efficiency of fecal feature fusion in dogs. Throughout the network, we decrease the number of parameters using depthwise separable convolution (DSC). In conclusion, MC-SCMNet outperforms all other models in terms of accuracy. On our self-built DFML dataset, it achieves an average identification accuracy of 88.27% and an F1 value of 88.91%. The results of the experiments demonstrate that it is more appropriate for dog fecal identification and maintains stable results even in complex backgrounds, which may be applied to dog gastrointestinal health checks.

Resveratrol enhances MUC2 synthesis via the ANRIL-miR-34a axis to mitigate IBD.

BACKGROUND/AIMS: Resveratrol (RSV) is a natural polyphenol with strong biological activity that confers a measure of protection against the development of inflammatory bowel disease (IBD), and the long noncoding RNA (lncRNA) antisense non-coding RNA in the INK4 locus (ANRIL) is closely related to inflammation. The present study determined whether resveratrol attenuated IBD by regulating ANRIL and its specific molecular mechanism. METHODS: In vivo model of IBD was induced by dextran sulfate sodium (DSS). In total, 60 BALB/c mice were randomly divided into 3 groups (normal control - NC, DSS, RSV), and their weight changes, fecal traits, colon length and tissue hematoxylin-eosin (H&E) were observed. Moreover, human colonic epithelial cells (HCoEpiC) treated with lipopolysaccharide (LPS) were used as cell models of IBD. The tumor necrosis factor α (TNF-α), interleukin (IL)-1ß, IL-6, IL-10, lncRNA ANRIL, and miR-34a levels were measured by RT-PCR. The expression of mucin 2 (MUC2) and an enzyme associated with MUC2 synthesis, polypeptide N-acetylgalactosaminyltransferase 7 (GALNT7), was measured by RT-PCR and western blot analysis. RESULTS: Resveratrol treatment mitigated colitis by significantly decreasing the expression of pro-inflammatory cytokines (i.e. TNF-α, IL-1ß, IL-6) and miR-34a, and increasing the levels of anti-inflammatory cytokine (i.e. IL-10), MUC2, GLNAT7, and lncRNA ANRIL in mice and HCoEpiC (all P<0.05). The elevated synthesis of MUC2 could be attributed to the ANRIL-miR-34a axis. CONCLUSIONS: Resveratrol attenuates IBD by promoting MUC2 synthesis via the ANRIL-miR-34a axis.

Salidroside ameliorates severe acute pancreatitis-induced cell injury and pyroptosis by inactivating Akt/NF-κB and caspase-3/GSDME pathways.

Our previous studies showed that Salidroside (Sal), a glucoside of the phenylpropanoid tyrosol isolated from Rhodiola rosea L, alleviated severe acute pancreatitis (SAP) by inhibiting inflammation. However, the detailed mechanism remains unclear. Recent evidence has indicated a critical role of Sal in ameliorating inflammatory disorders by regulating pyroptosis. The present study aimed to explore the involvement of Sal and pyroptosis in the pathogenesis of SAP and investigate the potential mechanism. The effects of Sal on pyroptosis were first evaluated using SAP rat and cell model. Our results revealed that Sal treatment significantly decreased SAP-induced pancreatic cell damage and pyroptosis in vivo and in vitro, as well as reduced the release of lactate dehydrogenase (LDH), IL-1ß and IL-18. Search Tool for Interacting Chemicals (STITCH) online tool identified 4 genes (CASP3, AKT1, HIF1A and IL10) as candidate targets of Sal in both rattus norvegicus and homo sapiens. Western blot and immunohistochemistry staining validated that Sal treatment decreased the phosphorylation levels of Akt and NF-κB p65, as well as cleaved caspase-3 and N-terminal fragments of GSDME (GSDME-N), suggesting that Sal might suppress pyroptosis through inactivating Akt/NF-κB and Caspase-3/GSDME pathways. Furthermore, overexpression of AKT1 or CASP3 could partially reverse the inhibitory effects of Sal on cell injury and pyroptosis, while downregulation of AKT1 or CASP3 promoted the inhibitory effects of Sal. Taken together, our data indicate that Sal suppresses SAP-induced pyroptosis through inactivating Akt/NF-κB and Caspase-3/GSDME pathways.

RNA-binding motif 4 promotes angiogenesis in HCC by selectively activating VEGF-A expression.

Increased angiogenesis in the liver plays a critical role in the progression of hepatocellular carcinoma (HCC). However, the molecular mechanism underlying increased angiogenesis in HCC is not well understood. Current study was designed to identify the potential angiogenic effect of RNA-binding motif 4 (RBM4)through a small-scale overexpression screening, followed by comparison of the expression level of RBM4 in cancer and adjacent tissues in multiple malignancies to explore the relationship between RBM4 and CD31 protein expression level and related clinical indicators, and understand the role of RBM4 in the hepatocellular carcinoma. To understand the specific mechanism of RBM4 in detail, transcriptome sequencing, mass spectrometry and multiple molecular cytological studies were performed. These cellular level results were verified by experiments in animal models of nude mice. The increased expression of RBM4 in cancer tissues, suggested its use as a prognostic biomarker. The RBM4 expression was found to be strongly correlated with tumor microvessel density. Mechanistically, RBM4 mediated its effects via interaction with HNRNP-M through the latter's WDR15 domain, which then stabilized RelA/p65 mRNA. Consequently, RBM4 induced the activation of the NF-kB signaling pathway, upregulating the expression of proangiogenic factor VEGF-A. The results confirmed the mechanism by which RBM4 promotes angiogenesis in hepatocellular carcinoma suggesting RBM4 as a crucial promoter of angiogenesis in HCC, helping understand regulation of NF-kB signaling in HCC.

DS-MENet for the classification of citrus disease.

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

Salidroside alleviates severe acute pancreatitis-triggered pancreatic injury and inflammation by regulating miR-217-5p/YAF2 axis.

BACKGROUND: Our previous studies have shown that salidroside (Sal) exerted a protective effect in severe acute pancreatitis (SAP) via inhibiting the inflammatory response. However, the molecular mechanism has not been fully elucidated. METHODS: Using SAP rat model and miRNA microarray, the effect of Sal on miRNA expression profiling was determined and then validated their changes by quantitative Real-time PCR (qRT-PCR). Then, SAP cell model, enzyme-linked immunosorbent assay (ELISA) and Cell Counting Kit-8 (CCK-8) assay were used to explore the biological function of miR-217-5p in vitro. Bioinformatics analysis, luciferase reporter assay and miRNA pulldown assay were performed to investigate the underlying mechanism of miR-217-5p in the protection of Sal against SAP. RESULTS: Compared with SAP group, 21 differentially expressed miRNAs were identified in SAP + Sal group. The target genes of these miRNAs were strongly associated with regulation of transcription, Axon guidance, Pathways in cancer and MAPK signaling pathway. Among these miRNAs, miR-217-5p was the most downregulated miRNA. Sal treatment alleviated cell injury and reduced the production of pro-inflammatory cytokines. Whereas overexpression of miR-217-5p reversed the effects of Sal. We identified YY1 associated factor 2 (YAF2) as a direct target gene of miR-217-5p and Sal treatment could upregulate YAF2 expression via targeting miR-217-5p. Furthermore, knockdown of YAF2 counteracted Sal-induced alleviation of cell injury and inflammation. Moreover, Sal could suppress the activation of p38 MAPK pathway by regulating miR-217-5p/YAF2 axis. CONCLUSIONS: Our findings for the first time highlighted that Sal alleviated pancreatic injury and inhibited inflammation by regulating miR-217-5p/YAF2 axis, which might provide new therapeutic strategies for SAP treatment.

Identification of key genes and infiltrating immune cells among acetaminophen-induced acute liver failure and HBV-associated acute liver failure.

Background: Acute liver failure (ALF) is a life-threatening complication that is relatively uncommon. ALF causes severe hepatocyte damage and necrosis, which can lead to liver dysfunction and even multi-organ failure. A growing body of evidence suggests that immune cell infiltration and some abnormally expressed genes are associated with ALF development. However, in ALF, they have yet to be thoroughly investigated. Methods: The Gene Expression Omnibus (GEO) database was used to obtain microarray datasets such as GSE74000, GSE120652, GSE38941, and GSE14668, which were then examined via GEO2R to determine differentially expressed genes (DEGs) associated with ALF. Metascape was employed to annotate the underlined genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The mechanism of IGF1 in 2 different kinds of ALF including acetaminophen-induced ALF and hepatitis B virus (HBV)-induced ALF was studied using gene set enrichment analysis (GSEA). Next, immune cell infiltration was investigated and differentiated in ALF using CIBERSORT. Results: Six genes (HAO2, IGF1, PLA2G7, SC5D, GNE, SLC1A1) were found to be abnormally expressed in the 2 distinct types of ALF i.e., acetaminophen-induced ALF and HBV-induced ALF. IGF1 was identified as a hub gene in ALF and was found to be associated with several developmental cascades including immune responses, inflammatory responses, and intracellular calcium homeostasis. Additionally, the number of CD4 naive T cells, CD8 T cells, and follicular helper T cells was increased in acetaminophen-induced ALF, whereas the number of activated NK cells, resting NK cells, and plasma cells was increased in HBV-induced ALF. Conclusions: The present study determined a potential molecular target, namely IGF1, in acetaminophen-induced ALF and HBV-induced ALF, which may provide novel insights into the pathophysiology and management of ALF. Concurrently, the putative immunological pathways have been found.

Exploring the Potential of Pyroptosis-Related Genes in Predicting Prognosis and Immunological Characteristics of Pancreatic Cancer From the Perspective of Genome and Transcriptome.

Objective: To probe into the role of pyroptosis-related genes in pancreatic carcinoma. Methods: Herein, we conducted a comprehensive bioinformatics analysis to evaluate tumor-immune infiltration and tumor mutation burden, the correlations between PRGs, and microsatellite instability and found that 33 PRGS were up- or down-regulated in PC. Then we built the PPI network, which was downloaded from the STRING database. Using TCGA cohort median risk score, PC subjects from the Gene Expression Composite cohort (GEO) data resource were stratified into two risk categories, with the low-PC risk group harboring a higher overall survival (OS) (P = 0.011). We employed the ssGSEA approach to quantify immune cell abundance in separate risk groups separated by risk signature while assessing variations in immune cell invasion. Chemotherapeutic drugs were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) data resource. Results: Eight prognostic PRG models (CASP4, GSDMC, IL-18, NLRP1, NLRP2, PLCG1, TIRAP, and TNF) were established via LASSO Cox regression to estimate the OS of PC subjects with medium-to-high accuracy. Conclusion: Our study is the first to identify a pyroptotic-related prognostic gene feature for PC, providing more options for the prognostic prediction of PC.