Traditional Chinese medicine combined with conventional treatment for the patients after percutaneous coronary intervention: A systematic review and meta-analysis

Purpose: To evaluate the efficacy, quality of care and safety of Traditional Chinese Medicine (TCM) after Percutaneous Coronary Intervention (PCI). using systematic review and meta-analysis of randomized controlled trials.Methods: Relevant studies published between January 1st 2010 and August 20th, 2021, on traditional Chinese medicine (TCM) and conventional treatment (CT) after PCI were sourced from different databases including CNKI, CBM, Web of Science, PubMed, Embase and Cochrane library. The TCM was composed of preparations of chinese eaglewood, peppermint, radix notoginseng, scabrous elephant foot herb, Tongxinluo, Danhong, Naoxintong capsule, Huxin Formula and liquorice root while the CT included aspirin (100 mg/day), clopidogrel (75 mg/day), and statins. PRISMA guidelines were used. Primary outcome was to evaluate the efficacy, quality of care and safety of TCM versus conventional treatment post percutaneous coronary intervention (PCI).Results: 110 randomized controlled trials (RCTs) were retrieved and analyzed. The results from metaanalysis showed an enhanced left ventricular ejection fraction (LVEF) % among patients that received TCM compared to those on CT [mean difference ± sd (MD)=5.17, 95% CI (3.29-7.06), Z = 5.38, (P < 0.001)]. Further, hypersensitive C-reactive protein (HS-CRP) level in TCM group was found to be relatively lower than that of the CT group (CG) [MD=-1.44, 95% CI (-2.87-0.00), Z=1.96, (P=0.05)]. In terms of safety, TCM group relative risk score in fixed-effect model was lower than that of the CG [RR=0.66, 95% CI (0.40, 1.10), Z=1.66,].Conclusion: It can be inferred from the results that TCM has more advantages in terms of clinical efficacy, quality of care and safety compared to conventional therapy. However, the lack of substantial research in deploying TCM for the treatment of CHD demands further exploration and strong evidence prior to clinical application of TCM

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases

Time Course of Gene Expression Profiling in the Liver of Experimental Mice Infected with Echinococcus multilocularis

BACKGROUND: Alveolar echinococcosis (AE) is a severe chronic parasitic disease which behaves like a slow-growing liver cancer. Clinical observations suggest that the parasite, Echinococcus multilocularis (E. multilocularis) influences liver homeostasis and hepatic cell metabolism. However, this has never been analyzed during the time course of infection in the common model of secondary echinococcosis in experimental mice. METHODOLOGY/PRINCIPAL FINDINGS: Gene expression profiles were assessed using DNA microarray analysis, 1, 2, 3 and 6 months after injection of E. multilocularis metacestode in the liver of susceptible mice. Data were collected at different time points to monitor the dynamic behavior of gene expression. 557 differentially expressed genes were identified at one or more time points, including 351 up-regulated and 228 down-regulated genes. Time-course analysis indicated, at the initial stage of E. multilocularis infection (month 1-2), that most of up-regulated pathways were related to immune processes and cell trafficking such as chemokine-, mitogen-activated protein kinase (MAPK) signaling, and down-regulated pathways were related to xenobiotic metabolism; at the middle stage (month 3), MAPK signaling pathway was maintained and peroxisome proliferator-activated receptor (PPAR) signaling pathway emerged; at the late stage (month 6), most of up-regulated pathways were related to PPAR signaling pathway, complement and coagulation cascades, while down-regulated pathways were related to metabolism of xenobiotics by cytochrome P450. Quantitative RT-PCR analysis of a random selection of 19 genes confirmed the reliability of the microarray data. Immunohistochemistry analysis showed that proliferating cell nuclear antigen (PCNA) was increased in the liver of E. multilocularis infected mice from 2 months to 6 months. CONCLUSIONS: E. multilocularis metacestode definitely exerts a deep influence on liver homeostasis, by modifying a number of gene expression and metabolic pathways. It especially promotes hepatic cell proliferation, as evidenced by the increased PCNA constantly found in all the experimental time-points we studied and by an increased gene expression of key metabolic pathways

Improved EfficientNet for corn disease identification

IntroductionCorn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time.MethodsTo address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance.ResultsCompared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs.DiscussionOverall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control

Phenotype and function of MAIT cells in patients with alveolar echinococcosis

Mucosal-associated invariant T (MAIT) cells are a subpopulation of unconventional T cells widely involved in chronic liver diseases. However, the potential role and regulating factors of MAIT cells in alveolar echinococcosis (AE), a zoonotic parasitic disease by Echinococcus multilocularis (E. multilocularis) larvae chronically parasitizing liver organs, has not yet been studied. Blood samples (n=29) and liver specimens (n=10) from AE patients were enrolled. The frequency, phenotype, and function of MAIT cells in peripheral blood and liver tissues of AE patients were detected by flow cytometry. The morphology and fibrosis of liver tissue were examined by histopathology and immunohistochemistry. The correlation between peripheral MAIT cell frequency and serologic markers was assessed by collecting clinicopathologic characteristics of AE patients. And the effect of in vitro stimulation with E. multilocularis antigen (Emp) on MAIT cells. In this study, MAIT cells are decreased in peripheral blood and increased in the close-to-lesion liver tissues, especially in areas of fibrosis. Circulating MAIT exhibited activation and exhaustion phenotypes, and intrahepatic MAIT cells showed increased activation phenotypes with increased IFN-γ and IL-17A, and high expression of CXCR5 chemokine receptor. Furthermore, the frequency of circulating MAIT cells was correlated with the size of the lesions and liver function in patients with AE. After excision of the lesion site, circulating MAIT cells returned to normal levels, and the serum cytokines IL-8, IL-12, and IL-18, associated with MAIT cell activation and apoptosis, were altered. Our results demonstrate the status of MAIT cell distribution, functional phenotype, and migration in peripheral blood and tissues of AE patients, highlighting their potential as biomarkers and therapeutic targets

Immunology and Immunodiagnosis of Cystic Echinococcosis: An Update

Cystic echinococcosis (CE) is a cosmopolitan zoonosis caused by the larval cystic stage of the dog tapeworm Echinococcus granulosus. This complex multicellular pathogen produces various antigens which modulate the host immune response and promote parasite survival and development. The recent application of modern molecular and immunological approaches has revealed novel insights on the nature of the immune responses generated during the course of a hydatid infection, although many aspects of the Echinococcus-host interplay remain unexplored. This paper summarizes recent developments in our understanding of the immunology and diagnosis of echinococcosis, indicates areas where information is lacking, and suggests possible new strategies to improve serodiagnosis for practical application