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The process of drum drying silk has complex characteristics of multivariability, strong coupling, and nonlinearity. Traditional Principal Component Analysis (PCA) method lacks strong nonlinear ability, and global modeling method is difficult to achieve accurate fault detection of the process. Therefore, this paper proposed a novel multi-block approach by integrating Autoencoder (AE) for feature extraction and PCA model. Initially, in order to capture local features, the process variables were divided into blocks according to the drying process principle of tobacco leaf. Secondly, autoencoder was used to extract the nonlinear features of each sub-block. Then, the corresponding PCA models are established for each sub-block, respectively. Lastly, the monitoring results of multiple subspaces were fused for decision-making by Bayesian inference. Two actual leaf silk drying cases were used for verification, and the results showed that the alarm rates of this method were as high as 91.67% and 98.21%. Compared to traditional PCA and AE-PCA detection methods, this algorithm could accurately reveal and characterize the overall operating status and local feature information of the drying process, improve the accuracy of anomaly detection in the drum leaf silk drying production process and achieve accurate alarm for quality anomalies, to ensure stable production of the drum leaf silk drying process. [ABSTRACT FROM AUTHOR]

Background: Vaccination is an important method for the prevention of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) transmission. There is currently a lack of real‐world clinical data regarding the safety and efficacy of coronavirus disease 2019 (COVID‐19) vaccines with respect to plaque psoriasis treatment involving tumor necrosis factor‐α (TNF‐α) and interleukin‐17A (IL‐17A) inhibitors. Methods: We longitudinally analyzed 152 patients with plaque psoriasis, 86 of whom received two doses of inactivated COVID‐19 vaccine (either BBIBP‐CorV or CoronaVac). Comparisons were made between patients undergoing treatment with biologics (TNF‐ α inhibitors or IL‐17A inhibitors) or acitretin. Routine blood tests were used to assess safety; the psoriasis area and severity index (PASI) and dermatology life quality index (DLQI) were used to assess efficacy. Results: After inactivated COVID‐19 vaccination, biologics retained considerable advantages in terms of improving skin lesions (measured by PASI) and quality of life (measured by DLQI), compared with conventional treatment (p < 0.05 and p < 0.01, respectively). Routine blood tests and hepatorenal function analyses suggested that inactivated SARS‐CoV‐2 vaccines did not alter the safety of biologics treatment (p > 0.05). Conclusions: Inactivated SARS‐CoV‐2 vaccines do not have significant impacts on the safety and efficacy of biologics (TNF‐α inhibitors or IL‐17A inhibitors) in patients with moderate to severe plaque psoriasis. [ABSTRACT FROM AUTHOR]

Byline: Jun. Li, Liangfeng. Wei, Bingyang. Xu, Xiaojun. Zhang, Shousen. Wang Objective: This study was designed to investigate the risk factors of, and the strategy for early diagnosis of cerebral [...]

Purpose: Psoriasis is not yet completely curable, and its etiology and pathogenesis are unclear. Necroptosis, also known as programmed necrosis, is a regulated mode of necrotic cell death. The interaction between inflammatory diseases and necrotic apoptosis has recently attracted significant attention. We explored the molecular mechanisms of necrotic apoptosis-related genes in psoriasis using bioinformatics methods to identify potential biomarkers for psoriasis., Patients and Methods: In this study, we screened psoriasis differentially expressed genes from the datasets GSE13355 and GSE14905 and took intersections with necrotic apoptosis-related genes for the next analysis. We used multiple machine learning algorithms to screen key genes and perform enrichment analysis. In addition, we performed an immune infiltration analysis. Transcription factors were predicted by the R package "RcisTarget". We also observed the cellular clustering of key genes in different cell types at the single-cell sequencing level. We used real-time fluorescence-based quantitative-polymerase chain reaction, Western blot, and immunohistochemistry to analyze gene expression in clinical samples. We constructed an imiquimod-induced psoriasis-like dermatitis model in mice for further validation., Results: Seven key genes were screened as follows: AIM2, CARD6, HPSE, MYD88, PYCARD, RAI14 , and TNFSF10 . Enrichment analysis showed that the key genes were mainly involved in inflammatory pathways. Immune infiltration analysis showed significantly higher levels of CD8 T cells, CD4 initial T cells, and CD4 memory-activated T cells in the disease group's samples than in the normal patients' samples. The key gene expression in single cells analyzed showed that PYCARD was significantly expressed in keratinocytes. PYCARD was selected for gene expression analysis; the results showed that its expression was significantly elevated in the skin lesion tissues of patients with psoriasis. We also verified that PYCARD might play a vital role in the development of psoriasis skin lesions using animal experiments., Conclusion: PYCARD plays a vital role in psoriasis development and is a potential biomarker for psoriasis., Competing Interests: The authors report no conflicts of interest in this work., (© 2024 Xu et al.)

Background: Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. Currently, the pathological mechanisms of MS are not fully understood, but research has suggested that iron metabolism disorder may be associated with the onset and clinical manifestations of MS., Methods and Materials: The study utilized publicly available databases and bioinformatics techniques for gene expression data analysis, including differential expression analysis, weighted correlation network analysis, gene enrichment analysis, and construction of logistic regression models. Subsequently, Mendelian randomization was used to assess the causal relationship between different iron metabolism markers and MS., Results: This study identified IREB2, LAMP2, ISCU, ATP6V1G1, ATP13A2, and SKP1 as genes associated with multiple sclerosis (MS) and iron metabolism, establishing their multi-gene diagnostic value for MS with an AUC of 0.83. Additionally, Mendelian randomization analysis revealed a potential causal relationship between transferrin saturation and MS (p=2.22E-02; OR 95%CI=0.86 (0.75, 0.98)), as well as serum transferrin and MS (p=2.18E-04; OR 95%CI=1.22 (1.10, 1.36))., Conclusion: This study comprehensively explored the relationship between iron metabolism and MS through integrated bioinformatics analysis and Mendelian randomization methods. The findings provide important insights for further research into the role of iron metabolism disorder in the pathogenesis of MS and offer crucial theoretical support for the treatment of MS., Competing Interests: 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., (Copyright © 2024 Tang, Yang, Zhu, Ding, Yang, Xu and He.)