The association of an elevated Th/Ts ratio and lupus anticoagulant with symptomatic osteonecrosis in systemic lupus erythematosus patients.

Objective: This study aimed to assess the risk factors for symptomatic osteonecrosis (ON) in systemic lupus erythematosus (SLE) and identify clinical characteristics and laboratory markers for predicting symptomatic ON occurrence in SLE patients. Methods: Seventy (6.0%) of 1175 SLE patients diagnosed with symptomatic ON were included in this study. An equal number of SLE patients without symptomatic ON, matched in terms of age and gender, were enrolled in the control group. Clinical symptoms, routine laboratory examinations, lymphocyte subsets, and treatments of these patients were retrospectively reviewed and compared between the two groups. Logistic regression analysis was employed to identify risk factors associated with symptomatic ON in SLE. Results: Among the 70 cases in the symptomatic ON group, 62 (88.6%) patients experienced femoral head necrosis, with bilateral involvement observed in 58 patients. Bone pain was reported in 32 cases (51.6%), and 19 cases (30.6%) presented with multiple symptoms. Univariate analysis revealed significant differences between the two groups in various factors, including disease duration (months), cumulative steroid exposure time, history of thrombosis, neurological involvement, the number of affected organs, myalgia/myasthenia, and the use of medications such as glucocorticoids, immunosuppressants, aspirin, and statins (P<0.05). Moreover, lupus anticoagulant (LA) levels were significantly higher in the symptomatic ON group than in the control group (P<0.05). Furthermore, notable distinctions were observed in peripheral blood immune cells, including an elevated white blood cell count (WBC), a decreased percentage of Ts cells (CD3+CD8+), and an elevated Th/Ts ratio. Logistic regression analysis revealed that a history of thrombosis, LA positivity, and an elevated Th/Ts ratio remained positive factors associated with symptomatic ON (P<0.05). Conclusion: Decreased Ts cells and changes in the T lymphocyte subset play an important regulatory role in the development of symptomatic ON. A history of thrombosis and LA are associated with an increased probability of symptomatic ON in SLE and may serve as potential predictors.

Exploring the Mechanism of Arctium Lappa L. Leaves in the Treatment of Alzheimer's Disease Based on Chemical Profile, Network Pharmacology and Molecular Docking.

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease, which urgently needs more effective treatment strategies. Arctium lappa L. leaf (burdock leaf) performs wide pharmacological activities, increasing evidence hinted that burdock leaves can ameliorate AD. This research aims to explore the bioactive ingredients and mechanisms of burdock leaves against AD by performing chemical profiles, network pharmacology, and molecular docking. 61 components are identified by liquid chromatography equipped with mass spectrometry. 792 targets of ingredients and 1661 AD-related genes are retrieved from public databases. Ten critical ingredients are identified from the topology analysis of the compound-target network. CytoNCA, AlzData database, and Aging Atlas database contribute to the foundation of 36 potential targets and four clinically significant targets (STAT3, RELA, MAPK8, and AR). The gene ontology (GO) analysis manifests that the included processes are close to the pathogenesis of AD. PI3K-Akt signaling pathway and AGE-RAGE signaling pathway may be important therapeutic mechanisms. Molecular docking results imply that network pharmacology results are reliable. Furthermore, the clinical meanings of core targets are also evaluated with the Gene Expression Omnibus (GEO) database. This research will provide research direction for the application of burdock leaves in the treatment of AD.

Characterization of a new selenoprotein methionine sulfoxide reductase from Haematococcus pluvialis and its antioxidant activity in response to high light intensity, hydrogen peroxide, glyphosate, and cadmium exposure.

Selenium incorporates into selenocysteine (Sec) which is a key component of selenoproteins implicated in antioxidant defense and redox homeostasis. Methionine sulfoxide reductases (Msr) play crucial roles in cellular defense against environmental stress. Whereas mammals have the MsrB selenoprotein form, unicellular organisms have MsrA. The Sec residue at the conserved catalytic sites of selenoprotein MsrA confers a metabolic advantage over the non-selenoprotein type MsrA. In the present study, the novel selenoprotein HpMsrA from Haematococcus pluvialis was cloned by the rapid amplification of cDNA ends and transformed into the model green alga Chlamydomonas reinhardtii. Alignment of homologs revealed the presence of the conserved catalytic domain GUFW and showed that the HpMsrA protein comprises Sec (U) at the N-terminus but no recycled Cys at the C-terminus. We studied the response of HpMsrA expression to selenite, high light intensity, hydrogen peroxide, cadmium nitrate, and glyphosate exposure via real-time quantitative PCR and enzyme activity analysis. The results demonstrated that HpMsrA protects cellular proteins against oxidative and environmental stressors. Compared with wild type C. reinhardtii, the transformant exhibited a superior antioxidant ability. The discoveries made herein shed light on the antioxidant physiology and environmental stress resistance mechanisms of the selenoproteins in microalgae. This information may aid in conducting environmental risk assessments of aquatic ecosystems involving microalgae known to respond rapidly and quantitatively to abiotic stress factors promoting excessive reactive oxygen species generation.

Learning Feature Channel Weighting for Real-Time Visual Tracking.

Recently, the siamese convolutional neural network plays an important role in the field of visual tracking, which can obtain high tracking accuracy and good real-time performance. However, the requirement of offline training a specific neural network results in the hardware source and time consumption. In order to improve the tracking efficiency and save computation resources, we adopt pre-trained densely connected neural network to extract robust target features. Since the pre-trained model is mainly used for classification task, it is not appropriate to directly adopt these deep features for visual tracking. We design a regression network to measure the importance of each channel to the target, and then propose a weighting fusion strategy to select the suitable features for visual tracking. Besides, we provide deep analysis about the proposed channel weighting method to demonstrate the superiority of this method through visualization of feature heatmaps. Extensive experiments on four classical benckmarks show that compared with state-of-the-art methods, our algorithm achieves the best results on several standard indicators and comparable results on other indicators.