The value of parotid sialography in the diagnosis and staging of Sjogren's syndrome.

Background/purpose: 2016 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) primary Sjögren's syndrome (SS) diagnostic criteria did not incorporate radiographic examination while staging SS according to salivary gland imaging and serological autoantibody tests was not discussed. The aim is to study the value of parotid sialography for diagnosing SS, and to initially explore the method of staging SS based on the results of imaging and serological autoantibody tests. Materials and methods: 287 patients' clinical records were included. The sensitivity and specificity of parotid sialography in the diagnosis of SS were investigated. SS patients were categorized into early stage (autoantibody positive, imaging does not support SS), active stage (autoantibody positive, imaging supports SS), and quiescent stage (autoantibody negative, imaging supports SS), clinical characteristics of different stages were compared. Results: The sensitivity of parotid sialography for the diagnosis of SS was 82.6%, the specificity was 71.5%. 10-minute USFR of the patients in the active stage (0.18 ± 0.38 ml/10min) was significantly lower than that of early stage (0.34 ± 0.47 ml/10min) and quiescent stage (0.54 ± 0.52 ml/10min), P = 0.010, and the rate of confirmed SS was significantly higher in the active stage (82.9%) than that in the early stage (44.4%) and the quiescent stages (14.8%), P < 0.001. Conclusion: Parotid sialography remains valuable in the diagnosis of SS. Performing imaging and serological autoantibody tests before lip gland biopsy may reduce invasive examinations for patients without significantly increasing the rate of missed diagnosis. According to imaging and serological autoantibody tests, SS can be categorized into early, active, and quiescent stages.

Engineered Extracellular Vesicle-Based Nanoformulations That Coordinate Neuroinflammation and Immune Homeostasis, Enhancing Parkinson's Disease Therapy.

Although conventional intervention to microglia can mitigate neuroinflammation in the short term, immune disorders by peripheral inflammatory cells can infiltrate continuously, resulting in an overactivated immune microenvironment of Parkinson's disease (PD). Here, we design engineered extracellular vesicle-based nanoformulations (EVNs) to address multiple factors for the management of PD. Specifically, EVN is developed by coating CCR2-enriched mesenchymal stem cell-derived extracellular vesicles (MSCCCR2 EVs) onto a dihydrotanshinone I-loaded nanocarrier (MSeN-DT). The MSCCCR2 EVs (the shell of EVN) can actively show homing to specific chemokines CCL2 in the substantia nigra, which enables them to block the infiltration of peripheral inflammatory cells. Interestingly, MSeN-DT (the core of EVN) can promote the Nrf2-GPX4 pathway for the suppression of the source of inflammation by inhibiting ferroptosis in microglia. In the PD model mice, a satisfactory therapeutic effect is achieved, with inhibition of peripheral inflammatory cell infiltration, precise regulation of inflammatory microglia in the substantia nigra, as well as promotion of behavioral improvement and repairing damaged neurons. In this way, the combinatorial code of alleviation of inflammation and modulation of immune homeostasis can reshape the immune microenvironment in PD, which bridges internal anti-inflammatory and external immunity. This finding reveals a comprehensive therapeutic paradigm for PD that breaks the vicious cycle of immune overactivation.

Toward Automatic Inference of Glycan Linkages Using MSn and Machine Learning─Proof of Concept Using Sialic Acid Linkages.

Glycosidic linkages in oligosaccharides play essential roles in determining their chemical properties and biological activities. MSn has been widely used to infer glycosidic linkages but requires a substantial amount of starting material, which limits its application. In addition, there is a lack of rigorous research on what MSn protocols are proper for characterizing glycosidic linkages. In this work, to deliver high-quality experimental data and analysis results, we propose a machine learning-based framework to establish appropriate MSn protocols and build effective data analysis methods. We demonstrate the proof-of-principle by applying our approach to elucidate sialic acid linkages (α2'-3' and α2'-6') in a set of sialyllactose standards and NIST sialic acid-containing N-glycans as well as identify several protocol configurations for producing high-quality experimental data. Our companion data analysis method achieves nearly 100% accuracy in classifying α2'-3' vs α2'-6' using MS5, MS4, MS3, or even MS2 spectra alone. The ability to determine glycosidic linkages using MS2 or MS3 is significant as it requires substantially less sample, enabling linkage analysis for quantity-limited natural glycans and synthesized materials, as well as shortens the overall experimental time. MS2 is also more amenable than MS3/4/5 to automation when coupled to direct infusion or LC-MS. Additionally, our method can predict the ratio of α2'-3' and α2'-6' in a mixture with 8.6% RMSE (root-mean-square error) across data sets using MS5 spectra. We anticipate that our framework will be generally applicable to analysis of other glycosidic linkages.