Differentiating progressive supranuclear palsy from other movement disorders using transcranial sonography: a systematic review and meta-analysis.

Progressive supranuclear palsy (PSP) is an atypical parkinsonism that presents with different phenotypes. There are still no validated diagnostic biomarkers for early diagnosis of PSP. Transcranial sonography (TCS) is a promising tool in the differential diagnosis of parkinsonian disorders; however, there are no systematic investigations about the application of TCS in PSP patients. Therefore, we performed a systematic review and meta-analysis to discuss the role of TCS in diagnosing PSP by systematically searching PubMed, Cochrane Library, Chinese National Knowledge Infrastructure and Wan Fang databases. Of 66 obtained records, 16 articles, including 366 patients with PSP, were included. Our results showed the estimated random-effects pooled prevalence of substantia nigra hyperechogenicity in patients with PSP was 22% (95% CI 12-32%), lenticular nucleus hyperechogenicity was 70% (95% CI 52-82%), and enlarged third ventricle was 71% (95% CI 55-85%). Additionally, a normal echogenicity substantia nigra in TCS showed 70% sensitivity (95% CI 56-81%) and 86% specificity (95% CI 75-86%) to differentiate PSP from Parkinson's disease. In conclusion, TCS is an important supplementary biomarker for diagnosing PSP. At the same time, the diagnostic value of TCS in discriminating PSP from other atypical parkinsonism and between different PSP phenotypes needs further exploration.

Lignan Glucosides from the Stem Barks of Illicium difengpi.

In this study, four new lignan glucosides, named difengpiosides A-D (1-4), were isolated from the stem barks of Illicium difengpi, together with seven known compounds 5-11. Their structures were identified on the basis of spectroscopic analyses (1D and 2D NMR, HRESIMS, CD) and a comparison with literature data. All the compounds were evaluated for their inhibitory effects on lipopolysaccharide (LPS)-induced nitric oxide (NO) production in RAW264.7 cells.

[Chemical constituents from EtOAc fraction of Sophora dunnii].

Sixteen compounds have been isolated from the EtOAc fraction of 95% ethanolic extract of Sophora dunnii through silica gel, Sephadex LH-20 and semi-prerarative HPLC column chromatographies. Their structures were identified on the basis of NMR and MS spectra data as phaseollidin (1), L-maackiain (2), 2-(2',4'-dihidroxyphenyl)-5,6-methylenedioxy benzofuran (3), 8-demethyl-farrerol (4), liquiritigenin (5), genistein (6), 6-methylgenistein (7), 5-O-methyl genistein (8), 7,2',4'-trihydroxys-5-methoxy-isoflavanone (9), 7, 3', 4'-trihydroxy-isoflavanone (10), erythribyssin D (11), calycosin (12), trans-resveratrol (13), cis-resveratrol (14), stigmasterol (15), ß-sitosterol (16). Among these, compounds 1-14 and 16 were isolated from this plant for the first time.

Ultrasonography-based radiomics and computer-aided diagnosis in thyroid nodule management: performance comparison and clinical strategy optimization.

Objectives: To compare ultrasonography (US) feature-based radiomics and computer-aided diagnosis (CAD) models for predicting malignancy in thyroid nodules, and to evaluate their utility for thyroid nodule management. Methods: This prospective study included 262 thyroid nodules obtained between January 2022 and June 2022. All nodules previously underwent standardized US image acquisition, and the nature of the nodules was confirmed by the pathological results. The CAD model exploited two vertical US images of the thyroid nodule to differentiate the lesions. The least absolute shrinkage and operator algorithm (LASSO) was applied to choose radiomics features with excellent predictive properties for building a radiomics model. Ultimately, the area under the receiver operating characteristic curve (AUC) and calibration curves were assessed to compare diagnostic performance between the models. DeLong's test was used to analyze the difference between groups. Both models were used to revise the American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR TI-RADS) to provide biopsy recommendations, and their performance was compared with the original recommendations. Results: Of the 262 thyroid nodules, 157 were malignant, and the remaining 105 were benign. The diagnostic performance of radiomics, CAD, and ACR TI-RADS models had an AUC of 0.915 (95% confidence interval (CI): 0.881-0.947), 0.814 (95% CI: 0.766-0.863), and 0.849 (95% CI: 0.804-0.894), respectively. DeLong's test showed a statistically significant between the AUC values of models (p < 0.05). Calibration curves showed good agreement in each model. When both models were applied to revise the ACR TI-RADS, our recommendations significantly improved the performance. The revised recommendations based on radiomics and CAD showed an increased sensitivity, accuracy, positive predictive value, and negative predictive value, and decreased unnecessary fine-needle aspiration rates. Furthermore, the radiomics model's improvement scale was more pronounced (33.3-16.7% vs. 33.3-9.7%). Conclusion: The radiomics strategy and CAD system showed good diagnostic performance for discriminating thyroid nodules and could be used to optimize the ACR TI-RADS recommendation, which successfully reduces unnecessary biopsies, especially in the radiomics model.