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| http://dx.doi.org/10.3343/alm.2021.41.3.339 | DOI Listing |
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9-Methylfascaplysin Prevents Neuroinflammation and Synaptic Damage via Cell-Specific Inhibition of Kinases in APP/PS1 Transgenic Mice.
CNS Neurosci Ther
November 2024
Translational Medicine Center of Pain, Emotion and Cognition, Health Science Center, Ningbo University, Zhejiang, China.
Background: Alzheimer's disease (AD) is a leading neurodegenerative disorder without effective treatments. The nonlinear dynamic nature of AD pathophysiology suggested that multiple pharmacological actions of anti-AD drugs should be elucidated. 9-Methylfascaplysin (9-MF) was previously designed and synthesized as a novel anti-AD candidate.
View Article and Find Full Text PDFMultimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer.
Cancer Imaging
October 2024
Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province, 529030, PR China.
Purpose: To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).
Materials And Methods: A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria.
Application of computed tomography-based radiomics analysis combined with lung cancer serum tumor markers in the identification of lung squamous cell carcinoma and lung adenocarcinoma.
J Cancer Res Ther
August 2024
Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China.
Objective: To establish a prediction model of lung cancer classification by computed tomography (CT) radiomics with the serum tumor markers (STM) of lung cancer.
Materials And Methods: Two-hundred NSCLC patients were enrolled in our study. Clinical data including age, sex, and STM (squamous cell carcinoma [SCC], neuron-specific enolase [NSE], carcinoembryonic antigen [CEA], pro-gastrin-releasing peptide [PRO-GRP], and cytokeratin 19 fragment [cYFRA21-1]) were collected.
Performance of the ERC/ESICM-recommendations for neuroprognostication after cardiac arrest: Insights from a prospective multicenter cohort.
Resuscitation
September 2024
AfterROSC Network Group, Paris, France; Université de Paris Cité, Inserm, Paris Cardiovascular Research Center, Paris, France; Ramsay Générale de Santé, Hôpital Privé Jacques Cartier, Massy, France.
Article Synopsis
- The study aimed to evaluate the effectiveness of the 2021 ERC/ESICM algorithm in predicting neurological outcomes for cardiac arrest survivors in intensive care.
- A total of 337 patients were examined, with the algorithm successfully identifying all 175 patients predicted to have poor neurological outcomes and showing high specificity for various predictive tools like EEG and clinical examination.
- For patients with uncertain outcomes, favorable indicators could help predict positive recovery, providing valuable guidance in prognosis and treatment decisions.
Development of a diagnostic algorithm to ascertain malignant pleural effusion utilizing clinical indicators and serum metal concentrations.
Front Oncol
June 2024
Department of Medical laboratory, the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
Background: Malignant pleural effusion (MPE) is prevalent among cancer patients, indicating pleural metastasis and predicting poor prognosis. However, accurately identifying MPE in clinical settings is challenging. The aim of this study was to establish an innovative nomogram-derived model based on clinical indicators and serum metal ion levels to identify MPE.
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