Risultati per: Innovativo test delle urine per una accuratezza diagnostica del cancro alla prostata

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Introduction
Pre-eclampsia is a condition associated with significant maternal and neonatal morbidity and mortality. The prediction of pre-eclampsia in high-risk populations using angiogenic markers, such as serum placental growth factor (PlGF) assessment, has been shown to improve maternal outcomes and is recommended by the National Institute for Health and Care Excellence (NICE). However, such tests are not yet available at the point of care (POC). Glycosylated fibronectin (GlyFn) level for the prediction of pre-eclampsia development is available as a POC test (Lumella) and has the potential to aid rapid clinical decision making. This study aimed to test the hypothesis that the sensitivity of the GlyFn test is not inferior to that of the current gold standard of soluble fms-like tyrosine kinase (sFlt)/PlGF-based laboratory testing for pre-eclampsia.

Methods and analysis
This is a multicentre prospective study. Women at risk for pre-eclampsia based on predefined clinical and/or obstetric risk factors will be invited to participate in the study. The recruitment target is 400 participants. Consenting participants will have paired samples for sFlt/PlGF together with POC GlyFn testing. Two follow-up visits are planned at 2 and 4 weeks after the initial recruitment where repeat testing with both tests will be performed. The clinical team will be blinded to the results of the GlyFn test but not that of the sFlt/PlGF test. Clinical care will be based on established protocols incorporating maternal/fetal evaluation and the results of sFlt/PlGF levels. Maternal and neonatal outcome data will be collected to compare the sensitivity and specificity of the tests, with the primary outcome being delivery for pre-eclampsia within 4 weeks.

Ethics and dissemination
Ethical approval has been obtained from the Health Research Authority and Health and Care Research Wales Ethics Committee. The results of this study will be published in peer-reviewed journals and presented at scientific conferences.

Trial registration number
ISRCTN13430018

Stroke, Volume 56, Issue Suppl_1, Page ATP370-ATP370, February 1, 2025. Introduction:Despite efforts to improve stroke outcomes in patients, a translational gap exists between preclinical and clinical studies. Due to this gap, the Stroke Preclinical Assessment Network has incorporated the corner test (CoT) for behavioral outcome as a primary measure for evaluating whether a treatment is successful or not. Standard behavioral analysis for CoT uses the laterality index to detect if there is mouse turning preference on a scale from -1 to 1. We sought to determine if a deep learning approach using “DeepLabCut” could be applied towards enriching our CoT data to better evaluate aspects of mouse locomotion.Methods:Six C57/Bl6 mice were subjected to an 1 hr transient middle cerebral artery occlusion in the right hemisphere of the brain. CoT were recorded with an isometric view and performed at both baseline (BL) prior to the stroke and one day post stroke (D1). The same set of six mice performed 10 turns per CoT, totalling to 60 turns for BL and 60 turns for D1. The pose estimation model was made using a ResNet-101 neural network trained on 1064 manually-labeled frames, with the assistance of “DeepLabCut” software packages. Videos were analyzed by the pose estimation model and sequentially processed through a newly developed R script and DLC Analyzer R script. Turns were defined as in SPAN with a 90 degree head turn upon vibrissae contact on both sides of the corner boards. Turn latency was defined as the time lapsed during a turn, and head turn speed as the average speed during a turning event.Results:The average laterality index showed clear preference towards ipsilateral turning in all D1 mice (-1.0 ± 0.04, N = 6). Furthermore, a comparison between BL data and respective D1 data showed significantly longer turn latencies and slower head turn speeds (p < 0.05) for D1 mice. The average turn latency for BL mice was 3.58 ± 0.57 s, which was 4.6 times shorter than that of D1 mice (16.45 ± 3.11 s). The average speed for BL mice was 2.01 cm/s ±0.21, which was 2.3 times faster than that of D1 mice (0.86 ± 0.14 cm/s).Conclusion:This deep learning approach enriches current stroke behavioral analysis methods by offering additional quantitative information upon which behavior can be assessed. Future studies can use these behavioral metrics for stratification or correlation with variables of interest (e.g. infarct size) to provide a more refined assessment of preclinical stroke behavior.

Objective
To investigate how various morbidities affect older patients’ performance on the Timed Up and Go (TUG) test.

Design
Cross-sectional study.

Setting
The seven government hospitals of Lahore, Pakistan, included are major tertiary care centres, representing an older patient population of Punjab, Pakistan.

Method
160 elderly participants completed the TUG test, frailty evaluations and Charlson Comorbidity Index (CCI) scoring to assess mobility, frailty and comorbidity burden. The Student’s t-test analysed differences between TUG groups (

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