Multivalent acetylated-sialic acid as recognition elements for the electrochemical sensing of viral antigens

Zhang, Zhen; Ji, Haijie; Zhuang, Xiwei; Xu, Yuning; Liu, Jianlei; Zeng, Chijia; Ding, Wen; Cui, Feiyun; Zhu, Sanyong

doi:10.1016/j.bios.2024.116883

Biosensors and Bioelectronics

2025

DOI: 10.1016/j.bios.2024.116883

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Multivalent acetylated-sialic acid as recognition elements for the electrochemical sensing of viral antigens

Zhen Zhang,

Haijie Ji,

Xiwei Zhuang

et al.

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Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment

Wu,

Qin,

Zhou

et al. 2024

Front. Neurol.

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BackgroundClassic trigeminal neuralgia (CTN) seriously affects patients’ quality of life. Percutaneous balloon compression (PBC) is a surgical program for treating trigeminal neuralgia. But some patients are ineffective or relapse after treatment. The aim is to use machine learning to construct clinical imaging models to predict relapse after treatment (PBC).MethodsThe clinical data and intraoperative balloon imaging data of CTN from January 2017 to August 2023 were retrospectively analyzed. The relationship between least absolute shrinkage and selection operator and random forest prediction of PBC postoperative recurrence, ROC curve and decision -decision curve analysis is used to evaluate the impact of imaging histology on TN recurrence.ResultsImaging features, like original_shape_Maximum2D, DiameterRow, Original_Shape_Elongation, etc. predict the prognosis of TN on PBC. The areas under roc curve were 0.812 and 0.874, respectively. The area under the ROC curve of the final model is 0.872. DCA and calibration curves show that nomogram has a promising future in clinical application.ConclusionThe combination of machine learning and clinical imaging and clinical information has the good potential of predicting PBC in CTN treatment. The efficacy of CTN is suitable for clinical applications of CTN patients after PBC.

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Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment

Wu,

Qin,

Zhou

et al. 2024

Front. Neurol.

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Multivalent acetylated-sialic acid as recognition elements for the electrochemical sensing of viral antigens

Cited by 1 publication

References 37 publications

Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment

Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment

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