深入学习肾病分层的临床纵向和免疫学数据。

Q3 Medicine

Recenti progressi in medicina Pub Date : 2025-10-01 DOI:10.1701/4573.45781

Giulia Ricci, Silvia Capuzzi, Martina Riganati, Alberto Eugenio Tozzi, Marina Vivarelli, Diana Ferro, Manuela Colucci

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引用次数: 0

摘要

本研究探讨淋巴细胞谱作为儿童肾病综合征（NS）分类的非侵入性生物标志物。利用来自205名患者的回顾性临床和免疫学数据，目的是建立一个基于长短期记忆的预测模型来识别NS亚型。通过比较有和没有免疫学数据的模型，该研究将评估免疫概况的价值。目标是支持个性化管理，同时减少对侵入性手术的需求。

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Deep learning su dati clinici longitudinali e immunologici per la stratificazione della sindrome nefrosica.

This study explores lymphocyte profiles as non-invasive biomarkers for classification of pediatric nephrotic syndrome (NS). Using retrospective clinical and immunological data from 205 patients, the aim is to develop a predictive model based on Long Short-Term Memory to identify NS subtypes. By comparing models with and without immunological data, the study will assess the value of immune profiles. The goal is to support personalized management while reducing the need for invasive procedures.

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来源期刊

Recenti progressi in medicina Medicine-Medicine (all)

CiteScore

0.90

自引率

0.00%

发文量

143

期刊介绍： Giunta ormai al sessantesimo anno, Recenti Progressi in Medicina continua a costituire un sicuro punto di riferimento ed uno strumento di lavoro fondamentale per l"ampliamento dell"orizzonte culturale del medico italiano. Recenti Progressi in Medicina è una rivista di medicina interna. Ciò significa il recupero di un"ottica globale e integrata, idonea ad evitare sia i particolarismi della informazione specialistica sia la frammentazione di quella generalista.