Journal of neurology · 2025
Evaluating the predictive potential of Th1 (IFN-γ+CD4+)/CD4+ in rapidly progressive amyotrophic lateral sclerosis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhu Jiahui, Zhang Yuechen, Hu Shuqi, He Xudong, Hong Xiaoqin, Wei Wan, Shu Song, Zhou Huixia, Yang Gaoyi, Zhang Hao
Аннотация
BACKGROUND: Th1 (IFN-γ+CD4+)/CD4+ cells exacerbate the release of pro-inflammatory cytokines, contributing to neuronal death. It is proposed that the peripheral immune system plays a pivotal role in the pathophysiology of amyotrophic lateral sclerosis (ALS). This study aims to develop an interpretable machine learning model based on blood Th1/CD4+ cells to predict rapidly progressive ALS.
METHODS: We enrolled 564 patients with sporadic ALS who met the eligibility inclusion criteria for further analysis. Immune cells and cytokines were quantified using flow cytometric cell counting and a flow cytometry-based fluorescent bead capture assay. Multivariate Cox proportional hazards models and restricted cubic spline analyses were applied to estimate the correlation between Th1/CD4+ cells and rapidly progressive ALS. The important variables identified through LASSO regression analysis were incorporated into the development of the machine learning model.
RESULTS: The multivariate Cox proportional hazards model revealed that, compared to the low Th1/CD4+ group (Th1/CD4+ < 16.21), the high Th1/CD4+ group (Th1/CD4+ ≥ 16.21) was positively associated with the rate of ALS progression (HR: 1.90, 95% CI: 1.34-2.70). Th1/CD4+ is also associated with the decline in forced vital capacity (r = 0.11, P = 0.01). The machine learning model was built using Th1/CD4+ in combination with the other 4 features. Xgboost performed best in the validation cohort, achieving an AUC of 0.804 and a G mean of 0.756.
CONCLUSIONS: Th1/CD4+ (with an optimal cutoff value of 16.21) was established as an independent risk factor for rapid progression in ALS. The machine learning model incorporating Th1/CD4+ demonstrated strong predictive performance.
TRIAL REGISTRATION: The prospective cohort study is registered with the Chinese Clinical Trial Registry (ID: ChiCTR2400079885) ( http://www.chictr.org.cn/ ).
METHODS: We enrolled 564 patients with sporadic ALS who met the eligibility inclusion criteria for further analysis. Immune cells and cytokines were quantified using flow cytometric cell counting and a flow cytometry-based fluorescent bead capture assay. Multivariate Cox proportional hazards models and restricted cubic spline analyses were applied to estimate the correlation between Th1/CD4+ cells and rapidly progressive ALS. The important variables identified through LASSO regression analysis were incorporated into the development of the machine learning model.
RESULTS: The multivariate Cox proportional hazards model revealed that, compared to the low Th1/CD4+ group (Th1/CD4+ < 16.21), the high Th1/CD4+ group (Th1/CD4+ ≥ 16.21) was positively associated with the rate of ALS progression (HR: 1.90, 95% CI: 1.34-2.70). Th1/CD4+ is also associated with the decline in forced vital capacity (r = 0.11, P = 0.01). The machine learning model was built using Th1/CD4+ in combination with the other 4 features. Xgboost performed best in the validation cohort, achieving an AUC of 0.804 and a G mean of 0.756.
CONCLUSIONS: Th1/CD4+ (with an optimal cutoff value of 16.21) was established as an independent risk factor for rapid progression in ALS. The machine learning model incorporating Th1/CD4+ demonstrated strong predictive performance.
TRIAL REGISTRATION: The prospective cohort study is registered with the Chinese Clinical Trial Registry (ID: ChiCTR2400079885) ( http://www.chictr.org.cn/ ).