Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm.

OBJECTIVE: To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models. METHODS: A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models. RESULTS: The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909. CONCLUSIONS: This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients' health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.

Research and Analysis of Molecules such as CD28, CD45RA, CD45RO, CD38, HLA-DR, and CD57 on T Cells in Multiple Myeloma.

BACKGROUND: For many years it has been postulated that the immune system controls the progress of multiple myeloma (MM). However, the phenotypes of T cells in MM remain to be elucidated. In this study, we compared the phenotypes of T cells, which were obtained from the peripheral blood, in MM patients with those in healthy donors (HD). The expression of CCR7, CD57, CD28, HLA-DR, CD38, CD45RA, and CD45RO were assessed on T cells from MM patients and HDs using multicolor flow cytometry (MFC). METHODS: For this study, 17 newly diagnosed MM patients were selected, and 20 healthy people were selected as a control group. MFC was used to detect the markers on T cells. RESULTS: We detected significant increases in the expression levels of HLA-DR, CD38, and CD57on CD8+ T cells, significant decreases in the expression levels of CD28 and CD45RA on CD8+ T cells, and a decrease of CD4+ effec-tor T cells in MM patients, compared to the HD group. CONCLUSIONS: Our study shows that the accumulation of peripheral CD8+CD57+T cells, CD8+CD38high T cells, and CD8+HLA-DR+CD38high T cells is reflective of an ongoing antitumor T cell response and a progressive immune dysfunction in MM. During chemotherapy, the recovery of immune function can be monitored by detecting the proportion of activated molecules of T lymphocytes.

Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance.

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians' lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.