Investigation of Clinical Value of NKT-like Cells in Tumor Diseases.

BACKGROUND: The goal is to study the changes of natural killer T-like (NKT-like) cells with age and explore the value of NKT-like cell changes in the evaluation of immune function and prognosis of tumor patients. METHODS: From January 2021 to December 2021, 19 patients with lung cancer, 37 patients with lymphoma, 16 patients with multiple myeloma (MM), 13 patients with acute lymphoblastic leukemia (ALL), 70 patients with acute myeloid leukemia (AML) treated in the Affiliated Hospital of Chengde Medical College and 141 healthy volunteers included in the healthy control group were recruited to study the change trend of NKT-like cells with age and changes in different tumor patients. RESULTS: With the increase of age, NKT-like cells in peripheral blood increased gradually in healthy people, mainly composed of CD8+NKT-like cells and CD8-CD4-NKT-like cells. The proportion of NKT-like cells in the lymphoma group and AML group was significantly higher than that in the control group (p < 0.05). The proportion of CD8+NKT-like cells decreased in the Lymphoma, AML, ALL, and lung cancer groups (p < 0.05), there was no statistical significance between MM group and control group (p > 0.05). CONCLUSIONS: With the increase of age, NKT-like cells in peripheral blood gradually increased in healthy people and were mainly composed of CD8+NKT-like cells and CD8-CD4-NKT-like cells. The increase of CD8+NKT-like cells in peripheral blood has important reference value for the evaluation of immune function and prognosis of patients with lymphoma, AML, ALL, and lung cancer and provides direction for immunotherapy.

Analysis of Peripheral Blood T Lymphocyte Subsets in Multiple Myeloma Patients.

BACKGROUND: The aim of this study was to investigate the changes of T lymphocyte subsets (Th1, Th2, Tc1, Tc2, and Th17) and memory T lymphocyte subsets (Tcm and Tem) in patients with multiple myeloma (MM) at different stages of the disease. METHODS: In total, 25 newly diagnosed patients with MM were selected as the study subjects and 30 healthy people were selected as the control group. The subsets of T lymphocytes such as Th1, Th2, Tc1, Tc2, Th17, Tcm, and Tem in the peripheral blood were detected by flow cytometry at the time of initial diagnosis, infection, and remission. RESULTS: Th1, Tem, and Tcm cells in MM patients showed a significant decrease compared to the control group. Th2 and Th17 cells in MM patients showed a significant increase compared to the control group. Total Th1 cells and memory Th1 cells in MM patients with bacterial infection were significantly higher than at initial diagnosis (p < 0.05). The Tcm of Th2 cells in the remission stage were significantly higher than those in MM patients with no remission. CONCLUSIONS: MM patients have decreased Th1 cells and increased Th2 and Th17 cells. The changes in memory Th1 cells were related to bacterial infection in MM patients. The increase of Tcm of Th2 cells may be associated with disease remission. The balance of T lymphocyte subsets plays an important role in the pathogenic course of MM.

Analysis of Th1/Th2 Balance and Related Cytokine Changes in the Peripheral Blood of Patients with Multiple Myeloma.

BACKGROUND: This study aimed to explore the changes in Th cells and cytokines in the peripheral blood of patients with multiple myeloma before and after treatment and at the time of the bacterial infection. METHODS: In total, 23 newly diagnosed MM patients admitted to the Hospital and 23 healthy individuals were selected as the study group and the control group, respectively. Flow cytometry was used to detect the Th1 and Th2 lymphocytes and cytokines, such as IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, INF-γ, IL-17A, IL-1b, TNF-α, TNF-ß, and IL-12P70, in the peripheral blood of the patients at initial diagnosis, before and after treatment, and at the time of the bacterial infection. RESULTS: The Th1% and Th1/Th2 ratio at the time of the initial diagnosis were lower in the MM patients than in the control group, whereas the Th2% at initial diagnosis was higher in the MM patients than in the control group. The levels of IL-6, IL-8, IL-10, and IL-17A at initial diagnosis were higher in the MM patients than in the control group. After 4 cycles of treatment, the Th2% in the patients was lower than before the treatment and the Th1/Th2 ratio in the patients was higher than before the treatment. The Th1% and the levels of IL-6, IL-8, IL-10, and INF-γ increased, while the level of IL-12P70 decreased, when MM patients got a bacterial infection. The abovementioned differences were statistically significant (p < 0.05). CONCLUSIONS: The Th1/Th2 deviation affects the immune function of the MM patients. There were significant changes in the Th1 and Th2 lymphocytes and cytokines in newly diagnosed MM patients after the treatment. The changes in the Th lymphocytes and cytokines may be an indicator of bacterial infection.

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.

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.

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.