Aging induces a series of immune related changes, which is called immunosenescence, playing important roles in many age-related diseases, especially neurodegenerative diseases, tumors, cardiovascular diseases, autoimmune diseases and coronavirus disease 2019(COVID-19). However, the mechanism of immunosenescence, the association with aging and successful aging, and the effects on diseases are not revealed obviously. In order to provide theoretical basis for preventing or controlling diseases effectively and achieve successful aging, we conducted the review and found that changes of aging-related phenotypes, deterioration of immune organ function and alterations of immune cell subsets participated in the process of immunosenescence, which had great effects on the occurrence and development of age-related diseases.

The dependence of gamma-radiation-induced neoplastic transformation frequency on position in the cell cycle was measured for a human hybrid cell line (HeLa X skin fibroblast). The end point used was the induction of a tumor-associated antigen which in these cells correlates with tumorigenicity. Induction was measured in cells at G2, M, and mid-G1 phases and compared with the frequency induced in asynchronous cells. For studies of cells in G2 phase, the cells of an asynchronous population were collected for 3 h post-irradiation using the mitotic shake-off technique. For studies of cells in M and mid-G1 phases, cells were collected by mitotic harvest and then treated at the appropriate time. The data show that cells in G2 and M phase are very radiosensitive in terms of both cell killing and induction of neoplastic transformation compared to cells in mid-G1 or asynchronous populations. At a dose of 1 Gy, the transformation frequency was 10- to 20-fold higher for cells in M and G2 phase than for cells in mid-G1 or for asynchronous cells. However, the data indicate that the transformation frequencies were similar in the different phases of the cell cycle when correlated with surviving fraction. The results indicate that transformation frequency is more sensitive to changes in dose than is cell survival.

Background: The incidence of depression in patients with systemic lupus erythematosus (SLE) is high and leads to a lower quality of life than that in undepressed SLE patients and healthy individuals. The causes of SLE depression are still unclear. Methods: A total of 94 SLE patients were involved in this study. A series of questionnaires (Hospital Depression Scale, Social Support Rate Scale and so on) were applied. Flow cytometry was used to test the different stages and types of T cells and B cells in peripheral blood mononuclear cells. Univariate and multivariate analyses were conducted to explore the key contributors to depression in SLE. Support Vector Machine (SVM) learning was applied to form the prediction model. Results: Depressed SLE patients showed lower objective support, severer fatigue, worse sleep quality and higher percentages of ASC%PBMC, ASC%CD19+, MAIT, TEM%Th, TEMRA%Th, CD45RA+CD27-Th, TEMRA%CD8 than non-depressed patients. A learning-based SVM model combining objective and patient-reported variables showed that fatigue, objective support, ASC%CD19+, TEM%Th and TEMRA%CD8 were the main contributing factors to depression in SLE. With the SVM model, the weight of TEM%Th was 0.17, which is the highest among objective variables, and the weight of fatigue was 0.137, which was the highest among variables of patients’ reported outcomes. Conclusions: Both patient-reported factors and immunological factors could be involved in the occurrence and development of depression in SLE. Scientists can explore the mechanism of depression in SLE or other psychological diseases from the above perspective.

Aim: Rates vary widely across hospitals globally and typically range from 3 to 11 falls per 1000 bed days and as 7–11 in Affiliated Hospital of Nantong University. This study determined to explore contributing factors and poor prognosis of fall in elderly tumour patients in China.Design: A cross‐sectional study.Methods: 161 older adults were invited to participate in this study and completed a self‐reported questionnaire, took blood tests, and received the exam of musculoskeletal ultrasound.Results: Among 161 patients, falls occurred in 41 cases, accounting for 24.8%. 51.6% of older adults suffered from intermediate‐to‐high risk of falls. Fall history, reduced self‐care ability, sleep disturbance, hearing impairment, hyperkyphosis, chronic disease, platelet count, and the thickness of left muscle rectus femoris (LF‐MLT), and left cross‐sectional area (LF‐CSA) were all contributing factors of fall, and higher risk of fall indicating lower quality of life. A fall prediction model was established in this study based on above contributing factors with good prediction efficiency (AUC = 0.920).Patient or public contribution: The patient volunteers participated in this study and provided valuable data for the final analysis and the acquisition of conclusion.