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Gastrointestinal probiotics play an important role in maintaining intestinal bacteria homeostasis. They might benefit people with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), which remains a global health challenge. However, there is a controversy regarding the efficacy of probiotics for the treatment of AIDS. This study systematically reviewed the evidence of the effects of existing probiotic interventions on AIDS and sought to provide information on the role of probiotics in the treatment of HIV/AIDS patients. A meta-analysis of studies identified by screening multiple databases was performed using a fixed-effects model in Review Manager 5.2 software. The meta-analysis showed that probiotics could reduce the incidence of AIDS-related diarrhea (RR = 0.60 (95% CI: 0.44–0.82), p = 0.001). The short-term use of probiotics (supplementation duration shorter than 30 days) did not reduce the incidence of diarrhea (RR = 0.76 (95% CI: 0.51–1.14), p = 0.19), while the long-term use of probiotics (supplementation duration longer than 30 days) reduced diarrhea (RR = 0.47 (95% CI: 0.29–0.76), p = 0.002). Probiotics had no effect on CD4 cell counts in HIV/AIDS patients (MD = 21.24 (95% CI: −12.95–55.39), p = 0.22). Our data support that probiotics were associated with an obvious reduction in AIDS-related diarrhea, which indicates the need for additional research on this potential preventive strategy for AIDS.

Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups.Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment.Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with shorter OS and low-risk group with longer OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p < 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher.Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.