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Abstract Background Retinal degenerative disorders (RDDs) cause vision loss by damaging retinal neurons and photoreceptors, affecting individuals of all ages. Cell-based therapy has emerged as an effective approach for the treatment of RDDs with promising results. This meta-analysis aims to comprehensively evaluate the efficacy of cell therapy in treating age-related macular degeneration (AMD), retinitis pigmentosa (RP), and Stargardt macular degeneration (SMD) as the most prevalent RDDs. Methods PubMed, Scopus, Web of Science, and Embase were searched using keywords related to various retinal diseases and cell therapy treatments until November 25th, 2023. The studies’ quality was evaluated using the Joanna Briggs Institute’s (JBI) checklist for quasi-experimental studies. Visual acuity measured as LogMAR score was used as our main outcome. A three-level random-effect meta-analysis was used to explore the visual acuity in patients who received cell-based therapy. Heterogeneity among the included studies was evaluated using subgroup and sensitivity analyses. Moreover, meta-regression for the type of cells, year of publication, and mean age of participants were performed. Results Overall, 8345 studies were retrieved by the search, and 39 met the eligibility criteria, out of which 18 studies with a total of 224 eyes were included in the meta-analysis. There were 12 studies conducted on AMD, 7 on SMD, and 2 on RP. Cell therapy for AMD showed significant improvement in LogMAR (p

Probiotics, the health-promoting members of the gut microbiota, have been shown to enhance mucosal and systemic immunity, stimulate cytokines, chemokines, and antibodies, and regulate T-cell activity. On the other hand, traditional adjuvants like alum have limitations, and researchers are exploring probiotics as a new generation of vaccine adjuvants to treat immune disorders and enhance vaccine effectiveness. For instance, studies show that probiotic strains like Lactobacillus rhamnosus GG (LGG) can improve the immune response to vaccines against rotavirus, SARS-CoV-2, and influenza. Besides, probiotic adjuvants delivered through various mucosal routes (oral and ocular) could offer broader protection against a wider range of infections. On the other hand, probiotics can not only boost the immune system but also trigger the production of anti-inflammatory cytokines and regulatory immune cells, as they have shown potential in cancer immunotherapy. Studies demonstrate that probiotics like LGG and Bifidobacterium longum can increase the efficacy of cancer vaccines by promoting T cell infiltration, thereby inhibiting tumor growth. Additionally, studies suggest specific probiotic strains like L. rhamnosus SL42 and L. casei might be beneficial for cow's milk allergies. In line with this, research on the use of probiotics for peanut allergies shows promise. However, there is a need for more research to understand mechanisms, identify optimal strains, and determine the timing and duration of administration for clinical outcomes. This review aimed to shed light on the impact of probiotics as immunomodulatory and immunogenic agents in vaccine immunizations, as well as their use as adjuvants for infectious diseases, allergies, and cancer suppression.

The human gut microbiome is an intricate ecosystem with profound implications for host metabolism, immune function, and neuroendocrine activity. Over the years, studies have strived to decode this microbial universe, especially its interactions with human health and underlying metabolic processes. Traditional analyses often struggle with the complex interplay within the microbiome due to presumptions of microbial independence. In response, machine learning (ML) and deep learning (DL) provide advanced multivariate and non-linear analytical tools that adeptly capture the complex interactions within the microbiota. With the influx of data from metagenomic next-generation sequencing (mNGS), there's an increasing reliance on these artificial intelligence (AI) subsets to derive actionable insights. This review delves deep into the cutting-edge ML techniques tailored for human gut microbiota research. It further underscores the potential of gut microbiota in shaping clinical diagnostics, prognosis, and intervention strategies, pointing to a future where computational methods bridge the gap between microbiome knowledge and targeted health interventions.