Secretory IgA (SIgA) is the dominant antibody class in mucosal secretions. The majority of plasma cells producing IgA are located within mucosal membranes lining the intestines. SIgA protects against the adhesion of pathogens and their penetration into the intestinal barrier. Moreover, SIgA regulates gut microbiota composition and provides intestinal homeostasis. In this review, we present mechanisms of SIgA generation: T cell-dependent and -independent; in different non-organized and organized lymphoid structures in intestinal lamina propria (i.e., Peyer’s patches and isolated lymphoid follicles). We also summarize recent advances in understanding of SIgA functions in intestinal mucosal secretions with focus on its role in regulating gut microbiota composition and generation of tolerogenic responses toward its members.

In recent years, there has been a surge of interest in tumor microenvironment-associated cancer vaccine therapies. These innovative treatments aim to activate and enhance the body’s natural immune response against cancer cells by utilizing specific antigens present in the tumor microenvironment. The goal is to achieve a complete clinical response, where all measurable cancer cells are either eliminated or greatly reduced in size. With their potential to revolutionize cancer treatment, these therapies represent a promising avenue for researchers and clinicians alike. Despite over 100 years of research, the success of therapeutic cancer vaccines has been variable, particularly in advanced cancer patients, with various limitations, including the heterogeneity of the tumor microenvironment, the presence of immunosuppressive cells, and the potential for tumor escape mechanisms. Additionally, the effectiveness of these therapies may be limited by the variability of the patient’s immune system response and the difficulty in identifying appropriate antigens for each patient. Despite these challenges, tumor microenvironment-targeted vaccine cancer therapies have shown promising results in preclinical and clinical studies and have the potential to become a valuable addition to current cancer treatment and “curative” options. While chemotherapeutic and monoclonal antibody treatments remain popular, ongoing research is needed to optimize the design and delivery of these therapies and to identify biomarkers that can predict response and guide patient selection. This comprehensive review explores the mechanisms of cancer vaccines, various delivery methods, and the role of adjuvants in improving treatment outcomes. It also discusses the historical background of cancer vaccine research and examines the current state of major cancer vaccination immunotherapies. Furthermore, the limitations and effectiveness of each vaccine type are analyzed, providing insights into the future of cancer vaccine development.

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

Background: Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease with high mortality. The identification of specific HNSCC biomarkers will increase treatment efficacy and limit the toxicity of current therapeutic strategies. Long non-coding RNAs (lncRNAs) are promising biomarkers. Accordingly, here we investigate the biological role of ZFAS1 and its potential as a biomarker in HNSCC. Methods: The expression level of ZFAS1 in HNSCC cell lines was analyzed using qRT-PCR. Based on the HNSCC TCGA data, the ZFAS1 expression profile, clinicopathological features, and expression of correlated genes were analyzed in patient tissue samples. The selected genes were classified according to their biological function using the PANTHER tool. The interaction between lncRNA:miRNA and miRNA:mRNA was tested using available online tools. All statistical analyses were accomplished using GraphPad Prism 5. Results: The expression of ZFAS1 was up-regulated in the metastatic FaDu cell line relative to the less aggressive SCC-25 and SCC-040 and dysplastic DOK cell lines. The TCGA data indicated an up-regulation of ZFAS1 in HNSCCs compared to normal tissue samples. The ZFAS1 levels typically differed depending on the cancer stage and T-stage. Patients with a lower expression of ZFAS1 presented a slightly longer disease-free survival and overall survival. The analysis of genes associated with ZFAS1, as well its targets, indicate that they are linked with crucial cellular processes. In the group of patients with low expression of ZFAS1, we detected the up-regulation of suppressors and down-regulation of genes associated with epithelial-to-mesenchymal transition (EMT) process, metastases, and cancer-initiating cells. Moreover, the negative correlation between ZFAS1 and its host gene, ZNFX1, was observed. The analysis of interactions indicated that ZFAS1 has a binding sequence for miR-150-5p. The expression of ZFAS1 and miR-150-5p is negatively correlated in HNSCC patients. miR-150-5p can regulate the 3′UTR of EIF4E mRNA. In the group of patients with high expression of ZFAS1 and low expression of miR-150-5p, we detected an up-regulation of EIF4E. Conclusions: In HNSCC, ZFAS1 displays oncogenic properties, regulates important processes associated with EMT, cancer-initiating cells, and metastases, and might affect patients’ clinical outcomes. ZFAS1 likely regulates the cell phenotype through miR-150-5p and its downstream targets. Following further validation, ZFAS1 might prove a new and valuable biomarker.

Endometriosis is one of the most common gynecological and systemic diseases, with a remarkable immune background. Patients suffer from pain and fertility reduction. Due to the distinct immune component, an immunotherapeutic approach may gain importance in the future. In endometriosis, shifts in the cell fractions of the immune system are well known. Moreover, hypoxia concomitant with inflammation causes a disturbed immune response. The removal of endometriosis has a therapeutic effect, normalizes the immune disorders, and remains the most effective causative treatment in terms of pain and infertility. A key issue is whether a similar effect can be achieved for fertility with non-invasive immunotherapy where surgery is inadvisable or cannot be performed for various reasons. Numerous immunotherapy trials, including vaccines, were conducted on animals only, although the research is encouraging. Among the promising methods of non-specific immunotherapy is the administration of an ethiodized oil contrast. Moreover, due to the significant successes of immunotherapy in oncology, the possibility of immunotherapy affecting NK cells has been postulated. NK cells are responsible for the surveillance and apoptosis of ectopic cells. Expanding the arsenal of endometriosis treatment by immunotherapy is promising due to the significant contribution of immunological factors and the limitations of current treatment methods.

Interleukin 11 (IL-11) is a pleiotropic cytokine with anti-apoptotic, anti-inflammatory and hematopoietic potential. The IL-11 activity is determined by the expression of the IL-11R receptor alpha (IL-11Rα) and the signal transducing subunit β (gp130) on the cell membrane. A recombinant soluble form of the IL-11Rα (sIL-11Rα) in combination with IL-11 acts as an agonist on cells expressing the gp130 molecule. We constructed a designer cytokine Hyper IL-11 (H11), which is exclusively composed of naturally existing components. It contains the full length sIL-11Rα connected with the mature IL-11 protein using their natural sequences only. Such a construct has two major advantages: (i) its components are as close as possible to the natural forms of both proteins and (ii) it lacks an artificial linker what should avoid induction of antibody production.