Ticks have coevolved with their hosts over millions of years, developing the ability to evade hemostatic, inflammatory, and immunological responses. Salivary molecules from these vectors bind to cytokines, chemokines, antibodies, complement system proteins, vasodilators, and molecules involved in coagulation and platelet aggregation, among others, inhibiting or blocking their activities. Initially studied to understand the complexities of tick–host interactions, these molecules have been more recently recognized for their potential clinical applications. Their ability to bind to soluble molecules and modulate important physiological systems, such as immunity, hemostasis, and coagulation, positions them as promising candidates for future therapeutic development. This review aims to identify the binding molecules present in tick saliva, determine their primary targets, and explore the tick species involved in these processes. By associating the binding molecules, the molecules to which they bind, and the effect caused, the review provides a basis for understanding how these molecules can contribute to possible future advances in clinical applications.

Objective Eclampsia is a hypertensive disorder that occurs during pregnancy and can lead to death. The literature has gaps by not providing comprehensive data on the epidemiology of the disease, restricting analysis to limited temporal intervals and geographical locations. This study aims to characterize the epidemiological profile of women who died from eclampsia in […] Abstract Objective Eclampsia is a hypertensive disorder that occurs during pregnancy and can lead to death. The literature has gaps by not providing comprehensive data on the epidemiology of the disease, restricting analysis to limited temporal intervals and geographical locations. This study aims to characterize the epidemiological profile of women who died from eclampsia in […]

To assess the therapeutic potential of Open-Label Placebo (OLP) for chronic low back pain (CLBP). A systematic literature search was conducted on 12 September 2024. Data extraction focused on follow-up and change-from-baseline data of the placebo or control groups. The effect sizes were calculated using the inverse variance statistical method. A meta-analysis of four articles involving 171 OLP patients and 161 controls demonstrated a modest reduction in pain on the numerical rating scale (Mean difference = -0.62; 95% confidence interval: -1.09 to -0.14; p = 0.01; I² = 31%). However, according to GRADE, the certainty of evidence for this finding is very low due to methodological limitations of the included studies and potential biases. These findings, even if small, suggest potential benefits of OLP for pain reduction, with clinical relevance of the effect size being important depending on the clinical context and the patient's perspective. OLP demonstrated a reduction in pain, with a slightly greater improvement observed during shorter intervention periods. However, the certainty of this evidence remains very low, indicating that these findings should be interpreted cautiously. Future clinical trials are needed to further explore the potential and limitations of this intervention. www.crd.york.ac.uk/prospero identifier is CRD42024568302.

The saliva of hematophagous arthropods, such as ticks and triatomines, contains bioactive ligands capable of modulating immune molecules, including cytokines. Cytokines play a critical role in immune regulation and have therapeutic relevance in inflammatory and immune-mediated diseases. Despite recent advances, identifying cytokine-binding molecules remains a significant challenge. Interferon-gamma (IFN-γ), interleukin-4 (IL-4), and interleukin-17 (IL-17) are key cytokines involved in inflammation, adaptive immunity, and host defense. This study evaluated the ability of salivary components from Amblyomma sculptum and compared the results to the triatomine Rhodnius neglectus (used as control) to bind to IL-2, IL-4, IL-6, IL-10, IL-17, IFN-γ, and TNF-α using ELISA assays with human cytokines. Saliva samples were tested at dilutions of 1:25, 1:50, and 1:100. Saliva from A. sculptum, which demonstrated significant anti-cytokine activity, was fractionated via HPLC to identify the active components. The results confirmed the inhibitory capacity of A. sculptum saliva on IFN-γ, IL-4, and IL-17, with inhibition rates ranging from 30% to 70%, depending on the cytokine and dilution. No inhibitory activity was observed against IL-2, IL-6, IL-10, or TNF-α. These findings underscore the immunomodulatory role of A. sculptum saliva during tick feeding and suggest its potential for the development of novel immunobiologics to treat inflammatory and immune-mediated diseases.

Fibromyalgia (FM) is a complex and multifactorial syndrome characterized by widespread pain, fatigue, cognitive impairment, and other systemic symptoms. The absence of specific biomarkers and the heterogeneous clinical presentation pose significant diagnostic challenges. This scoping review aims to explore the current applications of artificial intelligence (AI) and machine learning (ML) in the diagnosis and clinical management of FM. A systematic search was conducted in PubMed, EMBASE, and the Cochrane Library using defined keywords related to FM and AI/ML. Studies were included if they addressed ML applications in FM patients. Following PRISMA-ScR guidelines, 43 studies published between 2011 and 2024 were included and analyzed for ML techniques used, diagnostic targets, data types, and clinical relevance. As expected, the majority of studies done so far focused on improving diagnostic accuracy through supervised algorithms such as support vector machines, neural networks, and ensemble models, as well as unsupervised clustering and dimensionality reduction techniques. Notable findings include the identification of neurophysiological signatures via fMRI, gene expression patterns, retinal imaging changes, and metabolomic biomarkers that distinguish FM patients from controls. For instance, one study investigating circulating microRNAs used a Random Forest model to identify 11 microRNAs (e.g. hsa-miR-28-5p, hsa-miR-29a-3p, hsa-miR-150-5p) capable of differentiating patients with FM, ME/CFS, and healthy controls, suggesting their potential as biomarkers for more accurate diagnoses. Reported model accuracies ranged from 82% to 100%, although most studies were pilot-based with small and imbalanced samples, limiting generalizability. AI and ML offer promising tools to overcome longstanding limitations in FM diagnosis and treatment. While current findings demonstrate significant potential, larger, multicenter studies with rigorous validation protocols are essential to finally establish these approaches as clinically reliable solutions.