Neutrophils, which constitute the most abundant leukocytes in human blood, emerge as crucial players in the induction of endothelial cell death and the modulation of endothelial cell responses under both physiological and pathological conditions. The hallmark of preeclampsia is endothelial dysfunction induced by systemic inflammation, in which neutrophils, particularly through the formation of neutrophil extracellular traps (NETs), play a pivotal role in the development and perpetuation of endothelial dysfunction and the hypertensive state. Considering the potential of numerous pharmaceutical agents to attenuate NET formation (NETosis) in preeclampsia, a comprehensive assessment of the extensively studied candidates becomes imperative. This review aims to identify mechanisms associated with the induction and negative regulation of NETs in the context of preeclampsia. We discuss potential drugs to modulate NETosis, such as NF-κβ inhibitors, vitamin D, and aspirin, and their association with mutagenicity and genotoxicity. Strong evidence supports the notion that molecules involved in the activation of NETs could serve as promising targets for the treatment of preeclampsia.

Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise.

Non-coding RNAs (ncRNAs) and the innate immune system are closely related, acting as defense mechanisms and regulating gene expression and innate immunity. Both are modulators in the initiation, development and progression of cancer. We aimed to review the major types of ncRNAs, including small interfering RNAs (siRNAs), microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and long non-coding RNAs (lncRNAs), with a focus on cancer, innate immunity, and inflammation. We found that ncRNAs are closely related to innate immunity, epigenetics, chronic inflammation, and cancer and share properties such as inducibility, specificity, memory, and transfer. These similarities and interrelationships suggest that ncRNAs and modulators of trained immunity, together with the control of chronic inflammation, can be combined to develop novel therapeutic approaches for personalized cancer treatment. In conclusion, the close relationship between ncRNAs, the innate immune system, and inflammation highlights their importance in cancer pathways and their potential as targets for novel therapeutic strategies.

Background: Prostate cancer (PCa) arises from complex interactions among host genetics, androgen signaling, and microbial communities. Emerging genomic evidence supports the presence of microbial consortia within prostate tissue, suggesting that microbial genes, metabolites, and host–microbe interactions may contribute to chronic inflammation, oncogenic signaling, and therapeutic resistance. Methods: We conducted a narrative review using targeted searches of PubMed and Google Scholar for studies published between 2020 and 2025, complemented by selected mechanistic reports published in March 2026. Human studies and experimental research providing mechanistic insights into prostate models were prioritized. Due to the heterogeneous methodologies, evidence was synthesized qualitatively, with an emphasis on genomic and signaling perspectives. Results: Low-biomass microbial DNA is consistently detected in prostate tissue. Proteomic analyses of Corpora amylacea suggest a “fossil record” of past infections through sequestered microbial DNA and antimicrobial proteins, potentially priming tissue for long-term carcinogenic processes, although contamination remains a key limitation. Recurrent bacterial and viral signals, including Cutibacterium acnes, Escherichia coli, Pseudomonas, Acinetobacter, human papillomavirus, Epstein–Barr virus, and cytomegalovirus, appear to converge on a restricted set of tumor-relevant pathways, including TLR–NF-κB, MAPK, PI3K/AKT/mTOR, cGAS–STING, and p53/pRb disruption. These interactions may promote cytokine production, oxidative stress, DNA damage, epithelial–mesenchymal transition, extracellular matrix remodeling, immune evasion, and resistance to therapy. The gut–prostate axis further links intestinal dysbiosis and microbial metabolites with systemic IGF-1 signaling and castration resistance. Conclusions: Microbial genomic consortia in the prostate and gut may shape inflammatory, metabolic, and immune networks that influence PCa initiation and progression. However, most available data remain correlative and are limited by low-biomass sampling, contamination risk, and heterogeneous study designs. Future research should prioritize rigorous contamination control, longitudinal and prostate-specific mechanistic studies, and integrated multi-omic approaches to clarify causality and identify actionable microbial targets for prevention, diagnosis, and therapy.

This work presents a synthetic binary database of Dengue, Zika, Chikungunya, and Influenza constructed entirely from clinical information extracted from the scientific literature. Due to the limited availability and heterogeneity of clinical records in medical units—particularly for arboviral diseases—existing datasets are often insufficient for developing robust Machine Learning models. To address this limitation, an extensive search of PubMed and Google Scholar was conducted between February 2024 and May 2025, following strict selection criteria focused on diagnostic confirmation. The resulting dataset comprises 48,214 records and 67 standardized signs and symptoms, homogenized across all pathologies. Each record is fully binary, contains no missing values, and represents symptom presence or absence. The composition includes 22,379 Dengue records, 7135 Zika records, 7959 Chikungunya records, and 10,741 Influenza records. Symptom prevalence was analyzed, revealing consistency with patterns reported in epidemiological and clinical studies, supporting the dataset’s plausibility. This database enables statistical exploration and direct integration into Machine Learning pipelines without the need for imputation. It has been used in an in silico predictive study of arboviral diseases, employing Influenza as a negative control, and serves as a reproducible, literature-derived resource for computational modeling.