BioMarifa

Sexual dimorphism in migraine ovarian steroid signaling hormonal influences on pain processing gender-informed treatment neuroendocrine mechanisms women's headache disorders

Abstract:
Migraine stands as one of the most consequential disabling neurological disorders confronting modern medicine, characterized by recurrent episodes of severe head pain with neurological mani-festations that substantially diminish quality of life and impose considerable socioeconomic bur-den. Contemporary epidemiological investigations consistently document a striking disparity in disease prevalence between biological sexes, with women experiencing migraine attacks at rates exceeding those in men by approximately threefold, with documented prevalence reaching 18 per-cent among adult female populations. The female migraine phenotype characteristically includes am-plified pain intensity, elevated attack frequency, and more diverse symptomatological presenta-tions. This comprehensive review synthesizes accumulated evidence illuminating the complicated relationship between biological sex, hormonal steroid signaling pathways, central nervous system function, and the spectrum of therapeutic modalities available for migraine management. The over-arching conclusion emphasizes that truly effective, patient-centered migraine management neces-sarily incorporates biological sex and individual hormonal circumstances as core organizing princi-ples influencing both disease manifestation and therapeutic response patterns.

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TCR-T cell therapy precision medicine cancer immunotherapy neoantigen targeting adoptive cell transfer CRISPR engineering tumor microenvironment personalized oncology.

Abstract:
Abstract: Cancer care has arrived at a crossroads. While checkpoint inhibitors and CAR-T cells have rewritten the rules for hematologic malignancies, solid tumors continue to defy durable control. T cell receptor-engineered T cell (TCR-T) therapy occupies a unique niche: unlike CAR-T constructs, TCR-T cells recognize intracellular antigens displayed on major histocompatibil-ity complex molecules, effectively illuminating targets that remain invisible to conventional antibody-based approaches. The true promise of this modality, however, only materializes when it is woven into the fabric of precision medicine. By leveraging whole-exome sequenc-ing, artificial intelligence-driven neoantigen prediction, single-cell transcriptomics, and real-time liquid biopsy monitoring, clinicians can now select targets, engineer receptors, and triage patients with a granularity that was unimaginable a decade ago. This narrative review synthe-sizes peer-reviewed literature from 2015 to 2025, drawing from PubMed, Elsevier, Wiley, and Springer databases, to examine how the fusion of TCR-T therapy and precision oncology is re-shaping target identification, manufacturing workflows, and clinical outcomes. We highlight landmark trials—including the SPEARHEAD-1 study leading to FDA accelerated approval of afamitresgene autoleucel for synovial sarcoma—and discuss how CRISPR editing, allogeneic platforms, and rational combination regimens are addressing historical barriers such as tumor heterogeneity, T cell exhaustion, and prohibitive manufacturing costs. Our analysis suggests that the next chapter of adoptive cell therapy will not be defined by a single breakthrough, but by the creation of an integrated precision ecosystem that aligns tumor biology, immune engineer-ing, and patient-specific biomarkers from diagnosis through post-treatment surveillance.

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Haematological diagnostics Artificial intelligence Precision medicine Transfer learning Machine learning

Abstract:
Abstract:
Background: AI systems and machine learning techniques hold significant potential in haematology, demonstrat-ing capabilities in diagnostic areas like cytogenetics and molecular genetics by automating blood samples and genetic data analysis Aim: This review examines the utilisation of AI systems and machine-learning techniques in haematology, focusing on their applications, potential benefits, and challenges in clinical practice. Method: This study explored various AI approaches, including Machine learning, Deep learning techniques, applied to haematological diagnostics and research through various website and journals. It evaluates the performance of AI systems in differentiating cell types, conducting chromosome banding analyses, and interpreting complex im-aging data. Results: AI systems proficiently differentiate cell types in blood samples, aiding in diagnosing condi-tions like leukemia and lymphoma. They also show promise in chromosome banding analysis, identifying ab-normalities indicative of specific haematological diseases. Machine-learning techniques have been effectively used to predict patient responses and cluster patient profiles. Discussion: Integrating AI in haematology faces challenges such as data quality, algorithm interpretability, and clinical workflow integration. The "black box" na-ture of certain AI algorithms complicates interpretability and accountability. Collaborative efforts between AI developers and clinicians are needed to address these issues. Additionally, ensuring high-quality and repre-sentative training data is crucial to avoid bias and inaccuracies in AI outcomes. Conclusion: AI shows promise in improving haematology diagnostics, but faces implementation challenges. Continued research must balance technological advancement with ethical concerns, ensuring AI supports rather than replaces haematologists.

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