Abstract Post‐COVID syndrome is increasingly recognized as a multidimensional condition characterized by persistent cognitive, psychological, and functional impairment. Emerging evidence suggests a role for systemic inflammation; however, the integration of inflammatory, neuropsychological, and functional domains remains insufficiently explored. In this cross‐sectional study, 360 individuals with prior SARS‐CoV‐2 infection were evaluated. Systemic inflammatory markers, including interleukin‐6 (IL‐6) and C‐reactive protein (CRP), were assessed alongside validated neuropsychological measures, including PHQ‐9, BDI, STAI, and MoCA. Composite indices were constructed to quantify inflammatory burden, neuropsychological burden, and functional impairment. Correlation analyses and multivariable regression models were applied to evaluate independent associations between variables. Inflammatory burden demonstrated strong positive correlations with neuropsychological burden (ρ≈0.6, p< .001) and was independently associated with long COVID‐related neurological manifestations in regression models. Neuropsychological burden was strongly associated with functional impairment (ρ≈0.6, p< .001), indicating clinically meaningful translation of cognitive and affective dysfunction into real‐world disability. Disease severity during acute infection was positively associated with long‐term neurological burden, while time since infection showed an inverse relationship, suggesting partial recovery dynamics. Vascular risk burden did not demonstrate a consistent positive association with cognitive impairment after adjustment. Systemic inflammatory burden is strongly associated with neurocognitive and psychological dysfunction in post‐COVID syndrome, with downstream effects on functional status. These findings support a neuroimmune‐associated framework linking inflammation to persistent neurological burden and highlight the potential role of inflammatory markers in risk stratification. Further longitudinal and interventional studies are required to clarify causal mechanisms and therapeutic implications.
Abstract The increasing availability of heterogeneous biomedical data, including genomics, transcriptomics, proteomics, metabolomics, medical imaging, electronic health records, digital pathology, and wearable sensor data, has accelerated the development of multimodal artificial intelligence (AI) approaches for precision therapeutics. By integrating complementary information across multiple data modalities, multimodal AI aims to improve disease characterization, risk stratification, biomarker discovery, therapeutic target identification, and individualized treatment selection beyond what can be achieved using single‐modality analyses. This narrative review critically examines the current landscape of multimodal AI in medical biotechnology and precision therapeutics. Major biomedical data modalities, multimodal integration strategies, and emerging computational architectures are discussed, including deep‐learning frameworks, graph neural networks, biomedical foundation models, and multimodal large language models. Particular attention is given to the comparative strengths and limitations of early, late, and hybrid fusion approaches, as well as challenges associated with missing modalities, data heterogeneity, class imbalance, model calibration, and external validation. The review further evaluates representative applications in oncology, rare diseases, cardiovascular medicine, infectious diseases, neurodegenerative disorders, drug discovery, and companion diagnostics. Clinical translation remains constrained by limited prospective validation, inconsistent reporting standards, interoperability barriers, regulatory uncertainty, and concerns regarding fairness, privacy, and explainability. Emerging approaches such as federated learning and foundation‐model‐based architectures may help address some of these limitations, although their real‐world performance and governance requirements remain under active investigation. Overall, multimodal AI represents an important computational framework for integrating diverse biomedical data within precision therapeutics. Future progress will depend not only on methodological innovation but also on the development of robust validation frameworks, interoperable data ecosystems, equitable datasets, and clinically meaningful implementation studies capable of demonstrating improvements in patient outcomes.
Abstract Breast cancer is by far the most frequent cancer among females globally, besides being the top causes of cancer deaths. The reason for the complexity is the variety of its molecular and clinical subtypes causing huge difficulties with both diagnosis and management. Although screening and early detection have resulted in better survival rates in most countries, the prevalence of breast cancer is great in developing regions as compared to their populations. There has been an impressive development in the genetic and molecular insight into breast cancer development over the last 10 years. Clinicians can detect the presence of critical mutations and the progression of diseases in real time with the help of such technological advances as next‐generation sequencing and liquid biopsies. Treatment strategies based on molecular subtyping and multi‐omic data integration are beginning to transform care and customized treatment approaches. Besides, artificial intelligence is being used in diagnostic imaging and treatment planning, which will add to accuracy and improve results for patients. This review of molecular biology and genomics of breast cancer proclaims its potential in immune‐therapeutics, bioinformatics and precision medicine. The significance of the involvement of genomic data in everyday care, the potential of immunotherapeutic, and the place of computational tools in the development of precision medicine. Such breakthroughs are promising in reducing the mortality rate and optimizing long‐term survival.
Abstract Diabetic kidney disease (DKD) remains a leading cause of chronic kidney disease (CKD) and end‐stage renal failure, with outcomes that often vary despite comparable metabolic control. Increasing evidence positions cytokine‐driven inflammation at the core of this heterogeneity, linking metabolic stress to progressive renal injury. In DKD, a sustained, dysregulated cytokine milieu—often conceptualized as a chronic “cytokine storm”—amplifies immune activation, disrupts glomerular integrity, and accelerates the transition from reversible injury to irreversible renal scarring. This narrative review synthesizes current clinical and translational evidence on the role of cytokines in DKD, focusing on their dual relevance as mechanistic drivers and actionable biomarkers. Key inflammatory mediators, including TNF‐α, IL‐6, IL‐1β, and TGF‐βs, are examined within the context of disease initiation, progression, and fibrosis. We highlight the growing utility of circulating and urinary cytokine profiles—particularly TNF receptor signatures—in early detection, prognostication, and risk stratification, offering advantages over conventional markers that often reflect late‐stage damage. Importantly, the paper explores how these insights are reshaping therapeutic strategies. Beyond established renoprotective agents such as SGLT2 and RAAS inhibitors, emerging targeted interventions aimed at cytokine signaling pathways hold promise for modifying disease trajectory. Integrating biomarker‐guided risk assessment with tailored anti‐inflammatory therapies represents a critical step toward precision medicine in DKD. Bridging molecular mechanisms with clinical application, this review underscores a paradigm shift from reactive management to proactive, individualized care—where early identification of inflammatory signatures may define the window to prevent progression from cytokine‐driven injury to permanent renal scarring.
Abstract Endocrine Therapy is a critical treatment for hormone‐dependent cancers but frequently leads to cancer‐related fatigue, a prevalent side effect that significantly affects patients' quality of life. Cancer‐related fatigue results from a complex interaction of physiological and psychological factors, including hormonal changes, energy metabolism disruptions, and the emotional burden of living with cancer. This review summarizes the epidemiology of endocrine therapy‐induced cancer‐related fatigue, its contributing factors, and impacts on patients' daily functioning and treatment adherence, and underscores the necessity for comprehensive management strategies, combining pharmacological interventions and non‐pharmacological strategies. This review calls for further research to uncover the mechanisms connecting endocrine therapy and fatigue, consider the heterogeneity across different cancer types, ensure adequate sample sizes for statistical robustness, and adopt more objective and comprehensive assessment tools, which are pivotal for enabling the development of personalized care strategies aimed at alleviating the impact of cancer‐related fatigue and enhancing treatment efficacy.
