Search Results (62)

Sepsis is a life-threatening condition driven by a dysregulated host response to infection, with high mortality and few treatment options. Decades of failed drug development underscore the urgent need for therapies with novel mechanisms of action. Vimentin, an intermediate filament protein, acts as a network hub that senses and integrates cellular signals. Its involvement in key sepsis pathologies, including infection, hyperinflammation, immunosuppression, coagulopathy and metabolic dysregulation, positions it as a potential therapeutic target. This study evaluated the efficacy of ALD-R491, a novel small-molecule vimentin modulator, in a murine model of polymicrobial sepsis induced by cecal ligation and puncture (CLP). Mice received ALD-R491 prophylactically or therapeutically, alone or with ceftriaxone. The treatment significantly reduced serum levels of key biomarkers of sepsis, including C-reactive protein (CRP), lactate (Lac), tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), and dose-dependently improved the survival of septic mice. Organ-specific analysis confirmed the effects of ALD-R491 in mitigating hyperinflammation and multi-organ injury. The treatment reduced pulmonary edema and inflammation; preserved liver tissue architecture and improved hepatic function with lowered alanine aminotransferase/aspartate aminotransferase (ALT/AST); decreased kidney tubular damage; and improved renal function with lowered creatinine/blood urea nitrogen (BUN). These preclinical findings indicate that the vimentin-targeting agent ALD-R491 represents a promising therapeutic candidate for sepsis and merits further clinical investigation. Full article

Introduction: Gastrointestinal stromal tumors (GISTs) are primarily driven by mutations in KIT (KIT proto-oncogene receptor tyrosine kinase) or PDGFRA (platelet-derived growth factor receptor alpha), but resistance to tyrosine kinase inhibitors (TKIs) such as imatinib remains a major clinical challenge. Alterations in fibroblast growth factor receptor 2 (FGFR2), although rare, are emerging as important contributors to tumor progression and drug resistance. This review evaluates the molecular mechanisms, expression profiles, detection methods, and therapeutic implications of FGFR2 in GIST. Methods: We searched PubMed, Web of Science, Google Scholar, and ClinicalTrials.gov for studies published between January 2010 and June 2025, using combinations of keywords related to FGFR2, gastrointestinal stromal tumor, resistance mechanisms, gene fusion, amplification, polymorphisms, and targeted therapy. Eligible studies were critically assessed to distinguish GIST-specific data from evidence extrapolated from other cancers. Results:FGFR2 is expressed in multiple normal tissues and at variable levels in mesenchymal-derived tumors, including GIST. Its alterations occur in approximately 1–2% of GIST cases, most commonly as gene fusions (e.g., FGFR2::TACC2, <1%) or amplifications (1–2%); point mutations and clinically significant polymorphisms are extremely rare. These alterations activate the MAPK/ERK and PI3K/AKT pathways, contribute to bypass signaling, and enhance DNA damage repair, thereby promoting TKI resistance. Beyond mutations, mechanisms such as amplification, ligand overexpression, and microenvironmental interactions also play roles. FGFR2 alterations appear mutually exclusive with KIT/PDGFRA mutations but occasional co-occurrence has been reported. Current clinical evidence is largely limited to small cohorts, basket trials, or case reports. Conclusions:FGFR2 is an emerging oncogenic driver and biomarker of resistance in a rare subset of GISTs. Although direct evidence remains limited, particularly regarding DNA repair and polymorphisms, FGFR2-targeted therapies (e.g., erdafitinib, pemigatinib) show potential, especially in combination with TKIs or DNA-damaging agents. Future research should prioritize GIST-specific clinical trials, the development of FGFR2-driven models, and standardized molecular diagnostics to validate FGFR2 as a therapeutic target. Full article

Sex and gender play a critical role in allergic diseases, influencing immune response, clinical phenotypes, treatment strategies, outcomes, and health-related quality of life. Despite mounting evidence across multiple studies examining sex/gender differences in a multitude of allergic diseases, most address isolated conditions, not taking into consideration the vast interplay of hormonal, genetic, immunological, and sociocultural factors and their unique consequences for clinicians and researchers. With this position paper, we aim to assess currently available evidence on the sex- and gender-specific characteristics of the most common allergic diseases, providing an overview of present knowledge and future areas of improvement for clinicians and researchers. This position paper was developed by the Società Italiana di Allergologia, Asma ed Immunologia Clinica (SIAAIC): a panel of experts who conducted a literature review focusing on sex and gender differences across major allergic diseases. A consensus-based approach was employed to assess the immunological, clinical, and therapeutic implications of available evidence, offering a recommendation for researchers and clinicians alike. Data highlights marked differences driven by sex and gender in disease prevalence, immune pathways, clinical phenotype and severity, as well as therapeutic outcomes. Female patients appear to show a higher prevalence of Th2-driven ailments, autoimmune overlap, and allergic drug reactions, whereas males are more likely to experience fatal anaphylaxis and severe mastocytosis. Sex hormones can modulate multiple immune pathways leading to mast cell activation, antibody production, and cytokine expression, thus contributing to divergent disease trajectories. In conclusion, sex and gender are a key determinant in allergic diseases, and their integration in future research is essential to develop a tailored approach to treatment. Efforts should prioritise the identification of sex- and gender-specific biomarkers, therapeutic strategies, and equitable access to healthcare services. A sex- and gender-aware approach could potentially improve outcomes, optimise treatment strategies, and address current gaps in allergy practice. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article

Nasopharyngeal carcinoma (NPC) is a rare malignancy with a distinct epidemiological pattern and is most often associated with Epstein–Barr virus (EBV). EBV plays a critical role in NPC pathogenesis, with viral proteins driving oncogenesis by altering immune regulation, apoptosis, and tumor progression. The unique molecular landscape of NPC presents both challenges and opportunities for therapeutic development, particularly in the recurrent and metastatic (R/M) setting, where treatment resistance remains a major hurdle. While platinum-based chemotherapy has traditionally been the standard of care for R/M NPC, immune checkpoint inhibitors (ICIs) have emerged as a key component of treatment. However, both intrinsic and acquired resistance to PD-1/PD-L1 blockade underscore the need for alternative strategies, including modulation of alternative immune checkpoints and simultaneous engagement of non-redundant pathways to enhance responses and durability. Leveraging EBV-driven biology, emerging immunotherapeutic approaches, such as EBV-specific adoptive cellular therapies and therapeutic vaccines, aim to induce durable immunity to viral proteins. Additionally, targeted therapies including receptor tyrosine kinase inhibitors, epigenetic modulators, and antibody–drug conjugates are redefining precision medicine by selectively delivering cytotoxic agents to tumors. With growing insights into the biology of NPC and evolving therapeutics, the integration of immunotherapy, targeted agents, and biomarker-driven strategies is poised to transform NPC treatment, emphasizing biology-driven, multimodal approaches to optimize patient outcomes. Full article

Three-dimensional (3D) surface-enhanced Raman scattering (SERS) substrates have demonstrated remarkable abilities of ultrasensitive and reproducible molecular detection. The combination of both electromagnetic and chemical enhancement processes, light trapping, and multiple scattering effects of 3D structures are what enhance their performance. The principles of underlying enhancements are summarized systematically, and the main types of 3D substrates—vertically aligned nanowires, dendritic and fractal nanostructures, porous frameworks and aerogels, core–shell and hollow nanospheres, and hierarchical hybrid structures—are categorized in this review. Advances in fabrication techniques, such as template-assisted growth, electrochemical and galvanic deposition, dealloying and freeze-drying, self-assembly, and hybrid integration, are critically evaluated in terms of structural tunability and scalability. Novel developments in the field of biosensing are also highlighted, including non-enzymatic glucose sensing, tumor biomarker sensing, and drug delivery. The remaining limitations, such as low reproducibility, mechanical stability, and substrate standardization, are also noted, and future directions, such as stimuli-responsive designs, multifunctional hybrid platforms, and data-driven optimization strategies of SERS technologies, are also included. Full article

The overexpression of atypical protein kinase C-iota (PKC-ι) is a biomarker for carcinogenesis in various cell types, such as glioma, ovarian, renal, etc., manifesting as a potential drug target. In previous in vitro studies, ICA-1S and ICA-1T, experimental candidates for inhibiting PKC-ι, have demonstrated their specificity and promising efficacy against various cancers. Moreover, the in vivo studies have demonstrated low toxicity levels in acute and chronic murine models. Despite these prior developments, the binding affinities of the inhibitors were never thoroughly explored from a biophysical perspective. Here, we present the biophysical characterizations of PKC-ι in combination with ICA-1S/1T. Various methods based on molecular docking, light scattering, intrinsic fluorescence, thermal denaturation, and heat exchange were applied. The biophysical characteristics including particle sizing, thermal unfolding, aggregation profiles, enthalpy, entropy, free energy changes, and binding affinity (K_d) of the PKC-ι in the presence of ICA-1S were observed. The studies indicate the presence of domain-specific stabilities in the protein–ligand complex. Moreover, the results indicate a spontaneous reaction with an entropic gain, resulting in a possible entropy-driven hydrophobic interaction and hydrogen bonds in the binding pocket. Altogether, these biophysical studies reveal important insights into the binding interactions of PKC-ι and its inhibitors ICA-1S/1T. Full article

Background: Bipolar disorder (BD) is a chronic and disabling psychiatric condition characterized by recurring episodes of mania, hypomania, and depression. Despite the availability of mood stabilizers, antipsychotics, and antidepressants, long-term management remains challenging due to incomplete symptom control, adverse effects, and high relapse rates. Methods: This paper is a narrative review aimed at synthesizing emerging trends and future directions in the pharmacological treatment of BD. Results: Future pharmacotherapy for BD is likely to shift toward precision medicine, leveraging advances in genetics, biomarkers, and neuroimaging to guide personalized treatment strategies. Novel drug development will also target previously underexplored mechanisms, such as inflammation, mitochondrial dysfunction, circadian rhythm disturbances, and glutamatergic dysregulation. Physiological endophenotypes, such as immune-metabolic profiles, circadian rhythms, and stress reactivity, are emerging as promising translational tools for tailoring treatment and reducing associated somatic comorbidity and mortality. Recognition of the heterogeneous longitudinal trajectories of BD, including chronic mixed states, long depressive episodes, or intermittent manic phases, has underscored the value of clinical staging models to inform both pharmacological strategies and biomarker research. Disrupted circadian rhythms and associated chronotypes further support the development of individualized chronotherapeutic interventions. Emerging chronotherapeutic approaches based on individual biological rhythms, along with innovative monitoring strategies such as saliva-based lithium sensors, are reshaping the future landscape. Anti-inflammatory agents, neurosteroids, and compounds modulating oxidative stress are emerging as promising candidates. Additionally, medications targeting specific biological pathways implicated in bipolar pathophysiology, such as N-methyl-D-aspartate (NMDA) receptor modulators, phosphodiesterase inhibitors, and neuropeptides, are under investigation. Conclusions: Advances in pharmacogenomics will enable clinicians to predict individual responses and tolerability, minimizing trial-and-error prescribing. The future landscape may also incorporate digital therapeutics, combining pharmacotherapy with remote monitoring and data-driven adjustments. Ultimately, integrating innovative drug therapies with personalized approaches has the potential to enhance efficacy, reduce adverse effects, and improve long-term outcomes for individuals with bipolar disorder, ushering in a new era of precision psychiatry. Full article

Background: Melanoma metastasis, driven by tumor microenvironment (TME)-mediated crosstalk facilitated by extracellular vesicles (EVs), remains a major therapeutic challenge. A critical barrier to clinical translation is the overlap in protein cargo between tumor-derived and healthy cell EVs. Objective: To address this, we developed Scaffold-free Functional Deconvolution (SFD), a novel computational approach that leverages a comprehensive healthy cell EV protein database to deconvolute non-oncogenic background signals. Methods: Beginning with 1915 proteins (identified by MS/MS analysis on an Orbitrap Fusion Lumos Mass Spectrometer using the IonStar workflow) from melanoma EVs isolated using REIUS, SFD applies four sequential filters: exclusion of normal melanocyte EV proteins, prioritization of metastasis-linked entries (HCMDB), refinement via melanocyte-specific databases, and validation against TCGA survival data. Results: This workflow identified 21 high-confidence targets implicated in metabolic-associated acidification, immune modulation, and oncogenesis, and were analyzed for reduced disease-free and overall survival. SFD’s versatility was further demonstrated by surfaceome profiling, confirming enrichment of H7-B3 (CD276), ICAM1, and MIC-1 (GDF-15) in metastatic melanoma EV via Western blot and flow cytometry. Meta-analysis using Vesiclepedia and STRING categorized these targets into metabolic, immune, and oncogenic drivers, revealing a dense interaction network. Conclusions: Our results highlight SFD as a powerful tool for identifying clinically relevant biomarkers and therapeutic targets within melanoma EVs, with potential applications in drug development and personalized medicine. Full article

Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine. The disease classification model analyzes respiratory sound recordings and accurately distinguishes among eight clinical classes: bronchiectasis, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma, chronic obstructive pulmonary disease (COPD), bronchiolitis, and healthy respiratory state. The proposed model achieved a classification accuracy of 99.99% on a holdout test set, including 94.2% accuracy on pediatric samples. In parallel, the prescription module provides individualized treatment recommendations comprising drug, dosage, and frequency trained on a carefully constructed synthetic dataset designed to emulate real-world prescribing logic.The model achieved over 99% accuracy in medication prediction tasks, outperforming baseline models such as those discussed in research. Minimal misclassification in the confusion matrix and strong clinician agreement on 200 prescriptions (Cohen’s $\kappa$ = 0.91 [0.87–0.94] for drug selection, 0.78 [0.74–0.81] for dosage, 0.96 [0.93–0.98] for frequency) further affirm the system’s reliability. Adjusted clinician disagreement rates were 2.7% (drug), 6.4% (dosage), and 1.5% (frequency). SHAP analysis identified age and smoking as key predictors, enhancing model explainability. Dosage accuracy was 91.3%, and most disagreements occurred in renal-impaired and pediatric cases. However, our study is presented strictly as a proof-of-concept. The use of synthetic data and the absence of access to real patient records constitute key limitations. A trialed clinical deployment was conducted under a controlled environment with a positive rate of satisfaction from experts and users, but the proposed system must undergo extensive validation with de-identified electronic medical records (EMRs) and regulatory scrutiny before it can be considered for practical application. Nonetheless, the findings offer a promising foundation for the future development of clinically viable AI-assisted respiratory care tools. Full article

Background: Mycosis fungoides (MF), the most prevalent cutaneous T cell lymphoma, features clonal CD4⁺ T cell proliferation within a Th2-dominant microenvironment. Interleukin-4 (IL-4) promotes disease progression while Brentuximab Vedotin (BV), an anti-CD30 antibody–drug conjugate, shows efficacy but faces resistance challenges. Methods: We conducted a narrative literature review (2010–2024) synthesizing evidence on IL-4 signaling and BV’s efficacy in MF to develop a theoretical framework for combination therapy. Results: IL-4 may modulate CD30 expression and compromise BV’s effectiveness through immunosuppressive microenvironment remodeling. Theoretical mechanisms suggest that IL-4 pathway inhibition could reprogram the microenvironment toward Th1 dominance and restore BV sensitivity. However, no direct experimental evidence validates this combination, and safety concerns including potential disease acceleration require careful evaluation. Conclusions: The proposed IL-4/BV combination represents a biologically compelling but unproven hypothesis requiring systematic preclinical validation and biomarker-driven clinical trials. This framework could guide future research toward transforming treatment approaches for CD30-positive MF by targeting both malignant cells and their immunologically permissive microenvironment. Full article

Lung cancer remains one of the main causes of cancer-related death globally and a significant global health concern. There is an urgent need for safer and more effective therapeutic alternatives despite notable progress in therapy; issues such as drug resistance, side effects, metastasis, and recurrence still affect patient outcome and quality of life. The aim of this review is to examine recent developments in the application of herbal-drug-loaded nanoparticles as a new strategy for treating lung cancer. A thorough examination of different drug delivery systems based on nanoparticles is provided, highlighting their function in improving the solubility, bioavailability, and targeted delivery of herbal compounds. In addition, the review evaluates the biomarkers used for targeted therapy and examines how new personalised treatment approaches like wearable electronic patches, robotics-assisted interventions, smartphone-enabled therapies, AI-driven diagnostics, and lung-on-a-chip technologies can be integrated to improve the accuracy and effectiveness of lung cancer treatment. In conclusion, the combination of personalised medicine and nanotechnology may lead to revolutionary changes in lung cancer treatment in the future. Full article

Epidermal growth factor receptor (EGFR) mutations occur in approximately 10–20% of Caucasian and up to 50% of Asian patients with oncogene-addicted non-small cell lung cancer (NSCLC). Most frequently, alterations include exon 19 deletions and exon 21 L858R mutations, which confer sensitivity to EGFR tyrosine kinase inhibitors (TKIs). In the last decade, the third-generation EGFR-TKI osimertinib has represented the first-line standard of care for EGFR-mutant NSCLC. However, the development of acquired mechanisms of resistance significantly impacts long-term outcomes and represents a major therapeutic challenge. The mesenchymal–epithelial transition (MET) gene amplification and MET protein overexpression have emerged as prominent EGFR-independent (off-target) resistance mechanisms, detected in approximately 25% of osimertinib-resistant NSCLC. Noteworthy, variability in diagnostic thresholds, which differ between fluorescence in situ hybridization (FISH) and next-generation sequencing (NGS) platforms, complicates its interpretation and clinical applicability. To address MET-driven resistance, several therapeutic strategies have been explored, including MET-TKIs, antibody–drug conjugates (ADCs), and bispecific monoclonal antibodies, and dual EGFR/MET inhibition has emerged as the most promising strategy. In this context, the bispecific EGFR/MET antibody amivantamab has demonstrated encouraging efficacy, regardless of MET alterations. Furthermore, the combination of the ADC telisotuzumab vedotin and osimertinib has been associated with activity in EGFR-mutant, c-MET protein-overexpressing, osimertinib-resistant NSCLC. Of note, several novel agents and combinations are currently under clinical development. The success of these targeted approaches relies on tissue re-biopsy at progression and accurate molecular profiling. Yet, tumor heterogeneity and procedural limitations may challenge the feasibility of re-biopsy, making biomarker-agnostic strategies viable alternatives. Full article

The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows—from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS include variant calling, epigenomic profiling, transcriptomics, and single-cell sequencing, where AI models such as CNNs, RNNs, and hybrid architectures outperform traditional methods. In cancer research, AI enables precise tumor subtyping, biomarker discovery, and personalized therapy prediction, while in drug discovery, it accelerates target identification and repurposing. Despite these advancements, challenges persist, including data heterogeneity, model interpretability, and ethical concerns. This review also discusses the emerging role of AI in third-generation sequencing (TGS), addressing long-read-specific challenges, like fast and accurate basecalling, as well as epigenetic modification detection. Future directions should focus on implementing federated learning to address data privacy, advancing interpretable AI to improve clinical trust and developing unified frameworks for seamless integration of multi-modal omics data. By fostering interdisciplinary collaboration, AI promises to unlock new frontiers in precision medicine, making genomic insights more actionable and scalable. Full article

Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication of sepsis, characterized by high mortality and prolonged hospitalization. Early diagnosis and effective therapy remain difficult despite extensive investigation. To address this, we developed an AI-driven integrative framework that combines a Transformer-based deep learning model with established machine learning techniques (LASSO, SVM-RFE, Random Forest and neural networks) to uncover complex, nonlinear interactions among gene-expression biomarkers. Analysis of normalized microarray data from GEO (GSE95233 and GSE69063) identified differentially expressed genes (DEGs), and KEGG/GO enrichment via clusterProfiler revealed key pathways in immune response, protein synthesis, and antigen presentation. By integrating multiple transcriptomic cohorts, we pinpointed 617 SA-AKI-associated DEGs—21 of which overlapped between sepsis and AKI datasets. Our Transformer-based classifier ranked five genes (MYL12B, RPL10, PTBP1, PPIA, and TOMM7) as top diagnostic markers, with AUC values ranging from 0.9395 to 0.9996 (MYL12B yielding 0.9996). Drug–gene interaction mining using DGIdb (FDR < 0.05) nominated 19 candidate therapeutics for SA-AKI. Together, these findings demonstrate that melding deep learning with classical machine learning not only sharpens early SA-AKI detection but also systematically uncovers actionable drug targets, laying groundwork for precision intervention in critical care settings. Full article