The multi-target cardiac protection mechanism of butylphthalide and AI-driven precision medicine: From molecular basis to clinical translation.

DeepSTFSynergy: A multi-scale structural information fusion method for personalized drug combination prediction.

MASEA: A microfluidic system for in situ evaluation of tumor angiogenesis in PDO-endothelial co-culture.

Bacterial extracellular vesicles (BEVs) are nanoscale secretions containing proteins, lipids, nucleic acids, and metabolites that modulate immune pathways, reshape the tumor microenvironment, and influence multiple stages of tumor progression. These properties position BEVs as promising platforms for cancer immunotherapy and precision targeted therapies. However, clinical translation is limited by low and inconsistent yields, vesicle heterogeneity, and the lack of standardized methods for isolation, characterization, and bioengineering. Compared with eukaryotic extracellular vesicles and synthetic nanocarriers, BEVs combine intrinsic immunostimulatory activity, strain-level genetic programmability, and high stability in harsh microenvironments, which may help integrate adjuvant and cargo within a single platform and support oral or mucosal delivery when derived from appropriately selected and engineered bacterial strains, while also enabling the capture of microbe-derived signals for liquid biopsy. This review adopts a technology focused perspective to synthesize recent advances in BEV based biomedical strategies for cancer. We summarize progress in elucidating vesicle biogenesis, optimizing scalable separation and purification workflows, and applying multi-omics approaches for systematic cargo profiling. Particular emphasis is placed on emerging engineering methods, including genetic modification, chemical conjugation, and strain level design, that exploit post-translational modifications to fine tune BEV stability, targeting, and immune regulatory activity. By integrating and comparing preclinical studies on probiotic derived and synthetically engineered BEVs, this review outlines how BEV platforms are being used for precise drug and nucleic acid delivery, immune modulation, and metastasis inhibition, and how their stability and molecular signatures support applications in liquid biopsy and biomarker discovery. We further discuss cross cutting bottlenecks in standardization, quality control, manufacturing, and safety assessment, and highlight engineering innovations that begin to address these challenges. Together, these insights define key research priorities for refining BEV production and functional design and provide a roadmap for advancing BEVs toward clinical grade platforms for personalized cancer therapy. These applications remain subject to biological and manufacturing constraints, which are discussed in detail in the concluding section.

Multimodal synergistic effects and theranostic integration of hafnium-based nanoradiosensitizers for enhancing precision radiotherapy.

More than 10% of severe hemophilia A patients are paradoxically mild bleeders despite factor VIII (FVIII) levels being <1 IU/dl. Quantitation of FVIII levels <1 IU/dl is a challenge due to the detection limits of traditional one-stage activated partial thromboplastin time, two-stage chromogenic assay, and tissue factor-initiated thrombin generation assay (TF-TGA), impeding a precise characterization of the bleeding phenotype in severe hemophilia patients. The study aimed to enhance the sensitivity of TGA by modifying the trigger reagent for FVIII measurement. We optimized the fluorometric quantitation of thrombin generation triggered by factor IXa (FIXa) in artificial reconstituted plasma by blending varying proportions of normal pooled plasma (NPP) and FVIII-immunodepleted plasma containing a normal level of von Willebrand factor. FIXa-initiated thrombin generation depended on FVIII level, while TF could bypass FVIII deficiency to activate thrombin, making TF-TGA incapable of quantifying low levels of FVIII. When triggering with 0.6 nM FIXa, in the presence of 4 μM phospholipids and 40 μg/ml corn trypsin inhibitor, thrombin generation was highly dependent on FVIII levels even below 1 IU/dl. Among the five routinely used TGA parameters, peak thrombin, endogenous thrombin potential, and velocity index demonstrated the strongest linear correlation with FVIII levels down to 0.1 IU/dl. The study evaluated the performance of a modified TGA activated with FIXa. In addition to traditional TF-TGA, FIXa-TGA may provide additional information when assessing the bleeding tendency of patients with severe hemophilia A, facilitating personalized factor replacement therapy.

Pyrogenicity of phage active pharmaceutical ingredients used for personalized therapy in Belgium.

Despite extensive preclinical research identifying molecular targets and cardioprotective strategies, translation into effective clinical therapies remains challenging. Cardioprotection aims to mitigate ischemia/reperfusion injury (IRI) by modulating molecular pathways, such as the Reperfusion Injury Salvage Kinase (RISK) and Survivor Activating Factor Enhancement (SAFE) pathways, as well as autophagy, inflammation, and regulated cell death, to preserve myocardial function. However, a major limitation lies in the robustness of preclinical evidence. Many experimental studies rely on simplified models that fail to reproduce the complexity of human cardiac pathophysiology, resulting in inconsistent and poorly reproducible cardioprotective effects. It is likely that RISK-SAFE pathways represent an oversimplified framework. Moreover, most experimental approaches are cardiomyocyte-centered, overlooking the critical role of the vessels in IRI. Clinical translation is further compromised by patient-related factors, including comorbidities (e.g., diabetes, hypertension), concomitant medications, and heterogeneity in reperfusion protocols, all of which attenuate cardioprotective efficacy. Additional variables, such as timing of intervention and species differences, further contribute to translational failure. Emerging approaches include pharmacological therapies (e.g., SGLT2 inhibitors, PARP inhibitors, necroptosis and ferroptosis blockers, NLRP3-targeting compounds), cell- and organelle-based strategies (e.g., mitochondrial transplantation, extracellular vesicles, non-coding RNAs), and mechanical/device-based interventions (e.g., left ventricular unloading, ischemic conditioning, controlled reperfusion, selective intracoronary hypothermia). Future research should emphasize multi-target interventions, optimized timing and delivery, and advanced tools, such as nanocarriers, gene therapy, computational modeling, and adaptive clinical trials. Strengthening the robustness of preclinical models, including human ex vivo cardiac systems, remains essential to bridge the translational gap and improve the clinical success of cardioprotective therapies.

Preparation and evaluation of 3D-printed tacrolimus tablets based on mesoporous silica solubilization.

Biomaterials for endometriosis treatment: Design, advances, and prospects.

Endocrine therapy remains one of the primary treatment modalities for estrogen receptor (ER) positive breast cancer, serving a pivotal role in improving patient outcomes and extending survival. Nevertheless, the gradual emergence of endocrine resistance continues to limit its clinical efficacy. In recent years, advances in molecular biology and genomics have driven the development of innovative technologies and therapeutic strategies in this field. The present review highlights the latest progress in endocrine therapy for breast cancer, including the introduction of next-generation selective ER degraders (SERDs), ER antagonists/degraders and selective ER modulators (SERMs). In addition, combination strategies integrating endocrine therapy with small-molecule inhibitors of critical signaling pathways, such as PI3K/AKT/mTOR and CDK4/6, have demonstrated promising potential in overcoming resistance. Cutting-edge technologies, such as single-cell sequencing and organoid models, are providing novel insights into treatment monitoring and the implementation of personalized therapy. Looking ahead, precision medicine platforms powered by artificial intelligence and big data are expected to further refine therapeutic strategies and ultimately improve patient prognosis. Collectively, endocrine therapy for breast cancer is evolving toward a more diversified, precise and individualized approach, offering patients broader treatment options and enhanced survival benefits.

The bladder tumor microenvironment is a complex ecosystem composed of tumor cells, stromal cells, immune cells, and the extracellular matrix. Metabolic reprogramming and intercellular metabolic crosstalk within this microenvironment play a central role in tumor progression and immune evasion. This review systematically elucidates the three-dimensional interaction network among lactate shuttling, amino acid dependency, and immune checkpoint regulation in the bladder cancer microenvironment. It untangles how metabolites such as lactate, tryptophan, and glutamine directly suppress effector T-cell function and promote the activation of immunosuppressive cells, including regulatory T cells and tumor-associated macrophages, through mechanisms involving nutrient deprivation, signal transduction, and epigenetic modifications. Studies have revealed that lactate, transported via the MCT1/MCT4/CD147 complex, establishes a lactate-glutamine metabolic cycle between tumor and stromal cells. This cycle not only supports tumor proliferation but also significantly inhibits the immune response by acidifying the microenvironment and activating key signaling pathways. Concurrently, metabolic imbalances of amino acids such as arginine, cysteine, and serine contribute to immune cell dysfunction and synergize with the expression of immune checkpoint molecules to reinforce an immunosuppressive state. Clinical data indicate that metabolism-related biomarkers, including high MCT4 expression and elevated urinary lactate concentration, are closely associated with poor prognosis and resistance to immunotherapy. Furthermore, bladder cancers of different molecular subtypes and stages exhibit significant metabolic heterogeneity, underscoring the need to develop precise metabolic intervention strategies. Future research should integrate single-cell multi-omics and spatial metabolomics technologies to construct high-resolution microenvironmental maps, develop non-invasive monitoring systems based on metabolic biomarkers, and promote the clinical translation of combination treatment plans targeting metabolic crosstalk. These efforts will provide new avenues for achieving precise and personalized therapy for bladder cancer.

Circulating biomarkers in bladder cancer: emerging evidence and future directions for personalized therapy.

Heart failure with preserved ejection fraction (HFpEF) is a prevalent and heterogeneous syndrome with limited therapeutic options, making accurate risk stratification essential yet challenging. Traditional tools such as the H2FPEF and HFA-PEFF scores incorporate few variables and demonstrate modest prognostic performance. Machine learning (ML) offers enhanced risk prediction by integrating multidimensional clinical, imaging, biomarker, and molecular data. This review summarizes current ML applications in HFpEF, including random forests, gradient boosting, support vector machines, and deep learning, highlighting their superior discrimination and ability to reveal phenotypic subgroups with distinct outcomes. We also address practical considerations such as interpretability, real-world validation, and integration into clinical workflows, as well as challenges related to data bias, generalizability, and regulatory requirements. Future opportunities include real-time clinical decision support, digital health integration, and interventional ML to guide personalized therapy. ML holds significant potential to advance precision care and improve outcomes in HFpEF.

Hepatitis B surface antigen (HBsAg) clearance is a critical step toward functional cure and is associated with improved virological control, as well as a reduced risk of cirrhosis and hepatocellular carcinoma (HCC). Pegylated interferon-α (Peg-IFN-α) has shown promise in achieving HBsAg clearance in selected patient subsets. Precise identification of these "advantaged populations" and early assessment of treatment response are critical for optimizing personalized therapy. We retrospectively analyzed 239 chronic hepatitis B (CHB) patients, including 175 patients with baseline HBsAg levels < 1500 IU/mL, who received Peg-IFN-α therapy. Parameters were compared between HBsAg clearance and non-clearance groups. We developed/validated Model 1 (gender, age, baseline HBsAg) to identify treatment-advantaged patients. Model 2 incorporated week-12 dynamics: platelet count change (ΔPLT), alanine aminotransferase-to-aspartate aminotransferase ratio (ALT/AST) rates of change, and HBsAg level. The generalizability of Model 1 and Model 2 was further validated in an independent external cohort (n = 92). Model 1 demonstrated superior performance over individual predictors and existing models (ALT/qHBsAg and Zhang's model), with validation in an independent cohort. Decision curve analysis (DCA) further confirmed Model 1's clinical utility advantage over existing models (ALT/qHBsAg and Zhang's model). Additionally, through receiver operating characteristic (ROC) curve analysis, DCA, and Cox regression, Model 2 outperformed both week-12 HBsAg level and ALT/qHBsAg in predicting HBsAg clearance, providing a robust early on-treatment response indicator. We propose two clinically applicable models: Model 1 identifies advantaged patients suitable for Peg-IFN-α therapy based on baseline characteristics, while Model 2 predicts treatment response using week-12 dynamics. These tools provide a framework for precision medicine approaches to achieve HBsAg clearance in CHB.

Neurofibromatosis type 2 (NF2) is a hereditary tumor syndrome driven by mutations in the NF2 gene. The mutation leads to aberrant proliferation of Schwann cells along the vestibular division of cranial nerve VIII, resulting in bilateral vestibular schwannomas (VS) that cause progressive hearing loss and neurological dysfunction. Loss of the tumor suppressor merlin results in dysregulation of multiple oncogenic pathways, including VEGF, MAPK/ERK, PI3K/AKT/mTOR, EGFR/ErbB, PDGFR, and Hippo-YAP. The inability of conventional management modalities to address the multifocal and progressive nature of NF2-associated tumors has driven investigation into targeted therapies. This review summarizes the evolving landscape of targeted therapies in NF2-associated vestibular schwannomas. Management of NF2-VS is individualized, with active surveillance favored for stable or smaller tumors, surgical resection pursued for symptomatic or enlarging lesions, and bevacizumab increasingly utilized as either a primary or adjunctive systemic option. Radiation therapy, by contrast, is employed selectively given its potential to compromise auditory function and its association with malignant transformation. Targeted agents such as bevacizumab (anti-VEGF), MEK inhibitors (selumetinib, trametinib), EGFR inhibitors (lapatinib, erlotinib), and mTOR inhibitors (everolimus) are examined across preclinical models and clinical trials. Emerging approaches including dual pathway inhibition and immunologic strategies such as VEGF receptor vaccination are also discussed. Importantly, NF2-VS exhibits molecular and clinical heterogeneity, with differing responses observed across pediatric and adult populations. Therapeutic limitations of current targeted therapies include resistance, toxicity, and the modest efficacy of monotherapies. As such, future investigations must refine endpoints (e.g., hearing stabilization vs. tumor regression), optimize dosing strategies, and personalize therapy based on age, tumor biology, and clinical trajectory. This review highlights the translational challenges and opportunities that lie ahead in delivering clinically efficacious therapies specific to each patient.

Functional precision oncology aims to support personalized cancer therapy by assessing drug sensitivity in patient-derived tumor cells ex vivo. However, conventional drug sensitivity and resistance testing (DSRT) platforms typically require large numbers of cells, limiting their applicability to surgically resected tumors and posing challenges for patients diagnosed at advanced stages, where only small biopsy samples are available. To address this limitation, we developed a miniaturized DSRT workflow based on a Droplet Microarray (DMA) chip, which comprises 672 hydrophilic spots separated by superhydrophobic borders and enables high-throughput screening in nanoliter volumes. Using 300 cells per 200-nL droplet, lung cancer cells freshly isolated from surgical specimens were tested against 12 compounds across five concentrations and five replicates (360 experimental conditions). This required approximately 120,000 cells total, including additional cells for handling and processing. The approach generated drug-specific dose-response profiles and variable IC50 values across tumors of the same subtype. Comparable drug responses were also observed across three spatially distinct regions of the same tumor, indicating consistent assay performance. Overall, these results demonstrate that DSRT on the DMA platform is feasible with limited numbers of cells derived from clinical samples and may be useful for functional drug testing when tissue availability is constrained.

The assessment of tumor-infiltrating lymphocyte (TILs), together with gene expression signatures (GES), has the potential to guide personalized breast cancer therapy. We included 262 patients from the phase III NSABP B-41 trial, which evaluated neoadjuvant HER2-targeted therapies in combination with chemotherapy. We conducted a manual and artificial intelligence (AI)-based analyses of TILs, as well as GES from RNA sequencing. Higher manual TILs as a continuous variable were associated with pathologic complete response (pCR) in patients with estrogen receptor (ER)-negative disease. AI-based TILs were associated with pCR regardless of ER status. Immune GES (iGES) were associated with pCR. Manual TILs were not associated with event-free survival (EFS), while AI-TIL showed a marginal association. These results support the use of TIL assessment, complemented by GES, as a prognostic biomarker in HER2-positive breast cancer. Future studies are needed to evaluate their predictive utility to guide treatment decisions.

Innovative lipid nanoparticle formulations: Bridging the gap toward decentralized personalized gene therapies.

Global rehabilitation needs far exceed capacity, and artificial intelligence (AI) is proposed to extend access, personalise therapy, and support adherence. We aimed to synthesise evidence on clinical effectiveness, prognostic performance, and implementation feasibility of AI-enabled rehabilitation across conditions and care settings. We conducted a mixed-methods systematic review of AI-enabled rehabilitation and rehabilitation-led prevention relevant to physiotherapy practice. MEDLINE, Embase, Web of Science, CINAHL, Scopus, and IEEE Xplore were searched. Methodological quality was appraised with MMAT and PROBAST + AI (prediction/diagnostic models), with a priori AI-reporting minimums checklist. Given heterogeneity, evidence was integrated via prespecified thematic synthesis. Thirty studies from diverse regions met inclusion, comprising randomised trials, non-randomised comparisons, cohorts, surveys, qualitative work, and prediction/diagnostic models. Clinical effects were modest and heterogeneous. Most AI-enabled interventions were comparable to conventional rehabilitation; signals of benefit appeared in selected musculoskeletal and telerehabilitation contexts but rarely persisted beyond short follow-up. Internal validity was frequently limited by asymmetric adherence measurement objective telemetry in AI arms versus self-report or attendance in controls alongside low uptake and declining engagement over time. Safety and usability were generally favourable within short horizons, although surveillance and explicit attribution to AI components were inconsistently reported. Prognostic and adaptive models showed encouraging discrimination in development settings but lacked multicentre external validation, calibration, subgroup-error profiling, and prospective impact evaluation, leaving them unready for clinical use. Stakeholders reported willingness to adopt AI while highlighting gaps in training, governance and information technology support, costs, and digital equity. Overall certainty for comparative clinical outcomes was low to moderate, for models, very low. AI in rehabilitation presently acts more as a behavioural amplifier structuring home programmes and supporting execution than as a replacement for dose-matched therapist-delivered care. Credible scale-up should position AI as an adjunct and hinge on arm-symmetric capture of adherence and execution, clinically meaningful outcomes paired with verified behaviour change over treatment and longer-term follow-up, and model deployment only after transparent development, external validation with calibration and subgroup-error characterisation, and demonstration of clinical impact. Embedding equity and economic evaluation, together with living oversight and version control, will be essential to convert promising prototypes into trustworthy, durable, and widely accessible services.