Search Results (102)

Background/Objectives: Accurate absorbed dose estimation is essential for optimising targeted radionuclide therapy (TRT) in metastatic castration-resistant prostate cancer, where kidney toxicity is dose-limiting. [¹⁷⁷Lu]Lu-rhPSMA-10.1 is a novel PSMA-targeted radioligand with favourable tumour-to-kidney uptake ratios; however, inter-patient pharmacokinetic variability can lead to differences in organ and tumour absorbed doses under fixed-activity administration. Personalised dosimetry offers a means to address this variability. This work aims to create mouse PBPK model-based digital twins for [¹⁷⁷Lu]Lu-rhPSMA-10.1 to test the model’s resistance to noise and evaluate its impact on accuracy and absorbed dose calculations. Methods: Five CB-17 SCID mice bearing LNCaP tumour xenografts received 2.6–3.1 MBq [¹⁷⁷Lu]Lu-rhPSMA-10.1 intravenously. Biodistribution was assessed 24 h post-injection by organ weighing and gamma counting. The PBPK model, implemented in MATLAB SimBiology (R2023a), was fitted to individual biodistribution data using mouse-specific physiological parameters. Digital twins—combining the model with fitted parameters—were used to generate time–activity curves (TACs) for kidneys, tumours, and the whole body. Gaussian noise ($\sigma$ = 0–0.35) was added to TACs to simulate measurement error. The model was refitted, and absorbed doses from time-integrated activities (TIAs) were compared to digital twin references. Results: The digital twin approach reproduced experimental data with physiologically plausible parameters. Absorbed dose estimates remained consistent and robust, deviating by <2.3% in kidneys and <1.0% in tumours. Conclusions: PBPK-based digital twins enable reliable, individualised dosimetry, even under substantial measurement uncertainty. Full article

Background: Tacrolimus is a cornerstone immunosuppressant in kidney transplantation, but its narrow therapeutic index and marked inter-patient variability complicate dose optimization. Conventional therapeutic drug monitoring (TDM) relies on empirical adjustments that often overlook individual pharmacokinetics. Machine learning (ML) offers a precision dosing alternative by integrating diverse clinical and biochemical variables into predictive models. Methods: We retrospectively analyzed 1351 data points from 87 kidney transplant patients at Eunpyeong St. Mary’s Hospital (April 2019–November 2023). Clinical, demographic, and laboratory information, including tacrolimus trough levels and dosing history, were extracted from electronic medical records. Four predictive models—XGBoost, CatBoost, LightGBM, and a multilayer perceptron (MLP)—were trained to forecast next-day tacrolimus concentrations, and model serum creatinine level performance was evaluated using R-squared (R²), mean absolute error (MAE), and root-mean-squared error (RMSE). An ensemble model with weighted soft voting was applied to enhance predictive accuracy, and model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: The ensemble model achieved the best overall performance (R² = 0.6297, MAE = 1.0181, RMSE = 1.2999), outperforming all individual models, whereas the MLP model showed superior predictive power among single models, reflecting the significance of nonlinear interactions in tacrolimus pharmacokinetics. SHAP analysis highlighted prior tacrolimus levels, cumulative dose, renal function markers (eGFR level, serum creatinine level), and albumin concentration as the most influential predictors. Conclusions: We present a robust ML-based algorithm for tacrolimus dose optimization in hospitalized kidney transplant recipients. By improving predictions of tacrolimus concentrations, the model may help reduce inter-patient dose variability and lower the risk of nephrotoxicity, supporting safer and more individualized immunosuppressive management. This approach advances AI-driven precision medicine in transplant care, offering a pathway to safer and more effective immunosuppression. Full article

Background and Objectives: Post-surgical central nervous system (CNS) infections are severe complications associated with high morbidity and mortality. Vancomycin is a key antibiotic used in their management. However, because of the restrictive properties of the blood–brain barrier (BBB), plasma concentrations may not accurately reflect drug exposure in the brain extracellular fluid (ECF), the presumed site of infection. Cerebral microdialysis enables direct measurement of unbound drug levels in brain ECF. This study aimed to assess vancomycin penetration into brain ECF in patients with suspected or confirmed post-surgical CNS infection. Materials and Methods: Five patients with suspected or confirmed post-surgical CNS infections were enrolled. Paired brain ECF microdialysate and plasma samples (and cerebrospinal fluid (CSF) samples, when available) were collected over two consecutive days at vancomycin steady state. Vancomycin concentrations were determined using a homogeneous enzyme immunoassay and corrected for probe recovery based on in vitro calibration. Pharmacokinetic parameters, including mean concentrations and 24-h area under the concentration–time curve (AUC24), were calculated for plasma and ECF, and ECF-to-plasma ratios were derived. Results: Two subgroups could be identified: patients with negligible ECF concentrations (“low penetrators”), and those with higher ECF levels (“high penetrators”). Mean (SD) ECF-to-plasma concentration ratios were 0.07 (0.04) in “low penetrators” and 0.44 (0.10) in “high penetrators”. The corresponding AUC24 ratios were 0.06 (0.03) and 0.40 (0.03), respectively. The presence of systemic inflammatory response syndrome (SIRS) was considered the most plausible factor differentiating these two subgroups. Conclusions: Vancomycin exposure in brain ECF demonstrated substantial interpatient variability in post-surgical CNS infections, with some patients showing minimal drug penetration. Full article

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies due to late diagnosis and limited treatment options. Circulating tumor DNA (ctDNA) is a promising, minimally invasive biomarker that could improve the clinical outcomes of patients with PDAC by enabling early disease detection, minimal residual disease (MRD) assessment, precise prognostication, and accurate treatment monitoring. CtDNA has prognostic as well as predictive value in both resectable and metastatic settings, with serial measurements enhancing risk stratification and recurrence prediction beyond CA19-9. However, despite the promise, the true potential of ctDNA has not yet been fulfilled in patients with PDAC. The current limitations include a low sensitivity of ctDNA assays in early stage PDAC, challenges in the assay interpretation due to the specific nature of ctDNA shedding in PDAC, inter-patient heterogeneity, and technical variability. As precision oncology advances, ctDNA will be a powerful tool for personalized care in PDAC, but rigorous validation of its use within specific clinical contexts is still needed before the true potential of ctDNA is realized for patients with PDAC. Full article

Background: High-dose methotrexate (HDMTX) is widely used for acute lymphoblastic leukaemia (ALL), but its pharmacokinetic (PK) variability and toxicity require therapeutic drug monitoring (TDM). Our 10-year retrospective study investigated HDMTX PK parameters and their associations with renal and hepatic biomarkers in an Italian cohort of adult patients with ALL. Methods: Plasma MTX concentrations [MTX C(p)] were measured at 24-, 48-, and 72 h post-infusion. PK modelling was performed to calculate area under the curve (AUC_{0 → 72 h}) and half-life (t½). Creatinine, total bilirubin, and sample quality indices were retrieved from routine clinical laboratory analyses. Results: Mean (±SEM) MTX plasma concentrations were 36.09 ± 15.53 μmol/L, 0.93 ± 0.43 μmol/L, and 0.30 ± 0.07 μmol/L at 24, 48, and 72 h, respectively, with marked inter-patient variability. PK analysis showed a mean AUC_{0 → 72 h} of 112.85 ± 34.09 h·μmol/L and a t½ of 17.15 ± 2.40 h. MTX C(p) and AUC_{0 → 72 h} showed significant positive correlations with serum creatinine at all time points, confirming renal function as a major MTX clearance determinant. Age moderated the relationship at 72 h, with younger patients showing stronger correlations. Hepatic function measured by total bilirubin also correlated with MTX C(p) and AUC_{0 → 72 h} at 48 and 72 h, especially in younger patients, suggesting a hepatic contribution to MTX variability. No associations were found between the PK parameters and lipemic, icterus, or haemolysis indices. Conclusions: These findings highlight the value of integrating renal and hepatic biomarkers into HDMTX drug monitoring protocols. Such biomarker-informed TDM may improve the safety and efficacy by identifying patients at risk of delayed clearance and toxicity, especially younger individuals or those with renal insufficiency. Full article

Background: Sustained-release (SR) formulations of non-steroidal anti-inflammatory drugs (NSAIDs) aim to prolong therapeutic activity, reduce dosing frequency, and improve patient adherence. However, currently marketed SR NSAIDs exhibit persistent limitations, including incomplete control over release kinetics, high interpatient variability in bioavailability, limited reduction in gastrointestinal adverse effects, and insufficient dose flexibility for individualized therapy. In many cases, conventional excipients and release mechanisms remain predominant, leaving drug-specific physicochemical and pharmacokinetic constraints only partially addressed. These gaps highlight the need for a comprehensive synthesis of recent technological advances to guide the development of more effective, patient-centered delivery systems. Methods: A narrative literature review was conducted using Web of Science and PubMed databases to identify original research articles and comprehensive technological studies on oral SR formulations of NSAIDs and paracetamol published between January 2020 and March 2025. Inclusion criteria focused on preclinical and technological research addressing formulation design, excipient innovations, and manufacturing approaches. Results: Sixty-four studies met the inclusion criteria, encompassing polymeric matrices (31%), lipid-based carriers (18%), microspheres/hydrogel beads/interpenetrating polymer networks (30%), nanostructured systems (11%), and hybrid platforms (10%). The most common strategies involved pH-dependent release, mucoadhesive systems, and floating drug delivery, aiming to optimize release kinetics, minimize mucosal irritation, and sustain therapeutic plasma levels. Advances in manufacturing—such as hot-melt extrusion, 3D printing, electrospinning, and spray drying—enabled enhanced control of drug release profiles, improved stability, and in some cases up to 30–50% prolongation of release time or reduction in Cmax fluctuations compared with conventional formulations. Conclusions: Recent formulation strategies show substantial potential to overcome long-standing limitations of SR NSAID delivery, with expected benefits for patient compliance and quality of life through reduced dosing frequency, better tolerability, and more predictable therapeutic effects. Nevertheless, integration of in vitro performance with pharmacokinetic and clinical safety outcomes remains limited, and the translation to clinical practice is still in its early stages. This review provides a comprehensive overview of current technological trends, identifies persisting gaps, and proposes future research directions to advance SR NSAID systems toward safer, more effective, and patient-focused therapy. Full article

Background/Objectives: Acute myocardial infarction (MI) is a major cause of death worldwide, and it imposes a heavy burden on health care systems. Although diagnostic methods have improved, detecting the disease early and accurately is still difficult. Recently, AI has demonstrated increasing capability in improving ECG-based MI detection. From this perspective, this scoping review aimed to systematically map and evaluate AI applications for detecting MI through ECG data. Methods: A systematic search was performed in Ovid MEDLINE, Ovid Embase, Web of Science Core Collection, and Cochrane Central. The search covered publications from 2015 to 9 October 2024; non-English articles were included if a reliable translation was available. Studies that used AI to diagnose MI via ECG were eligible, and studies that used other diagnostic modalities were excluded. The review was performed per the PRISMA extension for scoping reviews (PRISMA-ScR) to ensure transparent and methodological reporting. Of a total of 7189 articles, 220 were selected for inclusion. Data extraction included parameters such as first author, year, country, AI model type, algorithm, ECG data type, accuracy, and AUC to ensure all relevant information was captured. Results: Publications began in 2015 with a peak in 2022. Most studies used 12-lead ECGs; the Physikalisch-Technische Bundesanstalt database and other public and single-center datasets were the most common sources. Convolutional neural networks and support vector machines predominated. While many reports described high apparent performance, these estimates frequently came from relatively small, single-source datasets and validation strategies prone to optimism. Cross-validation was reported in 57% of studies, whereas 36% did not specify their split method, and several noted that accuracy declined under inter-patient or external validation, indicating limited generalizability. Accordingly, headline figures (sometimes ≥99% for accuracy, sensitivity, or specificity) should be interpreted in light of dataset size, case mix, and validation design, with risks of spectrum/selection bias, overfitting, and potential data leakage when patient-level independence is not enforced. Conclusions: AI-based approaches for MI detection using ECGs have grown quickly. Diagnostic performance is limited by dataset and validation issues. Variability in reporting, datasets, and validation strategies have been noted, and standardization is needed. Future work should address clinical integration, explainability, and algorithmic fairness for safe and equitable deployment. Full article

Current fractional flow reserve computed tomography (FFR_CT) methods use static imaging, potentially missing critical hemodynamic changes during the cardiac cycle. We developed a novel dynamic FFR_CT framework using 4D-CTA data to capture temporal coronary dynamics throughout the complete cardiac cycle. Our automated pipeline integrates 4D-CTA processing, temporally weighted geometric modeling, and patient-specific boundary conditions derived from actual flow measurements. Preliminary validation in three patients (four vessels) showed that dynamic FFRCT values (0.720, 0.797, 0.811, and 0.952) closely matched invasive FFR measurements (0.70, 0.78, 0.78, and 0.94) with improved accuracy compared to conventional static methods. The dynamic approach successfully captured physiologically relevant hemodynamic variations, addressing inter-patient variability limitations of standardized approaches. This study establishes the clinical feasibility of dynamic FFR_CT computation, potentially improving non-invasive coronary stenosis assessment for clinical decision-making and treatment planning. Full article

Intron retention (IR) is one of the cellular mechanisms to perform alternative splicing and thus control gene expression in several mammalian cellular pathways. IR in PI-PLC γ1 mRNA was observed in some primary synoviocyte samples from osteoarthritis (OA) patients, likely due to inter-patient variability. The aim of the present manuscript was to explore the PI-PLC γ1 IR molecular mechanism as a consequence of nutraceutical treatment of synoviocytes and the molecular basis of individual response. To evaluate the gene expression modulation of molecules involved in mRNA splicing, an RNA-seq analysis was performed, and the transcription modulation of six differentially expressed genes was validated by RT-PCR. Moreover, through a silencing experiment, the relationship between PI-PLC γ1 IR and the six modulated genes was explored. Finally, two of them, the RNA-binding proteins CELF1 and PTBP3, whose mRNA levels were elevated in samples exhibiting IR, were analyzed in detail. CELF1 and PTBP3 were overexpressed in synoviocytes lacking PI-PLC γ1 IR, and we found that CELF1 was responsible for IR, whereas PTBP3 did not seem to be involved. In conclusion, in our experimental model, the role of CELF1 protein in PI-PLC γ1 IR was explored, opening new scenarios for understanding the molecular mechanisms underlying the IR phenomenon present in several kinds of diseases. Full article

Ankylosing spondylitis (AS) displays wide inter-patient variability that is not accounted for by HLA-B27 alone, suggesting that additional immune and metabolic modifiers contribute to disease severity. Using a genetically matched design, we profiled peripheral blood mononuclear cells from two brother pairs discordant for AS severity and one healthy brother pair. Strand-specific RNA-seq was analyzed with a family-blocked DESeq2 model, while untargeted metabolites were quantified using gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS). Differential features were defined as follows: differentially expressed genes (DEGs) (|log₂FC| ≥ 1 and FDR < 0.05) and metabolites (VIP > 1, FC ≥ 1.2, and BH-adjusted p < 0.05). Pathway enrichment was performed with KEGG and Gene Ontology (GO). A total of 325 genes were differentially expressed. Type I interferon and neutrophil granule transcripts (e.g., IFI44L, ISG15, S100A8/A9) were markedly up-regulated, whereas mitochondrial β-oxidation genes (ACADM, CPT1A, ACOT12) were repressed. Metabolomics revealed 110 discriminant features, including 25 MS/MS-annotated metabolites. Primary bile acid intermediates were depleted, whereas oxidized fatty acid derivatives such as 12-Z-octadecadienal and palmitic amide accumulated. Spearman correlation identified two antagonistic modules (i) interferon/neutrophil genes linked to pro-oxidative lipids and (ii) lipid catabolism genes linked to bile acid species that persisted when severe and mild siblings were compared directly. Enrichment mapping associated these modules with viral defense, neutrophil degranulation, fatty acid β-oxidation, and bile acid biosynthesis pathways. This sibling-paired peripheral blood mononuclear cell (PBMC) dual-omics study delineates an interferon-driven lipid–bile acid axis that tracks AS severity, supporting composite PBMC-based biomarkers for future prospective validation and highlighting mitochondrial lipid clearance and bile acid homeostasis as potential therapeutic targets. Full article

Ulcerative colitis (UC), a subtype of inflammatory bowel disease (IBD), is a chronic, relapsing inflammatory condition that significantly impairs the patient’s quality of life. While biologics have transformed disease management, a substantial number of patients remain unresponsive or lose efficacy over time. Tofacitinib (TOFA), an oral Janus kinase (JAK) inhibitor, introduces a novel therapeutic class of small-molecule drugs with a unique oral administration route, offering enhanced patient convenience and broader accessibility compared to parenterally administered biologics. As the first oral treatment approved for moderate to severe UC in years, TOFA acts by modulating the JAK/STAT pathway, influencing critical inflammatory mediators such as IL-6, IL-17, and IFN-γ. However, response rates are variable and appear dose-dependent, with up to 60% of patients showing inadequate therapeutic outcomes. This review represents the first comprehensive synthesis focused specifically on biomarkers of TOFA response in UC. Drawing on multi-omics data—epigenomics, transcriptomics, proteomics, and cellular profiling, we highlight emerging predictors of responsiveness, including CpG methylation signatures (e.g., LRPAP1 and FGFR2), transcriptomic regulators (e.g., REG3A and CLDN3), immune and epithelial cell shifts, and the cationic transporter MATE1. TOFA demonstrates a dual mechanism by modulating immune responses while supporting epithelial barrier restoration. Despite being promising, TOFA’s dose-dependent efficacy and interpatient variability underscore the critical need for non-invasive, predictive biomarkers to guide personalized treatment. As the first review of its kind, this work establishes a basis for precision medicine approaches to optimize the clinical utility of TOFA in UC management. Full article

Background: Maxillofacial bone defects present considerable challenges in oral and reconstructive surgery. While autologous bone grafts are the gold standard, their limitations, such as donor site morbidity and limited availability, have driven the search for alternative biomaterials. SmartBone^®, a xeno-hybrid graft, offers potential advantages due to its bioactivity and remodeling capacity. Methods: This analysis of a series of clinical cases, evaluated the performance of SmartBone^® in 10 patients presenting with various maxillofacial bone defects. The patient follow-up period spanned from 2017 to 2019, with a maximum duration of 30 months. Bone grafting was performed, and integration was monitored using Cone-Beam Computed Tomography at multiple timepoints. Bone density changes (ΔCT values) in selected anatomical sites were analyzed to assess graft transformation and integration. Results: SmartBone^® supported effective bone regeneration and selective remodeling in all cases. One patient required a revision procedure, after which successful integration was observed. Cellular colonization began within weeks, with complete remodeling into mature bone occurring between 6–12 months. Evidence of cortical wall resorption and reformation on the graft’s external surface confirmed this transformation. ΔCT values progressively aligned with native bone densities, indicating structural and functional integration. Conclusions: SmartBone^® demonstrates strong osteointegrative and site-specific remodeling capabilities, offering a reliable and predictable alternative for maxillofacial bone reconstruction. The study presents several limitations, including the small sample size, inter-patient variability, possible imaging artifacts due to metallic elements in Cone-Beam Computed Tomography scans and the lack of histological confirmation. Full article

Background/Objectives: HER2-positive breast cancer is an aggressive subtype with an established responsiveness to HER2-targeted therapies like ado-trastuzumab emtansine (T-DM1). However, inter-patient variability in treatment response and toxicity remains a challenge. Hormonal status, particularly menopausal state, may influence breast cancer behavior, therapeutic tolerance, and outcomes, yet data on its effect in patients treated with T-DM1 are scarce. This study aimed to evaluate whether menopausal status independently affects treatment response, side effects, and survival outcomes in HER2-positive breast cancer patients receiving T-DM1, accounting for the confounding role of age. Methods: This retrospective cohort study included 98 female patients with HER2-positive breast cancer treated with T-DM1: 53 premenopausal and 45 postmenopausal. The clinical characteristics, metastatic patterns, treatment history, T-DM1 outcomes, and toxicities were recorded. The statistical analysis included chi-square, t-tests, Mann–Whitney U tests, and Spearman’s correlations. Partial correlation analyses were conducted to isolate the effect of menopausal status by controlling for age. Results: The postmenopausal patients showed higher rates of lung metastasis (42.2% vs. 20.8%) and mortality (60.0% vs. 39.6%) than premenopausal patients. However, no significant differences were found in the T-DM1 response or toxicity profiles. After adjusting for age, menopausal status had no independent association with the treatment outcomes or side effects. Age was the dominant factor influencing performance status, metastatic burden, and mortality risk. Conclusions: Menopausal status affects disease presentation but not T-DM1 efficacy or toxicity when age is accounted for. Treatment decisions should consider age and clinical profile rather than menopausal classification alone when managing HER2-positive breast cancer with T-DM1. Full article

Myocardial infarction (MI) is a critical cardiovascular disease characterized by extensive myocardial necrosis occurring within a short timeframe. Traditional MI detection and localization techniques predominantly utilize single-domain features as input. However, relying solely on single-domain features of the electrocardiogram (ECG) proves challenging for accurate MI detection and localization due to the inability of these features to fully capture the complexity and variability in cardiac electrical activity. To address this, we propose a multi-domain feature fusion convolutional neural network (MFF–CNN) that integrates the time domain, frequency domain, and time-frequency domain features of ECG for automatic MI detection and localization. Initially, we generate 2D frequency domain and time-frequency domain images to combine with single-dimensional time domain features, forming multi-domain input features to overcome the limitations inherent in single-domain approaches. Subsequently, we introduce a novel MFF–CNN comprising a 1D CNN and two 2D CNNs for multi-domain feature learning and MI detection and localization. The experimental results demonstrate that in rigorous inter-patient validation, our method achieves 99.98% detection accuracy and 84.86% localization accuracy. This represents a 3.43% absolute improvement in detection and a 16.97% enhancement in localization over state-of-the-art methods. We believe that our approach will greatly benefit future research on cardiovascular disease. Full article

Semantic segmentation plays a critical role in medical image analysis, offering indispensable information for the diagnosis and treatment planning of liver diseases. However, due to the complex anatomical structure of the liver and significant inter-patient variability, the current methods exhibit notable limitations in feature extraction and fusion, which pose a major challenge to achieving accurate liver segmentation. To address these challenges, this study proposes an improved U-Net-based liver semantic segmentation method that enhances segmentation performance through optimized feature extraction and fusion mechanisms. Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. Secondly, an atrous spatial pyramid pooling (ASPP) module is incorporated into the bottleneck layer to capture features at various receptive fields using dilated convolutions, while global pooling is applied to enhance the acquisition of contextual information and ensure efficient feature transmission. Furthermore, a Channel Transformer module replaces the traditional skip connections to strengthen the interaction and fusion between encoder and decoder features, thereby reducing the semantic gap. The effectiveness of this method was validated on integrated public datasets, achieving an Intersection over Union (IoU) of 0.9315 for liver segmentation tasks, outperforming other mainstream approaches. This provides a novel solution for precise liver image segmentation and holds significant clinical value for liver disease diagnosis and treatment. Full article