Search Results (4,536)

Background: The blood–brain barrier (BBB) separates the circulation from the central nervous system (CNS) and serves to maintain brain homeostasis. The BBB comprises highly specialized brain endothelial cells (BECs) with unique properties that allow the BBB to maintain strict regulation of molecules entering and exiting the CNS. These characteristics include tight junctions, low endocytosis rates, and efflux and nutrient transporters. Breast cancer resistance protein (BCRP) is an efflux transporter found at the BBB that plays a key role in protecting the CNS. Together with other efflux transporters, BCRP contributes to multidrug-resistant cancers and difficulty delivering drugs and therapeutics to the brain and other organs. Methods: Using the hCMEC/D3 line, we utilized BCRP substrate rosuvastatin to effectively select for cells expressing high amounts of BCRP, thus generating hCMEC/D3-BCRP. To assess protein abundance, we utilized flow cytometry and confirmed expression via qPCR. To investigate BCRP efflux function in evolved hCMEC/D3-BCRP, we performed substrate accumulation assays with BCRP and P-gp substrates. Results: We found hCMEC/D3-BCRP had increased BCRP abundance and expression relative to parent hCMEC/D3. We also observed an increase in BCRP function via substrate accumulation of two BCRP substrates compared to parent hCMEC/D3. Conclusions: BCRP serves a protective role within the BBB and is a major hurdle in drug delivery. We generated a BCRP overexpression BEC cell line (hCMEC/D3-BCRP) under the influence of endogenous promoters. This cell line can be used to further investigate the role of BCRP in BECs and utilized in efflux transport studies. Full article

Breast cancer histopathological image classification remains a challenging task because reliable diagnosis depends on both fine-grained local lesion characteristics and multi-scale global tissue structures. However, current deep learning approaches often face challenges in effectively integrating these complementary cues, particularly in the presence of staining variations, ambiguous lesion boundaries, and limited annotated datasets. To address these challenges, we propose a novel method called UNI-Phase-Dual Network (UPDNet). This approach enhances the detection of stable lesion boundaries and subtle patterns by incorporating phase congruency, while combining it with global tissue information using the UNI foundation model. The method utilizes two branches to process features from different perspectives, one focusing on fine details and the other capturing broader context. Additionally, we apply a fine-tuning strategy that improves generalization and reduces overfitting in scenarios with small datasets. Experiments on three widely used breast cancer datasets, BRACS, BreakHis, and BACH, demonstrate that UPDNet significantly outperforms existing methods. Specifically, on the 7-class BRACS task, UPDNet achieves 68.58% accuracy, which is a 2.21% improvement over previous methods, and an increase of 1.48% in the weighted F1 score. These results demonstrate the strong potential of UPDNet in breast cancer histopathological image classification. Full article

Healthcare analytics is often limited by data silos and strict privacy requirements, which make it difficult to share patient-level records across organisations and to build robust predictive models. Federated learning (FL) provides an alternative by keeping data local and exchanging model updates instead of raw records. However, many existing FL solutions remain difficult to deploy in healthcare settings, as they provide limited support for auditability, governance-oriented evidence, and system-level transparency. This paper presents MediVault, an auditable and security-aware federated learning-based system for privacy-preserving healthcare collaboration. MediVault combines round-based federated training, prototype-level protected update exchange, audit-ready telemetry, and an interactive dashboard that exposes non-sensitive evidence of collaboration, model progress, and protocol execution. In addition, the system supports controlled reporting to improve stakeholder communication during pilot deployments. We evaluate MediVault on two public healthcare classification datasets, Breast Cancer Wisconsin (Diagnostic) and Heart Disease, under IID and label-skewed Non-IID settings. Experiments are conducted using logistic regression, linear SVM, and an additional lightweight MLP under matched settings. The observed results suggest that federated training remains competitive with centralised training under the evaluated settings. A prototype-level overhead analysis further shows that protected update exchange introduces measurable computational and communication costs, especially for larger update vectors. These findings indicate that MediVault can support initial system-level validation of auditable, privacy-preserving healthcare FL workflows, while further work is needed for larger-scale deployment, stronger adversarial evaluation, and real-world clinical validation. Full article

Background/Objective: MicroRNA-21 (miR-21) is one of the most widely studied oncogenic microRNAs and has been implicated in breast cancer progression, therapy resistance, and metastatic potential. However, its utility as a long-term prognostic biomarker in patients undergoing mastectomy remains insufficiently clarified. This study evaluated the prognostic significance of miR-21 expression in predicting overall and disease-free survival. Methods: A retrospective cohort of 426 breast cancer patients who underwent mastectomy between 2010 and 2017 was analyzed. Tumor miR-21 expression was measured using quantitative real-time PCR and categorized as high or low based on cohort-derived thresholds. Long-term outcomes were assessed over a median follow-up of 112 months. Kaplan–Meier survival curves, log-rank tests, and multivariable Cox proportional hazards models were used to estimate associations between miR-21 levels and survival outcomes. Results: High miR-21 expression was identified in 48.8% of cases. Patients with high miR-21 demonstrated significantly poorer overall survival (10-year OS: 61.4% vs. 82.7%; log-rank p < 0.001) and disease-free survival (10-year DFS: 54.9% vs. 78.3%; log-rank p < 0.001). In multivariable analysis, high miR-21 remained an independent predictor of decreased OS (HR = 2.18; 95% CI: 1.56–3.04) and DFS (HR = 2.44; 95% CI: 1.78–3.33). Conclusions: Elevated miR-21 expression is a significant independent biomarker of adverse long-term prognosis in breast cancer patients undergoing mastectomy. Integrating miR-21 into postoperative risk stratification may improve individualized management strategies. Full article

The Oncotype DX^® 21-gene recurrence score (RS) guides adjuvant chemotherapy decisions in estrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER+/HER2−) breast cancer, yet requires invasive tissue sampling and involves substantial costs. This study evaluated intratumoral tumor ecological diversity (iTED), a habitat imaging approach, as a non-invasive complement for predicting Oncotype DX^® high-risk classification (RS > 25). This retrospective multi-center study included 312 patients with ER+/HER2− invasive breast cancer who underwent Oncotype DX^® testing (development: n = 168; external validation: n = 144). The iTED framework employed superpixel-based habitat determination using Gaussian mixture models on pretreatment dynamic contrast-enhanced MRI. Four predictive models were compared: clinical, conventional whole-tumor radiomics (C-radiomics), iTED, and combined (Clinical + iTED). The iTED model achieved higher discriminative performance compared with C-radiomics in both development (area under the curve [AUC]: 0.868 ± 0.068 vs. 0.730 ± 0.112) and external validation (AUC: 0.811 vs. 0.587) sets. The combined model further improved performance (development AUC: 0.908 ± 0.043; external AUC: 0.889). Habitat imaging-based iTED features achieved numerically higher performance than conventional radiomics in predicting Oncotype DX^® high-risk classification. These findings suggest the potential of iTED as a non-invasive imaging biomarker to support molecular testing in clinical decision-making. Full article

Triple-negative breast cancer is defined by the absence of druggable receptor targets and by a biologically dynamic phenotype that renders static, single-timepoint biomarker strategies fundamentally inadequate. Current predictive markers, including PD-L1 expression, tumor mutational burden, and genomic profiling, fail to capture the therapy-induced transcriptional reprogramming, spatial heterogeneity, and drug-tolerant persister states that drive resistance and relapse. In this review, we argue that RNA, particularly non-coding RNA (ncRNA), represents a complementary and state-aware platform for biomarker development in TNBC, capable of capturing transcriptional adaptation, regulatory threshold dynamics, and cell state transitions that static genomic markers cannot fully detect. Unlike messenger RNAs, which reflect active transcriptional programs, long non-coding RNAs and circular RNAs modulate the stability of state transitions and are specifically induced under conditions of therapeutic stress, immune exclusion, and drug tolerance, which are properties that make them suitable as potential early and sensitive indicators of adaptive reprogramming. We review the biological rationale for RNA as a state-aware readout across five dimensions: tumor plasticity, immune context, stress response, therapy adaptation, and microenvironment composition. An examination is conducted regarding how spatial transcriptomics can map RNA-defined resistant niches within TNBC, how serial liquid biopsy RNA measurements, including extracellular vesicle RNA and circulating tumor RNA, enable temporal monitoring of transcriptional state shifts before radiologic progression, and what analytical and clinical standards deployable RNA assays must meet. Finally, a state-guided adaptive management framework is proposed in which RNA signatures function as iteratively updated measurement layers informing therapy selection, on-treatment monitoring, and early resistance detection. This review outlines trial design models and defines the validation standards required before RNA-guided adaptation can enter clinical practice. Full article

Background/Objectives: Integrating multi-omics data for cancer prognosis remains a challenging problem in bioinformatics because molecular profiles are high-dimensional, heterogeneous, and structured by incomplete biological relationships. Pathway databases provide biologically meaningful prior knowledge for modeling gene-level associations, but the sparsity and local incompleteness of pathway-derived networks often limit the performance of graph-based survival models. This study aimed to develop a pathway-guided framework for improving multi-omics survival prediction and identifying biologically relevant prognostic signals. Methods: We proposed PANA-Surv, a pathway-guided adaptive neighborhood augmentation framework for multi-omics cancer survival analysis. In this framework, KEGG pathways were used to construct gene graphs, and gene-level multi-omics profiles were encoded as node features. A conditional variational autoencoder module (PANA-VAE) was designed to enhance local representations through neighborhood reconstruction and adaptive weighting. The augmented features were then integrated into a graph convolutional survival model optimized with the Cox partial likelihood. Results: PANA-Surv was evaluated on 10 cancer cohorts from The Cancer Genome Atlas (TCGA). The proposed method achieved the highest mean concordance index (C-index) among all compared models and significantly outperformed Cox-EN, DeepSurv, GraphSurv, and LAGProg (all p < 0.01). Ablation analyses showed that both neighborhood reconstruction and adaptive weighting contributed to the observed performance gains, and KEGG-guided graph construction was more effective than alternative graph construction strategies. In a breast cancer (BRCA) case study, PANA-Surv identified 18 prognostic genes, including 12 genes supported by previous studies and 6 potentially novel candidates. Conclusions: These findings indicate that the integration of pathway prior knowledge with adaptive local feature enhancement can improve multi-omics survival modeling and support the identification of biologically relevant prognostic signals associated with cancer outcomes. Full article

Background: Adipocytes play a critical role in the breast cancer tumorigenic microenvironment. However, their effects and underlying mechanisms remain unclear. This study aims to investigate the role of adipocytes in luminal A breast cancer invasiveness at the cellular and molecular levels. Methods: Various adipocyte types were co-cultured with MCF7 breast cancer cells in direct and indirect manners. Invasiveness was assessed via proliferation, migration, and invasion, with alterations examined at morphological, cellular, and molecular levels. The role of adipocytes on MCF7 was further explored using an orthotopic breast cancer xenograft mouse model. Results: MCF7 co-cultured with adipocytes, especially brown adipocytes (BAC), showed increased invasiveness and tumorigenic potential. Morphologically, co-cultivation with BAC increased the proliferation, EMT, and stemness of MCF7. Mechanistically, co-culture of MCF7 with BAC exhibited disturbed expression of genes related to adipogenesis and mitochondrial dynamics; notably, IRX3 was the most prominently elevated one. Knockdown of IRX3 restored balanced mitochondrial function and reduced both the invasiveness of breast cancer cells in vitro and tumor growth in vivo. Conclusions: Brown adipocytes promote breast cancer invasiveness by upregulating adipogenesis-related IRX3, which acts via the mitochondrial functional regulation. Full article

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited targeted therapeutic options, underscoring the urgent need for novel treatment strategies. Feilike (FLK), a Traditional Chinese Medicine formula with heat-clearing and detoxifying properties, aligns with key pathological features implicated in breast cancer progression. In addition, several of its components have demonstrated anti-tumor activity, positioning FLK as a potential therapeutic candidate for TNBC. In this study, we employed an integrated approach combining network pharmacology, transcriptomic analysis, and experimental validation to investigate the anti-TNBC effects of FLK. Our results demonstrate that FLK significantly inhibits the proliferation of TNBC cell lines and patient-derived organoids and induces typical pyroptotic features, including cell swelling and increased lactate dehydrogenase (LDH) release. Mechanistically, FLK triggers a mutant p53 signaling cascade involving calcium dysregulation, endoplasmic reticulum stress (ERS) activation, mitochondrial dysfunction, and reactive oxygen species (ROS) accumulation, which collectively activate the P38/JNK–Caspase-3/GSDME pathway to induce pyroptosis. In vivo, FLK markedly suppresses tumor growth in a 4T1 orthotopic mouse model and enhances the anti-tumor efficacy of Cyclophosphamide. Furthermore, Licochalcone B (LCB) is identified as a key bioactive constituent that recapitulates the pyroptosis-inducing effects of FLK. Collectively, our findings uncover a previously unrecognized mutant p53–ERS–ROS–MAPK signaling axis underlying FLK-induced pyroptosis and provide mechanistic insight and experimental evidence supporting the repurposing of FLK as a potential therapeutic strategy for TNBC. Full article

Background/Objectives: The triglyceride–glucose (TyG) index, a reliable marker for insulin resistance, is strongly associated with T2DM, hypertension, and cardiovascular disease. Less well known is its relationship with cancer risk. The aim of this study was to quantify the association between the TyG index and risk of different types of cancer. Methods: Publications were searched in the PubMed, Web of Science, and Scopus databases using appropriate keywords. The PICOS framework was used to select the studies, and their quality was evaluated according to the “Newcastle–Ottawa Scale” (NOS). Meta-analysis was performed through a random-effects model using cancer risk parameters (RR: relative risk, OR: odds ratio and HR: hazard ratio) extracted from 26 selected studies associated with TyG index values. The weighted mean difference (WMD) was used to compare the mean of the TyG index in cancer patients to that of the control group. Heterogeneity was assessed by Cochran’s Q and I² statistics, while publication bias was evidenced using the Egger test and the Begg test, and funnel plot asymmetry. Results: A higher TyG index value was observed in cancer subjects (9483) compared to healthy controls (978,675) (WMD: 0.23, 95% CI: 0.16–0.31, p < 0.0001, n = 15). A statistically significant increase in cancer risk was associated with the TyG index level, expressed as both a categorical (OR 1.33, 95% CI 1.22–1.45, p < 0.0001, n = 29) and continuous (OR 1.14, 95% CI 1.10–1.19, p < 0.0001, n = 27) variable. The effect was more evident in case–control/cross-sectional studies compared to cohort studies (OR 1.78, 95% CI 1.51–2.09 vs. OR 1.19, 95% CI 1.10–1.29 TyG categorical; OR 1.46, 95% CI 1.21–1.76 vs. OR 1.09, 95% CI 1.05–1.12 TyG continuous). Stratified analysis showed an increased risk of cancer occurrence for gastrointestinal, gynecological, colorectal, breast, and gastric sites, while no association was observed for endometrial, ovarian, prostate, lung or esophageal cancers. Conclusions: Our results evidence an increase in cancer risk associated with higher TyG index values. However, due to the low number of studies, the effect on specific tumor sites was not statistically significant. Additional epidemiological studies with a cohort design are necessary to confirm these associations. Full article

Breast cancer screening is a fundamentally probabilistic diagnostic task that requires precise identification of complex imaging characteristics from diverse patient cohorts. Despite improvements in deep learning techniques, current automatic tools are typically trained on well-curated datasets and do not generalize to heterogeneous data, thereby limiting their application. This study aims to address these shortcomings by introducing a more effective and generalizable framework for breast cancer classification that focuses on the stability of features, the learning of complementary representations, and improved decision modeling. The proposed methodology incorporates stability-driven feature extraction (SDFE) with a multi-branch architecture that consists of EfficientNetV2 (Convolutional neural networks (CNNs)), EfficientFormer (Vision transformers (ViTs)), and multi-layer perceptron (MLP)-Mixer models to extract various feature representations. To improve non-linear decision boundaries, it uses a Kolmogorov–Arnold Network (KAN)-based classification head and selects the most credible prediction via an adaptive voting mechanism. This model is trained using patient-level splitting on the VinDr-Mammo dataset, evaluated using five-fold cross-validation, and subsequently externally validated on the CBIS-DDSM dataset. Experimental findings demonstrate the consistent performance of the proposed model, with accuracies of 94.5% in cross-validation, 93.3% on the VinDr-Mammo test set, and 94.6% on CBIS-DDSM, surpassing other recent state-of-the-art solutions. It demonstrates enhanced robustness and cross-dataset generalization, offering a scalable, consistent framework for breast cancer classification that supports the development of computer-aided diagnostic systems. Full article

Background/Objectives: The Mediterranean diet is widely recognized for its cardiovascular and metabolic benefits, including weight reduction; however, the metabolic mechanisms underlying these effects remain incompletely understood. This study investigated whether changes in circulating polyamines are associated with metabolic improvements following a Mediterranean diet intervention, particularly in breast cancer survivors. Methods: This exploratory secondary analysis used stored paired serum samples from a previously reported 8-week controlled intervention conducted in three groups: Group A (breast cancer survivors following a Mediterranean diet alone, n = 21), Group B (breast cancer survivors following a Mediterranean diet combined with naltrexone/bupropion, n = 23), and Group C (non-cancer participants receiving the combined intervention, n = 28). Paired polyamine data were available for 16, 9, and 16 participants, respectively. Breast cancer survivors were randomized to Groups A and B, whereas Group C was enrolled as a non-randomized active comparison group. Serum metabolic profiles were analyzed using liquid chromatography–mass spectrometry-based untargeted metabolomics, and nine polyamines were quantified using targeted analysis. An exploratory indirect-effect analysis examined associations between changes in serum polyamines and clinical outcomes, including body composition and lipid parameters. Results: Body weight, fat mass, and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) decreased significantly within all three groups after the 8-week intervention (median changes: −1.9 to −2.8 kg, −1.9 to −2.8 kg, and −0.3 to −0.7, respectively). LDL cholesterol decreased significantly only within the two groups receiving naltrexone/bupropion (median changes: −20.6 and −10.1 mg/dL). However, between-group differences in these changes were not statistically significant. N-acetylspermine increased nominally in all groups (p < 0.01), whereas spermine increased only in the Mediterranean diet alone group (p = 0.015). Conclusions: Mediterranean diet-related metabolic improvements were accompanied by changes in circulating polyamines. Spermine and N-acetylspermine may represent candidate metabolic response markers associated with nutritional and pharmacological interventions in breast cancer survivorship. Full article

MicroRNA (miRNA) regulation plays a pivotal role in intracellular gene expression. Analysis of miRNA profiles can provide critical insights into disease states. As cancer-associated molecules reported in previous studies, miRNAs may serve as candidate classificatory features for exploratory cancer classification. This research analyzed serum miRNA data from patients with 13 solid cancer types and individuals without cancer. The study comprised two distinct analyses: first, stratifying the dataset into cancer and non-cancer groups to identify miRNAs differentially represented in cancer patients; and second, subdividing the cancer patient data into 13 predefined solid-cancer types to identify candidate miRNA features that discriminate among these cancer types. We employed seven feature-ranking algorithms to evaluate miRNA contributions in both analyses and generate feature lists. Each list was examined using an incremental feature selection method to extract essential miRNAs and build good-performing classification models. Several candidate miRNAs were identified for distinguishing pan-cancer samples from non-cancer ones: miR-4783-3p has been linked to associated with the regulation of endocrine cell differentiation, and miR-663a has been reported in hepatocellular carcinoma and thyroid carcinoma. The analysis also highlighted miRNAs that differentiate solid cancer types, including miR-629-3p, reported to be upregulated in lung and breast cancer, and miR-6087, reported to be downregulated in osteosarcoma and bladder cancer. Full article

Background: Breast cancer (BC) survivors frequently experience depression, which is associated with poorer quality of life (QoL), increased healthcare utilization, and worse prognosis. Although traditional Chinese medicine (TCM) is commonly used as an adjunctive therapy among Chinese populations for cancer-related symptom relief and supportive care, population-based evidence remains limited regarding whether integrated Chinese and Western medicine (ICWM) confers measurable benefits over Western medicine (WM) alone in terms of healthcare utilization and survival. Taiwan’s National Health Insurance (NHI) system offers a unique nationwide setting to address this gap because it reimburses patients for both WM and TCM services and captures care from a large number of TCM clinics across Taiwan, allowing evaluation of adjunctive TCM use in routine clinical practice at a scale rarely possible in prior studies. We used emergency department visits, hospitalization, and length of stay as pragmatic proxy indicators of patients’ daily functioning and disease burden. Leveraging a 10-year enrollment window (2004–2013) and up to 17 years of follow-up, we hypothesized that ICWM would be associated with a reduced risk of acute care events and lower healthcare expenditures compared with WM alone. This hypothesis was examined in a large cohort of breast cancer patients treated across nearly 4000 medical facilities nationwide, encompassing the entire Taiwanese population. Methods: A retrospective cohort study was performed to analyze Taiwan’s National Health Insurance Research Database and Cancer Registry. Women newly diagnosed with breast cancer between 2004 and 2013 who subsequently developed depression (≥3 outpatient diagnoses or 1 hospitalization) were followed until death or 31 December 2021. Patients receiving ≥30 cumulative days of TCM after diagnosis were classified as the ICWM group, whereas those receiving <30 days were classified as the WM group. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) for all-cause mortality. Healthcare utilization, including emergency department visits, hospitalization, and medical expenditures, was analyzed on a per-person-year basis. Results: A total of 1193 patients were included, with 488 in the WM group and 705 in the ICWM group. Compared with WM users, ICWM users were younger, had lower body mass index, and were more likely to have stage 0–II disease. ICWM was associated with lower total, inpatient, and emergency healthcare expenditures per person-year, as well as fewer emergency visits per person-year, although outpatient and overall visits were higher. In stage-stratified multivariable analyses, ICWM was associated with lower all-cause mortality in both stage 0–II disease (aHR = 0.61, 95% CI: 0.39–0.94) and stage III–IV disease (aHR = 0.38, 95% CI: 0.21–0.67). Kaplan–Meier analyses likewise showed significantly better overall survival in the ICWM group in both early-stage and advanced-stage disease. Conclusions: In this nationwide retrospective cohort of breast cancer patients with depression, adjunctive ICWM was associated with better survival, lower acute care utilization, and lower healthcare expenditures compared with WM alone. However, because quality of life was not directly measured and the study was based on observational data, QoL-related interpretations should be made cautiously, with healthcare utilization outcomes viewed as indirect proxy indicators rather than direct evidence of improved daily QoL. Full article

Background: Abemaciclib (ABM) in combination with tamoxifen (TAM) is an extremely significant treatment regimen for hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer. It is approved for patients to reduce the risk of cancer recurrence. A bioanalytical method for the simultaneous determination of this new anti-breast cancer combination and its pharmacokinetic application has not yet been reported. Methods: An ultra-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS) method was developed for quantifying ABM, TAM, and its metabolites, including abemaciclib active metabolites M2, M18, and M20 and tamoxifen active metabolite N-desmethyl tamoxifen (NDTAM), in rat plasma using econazole as the internal standard (IS). Chromatographic separation was achieved on a Kinetex C18 column (100 × 2.1 mm ID, 2.6 µm) using gradient elution with 5 mM ammonium formate in water (eluent A) and 5 mM ammonium formate in water/methanol (1:9, v/v, eluent B) at a flow rate of 0.4 mL/min. Detection was performed on a TSQ Fortis Plus mass spectrometer employing multiple reaction monitoring mode under positive electrospray ionization. Results: The developed method was validated according to the guidance of the FDA. Linearity in rat plasma (ng/mL) was achieved from 1 to 1000 for ABM, TAM, and M20; 3 to 1000 for M2; 5 to 500 for M18; and 1 to 500 for NDTAM; with correlation coefficients ranging from 0.9991 to 0.9931 for all analytes using a weighting factor of 1/X². The lower limit of detection (LLOD) ranged between 0.3 and 1.5 ng/mL for all drugs. The accuracy ranged from 96 to 108% and the precision was less than 7.6% RSD for all analytes. For the first time, the newly developed approach was effectively used in a pharmacokinetic study on the simultaneous oral administration of ABM and TAM in rats that received 30.0 mg/kg of ABM and 8.0 mg/kg of TAM. Conclusions: To the best of our knowledge, this is the first reported UPLC–MS/MS method for the assay of ABM, TAM, and its active metabolites in plasma. This method offers a bioanalytical tool for assessing the pharmacokinetics of ABM and TAM. Therefore, this study makes a definite significant contribution to the field of bioanalytical research. Further validation in human plasma is required for future clinical or therapeutic drug monitoring applications, as the approach was developed in an animal model. Full article