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Kidney transplantation (KT) is the most effective treatment for end-stage renal disease. Hypothermic machine perfusion (HMP) can improve renal energy metabolism and reduce ischemia-reperfusion injury compared with static cold storage. This study aimed to evaluate the association between HMP parameters and graft function in deceased donor kidney transplantation (DDKT) and to develop a predictive model for early risk stratification. A retrospective analysis was conducted on 2,041 DDKT recipients from 1 January 2015 to 30 June 2023. The primary outcome, delayed graft function (DGF), was defined as the need for at least one dialysis session within the first week after transplantation. Consensus clustering (CC) and restricted cubic spline (RCS) analysis were used to evaluate the associations between clinical data, HMP parameters, and graft function. Feature selection was performed using Lasso-penalized logistic regression (LR), and multivariable LR was used to construct the predictive model. The model's performance was assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Among the DDKT recipients, 12.9% developed DGF. HMP parameters varied significantly between the two groups, with DGF recipients showing distinct patterns in perfusion resistance, flux, and pressure. CC identified two recipient clusters with distinct DGF risk profiles, graft function, and donor characteristics. Non-linear relationships were identified between HMP parameters and DGF risk, with thresholds for initial resistance, terminal resistance, and terminal flux. The predictive model integrating six variables achieved an AUC of 0.78 (95% CI: 0.76-0.82) in the test set. Calibration and DCA confirmed good reliability and net clinical benefit. Non-linear relationships between HMP parameters and DGF underscore graft perfusion complexity. The proposed model demonstrated robust internal performance and may support early post-transplant risk stratification. External validation in independent cohorts is warranted to confirm generalizability and clinical applicability.

Inadequate adherence to colorectal cancer screening reduces individual and population level health benefits. Blood-based tests offer a new modality that may help patients overcome barriers, but there are concerns about the impact of patients switching from existing guideline-recommended screening modalities. This study estimates the population health outcomes and cost-effectiveness of offering blood-based testing using a validated individual-level simulation model based on patient preference evidence from randomized controlled trials. In this study, a validated discrete-event simulation model was used to evaluate the performance of different combinations of colorectal cancer screening strategy preferences per 10,000 screened individuals beginning at age 45. Preferences for screening options were informed by randomized controlled trials of patients with and without the option of blood-based testing. Adherence to initial patient preferences over a simulated lifetime was modeled as: (1) assumed 100% adherence and (2) longitudinal using a calibrated model. Simulated outcomes included clinical outcomes and cost-effectiveness from a healthcare sector perspective. A strategy was deemed cost-effective at a willingness-to-pay threshold of $100,000 per quality-adjusted life-year gained. The introduction of blood-based testing to an unscreened population with evidence from randomized controlled trials is projected to increase colorectal cancer deaths averted by 35% to 116% and from 68% to 247% relative to no screening, for stated preference and revealed preference scenarios, respectively. These outcomes are cost-effective, with incremental cost-effectiveness ratios ranging from $63,994 to $85,497 and from $30,464 to $54,764 across stated preference and revealed preference scenarios, respectively. Given limited data, natural history and real-world longitudinal adherence to screening are based on evidence-informed assumptions. Using a simulation model to extrapolate data from two recent trials, we demonstrate that the introduction of blood-based tests has the potential to lead to cost-effective increases in the number of CRC deaths averted among the unscreened population.

This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug interactions in peripheral T-cell lymphoma (PTCL). Differentially expressed NET-RGs (DE-NRGs) between PTCL and normal tissues were screened. Functional enrichment analysis was conducted. Bioinformatics and machine learning were used to identify hub genes and assess their diagnostic value. Univariate and multivariate analyses were used to evaluate prognostic roles. Correlation and immune infiltration analyses were performed to explore relationships with the tumor microenvironment (TME). Clinical data were collected from PTCL patients who received potential agents (lenalidomide) as first-line treatment. A total of 31 DE-NRGs were identified (18 upregulated and 13 downregulated), enriched in inflammatory response, extracellular matrix organization, and infection involvement. Four hub genes (AKT2, MAPK14, IRF1, and TNF) were identified as effective PTCL diagnostic markers. Higher AKT2/MAPK14 expression correlated with poorer overall survival (OS), while elevated TNF expression associated with better OS; AKT2 and TNF independently predicted OS. These genes were implicated in modulating TME remodeling. Potential therapeutic agents (e.g. capivasertib, lenalidomide) were predicted, and lenalidomide may represent a feasible initial treatment option for PTCL, with an objective response rate (ORR) of 40.0% and a maximum survival duration exceeding 50 months. NET-RGs play crucial roles in diagnosis, prognosis, and TME regulation, and lenalidomide, a putative TNF-targeting agent, may represent a feasible initial treatment option in PTCL.

Diethyl phthalate (DEP), a common plasticiser and endocrine disruptor, has been linked to cancer, but its role in osteosarcoma (OS) remains unclear. This study integrated network toxicology, transcriptomics, protein-protein interaction (PPI) analysis, machine learning, molecular docking, molecular dynamics (MD), single-cell RNA sequencing (scRNA-seq), and external validation to investigate DEP-related mechanisms in OS. We identified 45 DEP-responsive genes enriched in extracellular matrix-related pathways. PPI network analysis revealed 11 hub genes, of which LASSO, SVM-RFE, and Boruta algorithms consistently prioritised P4HA2, COL18A1, and COL10A1. Docking and MD simulations supported stable binding of DEP to P4HA2 and COL18A1 via hydrogen bonds and hydrophobic interactions. scRNA-seq demonstrated celltype-specific expression of these genes. Validation cohorts confirmed their upregulation in OS, with AUC values up to 0.950. These findings suggest that DEP may promote OS progression by targeting extracellular matrix remodelling, offering new diagnostic biomarkers and hypothesis-generating evidence for environmental osteocarcinogenesis.

FMS-like tyrosine kinase 3 (FLT3/CD135) regulates haematopoiesis and is frequently mutated as FLT3-internal tandem duplication (FLT3-ITD) in acute myeloid leukaemia (AML), associated with poor prognosis. Although FLT3 inhibitors show clinical benefits, resistance remains a challenge. This study hypothesises that antibody-drug conjugate (ADC) efficacy depends on distinct FLT3 trafficking mechanisms in FLT3-wt and FLT3-ITD cells. Confocal imaging showed that in THP-1 (FLT3-wt) cells, FLT3 mAb trafficked to lysosomes, while in MV4-11 (FLT3-ITD) cells, it accumulated in the Golgi. To evaluate the impact of this trafficking difference, we synthesised an anti-FLT3 mAb-MMAE, linked via a Val-Cit-PAB linker at the Fc N-glycan, which exhibited lower cytotoxicity in MV4-11 than THP-1 cells, indicating that the impaired lysosomal trafficking of FLT3-ITD limits drug release and reduces ADC potency. These findings highlight that effective lysosomal targeting is essential for ADC activity and suggest that optimising linker design or restoring lysosome trafficking may enhance FLT3-targeted ADC in AML.

ABCB1-mediated drug efflux is a key determinant of multidrug resistance (MDR) in cancer. To overcome this mechanism, a series of thiol-substituted aminocoumarin-derived, coumarin-containing triazolo[1,5-a]pyrimidine derivatives (5a-5s) was synthesised, and compound 5r (NYH-707) was identified as the most potent ABCB1 inhibitor. NYH-707 markedly restored paclitaxel sensitivity in SW620/Ad300 MDR cells, reducing the IC50 from 4.55 ± 0.73 µM to 0.011 ± 0.002 µM (reversal factor = 413.6). Molecular docking predicted strong binding (-9.7 kcal/mol) through hydrogen bonding with LYS-826 and SER-880 and π-π stacking with PHE-994. CETSA confirmed direct ABCB1 engagement, while drug-accumulation assays demonstrated inhibition of ABCB1-mediated efflux. In vivo, co-administration of NYH-707 and paclitaxel significantly suppressed SW620/Ad300 xenograft growth without detectable systemic toxicity. These findings indicate that NYH-707 acts as a potent and selective ABCB1 modulator capable of reversing MDR likely by modulating ABCB1 conformational dynamics, thereby enhancing chemotherapeutic efficacy in resistant tumours.

Anti-PD-(L)1 agents, inhibitors of programmed cell death protein 1 (PD-1) or its ligand (PD-L1), are established therapies that improve cancer management as well as the disease and societal burden of specific metastatic and early-stage cancers. The aim of the study was to determine the impact of adopting anti-PD-(L)1 agents for the treatment of all eligible patients with early-stage cancers versus reserving anti-PD-(L)1 agents for patients with metastatic disease alone in Hungary. This study evaluated two scenarios, one where anti-PD-(L)1 agents were used to treat all eligible early-stage disease cases (ESD scenario) of melanoma (stage IIB-C and III), renal cell carcinoma (RCC), and triple-negative breast cancer (TNBC) versus a reference scenario where anti-PD-(L)1 agents were only used to treat metastatic disease cases in Hungary (2024-2033). A Markov-modeling approach estimated the health outcomes and productivity losses from each scenario from a societal perspective. Outcomes included recurrence-/event-/disease-free life-years, total life-years, quality-adjusted life-years (QALYs), productive years (patients and caregivers), recurrences/events, active treatments for metastatic disease, and deaths. The cumulative health and productivity impact of ESD treatment with anti-PD-(L)1 agents in Hungary was the difference in health and productivity outcomes between the ESD and reference scenarios for the time horizon of the model. ESD treatment with anti-PD-(L)1 agents was estimated to increase recurrence-/event-/disease-free life-years (+13.8%), total life-years (+3.7%), and QALYs (+4.7%), as well as productive work years for patients (+39.6%) and caregivers (+27.6%). Concurrently, there would be fewer recurrences/events (-31.0%), active treatments for metastatic disease (-34.0%), post-recurrence deaths (-30.3%), and total deaths (-23.1%). Investing in anti-PD-(L)1 agents for early-stage disease may not only increase the life expectancy and QALYs for patients in Hungary but also increase productive work years for both patients and caregivers in Hungary. In addition, it may also help to reduce metastatic disease treatments and cancer-related deaths.

The fungal priority pathogen and basidiomycete, Cryptococcus neoformans (Cn), causes lung and brain infection in predominantly immuno-compromised individuals and there is an urgent need for new treatment options. The pyrazolopyrimidine-based cyclin dependent kinase (CDK)7 inhibitor, BS-181, has anticancer properties, but its antifungal activity has not been investigated. We show that cryptococcal CDK7 more closely resembles the human enzyme than that of ascomycetes, and that BS-181 inhibits its activity. BS-181 inhibited growth of both Cn and Cryptococcus gattii (Cg), but not ascomycete fungi and delayed progression through the G2/M phase of the cell cycle. Transcriptomic analysis revealed that BS-181 induces splicing defects leading to elevated intron retention within the transcriptome and also suppresses translational processes. BS-181 displayed additive or synergistic activity with licensed antifungals against laboratory and clinical Cn and Cg strains, most notably with amphotericin B where synergy (2-4-fold reduction in the amphotericin B MIC) was achieved using low-sub micromolar concentrations of BS-181. Compared with either drug alone, BS-181-AmB combination therapy provided greater protection against Cn infection in a wax moth model (p ≤ 0.032) and extended survival of Cn-infected mice. These findings demonstrate that CDK7 inhibitors, already of interest as anticancer agents, could be repurposed to prevent or treat opportunistic fungal infections in cancer patients when combined with licensed antifungals limited by either toxicity or resistance.

PARP1 inhibitors are FDA-approved for BRCA1/2-mutated breast cancer but show limited efficacy in wild-type cancers and face resistance issues. To overcome these, we designed novel 6,7-dihydro-5H-pyrrolo[3,4-d]pyrimidine-based compounds integrating PARP1 inhibitor pharmacophores with the ATR inhibitor AZD6738 scaffold. Substituent modifications influenced PARP1 and ATR selectivity, yielding dual inhibitors or selective PARP1 inhibitors. Compound 38a, the lead candidate, exhibited potent dual inhibition (IC50 < 20 nM) and strong antitumor effects in MDA-MB-231 (IC50 < 0.048 μM) and MDA-MB-468 (IC50: 0.01 μM) cell lines in vitro. Mechanistically, 38a arrested cell cycle progression, induced apoptosis, inhibited colony formation and migration, and suppressed DNA damage repair pathways, outperforming combined Niraparib and AZD6738. These findings underscore the therapeutic potential of PARP1/ATR dual inhibitors for breast cancer and support further investigation.

Microscopy-based cancer cell classification traditionally relies on cell-based morphological features, while subcellular organelle organization remains underutilized. Existing machine learning methods often require manual preprocessing and handcrafted feature extraction, limiting scalability and introducing user bias. This study proposes an automated, interpretable, and organelle-focused deep learning framework for classifying breast cancer cell lines from high-resolution fluorescence microscopy images. We developed an end-to-end framework that incorporates patch-based sampling, sparsity filtering, and a channel-wise intermediate fusion strategy to independently extract and integrate organelle-specific features. Model interpretability was assessed using Grad-CAM visualizations and single-organelle classifier analyses. The framework was evaluated on fluorescence microscopy images from six breast cancer cell lines using 5-fold cross-validation. The proposed framework achieved a classification accuracy of 97.1 ± 1.1 %, performing comparably to or exceeding conventional handcrafted feature-based approaches while eliminating the need for manual segmentation and 3D rendering steps. Interpretability and classifier analyses revealed inter-organelle dependencies and mitochondria as the most informative contributors to classification decisions. Organelle morphology and spatial organization provide strong discriminative signals for cancer cell classification. The proposed framework offers a scalable, automated, and interpretable deep learning solution that advances microscopy-based phenotyping and supports broader applications in computational pathology and cellular informatics.