Enzyme-mediated nanoreactors with cascade metabolic modulation for enhanced chemo-chemodynamic combination therapy.

Characteristics and prognosis of PCA-1 (anti-Yo) autoimmunity and the utility of the Brief Ataxia Rating Scale in a seropositive cerebellar ataxia cohort.

A multifunctional oxidative stress amplifier for synergistic disruption of redox homeostasis and enhanced cancer therapy.

Thymidine kinase 1 (TK1) as a proteomic biomarker in cancer.

Patient-derived geometry-integrated multiphysics framework: Computational optimization of immunotherapeutic nanoparticle delivery via tumor microenvironment reprogramming in prostate cancer.

Discovery of a brain-penetrant fourth-generation EGFR inhibitor to overcome the human triple (L858R/T790M/C797S) mutation.

Platinum-based chemotherapeutic agents, such as cisplatin (Cis-Pt(II)), are widely used in cancer treatment but are limited by toxicity, DNA repair by cancer cells, and drug resistance. To address these limitations, we designed and synthesized in our laboratories a novel platinum-based compound, Aurkine16. In our previous studies, Aurkine16 demonstrated outstanding therapeutic efficacy, selectively inducing double-strand DNA breaks in both naïve and cisplatin-resistant cancer cells, without detectable toxic side effects at clinically relevant doses. In the present work, we report a computational analysis of Aurkine16, which reveals its unique activation pathway and its capacity to form stable [Aurki-GGG]3+ complexes. Unlike Cis-Pt(II), which induces single-strand DNA breaks, Aurkine16 simultaneously targets multiple nucleic bases, causing double-strand breaks and significant DNA disruption. Additionally, molecular dynamics simulations suggest that Aurkine16 is likely to exhibit specificity for cancer cells, avoiding off-target interactions within the nucleosome core. This selectivity, attributed to the steric hindrance from histone tails, underscores its potential for effectively targeting chromatin-accessible cancer cells. These computational findings position Aurkine16 as a promising alternative to platinum-based drugs, particularly for CisPt-resistant cancers.

Shark-derived variable new antigen receptors (VNARs) exhibit broad biomedical application prospects owing to their small size, exceptional thermal stability, and resistance to extreme pH conditions. Synthetic library construction enables the selection of specific VNARs without shark immunization, while employing stable universal scaffolds with rationally designed complementarity-determining region 3 (CDR3) length and amino acid composition. Here, a synthetic phage display library was constructed using a highly expressed and stable scaffold, guided by a systematic analysis of existing VNAR sequences. Its framework regions were retained, while the CDR1 and CDR3 were randomized with the conserved tryptophan (W) in CDR3 preserved. Using this library, VNARs specifically targeting the urinary tumor biomarkers hyaluronidase-1 (Hyal-1), Engrailed-2 (EN2), and prostate-specific antigen (PSA) were successfully identified. The affinity of all selected VNARs reached the micromolar (μM) level, and the expression level can reach 2.4-14.3 mg/mL. In summary, this study established a high-performance synthetic VNAR phage display library and preliminarily explored the role of the conserved tryptophan (W) in the CDR3. The VNARs targeting distinct epitopes of Hyal-1, EN2, and PSA obtained through screening represent promising candidate molecules for the diagnosis and treatment of related cancers.

Proton versus photon radiotherapy for patients with oropharyngeal cancer in the USA: a multicentre, randomised, open-label, non-inferiority phase 3 trial.

The proportion of lung cancers affecting individuals who have never smoked is growing, with these cancers being prone to harbor mutations in the EGFR gene. Little is known about risk factors and prognostic indicators for EGFR-mutant cancers, with current research limited by the scarcity of datasets integrating genomic, clinical, and environmental data. We created the Meyer Cancer Center Molecularly Enhanced Lung Cancer Database (MCC-MELD), including lung cancer cases from a large catchment area in New York City. We identified cases through linkage to our institution's cancer registry and a clinician-initiated, manually curated database. We linked all cases to the electronic health record and in-house tumor genomic testing results. We used natural language processing (NLP) to extract unstructured genomic testing results and detailed smoking history. We linked geocoded addresses to detailed area-level measures. MCC-MELD contains 9,573 patients with lung cancer diagnosed from 1988 to 2024, of whom 20% were non-Hispanic Asian, 14% were non-Hispanic Black, and 8% were Hispanic. We identified 1,092 (11.4%) EGFR-mutant cancers, with NLP identifying 397 cases not identified by structured data. NLP showed high accuracy in ascertaining EGFR status (97%) and quantitative smoking history variables (90%-98%). Never smokers made up 16% of the cases in MCC-MELD. MCC-MELD is an NLP-enhanced database containing clinical information, genomic testing results, and linkages to area-level data for patients with lung cancer from a diverse urban setting. This resource can facilitate studies on lung cancer risk factors, treatment patterns, and outcomes by EGFR and other driver mutation status.

Abstract Pancreas segmentation in CT images is fundamental for subsequent diagnosis and qualitative treatment of pancreatic cancer. Since the morphology of the pancreas may be influenced by issues such as class imbalance and boundary blurring across different individuals, segmenting the pancreas from abdominal CT images is a challenging task. To address these issues, this paper proposes a novel pancreas CT image segmentation network, REMC-UNet. First, we introduce the Residual Visual State Space block (ResVSS block) to capture extensive contextual information and effectively extract key features from pancreas CT images. Additionally, we design the Multi-Scale Hybrid Attention (MSHA) to aggregate long-range dependencies and leverage multi-scale spatial information to address the issue of unclear pancreatic boundaries. Finally, we propose the Feature Enhancement block (FE block), which allows the model to focus on global features while also attending to local regions during the feature recovery process. Through experiments on the public NIH dataset, we achieve an average Dice Similarity Coefficient (DSC) of 86.64±4.32%, improving by 2.73% over the baseline model and outperforming other segmentation models.

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality, underscoring the need for new therapeutic strategies. Screening a library of dihydroquinolinones, termed Factor Quinolinone Inhibitors (FQIs), identified compound 1 as a potent in vitro inhibitor of the oncogenic transcription factor LSF/TFCP2 and as an in vivo suppressor of HCC tumor growth without observable cytotoxicity. Unfortunately, 1 had limited bioavailability. In this report, we detail the identification and optimization of the structure-activity relationships (SAR) of chiral and achiral FQI analogs. The SAR study led to the discovery of achiral 12 (FQI2-34), a highly potent, selective compound with desirable absorption, distribution, metabolism, and excretion (ADME) properties and potent in vivo antitumor activity. We also demonstrated that FQIs directly bind to TFCP2 with affinities in the nanomolar ranges. Our results suggest that FQIs are promising chemotherapeutics for TFCP2-driven cancer, especially HCC.

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of cancer. However, the durable response to this therapy remains low, and there is a risk of moderate-to-severe toxicities following treatment that requires close monitoring. Over the past decade, we have learned that cytokines play an important role in mediating CAR T cell-associated toxicities and efficacy. As such, cytokine modulation has become a popular area of investigation to improve therapeutic responses. Although the relationship of many cytokines with CAR T-cell therapy has been investigated, several recent studies suggest paradoxical roles for cytokines such as IFNγ, IL2, IL4, and IL10 in CAR T-cell response and toxicity. In this review, we summarize the history of these cytokines in immunotherapies, detail the contexts in which these cytokines have been beneficial or harmful in the context of CAR T-cell therapy, and discuss factors that may be dictating their pleiotropy.

Accurate detection and segmentation of polyps during colonoscopy are of great significance for the early prevention and treatment of colorectal cancer. However, due to the considerable variations in polyp size and shape, as well as their blurred boundaries with surrounding tissues, polyps are often difficult to detect, making precise segmentation a challenging task. Although numerous deep learning (DL) based segmentation methods have been proposed in recent years and achieved certain progress, their results remain unstable and often unsatisfactory. To address these challenges, we propose PGMNet, an accurate and efficient network for polyp segmentation, which consists of a PVTv2 encoder, a Global-Local Interactive Relation Module (GLIRM), and a Multi-stage Feature Aggregation Module (MFAM). The PVTv2 encoder is capable of capturing both fine-grained details and global semantic representations, making it well-suited for complex medical image segmentation tasks. GLIRM performs multi-scale information fusion during upsampling to restore fine-grained details and global semantic context, while simultaneously introducing a bit-slice mechanism to effectively suppress noise. MFAM leverages a gating mechanism to efficiently aggregate GLIRM information from different stages, thereby improving the quality of the final predictions.Extensive experiments were conducted on five publicly available polyp datasets, and the results demonstrate that PGMNet achieved very promising performance in terms of segmentation accuracy and generalization ability. In particular, on the challenging ETIS dataset, PGMNet achieved an mDice of 82.33% and an mIoU of 74.29%, highlighting its superior performance.

Methotrexate, an antifolate therapy, is used for the treatment of cancers, autoimmune disease, and transplant patients and needs to be carefully monitored for toxicity. Plasma measurement of methotrexate concentrations is problematic as a result of analytical interference of its metabolites. Immunoassays are widely used for monitoring therapeutic drug levels of methotrexate; however, chromatographic assays, LC-MS/MS most particularly, are preferentially used since they are less prone to metabolite interference. Analytical performance was evaluated in a second-generation methotrexate immunoassay (ARK, Inc.) by testing precision, accuracy, linearity, interference, and method comparison with LC-MS/MS. Cross-reactivity of the immunoassay with 4-deoxy-4-amino-N10-methylpteroic acid (DAMPA), 7-OH methotrexate, and folic acid was also measured. Findings indicate that the within-run %CV ranged from 2.6% to 4.4% across control materials. The assay was linear across an analytical measuring range of 0.03 to 1.3 μM. The lower limit of the measuring interval and the lower limit of detection were established at 0.03 µM and 0.0067 μM, respectively. Notable interference from DAMPA was observed above 1 µM, whereas 7-OH methotrexate and folic acid showed no interference up to 5 µM and 1000 µM, respectively. Correlation studies showed an average 3% bias in DAMPA-free samples (R2 = 0.9935) and a significant bias and poorer correlation (R2 = 0.3241) in samples containing DAMPA. The assay performed well based on the validation experiments and is suitable for most clinical applications involving methotrexate. However, the significant interference with DAMPA highlights the necessity for careful assay selection and interpretation of glucarpidase-treated patients.

The construction of nano-drug carriers based on deoxyribonucleic acid (DNA) has demonstrated significant therapeutic potential. Similarly, supramolecular therapeutic systems utilizing host-guest interactions have emerged as promising in nanomedicine. Building upon these approaches, we designed a size-controllable, multi-responsive supramolecular DNA nanogel (SDN) based on host-guest recognition for dual-drug co-delivery in cancer combination therapy. The nanogel incorporates doxorubicin (DOX, a chemotherapeutic agent) and methylene blue (MB, a photosensitizer). The assembly of SDN is driven by cucurbit[8]uril (CB[8]), which selectively binds two MB molecules-one from each of two Y-shaped DNA building blocks-forming a 1 : 2 host-guest complex that crosslinks the structures into a nanogel network. Meanwhile, the double-stranded DNA scaffold efficiently encapsulates DOX via intercalation, enabling SDN@DOX to co-deliver both drugs in a precisely controlled ratio. Notably, MB's photodynamic activity is initially suppressed upon CB[8] binding. However, upon cellular uptake, SDN@DOX responds to overexpressed spermine or specific peptide sequences in the tumor microenvironment, triggering MB release and restoring its photodynamic function. Concurrently, DNase I-mediated DNA degradation liberates DOX, enabling synergistic chemo-photodynamic therapy (PDT). In vitro studies confirmed that SDN@DOX enhances reactive oxygen species (ROS) generation in cancer cells and achieves superior therapeutic efficacy through combined PDT and chemotherapy. This stimuli-responsive, dual-drug delivery system offers a potentially robust and controllable platform for precision cancer treatment.

Monitoring circulating tumor DNA in combination with drug screening of organoids enables dynamic, personalized breast cancer therapy adjustments to overcome resistance and improve treatment efficacy.

Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study.

Aframomum alboviolaceum (Ridl.) K.Schum. leaf essential oil protects against Benzo(a)pyrene induced prostate cancer in Wistar rats.

PLGA-integrated micro/nanoparticles for cargo delivery and nanomedicine of lung cancer.