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During development, entry of any substances from the circulation into the brain is tightly regulated by a series of blood-brain interfaces. Notably, the choroid plexuses, which form the blood-cerebrospinal fluid barrier, serve as a key interface for molecular exchange in early life. Control mechanisms within the choroid plexuses include efflux transporters and conjugating enzymes, such as glutathione S-transferases and UDP-glucuronosyltransferases, which have been shown to play key roles in safeguarding the developing brain. Sulphotransferases are another family of conjugating enzymes reported to be highly expressed in the choroid plexus in humans and rats during development. However, their activity and functional significance in the central nervous system remain poorly understood. In the present study, sulphotransferase activity was measured in the lateral and fourth ventricle choroid plexus from rats at embryonic Day 19 and postnatal Day (P)1, 3, 8 and 30. Activity was correlated with expression of isoenzymes by RT-qPCR. Inhibition studies were performed by co-incubating a prototypical sulphotransferase substrate with a potential substrate or inhibitor. Finally, assays in freshly isolated live tissue were conducted to assess sulphoconjugation under more physiologically relevant conditions. Results showed that both sulphotransferase activity and expression of Sult1a1 in the choroid plexus were markedly increased at P1 to P3. This distinct temporal pattern suggests age- and tissue-specific roles of choroidal sulphotransferase activity during the early postnatal period. Interactions with xenobiotics and neuroendocrine factors further suggest that these enzymes may contribute to multiple processes during this critical window, including protection against potentially harmful substances and regulation of neurotransmitters. Furthermore, the observed modulation of choroidal sulphotransferase activity by various exogenous substances suggests that developmental exposure could disrupt sulphotransferase-mediated biological processes, with potential consequences for normal neurodevelopment.
Ischemic stroke represents a dynamic metabolic disorder of the neurovascular unit (NVU) rather than a static vascular occlusion followed by neuronal demise. Immediate oxygen and glucose deprivation rapidly deplete ATP, disrupt the transmembrane ionic gradients, increase glutamate excitotoxicity, and overload mitochondrial with calcium. These events alter glycolytic, lipid, amino acid, and redox pathways. During the subacute and chronic phases, astrocytes, microglia, macrophages, endothelial cells, pericytes, oligodendrocytes, and surviving neurons continue to remodel substrate utilization. These phase-specific metabolic programs either accelerate infarct expansion and blood-brain barrier disruption or facilitate angiogenesis, synaptic plasticity, and tissue repair. Consequently, cell-based therapeutic paradigms have shifted from direct neuronal replacement toward metabolic rescue. Transplanted cells and cell-free derivatives deliver trophic factors, extracellular vesicles, microRNAs, antioxidant signals, mitochondrial cues, and immunoregulatory factors. These signals enhance mitochondrial fitness, restore redox homeostasis, attenuate pro-inflammatory glycolysis, and stabilize endothelial-pericyte coupling to stabilize a permissive neurorehabilitation microenvironment. This review synthesizes post-stroke metabolic landscapes and evaluates how mesenchymal stromal, neural stem/progenitor, endothelial progenitor, cord blood-derived, and mononuclear cells, and extracellular vesicles, may be incorporated into a phase-specific translational framework supported by target-engagement biomarkers and standardized potency assays.
Anti-Yo paraneoplastic cerebellar degeneration (PCD) is a rare autoimmune disorder linked to ovarian and breast cancers. Neurological symptoms often precede cancer diagnosis, yet conventional imaging techniques may fail to detect early cerebellar changes. This study quantitatively assessed cerebellar atrophy and network alterations in anti-Yo PCD patients compared to healthy controls and patients with spinocerebellar ataxia type 1 (SCA1). We analyzed structural MRI data from 11 antiYo PCD patients, 17 healthy controls, and 17 SCA1 patients. Cerebellar lobular segmentation and cortical thickness measurements were conducted. Structural covariance networks were built using inter-lobular Pearson correlation coefficients (threshold |r| > 0.5), with graph theory metrics assessing connectivity. Univariate and age-adjusted multivariate analyses evaluated group differences, and machine learning assessed the discriminative power of regional morphometric measures. AntiYo PCD patients showed pronounced anterior cortical thinning, while SCA1 atrophy was milder and more posterior. Two PCD subtypes emerged: one with severe atrophy, another with nearnormal thickness. Network analysis revealed increased node strength and clustering coefficients, but reduced betweenness centrality in PCD, suggesting altered network hierarchy and widespread clustering that may reflect pathological reorganization. In cross-validated analysis, regional cerebellar features distinguished PCD, SCA1, and controls with promising AUC values. Anti-Yo PCD is characterized by anterior cerebellar vulnerability and network reorganization distinct from SCA1. These morphometric and connectivity markers are candidate imaging biomarkers for early diagnosis and subgroup stratification in paraneoplastic cerebellar degeneration.
Neuromelanin-(NM) containing organelles are sub-cellular auto-lysosomal structures composed of three main compartments: NM pigment, protein matrix, and lipid bodies. These organelles accumulate during aging and are found predominantly in the catecholaminergic neurons of Substantia Nigra (SN) and Locus Coeruleus (LC), the main regions affected in Parkinson's disease (PD). NM serves a protective function by sequestering potentially toxic metals like Cu, Fe, and Al. However, NM released from degenerating neurons may lead to a cascade of events resulting in neuroinflammation and neurodegeneration. Therefore, elemental analysis of NM-containing organelles presents a crucial step to understand aging and PD. In LC, such studies are limited because NM isolation requires large postmortem cohorts and analyses may be impaired by tissue processing. By integrating high resolution electron microscopy (EM), nano-secondary ion mass spectrometry (nano-SIMS), and energy dispersive X-ray (EDX) microscpectroscopy, the elemental composition of intact NM-containing organelles was analyzed in seven postmortem LC tissues. Chemical mapping with down to 5-10 nm lateral resolution (EDX) fostered discrimination of structural composition (N, P, S, Cl) and metal storage (Al, Ca, Fe) across neurons and within individual NM-containing organelle sub-compartments (with diameters down to 0.2 μm for lipid bodies) from the same sample. NM-containing organelles were identified by an elemental fingerprint pattern. Metals accumulations were localized predominantly to the NM pigment compartment identified by its pheomelanin-rich portion (S). This confirms NM's role in accumulating physiological as well as potentially toxic metal species. Moreover, semi-quantitative analyses provided insights into inter- and intra-subject NM metal accumulation, showing that S and Fe exhibited a positive aging trend. Hemispheric asymmetry was observed for Al, Ca, and Fe, with higher levels observed in NMs of the right brain hemisphere suggesting region-specific accumulation that warrants further investigation to better understand aging-related changes and the neuronal vulnerability of the LC in PD.
Bacteria of the genus Moraxella are significant pathogens in both human clinical practice and veterinary medicine. A novel bacteriophage, designated vB_MboM_ZALNahr, was isolated from environmental sources (wastewater from the Moscow Region) and morphologically classified as a myovirus lytically active against Moraxella spp. The phage exhibits a broad spectrum of lytic activity, affecting 76% of the tested Moraxella isolates of various origins. Genome sequencing revealed integrase genes, indicating the temperate nature of the phage and limiting its therapeutic potential. Phylogenetic analysis demonstrated that the isolate is phylogenetically related to Bacillus phages. The obtained data indicate the potential of vB_MboM_ZALNahr as a specific diagnostic agent.
Phenothiazine derivatives have gained increasing attention as potential anticancer agents, while copper-based complexes represent promising cancer therapeutic candidates due to their redox activity, coordination versatility, and biological compatibility. In this study, five novel ternary copper(II) complexes were synthesized by combining phenothiazine derivatives with amino acids as secondary ligands to enhance structural diversity, solubility, and cellular uptake. The aim was to investigate their cytotoxic potency, selectivity, and underlying molecular mechanisms against human cancer cell lines, with a particular focus on hepatocellular carcinoma. The five copper(II) complexes were synthesized by combining phenothiazine derivatives-promazine (Prom) and triflupromazine (TFP)-with biologically relevant amino acids (methionine, tryptophan, tyrosine, and serine) as secondary ligands. Using MTT assays, the synthesized complexes [Cu(Prom)(Meth)Cl], [Cu(Prom)(Tryp)Cl]H2O, [Cu(Prom)(Tyr)Cl], [Cu(Prom)(Ser)Cl], and [Cu(TFP)(Ser)Cl] were assessed for their in vitro anticancer efficacy against five human cancer cell lines. Among them, [Cu(TFP)(Ser)Cl] exhibited the highest cytotoxic potency and selectivity toward HepG2 liver cancer cells, with an IC50 value of 15.15 µg/mL. Mechanistic studies revealed that this complex induces apoptosis, cell cycle arrest at G2/M phase, and autophagy, as confirmed by flow cytometric analysis. Gene expression analysis showed significant downregulation of the antiapoptotic BCL2 gene, marked upregulation of the autophagy marker LC3, and moderate upregulation of CDK4. These results were confirmed by molecular docking experiments, which revealed advantageous interactions with proteins involved in cell cycle regulation and apoptosis. These results highlight phenothiazine-copper-amino acid complexes as promising anticancer agents and identify [Cu(TFP)(Ser)Cl] as a potential candidate for hepatocellular carcinoma therapy.
Gastric cancer is a common digestive system malignancy worldwide, and its high metastatic potential is one of the main reasons for poor patient prognosis. Dihydrocapsaicin, a natural derivative of capsaicin found in chili peppers, has recently been shown to possess anti-tumor potential. However, the effects and underlying mechanisms of dihydrocapsaicin on gastric cancer cell migration and invasion remain unclear. This study aims to investigate the impact of dihydrocapsaicin on the migratory and invasive abilities of gastric cancer cells and to elucidate the regulatory role of TRPV1 in this process. Human gastric cancer cell lines NCI-N87 and HGC-27 were treated with different concentrations of dihydrocapsaicin. The changes in the migration and invasion abilities of gastric cancer cells were detected. Meanwhile, WB was used to detect the differential expression of TRPV1, E-cadherin, N-cadherin, Vimentin, and Snail. Subsequently, the expression of TRPV1 was interfered in the dihydrocapsaicin-treated gastric cancer cell lines, and the differential expressions of the above cell migration, invasion, and E-cadherin, N-cadherin, Vimentin, and Snail proteins were detected. Dihydrocapsaicin could significantly and dose-dependently inhibit the migration and invasion of gastric cancer cells NCI-N87 and HGC-27. In addition, dihydrocapsaicin treatment significantly increased the expression of TRPV1 and E-cadherin proteins in gastric cancer cells, and significantly inhibited the expression of N-cadherin, Vimentin, and Snail proteins in gastric cancer cells. Functional rescue experiments confirmed that when the expression of TRPV1 was inhibited, the inhibitory effect of dihydrocapsaicin on the migration and invasion of gastric cancer cells was significantly weakened. Dihydrocapsaicin can effectively inhibit the migration and invasion abilities of gastric cancer cells by upregulating the expression of TRPV1.
Colorectal cancer liver metastasis (CRLM) represents the leading cause of mortality in colorectal cancer (CRC). However, the molecular mechanisms enabling metastatic adaptation within the hepatic microenvironment remain unclear. We integrated single-cell RNA sequencing, spatial transcriptomics, and bulk transcriptomic data from CRC patients to characterize the immunometabolic landscape of CRLM. Machine learning models were used to identify key regulators, and functional assays were conducted to validate their biological roles. Nine major cell populations were delineated within CRLM, revealing enrichment of myeloid-derived suppressor cells and depletion of fibroblasts in metastatic lesions. Malignant cells displayed pronounced chromosomal instability and metabolic reprogramming. Among candidate regulators, PIGT emerged as a pivotal node linking metabolic adaptation and immune suppression. PIGT expression increased progressively from primary to metastatic states and was associated with immunosuppressive MIF, SPP1, and TGFβ signaling. Spatial transcriptomics demonstrated colocalization of PIGT-high tumor cells with ITGAM⁺ and CD163⁺ macrophages. Functionally, PIGT knockdown significantly suppressed cell invasion, migration, proliferation, and wound healing in vitro. Conversely, transcriptomic and qPCR analyses showed that PIGT-low tumors exhibited higher expression of inflammatory genes enriched in the IL-17 and TNF signaling pathways. Our integrative multi-omics and experimental analyses identify PIGT as a central regulator bridging tumor metabolism and immune modulation in CRLM. These findings highlight PIGT as a promising prognostic biomarker and potential therapeutic target for metastatic colorectal cancer.
Bosutinib, an orally administered dual Src and Bcr-Abl tyrosine kinase inhibitor (TKI), is approved for the treatment of chronic phase Philadelphia chromosome-positive chronic myelogenous leukemia, newly diagnosed or resistant/intolerant to previous treatment of one or more TKIs in adults and children ≥ 1 years (by the US Food and Drug Administration) and ≥ 6 years (by the European Medicines Agency). Owing to the limitations of non-compartmental analysis in pharmacokinetic (PK) characterization, this study applies a prior population PK modeling approach to describe the pediatric PK of bosutinib on the basis of its phase I dose-finding trial and the available bosutinib adult PK knowledge. From 26 pediatric patients in the phase I part of the ITCC-054/COG AAML1921 trial, 235 plasma bosutinib concentration samples were analyzed. A published adult bosutinib population PK (popPK) model was used as the reference model; the frequentist prior modeling approach was used during model development to integrate adult PK information, especially for parameters with poor identifiability. Bosutinib pediatric PK was well characterized by a two-compartment model with first-order absorption, an absorption lag time of 0.467 h, and allometric scaling with fixed exponents. The typical clearance was 74.9 L/h (normalized to 70 kg), higher than the reported adult value (56.3 L/h). Model-based simulations validated that the bosutinib recommended phase II dose (RP2D) could achieve the target exposure observed in adults and demonstrated the influence of higher clearance in pediatric patients. This study presents the first pediatric bosutinib popPK model, integrating prior adult PK knowledge to enable robust PK characterization using a small pediatric dataset from an early trial stage. In addition, the model confirms a higher clearance in children and supports the RP2D concluded in the phase I dose-finding part of the ITCC-054/COG AAML1921 trial.
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Selenium (Se) is an essential trace element for human health and longevity. However, the spatial association between soil Se content and longevity remains poorly understood at different spatial scales. Therefore, this study investigated the relationship between soil Se and longevity at both the national scale in China and at six representative smaller regions (Heilongjiang Province, Liaohe River Basin, Enshi Prefecture in Hubei, Ankang City in Shaanxi, Lianzhou City and Yingde City in Guangdong). Both traditional statistical methods (e.g., Pearson correlation, OLS regression) and spatial statistical methods (e.g., Moran's I, SLM, SEM) were employed. The results revealed that: (1) At the national scale, soil Se and longevity exhibited a highly significant positive correlation and strong spatial clustering. High-Se/high-longevity clusters were mainly located in southern China, while northern provinces were characterized by low-Se/low-longevity clusters. SEM (R² = 0.31) outperformed OLS (R² = 0.22) and SLM (R² = 0.28), and the SEM model parameters indicated that the positive association persists after controlling for spatial error dependence. (2) At the small-scale level, a highly significant positive correlation was only identified in Lianzhou and Yingde. In Lianzhou, clusters of high Se and high longevity were concentrated in the southern region. SLM (R² = 0.87) outperformed OLS (R² = 0.72); the SLM model parameters indicated that the positive effect of soil Se remains after controlling for spatial lag dependence. (3) Spatial error dependence dominated at the broader scale, whereas spatial lag dependence was more prominent in small regions. Linearity and model performance improved substantially at the small scale. Regions with suitable Se levels showed the strongest Se-longevity associations.
Prostate cancer remains the most prevalent malignancy and a leading cause of cancer-related mortality among men in Puerto Rico. This study evaluates regional disparities in prostate cancer incidence across 76 contiguous municipalities on the main island and characterizes areas exhibiting statistically significant spatial clustering. Using spatial analytical techniques-including Moran's I and Getis-Ord Gi*-within Geographic Information Systems (GIS), we analyze data from the Puerto Rico Central Cancer Registry (RCCPR) spanning 2016 to 2022. The findings reveal a non-random spatial distribution of prostate cancer incidence, with consistent clustering patterns observed throughout most of the study period, except in 2019. Notably, cold-spot regions were persistently identified in the northwest and western municipalities, while elevated incidence rates were concentrated in clusters located in the southern and eastern regions. These results underscore the presence of enduring geographic disparities in the prostate cancer burden across Puerto Rico.
High throughput metabolomic assays offer a huge opportunity to quantify the cellular processes underlying disease and intervention pathways. However, the multi-dimensional inter-relatedness between these processes coupled with the complex noisy measurement environment create a need for generation of new methods that move beyond simple pairwise associations. To evaluate the utility of a simple, multivariate, relational comparison method called CLARITY in the context of metabolomics data. Nuclear magnetic resonance (NMR)-derived metabolomics data collected from the same individuals (N = 125) before and after a weight management intervention in By-Band-Sleeve (BBS), a clinical trial of metabolic and bariatric surgery, were used. First a traditional (univariate) linear mixed model approach was taken to identify metabolites that were changed post-intervention. The CLARITY method was then used to generate a covariance-based relational anomaly score with a view to increasing classification performance of the underlying cause of changes to the levels of and covariances between metabolites. CLARITY enabled further characterisation of metabolites identified in univariate linear regression analyses, differentiating those that exhibited covariance changes from those with mean changes only. An additional cluster of metabolites were identified as undergoing a relational change that would not have been detected using traditional methods. Gathering insights about biological pathways from large-scale metabolomics data has the potential to inform the future design of modelling and laboratory experiments aimed at capturing the underlying biological processes relevant to disease.
Word-level psycholinguistic norms are necessary to test theories of language processing. However, obtaining such human-based measures is not always feasible or straightforward. One promising approach is to augment human norming datasets by using large language models (LLMs) to predict these characteristics directly, a practice that is rapidly gaining popularity in psycholinguistics and cognitive science. However, the novelty of this approach (and the relative inscrutability of LLMs) necessitates the adoption of rigorous methodologies. We discuss the range of possible approaches, and clarify limitations that are not immediately apparent. In this work, we present a comprehensive methodology for estimating word characteristics with LLMs, enriched with practical advice and lessons learned from our own experience. Our approach covers both the direct use of base LLMs and the fine-tuning of models, an alternative that can yield substantial performance gains in certain scenarios. A major emphasis in the guide is the need to validate LLM-generated data, at least with a small set of a few hundred human "gold standard" norms, before using the LLM-generated norms. We also present a software framework that implements our methodology and supports both commercial and open-weight models. We illustrate the proposed approach with a case study on estimating word familiarity in English. Using base models, we achieved a Spearman correlation of 0.8 with human ratings, which increased to 0.9 when employing fine-tuned models. This methodology, framework, and set of best practices can serve as a reference for future research on leveraging LLMs for psycholinguistic and lexical studies.
River pollution is increasingly driven by municipal sewage, industrial effluents, and agricultural runoff, posing significant threats to freshwater ecosystems. This study evaluated the impact of wastewater discharges on river water quality and irrigation suitability along the upper Ganga River in Uttarakhand, using 19 physicochemical and microbial indices. For this purpose, a total of 48 water samples were collected from eight sampling locations during the post-monsoon, monsoon, and pre-monsoon seasons from 2023 to 2025. Water Quality Index (WQI), water pollution index (WPI), principal component analysis (PCA), and irrigation indices (SAR, RSC, KI, Na%, MAR, and PI) were applied to samples collected from sewage treatment plants (STPs) and river water. Piper diagram and Gibbs plots were used to characterize the hydrochemical composition of the river water and identify the major mechanisms controlling its chemical evolution. Results showed that the total dissolved solids of river water, inlets, and outlets of STP range from 44 to 148 mg/l, 166 to 587 mg/l, and 67-446 mg/l, respectively. Treatment of wastewater substantially reduced pollutant loads; however, treated effluents still contained elevated BOD, COD, total coliforms (741-1540 MPN/100 mL), and E. coli (390-654 MPN/100 mL), indicating incomplete removal of organic matter and pathogens. River water exhibited good quality (WQI: 62.7-82.4; WPI: 0.38-0.50) and excellent irrigation suitability (SAR < 1.2; Na% < 25). In contrast, mixing zone samples showed moderate deterioration (WQI: 72.9-79.5; WPI: 0.61-0.67) due to residual effluent influence. The findings of this study indicate that the water quality of the Ganga River is significantly influenced by anthropogenic activities, including domestic sewage, industrial discharges, agricultural runoff, and tourism, as well as geogenic processes. Deteriorating water quality poses risks to human health and aquatic ecosystems. The study highlights the need for advanced wastewater treatment and stricter effluent management to protect river health and support Sustainable Development Goal 6.
Metastatic disease remains the leading cause of cancer-related death, yet most precision oncology strategies still emphasize profiling primary tumors and tracking cell-free tumor DNA (ctDNA). Although ctDNA has transformed genomic profiling, molecular residual disease monitoring, and early cancer detection, it cannot directly capture viable tumor cell states, phenotypic plasticity, or functional adaptations that drive metastatic spread. We propose that the next phase of precision oncology should integrate the cellular dimension of metastasis through systematic circulating tumor cell (CTC) profiling. The SCRUM-MONSTAR platform, one of the largest pan-cancer molecular profiling initiatives in Japan, offers an exceptional foundation for this transition through its nationwide infrastructure for multi-omics analysis, longitudinal biospecimen collection, and artificial intelligence-enabled clinical interpretation. By combining matched tissue profiling, serial ctDNA analysis, single-cell CTC transcriptomics, metabolomics, and organoid- and mouse-based functional modeling, SCRUM-MONSTAR-CTC could evolve into a translational ecosystem for anti-metastatic drug discovery. Within this framework, we highlight adherent-to-suspension transition (AST) as one representative, experimentally tractable plasticity program that enables tumor cells to survive in circulation and subsequently colonize distant organs. We envision that identifying and therapeutically targeting AST-related and other metastatic plasticity programs across tumor types will provide a path toward clinically actionable anti-metastatic therapies. More broadly, this framework could enable the identification of metastatic vulnerabilities, the development of biomarker-guided anti-metastatic trials, and the reverse translation of patient-derived discoveries into early-phase clinical testing. Precision oncology must move beyond cataloging tumor genomes and begin targeting metastasis as a dynamic biological process. UMIN000056873, approved by the Institutional Review Board of the National Cancer Center Hospital East.
The development of sustainable multifunctional hydrogels with antibacterial activity and protein delivery capability is of great interest for biomedical applications. In this study, plant oil-derived cationic hydrogels were developed by UV-induced polymerization using acrylated methyl ricinoleate (AMR), a renewable monomer derived from castor oil, and 2-aminoethyl methacrylate (AEMA). The influence of hydrogel composition on morphology, pH-responsive swelling, antibacterial activity, cytocompatibility, and protein encapsulation behavior was investigated. FTIR and SEM analyses confirmed successful hydrogel formation and revealed composition-dependent structural differences. The swelling behavior was strongly influenced by pH and monomer ratio. The hydrogels exhibited significant antibacterial activity against Escherichia coli and Staphylococcus aureus while maintaining good cytocompatibility toward human umbilical vein endothelial (HUVEC) cells, with cell viability exceeding 80%. Bovine serum albumin (BSA) was used as a model protein to evaluate encapsulation and release properties. Protein encapsulation efficiency was found to depend strongly on crosslinking density, with lower crosslinking ratios leading to enhanced loading. Overall, the results demonstrate that combining a renewable plant oil-derived monomer with a cationic component enables the fabrication of sustainable hydrogels with tunable physicochemical and biological properties, highlighting their potential for wound healing and localized protein delivery applications.
Immune checkpoint B7-H3 is an emerging target for immunotherapy. DS-7300a is an advanced B7-H3-targeting antibody-drug conjugate (ADC) warheaded with the topoisomerase I inhibitor DXd. DS-7300a has demonstrated clinical activity, but molecular biomarkers to predict its therapeutic response remain elusive. TP53 is one of the most mutated tumor suppressor genes across cancers, and effective therapies are urgently needed for TP53-deficient cancers. Using prostate cancer (PCa) as a model system, we reported that DS-7300a's anti-tumor efficacy is highly dependent on functional p53 in cancer cells, and TP53 defects confer resistance to DS-7300a. Mechanistically, we found that DS-7300a and its payload, DXd, induce DNA damage and activate the ATM/ATR/CHK signaling cascade, thereby stabilizing p53 and inducing a pro-apoptotic and senescence-associated transcriptome. In contrast, TP53-deficient cells fail to sense DXd-induced DNA damage, maintain a high proliferation rate, and exhibit low levels of apoptosis and senescence, thereby conferring resistance to DS-7300a. Ferroptosis is an iron-dependent form of regulated cell death triggered by lipid peroxidation, which is mechanistically and morphologically distinct from apoptosis. Interestingly, DS-7300a treatment elevates lipid peroxidation in TP53-deficient cancer cells and upregulates glutathione peroxidase 4 (GPX4), an antioxidant enzyme that mitigates lipid peroxidation. Using isogeneic xenograft models and a newly developed humanized B7-H3 PCa model, we demonstrated that inducing ferroptosis by pharmacological inhibition of GPX4 enhances DS-7300a's efficacy in TP53-deficient tumors. Our studies demonstrate that TP53 status dictates anti-tumor responses to DS-7300a, and ferroptosis induction represents a promising therapeutic approach to overcome resistance to DS-7300a in malignancies harboring TP53 defects.
Single-cell foundation models such as scGPT and Geneformer are large neural networks trained on human single-cell RNA-seq data. They were never shown chronological age during training. Do their internal representations nevertheless encode aging biology in a way that can be interpreted, and how should we test whether an apparent aging signal is real biology rather than an artifact of which donors and cell types happened to be sampled?. We applied a nine-step evaluation pipeline to two foundation models (frozen, no fine-tuning) and five PBMC datasets containing 4 to 5 million cells from ∼ 2,000 donors with chronological age. Each step is one specific test: can we read age out of the model's representation; does the representation place age along a clean axis; do sparse-feature decompositions surface aging-related programs; do the two models agree at the pathway level; do targeted perturbations of those features change predicted age in the expected direction; and finally, does the signal survive when we resample cells so that young and old donors have matched cell-type composition (removing the most obvious confound). (1) The foundation models encode age but do not predict it better than a 50-component PCA of gene expression: in all five cohorts the PCA baseline matches or exceeds the best foundation-model probe. What they add is a complementary interpretability mode-sparse-feature decomposition and activation-level intervention-rather than predictive power; a PCA of gene expression is itself interpretable through its loadings, so the contribution here is the evaluation framework that adjudicates such signals, not a claim that foundation models predict age better. Randomly reinitialising Geneformer's weights destroys most of its age signal ( - 0.107 balanced-accuracy points), while doing the same to scGPT's layer 9 changes essentially nothing-so the two models encode age asymmetrically. (2) Sparse autoencoders surface 132 robust aging-related features across the two models, of which 193 cross-model pairs match each other at pathway level, concentrated in inflammation. The shared inflammation signal resolves into specific submodules: TNF / NF- κ B classical and type-II IFN- γ (both models agree), complement (scGPT-specific). (3) The strongest aging signal is Geneformer's NF- κ B program in the AIDA phase 1 v2 cohort. Pushing those features in the "older" direction increases predicted age by 0.15 expected-age units; pushing them the opposite way decreases it; pushing along random unrelated directions does neither-a three-way directional check we call the "strict gate". When cells are resampled so that the age groups have matched cell-type composition (the strictest control), the directional effect shrinks ∼ 3 × but 7 of 8 resampling seeds still pass the strict gate. One in eight resamplings fully nullifies the effect. The directional aging signal therefore survives confounder removal on most realizations, at attenuated magnitude. An external check on the Yazar OneK1K cohort (981 donors, fully separate from AIDA) reproduces the workflow on a known-strong biological axis (sex), with results within 10% of the AIDA contrast-evidence that the test is calibrated and transfers off-cohort. The paper's primary contribution is an evaluation framework for deciding when an apparent aging signal in a single-cell foundation model is biology rather than sampling structure. Applied here, it shows that frozen foundation models carry a recoverable aging signal concentrated in NF- κ B and IFN- γ inflammation submodules-biology that is already established at the gene-expression level, recovered zero-shot from models never trained on age. Reporting both an unrestricted contrast and a composition-matched contrast as side-by-side specificity tests-not just the headline number-is the framework's central recommendation.