Biology subjects

Zhou, F.

Publications and source records attributed to Zhou, F..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Disgust propensity, not disgust sensitivity, shapes the reactivity of a subjective disgust circuit in humans

Disgust constitutes an evolutionary adaptive defensive-avoidance response, yet humans vary markedly in their dispositional tendency to experience disgust (disgust propensity) and in their negative appraisal of such experience (disgust sensitivity). Conceptual frameworks and neuroimaging studies suggest that these traits may differentially modulate neural responses to disgust-eliciting stimuli; however, methodological constraints have left their precise roles unresolved. Our comparably large fMRI study (n = 142) therefore aimed to systematically determine how trait disgust modulates neural responses to carefully selected and validated disgust-specific visual stimuli across varying levels of subjective disgust experience. The whole-brain voxel-wise regression analyses revealed a neural dissociation between the two disgust traits, with disgust propensity, but not disgust sensitivity, modulating disgust-related neural activity in the anterior, middle, and posterior insula, as well as the caudate, putamen, thalamus, hippocampus, and parahippocampal gyrus. Mediation and network-level analyses further supported this dissociation by showing that disgust propensity shapes disgust experience via insula - striatal - hippocampal pathways. Together, these findings provide evidence for a neurofunctional dissociation of disgust propensity and sensitivity and elucidate how trait disgust shapes subjective experiences. They further suggest that disgust propensity and the identified systems may represent promising targets for the regulation of disgust-related pathology.

neuroscience↗

bioRxiv2025

Rapid CE-MS with Real-Time Eco-AI Resolves Proteomic Heterogeneity Among Single Human Neutrophils

Single-cell proteomics by mass spectrometry is advancing rapidly, yet throughput and sensitivity remain limiting--particularly for small, protein-poor cell types such as neutrophils. As the most abundant circulating leukocytes in humans, neutrophils are central to immune defense and inflammation, but their proteomes comprehensive single-cell level characterization has only concurrently been reported1 and remains limited. Here, we introduce a rapid capillary electrophoresis-mass spectrometry (CE-MS) platform, which integrates electrophoresis-correlative real-time data acquisition with sub-7-minute separations and artificial intelligence (AI)-based data processing software to achieve deep, high-throughput profiling. Using single-cell-equivalent HeLa digests, the Rapid Eco-AI platform identified [~]1,350 proteins from 300 pg and [~]835 proteins from 75 pg of input-- approaching the complexity of a mammalian cell proteome. Applied to freshly isolated human neutrophils, the workflow identified 151 proteins from [~]2 pg of material, [~]3% of the total cell proteome. Analysis of 13 individual cells revealed marked functional heterogeneity across pathways of degranulation, neutrophil extracellular trap (NET) formation, chemotaxis, and innate immunity, with hierarchical clustering resolving at least four distinct proteomic subtypes. These results establish Rapid Eco-AI as a sensitive, scalable, and broadly applicable CE-MS approach for immune-cell phenotyping at single-cell and subcellular resolution, facilitating new research opportunities in systems immunology and clinical proteomics.

developmental biology↗

bioRxiv2025

Genomic Touchstone: Benchmarking Genomic Language Models in the Context of the Central Dogma

The emergence of genomic language models (gLMs) has revolutionized the analysis of genomic sequences, enabling robust capture of biologically meaningful patterns from DNA sequences for an improved understanding of human genome-wide regulatory programs, variant pathogenicity and therapeutic discovery. Given that DNA serves as the foundational blueprint within the central dogma, the ultimate evaluation of a gLM is its ability to generalize across this entire biological cascade. However, existing evaluations lack this holistic and consistent framework, leaving researchers uncertain about which model best translates sequence understanding into downstream biological prediction. Here we present Genomic Touchstone, a comprehensive benchmark designed to evaluate gLMs across 36 diverse tasks and 88 datasets structured along the central dogmas modalities of DNA, RNA, and protein, encompassing 5.34 billion base pairs of genomic sequences. We evaluate 34 representative models encompassing transformers and convolutional neural networks, as well as emerging efficient architectures such as Hyena and Mamba. Our analysis yield four key insights. First, gLMs achieve comparable or superior performance on RNA and protein tasks compared to models pretrained on these molecules. Second, transformer-based models continue to lead in overall performance, yet efficient sequence models show promising task-specific capabilities and deserve further exploration. Third, the scaling behavior of gLMs remains incompletely understood. While longer input sequences and more diverse pretraining data consistently improve performance, increases in model size do not always translate into better results. Fourth, pretraining strategies, including the choice of training objectives and the composition of pretraining corpora, exert substantial influence on downstream generalization across different genomic contexts. Genomic Touchstone establishes a unified evaluation framework tailored to human genomics. By spanning multiple molecular modalities and biological tasks, it offers a valuable foundation for guiding future gLM design and understanding model generalizability in complex biological contexts.

bioinformatics↗

bioRxiv2025

Frontal cortex organization supporting audiovisual processing during naturalistic viewing

AbstractOur brains dynamically adapt to a multisensory world by orchestrating diverse inputs across sensory streams. This process engages multiple brain regions, but it remains unclear how audiovisual stimuli are represented and evolve over time, especially in naturalistic scenarios. Here, we employed a movie-viewing paradigm to explore this question. We recorded intracranial electrocorticography (iEEG) to measure brain activity in 19 participants watching a short multilingual movie. Using unsupervised clustering and supervised encoding models, we identified a robust modality-specific gradient in the frontal cortex, wherein the ventral division primarily processes auditory information and the dorsal division processes visual inputs. Further, we found that this cortical organization dynamically changed, adapting to different movie contexts. This result potentially reflects flexible audiovisual-resource assignment to construct a coherent percept of the movie. Leveraging behavioral ratings, we found that the frontal cortex is the primary site in this modality assignment process. Together, our findings shed new light on the functional architecture of the frontal cortex underlying flexible multisensory representation and integration in natural contexts.

neuroscience↗

bioRxiv2025

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

neuroscience↗

bioRxiv2025

Functional Implications of the Conformational Landscape of a Multidrug Transporter Revealed by Structures of Zebrafish Abcb4

The hallmark of multidrug resistance conferred by human ABC transporter ABCB1 (hP-gp) is the recognition and efflux of diverse range of drugs, though the precise mechanism of polyspecificity remains unresolved. In aquatic animals such as zebrafish, Abcb4, a functional homolog to hP-gp, plays a vital role in surviving environmental toxicants. Here, we show that Abcb4 exhibits comparable basal and drug-stimulated ATPase activity to hP-gp. Using cryo-EM, we captured five inward-facing Abcb4 conformations with varying separations between its two lobes, illustrating its open-and- close motion. The range of separation exceeds that seen in published P-gp structures that appear to be conformationally restricted. This global open-and-close motion is coupled with individual helix movement, resulting in a highly fluid substrate-binding pocket. These dynamic changes, likely underlying the polyspecificity of substrate recognition, predict unconventional protein-ligand interactions that are supported by structures of Abcb4 bound to the P-gp inhibitors tariquidar and elacridar, and the substrate vincristine.

biochemistry↗

bioRxiv2025

Subcellular Mass Spectrometry Reveals Proteome Remodeling in an Asymmetrically Dividing (Frog) Embryonic Stem Cell

Subcellular proteomics holds the potential to reveal the molecular architecture of cellular processes with unprecedented spatial resolution. Performing these analyses deeply, at the level of hundreds to thousands of proteins in subcellular resolution, is a high and still unmet technical need. Here, we advance microprobe capillary electrophoresis-mass spectrometry (CE-MS) to achieve deep proteomic coverage--quantifying over 1,000 proteins within opposing poles of an asymmetrically dividing embryonic stem cell (blastomere). We integrated CE-electrospray ionization (CE-ESI) with trapped ion mobility spectrometry time-of-flight (timsTOF) MS, implementing data-independent acquisition (DIA) via parallel accumulation-serial fragmentation (diaPASEF). This CE-diaPASEF workflow identified 1,035 proteins from [~]200 pg of proteome digest, equivalent to [~]80% of the HeLa cells content, with high reproducibility (coefficient of variation <15% across technical triplicates). With microprobe sampling, this technology quantified 808 to 1,022 proteins in opposing poles of a dorsal-animal (D1) blastomere in the 8-cell Xenopus laevis embryo. Comparative proteomic analysis of the D1 blastomere and its descendants--the dorsal-animal-midline (D11) and dorsal-animal-lateral (D12) cells--revealed diverse molecular outcomes of asymmetric division: some protein profiles remained conserved, while others underwent significant or even reversed changes as these lineages descended into neural tissue and epidermal trajectories. Ultraviolet light-induced ventralization was performed to help disentangle subcellular gradients from dorsal-ventral patterning. Collectively, this work establishes microprobe CE-diaPASEF as a powerful platform for deep subcellular proteomics, enabling new insights into spatial proteome organization during key developmental processes.

developmental biology↗

bioRxiv2025

ZMS-6659, a selective Polθ helicase inhibitor, demonstrates anti-tumor activity as monotherapy and synergy effect with PARP inhibitors and topoisomerase inhibitors

The repair of DNA double strand break (DSB) is crucial for genome stability. Homologous recombination (HR) is a high-fidelity, error-free DSB repair pathway, and its dysfunction leads to genome instability and can result in tumorigenesis. In HR deficient contexts, microhomology-mediated end joining (MMEJ), dependent on DNA polymerase theta (Pol{theta}), is up-regulated to serve as a backup pathway for DSB repair. Several studies have proved that Pol{theta} inhibition causes synthetic lethality with HR deficiency, and Pol{theta} emerges as a potential DNA damage repair (DDR) target for the treatment of HR deficient tumors both as single agent or in combination with PARP inhibitors (PARPi). In addition, evidences show that the blockage of Pol{theta} can enhance the sensitivity to DNA damage agents. Herein, we characterized ZMS-6659, a highly potent small-molecule Pol{theta} inhibitor targeting the helicase domain. ZMS-6659 induces synthetic lethality in HR deficient models, elicits synergistic effects with even low dose of PARPi and delays PARPi resistance without additional hematotoxicity. Besides, ZMS-6659 also enhance the sensitivity to topoisomerase inhibitors in HR proficient cell lines. These findings support the clinical exploration of ZMS-6659 in combination with PARPi in HR deficient tumors, and suggest a potential application expansion beyond HR deficiency.

cancer biology↗

bioRxiv2025

Live Eco-AI, Electrophoresis-Correlative Data-Dependent Acquisition with Artificial Intelligence-Based Data Processing Democratizes Single-Cell Mass Spectrometry Proteomics

Single-cell mass spectrometry (MS) recently emerged with unprecedented sensitivity to characterize cellular proteomes. Still, limited access to sensitive and fast mass spectrometers impedes its broad adoption, due chiefly to budget constraints. Here, we democratize single-cell MS to a budget-conscious alternative with contemporary performance. A custom-built inexpensive capillary electrophoresis (CE) platform electrophoresis-correlatively (Eco) sorted peptide m/z signals. Detection re-employed a decade-old, slower yet affordable quadrupole-quadrupole-orbitrap (Q-Exactive Plus, QE+, Thermo). Data-dependent acquisition (DDA) with narrow, 1.6-to-4 Th isolation tracked the m/z trends in [~]100% efficiency, outperforming the data-independent reference. The ensuing chimeric spectra were deciphered using artificial intelligence (AI) via CHIMERYS (Thermo). Live Eco-AI augmented our QE+ to [~]15 peptides/spectrum, on par with more advanced analyzers (Lumos and Exploris compared). 1 ng HeLa proteome digest gave 2,142 proteins, topping 969 on a contemporary LC Qq-OT-ion trap reference (Fusion Lumos, Thermo). From [~]250 pg, approximately a cell, 1,799 HeLa-proteins were detected in <15-min-effective electrophoresis. To establish proof of principle, Live Eco-AI was employed to profile 1,524 proteins among differentially fated single stem cells (50-to-75 micrometer diameter) in the Xenopus laevis (frog) blastula. The quantitative data revealed previously unknown proteome reorganization during differentiation into the dorsal and ventral lineages.

cell biology↗

bioRxiv2025

Senescence-induced reparative fibroblasts enable scarless wound healing in aged murine skin.

It has been reported that elderly individuals exhibit reduced scarring during the wound healing process compared to younger adults. However, the underlying mechanisms responsible for this phenomenon remain poorly understood. Here, we revealed that aged mice exhibited more pronounced regenerative outcomes compared to young mice, characterized by increased hair follicle numbers and collagen fiber features closer to normal skin. Single-cell sequencing identified a reparative fibroblast subpopulation (Prss35+Fib) enriched in the aged group, which promotes regeneration through communication with epithelial cells, macrophages, and T cell subpopulations via PTN and EREG signaling. Spatial transcriptomics validated this communication pattern by elucidating cell proximity and locating the regenerative niche in the upper dermis. Finally, EREG treatment significantly enhanced regenerative outcomes in young mice, while the small wound model of aged mice, lacking the reparative fibroblast and EREG signaling, failed to achieve regeneration. Collectively, our findings advance the understanding of regenerative plasticity in aging and provide new insights for designing scarless therapeutic strategies.

cell biology↗

bioRxiv2025

The neurocomputational mechanisms of hierarchical linguistic predictions during narrative comprehension

Language comprehension requires a listener to predict the upcoming inputs of linguistic units with multiple timescales based on previous contexts, but how the prediction process is hierarchically represented and implemented in the human brain remains unclear. Combining the natural language processing (NLP) approach and functional Magnetic Resonance Imaging (fMRI) in a narrative comprehension task, we first applied the group-based general linear model (gGLM) to identify the neural underpinnings associated with the language prediction on word and sentence. Our results revealed a cortical architecture supporting the prediction, extending from the superior temporal cortices to the regions in the default mode network. Then, we investigated how these adjacent levels interact with each other by testing two rival hypotheses: the continuous updating hypothesis posits that the higher level of the representational hierarchy is continuously updated as inputs unfold over time, while the sparse updating hypothesis states that the higher level is only updated at the end of their preferred timescales of linguistic units. By conducting computational modeling and autocorrelation analysis, we found the sparse model outperformed the continuous model and the updating might occur at the sentence boundaries. Together, our results extend the linguistic prediction from the small timescales such as words to large timescales such as sentences, providing novel insights into the neurocomputational mechanisms of information updating within the linguistic prediction hierarchy.

neuroscience↗

bioRxiv2025

Benchmarking AlphaFold3-like Methods for Protein-Peptide Complex Prediction

Protein-short peptide interactions are central mechanisms regulating signal transduction, chaperone functions, and drug targeting design. However, the precise prediction of their complex structures has long been constrained by the limited accuracy of traditional methods. The breakthrough of AlphaFold2 resolved the challenge of predicting protein monomer structures. Its derivative method, AlphaFold2-multimer, surpassed traditional docking approaches in predicting protein complexes, yet its accuracy remains suboptimal. AlphaFold3 and its replication models, such as Protenix, Chai-1, and Boltz-1, have further improved prediction accuracy for protein-protein interactions. To evaluate the performance of these methods in protein-short peptide structure prediction, this study conducted predictions and comprehensive comparisons on two benchmark datasets of protein-short peptide complexes. One dataset did not intentionally exclude structures from the training set of these methods, while the other did. For the former, the results demonstrate that the new-generation structural method significantly increased the success rate under stringent criteria (Fnat[≥] 0.8) to 70-80%, compared to AlphaFold2-multimer (53%), with Protenix achieving the highest accuracy at 80.8%. For the second dataset, AlphaFold2-multimer showed consistent performance, but the success rates of the new-generation methods all dropped significantly (to 40-56%). Our analysis suggests that the main challenge for the performance decline comes from the accurate prediction of binding sites. For all datasets, A multi-method combination strategy enabled a higher-quality prediction success rate compared to the individual models, highlighting synergistic advantages among methods. Notably, inter-model consistency analysis provided an efficient metric for selecting optimal predictions.

bioinformatics↗

bioRxiv2025

Robust and Adaptive Non-Parametric Tests for Detecting General Distributional Shifts in Gene Expression

Differential expression analysis is crucial in genomics, yet existing methods primarily focus on detecting mean shifts. Variance shifts in gene expression are well-documented in studies of cellular signaling pathways, and more recently they have characterized aging, thus motivating the need for flexible detection approaches that include tests of expression variance changes. In this work, we present QRscore (Quantile Rank Score), a general method for detecting distributional shifts in gene expression by extending the Mann-Whitney test into a flexible family of rank-based tests. Here, we focus on implementing QRscore to detect shifts in mean and variance in gene expression, using weights designed from negative binomial (NB) and zero-inflated negative binomial (ZINB) models to combine the strengths of parametric and non-parametric approaches. We show through simulations that QRscore not only achieves high statistical power while controlling the false discovery rate (FDR), but also outperforms existing methods in detecting variance shifts and mean shifts. Applying QRscore to bulk RNA-seq data from the Genotype-Tissue Expression (GTEx) project, we identified numerous differentially dispersed genes and differentially expressed genes across 33 tissues. Notably, many genes have significant variance shifts but non-significant mean shifts. QRscore augments the genome bioinformatics toolkit by offering a powerful and flexible approach for differential expression analysis. QRscore is available in R, at https://github.com/songlab-cal/QRscore.

bioinformatics↗

bioRxiv2025

A versatile miniature two-photon microscope enabling multicolor deep-brain imaging

Here we present the FHIRM-TPM 3.0, a 2.6 g miniature two-photon microscope capable of multicolor deep-brain imaging in freely behaving mice. The system was integrated with a broadband anti-resonant hollow-core fiber featuring low transmission loss, minimal dispersion from 700-1060 nm, and high tolerance of laser power. By correcting chromatic and spherical aberrations and optimizing the fluorescence collection aperture, we achieved cortical neuronal imaging at depths exceeding 820 m and, using a GRIN lens, hippocampal Ca2+ imaging at single dendritic spine resolution. Moreover, we engineered three interchangeable parfocal objectives, allowing for a tenfold scalable field-of-view up to 1x0.8 mm{superscript 2}, with lateral resolutions ranging from 0.68 to 1.46 m. By multicolor imaging at excitation wavelengths of 780 nm, 920 nm and 1030 nm, we investigated mitochondrial and cytosolic Ca2+ activities relative to the deposition of amyloid plaques in the cortex of awake APP/PS1 transgenic mice. Thus, the FHIRM-TPM 3.0 provides a versatile imaging system suitable for diverse brain imaging scenarios.

neuroscience↗

bioRxiv2025

Posttranscriptional activity of the eukaryotic translation initiation factoreIF4E contributes to HoxA9-driven leukemogenesis

HoxA9, a homeodomain-containing transcription factor, is mis-expressed in over half of acute myeloid leukemia (AML) cases, and is associated with poor prognosis. Previous studies indicated that HoxA9 binds to the eukaryotic translation initiation factor eIF4E in primary specimens and that HoxA9 stimulated the RNA export and translation efficiency of selected RNAs via eIF4E. However, the relevance of this to its leukemogenic transformation capacity was unknown. Here, we used a double point mutation (HoxA9AA) to disrupt the physical and functional interaction between eIF4E and HoxA9 while retaining the HoxA9 transcriptional signature. Surprisingly, the mutation dramatically increased AML latency from a median of 90 to 280 days and resulted in incomplete penetrance. Re-transplantation of bone marrow cells from leukemic animals demonstrated even more pronounced differences in disease kinetics and penetrance with all animals succumbing to disease by day 60 in the wildtype group, while some HoxA9AA mice never developed leukemia. Collectively, these findings uncover a novel, transcription-independent mechanism of HoxA9-driven leukemogenesis through eIF4E and positions eIF4E as a potential therapeutic target AML patients expressing high levels of HoxA9. Key PointsO_LIA double point mutation in HoxA9 disrupted the physical and functional interaction between eIF4E and HoxA9 while retaining the HoxA9 transcriptional signature. C_LIO_LIEukaryotic translation initiation factor eIF4E contributes to HoxA9-driven leukemogenesis and is important for the maintenance of acute myeloid leukemia. C_LI

molecular biology↗

bioRxiv2025

High-pressure Golgi neuronal staining for X-ray whole-brain imaging

Whole-brain imaging has revolutionized neuroscience research, providing comprehensive insights into neural networks across the entire brain. This powerful approach has greatly advanced our understanding of brain functions and the mechanisms underlying various diseases. One primary challenge in whole-brain imaging technology is to achieve high-resolution observation of neural networks at large scales. Although Golgi method allows labeling random neurons in their entirety in the brain, visualizing individual dendritic trees, and tracing long-distance axonal projections, the lengthy processing time pose a limit on its use, i.e., staining a mouse whole-brain sample of just 300 mm3 takes over two weeks. Here, we developed a rapid staining technique for whole-brain neurons using high-pressure assisted Golgi (HP Golgi). This method significantly reduced the staining time for mouse whole-brain neurons from 16 days to only 4 days. We demonstrated the broad applicability of the HP Golgi method across various model organisms, achieving whole-brain neuronal staining in zebrafish, mice, and rats. Further, we successfully performed rapid staining of hippocampal neurons in an intact pig brain, which is difficult to achieve with the classic Golgi-Cox method. We also demonstrated that the combination of the HP Golgi method with synchrotron-based X-ray microscopy for high-resolution imaging of whole-brain neurons in mice. This HP Golgi method enables fast and high-resolution neuronal imaging in large model organisms, showcasing its broad applicability for diverse applications.

biophysics↗

bioRxiv2024

Dissociable neurofunctional and molecular characterizations of reward and punishment sensitivity

While the hyper-and hypo-reward or punishment sensitivities (RS, PS) have received considerable attention as prominent transdiagnostic features of psychopathology, the lack of an overarching neurobiological characterization currently limits their early identifications and neuromodulations. Here we combined microarray data from the Allen Human Brain Atlas with a multimodal fMRI approach to uncover the neurobiological signatures of RS and PS in a discovery-replication design (N=655 participants). Both RS and PS were mapped separately in the brain, with the intrinsic functional connectome in the fronto-striatal network encoding reward responsiveness, while the fronto-insular system was particularly engaged in punishment sensitivity. This dissociable functional connectome patterns related to RS and PS were also specific in differentiating decisions driven by social or monetary reward and punishment motivations. Further imaging transcriptomic analyses revealed that functional connectome variations for RS and PS were associated with topography of specific gene sets enriched in ontological pathways, including synaptic transmission, dopaminergic metabolism, immune response and stress adaptation. On the neurotransmitter level, the serotonin neuromodulator was identified as a pivotal hub regulating the intrinsic functional connectome patterns of RS and PS, with this process critically dependent on its interactions with dopaminergic, opioid and GABAergic systems. Overall, these findings indicate dissociable neural connectome mapping of RS and PS and highlight their linkage with transcriptomic profiles, which may offer valuable insights into the treatment evaluation for symptomatology relevant to reward/punishment processing deficits.

neuroscience↗

bioRxiv2024

Versatile Stain Transfer in Histopathology Using a Unified Diffusion Framework

Histological staining is vital in clinical pathology for visualizing tissue structures. However, these techniques are laborious and time-consuming. Digital virtual staining offers a promising solution, but existing methods typically rely on Generative Adversarial Networks (GANs), which may suffer from artifacts and mode collapse. Motivated by the success of diffusion models, we present DUST, a novel Diffusion-based Unified framework for versatile Stain Transfer in histopathology. To enhance domain awareness and task-specific performance, we propose a dual encoding strategy that integrates the stain types of both the source and target domains. Additionally, we introduce a dynamic dual-output head to address the unstable intensity issue encountered with conventional DDPM implementations. Validated on a curated fourstain kidney histopathological dataset (H&E, MT, PAS, and PASM), DUST demonstrates superior versatile stain transfer capabilities. Our research highlights the potential of diffusion models to advance virtual staining, paving the way for more efficient digital pathology analyses.

pathology↗