Biology subjects

Schmidt, F.

Publications and source records attributed to Schmidt, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Non-invasive in vivo lactate monitoring via NIR spectroscopy

We present a non-invasive approach for continuous monitoring of lactate dynamics in-vivo using near-infrared (NIR) spectroscopy. Lactate-related spectral features were measured non-invasively within the overtone region (1600-1850 nm). Several anatomical measurement sites were evaluated, and the middle phalanx of the dorsal finger emerged as the most promising location due to its superior spectral quality and stable tissue perfusion, becoming the exclusive site for all further experiments. Across multiple exercise sessions, predictive models achieved high within-day accuracy (R2[≥] 0.8), while cross-day performance was affected by spectral drift and physiological variability. A dynamic offset-correction procedure effectively mitigated these baseline shifts, enabling stable prediction accuracy across days, weeks, and subjects. These findings demonstrate the feasibility of NIR-based lactate estimation and highlight the importance of adaptive correction strategies for reliable long-term, non-invasive monitoring.

biophysics↗

bioRxiv2026

Comprehensive Profiling of Age- and Immune Cell- Specific Signaling Activation Using Multiplex Phosphoflow

Immune phenotyping represents a pillar in diagnostics, characterization of new genetic defects, and understanding mechanisms of diseases. Cell population distribution often does not cover the intrinsic function changes that may contribute to disease. Outcome of signaling activation can be used as proxy for cell function. To overcome the limitation of sample availability and standardization of signaling assays, we developed a multiplex full spectrum cytometry phosphoflow assay allowing the study of 6 phospho-proteins representing BCR/TCR, MAPK, PI3K/Akt/mTOR and canonical NF-{kappa}B signaling pathways in 18 immune cell subpopulations. Maximal stimulation and temporal dynamics were studied in response to pan-stimuli, activating cells regardless of receptor, and targeted stimuli for T, B, and innate immune cells. We studied healthy individuals between 1-69 years and discovered subpopulations-specific responses. Furthermore, pediatric donors showed broad differences in B cell and T cell function compared to adults. Hence, we established a tool to assess multiple signaling pathways at once and provide age- and subpopulation-specific references for signaling outcome. SummaryMultiplex full spectrum flow cytometry-based phosphoflow assay across 18 immune cell subpopulations, 6 phospho-proteins in response to 6 stimuli at 4 time points in individuals aged 1-69 years, reveals distinct age- and subpopulation-associated signaling patterns in magnitude and dynamics of pathways activation.

immunology↗

bioRxiv2026

Accelerated neural aging as a systems-level vulnerability for tinnitus

Tinnitus, the perception of sound without an external source, affects 10-15% of individuals, yet its neural mechanisms remain poorly understood. Building on evidence that chronological age predicts tinnitus risk beyond hearing loss, we tested the hypothesis that accelerated neural aging increases susceptibility to tinnitus by integrating cross-sectional resting-state MEG with longitudinal structural imaging data. In a large MEG dataset, spectral parametrization revealed that age-neural relationships were amplified in the tinnitus group compared to age-, sex- & hearing-matched controls. Complementing these findings, prospective analyses using data from the UK Biobank showed that among participants without tinnitus at baseline, stronger age-related declines in white-matter density predicted later tinnitus onset. Collectively, these converging functional and structural findings support accelerated brain aging as a key risk factor for tinnitus.

neuroscience↗

bioRxiv2026

Brain-body timing gates conscious access across sensory systems

Conscious awareness fluctuates with the brains internal state at the moment of perception, yet how these states are temporally coordinated remains unclear. Although such internal states are reflected in ongoing neural activity, their temporal structure may arise from intrinsic interactions between the brain and the body. To test this hypothesis, we simultaneously recorded neural, cardiac, and respiratory activity while participants detected auditory, tactile, or visual stimuli presented at perceptual threshold. Across sensory modalities, stimulus detection was preceded by transient bursts of alpha-band (8-13 Hz) activity localized to lateralized prefrontal and premotor regions. Crucially, these neural events were aligned with specific phases of the cardiac and respiratory cycles, revealing coordinated brain-body states at the time of perception. This cross-system coupling predicted whether sensory stimuli reached conscious awareness. Together, these findings show that brain-body synchrony acts as a temporal gate for conscious access across sensory systems.

neuroscience↗

bioRxiv2026

How we draw and recognize things that don't exist

How do we make sense of something weve never seen before? Classifying objects into superordinate classes like animal is a key step in interpreting novel experiences, but is challenging because radically different items (e.g., octopus, rabbit) must somehow be grouped together. In general, no single feature is shared by all members. We reasoned that to classify or imagine novel items from outside the distribution of previous experiences, observers parse objects into meaningful component features that they can mentally recombine ( compositionality). To test this, we asked participants to draw familiar and novel members of nine superordinate object classes. We then asked other participants to classify the drawings, and mark and label their defining parts. We find that human classification performance is well predicted by a Bayesian classifier that optimally combines the part labels, suggesting humans can create and classify out-of-distribution experiences through a compositional generative representation of object features.

neuroscience↗

bioRxiv2026

Membrane progesterone receptor signaling reverses hyperglycemia and insulin resistance in obese mice

Progesterone (P4) plays key roles in reproductive and metabolic function and signals through two receptor classes: classical nuclear receptors that regulate gene transcription and membrane progesterone receptors (mPR) that mediate rapid, non-genomic signaling. Whether mPR signaling influences systemic glucose homeostasis remains unclear. Here, we investigated whether mPR activation regulates glucose homeostasis and insulin sensitivity. Using the selective mPR agonist OD02-0, we show that mPR activation enhances glucose uptake in skeletal muscle and hepatocytes, associated with AMP-activated protein kinase (AMPK) activation. In HepG2 cells, mPR activation induces metabolic reprogramming characterized by reduced mitochondrial respiration and increased glycolytic flux. Pharmacological inhibition of AMPK suppresses this effect, indicating that these responses require AMPK activity. In diet-induced obese mice, chronic mPR activation reduces fasting glucose and insulin levels, improves glucose tolerance, and restores glucose-stimulated insulin secretion without detectable toxicity. Integrated proteomic and phosphoproteomic analyses in mouse liver reveal modulation of AMPK signaling and inhibition of mTORC1. Transcriptomic changes were limited, supporting a predominantly non-genomic mode of action. Together, these findings identify mPR signaling as a regulator of glucose homeostasis that engages central energy-sensing pathways to improve metabolic control in obesity.

physiology↗

bioRxiv2026

Eye movements reveal the intuitive understanding of materials and their behavior

Does a metal ball sink or float when dropped into water? Answering this intuitively simple question relies on our understanding of materials and their physics1. Knowing materials helps us to recognize objects, estimate their properties, and predict how they will behave when interacting with other objects or materials2,3. This leads to expectations about material behaviors already in young infants4,5, for example, that a metal ball should sink in water. Typically, properties and affordances of materials have been studied with perceptual tasks. But even though explicit perceptual judgments allow us to identify important determinants of material understanding, it is not clear how they relate to dynamic predictions of material behavior -- which we need for successful interactions with objects3. Here, we tap into material understanding by leveraging anticipatory eye movements, which have been shown to be direct readouts of trajectory predictions6 and can also reflect expectations about material properties such as elasticity or mass7. This allows us to reveal the temporal evolution of dynamic predictions based on material properties. Furthermore, by using novel stimuli that violate intuitive expectations about material behavior, we demonstrate that even though dynamic predictions can be learned over time, there remains a fundamental difference between predictions in line with and against our intuitive expectations.

neuroscience↗

bioRxiv2026

One-step generation of TCR knock-in mice targeted to the TCRβ locus results in functional mature T lymphocytes

Transgenic mouse models expressing predefined T cell receptors (TCRs) have been instrumental in advancing our understanding of T cell biology. However, these traditional models rely on random genomic insertion of large constructs, require labor-intensive embryo manipulation, and frequently result in aberrant TCR expression and phenotypes. These limitations render TCR transgenic models insufficient to meet the mounting demands for rapid and precise model systems to evaluate TCR specificities. To address these challenges, we developed a streamlined method that combines Adeno-Associated Virus (AAV), coupled with CRISPR/Cas9 genome editing to precisely integrate pre-rearranged TCR/{beta} sequences into the mouse Trb locus, enabling the rapid generation of first-of-its-kind TCR knock-in mice with physiological TCR expression and functional T cell differentiation. This approach bypasses the need for technically advanced embryo manipulation and enables rapid generation of models through a universally optimized AAV vector system, significantly enhancing the versatility and utility of monoclonal TCR mice in basic immunology and preclinical research such as cancer immunotherapy and vaccine development, providing a transformative resource to accelerate discovery and translation across disciplines.

immunology↗

bioRxiv2025

Human and generative AI integrate visual cues differently in a shape completion task

In amodal completion observers perceive complete objects despite partial occlusion. When two object parts are divided by an occluder, completion can result in perceiving one or two objects. This phenomenon involves both lower-level cues (e.g., symmetry, contour continuity) and higher-level cues (e.g., prior knowledge). Experiment 1 investigates how occluder size, familiarity, and symmetry affect human completions using a drawing task. Narrow occluders and asymmetry promote single-shape completions, while familiarity and (global) symmetry promote two-shape interpretations. Good continuation emerges as the strongest cue, with symmetry and familiarity playing increasingly important roles as occluder width increases. Experiment 2 compares human performance with three state-of-the-art generative AI models. Models often generated creative but non-compliant outputs, altering even unoccluded regions. We restricted analysis to instruction-following generations, identified through ratings by naive observers. Among compliant outputs, models showed some human-like biases (e.g., more two-shape completions for wide occluders), but failed with higher-level cues. They did not use symmetry to guide completions and showed reversed familiarity effects. Our findings highlight differences between human and AI completions. Humans integrate low- and high-level cues, whereas compliant outputs from the AI models rely primarily on low-level pattern continuation. Current AI models lack the flexible integration of multiple representational levels that characterize human perception. This work establishes an analytical framework for evaluating whether next-generation models achieve more human-like visual reasoning. HighlightsO_LIHumans see one or two objects behind occluders using geometric and semantic cues. C_LIO_LIHuman drawings and generative AI completions show how different cues modulate perception C_LIO_LIGood continuation dominates human and AI shape completions C_LIO_LISymmetry and familiarity guide humans, but not instruction-following AI outputs C_LIO_LIHumans integrate multiple levels; instruction-following AI engages lower-level-processing. C_LI

neuroscience↗

bioRxiv2025

Connecting the dots - Recognition of artificial and natural shapes relies on representing points of high information

Physiological and psychophysical evidence suggests that the visual system represents object outlines using prominent curvature features, particularly regions of extreme curvature (convex maxima and concave minima). These curvature extrema often coincide with points of high informational content ("surprisal"), but this relationship is only correlational. It remains unclear whether the visual system explicitly encodes curvature extrema or instead prioritizes the most informative contour locations. To address this, we conducted two shape-matching experiments comparing the roles of curvature extrema and surprisal in shape representation. Observers performed match-to-sample tasks in which smooth reference shapes were matched to simplified polygonal versions created by connecting subsets of contour points corresponding to (i) curvature maxima, (ii) curvature maxima and minima, or (iii) points of highest surprisal. Stimuli included artificial shapes composed of compound radial frequency patterns and natural shapes (animal outlines), the latter allowing us to dissociate curvature and information by restricting sampled points. Performance was higher for natural than artificial shapes (95% vs. [~]86%). Shapes defined by a small number of high-surprisal points matched performance in baseline and curvature maxima and minima conditions, but exceeded performance for curvature maxima alone ([~]90% vs. [~]65%). In a second experiment, we contrasted surprisal with curvature maxima and minima conditions while varying the number of sampled points (4-32). Performance increased with point number, approaching [~]90%. Critically, under strong simplification (4-6 points), surprisal-based shapes yielded higher accuracy than curvature-based shapes. These findings suggest that shape representation emphasizes features with high informational content rather than curvature extrema per se.

neuroscience↗

bioRxiv2025

Assessing Scale and Predictive Diversity in Models for Single-Cell Transcriptomics based on Geneformer

Foundation models are increasingly applied to single-cell transcriptomics, where they promise to capture generalizable representations that support diverse downstream analyzes. However, two central questions remain: Does scaling pre-training data reliably improve performance, and do models trained on rank-ordered expression profiles confer advantages for mitigating batch effects? We addressed these questions by systematically assessing transformer-based models pre-trained on ranked single-cell profiles with varying data scales. The models were evaluated on masked gene prediction and downstream tasks, including cell type classification, perturbation response prediction, and zero-shot batch integration. To complement prediction accuracy, we further quantified prediction repetition, uniqueness, and diversity. In addition, we evaluated architectural refinements that incorporate cumulative prediction adjustment and similarity-based regularization; however, the study mainly focused on comparative evaluations rather than the development of new models. Our results indicated that scaling pretraining corpora improved masked prediction accuracy but did not consistently enhance downstream performance. Smaller models often matched or exceeded larger ones, indicating diminishing returns relative to scale. Rank-based models offered limited robustness to batch effects and consistently underperformed relative to domain-specific correction methods. Across all scales, high redundancy in predicted genes remains a major limitation. Together, these findings challenge assumptions that larger datasets or rank-order modeling automatically confer stronger generalization. Progress in single-cell foundation models may depend less on scale and more on pre-training objectives that enhance predictive diversity and biological plausibility. Author summaryIn recent years, large machine learning models have been adapted to study single cells, raising hopes that simply training on more data would automatically lead to better biological insights. Our study aimed to test this assumption by carefully evaluating models trained on small versus very large collections of single-cell data. We also asked whether models that learn from ranked lists of gene expressions offer an advantage in correcting technical differences between experiments, a common challenge in biology. We found that bigger is not always better. Although larger models improved performance in predicting missing genes, this advantage did not consistently translate to stronger results in tasks such as cell type classification or data integration across studies. In fact, smaller models often matched or outperformed their larger counterparts. Models trained on ranked data were also not sufficient to reliably correct batch effects, and traditional tools still worked better for this purpose. Our findings suggest that future progress may come less from ever-larger models and more from rethinking how models learn from biological data. This work provides guidance for researchers designing new approaches and helps clarify realistic expectations for applying artificial intelligence to single-cell biology.

bioinformatics↗

bioRxiv2025

Ocular speech tracking persists in blindness, but its dynamics and oculo-cerebral connectivity depend on visual status

While eye movements have been shown to track the speech envelope, it is unknown whether this reflects a hard-wired mechanism or one shaped by (lifetime) audiovisual experience. Further, questions remain about whether ocular tracking is modulated by speech intelligibility and which brain regions drive these synchronized eye movements. Here, we investigate ocular speech tracking in blindfolded early blind, late blind, and sighted individuals using magnetoencephalography (MEG) and source-reconstructed oculomotor signals while participants listened to narrative speech of varying intelligibility. We found that oculomotor activity tracks acoustic speech features and, unlike neural speech tracking, is not modulated by intelligibility. Interestingly, we found effects reflected in two frequency-specific components: a low-frequency ([~]1 Hz) effect present across all groups, indicating that visual experience is not required, and a high-frequency ([~]6 Hz) effect reduced in early- and late-blind individuals. Moreover, this finding is not driven by cerebro-ocular connectivity, as late-blind individuals exhibit stronger connectivity between the eyes and the left temporal cortices without a corresponding increase in ocular tracking. In conclusion, ocular speech tracking seems to respond selectively to acoustic but not to intelligibility features of speech, and it does not require visual experience to develop. It may thus represent a hard-wired oculomotor mechanism within the oculo-cerebral network involved in speech processing. Significance StatementEye movements provide a unique window into the interaction between auditory and visual systems. By studying early blind, late blind, and sighted individuals, we demonstrate that speech-related eye movements arise from at least two distinct mechanisms: a low-frequency component that occurs independently of (lifetime) visual experience and is linked to processing of acoustic speech features, and a high-frequency component shaped by prior visual exposure. Importantly, speech intelligibility - unlike its impact on neural measures - does not modulate these ocular responses. This dissociation suggests that eye movements reflect mechanisms of spoken language processing that are independent of intelligibility, thereby revealing novel pathways of auditory-motor coupling and broadening our understanding of sensory integration in the absence of vision.

neuroscience↗

bioRxiv2025

Inflexible center-frequency shifts in neural speech tracking are linked to reading and phonological deficits in developmental dyslexia

Developmental dyslexia is marked by persistent deficits in reading and phonological awareness, potentially linked to atypical neural tracking of acoustic speech rhythms at frequencies below 10 Hz. Using magnetoencephalography, we compared adults with developmental dyslexia and matched controls while they listened to speech with varying levels of intelligibility (manipulated via noise vocoding). In controls, decreasing intelligibility led to systematic increases in the tracked frequency of the speech stream, replicating previous findings. This increase in tracking frequency was largely absent in individuals with developmental dyslexia. Unexpectedly, the magnitude of periodic speech tracking in the low frequency range was preserved in DD. Instead, the increase in central tracking frequency was linked to phonological awareness and reading performance.

neuroscience↗

bioRxiv2025

Robust CRISPR Screens Identify TPL1 as a Novel Long Noncoding RNA Driving Triple-Negative Breast Cancer Hallmarks

Despite the growing catalog of long noncoding RNAs (lncRNAs), the functional roles of their vast majority in cancer remain poorly defined. To systematically explore lncRNA dependencies in triple-negative breast cancer (TNBC), we compiled a comprehensive annotation by merging GENCODE, BIGTranscriptome, and MiTranscriptome databases and performed a CRISPR-Cas9 deletion screen targeting 1,029 TNBC-enriched lncRNAs. The screen revealed several essential lncRNAs and those modulating doxorubicin sensitivity, with TPL1 emerging among top hits. TPL1 silencing significantly impaired TNBC cell proliferation in both 2D and 3D cultures and reduced invasive capacity in an organ-on-chip model. Transcriptomic and proteomic profiling following TPL1 knockdown revealed downregulation of genes involved in ECM-receptor interaction, focal adhesion, cell migration, and PI3K-Akt signaling. Mechanistically, TPL1 directly interacted with key proteins including EIF4B, MDM2, TARBP2, TLE5, and GTPase RAN, suggesting TPL1 could regulate RNA processing, transcriptional repression, and translation, as well as modulate GTPase signaling pathways. Additionally, TPL1 functioned as a competing endogenous RNA (ceRNA), sequestering miR-10396b-5p, miR-486-3p, and miR-450a-2-3p, among others, thereby modulating expression of pro-tumorigenic targets. Clinically, TPL1 was significantly overexpressed in TNBC tissues, particularly in the BLIS subtype. Collectively, our findings highlight TPL1 as a key regulator of TNBC molecular networks and a promising therapeutic target.

cancer biology↗

bioRxiv2025

Establishment of menthol bleaching protocols for six stony coral species

The mutualistic symbiosis between stony corals and unicellular algae of the family Symbiodiniaceae forms the base of coral reef ecosystems. However, anthropogenic stressors, such as rising seawater temperatures, cause a breakdown of the coral-algal symbiosis, so-called coral bleaching, which leads to mass mortalities and a rapid loss of coral reefs. To functionally disassemble the coral-algal symbiosis, corals have been artificially rendered aposymbiotic using temperature stress, DCMU, or menthol. As menthol has proven to be an efficient and gentle bleaching agent, we tested four menthol treatments with six commonly investigated stony coral species. The overarching aim was to establish a broadly efficient bleaching protocol as a guide for future investigations. Menthol-induced bleaching was traced with chlorophyll fluorescence and tissue color analyses over time and confirmed as final symbiont cell density two weeks after the last day of menthol treatment. Here we found that the coral species varied greatly in their menthol bleaching tolerance, underlining the importance of establishing bespoke bleaching approaches. Acropora muricata and Stylophora pistillata were efficiently bleached within two days of menthol exposure with symbiont cell reductions of 94 % to 98 %. For Galaxea fascicularis, Montipora digitata and Porites cylindrica, six days of menthol exposure proved most successful in reducing symbiont density by 92 to 97 %. While these coral species suffered no mortality, fragments of Pocillopora verrucosa died or suffered severe necrosis in most of the protocols, making the tested menthol treatments unsuitable for this species. We demonstrate that repeated menthol treatment at low concentrations renders most coral species aposymbiotic within few days without visual or physiological damage. Our study, therefore, provides a guideline for efficient and customized application of menthol bleaching treatments for future coral symbiosis research.

systems biology↗

bioRxiv2025

NAD+ boosting by oral nicotinamide mononucleotide administration regulates key metabolic and immune pathways through SIRT1 dependent and independent mechanisms to mitigate diet-induced obesity and dyslipidemia in mice.

Sirtuins are NAD+-dependent histone deacetylases that play a key role in metabolism. Sirtuin activity is compromised in aging and metabolic disorders, and pharmacological strategies that promote sirtuin function including NAD+ boosting approaches show potential as therapeutics. To study the impact of nicotinamide mononucleotide (NMN) supplementation in mice in high fat diet (HFD) induced obesity and the role of SIRT1, a sirtuin family member, in mediating the NMN response, we administered NMN to mice in drinking water to boost NAD+ in control and in inducible SIRT1 knock-out mouse models and performed a combination of metabolic phenotyping, lipid profiling and plasma proteomics in these mice. We discovered that supplementation with NMN mitigated diet induced weight gain by enhancing energy expenditure, corrected dyslipidemia, and reversed perturbations in fasting blood glucose, all in a SIRT1-dependent manner. On the other hand, NMN-induced reductions in fat mass, fluid mass, eWAT and mesenteric WAT were SIRT1 independent. Proteomic approaches in plasma samples using O-Link and mass-spectrometry provided novel insights into key obesity- and NMN-dependent changes in circulating molecules with potential relevance to inflammation, liver function, and dyslipidemia. We discovered SIRT1 dependent and independent alterations in key circulating plasma proteins and identified key metabolic and molecular pathways that were significantly affected by HFD, several of which were reverted by oral NMN administration. Glucose metabolism, cholesterol metabolism and immune-related pathways are among the most significantly affected changes. Causal analysis of proteomic data suggests that observed effects could be mediated by transcription regulators FBXW7, ADIPOR2 and PRDM16. Collectively, our data supports the hypothesis that promoting SIRT1 function by boosting NAD+ levels in vivo may be a useful strategy to mitigate obesity and associated cardiovascular complications such as dyslipidemia.

physiology↗

bioRxiv2025

Convergent evolution of a fungal effector enabling phagosome membrane penetration

The ability of pathogens to evade phagosomal killing is critical for their pathogenicity. Previously, we had identified the HscA effector protein in the clinically important fungal pathogen Aspergillus fumigatus, which redirects conidia-containing phagosomes from the degradative to the non-degradative pathway. Here, we discovered a pathogenic form of this surface protein, determined by a single tyrosine residue (Y) at position 596, which is lacking in most fungi analyzed, that have a leucine (L) instead. Y596 enables HscA to penetrate the phagosomal membrane. In line, the introduction of a single L- to-Y exchange in the orthologous Ssb protein of Saccharomyces cerevisiae enabled the protein to penetrate phagosomal membranes that was reduced by deletion of one of the two Y-encoding SSB genes in the pathogenic fungus Candida glabrata. These data suggest a convergent evolution of HscA/Ssb proteins among human-pathogenic fungi and that a single amino acid exchange determines a virulence factor. HighlightsO_LIHscA with Y596 penetrates host cell membranes and causes phagosomal damage C_LIO_LIMost fungi (96%) have a leucine at the respective position C_LIO_LIAn L-to-Y mutation in Ssb enables S. cerevisiae to damage phagosomes C_LIO_LIRecruitment of ESCRT complex to the phagosomal membrane requires human p11 protein C_LI

microbiology↗

bioRxiv2025

c-Rel drives pancreatic cancer metastasis through Fibronectin-Integrin signaling-induced isolation stress resistance and EMT activation

Pancreatic ductal adenocarcinoma remains one of the deadliest malignancies, with limited treatment options and a high recurrence rate. Recurrence happens often with metastasis, for which cancer cells must adapt to isolation stress to successfully colonize distant organs. While the fibronectin-integrin axis has been implicated in this adaptation, its regulatory mechanisms require further elaboration. Here, we identify c-Rel as an oncogenic driver in PDAC, promoting epithelial-to-mesenchymal transition (EMT) plasticity, extracellular matrix (ECM) remodeling, and resistance to isolation stress. Mechanistically, c-Rel directly regulates fibronectin (Fn1) and CD61 (itgb3) transcription, enhancing cellular plasticity and survival under anchorage-independent conditions. Fibronectin is not essential for EMT, but its absence significantly impairs metastatic colonization, highlighting a tumor-autonomous role for FN1 in isolation stress adaptation. These findings establish c-Rel as a key regulator of PDAC metastasis by controlling circulating tumor cell (CTC) niche and survival, suggesting that targeting the c-Rel-fibronectin-integrin axis could provide new therapeutic strategies to mitigate disease progression and recurrence.

cancer biology↗