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At least 19 recordsLinked to original sources

PubMed2021

Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma.

Little is known about the transcriptomic plasticity and adaptive mechanisms of circulating tumor cells (CTCs) during hematogeneous dissemination. Here we interrogate the transcriptome of 113 single CTCs from 4 different vascular sites, including hepatic vein (HV), peripheral artery (PA), peripheral vein (PV) and portal vein (PoV) using single-cell full-length RNA sequencing in hepatocellular carcinoma (HCC) patients. We reveal that the transcriptional dynamics of CTCs were associated with stress response, cell cycle and immune-evasion signaling during hematogeneous transportation. Besides, we identify chemokine CCL5 as an important mediator for CTC immune evasion. Mechanistically, overexpression of CCL5 in CTCs is transcriptionally regulated by p38-MAX signaling, which recruites regulatory T cells (Tregs) to facilitate immune escape and metastatic seeding of CTCs. Collectively, our results reveal a previously unappreciated spatial heterogeneity and an immune-escape mechanism of CTC, which may aid in designing new anti-metastasis therapeutic strategies in HCC.

Aged↗

PubMedDate not supplied

[Spatially heterogeneous distribution of phytoplankton in a model of a regulated population].

With the help of the mathematical model is shown that the existence of the phitoplankton's mechanism of growth-speed regulation by means of excretion to the environment of biologically active substances, may lead to a considerable inhomogenity of it's spatial distribution, that is, patchness. There appears an intensive crowding of phitoplankton with homogenious distribution of nutrients, while some limitations of excretion rate and of dispersion coefficients are taken into account.

Mathematics↗

PubMed2025

Deep Learning on Histologic Slides Accurately Predicts Consensus Molecular Subtypes and Spatial Heterogeneity in Colon Cancer.

Colon cancer (CC) is the third most prevalent cancer type. It is highly heterogeneous, particularly in terms of molecular profiles, which have both prognostic and predictive impacts on the treatment efficacy. However, CC treatment in adjuvant situations is currently guided solely by T and N staging. In this context, consensus molecular subtypes (CMSs) were introduced to stratify patients with CC based on molecular profiles. Recent studies have shown that CMS can be heterogeneous in CC, leading to a worse prognosis. This study focused on predicting CMS and its heterogeneity in CC using deep learning on digitized hematoxylin and eosin ± saffron-stained whole-slide images. Data and whole-slide images of 1996 patients from the PETACC-8, The Cancer Genome Atlas-COAD, and PRODIGE-13 cohorts were used. The model is trained to predict a 4-dimensional CMS vector, reflecting intratumor heterogeneity (ITH). It comprises a self-supervised model for embedding image patches into vectors and a weakly supervised model predicting CMS calls. Ground-truth CMS scores are obtained with the CMSclassifier package. Interpretability analyses are performed at the slide and patch levels. For homogeneous tumors, the model trained on PETACC-8 achieves 93.0% (±1.4%) macroaverage area under the curve in internal cross-validation and 94.4% macroaverage area under the curve in external validation over PRODIGE-13, whereas the The Cancer Genome Atlas-COAD model reaches 85.4% (±3.0%) in cross-validation and 92.4% over PRODIGE-13. The trained models also provide spatial distributions of CMS across tumor slides and associate specific histologic features with each CMS. Finally, the models are able to predict ITH. The results show that a deep learning model trained on routine histology slides is capable of providing an efficient and robust method for predicting CMS and characterizing a patient's ITH, paving the way for the routine consideration of CMS/ITH in clinical decision making in the adjuvant setting.

Humans↗

PubMed2026

Integrated single-cell and spatial transcriptomic analyses reveal malignant epithelial glycolytic heterogeneity and spatial niche remodeling during colorectal cancer progression.

Colorectal cancer (CRC) progression is shaped by metabolic reprogramming and complex interactions within the tumor microenvironment. However, the cellular heterogeneity, spatial organization, and clinical relevance of glycolytic activity in CRC remain incompletely understood. In this study, we integrated single-cell RNA sequencing, bulk transcriptomics, and spatial transcriptomics data to systematically characterize glycolytic heterogeneity in CRC. Glycolytic activity was quantified using five independent scoring methods, consistently showing that epithelial cells exhibited the highest glycolytic activity across the two single-cell cohorts. Stratification of CopyKAT-verified aneuploid malignant epithelial cells into high-glycolysis (HG) and low-glycolysis (LG) subgroups by glycolysis scores revealed that HG cells exhibited higher stemness scores and chromosomal copy number variations. Cell-cell communication analysis revealed that, compared with LG cells, HG cells exhibited increased interaction frequency and strength with immune and stromal populations, indicating enhanced malignant epithelial-microenvironment crosstalk. Spatial transcriptomics analyses further revealed that glycolytic activity varied across normal colorectal tissue, primary CRC, and colorectal liver metastases, accompanied by progressive remodeling of epithelial-associated spatial niches and MIF-mediated intercellular communication. Bulk transcriptomic analysis identified a glycolysis-related prognostic signature with robust predictive performance, which served as an independent prognostic factor for overall survival in CRC cohorts. Collectively, these findings indicate that glycolytic heterogeneity is a key feature of CRC malignant epithelial cells and is closely associated with tumor progression, microenvironmental remodeling, and clinical outcomes.

Humans↗

PubMed2026

Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Advances in spatially resolved technologies enable the simultaneous acquisition of diverse data modalities within a tissue slice while preserving critical spatial context, which presents unprecedented opportunities to decipher intricate tissue heterogeneity. However, existing computational approaches lack the intrinsic flexibility to universally process both spatial multi-modal and multi-omics data. Here, we introduce STransformer, a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short-range cellular interactions and tissue-wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity. Systematic evaluations across diverse species, tissue types, and data modalities highlight its profound versatility. For spatial multi-modal data, STransformer delineates intricate anatomical structures in the human cortex, uncovers pathological mechanisms in Alzheimer's disease, and characterizes dynamic spatiotemporal developmental trajectories during chicken cardiogenesis. Scaling to spatial multi-omics data, STransformer synergizes spatial transcriptomic and proteomic profiles to decipher intricate immune microenvironments within the human tonsil, and jointly analyzes spatial epigenomic and transcriptomic data to infer regulatory mechanisms in the mouse embryonic brain. Consequently, STransformer serves as a highly versatile and robust analytical framework for advancing our understanding of tissue heterogeneity and disease pathogenesis.

Multiomics↗

PubMed2026

Radiomics as a spatial context for treatment decision-making in head and neck cancer.

Radiomics has been widely explored as a non-invasive biomarker in head and neck squamous cell carcinoma (HNSCC), yet its clinical role remains unclear. Tissue-based biomarkers differ in their susceptibility to spatial sampling. Biomarkers such as PD-L1 expression, immune-cell infiltration, necrosis, and immune exclusion may exhibit substantial spatial heterogeneity, whereas HPV/p16 status and some genomic alterations are generally more stable across the tumor. Nevertheless, localized sampling may incompletely capture heterogeneity in selected clinical contexts. This mismatch becomes clinically relevant when treatment decisions, particularly for chemoradiotherapy, immunotherapy, or de-escalation, are based on potentially non-representative biopsy findings. In this narrative review, we argue that the role of radiomics is not to outperform established biomarkers, but to contextualize them by capturing spatial heterogeneity related to hypoxia, necrosis, stromal architecture, and immune exclusion. We synthesize current evidence linking radiomic features to these biological processes and map them to specific clinical decision points, including larynx preservation, immunotherapy stratification, and recurrence assessment. Rather than serving as a standalone predictor, radiomics may provide complementary spatial information that helps identify situations in which biopsy-derived biomarkers should be interpreted with caution. Although current evidence is largely retrospective, radiomics offers a pragmatic framework for integrating spatial information into biomarker-guided clinical workflows.

Journal Article↗

PubMed2026

Toxoplasma gondii IgG seroprevalence in Mauritanian dromedary camels: First multi-regional survey.

Toxoplasma gondii is a globally distributed zoonotic parasite, and dromedary camels are important intermediate hosts in arid and semi-arid regions. However, information on T. gondii exposure in camels is lacking in Mauritania, which harbors one of the largest camel populations in West Africa. This study reports the first multi-regional seroepidemiological survey to estimate T. gondii seroprevalence and identify associated risk factors in Mauritanian dromedaries. Between 2023 and 2024, serum samples were collected from 953 camels across eight climatically distinct regions. Anti-T. gondii IgG antibodies were detected using the Modified Agglutination Test (MAT; cutoff≥1:20). Risk factors investigated included geographical region, sex, age group, and season of sampling, using multivariable logistic regression and a mixed-effects linear probability model accounting for regional clustering. The overall seroprevalence was 15.0% (143/953). Exposure varied markedly across regions, ranging from 0% in the hyper-arid northern regions of Adrar and Tagant to 41.7% in the southern Sahelian region of Guidimakha. This pronounced spatial gradient is consistent with contrasting climatic and ecological conditions, as higher rainfall and humidity in the south are hypothesized to favor environmental oocyst survival compared to the extreme aridity of the north. Geographical region and age were independent predictors of seropositivity. Compared with camels from Nouakchott, those from Guidimakha had higher odds of exposure (aOR = 2.63), whereas camels from Trarza had a markedly lower risk (aOR = 0.09). Camels older than 6 years were more than twice as likely to be seropositive as those aged 3-5 years, whereas sex and season were not associated with seropositivity. These findings indicate that T. gondii exposure is widespread in Mauritanian dromedaries and that ecological conditions may influence exposure patterns. The marked spatial heterogeneity supports targeted surveillance and One Health interventions to reduce the potential zoonotic risk associated with camel-derived food products.

Animals↗

PubMed2026

Longitudinal In Vivo Imaging at Single-Lesion Resolution Identifies Allele-Associated Response and Resistance Dynamics in EGFR-Mutant Lung Cancer.

Acquired resistance to targeted therapies is inevitable in EGFR-mutant non-small cell lung cancer (NSCLC), yet the principles governing its emergence in vivo remain incompletely understood. In particular, how lesion-level response patterns vary across distinct EGFR allele contexts during therapy has not been systematically examined at single-lesion resolution. Here, we establish a longitudinal in vivo imaging platform enabling single-lesion resolution tracking of tumor behavior during therapy in genetically engineered mouse models representing clinically relevant EGFR alleles. Using high-resolution micro-computed tomography (micro-CT) and three-dimensional reconstruction, we monitor tumor growth, therapeutic response, and resistance during osimertinib treatment. EGFR genotype is associated with distinct patterns of tumor growth, response kinetics, and resistance timing. Therapeutic response is spatially heterogeneous, with coexisting lesions undergoing complete regression, persistence, or progression within the same lung. During treatment, spatially distinct lesion-level behaviors included persistent growth during therapy and initial regression followed by regrowth. These findings demonstrate the utility of longitudinal micro-CT imaging to investigate allele-associated differences in treatment response and resistance timing at single-lesion resolution in vivo.

Animals↗

PubMed2026

Impact of spatial distribution of M2 macrophages on prognosis and neoadjuvant chemotherapy resistance in gastric cancer.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is a crucial treatment for locally advanced gastric cancer; however, approximately 30-40% of patients experience primary resistance, the mechanisms of which urgently require elucidation. The tumor microenvironment exhibits a high degree of spatial heterogeneity. M2 macrophages, as critical immune cells within this environment, are typically associated with poor prognosis. Yet, whether their spatial distribution impacts chemotherapy efficacy remains unclear. This study aims to investigate the relationship between the in situ spatial distribution characteristics of M2 macrophages and chemoresistance in gastric cancer. METHODS: Based on The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort, the association between M2 markers (CD163, MRC1) and histological grade as well as overall survival (OS) was evaluated. Spearman correlation and functional enrichment analyses were conducted to explore the mechanistic link between M2 macrophages and stromal barrier construction. Multiplex immunofluorescence (mIF) and digital pathology image analysis were utilized to calculate the areal density of M2 macrophages in the intratumoral core and the peritumoral stroma, respectively. The tumor-to-peritumoral ratio (TPR) was constructed, followed by a rank correlation analysis between TPR and the tumor regression grade (TRG). RESULTS: TCGA-STAD results confirmed that patients with high expression of M2 markers had worse OS (P=0.03), and the expression levels of M2 markers increased with histological grade. MRC1 was highly significantly and positively correlated with the pro-fibrotic factor TGFB1 (rho=0.447, P<0.001), with the gene set significantly enriched in pathways such as positive regulation of cytokine production and myeloid leukocyte activation. Histological examination revealed that in chemoresistant patients (TRG 3), M2 macrophages were primarily retained in the peritumoral stroma, with a median TPR of 0.50; in chemosensitive patients (TRG 1-2), a massive influx of M2 macrophages into the tumor core was observed, with a median TPR of 6.67. TPR was negatively correlated with TRG (rs=-0.65, P=0.043). CONCLUSIONS: The clinical impact of M2 macrophages in the gastric cancer microenvironment is highly dependent on their spatial distribution. The peritumoral-enriched pattern (TPR <1) mediates primary chemoresistance, whereas high infiltration in the core objectively reflects the pathological footprint following effective chemotherapy. The TPR serves as a novel tool for assessing neoadjuvant chemosensitivity in gastric cancer.

Gastric cancer (GC)↗

PubMed2026

Spatial inheritance patterns across maize ears are associated with alleles that reduce pollen fitness.

Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize (Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps fluorescently marked mutant (Ds-GFP) kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). Our dataset (1384 ears) represents 58 Ds-GFP insertion alleles. None of the 48 alleles with Mendelian inheritance showed any significant spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertional mutant of the gene encoding a putative actin-binding protein, base-to-apex gradient1* (bag1*), is associated with decreased mutant transmission at the ear base relative to the apex. Surprisingly, a mutant allele of another pollen-expressed gene (Zm00001eb236740) generates the opposite trend, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm cell attachment factor gamete expressed2 (gex2) can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants cause unexpectedly diverse spatial patterns of progeny genotypes.

Zea mays↗

PubMed2026

Multi-sampling allows intra-tumoral heterogeneity querying and vulnerability profiling in glioblastoma.

BACKGROUND: Glioblastoma (GBM) remains a devastating cancer with limited treatment options, largely due to its heterogeneity. While supramaximal resection has recently provided survival benefits, therapeutic profiling of different tumor compartments, particularly its infiltrative edge remains largely unexplored. METHODS: Here, we leveraged magnetic resonance imaging (MRI)-guided multi-sampling, collecting 2 cores and 2 margins per case, to query GBM heterogeneity. Whole-exome and RNA-seq with drug testing in two patient-derived 3D models were used to reveal similarities and differences in genomic and transcriptomic makeups, cellular compositions, and drug responses across cores and margins. Bioinformatics interrogations further identified response biomarkers. RESULTS: Mutation analysis showed that oncogenes exhibited a higher degree of spatial heterogeneity than tumor suppressor genes, regardless of MRI status. While the mesenchymal transcriptional subtype with extracellular matrix remodeling, stress response, and immune programs were preferentially enriched in enhancing cores, proneural tumors with neurological processes favored non-enhancing margins. Using a 15-drug GBM-targeted panel, ERK (ulixertinib) and PI3K pathway (paxalisib, CC-115) inhibitors showed preferential efficacy in enhancing cores and non-enhancing margins, respectively. The anti-apoptosis, pan-Bcl2 agent navitoclax and the epigenetic drug trotabresib represented the most effective, tumor-wide monotherapies. Importantly, drug combinations generally outperformed single agents across all regions. CONCLUSIONS: This work demonstrates the regional heterogeneity of therapeutic vulnerabilities in GBM ex vivo, showing various drugs with tumor-wide or MRI-enhancement informed activity. These findings offer preclinical bases of numerous monotherapies and drug combinations for future clinical trial design.

Humans↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Spatial Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Tissue by spRandom-seq.

The molecular pathogenesis of infectious diseases and cancer is orchestrated by nanoscale of host and microbial RNA transcripts within the tissue microenvironment. Nevertheless, spatially resolving the comprehensive transcriptional landscape within complex clinical tissues, like formalin-fixed paraffin-embedded (FFPE) specimens, still poses a formidable challenge. Here, we present spRandom-seq, a random primer-based spatial total RNA sequencing technology designed to spatially resolve complete transcriptomes from host, bacteria, and even nanoscale viruses in FFPE tissues. Capitalizing on the random primer design, our technology not only facilitated the discovery of specific lncRNAs and alternative splicing events in mouse brain and olfactory bulb, but also delineated pronounced spatial heterogeneity in clinical FFPE sections-across distinct tumor regions in breast cancer and microbial infection sites in Klebsiella pneumoniae-infected tissues. Importantly, integrated analysis of host and viral RNAs in FFPE samples from hepatitis B virus (HBV)‑positive hepatocellular carcinoma (HCC) demonstrated that complement and coagulation pathways were specifically activated across expansive HBV‑infected tumor areas, which also exhibited an increased burden of copy number variations (CNVs). Owing to its compatibility with existing spatial transcriptomics platforms and minimal operational complexity, spRandom-seq represents a practical and scalable approach for clinical pathology applications and infection diagnostics.

Paraffin Embedding↗

PubMed2026

Spatial Proteomics of the Normal Breast Collagen Stroma: Links to Density and Body Mass Index.

Collagen breast stroma can become a breast cancer risk factor, yet proteomic regulation of normal breast stroma remains poorly defined. This study evaluates the spatial regulation of the collagen proteome from normal breast tissue. Normal breast tissue sections from the Susan G. Komen tissue bank were used (n = 40), with data including genetic ancestry (n = 20 African ancestry; n = 20 European ancestry), body-mass-index (BMI), age, and mammogram density by the Breast Imaging Reporting and Data System (BI-RADS). 10-plex cell marker staining showed CD44 and COL1A1 markers modulated with BMI. Collagen fiber widths by second harmonic generation microscopy contrasted in BMI categories by genetic ancestry. Targeted extracellular matrix proteomics mass spectrometry imaging showed the collagen alpha-1(I) chain proteome was spatially heterogeneous across the normal breast microenvironment with site-specific post-translational modification of proline hydroxylation. Signatures computationally extracted from stroma-rich regions reported that 47 collagen peptides distinguished BI-RADS categories (area under the receiver operating curve >0.7; p-value >0.05). Multivariate modeling of collagen peptides, fiber metrics, and clinical features supported a strong positive association with BMI as a determinant of collagen alterations in the normal breast. This study provides a foundation for larger studies investigating the clinical value of spatial collagen proteome alterations in human breast.

Humans↗

PubMed2026

Light-activated CRISPR/dCas9 nanomedicine for programmable control of renal fibrosis.

Renal fibrosis is the final common pathway of progressive chronic kidney disease and is maintained by spatially heterogeneous interactions among injured epithelial cells, activated fibroblasts, immune cells, extracellular matrix remodeling, metabolic stress, and persistent profibrotic transcriptional programs. Current therapies slow renal functional decline but do not directly control the regulatory circuits that stabilize maladaptive repair. Photoresponsive renal nanomedicine offers a potential strategy to add external control to anti-fibrotic intervention by combining kidney-directed delivery with light-gated release or activation of molecular payloads. This review examines the emerging interface between photoresponsive nanomaterials and CRISPR/dCas9-based gene regulation for renal fibrosis, with emphasis on upconversion nanoparticles, photoresponsive polymers, ROS- and pH-responsive matrices, optogenetic switches, and renal-compartment-directed carrier design. We argue that the most defensible therapeutic objective is not permanent genome editing or autonomous organ regeneration, but spatially confined, temporally limited, and reversible regulation of validated fibrotic or protective gene programs using CRISPRa, CRISPRi, or dCas9-based epigenome editors. The review therefore evaluates material requirements, optical-dosimetry constraints, payload architecture, renal biodistribution, target-selection logic, safety risks, and preclinical validation criteria. By defining the engineering and biological conditions required for controlled anti-fibrotic regulation, this framework positions photoresponsive renal nanomedicine as a translationally testable route toward localized modulation of fibrotic cell states rather than an overextended claim of kidney regeneration.

Anti-fibrotic gene regulation↗

PubMed2025

Advances and challenges in human 3D solid tumor models.

The field of cancer biology and therapeutics has soared in the past several decades with new therapeutic modalities and options for patients, such as chemoradiotherapy, immunotherapy, and combination therapy. This dramatic success in expanding patient options is primarily attributed to the development of various model systems to elucidate drivers of oncogenesis, tumor maturation and evolution, and response to therapeutics. While mouse models have been a workhorse of cancer research, technological progress in ex vivo patient-derived tumor models has afforded more tunable and scrutable systems for patient-predictive platforms and mechanistic study. This review explores the technological innovations in 3D solid tumor models and their applicability to various aspects of cancer biology and identification of therapeutics. Features of the tumor and tumor microenvironment like spatial heterogeneity, multicellular populations and genomic variations are addressed and elaborated through the establishment of new in vitro models. We further address the integration of perfusable vasculature with 3D tumor models and the potentially wide-ranging applications of these more complex platforms in precision medicine and cancer immunotherapy. Finally, we provide an outlook on the future of experimental cancer models for both biological investigation and bench-to-bedside pipeline development.

Journal Article↗

PubMed2026

From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.

Sugarcane (Saccharum spp.) underpins global sugar and bioenergy supply and is increasingly valued as a renewable biomass feedstock. Sustained improvement in commercial traits and resilience is constrained by long breeding cycles, clonal propagation, multi-stage testing, and a highly polyploid, heterozygous, and frequently aneuploid genome with substantial non-additive genetic variation. Genomic selection has demonstrated value for predicting elite-clone performance, yet its operational use remains limited at earlier decision points, including family selection, parent evaluation, and cross design. This review examines the biological, statistical, and genomic factors that shape these decisions, with emphasis on the Australian breeding context based on progeny assessment trials (PATs), clonal assessment trials (CATs), and final assessment trials (FATs). We evaluate challenges arising from family plot means, the use of different full-sib samples as nominal family replicates, spatial heterogeneity, competition, genotype-by-environment interaction, and the partitioning of additive and non-additive effects. We also assess the integration of pedigree and genomic relationship, genotype representation, allele-dosage estimation, aneuploidy, genomic prediction models, and training-population design. We then consider genomic prediction of cross performance and constrained mate allocation as approaches for improving expected family performance, accounting for cross-specific non-additive effects and managing relatedness. We propose a decision-centred framework that links family and clonal data across breeding stages, tracks the propagation of information and uncertainty, and supports parent recycling and cross allocation. We conclude with a practical research agenda for stage-integrated mixed-model and single-step analyses that connect early family evaluation with genomic prediction and cross-level decision support in sugarcane breeding.

Saccharum↗

PubMed2025

Association of Lung Quantitative CT Scan Textures With Systemic Inflammation and Mortality in COPD.

BACKGROUND: COPD is characterized by persistent inflammation that is responsible for remodeling the bronchovascular bundles (BVBs), which may lead to poor quality of life. Quantitative CT (QCT) scan textures of the lung can capture local disease patterns of inflammation and related respiratory morbidity. RESEARCH QUESTION: Are BVB textures, obtained from the adaptive multiple feature method, associated with systemic inflammation, morbidity, and mortality in COPD? STUDY DESIGN AND METHODS: We analyzed data from the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS; n = 2,981) and the Genetic Epidemiology of COPD (COPDGene) study (n = 10,305). The predictors included 2 QCT scan biomarkers, the BVB and CT density gradient (CTDG) textures, age, sex, BMI, race, smoking status, pack-years of smoking, CT scan-detected emphysema, and square root of the wall area of a hypothetical airway with a 10-mm lumen perimeter (Pi10). Outcomes included plasma biomarker concentrations from Meso Scale Discovery proteomics assays and CBC counts, both as markers of inflammation, along with FEV1, FEV1 to FVC ratio, St. George's Respiratory Questionnaire score, 6-minute walk distance, and modified Medical Research Council dyspnea scale score. Associations of these QCT scan textures with FEV1 decline and all-cause mortality also were investigated. RESULTS: Increased BVB texture was associated significantly with elevated neutrophil and monocyte counts and the neutrophil to lymphocyte ratio, independent of clinical covariates, CT scan-detected emphysema, and Pi10. Elevated CTDG was associated with increased neutrophil count, NLR, and tumor necrosis factor α. Increased CTDG and BVB textures also were associated with a lower FEV1 and 6-minute walk distance. CTDG at baseline was also associated with decline in FEV1 at the 5-year follow-up in the COPDGene study. We observed a significant association of both BVB texture (SPIROMICS: hazard ratio [HR], 1.084 [95% CI, 1.035-1.135; P < .001]; COPDGene: HR, 1.106 [95% CI, 1.080-1.131; P < .001]) and CTDG texture (SPIROMICS: HR, 1.033 [95% CI, 1.003-1.064; P = .03]; COPDGene: HR, 1.079 [95% CI, 1.061-1.096; P < .001]) with all-cause mortality independent of CT scan-detected emphysema and Pi10. INTERPRETATION: QCT scan textures may provide imaging evidence of the spatial heterogeneity of lung inflammation and overall disease burden in COPD. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; Nos.: NCT01969344 (SPIROMICS) and NCT00608764 (COPDGene); URL: www. CLINICALTRIALS: gov.

Humans↗