Biology subjects

Youssef, A.

Publications and source records attributed to Youssef, A..

15 recordsLinked to original sources

bioRxiv2026

Maternal diet and genetics shape the human milk metabolome

Human milk contains a diverse array of metabolites that contribute to infant nutrition, immune development, and microbial colonization. The maternal factors shaping the milk metabolome, and the relative contribution of genetics or diet vs. other factors, remain poorly understood. Here, we profiled 458 milk metabolites in 349 one-month postpartum human milk samples and integrated metabolomic data with maternal diet, clinical, transcriptomic, and genomic measurements. Maternal diet was broadly associated with milk metabolite composition, with significant correlations identified between dietary features and 323 metabolites. Coffee consumption strongly predicted milk quinic acid and 1,3-dimethyluric acid abundance, while high-fiber dietary patterns were associated with metabolites including proline-betaine and N-acetylornithine. Integration of milk transcriptomic and metabolomic data via machine learning identified biologically plausible gene-metabolite pairs, including associations between QPRT expression and quinolinic acid, and DPEP1 and cysteine-glycine dipeptide. Genome-wide association analyses identified nine study-wide significant metabolite quantitative trait loci, including novel milk-specific associations near PDE6A affecting purine metabolites and near GNE affecting free sialic acid. Comparison with plasma metabolite studies demonstrated both shared and milk-specific genetic regulation of metabolites. Finally, we found that of all tested maternal features, diet explained the largest proportion of variation in the milk metabolome. Together, these findings demonstrate that the human milk metabolome reflects both maternal exposures and mammary gland-specific biology. This work establishes a framework for understanding how genetic and environmental factors shape milk composition.

genomics↗

bioRxiv2026

Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is an aggressive asbestos-linked cancer with limited therapeutic options and a dismal 5-year survival rate of [~]5%. While aberrant production of reactive oxygen and nitrogen species (ROS/RNS) is a hallmark of MPM, targeted approaches to exploit these redox vulnerabilities remain scarce. Here, using the MOSAIC multimodal cancer patient atlas, we identify Peroxiredoxin 5 (PRDX5) as being significantly upregulated in the epithelioid subtype of MPM. We show that MPM cells exhibit enhanced resistance to nitrosative and oxidative stress compared to healthy mesothelial cells, a phenotype correlated with basal PRDX5 expression. Next, utilising a machine learning guided discovery pipeline, we identified three putative allosteric pockets in PRDX5 and conducted a virtual screen of 3.6 million compounds. High-throughput biochemical validation of 452 candidates yielded 36 non-covalent hits, including sub-micromolar inhibitors. These findings establish PRDX5 as a novel, subtype specific therapeutic target in MPM and provide a chemical framework for the development of next-generation redox-modulating oncology treatments.

cancer biology↗

bioRxiv2026

DigitalPedon: A Novel Digital Twin Framework for Soil Profile Monitoring and Global Soil Data Interoperability

The Digital Pedon (DP) is an open-source Python framework that represents a soil profile as a continuously updated digital twin, bridging three persistent gaps in soil science: disconnected models and observations, cross-database interoperability, and the inference gap between raw sensor signals and agronomically meaningful variables. Integrating real-time sensor streams, model-based solver chains (Model-Zoo), GLOSIS-compliant ontology mapping, and a novel LLM agentic interface layer enabling natural language soil queries, the DP supports applications spanning precision agriculture, digital soil mapping, and environmental sustainability assessment. Four proof-of-concept experiments confirm automatic profile initialisation fidelity, solver chain consistency, ontology compliance, and user-defined solver extensibility.

bioengineering↗

bioRxiv2026

An Expert-Informed Synthetic Animal Data Generator: A Physiology-Consistent Generative Framework for High-Fidelity Animal Digital Twins

Digital twins are increasingly recognized as a transformative technology for precision livestock farming; however, a major bottleneck in their development remains the scarcity of high-quality, high-granularity physiological data. This study introduces the expert-informed conditional diffusion (EICD) framework, a novel approach to synthesizing high-fidelity metabolic time-series trajectories by embedding mechanistic biological principles directly into the generative process. While traditional generative models often prioritize statistical pattern-matching over biological reality, frequently resulting in physiological hallucinations, the EICD framework utilizes a physiology loss function (PhLF) to act as a form of mechanistic regularization. This guardrail penalizes samples that contradict expert-defined constraints, such as the laws of porcine bioenergetics, effectively steering the model toward a realistic physiological manifold. The framework was validated using an empirical dataset of growing pigs under varying thermal conditions. Quantitative results demonstrate near-perfect statistical distributional fidelity, with the model achieving an average Jensen-Shannon divergence (JSD) of 0.062 and a Kullback-Leibler divergence (KLD) of 0.19. The full EICD model produced a mean energy expenditure (EE) of 284.94 {+/-} 38.70 kJ/kg/day, mirroring the empirical average of 281.33 {+/-} 41.58 kJ/kg/day. In contrast, the standard generative diffusion model (i.e., with no physiology guardrail) exhibited significant distributional drift, yielding a mean EE of 334.41 kJ/kg/day. The biological integrity of the model was further assessed using the biological violation rate (BVR), a novel metric defined as the percentage of generated samples that fall outside the physically possible metabolic boundaries established by species-specific laws. While the standard diffusion model produced frequent biological artifacts, the EICD framework successfully suppressed these hallucinations, ensuring that synthetic trajectories remain strictly grounded in mechanistic laws. Despite these advancements, limitations remain at physiological extremes where individual stochasticity is high. By providing a reliable method for generating physiology-consistent synthetic data, this framework provides a robust foundation for the next generation of animal digital twins. HighlightsO_LIA novel expert-informed conditional diffusion (EICD) framework is proposed for physiology-consistent synthetic data generation in precision livestock farming. C_LIO_LIA physiology loss function (PhLF) embeds species-specific bioenergetic laws directly into the generative process as a mechanistic guardrail. C_LIO_LIThe framework achieves near-perfect distributional fidelity (JSD = 0.062) while suppressing physiological hallucinations (BVR = 0.93%). C_LIO_LIAn ablation study confirms that biological consistency is not an emergent property of standard diffusion models but requires explicit mechanistic constraints. C_LIO_LIThe framework provides a scalable solution for synthetic data augmentation in precision livestock farming, supporting the 3Rs and enabling high-throughput in silico experimentation. C_LI

bioengineering↗

bioRxiv2026

Multi-modal benchmarking of the Ultima UG100 and Illumina NovaSeq sequencing platforms using clinically relevant FFPE tissues

Emerging high-throughput sequencing technologies promise lower costs and higher scalability, yet their performance on archival clinical samples remains poorly characterized. Here, we benchmarked Ultima Genomics UG100 against Illumina Novaseq platforms across single-nuclei RNA-seq (snRNA-seq), whole-transcriptome (WTS), whole-exome (WES), and whole-genome sequencing (WGS) using FFPE tissues from oncologic and immune-mediated diseases. Across matched samples, we systematically assessed data quality, coverage profiles, error spectra, variant concordance and transcriptomic reproducibility. UG100 produced highly comparable results to Illumina, capturing key oncogenic and immune-related transcripts, accurately resolving cellular composition in snRNA-seq, and maintaining sensitivity for lowly expressed genes, despite characteristic insertion-biased indels and modest differences in multi-mapping reads. Discrepancies were subtle, largely limited to pseudogene and non-coding transcripts, and did not affect pathway-level conclusions. Ultima UG100 platform prioritized high precision and reduced low-frequency artifacts, offering a cleaner but more conservative variant-calling profile compared to the more sensitive, yet noisier, Illumina/DRAGEN workflow. This multimodal, clinically oriented assessment provides the first comprehensive evaluation of UG100, demonstrating its translational utility in population-scale genomics, and highlighting the potential for emerging sequencing technologies to lower the cost of biomedical research and clinical diagnostics.

genomics↗

bioRxiv2025

Differentiation-Dependent Proximity Proteomics Identifies Novel Host Factors Linked to HPV16 E2 Function

Human papillomavirus 16 (HPV16) is a causative agent of oropharyngeal, cervical and anogenital cancers. The viral E2 protein is essential for viral genome replication, transcriptional regulation, episome maintenance, and activation of the host DNA damage response. Despite its central role, the full network of HPV16 E2 interactions with host proteins remains incompletely defined, particularly under differentiating conditions which support the complete viral life cycle. In this study, we used TurboID-based proximity labeling to characterize the interactome of HPV16 E2 and known host partner protein TOPBP1, in both undifferentiated monolayer and differentiating keratinocytes. We generated stable keratinocyte lines expressing doxycycline-inducible TurboID-tagged HPV16 E2 and confirmed that the tagged protein retained transcriptional, replicative, and DNA damage-inducing functions. Mass spectrometry analysis of streptavidin-enriched proteins identified both known and novel E2-associated host factors, including chromatin regulators, DNA repair proteins, and nucleolar components. Comparative analysis revealed a substantial overlap between E2 and TOPBP1 interactomes, and in situ validation by proximity ligation assay identified nucleolin (NCL) as a differentiation-dependent factor whose interaction with E2 is stabilized by TOPBP1. Functional studies demonstrated that NCL is required for episomal genome maintenance, highlighting a cooperative E2-TOPBP1- NCL axis critical for viral genome stability during differentiation. These findings provide a comprehensive view of the E2-associated protein landscape in stratified epithelial cells and reveal a mechanistic pathway through which HPV16 co-opts host factors to support genome maintenance, productive replication, and persistence. ImportanceHuman papillomaviruses (HPVs) establish persistent infections in stratified epithelia and rely on host DNA damage and repair factors to support their replication. The E2 protein is central to viral genome replication and maintenance, and depends heavily on its interaction with the host factor TOPBP1 for these functions. Here, we define the E2 and TOPBP1 interactomes in differentiating keratinocytes, and identify nucleolin (NCL) a critical differentiation- and TOPBP1-dependent E2 partner required for episomal genome stability. These findings expand the understanding of how HPV16 coordinates viral replication with host chromatin and DNA repair networks, uncovering a cooperative E2-TOPBP1-NCL axis that may represent a new target for antiviral intervention.

cancer biology↗

bioRxiv2025

Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

BackgroundGlioblastoma (GBM), the most aggressive primary brain tumor, has a median survival of approximately 15 months. Twenty percent of patients survive beyond three years, but known clinical factors like age, performance status, resection extent, and MGMT promoter methylation status do not fully explain the observed outcomes. ObjectiveOur objective was to identify novel histology derived biomarkers associated with end-of-spectrum overall survival (OS) to provide novel biological insight with a translational potential. MethodsWe analyzed a total of 748 GBM patients from 3 different cohorts, uniquely enriched in long survivors (n=98 with overall survival (OS) > 5y including n=196 with OS[≥]3y), with clinical data and H&E slides obtained from the primary tumor at baseline. We propose an interpretable machine learning (ML) methodology for the discovery of histological biomarkers. Our method learned to segment each H&E slide into three distinct regions associated with long-term survival, short-term survival, and non-informative tissue. We characterized these regions by integrating unsupervised learning, nuclei segmentation, blood vessels detection, pathologist annotations, and multimodal data including spatial transcriptomics from n=31 patients of the GBM MOSAIC dataset to discover fully interpretable biomarkers. ResultsOur OS prediction model using histology and clinical data as input achieved an area under the curve (AUC) of 0.85 for the classification of patients between OS<2 and OS[≥]3y in external cohort validation, outperforming significantly models trained on clinical data or on histology alone (AUC of 0.76; 0.73, respectively). Two novel biomarkers were predicting poor survival: the presence of regions of lowly infiltrated white matter enriched in malignant cells with a mesenchymal-like phenotype, and lower levels of angiogenesis associated with higher hypoxia response in the main tumor regions. We also found that a subtype of immunosuppressive tumor macrophages - defined by high PLIN2 expression and lipid accumulation- is consistently enriched in histological areas predictive of poor prognosis. ConclusionOur interpretable ML methodology identified a novel prognostic impact of biological processes and cell types according to distinct tumor regions of GBM. These results pave the way for spatially-informed biomarkers to improve risk stratification and for personalized spatially-targeted therapeutic strategies. Key highlightsO_LIOur ML model identified histological biomarkers predicting prognosis independently from known clinical factors C_LIO_LIThe region of lowly infiltrated white matter enriched in malignant cells including a mesenchymal-like phenotype is predictive of poor prognosis C_LIO_LIAngiogenesis is increased in areas predictive of long survival in main non-necrotic tumor regions. C_LIO_LIThe subtype of macrophages expressing PLIN2 and associated with increased lipid metabolism was associated with poor prognosis in all GBM regions. C_LI Highlights O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/681087v4_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@45e619org.highwire.dtl.DTLVardef@105a4b0org.highwire.dtl.DTLVardef@17f257dorg.highwire.dtl.DTLVardef@7669b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

CDKN1A (p21Cip/Waf1) stabilizes Cyclin D3 by inhibiting its phosphorylation-dependent nuclear export following butyrate treatment

Butyrate-mediated inhibition of cell proliferation is part of the preventive role of dietary fiber against colorectal cancer (CRC). This effect notably involves the cyclin-dependent kinase inhibitor CDKN1A (p21Cip/Waf1) in human intestinal cells, yet the underlying molecular mechanisms remain incompletely understood. Previously, we observed a paradoxical increase in cyclin D3 (CCND3)--but not cyclin D1--levels upon butyrate exposure. Here, we demonstrate that the butyrate-induced accumulation of CCND3 protein results both from mRNA increase and a CDKN1A-dependent protein stabilization, specifically extending its nuclear half-life. Proteomic analyses of CCND3 co-immunoprecipitates identified complexes involving CDKN1A, CDK4, CDK6, and the CRC-associated kinase CDK5, particularly enriched in butyrate-treated cells. Phosphorylation at a conserved Thr residue, crucial for CCND nuclear export and subsequent proteasomal degradation, was notably reduced following butyrate treatment and inversely correlated with CDKN1A expression levels. Structural modeling based on AlphaFold2, complemented by molecular dynamics simulations, revealed possible differential interactions between CDKN1A and cyclins D1 and D3, predicting that CCND3-Thr283 becomes structurally buried upon CDKN1A binding, limiting its phosphorylation. Our findings provide novel mechanistic insights into how CDKN1A might regulate CCND3 stability, highlighting previously unexplored roles of cyclin D3-containing complexes in cell cycle arrest induced by butyrate.

cell biology↗

bioRxiv2025

CRISPRi-Linked Multi-Module Negative Feedback Loops to Address Winner-Take-All Resource Competition

Cellular resource limitations create unintended interactions among synthetic gene circuit modules, compromising circuit modularity. This challenge is particularly pronounced in circuits with positive feedback, where uneven resource allocation can lead to Winner-Takes-All (WTA) behavior, favoring one module at the expense of others. In this study, we experimentally implemented a Negatively Competitive Regulatory (NCR) controller using CRISPR interference (CRISPRi) and evaluated its effectiveness in mitigating WTA behavior in two gene circuits: dual self-activation and cascading bistable switch. We chromosomally integrated a tunable dCas9 gene and designed module-specific gRNAs, with each module encoding its own gRNA to self-repress via competition for limited dCas9. This configuration introduces strong negative feedback to the more active module while reallocating resources to the less active one, promoting balanced module activation. Compared to the control group lacking dCas9-mediated repression, the NCR controller significantly increased module coactivation and suppressed WTA behavior. Our quantitative results demonstrate that NCR provides an effective strategy for regulating resource competition and improving the modularity of synthetic gene circuits.

synthetic biology↗

bioRxiv2024

Phase Separation to Resolve Growth-Related Circuit Failures

Fluctuations in host cell growth poses a significant challenge to synthetic gene circuits, often disrupting circuit function. Existing solutions typically rely on circuit redesign with alternative topologies or additional control elements, yet a broadly applicable approach remains elusive. Here, we introduce a new strategy based on liquid-liquid phase separation (LLPS) to stabilize circuit performance. By engineering a self-activating circuit with transcription factors (TF) fused to an intrinsically disordered region (IDR), we enable the formation of TF condensates at the promoter region, maintaining local TF concentration despite growth-mediated dilution. This condensate formation preserves bistable memory in the self-activating circuit, demonstrating that phase separation can robustly counteract growth fluctuations, offering a novel design principle for resilient synthetic circuits.

synthetic biology↗

bioRxiv2023

Human Papillomavirus 16 replication converts SAMHD1 into a homologous recombination factor and promotes its recruitment to replicating viral DNA

We have demonstrated that SAMHD1 (sterile alpha motif and histidine-aspartic domain HD-containing protein 1) is a restriction factor for the HPV16 life cycle. Here we demonstrate that in HPV negative cervical cancer C33a cells and human foreskin keratinocytes immortalized by HPV16 (HFK+HPV16), SAMHD1 is recruited to E1-E2 replicating DNA. Homologous recombination (HR) factors are required for HPV16 replication and viral replication promotes phosphorylation of SAMHD1, which converts it from a dNTPase to an HR factor independent from E6/E7 expression. A SAMHD1 phosphor-mimic (SAMHD1 T592D) reduces E1-E2 mediated DNA replication in C33a cells and has enhanced recruitment to the replicating DNA. In HFK+HPV16 cells SAMHD1 T592D is recruited to the viral DNA and attenuates cellular growth, but does not attenuate growth in isogenic HFK cells immortalized by E6/E7 alone. SAMHD1 T592D also attenuates the development of viral replication foci following keratinocyte differentiation. The results indicated that enhanced SAMHD1 phosphorylation could be therapeutically beneficial in cells with HPV16 replicating genomes. Protein phosphatase 2A (PP2A) can dephosphorylate SAMHD1 and PP2A function can be inhibited by endothall. We demonstrate that endothall reduces E1-E2 replication and promotes SAMHD1 recruitment to E1-E2 replicating DNA, mimicking the SAMHD1 T592D phenotypes. Finally, we demonstrate that in head and neck cancer cell lines with HPV16 episomal genomes endothall attenuates their growth and promotes recruitment of SAMHD1 to the viral genome. The results suggest that targeting cellular phosphatases has therapeutic potential for the treatment of HPV infections and cancers. ImportanceHuman papillomaviruses are causative agents in around 5% of all human cancers. The development of anti-viral therapeutics depends upon an increased understanding of the viral life cycle. Here we demonstrate that HPV16 replication converts SAMHD1 into an HR factor via phosphorylation. This phosphorylation promotes recruitment of SAMHD1 to viral DNA to assist with replication. A SAMHD1 mutant that mimics phosphorylation is hyper-recruited to viral DNA and attenuates viral replication. Expression of this mutant in HPV16 immortalized cells attenuates the growth of these cells, but not cells immortalized by the viral oncogenes E6/E7 alone. Finally, we demonstrate that the phosphatase inhibitor endothall promotes hyper-recruitment of endogenous SAMHD1 to HPV16 replicating DNA and can attenuate the growth of both HPV16 immortalized human foreskin keratinocytes and HPV16 positive head and neck cancer cell lines. We propose that phosphatase inhibitors represent a novel tool for combating HPV infections and disease.

cancer biology↗

bioRxiv2023

DESP: Demixing Cell State Profiles from Dynamic Bulk Molecular Measurements

There is wide interest to determine the dynamic expression of proteins and other molecules that drive phenotypic remodeling in development and pathobiology, but due to technical limitations these systems remain largely unexplored at the foundational resolution of the underlying cell states. Here, we present DESP, a novel algorithm that leverages independent estimates of cell state proportions, such as from single-cell RNA-sequencing or cell sorting, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in-vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell state signatures from bulk-level measurements of both the proteome and transcriptome providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell state-level using established bulk-level workflows.

bioinformatics↗

bioRxiv2022

Quantifying the Effect of an Acute Stressor in Laying Hens using Thermographic Imaging and Vocalizations

The laying hen sector has multiple issues concerning the animals welfare. One crucial factor negatively impacts chicken welfare is stress. The conventional way of measuring and assessing chicken stress is time-consuming and subjective to the assessor. On the other hand, sensing and sensor technologies can be used to obtain objective, continuous and non-invasive/contactless measures of animal behavioural and physiological welfare indicators. The present study aims to investigate the use of thermographic imaging and microphones (sound) in obtaining objective indicators for acute stress in laying hens. During this study, 40 laying hens were stressed by opening an umbrella as a stressor starting from one day age until nine-weeks old. The birds were stressed every other day. Another 12 birds were housed in another cage. These birds were not stressed and served as a control group. The surface temperatures of the birds comb and beak decreased (1{degrees}C and 2.5{degrees}C respectively) in response to the applied stressor. This effect was only seen in the treatment group and not in the control birds. The number of vocalisations the birds produced significantly decreased shortly after stress. The number of calls in the stressed group decreased from 39.5 to 12.1 calls/minute, where the control group decreased from 27.8 to 22.5 calls/minute. It was hypothesized that the number of vocalisations would increase after stress. This difference could be due to the daily behavioural rhythm performed by the birds. The birds might naturally produce more calls at certain hours of the day because of certain behaviours they perform. Three different neural network algorithms were employed to differentiate between the vocalisation of stressed and the control group. This was done by converting the audio files to images and feeding them to the pretrained convolutional neural networks (CNN). The Resnet CNN had the highest categorising accuracy with an overall accuracy of 86 percent. The changes in surface temperature of the beak, comb, eye, and head, as well as the results from the audio analysis could serve as potential indicator for acute stress in laying hens. Future research is warranted to validate the methodologies and findings under different environmental conditions and stressors.

bioengineering↗

bioRxiv2021

A multi-tiered map of EMT defines major transition points and identifies vulnerabilities

Epithelial to mesenchymal transition (EMT) is a complex cellular program proceeding through a hybrid E/M state linked to cancer-associated stemness, migration and chemoresistance. Deeper molecular understanding of this dynamic physiological landscape is needed to define events which regulate the transition and entry into and exit from the E/M state. Here, we quantified >60,000 molecules across ten time points and twelve omic layers in human mammary epithelial cells undergoing TGF{beta}-induced EMT. Deep proteomic profiles of whole cells, nuclei, extracellular vesicles, secretome, membrane and phosphoproteome defined state-specific signatures and major transition points. Parallel metabolomics showed metabolic reprogramming preceded changes in other layers, while single-cell RNA sequencing identified transcription factors controlling entry into E/M. Covariance analysis exposed unexpected discordance between the molecular layers. Integrative causal modeling revealed co-dependencies governing entry into E/M that were verified experimentally using combinatorial inhibition. Overall, this dataset provides an unprecedented resource on TGF{beta} signaling, EMT and cancer.

systems biology↗

bioRxiv2021

Comparative investigation of the Diabetic foot ulcer microbiome

BackgroundMany factors may affect wound healing in Diabetic foot ulcer, namely, microbial density, microorganisms, microbial synergy, the host immune response, and infected tissue quality. MethodologyThis study used a cross-sectional design. We assessed 38 Subjects with DFUs, 23 neuroischaimic, and 15 neuropathic, for microbiota colonizing the DFU utilizing traditional cultures and 16S gene sequencing methods. All the relevant clinical factors were collected. Wound swabs were collected for both traditional microbiological analysis, direct swab (DSM), and cultured (CM) microbiome analysis. DNA isolation, and 16SrRNA hypervariable regions were amplified. Bioinformatics analysis was performed using IonReporter Software, statistical analysis, and diversity indices were computed with vegan R-package. ResultsThe traditional microbiological method was able to detect a maximum of one or two pathogens, and, in some cases, no pathogen was detected. The total number of the observed species was 176. The number of identified species was higher in the cultured microbiome (155) than the direct swab microbiome (136). Diversity analysis indicated that biological diversity is higher in the cultured microbiome compared with the DSM. The Shannon H index was 2.75 for the cultured microbiome and 2.63 for DSM. We observed some differences in the major bacterial taxa amongst neuroischaimic and neuropathic DFU microbiomes. ConclusionsCultured microbiome is superior to both the traditional method and direct swab microbiome. The Neuroischaimic group showed higher values for the tested diversity indices than the Neuroischaimic group. Neither Cluster Analysis nor Principal Component Analysis showed apparent clustering amongst the two types of ulcers.

microbiology↗