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At least 19 recordsLinked to original sources

PubMed2026

Rhinoplasty Difficulty Scale: Development and Psychometric Analysis of a Surgeon's Assessment of Rhinoplasty Technique and Nasal Deformity Correction.

BACKGROUND: Rhinoplasty surgeons lack a universal scale of the relative difficulty of rhinoplasty techniques and rhinoplasty deformities. OBJECTIVE: To compare the expert opinion of the difficulty of rhinoplasty techniques and rhinoplasty deformities among international rhinoplasty surgeons, as measured by a scale of difficulty. METHODS: A cross-sectional survey of rhinoplasty surgeons collected training levels, experience, case volume, and perceived expertise. Rhinoplasty techniques/deformities (n = 64) were rated from 1-10, representing the least to most technically demanding. Rasch analysis was used to examine the fit of the observed data to Rasch model requirements, assess rating scale functioning, and provide estimates of internal consistency. RESULTS: Respondents (n = 63) were in practice (<5 years, 14%; 5-10, 20%; 10-20, 20%; 20-30, 26%; >30, 20%), and rhinoplasty volume ranged from <25 (14%) to >100 cases/year (32%). Self-reported expertise was comfortably novice (32%), intermediate (10%), advanced (28%), and expert (30%). Otolaryngology (42%), facial plastic surgery (30%), and plastic surgery (28%) were represented. Rasch estimates of internal consistency reliability were excellent (0.96 for surgeons and 0.99 for items); the item difficulties were more heterogeneous (mean: 0, SD: 1.23) than the distribution of surgeons (mean: -0.09, SD: 0.58). Survey items were ordered by difficulty, ranging from least difficult (inferior turbinate reduction = 1.01) to most difficult (contracted nose repair post-infection = 8.24). CONCLUSION: The newly developed Rhinoplasty Difficulty Scale provides ratings of common rhinoplasty techniques and deformities with a high correlation among experts using this rating scale.

Humans↗

PubMed2025

Fluctuating DNA methylation tracks cancer evolution at clinical scale.

Cancer development and response to treatment are evolutionary processes1,2, but characterizing evolutionary dynamics at a clinically meaningful scale has remained challenging3. Here we develop a new methodology called EVOFLUx, based on natural DNA methylation barcodes fluctuating over time4, that quantitatively infers evolutionary dynamics using only a bulk tumour methylation profile as input. We apply EVOFLUx to 1,976 well-characterized lymphoid cancer samples spanning a broad spectrum of diseases and show that initial tumour growth rate, malignancy age and epimutation rates vary by orders of magnitude across disease types. We measure that subclonal selection occurs only infrequently within bulk samples and detect occasional examples of multiple independent primary tumours. Clinically, we observe faster initial tumour growth in more aggressive disease subtypes, and that evolutionary histories are strong independent prognostic factors in two series of chronic lymphocytic leukaemia. Using EVOFLUx for phylogenetic analyses of aggressive Richter-transformed chronic lymphocytic leukaemia samples detected that the seed of the transformed clone existed decades before presentation. Orthogonal verification of EVOFLUx inferences is provided using additional genetic data, including long-read nanopore sequencing, and clinical variables. Collectively, we show how widely available, low-cost bulk DNA methylation data precisely measure cancer evolutionary dynamics, and provides new insights into cancer biology and clinical behaviour.

Humans↗

PubMed2025

Newly Developed Structure-Based Methods Do Not Outperform Standard Sequence-Based Methods for Large-Scale Phylogenomics.

Recent developments in protein structure prediction have allowed the use of this previously limited source of information at genome-wide scales. It has been proposed that the use of structural information may offer advantages over sequences in phylogenetic reconstruction, due to their slower rate of evolution and direct correlation to function. Here, we examined how recently developed methods for structure-based homology search and tree reconstruction compare with current state-of-the-art sequence-based methods in reconstructing genome-wide collections of gene phylogenies (i.e. phylomes). While structure-based methods can be useful in specific scenarios, we found that their current performance does not justify using the newly developed structure-based methods as a default choice in large-scale phylogenetic studies. On the one hand, the best performing sequence-based tree reconstruction methods still outperform structure-based methods for this task. On the other hand, structure-based homology detection methods provide larger lists of candidate homologs, as previously reported. However, this comes at the expense of missing hits identified by sequence-based methods, as well as providing sets of homolog candidates with higher fractions of false positives. These insights help to guide the use of structural data in comparative genomics and highlight the need to continue improving structure-based approaches. Our pipeline is fully reproducible and has been implemented in a Snakemake workflow. This will facilitate a continuous assessment of future improvements of structure-based tools in the AlphaFold era.

Phylogeny↗

PubMed2025

Healthcare transition readiness in an adolescent and young adult urologic population: The ADHERENT study.

INTRODUCTION: There is a paucity of research regarding transition to adult services within pediatric and adolescent urology. Several recent articles have discussed the barriers in transitioning urologic patients from pediatric to adult health care, but empiric data that may drive intervention are lacking. This study proposes to begin to address this gap in literature and to provide information that may lead to improved understanding of how best to support transition in urologic care. OBJECTIVES: 1) to identify modifiable and non-modifiable factors related to transition readiness as measured by Transition Readiness Assessment Questionnaire (TRAQ) scores in a congenital urologic population and 2) to evaluate the relationships between TRAQ scores (a validated questionnaire measuring transition readiness) and scores measuring anxiety levels related to transition (using an adapted, non-validated questionnaire). STUDY DESIGN: This is a cross-sectional study of adolescent and young adult patients with complex congenital urologic diagnoses. Subjects were electronically administered the validated TRAQ and a study-developed ADHERENT survey, which assesses anxiety and worry surrounding transition. Regression models for the outcomes of the TRAQ and ADHERENT scales were developed to assess multivariable associations with variables of clinical importance. RESULTS: The youngest subgroup (14-17 years of age) compared to the oldest subgroup (21-25 years of age) had significantly lower TRAQ scores [regression estimate = 12.3 (95 % CI: 2.9, 21.7), p = 0.010]. Additionally, single participants versus those in a stable relationship had significantly lower TRAQ scores [estimate = 8.7 (95 % CI: 1.9, 15.4), p = 0.012]. The Spearman correlation coefficient between TRAQ and ADHERENT scores was 0.52 (p = <0.001), indicating a positive, moderate relationship between the two measures, suggesting more readiness correlated with less anxiety. DISCUSSION: This study found that age, higher education, and stable relationship status were associated with higher measures of transition readiness. There was a correlation found between more transition readiness and less anxiety surrounding transition. This finding can be used to inform future research and emphasizes the need for multidisciplinary support throughout the transition process. CONCLUSION: Early discussion of transition of care and education around transition readiness are not the only solution to improving transition success. The second phase of ADHERENT seeks to understand the patient experience and to include adolescents and young adults in shaping effective healthcare transition strategies.

Humans↗

PubMed2026

Transcranial Photobiomodulation Variables Assessment Battery: Development and Validation.

Transcranial photobiomodulation (tPBM) response variability is partly driven by biophysical characteristics such as skin tone and hair properties that attenuate photon penetration, and by lifestyle factors including sleep quality, alcohol use, and nicotine consumption that disrupt the mitochondrial and vascular pathways on which tPBM acts. To date, no validated self-report tool exists to capture these moderators systematically. To address this gap, the tPBM Variables Assessment Battery was developed and psychometrically evaluated. It integrates adapted versions of established measures (Brief Pittsburgh Sleep Quality Index, E-cigarette Dependence Scale, Hair Scale Assessment PRO, Monk Skin Tone Scale, and Heaviness of Smoking Index), validated wellbeing evaluators (Ryff's Psychological Wellbeing), and custom measures (Hairstyle Classification, Hair Color Classification). Face and content validity met recommended expert thresholds, internal consistency was acceptable across adapted subscales, and criterion validity analyses confirmed meaningful associations between the lifestyle components and PROMIS-10 global health outcomes. The battery is low-burden, digitally deployable, and psychometrically defensible, offering a practical tool for characterizing the variables most likely to moderate tPBM response in home-use studies.

Humans↗

PubMed2026

Spatially guided in vivo single-cell functional genomics of postnatal heart.

Understanding how spatial organization and cell-cell interactions shape gene regulatory programs is central to decoding tissue development and function. The transition at birth, marked by increased circulatory demands and rapid tissue growth, requires precise spatiotemporal coordination of cardiac maturation. In this study, we generated a high-resolution spatial and temporal atlas of the postnatal mouse heart by integrating single-nucleus RNA sequencing with image-based spatial transcriptomics. This framework revealed dynamic cellular interactions, niche-specific signaling and transcriptional programs guiding cardiomyocyte maturation. To functionally test prioritized regulators in vivo and at scale, we developed PIP-seq (probe-based indel-detectable Perturb-seq), a high-throughput platform that detects single guide RNA identity, infers gene editing and profiles transcription from fixed nuclei. Applying PIP-seq to the developing postnatal heart, we identified 21 previously uncharacterized regulators of cardiomyocyte maturation, including genes essential for sarcomere assembly, metabolic reprogramming and electrophysiological transitions. Together, our findings define how microenvironmental signals and intrinsic gene programs cooperate to guide heart maturation and establish a broadly applicable framework for functional genomics in complex tissues.

Animals↗

PubMed2026

Predicting cellular responses to perturbation across diverse contexts with State.

While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. State is trained using single-cell gene expression data to predict perturbation effects across sets of cells. State improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling, and chemical perturbations with significantly improved accuracy compared with baselines. Its cell embeddings trained on observational data from 167 million cells enable the identification of strong perturbations in cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that can be used to evaluate future models. Overall, the performance and flexibility of State set the stage for scaling the development of AI models of cell state.

Machine Learning↗

PubMed2025

Single-cell-scale spatial transcriptome of the developing and adult mouse ovary.

Mammalian ovary development is essential for female fertility, involving the complex spatial patterning of diverse cell types to establish the finite reserve of ovarian follicles. While single-cell transcriptome analyses have provided important insights into the mechanisms driving specification and developmental trajectories of ovarian cells, they disrupt this crucial spatial context. To overcome this limitation, we used 10X Genomics Visium HD spatial transcriptomics to analyze the developing mouse ovary while maintaining its native cellular architecture. We captured all ovarian cell types at eight key fetal and postnatal timepoints, generating a near single cell resolution library of spatial gene expression across ovarian development. This comprehensive dataset allows analysis of dynamic transcriptional signatures associated with unique spatial patterning throughout development, including the establishment of cortex and medulla and assembly of ovarian follicles in each region. This dataset represents a fundamental resource for the investigation of regulatory mechanisms driving spatial patterning of the ovary and opens new avenues to explore the spatial determinants of female fertility and reproductive longevity.

Journal Article↗

PubMed2025

Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution.

Computational multi-scale pandemic modelling remains a major and timely challenge. Here we identify specific requirements for a new class of models simulating pandemics across three scales: (1) pathogen evolution, often punctuated by the rapid emergence of new variants, (2) human interactions within a heterogeneous population, and (3) public health responses which constrain individual actions to control the disease transmission. We then present a pandemic modelling framework satisfying these requirements and capable of simulating feedback loops between dynamics unfolding at these different scales. The developed framework comprises a stochastic agent-based model of pandemic spread, coupled with a phylodynamic model that incorporates within-host pathogen evolution. It is validated with a case study, modelling the punctuated evolution of SARS-CoV-2, based on global and contemporary genomic surveillance data, which captures a large heterogeneous population. We demonstrate that the model replicates the essential features of the COVID-19 pandemic and virus evolution, while retaining computational tractability and scalability.

SARS-CoV-2↗

PubMed2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform.

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

Humans↗

PubMed2026

Premenarche risk factors for future dysmenorrhoea: a prospective cohort study.

BACKGROUND: Dysmenorrhoea, or pain during menstruation, is common in adolescence and is often dismissed or left untreated. Dysmenorrhoea can interfere with daily functioning and can lead to other chronic pain conditions; however, little is known about the risk factors for dysmenorrhoea. We aimed to characterise premenarche risk factors for the presence and severity of future dysmenorrhoea. METHODS: In this prospective cohort study, we obtained data for female adolescents from the population-based Adolescent Brain Cognitive Development Study (USA) who were premenarchal at baseline (age 9-10 years) and had both reached menarche and completed the Menstrual Cycle Survey at 3-year follow-up (age 12-13 years). Parents or guardians provided sociodemographic information and completed the Child Behavior Checklist, the Sleep Disturbance Scale for Children, and the Pubertal Development Scale, which captured data on non-painful somatic symptoms, attention problems, anxiety, depression, sleep disturbances, and pubertal development at baseline. Our primary objective was to analyse associations between dysmenorrhoea at 3-year follow-up (status and severity) with select symptom domains (sleep problems, attention problems, somatic symptoms, anxious or depressive symptoms, and baseline pain status) at baseline. We also investigated associations between dysmenorrhoea and participant characteristics (pubertal status, race or ethnicity, and income-to-needs ratio) that underlie social determinants of health. Differences by race were tested using Fisher exact tests. Differences by ethnicity and baseline pain status were tested using χ2 tests. Differences in continuous variables were assessed using ANOVA. Wilcoxon-Rank Sum tests were used in analyses of sleep problems, attention problems, and somatic symptoms, and ANOVA was used for pubertal status and income-to-needs ratio. Multinomial logistic regression was used to test associations with dysmenorrhoea severity, and linear regression was used to test associations with dysmenorrhoea status and menstrual pain interference. FINDINGS: 2254 female adolescents were included in this study. 1299 (57·6%) participants developed dysmenorrhoea at age 12-13 years, and 247 (19·0% of those with dysmenorrhoea) reported severe dysmenorrhoea. Non-painful somatic symptoms were prospectively associated with future dysmenorrhoea (odds ratio [OR] 1·17 [95% CI 1·04-1·32]; p=0·0070), whereas anxiety or depression, sleep disturbances, and attention problems were not. Sleep disturbances were prospectively associated with menstrual pain interference (β coefficient 0·16 [95% CI 0·01-0·31]). Advanced pubertal status at ages 9-10 years was prospectively associated with risk of dysmenorrhoea 3 years later (OR 1·79 [95% CI 1·47-2·17]; p<0·0001), as was lower income-to-needs ratio (0·96 [0·93-1·00]; p=0·031). Black (1·38 [1·05-1·83]; p=0·024) and Hispanic (1·34 [1·03-1·68]; p=0·010) young females were at a significantly greater risk of experiencing dysmenorrhoea than were White and non-Hispanic young females, respectively. INTERPRETATION: Sociodemographic characteristics and clinical symptoms present before menarche might help to identify at-risk individuals for dysmenorrhoea before pain becomes a lifelong issue. FUNDING: The National Institute of Nursing Research, the National Institute of Diabetes and Digestive and Kidney Diseases, and the Eunice Kennedy Shriver National Institute for Child Health and Human Development.

Humans↗

PubMed2026

Machine Learning-Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics.

BACKGROUND: Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk scores, and large-scale proteomics to develop a unified model for enhanced CAD risk prediction. METHODS: Using data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32 330) and internal validation (n=13 857) sets, and Scotland/Wales participants formed an external validation set (n=5775). Incident CAD was ascertained from linked health records. A 202-protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations-guided forward selection identified a compact protein panel. RESULTS: Across cohorts, the median age was 58 years and ∼45% were men. Protein proteomic risk score was dose-dependently associated with CAD risk. Compared with conventional risk factors alone, integrating polygenic risk scores and protein proteomic risk scores improved discrimination, with the area under the curve increasing from 0.750 (95% CI, 0.732-0.767) to 0.789 (95% CI, 0.772-0.805) in internal validation and from 0.717 (95% CI, 0.683-0.750) to 0.762 (95% CI, 0.732-0.791) in external validation. A 9-protein panel (GDF15 [growth differentiation factor 15], MMP12 [matrix metalloproteinase 12], NPPB [natriuretic peptide B], PGF [placental growth factor], REN [renin], ADGRG2 [adhesion G-protein coupled receptor], ACE2 [angiotensin-converting enzyme 2], CDCP1 [CUB domain-containing protein 1], CXCL17 [C-X-C motif chemokine ligand 17)]) captured most proteomic predictive information. CONCLUSIONS: Our findings demonstrate that integrating conventional risk factors, polygenic risk scores, and proteomic data improves CAD risk prediction. This study highlights the utility of proteomics in precision cardiovascular medicine and simplified risk stratification tools.

Humans↗

PubMed2025

Targeted, Genome-scale Overexpression in Proteobacteria.

Targeted, genome-scale gene perturbation screens using Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) and activation (CRISPRa) have revolutionized eukaryotic genetics, advancing medical, industrial, and basic research. Although CRISPRi knockdowns have been broadly applied in bacteria, options for genome-scale gene overexpression face key limitations. Here, we develop a facile approach for genome-scale overexpression in bacteria we call, "CRISPRtOE" (CRISPR transposition and OverExpression). We first create a platform for comprehensive gene targeting using CRISPR-associated transposons (CAST) and show that transposition occurs at a higher frequency in non-transcribed DNA. We then demonstrate that CRISPRtOE can upregulate gene expression in Proteobacteria with medical and industrial relevance by integrating synthetic promoters of varying strength upstream of target genes. Finally, we employ CRISPRtOE screening at the genome-scale in the model bacterium Escherichia coli and the non-model biofuel producer Zymomonas mobilis, recovering known and novel antibiotic and engineering targets. We envision that CRISPRtOE will be a valuable overexpression tool for antibiotic mode of action, industrial strain optimization, and gene function discovery in bacteria.

Journal Article↗

PubMed2025

Novel Predictive Spatial Biomarker in Non-Small Cell Lung Carcinoma: The Diversity of Niches Unlocking Treatment Sensitivity (DONUTS).

Probabilistic spatial modelling techniques developed on large-scale tumor-immune Atlases (~35M individually mapped cells; 50,000 high power fields) were used to characterize predictive features of treatment-responsive lung cancer. We identified CD8+FoxP3+ cell density as a robust pre-treatment biomarker for outcomes across disease stages and therapy types. In parallel, single-cell RNAseq studies of CD8+FoxP3+ T-cells revealed an activated, early effector phenotype, substantiating an anti-tumor role, and contrasting with CD4+FoxP3+ T-regulatory cells. A spatial biomarker was developed using an empirical probabilistic model to define the immediate cell neighbors or niche surrounding CD8+FoxP3+ cells and proximity to the tumor-stromal boundary. The resultant 'Diversity of Niches Unlocking Treatment Sensitivity (DONUTS)' are more prevalent than the CD8+FoxP3+ cells themselves, mitigating sampling error in small biopsies. Further, the DONUTS only require four markers, are additive to PD-L1, and associate with tertiary lymphoid structure counts. Taken together, the DONUTS represent a next-generation predictive biomarker poised for clinical implementation.

AstroPath↗

PubMed2025

Multicolor Cell Lineage Tracing Using MAGIC Markers Strategies.

Multicolor MAGIC Markers strategies are useful lineage tracing tools to study brain development at a multicellular scale. In this chapter, we describe an in utero electroporation method to simultaneously label multiple neighboring progenitors and their respective progeny using these multicolor reporters. In utero electroporation enables the introduction of any gene of interest into embryonic neural progenitors lining the brain ventricles through a simple pipeline consisting of a micro-injection followed by the application of electrical pulses. Successful in utero electroporation requires a concise yet complete understanding of each step of the surgical protocol, spanning from the preoperative preparation to the postoperative care, as well as the MAGIC Markers tool outlined in this study. Besides a detailed protocol, we present non-integrative and integrative approaches to demonstrate the range of cell and lineage tracking possibilities of multicolored progenitors and their descent over time.

Cell Lineage↗

PubMed2026

Dynamic Disentanglement Enables Highly Crystalline Fluorinated Polyimide Films Featuring Superior Dielectric Property and Intrinsic Thermal Conductivity.

The advancement of high-frequency communication and miniaturized electronics necessitates dielectric materials that combine high intrinsic thermal conductivity (λ) with low dielectric loss (Df) to mitigate signal delay and thermal accumulation. Conventional strategies, particularly nanocomposite approaches, often struggle to simultaneously achieve high λ and low Df without compromising processability or mechanical integrity. Herein, a semicrystalline polyimide (TAHQ/TFMB) architecture is initially established by circumventing the amorphous nature of fluorinated systems via rigid-rod backbone design and programmed thermal processing. Subsequently, minor dynamically exchangeable siloxane segments (≤ 1 mol%) act as molecular disentanglement switches, triggering topological rearrangement that promotes the formation of widely distributed crystal nuclei and enables the subsequent development of large-scale crystalline domains, ultimately yielding an enhanced crystallinity exceeding 50%. The optimally designed film with merely 0.25 mol% siloxane exhibits an enhanced in-plane λ of 2.33 W·m-1·K-1 and an ultralow Df of 0.00142 at 10 GHz. This synergy facilitates the first realization of a broadband thermoacoustic generator featuring an all-organic substrate and a flexible hairpin bandpass filter with excellent signal transmission performance. Furthermore, the DBPI-0.25 film exhibits excellent thermal stability (Td5% = 478.3°C), superior moisture resistance (water uptake of 0.41%), and good mechanical flexibility, rendering it highly suitable for advanced microelectronics.

crystalline polyimide↗

PubMed2026

Neurocorrelates of nocturnal enuresis in pre-adolescent children.

INTRODUCTION: Nocturnal enuresis (NE) is a common neurodevelopmental condition, yet its underlying neural mechanisms remain unclear. This study leverages the large-scale Adolescent Brain Cognitive Development (ABCD) dataset to identify structural and functional brain correlates associated with active symptoms and the resolution of bedwetting. METHODS: Using cross-sectional data from 3472 participants aged 9-10 years, children were categorized into three groups: active nocturnal enuresis (ANE, n = 225), history of nocturnal enuresis (HNE, n = 1171), and healthy control groups (CG, n = 2076). Multimodal neuroimaging protocol evaluated macrostructural properties via structural MRI (sMRI), microstructural white matter integrity via diffusion MRI (dMRI), and functional connectivity via resting-state fMRI (fMRI). Group differences were evaluated using linear models within an ANCOVA framework, adjusting for intracranial volume and handedness with False Discovery Rate (FDR) correction. RESULTS: Compared to controls, the ANE group exhibited a significant volume deficit in the right caudate, decreased sulcal depth in the left insula, and lower internal correlation within the Cingulo-Opercular Network (CON). Conversely, the dry HNE group demonstrated significant structural adaptations, including bilaterally larger putamen volumes and increased right caudate volume compared to the ANE group. The HNE group also showed increased microstructural density (decreased mean diffusivity) in the bilateral hippocampus and an increased cortical surface area in the left insula. Both NE groups demonstrated persistently reduced functional coupling within the CON. CONCLUSIONS: Nocturnal enuresis appears to be associated with a potential complex central signaling deficits. Reduced internal correlation within the CON across both active and former bedwetters indicates a potential for impairment in processing internal homeostatic bladder signals during sleep.

Humans↗

PubMed2005

Construction of a 2-Mb resolution BAC microarray for CGH analysis of canine tumors.

Recognition of the domestic dog as a model for the comparative study of human genetic traits has led to major advances in canine genomics. The pathophysiological similarities shared between many human and dog diseases extend to a range of cancers. Human tumors frequently display recurrent chromosome aberrations, many of which are hallmarks of particular tumor subtypes. Using a range of molecular cytogenetic techniques we have generated evidence indicating that this is also true of canine tumors. Detailed knowledge of these genomic abnormalities has the potential to aid diagnosis, prognosis, and the selection of appropriate therapy in both species. We recently improved the efficiency and resolution of canine cancer cytogenetics studies by developing a small-scale genomic microarray comprising a panel of canine BAC clones representing subgenomic regions of particular interest. We have now extended these studies to generate a comprehensive canine comparative genomic hybridization (CGH) array that comprises 1158 canine BAC clones ordered throughout the genome with an average interval of 2 Mb. Most of the clones (84.3%) have been assigned to a precise cytogenetic location by fluorescence in situ hybridization (FISH), and 98.5% are also directly anchored within the current canine genome assembly, permitting direct translation from cytogenetic aberration to DNA sequence. We are now using this resource routinely for high-throughput array CGH and single-locus probe analysis of a range of canine cancers. Here we provide examples of the varied applications of this resource to tumor cytogenetics, in combination with other molecular cytogenetic techniques.

Animals↗