'Capacity building skills in the implementation of evidence-based practice for community health nurses: an exploratory descriptive cohort study'.

Background: While nurses are strategically placed to support the achievement of universal health, their practice may not always be informed by evidence, especially in a context where research evidence is not commonly used to inform practice.Objective/Aim/Hypothesis: Improve management of clients with CNCDs in Caribbean community through evidence-based practice (EBP) capacity building workshops.Design/Methods: A descriptive, quantitative design was used for this study. Participants included Community Based Nurses who attended an initial and recall workshop on EBP in a Caribbean island. All participants were included as the sample. Data were collected as a Pre-Test and Post Test before the initial workshop and at the recall workshop. Analysis was done quantitatively. Since the sample was small, only descriptive statistics were used.Results: Data showed 64% of participants had no experience with EBP, 55% needed more essential resources to participate in EBP and 55% recognized a need to include EBP to change their clinical practice effectively.Conclusions: The nurses and district supervisors observed a high level of interest and commitment to initiating and completing EBP projects. However, the reality of significant workplace demands, and limitations in consistent logistical and supervisory support impacted long-term sustainability.

A common cis-regulatory variant impacts normal-range and disease-associated human facial shape through regulation of PKDCC during chondrogenesis.

Genome-wide association studies (GWAS) identified thousands of genetic variants linked to phenotypic traits and disease risk. However, mechanistic understanding of how GWAS variants influence complex morphological traits and can, in certain cases, simultaneously confer normal-range phenotypic variation and disease predisposition, is still largely lacking. Here, we focus on rs6740960, a single nucleotide polymorphism (SNP) at the 2p21 locus, which in GWAS studies has been associated both with normal-range variation in jaw shape and with an increased risk of non-syndromic orofacial clefting. Using in vitro derived embryonic cell types relevant for human facial morphogenesis, we show that this SNP resides in an enhancer that regulates chondrocytic expression of PKDCC - a gene encoding a tyrosine kinase involved in chondrogenesis and skeletal development. In agreement, we demonstrate that the rs6740960 SNP is sufficient to confer chondrocyte-specific differences in PKDCC expression. By deploying dense landmark morphometric analysis of skull elements in mice, we show that changes in Pkdcc dosage are associated with quantitative changes in the maxilla, mandible, and palatine bone shape that are concordant with the facial phenotypes and disease predisposition seen in humans. We further demonstrate that the frequency of the rs6740960 variant strongly deviated among different human populations, and that the activity of its cognate enhancer diverged in hominids. Our study provides a mechanistic explanation of how a common SNP can mediate normal-range and disease-associated morphological variation, with implications for the evolution of human facial features.

Automated 3D Landmarking of the Skull: A Novel Approach for Craniofacial Analysis.

Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising 9,999 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (>0.988), and automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.

Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population.

Human craniofacial shape is highly variable yet highly heritable with genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the normal population. We compared three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores revealed a polygenic basis for normal facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples showed craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing new insights into the genetic intersection of complex traits and Mendelian disorders.

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.

Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups. Discussion: netMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

ILIAD: a suite of automated Snakemake workflows for processing genomic data for downstream applications.

BACKGROUND: Processing raw genomic data for downstream applications such as imputation, association studies, and modeling requires numerous third-party bioinformatics software tools. It is highly time-consuming and resource-intensive with computational demands and storage limitations that pose significant challenges that increase cost. The use of software tools independent of one another, in a disjointed stepwise fashion, increases the difficulty and sets forth higher error rates because of fragmented job executions in alignment, variant calling, and/or build conversion complications. As sequencing data availability grows, the ability for biologists to process it using stable, automated, and reproducible workflows is paramount as it significantly reduces the time to generate clean and reliable data. RESULTS: The Iliad suite of genomic data workflows was developed to provide users with seamless file transitions from raw genomic data to a quality-controlled variant call format (VCF) file for downstream applications. Iliad benefits from the efficiency of the Snakemake best practices framework coupled with Singularity and Docker containers for repeatability, portability, and ease of installation. This feat is accomplished from the onset with download acquisitions of any raw data type (FASTQ, CRAM, IDAT) straight through to the generation of a clean merged data file that can combine any user-preferred datasets using robust programs such as BWA, Samtools, and BCFtools. Users can customize and direct their workflow with one straightforward configuration file. Iliad is compatible with Linux, MacOS, and Windows platforms and scalable from a local machine to a high-performance computing cluster. CONCLUSION: Iliad offers automated workflows with optimized time and resource management that are comparable to other workflows available but generates analysis-ready VCF files from the most common datatypes using a single command. The storage footprint challenge of genomic data is overcome by utilizing temporary intermediate files before the final VCF is generated. This file is ready for use in imputation, genome-wide association study (GWAS) pipelines, high-throughput population genetics studies, select gene candidate studies, and more. Iliad was developed to be portable, compatible, scalable, robust, and repeatable with a simplistic setup, so biologists that are less familiar with programming can manage their own big data with this open-source suite of workflows.

Joint multi-ancestry and admixed GWAS reveals the complex genetics behind human cranial vault shape.

The cranial vault in humans is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conduct a joint multi-ancestry and admixed multivariate genome-wide association study on 3D cranial vault shape extracted from magnetic resonance images of 6772 children from the ABCD study cohort yielding 30 genome-wide significant loci. Follow-up analyses indicate that these loci overlap with genomic risk loci for sagittal craniosynostosis, show elevated activity cranial neural crest cells, are enriched for processes related to skeletal development, and are shared with the face and brain. We present supporting evidence of regional localization for several of the identified genes based on expression patterns in the cranial vault bones of E15.5 mice. Overall, our study provides a comprehensive overview of the genetics underlying normal-range cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.

Transgenic ferret models define pulmonary ionocyte diversity and function.

Speciation leads to adaptive changes in organ cellular physiology and creates challenges for studying rare cell-type functions that diverge between humans and mice. Rare cystic fibrosis transmembrane conductance regulator (CFTR)-rich pulmonary ionocytes exist throughout the cartilaginous airways of humans1,2, but limited presence and divergent biology in the proximal trachea of mice has prevented the use of traditional transgenic models to elucidate ionocyte functions in the airway. Here we describe the creation and use of conditional genetic ferret models to dissect pulmonary ionocyte biology and function by enabling ionocyte lineage tracing (FOXI1-CreERT2::ROSA-TG), ionocyte ablation (FOXI1-KO) and ionocyte-specific deletion of CFTR (FOXI1-CreERT2::CFTRL/L). By comparing these models with cystic fibrosis ferrets3,4, we demonstrate that ionocytes control airway surface liquid absorption, secretion, pH and mucus viscosity-leading to reduced airway surface liquid volume and impaired mucociliary clearance in cystic fibrosis, FOXI1-KO and FOXI1-CreERT2::CFTRL/L ferrets. These processes are regulated by CFTR-dependent ionocyte transport of Cl- and HCO3-. Single-cell transcriptomics and in vivo lineage tracing revealed three subtypes of pulmonary ionocytes and a FOXI1-lineage common rare cell progenitor for ionocytes, tuft cells and neuroendocrine cells during airway development. Thus, rare pulmonary ionocytes perform critical CFTR-dependent functions in the proximal airway that are hallmark features of cystic fibrosis airway disease. These studies provide a road map for using conditional genetics in the first non-rodent mammal to address gene function, cell biology and disease processes that have greater evolutionary conservation between humans and ferrets.

Data-driven trait heritability-based extraction of human facial phenotypes.

A genome-wide association study (GWAS) of a complex, multi-dimensional morphological trait, such as the human face, typically relies on predefined and simplified phenotypic measurements, such as inter-landmark distances and angles. These measures are predominantly designed by human experts based on perceived biological or clinical knowledge. To avoid use handcrafted phenotypes (i.e., a priori expert-identified phenotypes), alternative automatically extracted phenotypic descriptors, such as features derived from dimension reduction techniques (e.g., principal component analysis), are employed. While the features generated by such computational algorithms capture the geometric variations of the biological shape, they are not necessarily genetically relevant. Therefore, genetically informed data-driven phenotyping is desirable. Here, we propose an approach where phenotyping is done through a data-driven optimization of trait heritability, defined as the degree of variation in a phenotypic trait in a population that is due to genetic variation. The resulting phenotyping process consists of two steps: 1) constructing a feature space that models shape variations using dimension reduction techniques, and 2) searching for directions in the feature space exhibiting high trait heritability using a genetic search algorithm (i.e., heuristic inspired by natural selection). We show that the phenotypes resulting from the proposed trait heritability-optimized training differ from those of principal components in the following aspects: 1) higher trait heritability, 2) higher SNP heritability, and 3) identification of the same number of independent genetic loci with a smaller number of effective traits. Our results demonstrate that data-driven trait heritability-based optimization enables the automatic extraction of genetically relevant phenotypes, as shown by their increased power in genome-wide association scans.

Insulin sensitization by small molecules enhancing GLUT4 translocation.

Insulin resistance (IR) is the root cause of type II diabetes, yet no safe treatment is available to address it. Using a high throughput compatible assay that measures real-time translocation of the glucose transporter glucose transporter 4 (GLUT4), we identified small molecules that potentiate insulin action. In vivo, these insulin sensitizers improve insulin-stimulated GLUT4 translocation, glucose tolerance, and glucose uptake in a model of IR. Using proteomic and CRISPR-based approaches, we identified the targets of those compounds as Unc119 proteins and solved the structure of Unc119 bound to the insulin sensitizer. This study identifies compounds that have the potential to be developed into diabetes treatment and establishes Unc119 proteins as targets for improving insulin sensitivity.

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.

Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

Rapid Conversion to a Completely Virtual Multidisciplinary ALS Clinic in Response to the COVID-19 Pandemic: Implications for Future Care Delivery.

OBJECTIVES: The goals of this study were to assess the feasibility of maintaining multidisciplinary remote care, patient preferences, and outcomes of this transition because of COVID-19. METHODS: From March 18, 2020 to June 3, 2020, 127 patients with amyotrophic lateral sclerosis (ALS) who were scheduled to be seen in our ALS clinic were contacted and scheduled according their preference for a telemedicine visit, telephone visit, or postponement until the next available in-person visit. Age, time from disease onset, ALS Functional Rating Scale-Revised, patient choices, and outcomes were recorded. RESULTS: Patient visit preferences were 69% telemedicine, 21% telephone, and 10% postpone for a later in-clinic visit. Patients with higher ALS Functional Rating Scale-Revised were more likely to choose the next in-person opening (P = 0.04). Age and time from disease onset were not related to visit type preference. There were 118 virtual encounters, of which 91 (77%) began as telemedicine and 27 (23%) as telephone visits. Most telemedicine visits were conducted successfully, but 10 were converted to a telephone visit. The clinic maintained 88.6% of patient volume compared with the prior year, during which most visits were in-person. CONCLUSIONS: Telemedicine care using synchronous videoconferencing is preferable and feasible for most patients on short notice, with telephone as back-up. Clinic volumes can be maintained. These findings support the conversion of a multidisciplinary ALS clinic to 1 with exclusively virtual visits when future events again disrupt in-person care.

Sequence-based allelic variations and frequencies for 22 autosomal STR loci in the Lebanese population.

This is the first study that characterizes the sequence-based allelic variations of 22 autosomal Short Tandem Repeat (aSTR) loci in a population dataset collected from Lebanon. Genomic DNA extracts from 195 unrelated Lebanese individuals were amplified with PowerSeq 46GY System Prototype. Targeted amplicons were subjected to DNA library preparation and sequenced on the Verogen MiSeq FGx Sequencing System. Raw FASTQ data files were processed by STRait Razor v3. Sequence strings were annotated according to the considerations of the DNA Commission of the International Society for Forensic Genetics (ISFG) and tabulated herein with their respective allelic frequencies and GeneBank accession and version numbers. The sequenced Lebanese dataset resulted in 429 distinct allelic sequences as compared to the 236 alleles identified by length only. The increase in the number of alleles was observed at 18 out of 22 aSTR loci and was attributed to the sequence variations residing in both the STR repeat motifs and flanking regions. The study uncovered 25 novel aSTR allelic sequences across 12 loci for which GenBank records did not previously exist in the STRSeq BioProject, PRJNA380127. For a concordance check, the length-based allelic calls derived from the full sequences were compared to those genotyped using capillary electrophoresis (CE) methods. Population genetic parameters relevant to the evaluation of forensic DNA evidence were assessed for the sequence-based data and compared to the parameters generated from the length-based information. Using the sequence-based data, Analysis of MOlecular VAriance (AMOVA), genetic distances, and population genetic structure were evaluated for 1231 individuals sampled from the Lebanese and four U.S. populations (African American, Asian, Caucasian, and Hispanic). The results were tabulated and visualized in a population tree, multidimensional scaling scatter plots, and bar plots. This newly established sequence-based database for the Lebanese population can be beneficial for extending NGS applicability to casework or paternity testing and assessing the strength of evidence for NGS-STR profiles. The described novel sequence variants at certain loci can further help in the effort to characterize the sequence diversity of STR markers from different populations around the world.

Using computed tomography to recover hidden medieval fragments beneath early modern leather bindings, first results.

Medieval bindings fragments have become increasingly interesting to Humanities researchers as sources for the textual and material history of medieval Europeans. Later book binders used these discarded and repurposed pieces of earlier medieval manuscripts to reinforce the structures of other manuscripts and printed books. That many of these fragments are contained within and obscured by decorative bindings that cannot be dismantled ethically has limited their discovery and description. Although previous attempts to recover these texts using IRT and MA-XRF scanning have been successful, the extensive time required to scan a single book, and the need to modify or create specialized IRT or MA-XRF equipment for this method are drawbacks. Our research proposes and tests the capabilities of medical CT scanning technologies (commonly available at research university medical schools) for making visible and legible these fragments hidden under leather bindings. Our research team identified three sixteenth-century printed codices in our university libraries that were evidently bound in tawed leather by one workshop. The damaged cover of one of these three had revealed medieval manuscript fragments on the book spine; this codex served as a control for testing the other two volumes to see if they, too, contain fragments. The use of a medical CT scanner proved successful in visualizing interior book-spine structures and some letterforms, but not all of the text was made visible. The partial success of CT-scanning points to the value of further experimentation, given the relatively wide availability of medical imaging technologies, with their potential for short, non-destructive, 3D imaging times.

Exploring regional aspects of 3D facial variation within European individuals.

Facial ancestry can be described as variation that exists in facial features that are shared amongst members of a population due to environmental and genetic effects. Even within Europe, faces vary among subregions and may lead to confounding in genetic association studies if unaccounted for. Genetic studies use genetic principal components (PCs) to describe facial ancestry to circumvent this issue. Yet the phenotypic effect of these genetic PCs on the face has yet to be described, and phenotype-based alternatives compared. In anthropological studies, consensus faces are utilized as they depict a phenotypic, not genetic, ancestry effect. In this study, we explored the effects of regional differences on facial ancestry in 744 Europeans using genetic and anthropological approaches. Both showed similar ancestry effects between subgroups, localized mainly to the forehead, nose, and chin. Consensus faces explained the variation seen in only the first three genetic PCs, differing more in magnitude than shape change. Here we show only minor differences between the two methods and discuss a combined approach as a possible alternative for facial scan correction that is less cohort dependent, more replicable, non-linear, and can be made open access for use across research groups, enhancing future studies in this field.

Using data-driven phenotyping to investigate the impact of sex on 3D human facial surface morphology.

The effects of sex on human facial morphology have been widely documented. Because sexual dimorphism is relevant to a variety of scientific and applied disciplines, it is imperative to have a complete and accurate account of how and where male and female faces differ. We apply a comprehensive facial phenotyping strategy to a large set of existing 3D facial surface images. We investigate facial sexual dimorphism in terms of size, shape, and shape variance. We also assess the ability to correctly assign sex based on shape, both for the whole face and for subregions. We applied a predefined data-driven segmentation to partition the 3D facial surfaces of 2446 adults into 63 hierarchically linked regions, ranging from global (whole face) to highly localized subparts. Each facial region was then analyzed with spatially dense geometric morphometrics. To describe the major modes of shape variation, principal components analysis was applied to the Procrustes aligned 3D points comprising each of the 63 facial regions. Both nonparametric and permutation-based statistics were then used to quantify the facial size and shape differences and visualizations were generated. Males were significantly larger than females for all 63 facial regions. Statistically significant sex differences in shape were also seen in all regions and the effects tended to be more pronounced for the upper lip and forehead, with more subtle changes emerging as the facial regions became more granular. Males also showed greater levels of shape variance, with the largest effect observed for the central forehead. Classification accuracy was highest for the full face (97%), while most facial regions showed an accuracy of 75% or greater. In summary, sex differences in both size and shape were present across every part of the face. By breaking the face into subparts, some shape differences emerged that were not apparent when analyzing the face as a whole. The increase in facial shape variance suggests possible evolutionary origins and may offer insights for understanding congenital facial malformations. Our classification results indicate that a high degree of accuracy is possible with only parts of the face, which may have implications for biometrics applications.

Hybrid autoencoder with orthogonal latent space for robust population structure inference.

Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. Namely, it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

Utilization of a Prenatal Palliative Care Consultation Pathway for Congenital Heart Disease.

Over 4 decades, short- and long-term survival for children with congenital heart disease (CHD) has significantly increased. Those with complex CHD have a 1-year mortality rate of approximately 25%. Prenatal access to palliative care ensures a comprehensive approach to the infant's physical, emotional, spiritual, and psychosocial well-being and offers enhanced support for parents and families while providing an interprofessional approach to care. Pediatric cardiac provider attitudes, knowledge, patterns of prenatal palliative care, and consultation for fetuses with complex heart disease to guide increased and appropriate prenatal consultation were explored for a quality improvement project completed at a single institution. A retrospective cardiac database review identified that 21.5% of maternal patients seen prenatally and carrying a fetus with complex CHD would have qualified for prenatal palliative care consultation. To increase prenatal palliative care consultation, a pathway for referral was developed. A pre-evaluation of pediatric cardiac provider attitudes regarding palliative care was measured. Survey data informed the intervention phase of prenatal palliative care educational material development, which was presented to pediatric cardiac providers. The number of prenatal palliative care consults was tracked. A postintervention survey completed by pediatric cardiac providers showed increased understanding and utilization of palliative care for comfort and support and not just reserved for death preparation. Greater than 93% agreed or strongly agreed that they gained a better understanding of appropriate use of palliative/supportive care. As pediatric cardiac providers become more knowledgeable about early integration of palliative care, parents and infants will benefit greatly from referrals and initiation of palliative care at the time of prenatal diagnosis.

Point-of-Care Ultrasound for Pulse Checks in Pediatric Cardiac Arrest: Two Illustrative Cases.

ABSTRACT: Cardiac arrest is an infrequent but high-stakes scenario in pediatrics. Manual central pulse checks are unreliable. Point-of-care ultrasound is a noninvasive technique to visualize the heart and central vessels during resuscitation. We describe 2 cases in which point-of-care ultrasound helped aid management decisions in pediatric cardiac arrest.