Dynamic evolution in the key honey bee pathogen deformed wing virus: Novel insights into virulence and competition using reverse genetics.

The impacts of invertebrate RNA virus population dynamics on virulence and infection outcomes are poorly understood. Deformed wing virus (DWV), the main viral pathogen of honey bees, negatively impacts bee health, which can lead to colony death. Despite previous reports on the reduction of DWV diversity following the arrival of the parasitic mite Varroa destructor, the key DWV vector, we found high genetic diversity of DWV in infested United States honey bee colonies. Phylogenetic analysis showed that divergent US DWV genotypes are of monophyletic origin and were likely generated as a result of diversification after a genetic bottleneck. To investigate the population dynamics of this divergent DWV, we designed a series of novel infectious cDNA clones corresponding to coexisting DWV genotypes, thereby devising a reverse-genetics system for an invertebrate RNA virus quasispecies. Equal replication rates were observed for all clone-derived DWV variants in single infections. Surprisingly, individual clones replicated to the same high levels as their mixtures and even the parental highly diverse natural DWV population, suggesting that complementation between genotypes was not required to replicate to high levels. Mixed clone-derived infections showed a lack of strong competitive exclusion, suggesting that the DWV genotypes were adapted to coexist. Mutational and recombination events were observed across clone progeny, providing new insights into the forces that drive and constrain virus diversification. Accordingly, our results suggest that Varroa influences DWV dynamics by causing an initial selective sweep, which is followed by virus diversification fueled by negative frequency-dependent selection for new genotypes. We suggest that this selection might reflect the ability of rare lineages to evade host defenses, specifically antiviral RNA interference (RNAi). In support of this hypothesis, we show that RNAi induced against one DWV strain is less effective against an alternate strain from the same population.

A Cross-Sectional Evaluation of Outcomes of Pediatric Thyroglossal Duct Cyst Excision.

INTRODUCTION: Thyroglossal duct cysts (TGDCs) are the most common form of congenital neck cysts. They may become infected causing dysphagia or respiratory distress. Accordingly, the treatment is always surgical removal. OBJECTIVES: The objectives of this article were to examine complications following TGDC excision by surgical specialty, demographics, and comorbid conditions. METHODS: A retrospective review of the National Surgical Quality Improvement Program database was performed. Pediatric cases from January 1, 2014 to November 1, 2015 with a current procedure terminology code of 60,280 (excision of TGDC or sinus) were included. Statistical analysis was performed to assess associations between complications and surgical specialty, demographics, and comorbidities. RESULTS: Of the 867 cases that met inclusion criteria, the median age was 4 years. There were 448 males (52.3%) and 408 females (47.7%). Thirty-six patients (4.2%) experienced at least one 30-day complication. The most predominant complications were reoperation (19 patients, 2.2%), readmission (18 patients, 2.1%), and surgical site infection (16 patients, 1.9%). There was no statistically significant difference between complications and surgical specialty. In those experiencing a complication, there was a statistically significant difference between males (86.1%) and females (13.9%). Of patients with at least one comorbidity, 36.67% had a complication, while 17.22% did not have a complication. There was also a statistically significant difference in the percentage of patients with a past medical history of asthma between those with at least one complication (16.67%) compared to those without any complications (4.76%). CONCLUSIONS: excision is a generally safe procedure across surgical specialties. There is a higher complication rate in males compared to females as well as those with a history of at least one medical comorbidity and those with asthma. The most common 30-day complications are reoperation, readmission, and surgical site infection.

Multi-tiered analyses of honey bees that resist or succumb to parasitic mites and viruses.

BACKGROUND: Varroa destructor mites, and the numerous viruses they vector to their honey bee hosts, are among the most serious threats to honey bee populations, causing mortality and morbidity to both the individual honey bee and colony, the negative effects of which convey to the pollination services provided by honey bees worldwide. Here we use a combination of targeted assays and deep RNA sequencing to determine host and microbial changes in resistant and susceptible honey bee lineages. We focus on three study sets. The first involves field sampling of sympatric western bees, some derived from resistant stock and some from stock susceptible to mites. The second experiment contrasts three colonies more deeply, two from susceptible stock from the southeastern U.S. and one from mite-resistant bee stock from Eastern Texas. Finally, to decouple the effects of mites from those of the viruses they vector, we experimentally expose honey bees to DWV in the laboratory, measuring viral growth and host responses. RESULTS: We find strong differences between resistant and susceptible bees in terms of both viral loads and bee gene expression. Interestingly, lineages of bees with naturally low levels of the mite-vectored Deformed wing virus, also carried lower levels of viruses not vectored by mites. By mapping gene expression results against current ontologies and other studies, we describe the impacts of mite parasitism, as well as viruses on bee health against two genetic backgrounds. We identify numerous genes and processes seen in other studies of stress and disease in honey bee colonies, alongside novel genes and new patterns of expression. CONCLUSIONS: We provide evidence that honey bees surviving in the face of parasitic mites do so through their abilities to resist the presence of devastating viruses vectored by these mites. In all cases, the most divergence between stocks was seen when bees were exposed to live mites or viruses, suggesting that gene activation, rather than constitutive expression, is key for these interactions. By revealing responses to viral infection and mite parasitism in different lineages, our data identify candidate proteins for the evolution of mite tolerance and virus resistance.

The EcoCyc database: reflecting new knowledge about Escherichia coli K-12.

EcoCyc (EcoCyc.org) is a freely accessible, comprehensive database that collects and summarizes experimental data for Escherichia coli K-12, the best-studied bacterial model organism. New experimental discoveries about gene products, their function and regulation, new metabolic pathways, enzymes and cofactors are regularly added to EcoCyc. New SmartTable tools allow users to browse collections of related EcoCyc content. SmartTables can also serve as repositories for user- or curator-generated lists. EcoCyc now supports running and modifying E. coli metabolic models directly on the EcoCyc website.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology.

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms.

Current management of childhood ptosis.

PURPOSE OF REVIEW: Blepharoptosis is a common problem encountered in the pediatric ophthalmology clinic. The presentation is obvious to both parents and referring physicians and often prompts urgent consultation. The current classification and management of childhood ptosis will be reviewed. RECENT FINDINGS: Recent refinements in techniques utilizing new materials hold promise for better, more predictable outcomes and improved long-term results. Autogenous tensor tendon fascia lata harvested from the patient's thigh remains the gold standard for many ptosis surgeons in frontalis suspension; however, other materials are commonly utilized, including silicone rod, Gore-Tex (ePTFE; W.L. Gore & Associates, Flagstaff, Arizona, USA), Mersilene polyester fiber mesh and Ethibond braided polyester (Ethicon US LLC, Somerville, New Jersey, USA), Supramid monofilament nylon (S. Jackson, Inc, Alexandria, Virginia, USA), prolene, and banked fascia lata. Other techniques include levator resection, posterior approach levatorpexy, and Muller's muscle conjunctival resection both with and without superior tarsectomy. Recent studies suggest that ptosis repair can be effectively combined with strabismus surgery. SUMMARY: The management of ptosis in infants and children demands a structured and disciplined approach to avoid the development of amblyopia and long-term visual compromise. Underlying systemic problems must be identified and surgical planning discussed in a timely fashion with caregivers. Recent studies help to further define the proper timing of surgical intervention and the optimal techniques to provide the best long-term results for these patients.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases.

The MetaCyc database (MetaCyc.org) is a freely accessible comprehensive database describing metabolic pathways and enzymes from all domains of life. The majority of MetaCyc pathways are small-molecule metabolic pathways that have been experimentally determined. MetaCyc contains more than 2400 pathways derived from >46,000 publications, and is the largest curated collection of metabolic pathways. BioCyc (BioCyc.org) is a collection of 5700 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems, and pathway-hole fillers. The BioCyc website offers a variety of tools for querying and analyzing PGDBs, including Omics Viewers and tools for comparative analysis. This article provides an update of new developments in MetaCyc and BioCyc during the last two years, including addition of Gibbs free energy values for compounds and reactions; redesign of the primary gene/protein page; addition of a tool for creating diagrams containing multiple linked pathways; several new search capabilities, including searching for genes based on sequence patterns, searching for databases based on an organism's phenotypes, and a cross-organism search; and a metabolite identifier translation service.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.

BACKGROUND: Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed. RESULTS: The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S-13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. CONCLUSIONS: jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Pathway collages: personalized multi-pathway diagrams.

BACKGROUND: Metabolic pathway diagrams are a classical way of visualizing a linked cascade of biochemical reactions. However, to understand some biochemical situations, viewing a single pathway is insufficient, whereas viewing the entire metabolic network results in information overload. How do we enable scientists to rapidly construct personalized multi-pathway diagrams that depict a desired collection of interacting pathways that emphasize particular pathway interactions? RESULTS: We define software for constructing personalized multi-pathway diagrams called pathway-collages using a combination of manual and automatic layouts. The user specifies a set of pathways of interest for the collage from a Pathway/Genome Database. Layouts for the individual pathways are generated by the Pathway Tools software, and are sent to a Javascript Pathway Collage application implemented using Cytoscape.js. That application allows the user to re-position pathways; define connections between pathways; change visual style parameters; and paint metabolomics, gene expression, and reaction flux data onto the collage to obtain a desired multi-pathway diagram. We demonstrate the use of pathway collages in two application areas: a metabolomics study of pathogen drug response, and an Escherichia coli metabolic model. CONCLUSIONS: Pathway collages enable facile construction of personalized multi-pathway diagrams.

A Method to Constrain Genome-Scale Models with 13C Labeling Data.

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases.

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37,000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

Analysis of archived residual newborn screening blood spots after whole genome amplification.

BACKGROUND: Deidentified newborn screening bloodspot samples (NBS) represent a valuable potential resource for genomic research if impediments to whole exome sequencing of NBS deoxyribonucleic acid (DNA), including the small amount of genomic DNA in NBS material, can be overcome. For instance, genomic analysis of NBS could be used to define allele frequencies of disease-associated variants in local populations, or to conduct prospective or retrospective studies relating genomic variation to disease emergence in pediatric populations over time. In this study, we compared the recovery of variant calls from exome sequences of amplified NBS genomic DNA to variant calls from exome sequencing of non-amplified NBS DNA from the same individuals. RESULTS: Using a standard alignment-based Genome Analysis Toolkit (GATK), we find 62,000-76,000 additional variants in amplified samples. After application of a unique kmer enumeration and variant detection method (RUFUS), only 38,000-47,000 additional variants are observed in amplified gDNA. This result suggests that roughly half of the amplification-introduced variants identified using GATK may be the result of mapping errors and read misalignment. CONCLUSIONS: Our results show that it is possible to obtain informative, high-quality data from exome analysis of whole genome amplified NBS with the important caveat that different data generation and analysis methods can affect variant detection accuracy, and the concordance of variant calls in whole-genome amplified and non-amplified exomes.

A peptide-based method for 13C Metabolic Flux Analysis in microbial communities.

The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species.

BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity.

BACKGROUND: Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a "gold standard" of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user's tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity. RESULTS: We assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC's superior variant-calling accuracy, both for somatic mutation detection and germline variant detection. CONCLUSIONS: BAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.

Finding the missing honey bee genes: lessons learned from a genome upgrade.

BACKGROUND: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. RESULTS: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. CONCLUSIONS: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.

Meta-Analysis of the Effects of Yeast Cell Wall Extract Supple-Mentation during Mycotoxin Challenges on the Performance of Laying Hens.

A random-effects meta-analysis was conducted to investigate the effect of mycotoxins (MT) without or with the inclusion of yeast cell wall extract (YCWE, Mycosorb®, Alltech, Inc., Nicholasville, KY, USA) on laying hen performance. A total of 25 trials were collected from a literature search, and data were extracted from 8 of these that met inclusion criteria, for a total of 12 treatments and 1774 birds. Laying hens fed MT had lower (p < 0.05) body weight (BW) by -50 g, egg production by -6.3 percentage points, and egg weight by -1.95 g than control fed hens (CTRL). Inclusion of YCWE during the mycotoxin challenges (YCWE + MT) resulted in numerically greater (p = 0.441) BW by 12.5 g, while egg production and egg weight were significantly (p < 0.0001) higher by 4.2 percentage points and 1.37 g, respectively. Furthermore, economic assessment calculations indicated that YCWE may not only support hen performance but also resulted in a positive return on investment. In conclusion, mycotoxins can play a role in negatively impacting laying hen performance and profitability. Inclusion of YCWE in feed with mycotoxin challenges provided benefits to egg production and egg weight and may support profitability. As such, the inclusion of YCWE could play an important role in minimizing mycotoxin effects and in turn aid farm efficiency and profitability.

HipA-triggered growth arrest and ß-lactam tolerance in Escherichia coli are mediated by RelA-dependent ppGpp synthesis.

Persistence is a phenomenon whereby a subpopulation of bacterial cells enters a transient growth-arrested state that confers antibiotic tolerance. While entrance into persistence has been linked to the activities of toxin proteins, the molecular mechanisms by which toxins induce growth arrest and the persistent state remain unclear. Here, we show that overexpression of the protein kinase HipA in Escherichia coli triggers growth arrest by activating synthesis of the alarmone guanosine tetraphosphate (ppGpp) by the enzyme RelA, a signal typically associated with amino acid starvation. We further demonstrate that chemically suppressing ppGpp synthesis with chloramphenicol relieves inhibition of DNA replication initiation and RNA synthesis in HipA-arrested cells and restores vulnerability to ß-lactam antibiotics. HipA-arrested cells maintain glucose uptake and oxygen consumption and accumulate amino acids as a consequence of translational inhibition. We harness the active metabolism of HipA-arrested cells to provide a bacteriophage-resistant platform for the production of biotechnologically relevant compounds, which may represent an innovative solution to the costly problem of phage contamination in industrial fermentations.

Population-genomic variation within RNA viruses of the Western honey bee, Apis mellifera, inferred from deep sequencing.

BACKGROUND: Deep sequencing of viruses isolated from infected hosts is an efficient way to measure population-genetic variation and can reveal patterns of dispersal and natural selection. In this study, we mined existing Illumina sequence reads to investigate single-nucleotide polymorphisms (SNPs) within two RNA viruses of the Western honey bee (Apis mellifera), deformed wing virus (DWV) and Israel acute paralysis virus (IAPV). All viral RNA was extracted from North American samples of honey bees or, in one case, the ectoparasitic mite Varroa destructor. RESULTS: Coverage depth was generally lower for IAPV than DWV, and marked gaps in coverage occurred in several narrow regions (< 50 bp) of IAPV. These coverage gaps occurred across sequencing runs and were virtually unchanged when reads were re-mapped with greater permissiveness (up to 8% divergence), suggesting a recurrent sequencing artifact rather than strain divergence. Consensus sequences of DWV for each sample showed little phylogenetic divergence, low nucleotide diversity, and strongly negative values of Fu and Li's D statistic, suggesting a recent population bottleneck and/or purifying selection. The Kakugo strain of DWV fell outside of all other DWV sequences at 100% bootstrap support. IAPV consensus sequences supported the existence of multiple clades as had been previously reported, and Fu and Li's D was closer to neutral expectation overall, although a sliding-window analysis identified a significantly positive D within the protease region, suggesting selection maintains diversity in that region. Within-sample mean diversity was comparable between the two viruses on average, although for both viruses there was substantial variation among samples in mean diversity at third codon positions and in the number of high-diversity sites. FST values were bimodal for DWV, likely reflecting neutral divergence in two low-diversity populations, whereas IAPV had several sites that were strong outliers with very low FST. CONCLUSIONS: This initial survey of genetic variation within honey bee RNA viruses suggests future directions for studies examining the underlying causes of population-genetic structure in these economically important pathogens.

Telemedicine for retinopathy of prematurity.

PURPOSE OF REVIEW: To examine and review digital retinal imaging via telemedicine as an important screening and diagnostic tool in the management of retinopathy of prematurity (ROP). RECENT FINDINGS: The use of wide-angle digital retinal photography to detect clinically significant ROP has been described in numerous reports since 2000. Comparisons with the gold standard of binocular indirect ophthalmoscopy have been favorable. Digital image capture can provide more objective information for disease detection, thereby facilitating internet consultation and retrospective analysis as part of the electronic medical record. As the presence of plus disease in ROP is now the most important criteria for determining the need for laser treatment, computer-based image analysis can potentially provide additional benefit to digital retinal imaging. Telemedicine screening also allows for the extension of diagnostic expertise to underserved areas in both the developed and third world. The role of telemedicine in ROP education will impact both screening efforts and traditional fellowship training in future years. SUMMARY: As the worldwide incidence of ROP continues to rise, the use of telemedicine for screening, diagnostic and educational purposes will assume increasing importance in the delivery of healthcare for premature infants.

Current Strategies in Medical Management of the Geriatric Hip Fracture Patient.

Orthogeriatric hip fractures have high morbidity and mortality rates. Modern management focuses on multidisciplinary collaboration for prompt surgical stabilization, early mobilization with multimodal pain control to avoid opioid consumption, and an enhanced recovery pathway. Despite these advances, postoperative complications and mortality rates remain higher than age-matched control subjects. The authors of this article represent the orthopaedic, anesthesia, and hospitalist medicine members of a multidisciplinary team at a single, Level 1 trauma center. Our goal was to provide an up-to-date comprehensive review of orthogeriatric hip fracture perioperative management from a multidisciplinary perspective that every orthopaedic surgeon should know.