Pregnancy Outcomes After Liver Transplantation: A Systematic Review and Meta-Analysis.

INTRODUCTION: Liver transplantation (LT) remains the gold standard for treatment of end-stage liver disease. Given the increasing number of liver transplantation in females of reproductive age, our aim was to conduct a systematic review and meta-analysis evaluating pregnancy outcomes after LT. METHODS: MEDLINE, Embase, and Scopus databases were searched for relevant studies. Study selection, quality assessment, and data extraction were conducted independently by 2 reviewers. Estimates of pregnancy-related outcomes in LT recipients were generated and pooled across studies using the random-effects model. RESULTS: A comprehensive search identified 1,430 potential studies. Thirty-eight studies with 1,131 pregnancies among 838 LT recipients were included in the analysis. Mean maternal age at pregnancy was 27.8 years, with a mean interval from LT to pregnancy of 59.7 months. The live birth rate was 80.4%, with a mean gestational age of 36.5 weeks. The rate of miscarriages (16.7%) was similar to the general population (10%-20%). The rates of preterm birth, preeclampsia, and cesarean delivery (32.1%, 12.5%, and 42.2%, respectively) among LT recipients were all higher than the rates for the general US population (9.9%, 4%, and 32%, respectively). Most analyses were associated with substantial heterogeneity. DISCUSSION: Pregnancy outcomes after LT are favorable, but the risk of maternal and fetal complications is increased. Large studies along with consistent reporting to national registries are necessary for appropriate patient counseling and to guide clinical management of LT recipients during pregnancy.

Making a case for the right '-ase' in acute ischemic stroke: alteplase, tenecteplase, and reteplase.

INTRODUCTION: Alteplase, reteplase, and tenecteplase are tissue plasminogen activators (TPA) approved for the management of acute myocardial infarction. Only alteplase is also approved for the treatment of acute ischemic stroke (AIS). The US Food and Drug Administration has received reports of accidental administration of tenecteplase or reteplase instead of alteplase in patients with AIS, which can result in failure to treat patients with the intended agent and lead to potential overdose. AREAS COVERED: This review compares the molecular and clinical features of alteplase, reteplase, and tenecteplase (TNK), identifies factors contributing to medication errors among these agents, and provides steps to reduce medication errors. EXPERT OPINION: Primary factors contributing to medication errors among tissue plasminogen activators include the use of the abbreviations 'TPA,' 'tPA,' or 'TNK' in written or verbal orders and use of these agents in similar settings (e.g. emergency departments and critical care areas). Steps to reduce the likelihood of accidental substitution of tenecteplase or reteplase for alteplase in patients with AIS include the use of full brand or generic names and inclusion of the indication in written and verbal orders, the addition of alerts in automated dispensing machines and ordering systems and use of stroke boxes containing alteplase and materials for administration.

Limbform: a functional ontology-based database of limb regeneration experiments.

SUMMARY: The ability of certain organisms to completely regenerate lost limbs is a fascinating process, far from solved. Despite the extraordinary published efforts during the past centuries of scientists performing amputations, transplantations and molecular experiments, no mechanistic model exists yet that can completely explain patterning during the limb regeneration process. The lack of a centralized repository to enable the efficient mining of this huge dataset is hindering the discovery of comprehensive models of limb regeneration. Here, we introduce Limbform (Limb formalization), a centralized database of published limb regeneration experiments. In contrast to natural language or text-based ontologies, Limbform is based on a functional ontology using mathematical graphs to represent unambiguously limb phenotypes and manipulation procedures. The centralized database currently contains >800 published limb regeneration experiments comprising many model organisms, including salamanders, frogs, insects, crustaceans and arachnids. The database represents an extraordinary resource for mining the existing knowledge of functional data in this field; furthermore, its mathematical nature based on a functional ontology will pave the way for artificial intelligence tools applied to the discovery of the sought-after comprehensive limb regeneration models.

A bioinformatics expert system linking functional data to anatomical outcomes in limb regeneration.

Amphibians and molting arthropods have the remarkable capacity to regenerate amputated limbs, as described by an extensive literature of experimental cuts, amputations, grafts, and molecular techniques. Despite a rich history of experimental efforts, no comprehensive mechanistic model exists that can account for the pattern regulation observed in these experiments. While bioinformatics algorithms have revolutionized the study of signaling pathways, no such tools have heretofore been available to assist scientists in formulating testable models of large-scale morphogenesis that match published data in the limb regeneration field. Major barriers preventing an algorithmic approach are the lack of formal descriptions for experimental regenerative information and a repository to centralize storage and mining of functional data on limb regeneration. Establishing a new bioinformatics of shape would significantly accelerate the discovery of key insights into the mechanisms that implement complex regeneration. Here, we describe a novel mathematical ontology for limb regeneration to unambiguously encode phenotype, manipulation, and experiment data. Based on this formalism, we present the first centralized formal database of published limb regeneration experiments together with a user-friendly expert system tool to facilitate its access and mining. These resources are freely available for the community and will assist both human biologists and artificial intelligence systems to discover testable, mechanistic models of limb regeneration.