Psychopharmacological Treatment Algorithms of Manic/Mixed and Depressed Episodes in Pediatric Bipolar Disorder.

Introduction: Pediatric bipolar disorder (PBD) is a severe psychiatric illness diagnosed before the age of 18, which is associated with extreme shifts in mood characterized by manic and depressive episodes. In 2005, AACAP published algorithms to guide pharmacological treatment of manic/mixed episodes associated with PBD. At that time, lithium was the only Food and Drug Administration (FDA)-approved treatment for pediatric bipolar manic/mixed episodes. The goal of this article is to review evidence that has emerged since the AACAP algorithm in 2005. Methods: Literature searches were conducted through PubMed and limited to studies published between 2005 and 2021, using keywords that focused on randomized controlled trials (RCTs) for available psychopharmacological medications. In addition, the authors conducted in-depth searches for articles providing evidence for agents included in the 2005 AACAP algorithm. Results: Since the publication of the AACAP algorithm in 2005, multiple RCTs have been conducted in PBD, leading to FDA approval of five medications (aripiprazole, asenapine, olanzapine, quetiapine, and risperidone) for the treatment of manic/mixed episodes and two medications (lurasidone and olanzapine-fluoxetine combination) for the treatment of depressed episodes. Divalproex sodium and oxcarbazepine were studied in pediatric RCTs and failed to separate from placebo. Conclusions: We offer an update to the 2005 AACAP algorithms for the treatment of pediatric bipolar mixed/manic episodes and added an evidence-based algorithm for the treatment of depression in PBD. In addition to treatment algorithms, we review current evidence for efficacy of agents proposed in the AACAP algorithm and provide tables summarizing medication side effects and efficacy.

ECO: the Evidence and Conclusion Ontology, an update for 2022.

The Evidence and Conclusion Ontology (ECO) is a community resource that provides an ontology of terms used to capture the type of evidence that supports biomedical annotations and assertions. Consistent capture of evidence information with ECO allows tracking of annotation provenance, establishment of quality control measures, and evidence-based data mining. ECO is in use by dozens of data repositories and resources with both specific and general areas of focus. ECO is continually being expanded and enhanced in response to user requests as well as our aim to adhere to community best-practices for ontology development. The ECO support team engages in multiple collaborations with other ontologies and annotating groups. Here we report on recent updates to the ECO ontology itself as well as associated resources that are available through this project. ECO project products are freely available for download from the project website (https://evidenceontology.org/) and GitHub (https://github.com/evidenceontology/evidenceontology). ECO is released into the public domain under a CC0 1.0 Universal license.

ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts.

Analysis of high-throughput experiments in the life sciences frequently relies upon standardized information about genes, gene products, and other biological entities. To provide this information, expert curators are increasingly relying on text mining tools to identify, extract and harmonize statements from biomedical journal articles that discuss findings of interest. For determining reliability of the statements, curators need the evidence used by the authors to support their assertions. It is important to annotate the evidence directly used by authors to qualify their findings rather than simply annotating mentions of experimental methods without the context of what findings they support. Text mining tools require tuning and adaptation to achieve accurate performance. Many annotated corpora exist to enable developing and tuning text mining tools; however, none currently provides annotations of evidence based on the extensive and widely used Evidence and Conclusion Ontology. We present the ECO-CollecTF corpus, a novel, freely available, biomedical corpus of 84 documents that captures high-quality, evidence-based statements annotated with the Evidence and Conclusion Ontology.

ECO, the Evidence & Conclusion Ontology: community standard for evidence information.

The Evidence and Conclusion Ontology (ECO) contains terms (classes) that describe types of evidence and assertion methods. ECO terms are used in the process of biocuration to capture the evidence that supports biological assertions (e.g. gene product X has function Y as supported by evidence Z). Capture of this information allows tracking of annotation provenance, establishment of quality control measures and query of evidence. ECO contains over 1500 terms and is in use by many leading biological resources including the Gene Ontology, UniProt and several model organism databases. ECO is continually being expanded and revised based on the needs of the biocuration community. The ontology is freely available for download from GitHub (https://github.com/evidenceontology/) or the project's website (http://evidenceontology.org/). Users can request new terms or changes to existing terms through the project's GitHub site. ECO is released into the public domain under CC0 1.0 Universal.

Gross Pathology of Routine Pediatric Tonsillectomy Specimens: Optimizing the Value of Patient Care.

OBJECTIVE: This investigation seeks to evaluate the effect of gross pathologic analysis on our management of patients undergoing routine tonsillectomy and to evaluate charges and reimbursement. STUDY DESIGN: Retrospective chart review from 2005 through 2016. SETTING: Academic medical center. SUBJECTS AND METHODS: Participants were pediatric patients aged 14 years and younger undergoing tonsillectomy for either sleep-disordered breathing or tonsillitis, with tonsillectomy specimens evaluated by pathology, and without any risk factors for pediatric malignancy. Records were reviewed for demographics, surgical indications, and pathology. Abnormal reports prompted an in-depth review of the chart. Charges and reimbursement related to both hospital and professional fees for gross tonsil analysis were evaluated. RESULTS: From 2005 to 2016, 3183 routine pediatric tonsillectomy cases were performed with corresponding specimens that were sent for gross analysis revealing no significant pathologic findings; 1841 were males and 1342 were females. Ten cases underwent microscopy by pathologist order, revealing normal tonsillar tissue. The mean charge per patient for gross analysis was $60.67 if tonsils were together as 1 specimen and $77.67 if tonsils were sent as 2 separate specimens; respective reimbursement amounts were $28.74 and $35.90. CONCLUSIONS: Gross pathologic analysis did not change our management of routine pediatric tonsillectomy patients. Foregoing the practice at our institution would eliminate $19,171.72 to $24,543.72 in charges and $9081.40 to $11,344.40 in reimbursement per year. Eliminating this test would improve the value of patient care by saving health care resources without compromising clinical outcomes.

A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data.

BACKGROUND: Metagenomics enables the analysis of bacterial population composition and the study of emergent population features, such as shared metabolic pathways. Recently, we have shown that metagenomics datasets can be leveraged to characterize population-wide transcriptional regulatory networks, or meta-regulons, providing insights into how bacterial populations respond collectively to specific triggers. Here we formalize a Bayesian inference framework to analyze the composition of transcriptional regulatory networks in metagenomes by determining the probability of regulation of orthologous gene sequences. We assess the performance of this approach on synthetic datasets and we validate it by analyzing the copper-homeostasis network of Firmicutes species in the human gut microbiome. RESULTS: Assessment on synthetic datasets shows that our method provides a robust and interpretable metric for assessing putative regulation by a transcription factor on sets of promoter sequences mapping to an orthologous gene cluster. The inference framework integrates the regulatory contribution of secondary sites and can discern false positives arising from multiple instances of a clonal sequence. Posterior probabilities for orthologous gene clusters decline sharply when less than 20 % of mapped promoters have binding sites, but we introduce a sensitivity adjustment procedure to speed up computation that enhances regulation assessment in heterogeneous ortholog clusters. Analysis of the copper-homeostasis regulon governed by CsoR in the human gut microbiome Firmicutes reveals that CsoR controls itself and copper-translocating P-type ATPases, but not CopZ-type copper chaperones. Our analysis also indicates that CsoR frequently targets promoters with dual CsoR-binding sites, suggesting that it exploits higher-order binding conformations to fine-tune its activity. CONCLUSIONS: We introduce and validate a method for the analysis of transcriptional regulatory networks from metagenomic data that enables inference of meta-regulons in a systematic and interpretable way. Validation of this method on the CsoR meta-regulon of gut microbiome Firmicutes illustrates the usefulness of the approach, revealing novel properties of the copper-homeostasis network in poorly characterized bacterial species and putting forward evidence of new mechanisms of DNA binding for this transcriptional regulator. Our approach will enable the comparative analysis of regulatory networks across metagenomes, yielding novel insights into the evolution of transcriptional regulatory networks.