Association Between Obstructive Sleep Apnea and Postoperative Adverse Events

Purpose: Adults with obstructive sleep apnea (OSA) arouse from sleep repeatedly due to hypoxemia and hyerpcapnea. General anesthesia, analgesics, and sedatives may interfere with these arousals and, thus, increase adverse events. Therefore, the purpose of this study is to compare postoperative recovery scores in adult surgical patients with and without diagnosed OSA. Significant differences in postoperative recovery scores between these groups may suggest an opportunity to improve patient care in the postoperative environment. Methods: We performed a retrospective electronic data review to compare postoperative recovery scores in two matched cohorts of patients admitted to a large urban medical center between November 2009 and July 2011 for procedures requiring anesthesia. OSA and non-OSA cohorts were matched based on gender, age, and type of surgical procedure. We collected data regarding patients' post-anesthesia recovery scores in four categories: oxygen saturation, respiration rate, blood pressure, and level of consciousness. Results: Our cohorts included 61 people with an ICD-9 code for OSA and 55 people who did not have an OSA diagnosis. We noted no significant differences in mean post-anesthesia recovery scores between the two cohorts in each of the four categories. We did find a significant difference (p = .05) between the number of assessments the OSA cohort received (M= 5.80, SD = 2.52) and the number of assessments the non-OSA cohort received (M=4.87, SD= 2.62). We also found that the OSA cohort's mean initial scores upon arrival to the post-anesthesia care unit (PACU) were significantly better for respiration (p = .05) and level of consciousness (p = .03) than were the non-OSA cohort's scores. Conclusions: While the OSA cohort received better initial recovery scores upon arrival to the post anesthesia care unit (PACU), they had a higher number of assessments overall, indicating that they spent more time on the PACU before discharge. Numerous explanations exist to explain these results, indicating a need for further research.University of Kansas School of Nursing. Bachelor of Science in Nursing Honors Progra

The Journal of BSN Honors Research, Volume 5, Issue 1, Summer 2012

University of Kansas School of Nursing. Bachelor of Science in Nursing Honors ProgramExploration Of Health Care Needs Among Sudanese Refugee Women - Albin, J M, Domian, E. Is There An App For That? Developing An Evaluation Rubric For Apps For Use With Adults With Special Needs - Buckler, T, Peterson, M. The Relationship Between Nursing Characteristics And Pain Care Quality - Davis, E, Dunton, N. The Relationship Between Sleep And Night Eating On Weight Loss In Individuals With Severe Mental Illness - Huynh, Thu Nhi, Hamera, E. Examining Nurse Leader/Manager-Physician Communication Strategies: A Pilot Study - Jantzen, M, Ford, D J. Comparison Of Personal, Health And Family Characteristic Of Children With And Without Autism - Martin, A, Bott, M J. Association Between Obstructive Sleep Apnea And Postoperative Adverse Events - Nielsenshultz, Y, Smith, C, Bott, M, Schultz, M P, Cole, C. Challenges Associated With Partnering With Sudanese Refugee Women In Addressing Their Health Issues - Pauls, K L, Baird, M B. Complementary Therapy To Relieve Pediatric Cancer Therapy-Related Symptoms In The Usa - Slaven, A, Williams, P D

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation