Sleep Apnea and Fetal Growth Restriction (SAFER) study: Protocol for a pragmatic randomised clinical trial of positive airway pressure as an antenatal therapy for fetal growth restriction in maternal obstructive sleep apnoea

INTRODUCTION: Fetal growth restriction (FGR) is a major contributor to fetal and neonatal morbidity and mortality with intrauterine, neonatal and lifelong complications. This study explores maternal obstructive sleep apnoea (OSA) as a potentially modifiable risk factor for FGR. We hypothesise that, in pregnancies complicated by FGR, treating mothers who have OSA using positive airway pressure (PAP) will improve birth weight and neonatal outcomes. METHODS AND ANALYSIS: The Sleep Apnea and Fetal Growth Restriction study is a prospective, block-randomised, single-blinded, multicentre, pragmatic controlled trial. We enrol pregnant women aged 18-50, between 22 and 31 weeks of gestation, with established FGR based on second trimester ultrasound, who do not have other prespecified known causes of FGR (such as congenital anomalies or intrauterine infection). In stage 1, participants are screened by questionnaire for OSA risk. If OSA risk is identified, participants proceed to stage 2, where they undergo home sleep apnoea testing. Participants are determined to have OSA if they have an apnoea-hypopnoea index (AHI) ≥5 (if the oxygen desaturation index (ODI) is also ≥5) or if they have an AHI ≥10 (even if the ODI is \u3c5). These participants proceed to stage 3, where they are randomised to nightly treatment with PAP or no PAP (standard care control), which is maintained until delivery. The primary outcome is unadjusted birth weight; secondary outcomes include fetal growth velocity on ultrasound, enrolment-to-delivery interval, gestational age at delivery, birth weight corrected for gestational age, stillbirth, Apgar score, rate of admission to higher levels of care (neonatal intensive care unit or special care nursery) and length of neonatal stay. These outcomes are compared between PAP and control using intention-to-treat analysis. ETHICS AND DISSEMINATION: This study has been approved by the Institutional Review Boards at Washington University in St Louis, Missouri; Hadassah Hebrew University Medical Center, Jerusalem; and the University of Rochester, New York. Recruitment began in Washington University in November 2019 but stopped from March to November 2020 due to COVID-19. Recruitment began in Hadassah Hebrew University in March 2021, and in the University of Rochester in May 2021. Dissemination plans include presentations at scientific conferences and scientific publications. TRIAL REGISTRATION NUMBER: NCT04084990

Mitochondrial Phenotypes in Purified Human Immune Cell Subtypes and Cell Mixtures

Using a high-throughput mitochondrial phenotyping platform to quantify multiple mitochondrial features among molecularly defined immune cell subtypes, we quantify the natural variation in mitochondrial DNA copy number (mtDNAcn), citrate synthase, and respiratory chain enzymatic activities in human neutrophils, monocytes, B cells, and naïve and memory T lymphocyte subtypes. In mixed peripheral blood mononuclear cells (PBMCs) from the same individuals, we show to what extent mitochondrial measures are confounded by both cell type distributions and contaminating platelets. Cell subtype-specific measures among women and men spanning four decades of life indicate potential age- and sex-related differences, including an age-related elevation in mtDNAcn, which are masked or blunted in mixed PBMCs. Finally, a proof-of-concept, repeated-measures study in a single individual validates cell type differences and also reveals week-to-week changes in mitochondrial activities. Larger studies are required to validate and mechanistically extend these findings. These mitochondrial phenotyping data build upon established immunometabolic differences among leukocyte subpopulations, and provide foundational quantitative knowledge to develop interpretable blood-based assays of mitochondrial health

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

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

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

Pitfalls of vaccinations with WT1-, Proteinase3- and MUC1-derived peptides in combination with MontanideISA51 and CpG7909

T cells with specificity for antigens derived from Wilms Tumor gene (WT1), Proteinase3 (Pr3), and mucin1 (MUC1) have been demonstrated to lyse acute myeloid leukemia (AML) blasts and multiple-myeloma (MM) cells, and strategies to enhance or induce such tumor-specific T cells by vaccination are currently being explored in multiple clinical trials. To test safety and immunogenicity of a vaccine composed of WT1-, Pr3-, and MUC1-derived Class I-restricted peptides and the pan HLA-DR T helper cell epitope (PADRE) or MUC1-helper epitopes in combination with CpG7909 and MontanideISA51, four patients with AML and five with MM were repetitively vaccinated. No clinical responses were observed. Neither pre-existing nor naive WT1-/Pr3-/MUC1-specific CD8+ T cells expanded in vivo by vaccination. In contrast, a significant decline in vaccine-specific CD8+ T cells was observed. An increase in PADRE-specific CD4+ T helper cells was observed after vaccination but these appeared unable to produce IL2, and CD4+ T cells with a regulatory phenotype increased. Taken into considerations that multiple clinical trials with identical antigens but different adjuvants induced vaccine-specific T cell responses, our data caution that a vaccination with leukemia-associated antigens can be detrimental when combined with MontanideISA51 and CpG7909. Reflecting the time-consuming efforts of clinical trials and the fact that 1/3 of ongoing peptide vaccination trails use CpG and/or Montanide, our data need to be taken into consideration

Quality indicators for patients with traumatic brain injury in European intensive care units

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

The 13th Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-IV Survey Mapping Nearby Galaxies at Apache Point Observatory

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2,MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper describes the first data release from SDSS-IV, Data Release 13 (DR13), which contains new data, reanalysis of existing data sets and, like all SDSS data releases, is inclusive of previously released data. DR13 makes publicly available 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA,the first data released from this survey. It includes new observations from eBOSS, completing SEQUELS. In addition to targeting galaxies and quasars, SEQUELS also targeted variability-selected objects from TDSS and X-ray selected objects from SPIDERS. DR13 includes new reductions ofthe SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification. DR13 releases new reductions of the APOGEE-1data from SDSS-III, with abundances of elements not previously included and improved stellar parameters for dwarf stars and cooler stars. For the SDSS imaging data, DR13 provides new, more robust and precise photometric calibrations. Several value-added catalogs are being released in tandem with DR13, in particular target catalogs relevant for eBOSS, TDSS, and SPIDERS, and an updated red-clump catalog for APOGEE.This paper describes the location and format of the data now publicly available, as well as providing references to the important technical papers that describe the targeting, observing, and data reduction. The SDSS website, http://www.sdss.org, provides links to the data, tutorials and examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ~6-year operations of SDSS-IV.PostprintPeer reviewe

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations