tbuckley/cabot-search huggingface dataset:
Abstract PR-09: Genetic risk scores for breast cancer based on machine learning analysis of chromosomal-scale length variation
Authors: Charmeine Ko, Christopher Toh, James P. Brody
Journal: Clinical Cancer Research (2021)

Abstract Inherited factors are thought to be responsible for a substantial fraction of breast cancers, but only a small fraction of breast cancers are caused by mutations to known genes, like BRCA1. Most current genetic risk scores for breast cancer focus on the additive effects of single nucleotide polymorphisms (SNPs) found in germ-line DNA. This process ignores epistatic interactions between different genes. We sought to use modern machine learning algorithms to identify complex patterns in germ line DNA that are correlated with the incidence of breast cancer, a strategy which should include epistatic interactions. We looked for these patterns in measurements of chromosomal-scale length variation of germ line DNA. These measurements represents the sum of many insertions, deletions, and copy number variations across the chromosome. We evaluated several different machine learning models, including: a generalized linear model, a distributed random forest, a gradient boosting machine, and a deep learning model. We evaluated these models on two independent large datasets, the Cancer Genome Atlas (TCGA) and the UK Biobank. We found that chromosomal-scale length variation of germline DNA, in combination with machine learning models, can be used to predict whether a woman will develop breast cancer. In the Cancer Genome Atlas (TCGA) dataset, we used a dataset with 968 (cases) women who had been diagnosed with breast cancer and 3715 (controls) women who had not been diagnosed with breast cancer. We found that a gradient boosting algorithm could differentiate between those two classes of women with an AUC=0.73. In the UK Biobank dataset, we identified 1534 (cases) women who had been diagnosed with breast cancer and compared them with a set of 4391 (controls) women who had no report of any type of cancer. In this UK Biobank dataset, we could distinguish between the two classes of women with an AUC =0.83. In conclusion, we found that artificial intelligence algorithms could be used to develop effective genetic risk scores for breast cancer. Citation Format: Charmeine Ko, Christopher Toh, James P. Brody. Genetic risk scores for breast cancer based on machine learning analysis of chromosomal-scale length variation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-09.
Effect of Ventral vs Dorsal Spinal Surgery on Patient-Reported Physical Functioning in Patients With Cervical Spondylotic Myelopathy
Authors: Zoher Ghogawala, Norma Terrin, Melissa R. Dunbar, Janis L. Breeze, Karen M. Freund, Adam S. Kanter, Praveen V. Mummaneni, Erica F. Bisson, Fred G. Barker, J. Sanford Schwartz, James S. Harrop, Subu N. Magge, Robert F. Heary, Michael G. Fehlings, Todd J. Albert, Paul M. Arnold, K. Daniel Riew, Michael P. Steinmetz, Marjorie C. Wang, Robert G. Whitmore, John G. Heller, Edward C. Benzel
Journal: JAMA (2021)

Cervical spondylotic myelopathy is the most common cause of spinal cord dysfunction worldwide. It remains unknown whether a ventral or dorsal surgical approach provides the best results.
Estimated SARS-CoV-2 Seroprevalence Among Persons Aged &lt;18 Years — Mississippi, May–September 2020
Authors: Charlotte V. Hobbs, Jan Drobeniuc, Theresa Kittle, John Williams, Paul Byers, Panayampalli S. Satheshkumar, Kengo Inagaki, Meagan Stephenson, Sara S. Kim, Manish M. Patel, Brendan Flannery, Bailey Alston, Shanna J. Bolcen, Darbi Boulay, Peter Browning, Li Cronin, Ebenezer David, Tonya Hayden, Han Li, Travis Lim, Panagiotis Maniatis, Palak Patel, Mathew Pauly, Amanda Poe, Lili Punkova, Vera Semenova, Evelene P. Steward-Clark, Alexandra Tejada, Briana Zellner
Journal: MMWR Morbidity and Mortality Weekly Report (2021)

Response TeamAs of March 1, 2021, persons aged <18 years accounted for approximately 11% of 28.4 million reported COVID-19 cases in the United States*; however, data on pediatric infections with SARS-CoV-2, the virus that causes COVID-19, are limited (1).Surveys of SARS-CoV-2 antibody seroprevalence suggest that cumulative incidence of infection is much higher than that ascertained by reported COVID-19 cases (2,3).Evidence of previous SARS-CoV-2 infections among young persons in Mississippi was assessed by testing for antibodies to SARS-CoV-2 on a convenience sample of residual serum specimens collected for routine testing by an academic medical center laboratory during May 17-September 19, 2020.Seroprevalence by calendar month was standardized to the state population by race/ethnicity; cumulative numbers of infections were estimated by extrapolating seroprevalence to all persons aged <18 years in Mississippi.Serum specimens from 1,603 persons were tested; 175 (10.9%) were positive for SARS-CoV-2 antibodies.Among 1,579 (98.5%) specimens for which the race/ethnicity of the person tested was known, specimens from 16 (23.2%) of 69 Hispanic persons, 117 (13.0%) of 901 non-Hispanic Black persons, and 30 (5.3%) of 565 non-Hispanic White persons tested positive.Populationweighted seroprevalence estimates among persons aged <18 years increased from 2.5% in May to 16.3% in September 2020.Based on these estimates, 113,842 (95% confidence interval [CI] = 90,096-153,652) persons aged <18 years in Mississippi might have been infected with SARS-CoV-2 by mid-September 2020.The number of COVID-19 cases reported in this age group through August 31, 2020 was 8,993.Serosurveys that include pediatric age groups can help provide evidence of cumulative disease incidence, estimate frequency of undiagnosed cases of SARS-CoV-2 among young persons, and guide prevention efforts.Most persons who are infected with SARS-CoV-2 develop antibodies to SARS-CoV-2 proteins within 1-2 weeks of disease onset (4).Serologic testing for SARS-CoV-2 antibodies, albeit having imperfect sensitivity and specificity, † is useful to identify past SARS-CoV-2 infections.Serology tests are * https://covid.cdc.gov/covid-data-tracker/#demographics† https://www.cdc.gov/coronavirus/2019-ncov/lab/resources/antibody-testsguidelines.html
Abstract PO-049: Pseudo-alignment resolution in discriminating mouse reads
Authors: Soheil Jahangiri-Tazehkand, Farnoosh Agha-Babazadeh, Benjamin Haibe-Kains
Journal: Clinical Cancer Research (2021)

Abstract Patient-Derived Xenografts (PDX) are the best preclinical models to study cancer biology preclinical drug screening. PDXs are generated by implanting a piece of fresh cancer tissue directly into an immunodeficient mouse to grow and imitate a human tumor. Previous studies have shown that PDXs are able to accurately recapitulate the (epi-)genomic characteristics of the original tumor. Yet the inclusion of the mouse cells in tumor biopsies can introduce unwanted noise and bias in high throughput (epi-)genomics analyses and interpretation. Thus, filtering the mouse reads is one of the first and important steps in analyzing high throughput PDX sequencing data. There are two main approaches to account for mouse reads in analyzing bulk RNAseq data generated from PDXs. In the first approach, an external tool is used to classify the raw or aligned reads into a human, mouse, or ambiguous reads. Examples include XenoFilter, BBsplit, disambiguate, and Xenome. In the second approach, a mixed human-mouse reference genome is used when aligning the raw reads. Multiple studies have shown that the mixed reference approach produces better or similar results compared to when an external tool is used for mouse read discrimination. However, these comparisons have employed exact alignment methods when using a mixed reference approach. Recently, the significant deluge of sequencing data has brought attention to pseudo-alignment based methods, specifically Kallisto and Salmon. By taking advantage of pseudo-alignment to a reference transcriptome, these tools are capable of analyzing millions of reads in minutes while demonstrating near-optimal quantification performance. Yet their performance in analyzing PDX RNAseq data while using a mixed reference is unclear. Moreover, among the external methods, only Xenome has a pseudo-alignment strategy for read classification and the rest of the methods use exact alignment making them labor-intensive and intractable for large datasets. In this study, we compare two main mouse read discrimination pipelines when using Kallisto and Salmon. By sampling from two human and mouse lung RNAseq datasets, we have simulated five pseudo-PDX RNAseq datasets with different contamination ratios. By comparing outputs of different discriminating strategies to the ground truth, we show that Kallisto and Salmon when used with mixed human-mouse transcriptome produce results that are more correlated with the ground truth. Moreover, by differential gene expression analyses we show that fewer genes are significantly affected when using the mixed reference approach. Citation Format: Soheil Jahangiri-Tazehkand, Farnoosh Agha-Babazadeh, Benjamin Haibe-Kains. Pseudo-alignment resolution in discriminating mouse reads [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-049.
Factors linked to severe outcomes in multisystem inflammatory syndrome in children (MIS-C) in the USA: a retrospective surveillance study
Authors: Joseph Y Abrams, Matthew E Oster, Shana E Godfred-Cato, Bobbi Bryant, S Deblina Datta, Angela P Campbell, Jessica W Leung, Clarisse A Tsang, Timmy J Pierce, Jordan L Kennedy, Teresa A Hammett, Ermias D Belay
Journal: The Lancet Child & Adolescent Health (2021)
Covid-19: School staff testing positive for antibodies rose to around 15% in December
Authors: Elisabeth Mahase
Journal: BMJ (2021)

Covid-19: School staff testing positive for antibodies rose to around 15% in December Elisabeth MahaseThe proportion of school staff testing positive for antibodies against SARS-CoV-2 rose from 12.63% to 14.61% for primary staff and from 12.27% to 15.72% for secondary staff between November and December, the latest results from the Schools Infection Survey have shown.2
Intermediate clinical endpoints for surrogacy in localised prostate cancer: an aggregate meta-analysis
Authors: Laila A Gharzai, Ralph Jiang, David Wallington, Gavin Jones, Samuel Birer, Neil Jairath, Elizabeth M Jaworski, Matthew R McFarlane, Brandon A Mahal, Paul L Nguyen, Howard Sandler, Todd M Morgan, Zachery R Reichert, Joshi J Alumkal, Rohit Mehra, Amar U Kishan, Karim Fizazi, Susan Halabi, Edward M Schaeffer, Felix Y Feng, David Elliott, Robert T Dess, William C Jackson, Matthew J Schipper, Daniel E Spratt
Journal: The Lancet Oncology (2021)
Consequences of Selling a Kidney in India—Reply
Authors: David J. Rothman
Journal: JAMA (2003)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA HomeNew OnlineCurrent IssueFor Authors Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) Podcasts Clinical Reviews Editors' Summary Medical News Author Interviews More JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA journal
Pharmacological treatment with FGF21 strongly improves plasma cholesterol metabolism to reduce atherosclerosis
Authors: Cong Liu, Milena Schönke, Enchen Zhou, Zhuang Li, Sander Kooijman, Mariëtte R Boon, Mikael Larsson, Kristina Wallenius, Niek Dekker, Louise Barlind, Xiao-Rong Peng, Yanan Wang, Patrick C N Rensen
Journal: Cardiovascular Research (2021)

Abstract Aims Fibroblast growth factor (FGF) 21, a key regulator of energy metabolism, is currently evaluated in humans for treatment of type 2 diabetes and non-alcoholic steatohepatitis. However, the effects of FGF21 on cardiovascular benefit, particularly on lipoprotein metabolism in relation to atherogenesis, remain elusive. Methods and results Here, the role of FGF21 in lipoprotein metabolism in relation to atherosclerosis development was investigated by pharmacological administration of a half-life extended recombinant FGF21 protein to hypercholesterolaemic APOE*3-Leiden.CETP mice, a well-established model mimicking atherosclerosis initiation and development in humans. FGF21 reduced plasma total cholesterol, explained by a reduction in non-HDL-cholesterol. Mechanistically, FGF21 promoted brown adipose tissue (BAT) activation and white adipose tissue (WAT) browning, thereby enhancing the selective uptake of fatty acids from triglyceride-rich lipoproteins into BAT and into browned WAT, consequently accelerating the clearance of the cholesterol-enriched remnants by the liver. In addition, FGF21 reduced body fat, ameliorated glucose tolerance and markedly reduced hepatic steatosis, related to up-regulated hepatic expression of genes involved in fatty acid oxidation and increased hepatic VLDL-triglyceride secretion. Ultimately, FGF21 largely decreased atherosclerotic lesion area, which was mainly explained by the reduction in non-HDL-cholesterol as shown by linear regression analysis, decreased lesion severity, and increased atherosclerotic plaque stability index. Conclusion FGF21 improves hypercholesterolaemia by accelerating triglyceride-rich lipoprotein turnover as a result of activating BAT and browning of WAT, thereby reducing atherosclerotic lesion severity and increasing atherosclerotic lesion stability index. We have thus provided additional support for the clinical use of FGF21 in the treatment of atherosclerotic cardiovascular disease.
Treatment response in the intact primary renal mass (P-Rmass) and its relationship to the overall response to treatment in patients with metastatic renal cell carcinoma (mRCC).
Authors: Hamid Emamekhoo, Hameem I Kawsar, Jens C. Eickhoff, Danubia Hester, Tristan Bice, Luna Acharya, Ellen Jaeger, Pedro C. Barata, Yousef Zakharia, Deepak Kilari, Elizabeth Marie Wulff-Burchfield, Christos Kyriakopoulos
Journal: Journal of Clinical Oncology (2021)

329 Background: With the approval of more effective systemic treatments (syst Rx) such as tyrosine kinase inhibitors (TKI) and immune checkpoint inhibitors (ICI), the impact of cytoreductive nephrectomy (CN) on response to Rx and survival remains unknown. The majority of patients (pts) previously enrolled in clinical trials have had radical nephrectomy (RN) or CN prior to syst Rx. Therefore, the response of the P-Rmass to ICIs and the effect of intact P-Rmass on response to syst Rx is not well described. Methods: A retrospective review of 209 pts with mRCC who were treated with ICI in the first or second-line was conducted. Following the appropriate regulatory process, collaborators from 5 US sites collected clinical, pathological, and outcome data via chart review. The response was investigator-assessed for all pts with at least one post-treatment scan or evidence of clinical progression after treatment initiation. Overall radiographic response (ORR) includes complete response (CR) and radiographic response (Rad-resp) to treatment. Disease control rate (DCR) includes CR, Rad-resp, and stable disease. Results: Median age at diagnosis was 63 yrs and 69% were male. 102 pts (49%) had localized disease at diagnosis and underwent radical or partial nephrectomy, 3 (1%) had ablation/radiation of P-Rmass, 26 (12%) had CN, 9 (4%) had CN after an excellent response to syst Rx, 12 (6%) had a previous nephrectomy but developed a new Rmass (measurable target lesion), and 57 (27%) did not have CN and had an intact P-Rmass. 176 (84%) pts had clear cell histology. 27 (14%) and 23 (12%) had known sarcomatoid and rhabdoid features, respectively. Overall, 77 (37%) pts had a measurable Rmass while receiving syst Rx. 84 (40%), 93 (45%), and 10 (5%) pts received ICI (Ipilimumab/Nivolumab or Nivo), TKI, or Pembrolizumab/Axitinib in the first-line. 143 (68%) and 70 (33%) pts received second- and third-line treatment. 103 (72%) and 28 (19%) pts received ICI and TKI in the second-line, respectively. The best ORR and the Rad-resp in the intact P-Rmass in evaluable pts are summarized in the table below. ORR to ICI in the first or second-line were numerically higher in pts with an intact P-Rmass compared to pts who had nephrectomy, but this difference was not statistically significant (p= .38 and .35 respectively). Conclusions: The intact P-Rmass had a good response (62-70%) to the first-line syst Rx. Although the overall Rad-resp rates to ICI are numerically higher in pts with intact P-Rmass, this difference was not statistically significant. [Table: see text]
Physician's Visiting List for 1899.
Journal: JAMA (1898)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA HomeNew OnlineCurrent IssueFor Authors Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) Podcasts Clinical Reviews Editors' Summary Medical News Author Interviews More JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA journal
Diversity Is Needed Throughout All Aspects of the Editorial Peer Review Process for Surgery Journals
Authors: Alex O. Wamalwa, Isaac Cheruiyot, James Kigera
Journal: JAMA Surgery (2021)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Surgery HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Surgery journal
Abstract IA-06: Artificial intelligence in cancer imaging
Authors: Hugo Aerts
Journal: Clinical Cancer Research (2021)

Abstract Medical imaging in oncology has traditionally been restricted to the diagnosis and staging of cancer. But technological advances in Artificial Intelligence (AI), deep learning in particular, are moving imaging modalities into the heart of patient care. Imaging can address a critical barrier in precision medicine as solid tumors can be spatial and temporal heterogeneous, and the standard approach to tumor sampling, often invasive needle biopsy, is unable to fully capture the spatial state of the tumor. Radiomics refers to the automatic quantification of this radiographic phenotype. Radiomic methods heavily rely on AI technologies to quantify phenotypic characteristics that can be used to develop non-invasive biomarkers. In this talk, Dr. Aerts will discuss recent developments from his group and collaborators performing research at the intersection of radiology, bioinformatics, and data science. Also, he will discuss recent work of building a computational image analysis system to extract a rich radiomics set and use these features to build radiomic signatures. The presentation will conclude with a discussion of future work on building integrative systems incorporating both molecular and phenotypic data to improve cancer therapies. Citation Format: Hugo Aerts. Artificial intelligence in cancer imaging [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-06.
Annual incidence and severity of acute episodes in hereditary thrombotic thrombocytopenic purpura
Authors: Erika Tarasco, Lukas Bütikofer, Kenneth D. Friedman, James N. George, Ingrid Hrachovinova, Paul N. Knöbl, Masanori Matsumoto, Anne Sophie von Krogh, Isabella Aebi-Huber, Zuzana Cermakova, Magdalena Górska-Kosicka, Katarzyna A. Jalowiec, Carlo R. Largiadèr, Zoltán Prohászka, György Sinkovits, Jerzy Windyga, Bernhard Lämmle, Johanna A. Kremer Hovinga
Journal: Blood (2021)