Osteosarcoma: Novel prognostic biomarkers using circulating and cell-free tumour DNA

AIM: Osteosarcoma (OS) is the most common primary bone tumour in children and adolescents. Circulating free (cfDNA) and circulating tumour DNA (ctDNA) are promising biomarkers for disease surveillance and prognostication in several cancer types; however, few such studies are reported for OS. The purpose of this study was to discover and validate methylation-based biomarkers to detect plasma ctDNA in patients with OS and explore their utility as prognostic markers. METHODS: Candidate CpG markers were selected through analysis of methylation array data for OS, non-OS tumours and germline samples. Candidates were validated in two independent OS datasets (n = 162, n = 107) and the four top-performing markers were selected. Methylation-specific digital droplet PCR (ddPCR) assays were designed and experimentally validated in OS tumour samples (n = 20) and control plasma samples. Finally, ddPCR assays were applied to pre-operative plasma and where available post-operative plasma from 72 patients with OS, and findings correlated with outcome. RESULTS: Custom ddPCR assays detected ctDNA in 69% and 40% of pre-operative plasma samples (n = 72), based on thresholds of one or two positive markers respectively. ctDNA was detected in 5/17 (29%) post-operative plasma samples from patients, which in four cases were associated with or preceded disease relapse. Both pre-operative cfDNA levels and ctDNA detection independently correlated with overall survival (p = 0.0015 and p = 0.0096, respectively). CONCLUSION: Our findings illustrate the potential of mutation-independent methylation-based ctDNA assays for OS. This study lays the foundation for multi-institutional collaborative studies to explore the utility of plasma-derived biomarkers in the management of OS

Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe

HIV drug resistance among adults initiating antiretroviral therapy in Uganda.

BACKGROUND: WHO revised their HIV drug resistance (HIVDR) monitoring strategy in 2014, enabling countries to generate nationally representative HIVDR prevalence estimates from surveys conducted using this methodology. In 2016, we adopted this strategy in Uganda and conducted an HIVDR survey among adults initiating or reinitiating ART. METHODS: A cross-sectional survey of adults aged ≥18 years initiating or reinitiating ART was conducted at 23 sites using a two-stage cluster design sampling method. Participants provided written informed consent prior to enrolment. Whole blood collected in EDTA vacutainer tubes was used for preparation of dried blood spot (DBS) specimens or plasma. Samples were shipped from the sites to the Central Public Health Laboratory (CPHL) for temporary storage before transfer to the Uganda Virus Research Institute (UVRI) for genotyping. Prevalence of HIVDR among adults initiating or reinitiating ART was determined. RESULTS: Specimens from 491 participants (median age 32 years and 61.5% female) were collected between August and December 2016. Specimens from 351 participants were successfully genotyped. Forty-nine had drug resistance mutations, yielding an overall weighted HIVDR prevalence of 18.2% with the highest noted for NNRTIs at 14.1%. CONCLUSIONS: We observed a high HIVDR prevalence for NNRTIs among adults prior to initiating or reinitiating ART in Uganda. This is above WHO's recommended threshold of 10% when countries should consider changing from NNRTI- to dolutegravir-based first-line regimens. This recommendation was adopted in the revised Ugandan ART guidelines. Dolutegravir-containing ART regimens are preferred for first- and second-line ART regimens

The SLUGGS Survey: kinematics for over 2500 globular clusters in twelve early-type galaxies

We present a spectrophotometric survey of 2522 extragalactic globular clusters (GCs) around 12 early-type galaxies, nine of which have not been published previously. Combining space-based and multicolour wide-field ground-based imaging, with spectra from the Keck/DEep Imaging Multi-Object Spectrograph (DEIMOS) instrument, we obtain an average of 160 GC radial velocities per galaxy, with a high-velocity precision of ∼15 km s−1 per GC. After studying the photometric properties of the GC systems, such as their spatial and colour distributions, we focus on the kinematics of metal-poor (blue) and metal-rich (red) GC subpopulations to an average distance of ∼8 effective radii from the galaxy centre. Our results show that for some systems the bimodality in GC colour is also present in GC kinematics. The kinematics of the red GC subpopulations are strongly coupled with the host galaxy stellar kinematics. The blue GC subpopulations are more dominated by random motions, especially in the outer regions, and decoupled from the red GCs. Peculiar GC kinematic profiles are seen in some galaxies: the blue GCs in NGC 821 rotate along the galaxy minor axis, whereas the GC system of the lenticular galaxy NGC 7457 appears to be strongly rotation supported in the outer region. We supplement our galaxy sample with data from the literature and carry out a number of tests to study the kinematic differences between the two GC subpopulations. We confirm that the GC kinematics are coupled with the host galaxy properties and find that the velocity kurtosis and the slope of their velocity dispersion profiles are different between the two GC subpopulations in more massive galaxies

Structural Forms of the Human Amylase Locus and Their Relationships to SNPs, Haplotypes, and Obesity

Hundreds of human genes reside in structurally complex loci that elude molecular analysis and assessment in genome-wide association studies (GWAS). One such locus contains the three different amylase genes (AMY2B, AMY2A, and AMY1) responsible for digesting starch into sugar. The copy number of AMY1 is reported to be the genome’s largest influence on obesity, yet has gone undetected in GWAS. Using droplet digital PCR (ddPCR), sequence analysis, and optical mapping, we characterized eight common structural forms of the amylase locus, their mutational histories, and their relationships to SNPs. We found that AMY1 copy number has a unique distribution undetectable to earlier methods that can be understood from an underlying set of structural forms and their allele frequencies. Despite a history of recurrent structural mutations, AMY1 copy number has maintained partial correlations to nearby SNPs; these SNPs do not associate with body mass index (BMI). To directly test for association, we measured amylase gene copy number using ddPCR in 1,000 Estonians selected for being either obese or lean and in two cohorts totaling ~3,500 individuals using sequence analysis. We had 99% power to detect even the lower bound of the reported effects on BMI and obesity, yet found no association. This study model of using multiple methods to analyze the copy number, structural haplotypes, and surrounding SNP haplotypes of multi-allelic variants will likely facilitate more robust disease association results in future studies.Medical Science

Recent progress in biohydrometallurgy and microbial characterisation

Since the discovery of microbiological metal dissolution, numerous biohydrometallurgical approaches have been developed to use microbially assisted aqueous extractive metallurgy for the recovery of metals from ores, concentrates, and recycled or residual materials. Biohydrometallurgy has helped to alleviate the challenges related to continually declining ore grades by transforming uneconomic ore resources to reserves. Engineering techniques used for biohydrometallurgy span from above ground reactor, vat, pond, heap and dump leaching to underground in situ leaching. Traditionally biohydrometallurgy has been applied to the bioleaching of base metals and uranium from sulfides and the biooxidation of sulfidic refractory gold ores and concentrates before cyanidation. More recently the interest in using bioleaching for oxide ore and waste processing, as well as extracting other commodities such as rare earth elements has been growing. Bioprospecting, adaptation, engineering and storing of microorganisms has increased the availability of suitable biocatalysts for biohydrometallurgical applications. Moreover, the advancement of microbial characterisation methods has increased the understanding of microbial communities and their capabilities in the processes. This paper reviews recent progress in biohydrometallurgy and microbial characterisation.</p

Severity Index for Suspected Arbovirus (SISA):machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

BACKGROUND:Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. METHODOLOGY/PRINCIPAL FINDINGS:Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. CONCLUSIONS/SIGNIFICANCE:Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection