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Fulltext Verifying Death Reports in the Platelet Inhibition and Patient Outcomes (PLATO) Trial

Serebruany V, Tanguay JF, Benavides MA, Cabrera-Fuentes H, Eisert W, Kim MH, et al.

Am J Ther, 2020 10 29;27(6):e563-e572.
PMID: 33109913 DOI: 10.1097/MJT.0000000000001286

BACKGROUND: Excess vascular deaths in the PLATO trial comparing ticagrelor to clopidogrel have been repeatedly challenged by the Food and Drug Administration (FDA) reviewers and academia. Based on the Freedom of Information Act, BuzzFeed won a court order and shared with us the complete list of reported deaths for the ticagrelor FDA New Drug Application (NDA) 22-433. This dataset was matched against local patient-level records from PLATO sites monitored by the sponsor.
STUDY QUESTION: Whether FDA death data in the PLATO trial matched the local site records.
STUDY DESIGN: The NDA spreadsheet contains 938 precisely detailed PLATO deaths. We obtained and validated local evidence for 52 deaths among 861 PLATO patients from 14 enrolling sites in 8 countries and matched those with the official NDA dataset submitted to the FDA.
MEASURES AND OUTCOMES: Existence, precise time, and primary cause of deaths in PLATO.
RESULTS: Discrepant to the NDA document, sites confirmed 2 extra unreported deaths (Poland and Korea) and failed to confirm 4 deaths (Malaysia). Of the remaining 46 deaths, dates were reported correctly for 42 patients, earlier (2 clopidogrel), or later (2 ticagrelor) than the actual occurrence of death. In 12 clopidogrel patients, cause of death was changed to "vascular," whereas 6 NDA ticagrelor "nonvascular" or "unknown" deaths were site-reported as of "vascular" origin. Sudden death was incorrectly reported in 4 clopidogrel patients, but omitted in 4 ticagrelor patients directly affecting the primary efficacy PLATO endpoint.
CONCLUSIONS: Many deaths were inaccurately reported in PLATO favoring ticagrelor. The full extent of mortality misreporting is currently unclear, while especially worrisome is a mismatch in identifying primary death cause. Because all PLATO events are kept in the cloud electronic Medidata Rave capture system, securing the database content, examining the dataset changes or/and repeated entries, identifying potential interference origin, and assessing full magnitude of the problem are warranted.

Matched MeSH terms: Datasets as Topic
Fulltext Use of Medications with Anticholinergic Properties and the Long-Term Risk of Hospitalization for Falls and Fractures in the EPIC-Norfolk Longitudinal Cohort Study

Tan MP, Tan GJ, Mat S, Luben RN, Wareham NJ, Khaw KT, et al.

Drugs Aging, 2020 02;37(2):105-114.
PMID: 31808140 DOI: 10.1007/s40266-019-00731-3

The consumption of medications with anticholinergic activity has been suggested to result in the adverse effects of mental confusion, visual disturbance, and muscle weakness, which may lead to falls. Existing published evidence linking anticholinergic drugs with falls, however, remains weak. This study was conducted to evaluate the relationship between anticholinergic cognitive burden (ACB) and the long-term risk of hospitalization with falls and fractures in a large population study. The dataset comprised information from 25,639 men and women (aged 40-79 years) recruited from 1993 to 1997 from Norfolk, United Kingdom into the European Prospective Investigation into Cancer (EPIC)-Norfolk study. The time to first hospital admission with a fall with or without fracture was obtained from the National Health Service hospital information system. Cox-proportional hazards analyses were conducted to adjust for confounders and competing risks. The fall hospitalization rate was 5.8% over a median follow-up of ~ 19.4 years. The unadjusted incidence rate ratio for the use of any drugs with anticholinergic properties was 1.79 (95% CI 1.66-1.93). The hazard ratios (95% CI) for ACB scores of 1, 2-3, and ≥ 4 compared with ACB = 0 for fall hospitalization were 1.20 (1.09-1.33), 1.42 (1.25-1.60), and 1.39 (1.21-1.60) after adjustment for age, gender, medical conditions, physical activity, and blood pressure. Medications with anticholinergic activity are associated with an increased risk of subsequent hospitalization with a fall over a 19-year follow-up period. The biological mechanisms underlying the long-term risk of hospitalization with a fall or fracture following baseline ACB exposure remains unclear and requires further evaluation.

Matched MeSH terms: Datasets as Topic
Fulltext Three new pancreatic cancer susceptibility signals identified on chromosomes 1q32.1, 5p15.33 and 8q24.21

Zhang M, Wang Z, Obazee O, Jia J, Childs EJ, Hoskins J, et al.

Oncotarget, 2016 Oct 11;7(41):66328-66343.
PMID: 27579533 DOI: 10.18632/oncotarget.11041

Genome-wide association studies (GWAS) have identified common pancreatic cancer susceptibility variants at 13 chromosomal loci in individuals of European descent. To identify new susceptibility variants, we performed imputation based on 1000 Genomes (1000G) Project data and association analysis using 5,107 case and 8,845 control subjects from 27 cohort and case-control studies that participated in the PanScan I-III GWAS. This analysis, in combination with a two-staged replication in an additional 6,076 case and 7,555 control subjects from the PANcreatic Disease ReseArch (PANDoRA) and Pancreatic Cancer Case-Control (PanC4) Consortia uncovered 3 new pancreatic cancer risk signals marked by single nucleotide polymorphisms (SNPs) rs2816938 at chromosome 1q32.1 (per allele odds ratio (OR) = 1.20, P = 4.88x10 -15), rs10094872 at 8q24.21 (OR = 1.15, P = 3.22x10 -9) and rs35226131 at 5p15.33 (OR = 0.71, P = 1.70x10 -8). These SNPs represent independent risk variants at previously identified pancreatic cancer risk loci on chr1q32.1 ( NR5A2), chr8q24.21 ( MYC) and chr5p15.33 ( CLPTM1L- TERT) as per analyses conditioned on previously reported susceptibility variants. We assessed expression of candidate genes at the three risk loci in histologically normal ( n = 10) and tumor ( n = 8) derived pancreatic tissue samples and observed a marked reduction of NR5A2 expression (chr1q32.1) in the tumors (fold change -7.6, P = 5.7x10 -8). This finding was validated in a second set of paired ( n = 20) histologically normal and tumor derived pancreatic tissue samples (average fold change for three NR5A2 isoforms -31.3 to -95.7, P = 7.5x10 -4-2.0x10 -3). Our study has identified new susceptibility variants independently conferring pancreatic cancer risk that merit functional follow-up to identify target genes and explain the underlying biology.

Matched MeSH terms: Datasets as Topic
Taking it apart and putting it back together again: Using Item Pool Visualisation to summarise complex data patterns in (positive) body image research

Swami V, Furnham A, Horne G, Stieger S

Body Image, 2020 Sep;34:155-166.
PMID: 32593946 DOI: 10.1016/j.bodyim.2020.05.004

Issues of construct commonality and distinguishability in body image research are typically addressed using structural equal models, but such methods can sometimes present problems of interpretation when data patterns are complex. One recent-developed tool that could help in summarising complex data patterns is Item Pool Visualisation (IPV), an illustrative method that locates item pools from within the same dataset and illustrates these in the form of single or nested radar charts. Here, we demonstrate the utility of IPV in visualising data patterns vis-à-vis positive body image. Five-hundred-and-one adults from the United Kingdom completed seven widely-used measures of positive body image and data were subjected IPV. Results demonstrated that, of the included measures, the Body Appreciation Scale-2 provided the closest and most precise measurement of a core positive body image construct. The Functionality Appreciation Scale and the Authentic Pride subscale of the Body and Appearance Self-Conscious Emotions Scale tapped more distal aspects. Our results also highlight possible limitations with the use of several other instruments as measures of positive body image. We discuss implications for research aimed at better understanding the nature of positive body image and interpreting complex data patterns in body image research more generally.

Matched MeSH terms: Datasets as Topic
Study on the performance of different craniofacial superimposition approaches (I)

Ibáñez O, Vicente R, Navega DS, Wilkinson C, Jayaprakash PT, Huete MI, et al.

Forensic Sci Int, 2015 Dec;257:496-503.
PMID: 26060056 DOI: 10.1016/j.forsciint.2015.05.030

As part of the scientific tasks coordinated throughout The 'New Methodologies and Protocols of Forensic Identification by Craniofacial Superimposition (MEPROCS)' project, the current study aims to analyse the performance of a diverse set of CFS methodologies and the corresponding technical approaches when dealing with a common dataset of real-world cases. Thus, a multiple-lab study on craniofacial superimposition has been carried out for the first time. In particular, 26 participants from 17 different institutions in 13 countries were asked to deal with 14 identification scenarios, some of them involving the comparison of multiple candidates and unknown skulls. In total, 60 craniofacial superimposition problems divided in two set of females and males. Each participant follow her/his own methodology and employed her/his particular technological means. For each single case they were asked to report the final identification decision (either positive or negative) along with the rationale supporting the decision and at least one image illustrating the overlay/superimposition outcome. This study is expected to provide important insights to better understand the most convenient characteristics of every method included in this study.

Matched MeSH terms: Datasets as Topic
Population data of 23 Y chromosome STR loci for the five major human subpopulations of Ghana

Kofi AE, Hakim HM, Khan HO, Ismail SA, Ghansah A, David AA, et al.

Int J Legal Med, 2020 Jul;134(4):1313-1315.
PMID: 31154498 DOI: 10.1007/s00414-019-02099-w

In this study, 268 samples for unrelated males belonging to the five major human subpopulation groups in Ghana (Akan, Ewe, Mole-Dagbon, Ga-Dangme and Guang) were genetically characterised for 23 Y chromosome short tandem repeat (STR) loci using the Powerplex® Y23 STR kit. A total of 263 complete haplotypes were recorded of which 258 were unique. The haplotype diversity, discriminating capacity and match probability for the pooled population data were 0.9998, 0.9627 and 0.0039, respectively. The pairwise genetic distance (RST) for the Ghanaian datasets and other reference populations deposited in the Y-STR Haplotype Reference Database (YHRD) were estimated and mapped using multidimensional scaling (MDS) plot. The Guang and Ewe were significantly different from the Akan, Mole-Dagbon and Ga-Dangme. However, the five Ghanaian datasets were all plotted close together with other African populations in the MDS data mapping.

Matched MeSH terms: Datasets as Topic
Fulltext Parasitic egg recognition using convolution and attention network

AlDahoul N, Karim HA, Momo MA, Escobar FIF, Magallanes VA, Tan MJT

Sci Rep, 2023 Sep 02;13(1):14475.
PMID: 37660120 DOI: 10.1038/s41598-023-41711-3

Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.

Matched MeSH terms: Datasets as Topic
Optimising the production of succinate and lactate in Escherichia coli using a hybrid of artificial bee colony algorithm and minimisation of metabolic adjustment

Tang PW, Choon YW, Mohamad MS, Deris S, Napis S

J Biosci Bioeng, 2015 Mar;119(3):363-8.
PMID: 25216804 DOI: 10.1016/j.jbiosc.2014.08.004

Metabolic engineering is a research field that focuses on the design of models for metabolism, and uses computational procedures to suggest genetic manipulation. It aims to improve the yield of particular chemical or biochemical products. Several traditional metabolic engineering methods are commonly used to increase the production of a desired target, but the products are always far below their theoretical maximums. Using numeral optimisation algorithms to identify gene knockouts may stall at a local minimum in a multivariable function. This paper proposes a hybrid of the artificial bee colony (ABC) algorithm and the minimisation of metabolic adjustment (MOMA) to predict an optimal set of solutions in order to optimise the production rate of succinate and lactate. The dataset used in this work was from the iJO1366 Escherichia coli metabolic network. The experimental results include the production rate, growth rate and a list of knockout genes. From the comparative analysis, ABCMOMA produced better results compared to previous works, showing potential for solving genetic engineering problems.

Matched MeSH terms: Datasets as Topic
Fulltext Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

Wan Ahmad WS, Zaki WM, Ahmad Fauzi MF

Biomed Eng Online, 2015;14:20.
PMID: 25889188 DOI: 10.1186/s12938-015-0014-8

Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.

Matched MeSH terms: Datasets as Topic
Fulltext Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Ong SQ, Ahmad H, Nair G, Isawasan P, Majid AHA

Sci Rep, 2021 05 10;11(1):9908.
PMID: 33972645 DOI: 10.1038/s41598-021-89365-3

Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

Matched MeSH terms: Datasets as Topic
Identification of inhibitors synergizing gemcitabine sensitivity in the squamous subtype of pancreatic ductal adenocarcinoma (PDAC)

Er JL, Goh PN, Lee CY, Tan YJ, Hii LW, Mai CW, et al.

Apoptosis, 2018 Jun;23(5-6):343-355.
PMID: 29740790 DOI: 10.1007/s10495-018-1459-6

Pancreatic adenocarcinoma (PDAC) is a highly aggressive cancer with a high chance of recurrence, limited treatment options, and poor prognosis. A recent study has classified pancreatic cancers into four molecular subtypes: (1) squamous, (2) immunogenic, (3) pancreatic progenitor and (4) aberrantly differentiated endocrine exocrine. Among all the subtypes, the squamous subtype has the worst prognosis. This study aims to utilize large scale genomic datasets and computational systems biology to identify potential drugs targeting the squamous subtype of PDAC through combination therapy. Using the transcriptomic data available from the International Cancer Genome Consortium, Cancer Cell Line Encyclopedia and Connectivity Map, we identified 26 small molecules that could target the squamous subtype of PDAC. Among them include inhibitors targeting the SRC proto-oncogene (SRC) and the mitogen-activated protein kinase kinase 1/2 (MEK1/2). Further analyses demonstrated that the SRC inhibitors (dasatinib and PP2) and MEK1/2 inhibitor (pimasertib) synergized gemcitabine sensitivity specifically in the squamous subtype of PDAC cells (SW1990 and BxPC3), but not in the PDAC progenitor cells (AsPC1). Further analysis revealed that the synergistic effects are dependent on SRC or MEK1/2 activities, as overexpression of SRC or MEK1/2 completely abrogated the synergistic effects SRC inhibitors (dasatinib and PP2) and MEK1/2 inhibitor (pimasertib). In contrast, no significant toxicity was observed in the MRC5 human lung fibroblast and ARPE-19 human retinal pigment epithelial cells. Together, our findings suggest that combinations of SRC or MEK inhibitors with gemcitabine possess synergistic effects on the squamous subtype of PDAC cells and warrant further investigation.

Matched MeSH terms: Datasets as Topic
Fulltext High-Throughput Sequencing-Based Investigation of Viruses in Human Cancers by Multienrichment Approach

Mollerup S, Asplund M, Friis-Nielsen J, Kjartansdóttir KR, Fridholm H, Hansen TA, et al.

J Infect Dis, 2019 09 13;220(8):1312-1324.
PMID: 31253993 DOI: 10.1093/infdis/jiz318

BACKGROUND: Viruses and other infectious agents cause more than 15% of human cancer cases. High-throughput sequencing-based studies of virus-cancer associations have mainly focused on cancer transcriptome data.
METHODS: In this study, we applied a diverse selection of presequencing enrichment methods targeting all major viral groups, to characterize the viruses present in 197 samples from 18 sample types of cancerous origin. Using high-throughput sequencing, we generated 710 datasets constituting 57 billion sequencing reads.
RESULTS: Detailed in silico investigation of the viral content, including exclusion of viral artefacts, from de novo assembled contigs and individual sequencing reads yielded a map of the viruses detected. Our data reveal a virome dominated by papillomaviruses, anelloviruses, herpesviruses, and parvoviruses. More than half of the included samples contained 1 or more viruses; however, no link between specific viruses and cancer types were found.
CONCLUSIONS: Our study sheds light on viral presence in cancers and provides highly relevant virome data for future reference.

Matched MeSH terms: Datasets as Topic
Fulltext GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Saha P, Mukherjee D, Singh PK, Ahmadian A, Ferrara M, Sarkar R

Sci Rep, 2021 04 15;11(1):8304.
PMID: 33859222 DOI: 10.1038/s41598-021-87523-1

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .

Matched MeSH terms: Datasets as Topic
Fulltext Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types

Kar SP, Beesley J, Amin Al Olama A, Michailidou K, Tyrer J, Kote-Jarai Z, et al.

Cancer Discov, 2016 Sep;6(9):1052-67.
PMID: 27432226 DOI: 10.1158/2159-8290.CD-15-1227

Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.
SIGNIFICANCE: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.

Matched MeSH terms: Datasets as Topic
Fulltext Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R

Sci Rep, 2021 Jul 08;11(1):14133.
PMID: 34238992 DOI: 10.1038/s41598-021-93658-y

COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

Matched MeSH terms: Datasets as Topic
Evaluation of Development Datasets for Hermetia illucens (L.) (Diptera: Stratiomyidae) for Estimating the Time of Placement of Human and Swine Remains in Texas, USA

Cuttiford L, Pimsler ML, Heo CC, Zheng L, Karunaratne I, Trissini G, et al.

J Med Entomol, 2021 07 16;58(4):1654-1662.
PMID: 33970239 DOI: 10.1093/jme/tjab081

A basic tenet of forensic entomology is development data of an insect can be used to predict the time of colonization (TOC) by insect specimens collected from remains, and this prediction is related to the time of death and/or time of placement (TOP). However, few datasets have been evaluated to determine their accuracy or precision. The black soldier fly, Hermetia illucens (L.) (Diptera: Stratiomyidae) is recognized as an insect of forensic importance. This study examined the accuracy and precision of several development datasets for the black soldier fly by estimating the TOP of five sets of human and three sets of swine remains in San Marcos and College Station, TX, respectively. Data generated from this study indicate only one of these datasets consistently (time-to-prepupae 52%; time-to-eclosion 75%) produced TOP estimations that occurred within a day of the actual TOP of the remains. It is unknown if the precolonization interval (PreCI) of this species is long, but it has been observed that the species can colonize within 6 d after death. This assumption remains untested by validation studies. Accounting for this PreCI improved accuracy for the time-to-prepupae group, but reduced accuracy in the time-to-eclosion group. The findings presented here highlight a need for detailed, forensic-based development data for the black soldier fly that can reliably and accurately be used in casework. Finally, this study outlines the need for a basic understanding of the timing of resource utilization (i.e., duration of the PreCI) for forensically relevant taxa so that reasonable corrections may be made to TOC as related to minimum postmortem interval (mPMI) estimates.

Matched MeSH terms: Datasets as Topic
Fulltext Estimating the actual importation risk of dengue virus infection among Japanese travelers

Yuan B, Nishiura H

PLoS One, 2018;13(6):e0198734.
PMID: 29924819 DOI: 10.1371/journal.pone.0198734

BACKGROUND: Frequent international travel facilitates the global spread of dengue fever. Japan has experienced an increasing number of imported case notifications of dengue virus (DENV) infection, mostly arising from Japanese travelers visiting South and Southeast Asian countries. This has led an autochthonous dengue outbreak in 2014 in Japan. The present study aimed to infer the risk of DENV infection among Japanese travelers to Asian countries, thereby obtaining an actual estimate of the number of DENV infections among travelers.
METHODOLOGY/PRINCIPAL FINDINGS: For eight destination countries (Indonesia, Philippines, Thailand, India, Malaysia, Vietnam, Sri Lanka, and Singapore), we collected age-dependent seroepidemiological data. We also retrieved the number of imported cases, who were notified to the Japanese government, as well as the total number of travelers to each destination. Using a mathematical model, we estimated the force of infection in each destination country with seroepidemiological data while jointly inferring the reporting coverage of DENV infections among Japanese travelers from datasets of imported cases and travelers. Assuming that travelers had a risk of infection that was identical to that of the local population during travel, the reporting coverage of dengue appeared to range from 0.6% to 4.3%. The risk of infection per journey ranged from 0.02% to 0.44%.
CONCLUSIONS/SIGNIFICANCE: We found that the actual number of imported cases of DENV infection among Japanese travelers could be more than 20 times the notified number of imported cases. This finding may be attributed to the substantial proportion of asymptomatic and under-ascertained infections.

Matched MeSH terms: Datasets as Topic/statistics & numerical data
Fulltext Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning

Ab Ghani NS, Ramlan EI, Firdaus-Raih M

Nucleic Acids Res, 2019 07 02;47(W1):W350-W356.
PMID: 31106379 DOI: 10.1093/nar/gkz391

A common drug repositioning strategy is the re-application of an existing drug to address alternative targets. A crucial aspect to enable such repurposing is that the drug's binding site on the original target is similar to that on the alternative target. Based on the assumption that proteins with similar binding sites may bind to similar drugs, the 3D substructure similarity data can be used to identify similar sites in other proteins that are not known targets. The Drug ReposER (DRug REPOSitioning Exploration Resource) web server is designed to identify potential targets for drug repurposing based on sub-structural similarity to the binding interfaces of known drug binding sites. The application has pre-computed amino acid arrangements from protein structures in the Protein Data Bank that are similar to the 3D arrangements of known drug binding sites thus allowing users to explore them as alternative targets. Users can annotate new structures for sites that are similarly arranged to the residues found in known drug binding interfaces. The search results are presented as mappings of matched sidechain superpositions. The results of the searches can be visualized using an integrated NGL viewer. The Drug ReposER server has no access restrictions and is available at http://mfrlab.org/drugreposer/.

Matched MeSH terms: Datasets as Topic
Fulltext Dense sampling of bird diversity increases power of comparative genomics

Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH, et al.

Nature, 2020 11;587(7833):252-257.
PMID: 33177665 DOI: 10.1038/s41586-020-2873-9

Whole-genome sequencing projects are increasingly populating the tree of life and characterizing biodiversity1-4. Sparse taxon sampling has previously been proposed to confound phylogenetic inference5, and captures only a fraction of the genomic diversity. Here we report a substantial step towards the dense representation of avian phylogenetic and molecular diversity, by analysing 363 genomes from 92.4% of bird families-including 267 newly sequenced genomes produced for phase II of the Bird 10,000 Genomes (B10K) Project. We use this comparative genome dataset in combination with a pipeline that leverages a reference-free whole-genome alignment to identify orthologous regions in greater numbers than has previously been possible and to recognize genomic novelties in particular bird lineages. The densely sampled alignment provides a single-base-pair map of selection, has more than doubled the fraction of bases that are confidently predicted to be under conservation and reveals extensive patterns of weak selection in predominantly non-coding DNA. Our results demonstrate that increasing the diversity of genomes used in comparative studies can reveal more shared and lineage-specific variation, and improve the investigation of genomic characteristics. We anticipate that this genomic resource will offer new perspectives on evolutionary processes in cross-species comparative analyses and assist in efforts to conserve species.

Matched MeSH terms: Datasets as Topic
Fulltext Comprehensive genomic and immunological characterization of Chinese non-small cell lung cancer patients

Zhang XC, Wang J, Shao GG, Wang Q, Qu X, Wang B, et al.

Nat Commun, 2019 04 16;10(1):1772.
PMID: 30992440 DOI: 10.1038/s41467-019-09762-1

Deep understanding of the genomic and immunological differences between Chinese and Western lung cancer patients is of great importance for target therapy selection and development for Chinese patients. Here we report an extensive molecular and immune profiling study of 245 Chinese patients with non-small cell lung cancer. Tumor-infiltrating lymphocyte estimated using immune cell signatures is found to be significantly higher in adenocarcinoma (ADC, 72.5%) compared with squamous cell carcinoma (SQCC, 54.4%). The correlation of genomic alterations with immune signatures reveals that low immune infiltration was associated with EGFR mutations in ADC samples, PI3K and/or WNT pathway activation in SQCC. While KRAS mutations are found to be significantly associated with T cell infiltration in ADC samples. The SQCC patients with high antigen presentation machinery and cytotoxic T cell signature scores are found to have a prolonged overall survival time.

Matched MeSH terms: Datasets as Topic

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