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Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.

A computational method to identify cell types within a complex tissue, based on analysis of gene expression profiles, is described in this paper. We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets ( http://cibersort.stanford.edu/ ).

A searchable pan-cancer resource generated using data from nearly 18,000 human tumors reveals links between tumor infiltration by particular leukocyte subsets, tumor expression of particular gene signatures, and patient prognosis. Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI ( FOXM1 ) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools ( http://precog.stanford.edu ) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.

Although extending the duration of ambulatory electrocardiographic monitoring beyond 24 to 48 hours can improve the detection of arrhythmias, lead-based (Holter) monitors might be limited by patient compliance and other factors. We, therefore, evaluated compliance, analyzable signal time, interval to arrhythmia detection, and diagnostic yield of the Zio Patch, a novel leadless, electrocardiographic monitoring device in 26,751 consecutive patients. The mean wear time was 7.6 ± 3.6 days, and the median analyzable time was 99% of the total wear time. Among the patients with detected arrhythmias (60.3% of all patients), 29.9% had their first arrhythmia and 51.1% had their first symptom-triggered arrhythmia occur after the initial 48-hour period. Compared with the first 48 hours of monitoring, the overall diagnostic yield was greater when data from the entire Zio Patch wear duration were included for any arrhythmia (62.2% vs 43.9%, p <0.0001) and for any symptomatic arrhythmia (9.7% vs 4.4%, p <0.0001). For paroxysmal atrial fibrillation (AF), the mean interval to the first detection of AF was inversely proportional to the total AF burden, with an increasing proportion occurring after 48 hours (11.2%, 10.5%, 20.8%, and 38.0% for an AF burden of 51% to 75%, 26% to 50%, 1% to 25%, and <1%, respectively). In conclusion, extended monitoring with the Zio Patch for ≤14 days is feasible, with high patient compliance, a high analyzable signal time, and an incremental diagnostic yield beyond 48 hours for all arrhythmia types. These findings could have significant implications for device selection, monitoring duration, and care pathways for arrhythmia evaluation and AF surveillance.

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.Measurement(s)diseases and abnormal findings from chest X-ray scansTechnology Type(s)AI is used to detect diseases and abnormal findingsSample Characteristic - LocationVietnam

We aimed to determine the efficacy of multiple micronutrient supplementation on the biomarkers of iron, zinc, and vitamin A status across anthropometric status categories in Vietnamese school children. In this 22-week randomised controlled trial, 347 undernourished, normal weight, or overweight/obese children aged 6–9 years were allocated to receive every school day a multiple micronutrient supplement (10 mg iron, 10 mg zinc, 400 µg vitamin A) or a placebo. Haematological indices; circulating ferritin, zinc, and retinol (corrected for inflammation); and C-reactive protein were measured at baseline and 22 weeks. At week 22, linear mixed models showed that mean corpuscular volume increased by 0.3 fL, serum ferritin by 9.1 µg/L, plasma zinc by 0.9 µmol/L, and plasma retinol by 15%, and the prevalence of zinc deficiency decreased by 17.3% points in the intervention group compared to placebo. No intervention effects were found for other haematological indices, or the prevalence of anaemia. Multiple micronutrient supplementation for 22 weeks improved the biomarkers of zinc and vitamin A status and some biomarkers of iron status, and reduced the prevalence of zinc deficiency in Vietnamese school children. Trial registration: This trial was registered on 06/09/2016 at www.anzctr.org.au as ACTRN12616001245482.

There is urgent need to develop new acceleration techniques capable of exceeding gigaelectron-volt-per-metre (GeV m −1 ) gradients in order to enable future generations of both light sources and high-energy physics experiments. To address this need, short wavelength accelerators based on wakefields, where an intense relativistic electron beam radiates the demanded fields directly into the accelerator structure or medium, are currently under intense investigation. One such wakefield based accelerator, the dielectric wakefield accelerator, uses a dielectric lined-waveguide to support a wakefield used for acceleration. Here we show gradients of 1.347±0.020 GeV m −1 using a dielectric wakefield accelerator of 15 cm length, with sub-millimetre transverse aperture, by measuring changes of the kinetic state of relativistic electron beams. We follow this measurement by demonstrating accelerating gradients of 320±17 MeV m −1 . Both measurements improve on previous measurements by and order of magnitude and show promise for dielectric wakefield accelerators as sources of high-energy electrons. Wakefield accelerators are a cheaper and compact alternative to conventional particle accelerators for high-energy physics and coherent x-ray sources. Here, the authors demonstrate a field gradient in excess of a gigaelectron-volt-per-metre using a terahertz-frequency wakefield supported by a dielectric lined-waveguide.

Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient’s tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.

It was necessary to have a tool that could predict the amount of protein and optimize the gene sequences to produce recombinant proteins efficiently. The Transim model published by Tuller et al . in 2018 can calculate the translation rate in E . coli using features on the mRNA sequence, achieving a Spearman correlation with the amount of protein per mRNA of 0.36 when tested on the dataset of operons’ first genes in E . coli K-12 MG1655 genome. However, this Spearman correlation was not high, and the model did not fully consider the features of mRNA and protein sequences. Therefore, to enhance the prediction capability, our study firstly tried expanding the testing dataset, adding genes inside the operon, and using the microarray of the mRNA expression data set, thereby helping to improve the correlation of translation rate with the amount of protein with more than 0.42. Next, the applicability of 6 traditional machine learning models to calculate a \"new translation rate\" was examined using initiation rate and elongation rate as inputs. The result showed that the SVR algorithm had the most correlated new translation rates, with Spearman correlation improving to R = 0.6699 with protein level output and to R = 0.6536 with protein level per mRNA. Finally, the study investigated the degree of improvement when combining more features with the new translation rates. The results showed that the model’s predictive ability to produce a protein per mRNA reached R = 0.6660 when using six features, while the correlation of this model’s final translation rate to protein level was up to R = 0.6729. This demonstrated the model’s capability to predict protein expression of a gene, rather than being limited to predicting expression by an mRNA and showed the model’s potential for development into gene expression predicting tools.

The effects of nutrient and mechanical sensing on gut motility and intestinal metabolism in lower vertebrates remains largely unknown. Here we present the transcriptome response to luminal stimulation by nutrients and an inert bolus on nutrient response pathways and also the response on gut motility in a stomachless fish with a short digestive tract; the ballan wrasse ( Labrus berggylta ). Using an in vitro model, we differentiate how signals initiated by physical stretch (cellulose and plastic beads) and nutrients (lipid and protein) modulate the gut evacuation rate, motility patterns and the transcriptome. Intestinal stretch generated by inert cellulose initiated a faster evacuation of digesta out of the anterior intestine compared to digestible protein and lipid. Stretch on the intestine upregulated genes associated with increased muscle activity, whereas nutrients stimulated increased expression of several neuropeptides and receptors which are directly involved in gut motility regulation. Although administration of protein and lipid resulted in similar bulbous evacuation times, differences in intestinal motility, transit between the segments and gene expression between the two were observed. Lipid induced increased frequency of ripples and standing contraction in the middle section of the intestine compared to the protein group. We suggest that this difference in motility was modulated by factors [prepronociceptin ( pnoca ), prodynorphin ( pdyn ) and neuromedin U ( nmu ), opioid neurotransmitters and peptides] that are known to inhibit gastrointestinal motility and were upregulated by protein and not lipid. Our findings show that physical pressure in the intestine initiate contractions propelling the bolus distally, directly towards the exit, whereas the stimuli from nutrients modulates the motility to prolong the residence time of digesta in the digestive tract for optimal digestion.