Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images

Micropropagation and conservation of selected endangered anticancer medicinal plants from the Western Ghats of India

Globally, cancer is a constant battle which severely affects the human population. The major limitations of the anticancer drugs are the deleterious side effects on the quality of life. Plants play a vital role in curing many diseases with minimal or no side effects. Phytocompounds derived from various medicinal plants serve as the best source of drugs to treat cancer. The global demand for phytomedicines is mostly reached by the medicinal herbs from the tropical nations of the world even though many plant species are threatened with extinction. India is one of the mega diverse countries of the world due to its ecological habitats, latitudinal variation, and diverse climatic range. Western Ghats of India is one of the most important depositories of endemic herbs. It is found along the stretch of south western part of India and constitutes rain forest with more than 4000 diverse medicinal plant species. In recent times, many of these therapeutically valued herbs have become endangered and are being included under the red-listed plant category in this region. Due to a sharp rise in the demand for plant-based products, this rich collection is diminishing at an alarming rate that eventually triggered dangerous to biodiversity. Thus, conservation of the endangered medicinal plants has become a matter of importance. The conservation by using only in situ approaches may not be sufficient enough to safeguard such a huge bio-resource of endangered medicinal plants. Hence, the use of biotechnological methods would be vital to complement the ex vitro protection programs and help to reestablish endangered plant species. In this backdrop, the key tools of biotechnology that could assist plant conservation were developed in terms of in vitro regeneration, seed banking, DNA storage, pollen storage, germplasm storage, gene bank (field gene banking), tissue bank, and cryopreservation. In this chapter, an attempt has been made to critically review major endangered medicinal plants that possess anticancer compounds and their conservation aspects by integrating various biotechnological tool

Efficacy of early neonatal vitamin A supplementation in reducing mortality during infancy in Ghana, India and Tanzania: study protocol for a randomized controlled trial

Vitamin A supplementation of 6-59 month old children is currently recommended by the World Health Organization based on evidence that it reduces mortality. There has been considerable interest in determining the benefits of neonatal vitamin A supplementation, but the results of existing trials are conflicting. A technical consultation convened by WHO pointed to the need for larger scale studies in Asia and Africa to inform global policy on the use of neonatal vitamin A supplementation. Three trials were therefore initiated in Ghana, India and Tanzania to determine if vitamin A supplementation (50,000 IU) given to neonates once orally on the day of birth or within the next two days will reduce mortality in the period from supplementation to 6 months of age compared to placebo. The trials are individually randomized, double masked, and placebo controlled. The required sample size is 40,200 in India and 32,000 each in Ghana and Tanzania. The study participants are neonates who fulfil age eligibility, whose families are likely to stay in the study area for the next 6 months, who are able to feed orally, and whose parent(s) provide informed written consent to participate in the study. Neonates randomized to the intervention group receive 50,000 IU vitamin A and the ones randomized to the control group receive placebo at the time of enrollment. Mortality and morbidity information are collected through periodic home visits by a study worker during infancy. The primary outcome of the study is mortality from supplementation to 6 months of age. The secondary outcome of the study is mortality from supplementation to 12 months of age. The three studies will be analysed independent of each other. Subgroup analysis will be carried out to determine the effect by birth weight, sex, and timing of DTP vaccine, socioeconomic groups and maternal large-dose vitamin A supplementation. The three ongoing studies are the largest studies evaluating the efficacy of vitamin A supplementation to neonates. Policy formulation will be based on the results of efficacy of the intervention from the ongoing randomized controlled trials combined with results of previous studies

Structure-based functional inference of hypothetical proteins from Mycoplasma hyopneumoniae

Enzootic pneumonia caused by Mycoplasma hyopneumoniae is a major constraint to efficient pork production throughout the world. This pathogen has a small genome with 716 coding sequences, of which 418 are homologous to proteins with known functions. However, almost 42% of the 716 coding sequences are annotated as hypothetical proteins. Alternative methodologies such as threading and comparative modeling can be used to predict structures and functions of such hypothetical proteins. Often, these alternative methods can answer questions about the properties of a model system faster than experiments. In this study, we predicted the structures of seven proteins annotated as hypothetical in M. hyopneumoniae, using the structure-based approaches mentioned above. Three proteins were predicted to be involved in metabolic processes, two proteins in transcription and two proteins where no function could be assigned. However, the modeled structures of the last two proteins suggested experimental designs to identify their functions. Our findings are important in diminishing the gap between the lack of annotation of important metabolic pathways and the great number of hypothetical proteins in the M. hyopneumoniae genome

Sulfated Polysaccharide, Curdlan Sulfate, Efficiently Prevents Entry/Fusion and Restricts Antibody-Dependent Enhancement of Dengue Virus Infection In Vitro: A Possible Candidate for Clinical Application

10.1371/journal.pntd.0002188PLoS Neglected Tropical Diseases74

The NRF2-mediated oxidative stress response pathway is associated with tumor cell resistance to arsenic trioxide across the NCI-60 panel

<p>Abstract</p> <p>Background</p> <p>Drinking water contaminated with inorganic arsenic is associated with increased risk for different types of cancer. Paradoxically, arsenic trioxide can also be used to induce remission in patients with acute promyelocytic leukemia (APL) with a success rate of approximately 80%. A comprehensive study examining the mechanisms and potential signaling pathways contributing to the anti-tumor properties of arsenic trioxide has not been carried out.</p> <p>Methods</p> <p>Here we applied a systems biology approach to identify gene biomarkers that underlie tumor cell responses to arsenic-induced cytotoxicity. The baseline gene expression levels of 14,500 well characterized human genes were associated with the GI₅₀ data of the NCI-60 tumor cell line panel from the developmental therapeutics program (DTP) database. Selected biomarkers were tested <it>in vitro</it> for the ability to influence tumor susceptibility to arsenic trioxide.</p> <p>Results</p> <p>A significant association was found between the baseline expression levels of 209 human genes and the sensitivity of the tumor cell line panel upon exposure to arsenic trioxide. These genes were overlayed onto protein-protein network maps to identify transcriptional networks that modulate tumor cell responses to arsenic trioxide. The analysis revealed a significant enrichment for the oxidative stress response pathway mediated by nuclear factor erythroid 2-related factor 2 (NRF2) with high expression in arsenic resistant tumor cell lines. The role of the NRF2 pathway in protecting cells against arsenic-induced cell killing was validated in tumor cells using shRNA-mediated knock-down.</p> <p>Conclusions</p> <p>In this study, we show that the expression level of genes in the NRF2 pathway serve as potential gene biomarkers of tumor cell responses to arsenic trioxide. Importantly, we demonstrate that tumor cells that are deficient for NRF2 display increased sensitivity to arsenic trioxide. The results of our study will be useful in understanding the mechanism of arsenic-induced cytotoxicity in cells, as well as the increased applicability of arsenic trioxide as a chemotherapeutic agent in cancer treatment.</p

Child wasting and concurrent stunting in low- and middle-income countries

Sustainable Development Goal 2.2—to end malnutrition by 2030—includes the elimination of child wasting, defined as a weight-for-length z-score that is more than two standard deviations below the median of the World Health Organization standards for child growth 1. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery and persistence—key features that inform preventive interventions and estimates of disease burden. Here we analyse 21 longitudinal cohorts and show that wasting is a highly dynamic process of onset and recovery, with incidence peaking between birth and 3 months. Many more children experience an episode of wasting at some point during their first 24 months than prevalent cases at a single point in time suggest. For example, at the age of 24 months, 5.6% of children were wasted, but by the same age (24 months), 29.2% of children had experienced at least one wasting episode and 10.0% had experienced two or more episodes. Children who were wasted before the age of 6 months had a faster recovery and shorter episodes than did children who were wasted at older ages; however, early wasting increased the risk of later growth faltering, including concurrent wasting and stunting (low length-for-age z-score), and thus increased the risk of mortality. In diverse populations with high seasonal rainfall, the population average weight-for-length z-score varied substantially (more than 0.5 z in some cohorts), with the lowest mean z-scores occurring during the rainiest months; this indicates that seasonally targeted interventions could be considered. Our results show the importance of establishing interventions to prevent wasting from birth to the age of 6 months, probably through improved maternal nutrition, to complement current programmes that focus on children aged 6–59 months

Causes and consequences of child growth faltering in low-resource settings

Growth faltering in children (low length for age or low weight for length) during the first 1,000 days of life (from conception to 2 years of age) influences short-term and long-term health and survival 1,2. Interventions such as nutritional supplementation during pregnancy and the postnatal period could help prevent growth faltering, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. Identification of age windows and population subgroups on which to focus will benefit future preventive efforts. Here we use a population intervention effects analysis of 33 longitudinal cohorts (83,671 children, 662,763 measurements) and 30 separate exposures to show that improving maternal anthropometry and child condition at birth accounted for population increases in length-for-age z-scores of up to 0.40 and weight-for-length z-scores of up to 0.15 by 24 months of age. Boys had consistently higher risk of all forms of growth faltering than girls. Early postnatal growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits exhibited higher mortality rates from birth to 2 years of age than children without growth deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes and severe consequences for children who experienced early growth faltering support a focus on pre-conception and pregnancy as a key opportunity for new preventive interventions

Early-childhood linear growth faltering in low- and middle-income countries

Globally, 149 million children under 5 years of age are estimated to be stunted (length more than 2 standard deviations below international growth standards) 1,2. Stunting, a form of linear growth faltering, increases the risk of illness, impaired cognitive development and mortality. Global stunting estimates rely on cross-sectional surveys, which cannot provide direct information about the timing of onset or persistence of growth faltering—a key consideration for defining critical windows to deliver preventive interventions. Here we completed a pooled analysis of longitudinal studies in low- and middle-income countries (n = 32 cohorts, 52,640 children, ages 0–24 months), allowing us to identify the typical age of onset of linear growth faltering and to investigate recurrent faltering in early life. The highest incidence of stunting onset occurred from birth to the age of 3 months, with substantially higher stunting at birth in South Asia. From 0 to 15 months, stunting reversal was rare; children who reversed their stunting status frequently relapsed, and relapse rates were substantially higher among children born stunted. Early onset and low reversal rates suggest that improving children’s linear growth will require life course interventions for women of childbearing age and a greater emphasis on interventions for children under 6 months of age