Early Prediction of Hemodynamic Shock in Pediatric Intensive Care Units With Deep Learning on Thermal Videos

Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives

Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study

BackgroundEvidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In massive and rapidly growing corpuses, such as COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeline that evaluates multiple aspects, such as network topological features, communities, and their temporal trends, can make this process more efficient. ObjectiveWe aimed to show that new knowledge can be captured and tracked using the temporal change in the underlying unsupervised word embeddings of the literature. Further imminent themes can be predicted using machine learning on the evolving associations between words. MethodsFrequently occurring medical entities were extracted from the abstracts of more than 150,000 COVID-19 articles published on the World Health Organization database, collected on a monthly interval starting from February 2020. Word embeddings trained on each month’s literature were used to construct networks of entities with cosine similarities as edge weights. Topological features of the subsequent month’s network were forecasted based on prior patterns, and new links were predicted using supervised machine learning. Community detection and alluvial diagrams were used to track biomedical themes that evolved over the months. ResultsWe found that thromboembolic complications were detected as an emerging theme as early as August 2020. A shift toward the symptoms of long COVID complications was observed during March 2021, and neurological complications gained significance in June 2021. A prospective validation of the link prediction models achieved an area under the receiver operating characteristic curve of 0.87. Predictive modeling revealed predisposing conditions, symptoms, cross-infection, and neurological complications as dominant research themes in COVID-19 publications based on the patterns observed in previous months. ConclusionsMachine learning–based prediction of emerging links can contribute toward steering research by capturing themes represented by groups of medical entities, based on patterns of semantic relationships over time

Genetic variations in olfactory receptor gene OR2AG2 in a large multigenerational family with asthma

It is estimated from twin studies that heritable factors account for at-least half of asthma-risk, of which genetic variants identified through population studies explain only a small fraction. Multi-generation large families with high asthma prevalence can serve as a model to identify highly penetrant genetic variants in closely related individuals that are missed by population studies. To achieve this, a four-generation Indian family with asthma was identified and recruited for examination and genetic testing. Twenty subjects representing all generations were selected for whole genome genotyping, of which eight were subjected to exome sequencing. Non-synonymous and deleterious variants, segregating with the affected individuals, were identified by exome sequencing. A prioritized deleterious missense common variant in the olfactory receptor gene OR2AG2 that segregated with a risk haplotype in asthma, was validated in an asthma cohort of different ethnicity. Phenotypic tests were conducted to verify expected deficits in terms of reduced ability to sense odors. Pathway-level relevance to asthma biology was tested in model systems and unrelated human lung samples. Our study suggests that OR2AG2 and other olfactory receptors may contribute to asthma pathophysiology. Genetic studies on large families of interest can lead to efficient discovery

Geographic information system-based mapping of air pollution & emergency room visits of patients for acute respiratory symptoms in Delhi, India (March 2018-February 2019)

Background & objectives: Studies assessing the spatial and temporal association of ambient air pollution with emergency room visits of patients having acute respiratory symptoms in Delhi are lacking. Therefore, the present study explored the relationship between spatio-temporal variation of particulate matter (PM)2.5 concentrations and air quality index (AQI) with emergency room (ER) visits of patients having acute respiratory symptoms in Delhi using the geographic information system (GIS) approach. Methods: The daily number of ER visits of patients having acute respiratory symptoms (less than or equal to two weeks) was recorded from the ER of four hospitals of Delhi from March 2018 to February 2019. Daily outdoor PM2.5 concentrations and air quality index (AQI) were obtained from the Delhi Pollution Control Committee. Spatial distribution of patients with acute respiratory symptoms visiting ER, PM2.5 concentrations and AQI were mapped for three seasons of Delhi using ArcGIS software. Results: Of the 70,594 patients screened from ER, 18,063 eligible patients were enrolled in the study. Winter days had poor AQI compared to moderate and satisfactory AQI during summer and monsoon days, respectively. None of the days reported good AQI (<50). During winters, an increase in acute respiratory ER visits of patients was associated with higher PM2.5 concentrations in the highly polluted northwest region of Delhi. In contrast, a lower number of acute respiratory ER visits of patients were seen from the 'moderately polluted' south-west region of Delhi with relatively lower PM2.5 concentrations. Interpretation & conclusions: Acute respiratory ER visits of patients were related to regional PM2.5 concentrations and AQI that differed during the three seasons of Delhi. The present study provides support for identifying the hotspots and implementation of focused, intensive decentralized strategies to control ambient air pollution in worst-affected areas, in addition to the general city-wise strategies