EP11 Using machine learning to recover unrecorded prehospital data

Background The recording practices, of electronic patient records for ambulance crews, are continuously developing. South Central Ambulance Service (SCAS) adapted the common AVPU-scale (Alert, Voice, Pain, Unresponsive) in 2019 to include an option for ‘New Confusion’. Progressing to this new AVCPU-scale made comparisons with older data impossible. We demonstrate a method to retrospectively classify patients into the alertness levels most influenced by this update. Methods SCAS provided ~1.6 million Electronic Patient Records, including vital signs, demographics, and presenting complaint free-text, these were split into training, validation, and testing datasets (80%, 10%, 10% respectively), and under sampled to the minority class. These data were used to train and validate predictions of the classes most affected by the modification of the scale (Alert, New Confusion, Voice). A transfer-learning natural language processing (NLP) classifier was used, using a language model described by Smerity et al. (2017) to classify the presenting complaint free-text. A second approach used vital signs, demographics, conveyance, and assessments (30 metrics) for classification. Categorical data were binary encoded and continuous variables were normalised. 20 machine learning algorithms were empirically tested and the best 3 combined into a voting ensemble combining three vital-sign based algorithms (Random Forest, Extra Tree Classifier, Decision Tree) with the NLP classifier using a Random Forest output layer. Results The ensemble method resulted in a weighted F1 of 0.78 for the test set. The sensitivities/specificities for each of the classes are: 84%/ 90% (Alert), 73%/ 89% (Newly Confused) and 68%/ 93% (Voice). Conclusions The ensemble combining free text and vital signs resulted in high sensitivity and specificity when reclassifying the alertness levels of prehospital patients. This study demonstrates the capabilities of machine learning classifiers to recover missing data, allowing the comparison of data collected with different recording standards

Exercise Martian Attack!: Using VR feedback as a reflective tool for paramedic science students.

Paramedic students have had to overcome the restrictions Covid-19 with many of their clinical skills moving online, limiting opportunities to engage with clinical practice partners, a key requirement of their professional programme. Social distancing has been challenging to overcome and the paramedic teaching team’s solution was to offer a the University underground carpark to stage a simulated Casualty Clearing Point for a Major Incident Martian Attack

Code-assisted discovery of TAL effector targets in bacterial leaf streak of rice reveals contrast with bacterial blight and a novel susceptibility gene

Citation: Cernadas RA, Doyle EL, Nin˜o-Liu DO, Wilkins KE, Bancroft T, et al. (2014) Code-Assisted Discovery of TAL Effector Targets in Bacterial Leaf Streak of Rice Reveals Contrast with Bacterial Blight and a Novel Susceptibility Gene. PLoS Pathog 10(2): e1003972. https://doi.org/10.1371/journal.ppat.1003972Bacterial leaf streak of rice, caused by Xanthomonas oryzae pv. oryzicola (Xoc) is an increasingly important yield constraint in this staple crop. A mesophyll colonizer, Xoc differs from X. oryzae pv. oryzae (Xoo), which invades xylem to cause bacterial blight of rice. Both produce multiple distinct TAL effectors, type III-delivered proteins that transactivate effector-specific host genes. A TAL effector finds its target(s) via a partially degenerate code whereby the modular effector amino acid sequence identifies nucleotide sequences to which the protein binds. Virulence contributions of some Xoo TAL effectors have been shown, and their relevant targets, susceptibility (S) genes, identified, but the role of TAL effectors in leaf streak is uncharacterized. We used host transcript profiling to compare leaf streak to blight and to probe functions of Xoc TAL effectors. We found that Xoc and Xoo induce almost completely different host transcriptional changes. Roughly one in three genes upregulated by the pathogens is preceded by a candidate TAL effector binding element. Experimental analysis of the 44 such genes predicted to be Xoc TAL effector targets verified nearly half, and identified most others as false predictions. None of the Xoc targets is a known bacterial blight S gene. Mutational analysis revealed that Tal2g, which activates two genes, contributes to lesion expansion and bacterial exudation. Use of designer TAL effectors discriminated a sulfate transporter gene as the S gene. Across all targets, basal expression tended to be higher than genome-average, and induction moderate. Finally, machine learning applied to real vs. falsely predicted targets yielded a classifier that recalled 92% of the real targets with 88% precision, providing a tool for better target prediction in the future. Our study expands the number of known TAL effector targets, identifies a new class of S gene, and improves our ability to predict functional targeting

The Retinoic Acid Receptor Agonist Am80 Increases Mucosal Inflammation in an IL-6 Dependent Manner During Trichuris muris Infection

PURPOSE: Vitamin A metabolites, such as all-trans-retinoic acid (RA) that act through the nuclear receptor; retinoic acid receptor (RAR), have been shown to polarise T cells towards Th2, and to be important in resistance to helminth infections. Co-incidentally, people harbouring intestinal parasites are often supplemented with vitamin A, as both vitamin A deficiency and parasite infections often occur in the same regions of the globe. However, the impact of vitamin A supplementation on gut inflammation caused by intestinal parasites is not yet completely understood. METHODS: Here, we use Trichuris muris, a helminth parasite that buries into the large intestine of mice causing mucosal inflammation, as a model of both human Trichuriasis and IBD, treat with an RARα/β agonist (Am80) and quantify the ensuing pathological changes in the gut. RESULTS: Critically, we show, for the first time, that rather than playing an anti-inflammatory role, Am80 actually exacerbates helminth-driven inflammation, demonstrated by an increased colonic crypt length and a significant CD4(+) T cell infiltrate. Further, we established that the Am80-driven crypt hyperplasia and CD4(+) T cell infiltrate were dependent on IL-6, as both were absent in Am80-treated IL-6 knock-out mice. CONCLUSIONS: This study presents novel data showing a pro-inflammatory role of RAR ligands in T. muris infection, and implies an undesirable effect for the administration of vitamin A during chronic helminth infection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10875-013-9936-8) contains supplementary material, which is available to authorized users

Whipworm genome and dual-species transcriptome analyses provide molecular insights into an intimate host-parasite interaction.

Whipworms are common soil-transmitted helminths that cause debilitating chronic infections in man. These nematodes are only distantly related to Caenorhabditis elegans and have evolved to occupy an unusual niche, tunneling through epithelial cells of the large intestine. We report here the whole-genome sequences of the human-infective Trichuris trichiura and the mouse laboratory model Trichuris muris. On the basis of whole-transcriptome analyses, we identify many genes that are expressed in a sex- or life stage-specific manner and characterize the transcriptional landscape of a morphological region with unique biological adaptations, namely, bacillary band and stichosome, found only in whipworms and related parasites. Using RNA sequencing data from whipworm-infected mice, we describe the regulated T helper 1 (TH1)-like immune response of the chronically infected cecum in unprecedented detail. In silico screening identified numerous new potential drug targets against trichuriasis. Together, these genomes and associated functional data elucidate key aspects of the molecular host-parasite interactions that define chronic whipworm infection

Explanatory pluralism in the medical sciences: theory and practice

Explanatory pluralism is the view that the best form and level of explanation depends on the kind of question one seeks to answer by the explanation, and that in order to answer all questions in the best way possible, we need more than one form and level of explanation. In the first part of this article, we argue that explanatory pluralism holds for the medical sciences, at least in theory. However, in the second part of the article we show that medical research and practice is actually not fully and truly explanatory pluralist yet. Although the literature demonstrates a slowly growing interest in non-reductive explanations in medicine, the dominant approach in medicine is still methodologically reductionist. This implies that non-reductive explanations often do not get the attention they deserve. We argue that the field of medicine could benefit greatly by reconsidering its reductive tendencies and becoming fully and truly explanatory pluralist. Nonetheless, trying to achieve the right balance in the search for and application of reductive and non-reductive explanations will in any case be a difficult exercise

Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials