Sepsis biomarkers and diagnostic tools with a focus on machine learning.

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers

Large-area CCD imagers for spacecraft applications

Backside illuminated CCD imagers with 100 x 160 resolution elements have been fabricated using double level metal technology. Detailed study of the optical performance of such arrays has been performed between 24 C and -40 C using data rates from 10 kHz to 1 MHz. A 400 x 400 array is presently being fabricated

Profiling inflammatory markers in patients with pneumonia on intensive care

Clinical investigations lack predictive value when diagnosing pneumonia, especially when patients are ventilated and develop ventilator associated pneumonia (VAP). New tools to aid diagnosis are important to improve outcomes. This pilot study examines the potential for a panel of inflammatory mediators to aid in the diagnosis. Forty-four ventilated patients, 17 with pneumonia and 27 with brain injuries, eight of whom developed VAP, were recruited. 51 inflammatory mediators, including cytokines and oxylipins, were measured in patients’ serum using flow cytometry and mass spectrometry. The mediators could separate patients admitted to ICU with pneumonia compared to brain injury with an area under the receiver operating characteristic curve (AUROC) 0.75 (0.61–0.90). Changes in inflammatory mediators were similar in both groups over the course of ICU stay with 5,6-dihydroxyeicosatrienoic and 8,9-dihydroxyeicosatrienoic acids increasing over time and interleukin-6 decreasing. However, brain injured patients who developed VAP maintained inflammatory profiles similar to those at admission. A multivariate model containing 5,6-dihydroxyeicosatrienoic acid, 8,9-dihydroxyeicosatrienoic acid, intercellular adhesion molecule-1, interleukin-6, and interleukin-8, could differentiate patients with VAP from brain injured patients without infection (AUROC 0.94 (0.80–1.00)). The use of a selected group of markers showed promise to aid the diagnosis of VAP especially when combined with clinical data

Machine learning and synthetic outcome estimation for individualised antimicrobial cessation

The decision on when it is appropriate to stop antimicrobial treatment in an individual patient is complex and under-researched. Ceasing too early can drive treatment failure, while excessive treatment risks adverse events. Under- and over-treatment can promote the development of antimicrobial resistance (AMR). We extracted routinely collected electronic health record data from the MIMIC-IV database for 18,988 patients (22,845 unique stays) who received intravenous antibiotic treatment during an intensive care unit (ICU) admission. A model was developed that utilises a recurrent neural network autoencoder and a synthetic control-based approach to estimate patients’ ICU length of stay (LOS) and mortality outcomes for any given day, under the alternative scenarios of if they were to stop vs. continue antibiotic treatment. Control days where our model should reproduce labels demonstrated minimal difference for both stopping and continuing scenarios indicating estimations are reliable (LOS results of 0.24 and 0.42 days mean delta, 1.93 and 3.76 root mean squared error, respectively). Meanwhile, impact days where we assess the potential effect of the unobserved scenario showed that stopping antibiotic therapy earlier had a statistically significant shorter LOS (mean reduction 2.71 days, p-value <0.01). No impact on mortality was observed. In summary, we have developed a model to reliably estimate patient outcomes under the contrasting scenarios of stopping or continuing antibiotic treatment. Retrospective results are in line with previous clinical studies that demonstrate shorter antibiotic treatment durations are often non-inferior. With additional development into a clinical decision support system, this could be used to support individualised antimicrobial cessation decision-making, reduce the excessive use of antibiotics, and address the problem of AMR

Remarkable preservation of brain tissues in an Early Cretaceous iguanodontian dinosaur

It has become accepted in recent years that the fossil record can preserve labile tissues. We report here the highly detailed mineralization of soft tissues associated with a naturally occurring brain endocast of an iguanodontian dinosaur found in c. 133 Ma fluvial sediments of the Wealden at Bexhill, Sussex, UK. Moulding of the braincase wall and the mineral replacement of the adjacent brain tissues by phosphates and carbonates allowed the direct examination of petrified brain tissues. Scanning electron microscopy (SEM) imaging and computed tomography (CT) scanning revealed preservation of the tough membranes (meninges) that enveloped and supported the brain proper. Collagen strands of the meningeal layers were preserved in collophane. The blood vessels, also preserved in collophane, were either lined by, or infilled with, microcrystalline siderite. The meninges were preserved in the hindbrain region and exhibit structural similarities with those of living archosaurs. Greater definition of the forebrain (cerebrum) than the hindbrain (cerebellar and medullary regions) is consistent with the anatomical and implied behavioural complexity previously described in iguanodontian-grade ornithopods. However, we caution that the observed proximity of probable cortical layers to the braincase walls probably resulted from the settling of brain tissues against the roof of the braincase after inversion of the skull during decay and burial

Remarkable preservation of microbial mats in Neoproterozoic siliciclastic settings : Implications for Ediacaran taphonomic models

The authors thank Duncan McIlroy and Alex Liu for their discussions, help, comments and field support, the National Trust for access to Longmyndian localities, and the staff of the British Geological Survey Palaeontology unit and the Oxford University Museum of Natural History for their assistance with access to materials. The comments and suggestions of two anonymous reviewers and Nora Noffke significantly improved the manuscript.Peer reviewedPostprin

Cryptic Disc Structures Resembling Ediacaran Discoidal Fossils from the Lower Silurian Hellefjord Schist, Arctic Norway

The Hellefjord Schist, a volcaniclastic psammite-pelite formation in the Caledonides of Arctic Norway contains discoidal impressions and apparent tube casts that share morphological and taphonomic similarities to Neoproterozoic stem-holdfast forms. U-Pb zircon geochronology on the host metasediment indicates it was deposited between 437 ± 2 and 439 ± 3 Ma, but also indicates that an inferred basal conglomerate to this formation must be part of an older stratigraphic element, as it is cross-cut by a 546 ± 4 Ma pegmatite. These results confirm that the Hellefjord Schist is separated from underlying older Proterozoic rocks by a thrust. It has previously been argued that the Cambrian Substrate Revolution destroyed the ecological niches that the Neoproterozoic frond-holdfasts organisms occupied. However, the discovery of these fossils in Silurian rocks demonstrates that the environment and substrate must have been similar enough to Neoproterozoic settings that frond-holdfast bodyplans were still ecologically viable some hundred million years later

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease