A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Requirement Text Detection from Contract Packages to Support Project Definition Determination

Project requirements are wishes and expectations of the client toward the design, construction, and other project management processes. The project definition is typically specified in a contract package including a contract document and many other related documents such as drawings, specifications, and government codes. Project definition determination is critical to the success of a project. Due to the lack of efficient tools for requirement processing, the current practices regarding project scoping still heavily rely on a manual basis which is tedious, time-consuming, and error-prone. This study aims to fill that gap by developing an automated method for identifying requirement texts from contractual documents. The study employed Naïve Bayes to train a classification model that can be used to separate requirement statements from non-requirement statements. An experiment was conducted on a manually labeled dataset of 1191 statements. The results revealed that the developed requirement detection model achieves a promising accuracy of over 90%

Henipavirus RNA in African Bats

BACKGROUND: Henipaviruses (Hendra and Nipah virus) are highly pathogenic members of the family Paramyxoviridae. Fruit-eating bats of the Pteropus genus have been suggested as their natural reservoir. Human Henipavirus infections have been reported in a region extending from Australia via Malaysia into Bangladesh, compatible with the geographic range of Pteropus. These bats do not occur in continental Africa, but a whole range of other fruit bats is encountered. One of the most abundant is Eidolon helvum, the African Straw-coloured fruit bat. METHODOLOGY/PRINCIPAL FINDINGS: Feces from E. helvum roosting in an urban setting in Kumasi/Ghana were tested for Henipavirus RNA. Sequences of three novel viruses in phylogenetic relationship to known Henipaviruses were detected. Virus RNA concentrations in feces were low. CONCLUSIONS/SIGNIFICANCE: The finding of novel putative Henipaviruses outside Australia and Asia contributes a significant extension of the region of potential endemicity of one of the most pathogenic virus genera known in humans

Recombinase technology: applications and possibilities

The use of recombinases for genomic engineering is no longer a new technology. In fact, this technology has entered its third decade since the initial discovery that recombinases function in heterologous systems (Sauer in Mol Cell Biol 7(6):2087–2096, 1987). The random insertion of a transgene into a plant genome by traditional methods generates unpredictable expression patterns. This feature of transgenesis makes screening for functional lines with predictable expression labor intensive and time consuming. Furthermore, an antibiotic resistance gene is often left in the final product and the potential escape of such resistance markers into the environment and their potential consumption raises consumer concern. The use of site-specific recombination technology in plant genome manipulation has been demonstrated to effectively resolve complex transgene insertions to single copy, remove unwanted DNA, and precisely insert DNA into known genomic target sites. Recombinases have also been demonstrated capable of site-specific recombination within non-nuclear targets, such as the plastid genome of tobacco. Here, we review multiple uses of site-specific recombination and their application toward plant genomic engineering. We also provide alternative strategies for the combined use of multiple site-specific recombinase systems for genome engineering to precisely insert transgenes into a pre-determined locus, and removal of unwanted selectable marker genes

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC

Construction project simulation using CYCLONE

Construction simulation is a powerful tool that can be used by a construction company for a number of tasks such as productivity measurement, risk analysis, resource planning, design and analysis of construction methods, and site planning. The success of simulation at the construction-process level has led to a natural attempt to use simulation at the construction-project level. Cyclic Operations Network (CYCLONE) and related simulation methodologies have provided the fundamental basis of these developments. This paper describes the enhancements made to the CYCLONE modeling methodology to allow simultaneous simulation of processes involved in a construction project. The objective of these enhancements is to allow development of individual models for all the processes that constitute a project and then to link them so as to simulate them simultaneously using a common resource pool. Such a simulation experiment will allow the construction manager to realistically model, analyze, and plan construction projects. This paper provides the specifications required to perform a project-level simulation using the CYCLONE modeling concepts