A novel haemocytometric covid-19 prognostic score developed and validated in an observational multicentre european hospital-based study

COVID-19 induces haemocytometric changes. Complete blood count changes, including new cell activation parameters, from 982 confirmed COVID-19 adult patients from 11 European hospitals were retrospectively analysed for distinctive patterns based on age, gender, clinical severity, symptom duration and hospital days. The observed haemocytometric patterns formed the basis to develop a multi-haemocytometric-parameter prognostic score to predict, during the first three days after presentation, which patients will recover without ventilation or deteriorate within a two-week timeframe, needing intensive care or with fatal outcome. The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781) increasing to 0.875 (95% CI 0.806-0.926) on day 3, was superior to any individual parameter at distinguishing between clinical severity. Findings were confirmed in a validation cohort. Aim is that the score and haemocytometry results are simultaneously provided by analyser software, enabling wide applicability of the score as haemocytometry is commonly requested in COVID-19 patients

A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia

BACKGROUND: Distinguishing arboviral infections from bacterial causes of febrile illness is of great importance for clinical management. The Infection Manager System (IMS) is a novel diagnostic algorithm equipped on a Sysmex hematology analyzer that evaluates the host response using novel techniques that quantify cellular activation and cell membrane composition. The aim of this study was to train and validate the IMS to differentiate between arboviral and common bacterial infections in Southeast Asia and compare its performance against C-reactive protein (CRP) and procalcitonin (PCT). METHODOLOGY/PRINCIPAL FINDINGS: 600 adult Indonesian patients with acute febrile illness were enrolled in a prospective cohort study and analyzed using a structured diagnostic protocol. The IMS was first trained on the first 200 patients and subsequently validated using the complete cohort. A definite infectious etiology could be determined in 190 of 463 evaluable patients (41%), including 89 arboviral infections (81 dengue and 8 chikungunya), 94 bacterial infections (26 murine typhus, 16 salmonellosis, 6 leptospirosis and 46 cosmopolitan bacterial infections), 3 concomitant arboviral-bacterial infections, and 4 malaria infections. The IMS detected inflammation in all but two participants. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the IMS for arboviral infections were 69.7%, 97.9%, 96.9%, and 77.3%, respectively, and for bacterial infections 77.7%, 93.3%, 92.4%, and 79.8%. Inflammation remained unclassified in 19.1% and 22.5% of patients with a proven bacterial or arboviral infection. When cases of unclassified inflammation were grouped in the bacterial etiology group, the NPV for bacterial infection was 95.5%. IMS performed comparable to CRP and outperformed PCT in this cohort. CONCLUSIONS/SIGNIFICANCE: The IMS is an automated, easy to use, novel diagnostic tool that allows rapid differentiation between common causes of febrile illness in Southeast Asia

A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study

COVID-19 induces haemocytometric changes. Complete blood count changes, including new cell activation parameters, from 982 confirmed COVID-19 adult patients from 11 European hospitals were retrospectively analysed for distinctive patterns based on age, gender, clinical severity, symptom duration, and hospital days. The observed haemocytometric patterns formed the basis to develop a multi-haemocytometric-parameter prognostic score to predict, during the first three days after presentation, which patients will recover without ventilation or deteriorate within a two-week timeframe, needing intensive care or with fatal outcome. The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781) increasing to 0.875 (95% CI 0.806-0.926) on day 3, was superior to any individual parameter at distinguishing between clinical severity. Findings were confirmed in a validation cohort. Aim is that the score and haemocytometry results are simultaneously provided by analyser software, enabling wide applicability of the score as haemocytometry is commonly requested in COVID-19 patients.Afdeling Klinische Chemie en Laboratoriumgeneeskunde (AKCL

Approaches to modelling land erodibility by wind

Land susceptibility to wind erosion is governed by complex multiscale interactions between soil erodibility and non-erodible roughness elements populating the land surface. Numerous wind erosion modelling systems have been developed to quantify soil loss and dust emissions at the field, regional and global scales. All of these models require some component that defines the susceptibility of the land surface to erosion, ie, land erodibility. The approaches taken to characterizing land erodibility have advanced through time, following developments in empirical and process-based research into erosion mechanics, and the growing availability of moderate to high-resolution spatial data that can be used as model inputs. Most importantly, the performance of individual models is highly dependent on the means by which soil erodibility and surface roughness effects are represented in their land erodibility characterizations. This paper presents a systematic review of a selection of wind erosion models developed over the last 50 years. The review evaluates how land erodibility has been modelled at different spatial and temporal scales, and in doing this the paper identifies concepts behind parameterizations of land erodibility, trends in model development, and recent progress in the representation of soil, vegetation and land management effects on the susceptibility of landscapes to wind erosion. The paper provides a synthesis of the capabilities of the models in assessing dynamic patterns of land erodibility change, and concludes by identifying key areas that require research attention to enhance our capacity to achieve this task