Development of crash prediction models by assessing the role of perpetrators and victims: a comparison of ANN & logistic model using historical crash data.

Road traffic injuries cost countries 3% of their annual GDP. In developing countries like India, every year around 150,000 people die on roads. The type of vehicles involved in a crash contribute majorly to the outcome of casualty (injury/death). Barring few studies, literature are less regarding the role of vehicle as perpetrator and victim on road crash fatalities. Historical crash data has been used in the present study to examine the role of vehicles (both as perpetrator & victim). The study reveals that victim's effect is more as compared to perpetrator/accused for determining the outcome of crash. Heavy vehicles as perpetrator, and self-hitting vehicles along with pedestrians as victims have higher fatality rates. Binary logistic regression and artificial neural network (ANN) has been utilized for developing prediction models. Binary logistic model predicted around 75% of outcomes correctly with default cut-off value (0.5). However, based on reported crash data, where 19% of total crashes lead to deaths, 0.19 has been proposed as cut-off value which increases the accuracy of the predictions. Accuracy of ANN technique directly depends on the number of crashes reported for a definite pair of perpetrator and victim and the type of validation technique used (Holdback/K-Fold) along with the type of hidden layer chosen for the study based on different types of sigmoid activation function. ROC curves in ANN suggest that the analysis can predict 75% of the outcomes which can be increased by deleting the pairs of vehicles which are present/have occurred in very less number. A comparison has been made between the two techniques based on their advantages and limitations. The developed models can be used as safety indicators based on composition of traffic flow on urban roads.

Factors propelling fatalities during road crashes: A detailed investigation and modelling of historical crash data with field studies.

One of the major concerns in developing countries like India is to maintain traffic safety under mixed and heterogenous scenario. Although zero accidents is the need of the hour, the first step to attain it is ensuring zero deaths and no serious long-term disabling injuries in road crashes. To reduce the road crash fatalities, explicit and detailed studies have been conducted by utilising historical road crash data of two emerging smart cities of India - Bhubaneswar and Visakhapatnam. Traffic flow data and characteristics of road infrastructure has also been collected by performing field studies at accident prone locations. Various factors including vehicular characteristics, road user characteristics, and road infrastructure have been analyzed using various non-parametric tests to identify the contributing factors resulting in fatalities. It is observed that out of 14 variables used for study, 8 factors were significantly related to fatal crashes. These included categories of victim and accused, 85th percentile speed, presence of road markings, availability of sight distance, etc. The significant factors were subjected to binary logistic regression to determine the odd's ratio of significant factors. The logistic regression predicted 79% of deaths correctly. Crash fatality prediction models are developed using both Classification and Regression Tree (CART) classification tree with 83% accuracy. Although CART classification led to higher accuracy, binary logistic regression is more robust as it considered more significant factors as compared to CART. Subsequently, a severity index has been proposed based on proportions of actual fatal crashes and usage of K-means clustering technique. The proposed indices shall be really helpful in traffic safety management, specifically in reduction of fatalities during road crashes.

A functionally distinct neutrophil landscape in severe COVID-19 reveals opportunities for adjunctive therapies.

Acute respiratory distress syndrome (ARDS) is a life-threatening syndrome, constituted by respiratory failure and diffuse alveolar damage that results from dysregulated local and systemic immune activation, causing pulmonary vascular, parenchymal, and alveolar damage. SARS-CoV-2 infection has become the dominant cause of ARDS worldwide, and emerging evidence implicates neutrophils and their cytotoxic arsenal of effector functions as central drivers of immune-mediated lung injury in COVID-19 ARDS. However, key outstanding questions are whether COVID-19 drives a unique program of neutrophil activation or effector functions that contribute to the severe pathogenesis of this pandemic illness and whether this unique neutrophil response can be targeted to attenuate disease. Using a combination of high-dimensional single-cell analysis and ex vivo functional assays of neutrophils from patients with COVID-19 ARDS, compared with those with non-COVID ARDS (caused by bacterial pneumonia), we identified a functionally distinct landscape of neutrophil activation in COVID-19 ARDS that was intrinsically programmed during SARS-CoV-2 infection. Furthermore, neutrophils in COVID-19 ARDS were functionally primed to produce high amounts of neutrophil extracellular traps. Surprisingly, this unique pathological program of neutrophil priming escaped conventional therapy with dexamethasone, thereby revealing a promising target for adjunctive immunotherapy in severe COVID-19.

Circulating extracellular DNA is an independent predictor of mortality in elderly patients with venous thromboembolism.

BACKGROUND: Venous thromboembolism (VTE) is a major cause of morbidity and mortality in elderly patients. Extracellular DNA is a pro-inflammatory and pro-thrombotic mediator in vitro and in animal models. Levels of circulating extracellular DNA (ceDNA) are increased in VTE patients, but the association of ceDNA with VTE extent and clinical outcome is poorly understood. OBJECTIVES: We analyzed the association of ceDNA with the extent of VTE, categorized as distal and proximal deep vein thrombosis and pulmonary embolism, and with the clinical outcomes VTE recurrence and mortality. METHODS: We quantified ceDNA by a fluorescent probe, as well as circulating nucleosomes and neutrophil extracellular traps (NETs) by ELISA in plasma from 611 patients aged ≥ 65 years with acute VTE of a prospective cohort study (SWITCO65+). RESULTS: Levels of ceDNA and nucleosomes, but not NETs, correlated with VTE extent. Infectious comorbidities independently increased ceDNA levels in VTE. CeDNA strongly correlated with C-reactive protein and leukocytosis, suggesting an association of ceDNA with inflammation in VTE patients. CeDNA furthermore predicted PE-related and all-cause mortality, but not VTE recurrence, during a 3-year follow-up. CONCLUSIONS: Our study suggests that ceDNA levels in VTE patients reflect the degree of inflammation and may serve as a biomarker to stratify VTE patients at risk for mortality.

Host DNases prevent vascular occlusion by neutrophil extracellular traps.

Platelet and fibrin clots occlude blood vessels in hemostasis and thrombosis. Here we report a noncanonical mechanism for vascular occlusion based on neutrophil extracellular traps (NETs), DNA fibers released by neutrophils during inflammation. We investigated which host factors control NETs in vivo and found that two deoxyribonucleases (DNases), DNase1 and DNase1-like 3, degraded NETs in circulation during sterile neutrophilia and septicemia. In the absence of both DNases, intravascular NETs formed clots that obstructed blood vessels and caused organ damage. Vascular occlusions in patients with severe bacterial infections were associated with a defect to degrade NETs ex vivo and the formation of intravascular NET clots. DNase1 and DNase1-like 3 are independently expressed and thus provide dual host protection against deleterious effects of intravascular NETs.

Neutrophil Extracellular Traps Contain Selected Antigens of Anti-Neutrophil Cytoplasmic Antibodies.

Neutrophil extracellular traps (NETs) are chromatin filaments decorated with enzymes from neutrophil cytoplasmic granules. Anti-neutrophil cytoplasmic antibodies (ANCAs) bind to enzymes from neutrophil cytoplasmic granules and are biomarkers for the diagnosis of systemic vasculitides. ANCA diagnostics are based on indirect immunofluorescence (IIF) of ethanol-fixed neutrophils. IIF shows a cytoplasmic staining pattern (C-ANCA) due to autoantibodies against proteinase 3 (PR3) or a perinuclear staining pattern (P-ANCA) due to autoantibodies against myeloperoxidase (MPO). The distinct ANCA-staining patterns are an artifact of ethanol fixation. Here, we tested NETs as a substrate for the detection of ANCAs in human sera. We observed that P-ANCAs specifically stained NETs, while C-ANCAs targeted the cell bodies of netting neutrophils. The distinct ANCA-staining patterns were caused by the presence of MPO, but not PR3, in NETs. Using NETs as a substrate for IIF, we characterized ANCAs in sera of patients with ANCA-associated vasculitis (AAV). Furthermore, we inhibited serine proteases by diisopropylfluorophosphate to prevent chromatin unfolding and the release of NETs and thus generated neutrophils with MPO-positive nuclei and PR3-positive cytoplasm, which resembled the appearance of ethanol-fixed neutrophils. In conclusion, our data suggest that NETs are selectively loaded with antigens recognized by P-ANCAs, and netting neutrophils provide a physiological substrate for ANCA detection in patients with AAV.

The factor XIIa blocking antibody 3F7: a safe anticoagulant with anti-inflammatory activities.

The plasma protein factor XII (FXII) is the initiating protease of the procoagulant and proinflammatory contact system. FXII activates both the bradykinin (BK) producing kallikrein-kinin system and the intrinsic pathway of coagulation. Contact with negatively charged surfaces induces auto-activation of zymogen FXII that results in activated FXII (FXIIa). Various in vivo activators of FXII have been identified including heparin, misfolded protein aggregates, nucleic acids and polyphosphate. Murine models have established a central role of FXII in arterial and venous thromboembolic diseases. Despite the central function of FXII in pathologic thrombosis, its deficiency does not impair hemostasis in animals or humans. The selective role of FXIIa in thrombosis, but not hemostasis, offers an exciting novel strategy for safe anticoagulation based on interference with FXIIa. We have generated the recombinant fully human FXIIa-blocking antibody 3F7, which abolished FXIIa enzymatic activity and prevented thrombosis in a cardiopulmonary bypass system in large animals, in the absence of increased therapy-associated bleeding. Furthermore, 3F7 also interfered with BK-driven edema in the severe swelling disorder hereditary angioedema (HAE) type III. Taken together, targeting FXIIa with 3F7 appears to be a promising approach to treat edema disorders and thrombosis.