ABSTRACT
BACKGROUND: Swimming-Induced Pulmonary Edema (SIPE) is a respiratory condition frequently seen amongst Naval Special Warfare (NSW) trainees. The incidence of positive respiratory panels (RPs) in trainees diagnosed with SIPE is currently unknown. RESEARCH QUESTION: Is there a significant difference in the incidence of respiratory pathogens in nasopharyngeal samples of NSW candidates with SIPE and a control group? STUDY DESIGN AND METHODS: Retrospective analysis of clinical information from NSW Sea Air and Land (SEAL) candidates diagnosed with SIPE over a 12-month period. Candidates who presented with the common signs and symptoms of SIPE received a nasopharyngeal swab and RP test for common respiratory pathogens. SIPE diagnoses were supported by two-view chest radiograph. RP tests were obtained for a selected control group of 1st phase trainees without SIPE. RESULTS: 45 of 1048 SEAL candidates were diagnosed with SIPE (4.3%). 5 had superimposed pneumonia. 36 of 45 tested positive for at least one microorganism on the RP (80%). In the study group, human rhinovirus/enterovirus (RV/EV) was the most frequently detected organism (37.8%), followed by coronavirus OC43 (17.8%), and parainfluenza virus 3 (17.8%). 16 of 68 candidates from the control group had positive RPs (24%). Patients with SIPE and positive RPs reported dyspnea (94%), pink-frothy sputum (44%), and hemoptysis (22%) more frequently than the controls with positive RPs. Those who reported respiratory infection symptoms in both the study and control groups had higher incidences of positive RPs (P=.046). INTERPRETATION: We observed that 80% of trainees diagnosed with SIPE tested positive on a point of care RP. This positivity rate was significantly higher than RP test results from the control cohort. These findings suggest an association between colonization with a respiratory pathogen and the development of SIPE in NSW candidates.
ABSTRACT
BACKGROUND/AIMS: Chronic kidney disease CKD patients on intermittent hemodialysis IHD are exposed to SARS-CoV-2 infection and carry a risk of developing severe symptoms. The aim of this study was to evaluate the humoral and cellular immunity induced by two doses of mRNA vaccines, the Pfizer-BioNTech (Comirnaty) COVID-19 Vaccine and the Moderna (mRNA-1273) COVID-19 vaccine. PATIENTS AND METHODS: The study included 281 patients from five dialysis centers in northern Poland. Within 2 weeks prior to the first dose of the vaccine, a blood sample was collected for an evaluation of SARS-CoV-2 antibodies. Thirty to forty-five days after the second dose of the vaccine, a blood sample was taken to evaluate humoral and cellular response. RESULTS: Patients with stage 5 CKD on IHD were characterized by a considerable SARS-CoV-2 vaccine-induced seroconversion rate. The strongest factors influencing the antibodies AB level after vaccination were a pre-vaccination history of SARS-CoV-2 infection, age, the neutrophil-to-lymphocyte ratio NLR, neutrophil absolute count, and the hemoglobin level. Cellular immunity was higher in patients with a pre-vaccination history of SARS-CoV-2 infection. Cellular immunity depended on the albumin level. Positive cellular response to vaccination was a positive factor reducing all-cause mortality, except for COVID-19 mortality (no such deaths were reported during our follow-up). Cellular immunity and humoral immunity were positively mutually dependent. High levels of albumin and hemoglobin, low neutrophil count, and a reduced NLR, translated into better response to vaccination. CONCLUSIONS: Patients with stage 5 CKD on IHD were characterized by a considerable SARS-CoV-2 vaccine-induced seroconversion rate and a good rate of cellular immunity. The factors that change with exacerbating inflammation and malnutrition (albumin, hemoglobin, neutrophil count, the NLR) affected the efficacy of the vaccination.
ABSTRACT
The Covid-19 pandemic caused serious turbulences in most aspects of humans activities. Due to the need to address the epidemic developments at extreme scales, ranging from the entire population of the country down to the level of individual citizens, a construction of adequate mathematical models faces substantial difficulties caused by lacking knowledge of the mechanisms driving transmission of the infections and the very nature of the resulting disease. Therefore, in modeling Covid-19 and its effects, a shift from the knowledge-intensive systems paradigm to the data-intensive one is needed. The current paper is devoted to the architecture of ProME, a data-intensive system for forecasting the Covid-19 and decision making support needed to mitigate the pandemics effects. The system has been constructed to address the mentioned challenges and to allow further relatively easy adaptations to the dynamically changing situation. The system is mainly based on open-source solutions so can be reproduced whenever similar challenges occur.