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Abstract Severe acute respiratory infections, such as community-acquired pneumonia, hospital-acquired pneumonia, and ventilator-associated pneumonia, constitute frequent and lethal pulmonary infections in the intensive care unit (ICU). Despite optimal management with early appropriate empiric antimicrobial therapy and adequate supportive care, mortality remains high, in part attributable to the aging, growing number of comorbidities, and rising rates of multidrug resistance pathogens. Biomarkers have the potential to offer additional information that may further improve the management and outcome of pulmonary infections. Available pathogen-specific biomarkers, for example, Streptococcus pneumoniae urinary antigen test and galactomannan, can be helpful in the microbiologic diagnosis of pulmonary infection in ICU patients, improving the timing and appropriateness of empiric antimicrobial therapy since these tests have a short turnaround time in comparison to classic microbiology. On the other hand, host-response biomarkers, for example, C-reactive protein and procalcitonin, used in conjunction with the clinical data, may be useful in the diagnosis and prediction of pulmonary infections, monitoring the response to treatment, and guiding duration of antimicrobial therapy. The assessment of serial measurements overtime, kinetics of biomarkers, is more informative than a single value. The appropriate utilization of accurate pathogen-specific and host-response biomarkers may benefit clinical decision-making at the bedside and optimize antimicrobial stewardship.

Abstract The objective was to evaluate the spatial distribution of chemical and textural soil variables in a multistrata agroforestry system. A total of 73 georeferenced soil samples were collected at depths of 10-20 cm and 20-40 cm. The studied parameters were: pH H2O , potential acidity (H+Al), calcium (Ca 2+ ), magnesium (Mg 2+ ), aluminum (Al 3+ ), sodium (Na + ), potassium (K + ), phosphorus (P), organic carbon (C org ), cation exchange capacity (T-value), base saturation (V-value), total clay, total sand, and silt. Principal component analysis (PCA) was performed in R software using the FactoMineR and Factoextra packages. For variables with spatial dependence, ordinary kriging was performed using the best-fitted model. For variables without spatial dependence, inverse distance weighted (IDW) interpolation was applied (power = 2). The spherical model was the best fit for chemical attributes. IDW interpolation accurately mapped the textural attributes. It was concluded that geostatistics enabled a detailed analysis of chemical and textural attributes.