The nasopharyngeal commensal Streptococcus pneumoniae can become invasive and cause metastatic infection. This requires the pneumococcus to have the ability to adapt, grow, and reside in diverse host environments. Therefore, we studied whether the likelihood of severe disease manifestations was related to pneumococcal growth kinetics. For 383 S. pneumoniae blood isolates and 25 experimental mutants, we observed highly reproducible growth curves in nutrient-rich medium. The derived growth features were lag time, maximum growth rate, maximum density, and stationary-phase time before lysis. First, the pathogenicity of each growth feature was probed by comparing isolates from patients with and without marked preexisting comorbidity. Then, growth features were related to the propensity of causing severe manifestations of invasive pneumococcal disease (IPD). A high maximum bacterial density was the most pronounced pathogenic growth feature, which was also an independent predictor of 30-day mortality (P = 0.03). Serotypes with an epidemiologically higher propensity for causing meningitis displayed a relatively high maximum density (P < 0.005) and a short stationary phase (P < 0.005). Correspondingly, isolates from patients diagnosed with meningitis showed an especially high maximum density and short stationary phase compared to isolates from the same serotype that had caused uncomplicated bacteremic pneumonia. In contrast, empyema-associated strains were characterized by a relatively long lag phase (P < 0.0005), and slower growth (P < 0.005). The course and dissemination of IPD may partly be attributable to the pneumococcal growth features involved. If confirmed, we should tailor the prevention and treatment strategies for the different infection sites that can complicate IPD.
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Respiratory pathogens like Streptococcus pneumoniae can cause severe pneumonia. Nonetheless, mechanically ventilated intensive care patients, who have a high risk of contracting pneumonia, rarely develop pneumococcal pneumonia. Mechanically ventilated patients are at risk of contracting pneumonia. Therefore, these patients often receive prophylactic systemic antimicrobial therapy. Intriguingly however, a previous study showed that antimicrobial activity in bronchoalveolar aspirates (here referred to as “sputa”) from ventilated patients was only partially explained by antibiotic therapy. Here we report that sputa from these patients presented distinct proteome signatures depending on the presence or absence of antimicrobial activity. Moreover, we show that the same distinction applied to antibodies against Streptococcus pneumoniae , which is a major causative agent of pneumonia. Specifically, the investigated sputa that inhibited growth of S. pneumoniae , while containing subinhibitory levels of the antibiotic cefotaxime, presented elevated levels of proteins implicated in innate immune defenses, including complement and apolipoprotein-associated proteins. In contrast, S. pneumoniae -inhibiting sputa with relatively high cefotaxime concentrations or noninhibiting sputa contained higher levels of proteins involved in inflammatory responses, such as neutrophil elastase-associated proteins. In an immunoproteomics analysis, 18 out of 55 S. pneumoniae antigens tested showed significantly increased levels of IgGs in inhibiting sputa. Hence, proteomics and immunoproteomics revealed elevated levels of antimicrobial host proteins or S. pneumoniae antigen-specific IgGs in pneumococcal growth-inhibiting sputa, thus explaining their anti-pneumococcal activity. IMPORTANCE Respiratory pathogens like Streptococcus pneumoniae can cause severe pneumonia. Nonetheless, mechanically ventilated intensive care patients, who have a high risk of contracting pneumonia, rarely develop pneumococcal pneumonia. This suggests the presence of potentially protective antimicrobial agents in their lung environment. Our present study shows for the first time that bronchoalveolar aspirates, “sputa,” of ventilated patients in a Dutch intensive care unit were characterized by three distinct groups of proteome abundance signatures that can explain their anti-pneumococcal activity. Importantly, this anti-pneumococcal sputum activity was related either to elevated levels of antimicrobial host proteins or to antibiotics and S. pneumoniae -specific antibodies. Further, the sputum composition of some patients changed over time. Therefore, we conclude that our study may provide a novel tool to measure changes that are indicative of infection-related conditions in the lungs of mechanically ventilated patients.
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Background: Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Objective: The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Methods: This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization’s (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy. Results: Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations’ inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects.
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