Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample’s Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient’s clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden.

Introduction: The aim of this study was to investigate the kinetics of immunoglobulin M (IgM) during the different stages of sepsis. Methods: In this prospective multicenter study, blood sampling for IgM measurement was done within the first 24 hours from diagnosis in 332 critically ill patients; in 83 patients this was repeated upon progression to more severe stages. Among these 83 patients, 30 patients with severe sepsis progressed into shock and IgM was monitored daily for seven consecutive days. Peripheral blood mononuclear cells (PBMCs) were isolated from 55 patients and stimulated for IgM production. Results: Serum IgM was decreased in septic shock compared to patients with systemic inflammatory response syndrome (SIRS) and patients with severe sepsis. Paired comparisons at distinct time points of the sepsis course showed that IgM was decreased only when patients deteriorated from severe sepsis to septic shock. Serial measurements in these patients, beginning from the early start of vasopressors, showed that the distribution of IgM over time was significantly greater for survivors than for non-survivors. Production of IgM by PBMCs was significantly lower at all stages of sepsis compared with healthy controls. Conclusions: Specific changes of circulating IgM occur when patients with severe sepsis progress into septic shock. The distribution of IgM is lower among non-survivors.

Evidence on the changes in the absolute counts of monocyte subpopulations in sepsis is missing. Firstly, absolute counts of circulating CD14pos/HLA-DRpos/CD45pos monocytes were measured by flow cytometry in 70 patients with Gram-negative sepsis and in 10 healthy volunteers. In the second phase, immunophenotyping was performed and the absolute count of circulating inflammatory monocytes and of circulating CD14dim/CD16pos/CD45pos patrolling monocytes were measured in another 55 patients and 10 healthy volunteers. Measurements were repeated on days 3, 7, and 10. Results were correlated with survival after 28 days. Greater numbers of CD14pos/HLA-DRpos/CD45pos monocytes were found on day 1 in survivors compared to nonsurvivors (p = 0.030). Receiver operating characteristic (ROC) analysis showed that a cutoff higher than 337 cells/mm3 on day 1 could discriminate between survivors and nonsurvivors with a positive predictive value (PPV) of 91.1%. Logistic regression including Sequential Organ Failure Assessment (SOFA) score and Acute Physiology and Chronic Health Evaluation (APACHE) II score showed that an absolute count greater than 337 cells/mm3 was independently associated with unfavorable outcome (odds ratio (OR) 0.19, p = 0.050). The absolute counts of inflammatory and of CD14dim/CD16pos/CD45pos monocytes were greater in patients than healthy controls during the entire 10 days of follow-up. The absolute counts on day 3 of CD14dim/CD16pos/CD45pos monocytes were greater in survivors than nonsurvivors (p = 0.027). ROC analysis revealed that the cutoff at 27 cells/mm3 could discriminate between survivors and nonsurvivors with PPV of 94.1%. Logistic regression including age, SOFA score, and APACHE II score showed that an absolute count greater than 27 cells/mm3 was independently associated with unfavorable outcome (OR 0.06, p = 0.033). Logistic regression analysis showed that intra-abdominal infection on day 1 was predictive of low CD14dim/ CD16pos/CD45pos count on day 3. Circulating counts of inflammatory and patrolling monocytes are greatly increased in Gram-negative sepsis. Absolute counts of CD14pos/HLA-DRpos/CD45pos monocytes on day 1 and CD14dim/CD16pos/CD45pos monocytes on day 3 are independently associated with final outcome. ClinicalTrials.gov, NCT01223690 . Registered retrospectively on 18 October 2010.

The presence of human immunodeficiency virus type 1 (HIV-1) drug resistance among drug-naïve patients remains stable, although the proportion of patients with virological failure to therapy is decreasing. The dynamics of transmitted resistance among drug-naïve patients remains largely unknown. The prevalence of non-nucleoside reverse transcriptase inhibitors (NNRTI) resistance was 16.9% among treatment-naïve individuals in Greece. We aimed to investigate the transmission dynamics and the effective reproductive number (R_e) of the locally transmitted NNRTI resistance. We analyzed sequences with dominant NNRTI resistance mutations (E138A and K103N) found within monophyletic clusters (local transmission networks (LTNs)) from patients in Greece. For the K103N LTN, the R_e was >1 between 2008 and the first half of 2013. For all E138A LTNs, the R_e was >1 between 1998 and 2015, except the most recent one (E138A_4), where the R_e was >1 between 2006 and 2011 and approximately equal to 1 thereafter. K103N and E138A_4 showed similar characteristics with a more recent origin, higher R_e during the first years of the sub-epidemics, and a declining trend in the number of transmissions during the last two years. In the remaining LTNs the epidemic was still expanding. Our study highlights the added value of molecular epidemiology to public health.

Our aim was to investigate the dispersal patterns and parameters associated with local molecular transmission clusters (MTCs) of subtypes A1 and B in Greece (predominant HIV-1 subtypes). The analysis focused on 1751 (28.4%) and 2575 (41.8%) sequences of subtype A1 and B, respectively. Identification of MTCs was based on phylogenetic analysis. The analyses identified 38 MTCs including 2–1518 subtype A1 sequences and 168 MTCs in the range of 2–218 subtype B sequences. The proportion of sequences within MTCs was 93.8% (1642/1751) and 77.0% (1982/2575) for subtype A1 and B, respectively. Transmissions within MTCs for subtype A1 were associated with risk group (Men having Sex with Men vs. heterosexuals, OR = 5.34, p < 0.001) and Greek origin (Greek vs. non-Greek origin, OR = 6.05, p < 0.001) and for subtype B, they were associated with Greek origin (Greek vs. non-Greek origin, OR = 1.57, p = 0.019), younger age (OR = 0.96, p < 0.001), and more recent sampling (time period: 2011–2015 vs. 1999–2005, OR = 3.83, p < 0.001). Our findings about the patterns of across and within country dispersal as well as the parameters associated with transmission within MTCs provide a framework for the application of the study of molecular clusters for HIV prevention.