Pseudomonas aeruginosa is one of the leading causes of healthcare-associated infections globally. High-risk carbapenemase-encoding P. aeruginosa clones are disseminating in many regions. The aim of this study was to learn more about the lineages and mechanisms of resistance of P. aeruginosa circulating in Peru. A total of 141 carbapenemase-producing isolates recovered from hospitalized and ambulatory patients in Lima were sequenced and analyzed to infer their lineages through whole-genome sequence typing (wgST) and to identify their antimicrobial resistance genes. wgST identified nine sequence types (STs); ST111 and ST357 were the most frequently encountered (44.0% and 38.3%, respectively), followed by ST179 (8.5%), with the remaining six detected only sporadically. Among ST357 isolates, 96.3% carried the novel bla_IMP-93 allele, whereas the remainder harbored bla_IMP-74. 74.2% of ST111 isolates co-harbored bla_IMP-18 and bla_VIM-2, while the rest carried either of these genes individually_. All other ST lineages carried a single carbapenemase, which was either bla_IMP-16, bla_IMP-74, or bla_VIM-2. Our study shows that the high-risk P. aeruginosa clones ST357, which harbors the novel bla_IMP-93, and ST111, which carries bla_IMP-18 and bla_VIM-2, have apparently become endemic in the region.

Information on the effects of a heterologous booster in adult patients first vaccinated with the BBIBP-CorV vaccine is limited. This prospective cohort study evaluated the humoral response of 152 healthcare workers (HCWs) from a private laboratory in Lima (Peru) before and after receiving the BNT162b2 vaccine, with a seven-month interval since the BBIBP-CorV doses. We employed the Elecsys^® anti-SARS-CoV-2 S and the cPass™ SARS-CoV-2 Neutralization Antibody (NAbs) assays to evaluate anti-S-RBD IgG and NAbs, respectively. Of the 152 HCWs, 79 (51.98%) were previously infected (PI) with SARS-CoV-2 and 73 (48.02%) were not previously infected (NPI). The proportion of HCWs with positive NAbs, seven months after the BBIBP-CorV immunization, was 49.31% in NPI and 92.40% in PI. After the booster, this ratio increased to 100% in both groups. The anti-S-RBD IgG and NAbs in the HCWs’ NPI increased by 32.7 and 3.95 times more, respectively. In HCWs’ PI, this increment was 5 and 1.42 times more, respectively. There was no statistical association between the history of previous SARS-CoV-2 infection and the titer of anti-S-RBD IgG and NAbs after the booster. The humoral immunity presented a robust increase after receiving the BNT162b2 booster and was more pronounced in NPI.

Background: Bloodstream infections continue to pose a serious global health threat due to their high morbidity and mortality, further worsened by rising antimicrobial resistance and delays in starting targeted therapy. This study assesses the accuracy and timeliness of therapeutic recommendations produced by an artificial intelligence (AI)-driven and machine-learning (ML) clinical decision support system (CDSS), comparing results based on molecular diagnostics alone with those that combine molecular and phenotypic data (standard cultures). Methods: In a prospective cross-sectional study conducted in Lima, Peru, 117 blood cultures were analyzed using FilmArray/GeneXpert for molecular identification and MALDI-TOF/VITEK 2.0 for phenotypic profiling. The AI/ML-based CDSS provided treatment recommendations in two formats, which were assessed for concordance and turnaround time. Results: Therapeutic recommendations showed 80.3% consistency between data types, with 86.3% concordance in pathogen and resistance detection. Notably, molecular-only recommendations were delivered 29 h earlier than those incorporating phenotypic data. Escherichia coli was the most frequently isolated pathogen, with a 95% concordance in suggested therapy. A substantial agreement was observed in treatment consistency (Kappa = 0.80). Conclusions: These findings highlight the potential of using AI-powered CDSS in conjunction with molecular diagnostics to accelerate clinical decision-making in bacteremia, supporting more timely interventions and improved antimicrobial stewardship. Further research is warranted to assess scalability and impact across diverse clinical settings.

Objective. To determine the diagnostic performance of blood culture positivity times for distinguishing true bacteremia from contaminants in the automated “BACT/ALERT®” system. Materials and methods. A cross-sectional, diagnostic test-type study was conducted from a database of blood culture samples processed between January 2016 and August 2021. All blood culture samples from patients with suspected bacteremia were included; blood culture samples were entered into the “BACT/ALERT®” system to differentiate true bacteremia from contaminants. Results. We obtained 33,951 blood cultures samples, of which 3875 were positive. Of the total number of positive blood cultures, 75.2% (n=2913) were true bacteremia and 24.8% (n=962) were contaminants. The median time to positivity in blood cultures with true bacteremia was significantly shorter (16.3 hours; IQR: 11.2 - 24.9) than the median time to positivity of blood cultures with contaminants (22.5 hours; IQR: 18.4 - 31.8; p<0.001). Thepositivity time showed the capacity to differentiate true bacteremia from contaminants, with an AUC-ROC of 0.73(95%CI: 0.71 - 0.75), with 85% and 63% sensitivity and specificity respectively for the diagnosis of contaminantswhen the positivity time exceeds 16.5 hours. The use of antibiotics prior to sampling delayed the time to positivity, while having fever before sampling shortened the time to positivity. Conclusions. Our results show good diagnostic performance of blood culture positivity times to differentiate true bacteremia from contaminants using the “BACT/ALERT®” system when the positivity time was longer than 16.5 hours.

Insufficient data have been reported about the effect of the inactivated SARS-CoV-2 vaccine (BBIBP-CorV) on the humoral response through time in healthcare workers (HCW). This retrospective cohort studied the information of 252 HCW from a private laboratory, comparing the antibody-mediated response provoked by BBIBP-CorV between HCW previously infected with SARS-CoV-2 (PI) and not previously infected (NPI), employing the Elecsys^® anti-SARS-CoV-2 S and the cPass™ SARS-CoV-2 Neutralization Antibody Detection kit at intervals of 21, 90, and 180 days after vaccination. The presence of neutralizing antibodies in HCW 21 days after full vaccination was 100% in PI and 91.60% in NPI. We observed a progressive decrease in antibody levels over time in both groups. Comparing HCW PI with NPI, PI had a 10.9, 14.3, and 8.6-fold higher antibody titer with the Elecsys^® anti-SARS-CoV-2 S at 21 (p < 0.001), 90 (p< 0.001) and 180 days (p < 0.001) respectively, compared to NPI. Using the percent of signal inhibition (PSI) of the antibody neutralization cPass™, HCW PI showed a level of 1.3, 2.0, and 3.1 times more antibodies, at 21 (p < 0.001), 90 (p < 0.001), and 180 days (p < 0.001) respectively, compared to NPI. We determined a progressive decrease in humoral immunity over time, particularly higher in those NPI.