Internet can accelerate information exchange. Social networks are the most accessed especially Facebook. This kind of networks might create dependency with several negative consequences in people’s life. The aim of this study was to assess potential association between Facebook dependence and poor sleep quality. A cross sectional study was performed enrolling undergraduate students of the Universidad Peruana de Ciencias Aplicadas, Lima, Peru. The Internet Addiction Questionnaire, adapted to the Facebook case, and the Pittsburgh Sleep Quality Index, were used. A global score of 6 or greater was defined as the cutoff to determine poor sleep quality. Generalized linear model were used to determine prevalence ratios (PR) and 95% confidence intervals (95%CI). A total of 418 students were analyzed; of them, 322 (77.0%) were women, with a mean age of 20.1 (SD: 2.5) years. Facebook dependence was found in 8.6% (95% CI: 5.9%–11.3%), whereas poor sleep quality was present in 55.0% (95% CI: 50.2%–59.8%). A significant association between Facebook dependence and poor sleep quality mainly explained by daytime dysfunction was found (PR = 1.31; IC95%: 1.04–1.67) after adjusting for age, sex and years in the faculty. There is a relationship between Facebook dependence and poor quality of sleep. More than half of students reported poor sleep quality. Strategies to moderate the use of this social network and to improve sleep quality in this population are needed.

Among different factors, diet patterns seem to be related to depression. The aim of this study was to evaluate the association between the consumption of fruits and/or vegetables and depressive symptoms. A secondary data analysis was conducted using information from a population-based survey from 25 regions from Peru. The outcome was the presence of depressive symptoms according to the Patient Health Questionnaire (cutoff ≥15 to define major depressive syndrome); whereas the exposure was the self-reported consumption of fruits and/or vegetables (in tertiles and using WHO recommendation ≥5 servings/day). The association of interest was evaluated using Poisson regression models controlling for the complex-sample survey design and potential confounders. Data from 25,901 participants were analyzed, mean age 44.2 (SD: 17.7) and 13,944 (54.0%) women. Only 910 (3.8%; 95%CI: 3.5%–4.2%) individuals reported consuming ≥5 servings of fruits and/or vegetables/day; whereas 819 (2.8%; 95%CI: 2.5%–3.1%) had depressive symptoms. Those in the lowest tertile of fruits and/or vegetables consumption had greater prevalence of depressive symptoms (PR = 1.88; 95%CI: 1.39–2.55) than those in the highest tertile. This association was stronger with fruits (PR = 1.92; 95%CI: 1.46–2.53) than vegetables (PR = 1.42; 95%CI: 1.05–1.93) alone. An inverse relationship between consumption of fruits and/or vegetables and depressive symptoms is reported. Less than 5% of subjects reported consuming the amount of fruits and vegetables recommended by the WHO. There is a need to implement strategies to promote better diet patterns with potential impact on mental health.

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.

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.

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.