Introduction: Pediatric and neonatal sepsis is one of the main causes of mortality and morbidity in these age groups. Accurate and early etiological identification is essential for guiding antibiotic treatment, improving survival, and reducing complications and sequelae. Currently, the identification is based on culture-dependent methods, which has many limitations for its use in clinical practice, and obtaining its results is delayed. Next-generation sequencing enables rapid, accurate, and unbiased identification of multiple microorganisms in biological samples at the same time. The objective of this study was to characterize the etiology of neonatal and pediatric sepsis by metagenomic techniques.Methods: A systematic review of the literature was carried out using the PRISMA-2020 guide. Observational, descriptive, and case report studies on pediatric patients were included, with a diagnostic evaluation by clinical criteria of sepsis based on the systemic inflammatory response, in sterile and non-sterile biofluid samples. The risk of bias assessment of the observational studies was carried out with the STROBE-metagenomics instrument and the CARE checklist for case reports.Results and Discussion: Five studies with a total of 462 patients were included. Due to the data obtained from the studies, it was not possible to perform a quantitative synthesis (meta-analysis). Based on the data from the included studies, the result identified that mNGS improves the etiological identification in neonatal and pediatric sepsis, especially in the context of negative cultures and in the identification of unusual microorganisms (bacteria that are difficult to grow in culture, viruses, fungi, and parasites). The number of investigations is currently limited, and the studies are at high risk of bias. Further research using this technology would have the potential to improve the rational use of antibiotics.

Background: Skin-to-skin contact (SSC) compared to separation at birth has a positive effect on breastfeeding. However, separation at birth is common with negative impact on breastfeeding. The aim was to determine the effect of immediate SSC compared to early SSC on the duration of exclusive breastfeeding. Methods: A randomized multicentre parallel clinical trial was conducted in two hospitals in Cundinamarca (Colombia) between November 2018 and January 2020. Low-risk full term newborns at birth were included. Neonates were assigned to immediate (in the first minute after birth) or early onset (start exactly at 60 min of life) skin to skin contact. Monthly follow-up was performed until 6 months of age. The primary outcome was the percentage of exclusively breastfed infants at 6 months (time in months with human milk as the only source of food). Secondary outcomes were the percentage of infants with exclusive breastfeeding at 3 months, duration in months of exclusive breastfeeding, neonate’s breastfeeding ability, percentage of weight change between birth and the first week of life and hospitalization in the neonatal unit in the first week. A bivariate analysis was performed to determine the variables associated with exclusive breastfeeding at 6 months. A survival analysis was performed to evaluate the effect of the onset of SSC on exclusive breastfeeding duration. Results: A total of 297 newborns were included: 49.8% (n = 148) in the immediate SSC group, and 50.2% (n = 149) in the early SSC group. The mean duration of exclusive breastfeeding in both groups was 5 months. There were no differences between the groups in the percentage of exclusive breastfeeding at 6 months (relative risk [RR] 1.06, 95% CI 0.72, 1.58) or in the duration of exclusive breastfeeding (hazard ratio [HR] 0.98, 95% CI 0.74, 1.28). Conclusions: The percentage of infants and the duration of exclusive breastfeeding in the first 6 months of age were the same between the two groups of SSC interventions. Given the current barriers to its implementation, the results of this study could positively impact the use of SSC at birth and standardize the intervention and improve breastfeeding indicators. Trial registration: ClinicalTrials.gov NCT02687685.

Objective: To evaluate two debriefing strategies for the development of neonatal resuscitation skills in health professionals responsible for the critical newborn care in a high-complexity university Hospital. Results: A simple blind randomized clinical trial was conducted. Twenty-four professionals (pediatricians, nurses, and respiratory therapists) were randomly assigned for two interventions; one group received oral debriefing and the other oral debriefing assisted by video. Three standardized clinical scenarios that were recorded on video were executed. A checklist was applied for the evaluation, administered by a reviewer blinded to the assignment of the type of debriefing. The two debriefing strategies increased the technical and behavioral neonatal resuscitation skills of the participants, without one being superior to the other. The coefficient of the difference in the compliance percentage between the two types of debriefing was − 3.6% (95% CI − 13.77% to 6.47%). When comparing the development of technical and behavioral skills among the professionals evaluated, no significant differences were found between the types of debriefing. The two debriefing strategies increase compliance percentages, reaching or approaching 100%. Trial Registration ClinicalTrials.gov NCT03606278. July 30, 2018. Retrospectively registered

Background and objectives: In low- and middle-income countries, the leading cause of neonatal mortality is perinatal asphyxia. Training in neonatal resuscitation has been shown to decrease this cause of mortality. The program “Helping Babies Breathe” (HBB) is a program to teach basic neonatal resuscitation focused on countries and areas with limited economic resources. The aim of the study was to determine the effect of the implementation of the HBB program on newborn outcomes: mortality and morbidity. Material and Methods: A systematic review was carried out on observational studies and clinical trials that reported the effect of the implementation in low- and middle-income countries of the HBB program on neonatal mortality and morbidity. We carried out a meta-analysis of the extracted data. Random-effect models were used to evaluate heterogeneity, using the Cochrane Q and I2 tests, and stratified analyses were performed by age and type of outcome to determine the sources of heterogeneity. Results: Eleven studies were identified. The implementation of the program includes educational strategies focused on the training of doctors, nurses, midwives, and students of health professions. The poled results showed a decrease in overall mortality (OR 0.67; 95% CI 0.57, 0.80), intrapartum stillbirth mortality (OR 0.62; 95% CI 0.51, 0.75), and first-day mortality (OR 0.70; 95% IC 0.64, 0.77). High heterogeneity was found, which was partly explained by differences in the gestational age of the participants. Conclusions: The implementation of the program HBB in low- and medium-income countries has a significant impact on reducing early neonatal mortality.

Breastfeeding rates remain below the globally recommended levels, a situation associated with higher infant and neonatal mortality rates. The implementation of artificial intelligence (AI) could help improve and increase breastfeeding rates. This study aimed to identify and synthesize the current information on the use of AI in the analysis of human milk and breastfeeding. A scoping review was conducted according to the PRISMA Extension for Scoping Reviews guidelines. The literature search, performed in December 2023, used predetermined keywords from the PubMed, Scopus, LILACS, and WoS databases. Observational and qualitative studies evaluating AI in the analysis of breastfeeding patterns and human milk composition have been conducted. A thematic analysis was employed to categorize and synthesize the data. Nineteen studies were included. The primary AI approaches were machine learning, neural networks, and chatbot development. The thematic analysis revealed five major categories: 1. Prediction of exclusive breastfeeding patterns: AI models, such as decision trees and machine learning algorithms, identify factors influencing breastfeeding practices, including maternal experience, hospital policies, and social determinants, highlighting actionable predictors for intervention. 2. Analysis of macronutrients in human milk: AI predicted fat, protein, and nutrient content with high accuracy, improving the operational efficiency of milk banks and nutritional assessments. 3. Education and support for breastfeeding mothers: AI-driven chatbots address breastfeeding concerns, debunked myths, and connect mothers to milk donation programs, demonstrating high engagement and satisfaction rates. 4. Detection and transmission of drugs in breast milk: AI techniques, including neural networks and predictive models, identified drug transfer rates and assessed pharmacological risks during lactation. 5. Identification of environmental contaminants in milk: AI models predict exposure to contaminants, such as polychlorinated biphenyls, based on maternal and environmental factors, aiding in risk assessment. AI-based models have shown the potential to increase breastfeeding rates by identifying high-risk populations and providing tailored support. Additionally, AI has enabled a more precise analysis of human milk composition, drug transfer, and contaminant detection, offering significant insights into lactation science and maternal-infant health. These findings suggest that AI can promote breastfeeding, improve milk safety, and enhance infant nutrition.