The healthy growth of infants during early life is associated with lifelong consequences. Breastfeeding has positive impacts on reducing obesity risk, which is likely due to the varied components of breast milk, such as N-acetylneuraminic acid (Neu5Ac). However, the effect of breast milk Neu5Ac on infant growth has not been well studied. In this study, targeted metabolomic and metagenomic analyses were performed to illustrate the association between breast milk Neu5Ac and infant growth. Results demonstrated that Neu5Ac was significantly abundant in breast milk from infants with low obesity risk in two independent Chinese cohorts. Neu5Ac from breast milk altered infant gut microbiota and bile acid metabolism, resulting in a distinct fecal bile acid profile in the high-Neu5Ac group, which was characterized by reduced levels of primary bile acids and elevated levels of secondary bile acids. Taurodeoxycholic acid 3-sulfate and taurochenodeoxycholic acid 3-sulfate were correlated with high breast milk Neu5Ac and low obesity risk in infants, and their associations with healthy growth were reproduced in mice colonized with infant-derived microbiota. Parabacteroides might be linked to bile acid metabolism and act as a mediator between Neu5Ac and infant growth. These results showed the gut microbiota-dependent crosstalk between breast milk Neu5Ac and infant growth.

Background: Neonatal Respiratory Distress Syndrome (NRDS) is a common and severe respiratory disorder in neonates, particularly among preterm infants (PTIs), and is often associated with hypoxemia and multiple organ dysfunction. This study aims to investigate the gut microbiota characteristics in NRDS and the potential regulatory role of probiotics in restoring gut microbiota dysbiosis.Methods: This study enrolled 55 PTIs diagnosed with NRDS and 26 preterm infants without NRDS. The NRDS group was classified into two groups based on treatment: an antibiotic-only group (TA group, N = 30) and an antibiotic plus probiotics group (TB group, N = 25). Fecal samples were collected within 48 h of birth and again after recovery, for 16S rRNA sequencing.Results: The study revealed that the gut microbiota diversity in the NRDS group was significantly greater than in the non-NRDS group, and the microbiota composition in the NRDS group was closely associated with multiple clinical indicators, including Apgar score, pH, PaO₂, and PaCO₂. Notably, the abundance of bacteria such as Muribaculaceae Incertae Sedis, Rhodococcus, and Corynebacterium was significantly higher in the NRDS group, which may contribute to disease progression. ROC analysis suggested that gut microbiota could serve as potential biomarkers for diagnosing NRDS. Probiotic intervention notably restored the gut microbiota structure in the NRDS group, particularly by enhancing the abundance of beneficial genera such as Streptococcus, Bifidobacterium, and Clostridium. This intervention reduced the microbiota disparity between the NRDS group and normal one-month-old children, thereby slowing disease progression.Conclusion: This study demonstrated that the NRDS displayed an increase in gut microbiota diversity and alterations in specific bacterial populations, both of which were closely correlated with clinical data. Probiotic treatment aids in restoring the disrupted gut microbiota in NRDS infants, promoting disease recovery, and providing new biomarkers and clinical strategies for managing NRDS.

Background: Azacitidine is used to treat myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). It acts as a cytosine analog and DNA methyltransferase inhibitor, inducing DNA hypomethylation to reverse epigenetic modifications and restore normal gene expression. However, adverse events (AEs) associated with azacitidine are mainly reported in clinical trials, with limited real-world evidence. This study aims to assess the AE profile of azacitidine by utilizing data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and WHO-VigiAccess databases.Methods: We extracted adverse event (AE) reports related to azacitidine from the FAERS and WHO-VigiAccess databases, covering the period from the drug’s market introduction to the third quarter of 2024. We used statistical methods including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM) to analyze the association between azacitidine and documented AEs.Results: The investigation unveiled 16,056 azacitidine-related adverse event (AE) reports from FAERS and 19,867 reports from WHO-VigiAccess. The median duration for the occurrence of these AEs during the observation period was 36 days, with an interquartile range (IQR) spanning from 11 to 126 days. Our statistical analysis identified 27 organ systems associated with AEs induced by azacitidine. Among these, the notable System Organ Classes (SOCs) that met four specific criteria included: infections and infestations, blood and lymphatic system disorders, and neoplasms benign, malignant, and unspecified (including cysts and polyps). Four algorithms identified 443 significant disproportionality preferred terms (PTs), including previously unreported AEs such as death, sepsis, septic shock, respiratory failure, cardiac failure, tumor lysis syndrome, bone marrow failure, interstitial lung disease, and pericarditis. Analysis from the WHO-VigiAccess database showed a ROR of 3.65 and a PRR of 3.30 for the SOC of infections and infestations.Conclusion: This research not only confirms the widely acknowledged AEs linked to azacitidine but also uncovers several potentially new safety concerns noted in actual clinical practice. These results may offer important vigilance information for clinicians and pharmacists when addressing safety issues associated with azacitidine.

Background: The gut microbiome is increasingly recognized for its role in the pathogenesis of neonatal conditions commonly associated with retinopathy of prematurity (ROP). This study aimed to identify key intestinal microbiota and metabolites in ROP and examine their relationships.Methods: Fecal samples were collected from infants with and without ROP at weeks 2 (T1) and 4 (T2) for 16S rRNA sequencing. At T2, additional fecal samples underwent non-targeted metabolomic analyses. A combined analysis of the 16S rRNA sequencing and metabolomics data was performed.Results: No significant differences in α-diversity indexes were observed between the ROP and non-ROP at T1. However, at T2, the Chao, ACE, and Shannon indices were significantly higher, whereas the Simpson index was lower in ROP compared to non-ROP. At the phylum level, the dominant phyla at T2 included Pseudomonadota, Bacillota, Actinomycetota, Bacteroidota, and Verrucomicrobiota. LEfSe analysis of T2 showed that Bifidobacterium, Rhodococcus, Staphyloococcus, Caulobacter, Sphingomonas, Aquabacterium, and Klebsiella as key genera associated with ROP. Metabolomic analysis identified 382 differentially accumulated metabolites, which were enriched in steroid hormone biosynthesis; the PPAR signaling pathway; linoleic acid metabolism; histidine metabolism; and alanine, aspartate, and glutamate metabolism. Additionally, the AUC of the combined analysis exceeded that of differential bacterial communities (0.9958) alone.Conclusion: This study revealed characteristic changes in the intestinal flora and metabolites in ROP, which provide promising targets/pathways for ROP diagnosis and therapy.

Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and effective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in proteins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty important biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By using the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jackknife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, respectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease.