Ching-Yu Chang

Pregnancy induces notable alterations in the gut, vaginal, and oral microbiota driven by hormonal, immune, metabolic, dietary, and environmental factors. During pregnancy, the gut microbiota is characterized by increased proportions of the genus Bifidobacterium and the phyla Pseudomonadota (formerly Proteobacteria) and Actinomycetota (formerly Actinobacteria). These changes occur alongside reduced alpha diversity and greater beta diversity, changes that influence maternal metabolism and fetal development. Shifts in gut and oral microbiota have been associated with complications such as preterm birth (PTB), pre-eclampsia, and gestational diabetes (GDM), though patterns are sometimes inconsistent. The vaginal microbiota remains Lactobacillus-dominant during pregnancy, with reduced diversity leading to reduced risk of pathogenic infection and increased diversity has been linked with a higher risk of PTB. Hormonal changes also affect the oral microbiota, potentially increasing pathogenic species and contributing to adverse outcomes like PTB. Probiotic supplementation during pregnancy has significant potential to reduce adverse pregnancy outcomes; however, clinical studies are still limited. Probiotics may be effective in alleviating maternal constipation and lead to lower PTB risk, particularly by modulating the vaginal microbiota, but they have limited impact on GDM. In the context of maternal mental health, some studies suggest benefits of probiotics in reducing anxiety, but effects on depression are inconclusive. This perspective examines how pregnancy-related microbial shifts, both natural and probiotic-induced, affect maternal and fetal health and highlights potential opportunities for the innovative use of probiotics during the gestation period.

Context: Early dietary habits play a crucial role in shaping long-term health outcomes. Understanding the effects of different carbohydrate types on physiological markers is essential for developing evidence-based nutritional guidelines for toddlers. Objective: The aim was to systematically evaluate the impact of both digestible and nondigestible carbohydrate intake during early childhood (1–4 years of age) on various health outcomes, including growth patterns, metabolic parameters, and the development of risk of cardiovascular diseases. Data Sources: PubMed, Embase, and CENTRAL databases were searched up to April 2022 to identify studies investigating carbohydrate consumption in toddlers. Data Extraction: The types of carbohydrates consumed, their sources, and their associations with growth parameters and metabolic markers were extracted. Thirty-one publications, including 18 cohort studies and 2 randomized controlled trials, were included. Data Analysis: The risk of bias was assessed using the Mixed Methods Appraisal Tool. A narrative synthesis was performed, with a visual summary table of the direction of effects. Conclusion: In toddlers, the negative impact on health risks later in life is more pronounced for digestible dietary carbohydrate intake in liquid forms, such as sugar-sweetened beverages and fruit juice, compared with solid forms. Higher nondigestible carbohydrate (dietary fiber) intake during early childhood showed a beneficial trend on later lipid profile. Further studies are required to comprehensively assess the effect of digestible and nondigestible carbohydrate intake in toddlers on cognitive and psychomotor development, infections, bowel function, and gut microbiota.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a clinically critical pathogen that causes severe infection. Due to improper antibiotic administration, the prevalence of CRKP infection has been increasing considerably. In recent years, the utilization of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has enabled the identification of bacterial isolates at the families and species level. Moreover, machine learning (ML) classifiers based on MALDI-TOF MS have been recently considered a novel method to detect clinical antimicrobial-resistant pathogens. Methods: A total of 2683 isolates (369 CRKP cases and 2314 carbapenem-susceptible Klebsiella pneumoniae [CSKP]) collected in the clinical laboratories of Taipei Medical University Hospital (TMUH) were included in this study, and 80% of data was split into the training data set that were submitted for the ML model. The remaining 20% of data was used as the independent data set for external validation. In this study, we established an artificial neural network (ANN) model to analyze all potential peaks on mass spectrum simultaneously. Results: Our artificial neural network model for detecting CRKP isolates showed the best performance of area under the receiver operating characteristic curve (AUROC = 0.91) and of area under precision–recall curve (AUPRC = 0.90). Furthermore, we proposed the top 15 potential biomarkers in probable CRKP isolates at 2480, 4967, 12,362, 12,506, 12,855, 14,790, 15,730, 16,176, 16,218, 16,758, 16,919, 17,091, 18,142, 18,998, and 19,095 Da. Conclusions: Compared with the prior MALDI-TOF and machine learning studies of CRKP, the amount of data in our study was more sufficient and allowing us to conduct external validation. With better generalization abilities, our artificial neural network model can serve as a reliable screening tool for CRKP isolates in clinical practice. Integrating our model into the current workflow of clinical laboratories can assist the rapid identification of CRKP before the completion of traditional antimicrobial susceptibility testing. The combination of MADLI-TOF MS and machine learning techniques can support physicians in selecting suitable antibiotics, which has the potential to enhance the patients’ outcomes and lower the prevalence of antimicrobial resistance.