Piloting the better research interactions for every family (BRIEF) researcher intervention to support recruitment for a neonatal clinical trial: parent experience and infant enrollment
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Severity of neonatal influenza infection is driven by type I interferon and oxidative stress
Neonates exhibit increased susceptibility to respiratory viral infections, attributed to inflammation at the developing pulmonary air-blood interface. IFN I are antiviral cytokines critical to control viral replication, but also promote inflammation. Previously, we established a neonatal murine influenza virus (IV) model, which demonstrates increased mortality. Here, we sought to determine the role of IFN I in this increased mortality. We found that three-day-old IFNAR-deficient mice are highly protected from IV-induced mortality. In addition, exposure to IFNβ 24 h post IV infection accelerated death in WT neonatal animals but did not impact adult mortality. In contrast, IFN IIIs are protective to neonatal mice. IFNβ induced an oxidative stress imbalance specifically in primary neonatal IV-infected pulmonary type II epithelial cells (TIIEC), not in adult TIIECs. Moreover, neonates did not have an infection-induced increase in antioxidants, including a key antioxidant, superoxide dismutase 3, as compared to adults. Importantly, antioxidant treatment rescued IV-infected neonatal mice, but had no impact on adult morbidity. We propose that IFN I exacerbate an oxidative stress imbalance in the neonate because of IFN I-induced pulmonary TIIEC ROS production coupled with developmentally regulated, defective antioxidant production in response to IV infection. This age-specific imbalance contributes to mortality after respiratory infections in this vulnerable population.
Analysis of microbial composition and sharing in low-biomass human milk samples: a comparison of DNA isolation and sequencing techniques
Human milk microbiome studies are currently hindered by low milk bacterial/human cell ratios and often rely on 16S rRNA gene sequencing, which limits downstream analyses. Here, we aimed to find a method to study milk bacteria and assess bacterial sharing between maternal and infant microbiota. We tested four DNA isolation methods, two bacterial enrichment methods and three sequencing methods on mock communities, milk samples and negative controls. Of the four DNA isolation kits, the DNeasy PowerSoil Pro (PS) and MagMAX Total Nucleic Acid Isolation (MX) kits provided consistent 16S rRNA gene sequencing results with low contamination. Neither enrichment method substantially decreased the human metagenomic sequencing read-depth. Long-read 16S-ITS-23S rRNA gene sequencing biased the mock community composition but provided consistent results for milk samples, with little contamination. In contrast to 16S rRNA gene sequencing, 16S-ITS-23S rRNA gene sequencing of milk, infant oral, infant faecal and maternal faecal DNA from 14 mother-infant pairs provided sufficient resolution to detect significantly more frequent sharing of bacteria between related pairs compared to unrelated pairs. In conclusion, PS or MX kit-DNA isolation followed by 16S rRNA gene sequencing reliably characterises human milk microbiota, and 16S-ITS-23S rRNA gene sequencing enables studies of bacterial transmission in low-biomass samples.
Professional demand analysis for teaching Chinese to speakers of other languages: a text mining approach on internet recruitment platforms
The rapid development of international education in China highlights the growing importance of employment analysis in Teaching Chinese to Speakers of Other Languages (TCSOL). This study explores the enterprise demands for TCSOL professionals using text mining techniques to analyze recruitment data collected from four major platforms: Boss Zhipin, Zhaopin.com, 51job.com, and Liepin.com. Combining descriptive statistics, LDA topic modeling, BERT-BiLSTM-CRF-based named entity recognition, and co-occurrence network analysis were used. Results show that there is a high demand for TCSOL professionals, especially for small-scale enterprises located in first-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen. Employers tend to favor candidates with at least a bachelor’s degree and 1–3 years of work experience. The topic model highlighted three central themes in job descriptions, emphasizing a shift toward a more diverse skill set. Named entity recognition identified essential attributes such as “communication ability”, “teaching experience”, “bachelor’s degree or above” and “responsibility” as core recruitment requirements. The co-occurrence network analysis revealed the importance of “teaching” and “priority” as core skill nodes. Time series analysis showed seasonal fluctuations in recruitment demand, peaking during spring recruitment and graduation periods. A hierarchical model of talent demand and development in TCSOL is proposed, integrating the perspectives of employers, job seekers, educators, and policymakers. This study provides valuable insights for aspiring TCSOL professionals, offering guidance to better align talent training with market needs and improve employment prospects.
Smartwatch- and smartphone-based remote assessment of brain health and detection of mild cognitive impairment
Consumer-grade mobile devices are used by billions worldwide. Their ubiquity provides opportunities to robustly capture everyday cognition. ‘Intuition’ was a remote observational study that enrolled 23,004 US adults, collecting 24 months of longitudinal multimodal data via their iPhones and Apple Watches using a custom research application that captured routine device use, self-reported health information and cognitive assessments. The study objectives were to classify mild cognitive impairment (MCI), characterize cognitive trajectories and develop tools to detect and track cognitive health at scale. The study addresses sources of bias in current cognitive health research, including limited representativeness (for example, racial/ethnic, geographic) and accuracy of cognitive measurement tools. We describe study design and provide baseline cohort characteristics. Next, we present foundational proof-of-concept MCI classification modeling results using interactive cognitive assessment data. Initial findings support the reliability and validity of remote MCI detection and the usefulness of such data in describing at-risk cognitive health trajectories in demographically diverse aging populations. ClinicalTrials.gov identifier: NCT05058950.
A network analysis of postpartum depression and mother-to-infant bonding shows common and unique symptom-level connections across three postpartum periods
Postpartum depression and mother-to-infant bonding difficulties (MIBD), two issues crucial to maternal and infant mental health, often coexist and affect each other. Our study aims to dissect their complex relationship through a graphical LASSO network analysis of individual symptoms in 5594 Japanese postpartum women, whose geographical distribution was nationally representative. We identified ‘fear’, ‘enjoyment’, ‘overwhelm’, and ‘insomnia’ as common bridge symptoms linking postpartum depression and MIBD across three distinct postpartum periods. Moreover, ‘self-harm’ emerged as a bridge symptom in the first 6 months and the 7–12 month period, while ‘laugh’ was a bridge symptom in the first 6 months and the 13–24 month period. Notably, ‘self-blame’ was identified as a unique bridge symptom specific to the 13–24 month period. Our analysis highlights the complexities of symptom connectivity across postpartum stages and underscores the critical need for interventions that address both common and stage-specific bridge symptoms to effectively support maternal mental health and strengthen mother-to-infant bonding.
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