A dataset for predicting cloud cover over Europe.

Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections difficult. In this paper, we present the European Cloud Cover dataset, which can be used to learn statistical relations between cloud cover and other environmental variables, to potentially improve future climate projections. The dataset was created using a novel technique called Area Weighting Regridding Scheme to map satellite observations to cloud fractional cover on the same grid as the other variables in the dataset. Baseline experiments using autoregressive models document that it is possible to use the dataset to predict cloud fractional cover.

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

CELLULAR, A Cell Autophagy Imaging Dataset.

Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target. Image analysis to follow autophagy in cells, especially on high-content screens, has proven to be a bottleneck. Machine learning (ML) algorithms have recently emerged as crucial in analyzing images to efficiently extract information, thus contributing to a better understanding of the questions at hand. This paper presents CELLULAR, an open dataset consisting of images of cells expressing the autophagy reporter mRFP-EGFP-Atg8a with cell-specific segmentation masks. Each cell is annotated into either basal autophagy, activated autophagy, or unknown. Furthermore, we introduce some preliminary experiments using the dataset that can be used as a baseline for future research.

Sperm motility assessed by deep convolutional neural networks into WHO categories.

Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson's correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson's r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson's r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.

Infant Saliva Microbiome Activity Modulates Nutritional Impacts on Neurodevelopment.

Neurodevelopment is influenced by complex interactions between environmental factors, including social determinants of health (SDOH), nutrition, and even the microbiome. This longitudinal cohort study of 142 infants tested the hypothesis that microbial activity modulates the effects of nutrition on neurodevelopment. Salivary microbiome activity was measured at 6 months using RNA sequencing. Infant nutrition was assessed longitudinally with the Infant Feeding Practices survey. The primary outcome was presence/absence of neurodevelopmental delay (NDD) at 18 months on the Survey of Wellbeing in Young Children. A logistic regression model employing two microbial factors, one nutritional factor, and two SDOH accounted for 33.3% of the variance between neurodevelopmental groups (p < 0.001, AIC = 77.7). NDD was associated with Hispanic ethnicity (OR 18.1, 2.36-139.3; p = 0.003), no fish consumption (OR 10.6, 2.0-54.1; p = 0.003), and increased Candidatus Gracilibacteria activity (OR 1.43, 1.00-2.07; p = 0.007). Home built after 1977 (OR 0.02, 0.001-0.53; p = 0.004) and Chlorobi activity (OR 0.76, 0.62-0.93, p = 0.001) were associated with reduced risk of NDD. Microbial alpha diversity modulated the effect of fish consumption on NDD (X2 = 5.7, p = 0.017). These data suggest the benefits of fish consumption for neurodevelopment may be mediated by microbial diversity. Confirmation in a larger, randomized trial is required.

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis.

Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of "black box" models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.

Increased Sleep Latency and Decreased Sleep Duration are Associated With Elevated Risk of Bed Sharing Among Mother-Infant Dyads.

Bed sharing increases risk of sleep-related infant deaths. We hypothesized that infant sleep difficulties increase bed sharing, independent of social determinants of health (SDOH). In total, 191 mother-infant dyads in a prospective, longitudinal cohort study completed the Brief Infant Sleep Questionnaire at 1, 4, 6, and 12 months. Sleep characteristics at 1 month (latency, duration, night awakenings) were compared between dyads with/without bed sharing in the first 12 months. Infants who participated in bed sharing slept fewer hours at night (7.1 ± 1.7 hours vs 8.3 ± 1.5 hours, P = .001, d = -0.79), and took longer to fall asleep (0.7 ± 0.6 hours vs 0.5 ± 0.5 hours, P = .021, d = 0.43), even when controlling for SDOH variables that influence bed sharing. Maternal perception of sleep problems did not differ between groups (P = .12). Our findings suggest that infants with quantifiable sleep difficulties at 1 month are more likely to bed share.

VISEM-Tracking, a human spermatozoa tracking dataset.

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet semen preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.

Saliva microRNA Profile in Children with and without Severe SARS-CoV-2 Infection.

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) may impair immune modulating host microRNAs, causing severe disease. Our objectives were to determine the salivary miRNA profile in children with SARS-CoV-2 infection at presentation and compare the expression in those with and without severe outcomes. Children <18 years with SARS-CoV-2 infection evaluated at two hospitals between March 2021 and February 2022 were prospectively enrolled. Severe outcomes included respiratory failure, shock or death. Saliva microRNAs were quantified with RNA sequencing. Data on 197 infected children (severe = 45) were analyzed. Of the known human miRNAs, 1606 (60%) were measured and compared across saliva samples. There were 43 miRNAs with ≥2-fold difference between severe and non-severe cases (adjusted p-value < 0.05). The majority (31/43) were downregulated in severe cases. The largest between-group differences involved miR-4495, miR-296-5p, miR-548ao-3p and miR-1273c. These microRNAs displayed enrichment for 32 gene ontology pathways including viral processing and transforming growth factor beta and Fc-gamma receptor signaling. In conclusion, salivary miRNA levels are perturbed in children with severe COVID-19, with the majority of miRNAs being down regulated. Further studies are required to validate and determine the utility of salivary miRNAs as biomarkers of severe COVID-19.

The Association between Infant Colic and the Multi-Omic Composition of Human Milk.

Infant colic is a common condition with unclear biologic underpinnings and limited treatment options. We hypothesized that complex molecular networks within human milk (i.e., microbes, micro-ribonucleic acids (miRNAs), cytokines) would contribute to colic risk, while controlling for medical, social, and nutritional variables. This hypothesis was tested in a cohort of 182 breastfed infants, assessed with a modified Infant Colic Scale at 1 month. RNA sequencing was used to interrogate microbial and miRNA features. Luminex assays were used to measure growth factors and cytokines. Milk from mothers of infants with colic (n = 28) displayed higher levels of Staphylococcus (adj. p = 0.038, d = 0.30), miR-224-3p (adj. p = 0.023, d = 0.33), miR-125b-5p (adj. p = 0.028, d = 0.29), let-7a-5p (adj. p = 0.028, d = 0.27), and miR-205-5p (adj. p = 0.029, d = 0.26) compared to milk from non-colic mother-infant dyads (n = 154). Colic symptom severity was directly associated with milk hepatocyte growth factor levels (R = 0.21, p = 0.025). A regression model involving let-7a-5p, miR-29a-3p, and Lactobacillus accurately modeled colic risk (X2 = 16.7, p = 0.001). Molecular factors within human milk may impact colic risk, and provide support for a dysbiotic/inflammatory model of colic pathophysiology.

Human Milk-Derived Levels of let-7g-5p May Serve as a Diagnostic and Prognostic Marker of Low Milk Supply in Breastfeeding Women.

Low milk supply (LMS) is associated with early breastfeeding cessation; however, the biological underpinnings in the mammary gland are not understood. MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally downregulate gene expression, and we hypothesized the profile of miRNAs secreted into milk reflects lactation performance. Longitudinal changes in milk miRNAs were measured using RNAseq in women with LMS (n = 47) and adequate milk supply (AMS; n = 123). Relationships between milk miRNAs, milk supply, breastfeeding outcomes, and infant weight gain were assessed, and interactions between milk miRNAs, maternal diet, smoking status, and BMI were determined. Women with LMS had lower milk volume (p = 0.003), were more likely to have ceased breast feeding by 24 wks (p = 0.0003) and had infants with a lower mean weight-for-length z-score (p = 0.013). Milk production was significantly associated with milk levels of miR-16-5p (R = -0.14, adj p = 0.044), miR-22-3p (R = 0.13, adj p = 0.044), and let-7g-5p (R = 0.12, adj p = 0.046). Early milk levels of let-7g-5p were significantly higher in mothers with LMS (adj p = 0.0025), displayed an interaction between lactation stage and milk supply (p < 0.001), and were negatively related to fruit intake (p = 0.015). Putative targets of let-7g-5p include genes important to hormone signaling, RNA regulation, ion transport, and the extracellular matrix, and down-regulation of two targets (PRLR and IGF2BP1/IMP1) was confirmed in mammary cells overexpressing let-7g-5p in vitro. Our data provide evidence that milk-derived miRNAs reflect lactation performance in women and warrant further investigation to assess their utility for predicting LMS risk and early breastfeeding cessation.

Comparison of Symptom Duration Between Children With SARS-CoV-2 and Peers With Other Viral Illnesses During the COVID-19 Pandemic.

Some children and young people (CYP) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) experience persistent symptoms, commonly called "long COVID." It remains unclear whether symptoms of SARS-CoV-2 persist longer than those of other respiratory viruses, particularly in young children. This cross-sectional study involved 372 CYP (0-15 years) tested for SARS-CoV-2. Character and duration of symptoms (cough, runny nose, sore throat, rash, diarrhea, vomiting, sore muscles, fatigue, fever, loss of smell) were compared between CYP with a positive test (n = 100) and those with a negative test (n = 272), while controlling for medical/demographic covariates. The average duration of symptoms for CYP with a positive SARS-CoV-2 test (8.5 ± 10 days) did not differ from that of CYP with a negative test (7.2 ± 5 days, P = .71, d = 0.046). A positive SARS-CoV-2 test did not increase the risk (36/372, 10%) of symptoms persisting for ≥3 weeks (odds ratio = 0.96, 95% confidence interval = 0.45-2.0). These results suggest CYP with non-SARS-CoV-2 infections experience a similar duration of symptoms as peers with SARS-CoV-2 infection.

Multi-Omic Factors Associated with Frequency of Upper Respiratory Infections in Developing Infants.

Susceptibility to upper respiratory infections (URIs) may be influenced by host, microbial, and environmental factors. We hypothesized that multi-omic analyses of molecular factors in infant saliva would identify complex host-environment interactions associated with URI frequency. A cohort study involving 146 infants was used to assess URI frequency in the first year of life. Saliva was collected at 6 months for high-throughput multi-omic measurement of cytokines, microRNAs, transcripts, and microbial RNA. Regression analysis identified environmental (daycare attendance, atmospheric pollution, breastfeeding duration), microbial (Verrucomicrobia, Streptococcus phage), and host factors (miR-22-5p) associated with URI frequency (p < 0.05). These results provide pathophysiologic clues about molecular factors that influence URI susceptibility. Validation of these findings in a larger cohort could one day yield novel approaches to detecting and managing URI susceptibility in infants.

Infant Saliva Levels of microRNA miR-151a-3p Are Associated with Risk for Neurodevelopmental Delay.

Prompt recognition of neurodevelopmental delay is critical for optimizing developmental trajectories. Currently, this is achieved with caregiver questionnaires whose sensitivity and specificity can be limited by socioeconomic and cultural factors. This prospective study of 121 term infants tested the hypothesis that microRNA measurement could aid early recognition of infants at risk for neurodevelopmental delay. Levels of four salivary microRNAs implicated in childhood autism (miR-125a-5p, miR-148a-5p, miR-151a-3p, miR-28-3p) were measured at 6 months of age, and compared between infants who displayed risk for neurodevelopmental delay at 18 months (n = 20) and peers with typical development (n = 101), based on clinical evaluation aided by the Survey of Wellbeing in Young Children (SWYC). Accuracy of microRNAs for predicting neurodevelopmental concerns at 18 months was compared to the clinical standard (9-month SWYC). Infants with neurodevelopmental concerns at 18 months displayed higher levels of miR-125a-5p (d = 0.30, p = 0.018, adj p = 0.049), miR-151a-3p (d = 0.30, p = 0.017, adj p = 0.048), and miR-28-3p (d = 0.31, p = 0.014, adj p = 0.048). Levels of miR-151a-3p were associated with an 18-month SWYC score (R = -0.19, p = 0.021) and probability of neurodevelopmental delay at 18 months (OR = 1.91, 95% CI, 1.14-3.19). Salivary levels of miR-151a-3p enhanced predictive accuracy for future neurodevelopmental delay (p = 0.010, X2 = 6.71, AUC = 0.71) compared to the 9-month SWYC score alone (OR = 0.56, 95% CI, 0.20-1.58, AUC = 0.567). This pilot study provides evidence that miR-151a-3p may aid the identification of infants at risk for neurodevelopmental delay. External validation of these findings is necessary.

Putting the "mi" in omics: discovering miRNA biomarkers for pediatric precision care.

In the past decade, growing interest in micro-ribonucleic acids (miRNAs) has catapulted these small, non-coding nucleic acids to the forefront of biomarker research. Advances in scientific knowledge have made it clear that miRNAs play a vital role in regulating cellular physiology throughout the human body. Perturbations in miRNA signaling have also been described in a variety of pediatric conditions-from cancer, to renal failure, to traumatic brain injury. Likewise, the number of studies across pediatric disciplines that pair patient miRNA-omics with longitudinal clinical data are growing. Analyses of these voluminous, multivariate data sets require understanding of pediatric phenotypic data, data science, and genomics. Use of machine learning techniques to aid in biomarker detection have helped decipher background noise from biologically meaningful changes in the data. Further, emerging research suggests that miRNAs may have potential as therapeutic targets for pediatric precision care. Here, we review current miRNA biomarkers of pediatric diseases and studies that have combined machine learning techniques, miRNA-omics, and patient health data to identify novel biomarkers and potential therapeutics for pediatric diseases. IMPACT: In the following review article, we summarized how recent developments in microRNA research may be coupled with machine learning techniques to advance pediatric precision care.

Refinement of saliva microRNA biomarkers for sports-related concussion.

BACKGROUND: Recognizing sport-related concussion (SRC) is challenging and relies heavily on subjective symptom reports. An objective, biological marker could improve recognition and understanding of SRC. There is emerging evidence that salivary micro-ribonucleic acids (miRNAs) may serve as biomarkers of concussion; however, it remains unclear whether concussion-related miRNAs are impacted by exercise. We sought to determine whether 40 miRNAs previously implicated in concussion pathophysiology were affected by participation in a variety of contact and non-contact sports. Our goal was to refine a miRNA-based tool capable of identifying athletes with SRC without the confounding effects of exercise. METHODS: This case-control study harmonized data from concussed and non-concussed athletes recruited across 10 sites. Levels of salivary miRNAs within 455 samples from 314 individuals were measured with RNA sequencing. Within-subjects testing was used to identify and exclude miRNAs that changed with either (a) a single episode of exercise (166 samples from 83 individuals) or (b) season-long participation in contact sports (212 samples from 106 individuals). The miRNAs that were not impacted by exercise were interrogated for SRC diagnostic utility using logistic regression (172 samples from 75 concussed and 97 non-concussed individuals). RESULTS: Two miRNAs (miR-532-5p and miR-182-5p) decreased (adjusted p < 0.05) after a single episode of exercise, and 1 miRNA (miR-4510) increased only after contact sports participation. Twenty-three miRNAs changed at the end of a contact sports season. Two of these miRNAs (miR-26b-3p and miR-29c-3p) were associated (R > 0.50; adjusted p < 0.05) with the number of head impacts sustained in a single football practice. Among the 15 miRNAs not confounded by exercise or season-long contact sports participation, 11 demonstrated a significant difference (adjusted p < 0.05) between concussed and non-concussed participants, and 6 displayed moderate ability (area under curve > 0.70) to identify concussion. A single ratio (miR-27a-5p/miR-30a-3p) displayed the highest accuracy (AUC = 0.810, sensitivity = 82.4%, specificity = 73.3%) for differentiating concussed and non-concussed participants. Accuracy did not differ between participants with SRC and non-SRC (z = 0.5, p = 0.60). CONCLUSION: Salivary miRNA levels may accurately identify SRC when not confounded by exercise. Refinement of this approach in a large cohort of athletes could eventually lead to a non-invasive, sideline adjunct for SRC assessment.

Multi-omic factors associated with future wheezing in infants.

BACKGROUND: The pathophysiology of wheezing is multifactorial, impacted by medical, demographic, environmental, and immunologic factors. We hypothesized that multi-omic analyses of host and microbial factors in saliva would enhance the ability to identify infants at risk for wheezing. METHODS: This longitudinal cohort study included 161 term infants. Infants who developed wheezing (n = 27) within 24 months of delivery were identified using the International Study of Asthma and Allergies in Childhood Written Questionnaire and review of the medical record. Standardized surveys were used to assess infant traits and environmental exposures. Saliva was collected for multi-omic assessment of cytokines, microRNAs, mRNAs, and microbiome/virome RNAs. RESULTS: Two infant factors (daycare attendance, family history of asthma) and three salivary "omic" features (miR-26a-5p, Elusimicrobia, Streptococcus phage phiARI0131-1) differed between the two groups (adjusted p < 0.05). miR-26a-5p levels were correlated with Elusimicrobia (R = -0.87, p = 3.7 × 10-31). A model employing the three omic features plus daycare attendance and family asthma history yielded the highest predictive accuracy for future wheezing episodes (AUC = 0.74, 95% CI: 0.703-0.772, 77% sensitivity, 62% specificity). CONCLUSIONS: Host-microbiome interactions in saliva may yield pathophysiologic clues about the origins of wheezing and aid identification of infants at risk of future wheezing episodes. IMPACT: Wheezing is multi-factorial, but the relative contributions of infant traits, environment, and underlying biology are poorly understood. This multi-omic study identifies three molecular factors, including salivary microRNAs, microbes, and viral phages associated with increased risk of infant wheezing. Measurement of these molecular factors enhanced predictive accuracy for future wheezing when combined with family asthma history and daycare attendance. Validation of this approach could be used to identify infants at risk for wheezing and guide personalized medical management.

Multi-Omic Profiles in Infants at Risk for Food Reactions.

Food reactions (FR) are multifactorial and impacted by medical, demographic, environmental, and immunologic factors. We hypothesized that multi-omic analyses of host-microbial factors in saliva would enhance our understanding of FR development. This longitudinal cohort study included 164 infants followed from birth through two years. The infants were identified as FR (n = 34) or non-FR (n = 130) using the Infant Feeding Practice II survey and medical record confirmation. Saliva was collected at six months for the multi-omic assessment of cytokines, mRNAs, microRNAs, and the microbiome/virome. The levels of one miRNA (miR-203b-3p, adj. p = 0.043, V = 2913) and one viral phage (Proteus virus PM135, adj. p = 0.027, V = 2955) were lower among infants that developed FRs. The levels of one bacterial phylum (Cyanobacteria, adj. p = 0.048, V = 1515) were higher among infants that developed FR. Logistical regression models revealed that the addition of multi-omic features (miR-203b-3p, Cyanobacteria, and Proteus virus PM135) improved predictiveness for future FRs in infants (p = 0.005, X2 = 12.9), predicting FRs with 72% accuracy (AUC = 0.81, sensitivity = 72%, specificity = 72%). The multi-omic analysis of saliva may enhance the accurate identification of infants at risk of FRs and provide insights into the host/microbiome interactions that predispose certain infants to FRs.

Confounding Factors Impacting microRNA Expression in Human Saliva: Methodological and Biological Considerations.

There is growing interest in saliva microRNAs (miRNAs) as non-invasive biomarkers for human disease. Such an approach requires understanding how differences in experimental design affect miRNA expression. Variations in technical methodologies, coupled with inter-individual variability may reduce study reproducibility and generalizability. Another barrier facing salivary miRNA biomarker research is a lack of recognized "control miRNAs". In one of the largest studies of human salivary miRNA to date (922 healthy individuals), we utilized 1225 saliva samples to quantify variability in miRNA expression resulting from aligner selection (Bowtie1 vs. Bowtie2), saliva collection method (expectorated vs. swabbed), RNA stabilizer (presence vs. absence), and individual biological factors (sex, age, body mass index, exercise, caloric intake). Differential expression analyses revealed that absence of RNA stabilizer introduced the greatest variability, followed by differences in methods of collection and aligner. Biological factors generally affected a smaller number of miRNAs. We also reported coefficients of variations for 643 miRNAs consistently present in saliva, highlighting several salivary miRNAs to serve as reference genes. Thus, the results of this analysis can be used by researchers to optimize parameters of salivary miRNA measurement, exclude miRNAs confounded by numerous biologic factors, and identify appropriate miRNA controls.

Infant consumption of microRNA miR-375 in human milk lipids is associated with protection from atopy.

BACKGROUND: Human milk is thought to reduce infant atopy risk. The biologic mechanism for this protective effect is not fully understood. OBJECTIVES: We tested the hypothesis that infant consumption of 4 microRNAs (miR-146b-5p, miR-148b-3p, miR-21-5p, and miR-375-3p) in human milk would be associated with reduced atopy risk. METHODS: The Breast Milk Influence of the Microtranscriptome Profile on Atopy in Children over Time (IMPACT) study involved a cohort of mother-infant dyads who planned to breastfeed beyond 4 mo. Infant consumption of the 4 human milk microRNAs (miRNAs) in the first 6 mo was calculated as the product of milk miRNA concentration and the number of human milk feeds, across 3 lactation stages: early milk (0-4 wk), transitional milk (4-16 wk), and mature milk (16-24 wk). The primary outcome was infant atopy in the first year, defined as atopic dermatitis (AD), food allergies, or wheezing. The final analysis included 432 human milk samples and 7824 wk of longitudinal health data from 163 dyads. RESULTS: Seventy-three infants developed atopy. Forty-one were diagnosed with AD (25%), 33 developed food allergy (20%), and 10 had wheezing (6%). Eleven developed >1 condition (7%). Infants who did not develop atopy consumed higher concentrations of miR-375-3p (d = 0.18, P = 0.022, adj P = 0.044) and miR-148b-3p (d = 0.23, P = 0.007, adj P = 0.028). The consumption of miR-375-3p (X2 = 5.7, P = 0.017, OR: 0.92, 95% CI: 0.86, 0.99) was associated with reduced atopy risk. Concentrations of miR-375-3p increased throughout lactation (r = 0.46, F = 132.3, P = 8.4 × 10-34) and were inversely associated with maternal body mass (r = -0.11, t = -2.1, P = 0.032). CONCLUSIONS: This study provides evidence that infant consumption of miR-375-3p may reduce atopy risk.