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ObjectivesLate preterm and term infants comprise 97.3% of annual births in the United States. Admission criteria and the availability of medical interventions in well newborn nurseries are key determinants of these infants remaining within a mother-infant dyad or requiring a NICU admission and resultant separation of the dyad. The objective of this study was to identify national patterns for well newborn nursery care practices.MethodsWe surveyed a physician representative from each nursery in the Better Outcomes through Research for Newborns Network. We described the admission criteria and clinical management of common newborn morbidities and analyzed associations with nursery demographics.ResultsOf 96 eligible nursery representatives, 69 (72%) completed surveys. Among respondents, 59 (86%) used a minimal birth weight criterion for admission to their well newborn nursery. The most commonly used criteria were 2000 g (n = 29, 49%) and 1800 g (n = 19, 32%), with a range between 1750 and 2500 g. All nurseries used a minimal gestational age criterion for admission; the most commonly used criterion was 35 weeks (n = 55, 80%). Eleven percent of sites required transfer to the NICU for phototherapy. Common interventions in the mother's room included dextrose gel (n = 56, 81%), intravenous antibiotics (n = 35, 51%), opiates for neonatal abstinence syndrome (n = 15, 22%), and an incubator for thermoregulation (n = 14, 20%).ConclusionsWide variation in admission criteria and medical interventions exists in well newborn nurseries. Further studies may help identify evidence-based optimal admission criteria to maximize care within the mother-infant dyad.

The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling. This study explored single cells and single organelles to reveal unique molecular characteristics such as mutant peptides and modulation of diverse post-translational modifications that are inaccessible through DNA/RNA profiling. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms release toxins and form thick blanket layers on the water surface causing widespread problems, including serious threats to human health, water ecosystem, economics, and recreation. To identify the potential drivers for the bloom, there is a need for extensive observations of the water sources with bloom occurrences. However, the traditional methods for monitoring water sources, such as collection of point ground samples, have proven limited due to spatial and temporal variability of water resources, and the cost associated with collecting samples that accurately represent this variability. These limitations can be addressed through the use of high-frequency satellite data. In this study, we explored the use of Random Forest (RF), which is one of the widely used machine learning architectures, to evaluate the performance of Sentinel-3 OLCI (Ocean and Land Color Imager) images in predicting bloom proxies in the western region of Lake Erie. The sixteen available bands of Sentinel-3 images were used as the predictor variables, while four proxies of the cyanobacterial masses, including Chlorophyll-a, Microcystin, Phycocyanin, and Secchi-depth, were considered as response variables in the RF models, with one RF model per proxy. Each of the proxies comes with a unique set of traits that can help with bloom detection. Among four RF models, the model for Chlorophyll-a performed the best with R2 = 0.55 and RMSE = 20.84 µg/L, while R2 performance for the rest of the other proxies was less than 0.5. This is because Chlorophyll-a is the most dominant and optically active pigment in water, while Phycocyanin, which is a strong indicator of harmful bloom, is present in low concentrations. Additionally, Microcystin, responsible for bloom toxicity, has limited spectral sensitivity, and Secchi-depth could be influenced by various factors besides blooms, such as colored dissolved organic and inorganic matter. On further examining the relationship between the proxies, Microcystin and Secchi-depth were significantly correlated with Chlorophyll-a, which enhances the usefulness of Chlorophyll-a in accurately identifying the presence of algal blooms. [ABSTRACT FROM AUTHOR]

Background: Ketamine is increasingly used for treatment‐resistant depression (TRD) while its mechanism of action is still being investigated. In this systematic review, we appraise the current evidence of metabolomic biomarkers for racemic ketamine and esketamine in patients with TRD and healthy controls (HCs). Methods: A comprehensive search of several databases (Ovid MEDLINE®, Embase, and Epub Ahead of Print) was performed from each database's inception to June 29, 2022, in any language, was conducted. We included studies wherein the metabolomic biomarkers for racemic ketamine or esketamine were investigated in TRD or HCs. Our main outcomes were to examine changes in metabolites among patients treated with ketamine/esketamine and explore the association with response to ketamine/esketamine. Results: A total of 1859 abstracts were screened of which 11 were included for full‐text review. Of these, a total of five articles were included (N = 147), including three RCTs (n = 129) and two open‐label trials (n = 18). All studies used racemic ketamine; one study additionally used esketamine. The included studies evaluated patients with treatment‐resistant bipolar depression (n = 22), unipolar depression (n = 91), and HCs (n = 34). The included studies reported alteration in several metabolites including acylcarnitines, lipids, kynurenine (KYN), and arginine with ketamine in TRD. Studies suggest the involvement of energy metabolism, KYN, and arginine pathways. In HCs, acetylcarnitine decreased post‐infusion, whereas inconsistent findings were observed after the ketamine infusion in TRD patients. Conclusions: This systematic review provides preliminary evidence that ketamine may cause changes in several important pathways involved in energy metabolism and inflammation. Larger and more rigorous studies are needed. [ABSTRACT FROM AUTHOR]