Balanced Permeability Index: A Multiparameter Index for Improved In Vitro Permeability.

The optimization of passive permeability is a key objective for orally available small molecule drug candidates. For drugs targeting the central nervous system (CNS), minimizing P-gp-mediated efflux is an additional important target for optimization. The physicochemical properties most strongly associated with high passive permeability and lower P-gp efflux are size, polarity, and lipophilicity. In this study, a new metric called the Balanced Permeability Index (BPI) was developed that combines these three properties. The BPI was found to be more effective than any single property in classifying molecules based on their permeability and efflux across a diverse range of chemicals and assays. BPI is easy to understand, allowing researchers to make decisions about which properties to prioritize during the drug development process.

Ara h 2 Peptide Mix Improves the Diagnosis of Peanut Allergy and Is Relevant for Ara h 2-Induced Mast Cell Activation.

BACKGROUND: A precise diagnosis of peanut allergy is extremely important. We identified 4 Ara h 2 peptides that improved Ara h 2-specific IgE (sIgE) diagnostic accuracy. OBJECTIVE: To assess the diagnostic utility of sIgE to the mixture of these peptides and their role in mast cell response to peanut allergens. METHODS: sIgE to the peptide mix was determined using ImmunoCAP. Its diagnostic utility was compared with Ara h 2-sIgE and sIgE to the individual peptides. The functional relevance of the peptides was tested on the mast cell activation test using laboratory of allergic diseases 2 cell line and flow cytometry. RESULTS: A total of 52 peanut-allergic (PA), 36 peanut-sensitized but tolerant, and 9 nonsensitized nonallergic children were studied. Peptide mix-sIgE improved the diagnostic performance of Ara h 2-sIgE compared with Ara h 2-sIgE alone (area under the receiver operating characteristic curve .92 vs .89, respectively; P = .056). The sensitivity and specificity of Ara h 2-sIgE combined with the peptide mix were 85% and 96%, respectively. sIgE to individual peptides had the highest specificity (91%-96%) but the lowest sensitivity (10%-52%) compared with Ara h 2-sIgE (69% specificity and 87% sensitivity) or with peptide mix-sIgE (82% specificity and 63% sensitivity). Peptide 3 directly induced mast cell activation, and the peptide mix inhibited Ara h 2-induced activation of mast cells sensitized with plasma from Ara h 2-positive PA patients. CONCLUSIONS: sIgE to the peptide mix improved the diagnostic performance of Ara h 2-sIgE similarly to sIgE to individual peptides. The peptides interfered with Ara h 2-induced mast cell activation, confirming its relevance in peanut allergy.

Understanding the Geographic Distribution of Diverticulitis Hospitalizations in Washington State.

BACKGROUND: The incidence of diverticulitis in the United States is increasing, and hospitalization remains a surrogate for disease severity. State-level characterization of diverticulitis hospitalization is necessary to better understand the distribution of disease burden and target interventions. METHODS: A retrospective cohort of diverticulitis hospitalizations from 2008 through 2019 was created using Washington State's Comprehensive Hospital Abstract Reporting System. Hospitalizations were stratified by acuity, presence of complicated diverticulitis, and surgical intervention using ICD diagnosis and procedure codes. Patterns of regionalization were characterized by hospital case burden and distance travelled by patients. RESULTS: During the study period, 56,508 diverticulitis hospitalizations occurred across 100 hospitals. Most hospitalizations were emergent (77.2%). Of these, 17.5% were for complicated diverticulitis, and 6.6% required surgery. No single hospital received more than 5% (n = 235) of average annual hospitalizations. Surgeons operated in 26.5% of total hospitalizations (13.9% of emergent hospitalizations, and 69.2% of elective hospitalizations). Operations for complicated disease made up 40% of emergent surgery and 28.7% of elective surgery. Most patients traveled fewer than 20 miles for hospitalization, regardless of acuity (84% for emergent hospitalization and 77.5% for elective hospitalization). DISCUSSION: Hospitalizations for diverticulitis are primarily emergent, nonoperative, and broadly distributed across Washington State. Hospitalization and surgery occur close to patients' homes, regardless of acuity. This decentralization needs to be considered if improvement initiatives and research in diverticulitis are to have meaningful, population-level impact.

High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome.

Characterizing complex microbial communities with single-cell resolution has been a long-standing goal of microbiology. We present Microbe-seq, a high-throughput method that yields the genomes of individual microbes from complex microbial communities. We encapsulate individual microbes in droplets with microfluidics and liberate their DNA, which we then amplify, tag with droplet-specific barcodes, and sequence. We explore the human gut microbiome, sequencing more than 20,000 microbial single-amplified genomes (SAGs) from a single human donor and coassembling genomes of almost 100 bacterial species, including several with multiple subspecies strains. We use these genomes to probe microbial interactions, reconstructing the horizontal gene transfer (HGT) network and observing HGT between 92 species pairs; we also identify a significant in vivo host-phage association between crAssphage and one strain of Bacteroides vulgatus. Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

The Effect of Resistance Training on Motor Unit Firing Properties: A Systematic Review and Meta-Analysis.

While neural changes are thought to be responsible for early increases in strength following resistance training (RT), the exact changes in motor unit (MU) firing properties remain unclear. This review aims to synthesize the available evidence on the effect of RT on MU firing properties. MEDLINE (OVID interface), EMBASE (OVID interface), Web of Science (all databases), Cochrane Library, EBSCO CINAHL Plus, PubMed, and EBSCO SportDiscus were searched from inception until June 2021. Randomized controlled trials and non-randomized studies of interventions that compared RT to no intervention (control) were included. Two reviewers independently extracted data from each trial, assessed the risk of bias and rated the cumulative quality of evidence. Motor unit discharge rate (MUDR), motor unit recruitment threshold (MURT), motor unit discharge rate variability (MUDRV), MU discharge rate at recruitment vs. recruitment threshold relationship, and MU discharge rate vs. recruitment threshold relationship were assessed. Seven trials including 167 participants met the inclusion criteria. Meta-analysis (four studies) revealed that MUDR did not change significantly (P = 0.43), but with considerable heterogeneity likely to be present (I 2 = 91). Low to moderate evidence supports changes in MUDRV, MUDR at recruitment vs. recruitment threshold relationship, and the MUDR vs. recruitment threshold relationship. Overall, this systematic review revealed that there is a lack of high-quality evidence for the effect of RT on MU firing properties. Heterogeneity across studies undermines the quality of the evidence for multiple outcomes and affects the conclusions that can be drawn.

"I need to have a fulfilling job": A qualitative study of surgeon well-being and professional fulfillment.

BACKGROUND: Burnout, often regarded as an individual failing, rather than a systemic one, negatively impacts quality of care, patient safety and healthcare costs. Focusing on improving well-being can help mitigate burnout. This study examined protective factors that promote well-being and professional fulfillment in surgeons. METHODS: Using a purposive sample, 32 semi-structured 30-60-min interviews were conducted with surgeons of varying sub-specialties and rank. Abductive exploratory analysis was used to code and interpret interview transcripts and to build a conceptual model of surgeon well-being. RESULTS: Emergent protective factors were placed into one of three levels of implementation: individual, team-level, and institutional (figure). Individual factors for well-being included autonomy and adequate time to pursue non-clinical endeavors. Team-level factors consisted of adaptability, boundaries, and cohesion. Institutional factors related to diversifying performance evaluations and celebrating and recognizing individual value and contributions. CONCLUSIONS: The conceptual model developed from the results of this study highlights factors important to surgeons' professional well-being. This model can be used to guide quality improvement efforts.

Leveraging Stochasticity for Open Loop and Model Predictive Control of Spatio-Temporal Systems.

Stochastic spatio-temporal processes are prevalent across domains ranging from the modeling of plasma, turbulence in fluids to the wave function of quantum systems. This letter studies a measure-theoretic description of such systems by describing them as evolutionary processes on Hilbert spaces, and in doing so, derives a framework for spatio-temporal manipulation from fundamental thermodynamic principles. This approach yields a variational optimization framework for controlling stochastic fields. The resulting scheme is applicable to a wide class of spatio-temporal processes and can be used for optimizing parameterized control policies. Our simulated experiments explore the application of two forms of this approach on four stochastic spatio-temporal processes, with results that suggest new perspectives and directions for studying stochastic control problems for spatio-temporal systems.

Predicting human health from biofluid-based metabolomics using machine learning.

Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified-where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites.

Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features-providing orthogonal information to physicochemical properties-illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.