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Anomalous pulmonary venous return (APVR) frequently occurs with other congenital heart defects (CHDs) or extra-cardiac anomalies. While some genetic causes have been identified, the optimal approach to genetic testing in individuals with APVR remains uncertain, and the etiology of most cases of APVR is unclear. Here, we analyzed molecular data from 49 individuals to determine the diagnostic yield of clinical exome sequencing (ES) for non-isolated APVR. A definitive or probable diagnosis was made for 8 of those individuals yielding a diagnostic efficacy rate of 16.3%. We then analyzed molecular data from 62 individuals with APVR accrued from three databases to identify novel APVR genes. Based on data from this analysis, published case reports, mouse models, and/or similarity to known APVR genes as revealed by a machine learning algorithm, we identified 3 genes—EFTUD2, NAA15, and NKX2-1—for which there is sufficient evidence to support phenotypic expansion to include APVR. We also provide evidence that 3 recurrent copy number variants contribute to the development of APVR: proximal 1q21.1 microdeletions involving RBM8A and PDZK1, recurrent BP1-BP2 15q11.2 deletions, and central 22q11.2 deletions involving CRKL. Our results suggest that ES and chromosomal microarray analysis (or genome sequencing) should be considered for individuals with non-isolated APVR for whom a genetic etiology has not been identified, and that genetic testing to identify an independent genetic etiology of APVR is not warranted in individuals with EFTUD2-, NAA15-, and NKX2-1-related disorders.

The spread of antimicrobial resistance has become a serious public health concern, making once-treatable diseases deadly again and undermining the achievements of modern medicine 1 , 2 . Drug combinations can help to fight multi-drug-resistant bacterial infections, yet they are largely unexplored and rarely used in clinics. Here we profile almost 3,000 dose-resolved combinations of antibiotics, human-targeted drugs and food additives in six strains from three Gram-negative pathogens— Escherichia coli , Salmonella enterica serovar Typhimurium and Pseudomonas aeruginosa —to identify general principles for antibacterial drug combinations and understand their potential. Despite the phylogenetic relatedness of the three species, more than 70% of the drug–drug interactions that we detected are species-specific and 20% display strain specificity, revealing a large potential for narrow-spectrum therapies. Overall, antagonisms are more common than synergies and occur almost exclusively between drugs that target different cellular processes, whereas synergies are more conserved and are enriched in drugs that target the same process. We provide mechanistic insights into this dichotomy and further dissect the interactions of the food additive vanillin. Finally, we demonstrate that several synergies are effective against multi-drug-resistant clinical isolates in vitro and during infections of the larvae of the greater wax moth Galleria mellonella , with one reverting resistance to the last-resort antibiotic colistin. Screening pairwise combinations of antibiotics and other drugs against three bacterial pathogens reveals that antagonistic and synergistic drug–drug interactions are specific to microbial species and strains.

Pervaporation, which is a non-pressure driven membrane process, was evaluated to determine its viability for desalinating high-salinity source waters like those originating from oil and natural gas development (produced water). Two types of membrane material chemistries were studied in order to identify the optimal properties for maximizing the permeate flux under a given set of operating conditions. Permeate flux was determined to be a significant function of membrane thickness and the diffusion coefficient of water through the membrane. The diffusion coefficient is in turn a function of the membrane's affinity for water (hydrophilicity) and its fractional free volume space. A cellulose triacetate membrane (Membrane B) achieved fluxes of 0.06 m3m–2day–1 when treating solutions having salt concentrations of 100 g L–1, comparable to fluxes achieved by other types of non-pressure driven membrane processes. The flux increased in a linear fashion with decreasing ionic strength and improved through increases in the vapor pressure gradient and/or inclusion of a feed channel spacer into the test cell. Salt rejection efficiencies by all membranes were >99%; however, co-ions were able to penetrate into the membrane material matrix over time.

A proprietary nonporous hydrophilic pervaporation membrane was examined as a potential produced water treatment and irrigation reuse technology. Flat-sheet and corrugated tubular configurations of the proprietary membranes were analyzed, as well as a cellulose triacetate membrane for comparison. Membrane performance as a function of membrane thickness, vapor pressure difference and feed water salinity was analyzed. The cellulose triacetate membranes and flat sheet proprietary membranes exhibited flux rates of 0.97 – 5.65x10-2 m 3 m-2 day-1 and 0.159 – 1.19 x10 -2 m3 m-2 day-1, respectively, and both membranes displayed salt rejection capabilities ≥ 99%. The tubular proprietary membranes exhibited flux rates of 1.74 – 5.74x10 -4 m3 m-2 day-1. The tubular proprietary membranes displayed excellent salt rejection capabilities (linear regression showed R2 ≥ 0.90 assuming 100% rejection), and flux rates were found to remain stable even as the feed water salinity increased over time indicating potential for long term use as an irrigation system.

Background Faltering postnatal growth in preterm babies is associated with adverse neurodevelopment. However, which growth reference is most helpful for predicting neurodevelopment is unknown. We examined associations between faltering growth and developmental delay in extremely low birthweight (ELBW) infants. Methods We categorized faltering growth (z-score decrease ≥0.8 for weight/length, >1 for head circumference) between birth, 4 weeks, 36 weeks’ postmenstrual age and 2 years’ corrected age using fetal (Fenton, UK-WHO and Olsen) and healthy preterm (INTERGROWTH-21st) references. Associations between faltering growth and developmental delay were examined using binary logistic regression and area under the receiver operating curve (AUC). Results In 327 infants, Olsen charts identified the highest prevalence of faltering growth (weight 37%, length 63%, head 45%). Agreement in classification was higher amongst fetal references (kappa coefficient, ĸ  = 0.46–0.94) than between INTERGROWTH-21st and fetal references ( ĸ  = 0.10–0.81). Faltering growth in all measures between 4–36 weeks (odds ratio, OR 2.0–4.7) compared with other time intervals (OR 1.7–2.7) were more strongly associated with developmental delay, particularly motor delay (OR 2.0–4.7). All growth references were poorly predictive of developmental delay at 2 years (AUC ≤ 0.62). Conclusions Faltering postnatal growth in ELBW infants is associated with, but is poorly predictive of, developmental delay at 2 years. Impact In babies born preterm, different growth references result in wide variation in categorization of faltering postnatal growth. Faltering growth in weight, length, and head circumference from 4 weeks to 36 weeks’ postmenstrual age are associated with developmental delay at 2 years’ corrected age, particularly motor delay. However, postnatal growth is a poor predictor of later developmental delay in extremely low birthweight infants irrespective of the growth reference used.

Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel protocol for audio data collection and a corresponding application that captures health information through guided questions. To demonstrate the potential of Voice EHR as a biomarker of health, initial experiments on data quality and multiple case studies are presented in this report. Large language models (LLMs) were used to compare transcribed Voice EHR data with data (from the same patients) collected through conventional techniques like multiple choice questions. Information contained in the Voice EHR samples was consistently rated as equally or more relevant to a health evaluation. The HEAR application facilitates the collection of an audio electronic health record (\"Voice EHR\") that may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context-potentially compensating for the typical limitations of unimodal clinical datasets.

Background Approximately 5–10% of children exhibit developmental deviations in motor skills or other domains; however, physicians detect less than one-third of these abnormalities. Systematic tracking and early identification of motor deviations are fundamental for timely intervention. Methods Term and preterm neonates were prospectively assessed at the newborn (NB) period in a study of the psychometric properties of the Motor (MOT) domain of PediaTrac TM v3.0, a novel caregiver-based development tracking instrument. Item response theory graded response modeling was used to model item parameters and estimate theta, an index of the latent trait, motor ability. Exploratory factor analysis (EFA) was conducted to examine the dimensionality and factor structure. Results In a cohort of 571 caregiver/infant dyads (331 term, 240 preterm), NB MOT domain reliability was high (rho = 0.94). Item discrimination and item difficulty of each of the 15 items could be reliably modeled across the range of motor ability. EFA confirmed that the items constituted a single dimension with second-order factors, accounting for 43.20% of variance. Conclusions The latent trait, motor ability, could be reliably estimated at the NB period. Impact The caregiver-reported Motor domain of PediaTrac provides a reliable estimate of the latent trait of motor ability during the newborn period. This is the first known caregiver-reported instrument that can assess motor ability in the newborn period with high reliability in term and preterm infants. Item response theory methods were employed that will allow for future characterization of developmental subgroups and motor trajectories. The PediaTrac Motor domain can support early identification of at-risk infants. Including caregivers in digital reporting and child-centered monitoring of motor functioning may improve access to care.

Objectives Cerebral palsy (CP) is the most common childhood motor disability, yet its link to single‐gene disorders is under‐characterized. To explore the genetic landscape of CP, we conducted whole exome sequencing (WES) in a cohort of patients with CP. Methods We performed comprehensive phenotyping and WES on a prospective cohort of individuals with cryptogenic CP (who meet criteria for CP; have no risk factors), non‐cryptogenic CP (who meet criteria for CP; have at least one risk factor), and CP masqueraders (who could be diagnosed with CP, but have regression/progressive symptoms). We characterized motor phenotypes, ascertained medical comorbidities, and classified brain MRIs. We analyzed WES data using an institutional pipeline. Results We included 50 probands in this analysis (20 females, 30 males). Twenty‐four had cryptogenic CP, 20 had non‐cryptogenic CP, five had CP masquerader classification, and one had unknown classification. Hypotonic‐ataxic subtype showed a difference in prevalence across the classification groups (p = 0.01). Twenty‐six percent of participants (13/50) had a pathogenic/likely pathogenic variant in 13 unique genes (ECHS1, SATB2, ZMYM2, ADAT3, COL4A1, THOC2, SLC16A2, SPAST, POLR2A, GNAO1, PDHX, ACADM, ATL1), including one patient with two genetic disorders (ACADM, PDHX) and two patients with a SPAST‐related disorder. The CP masquerader category had the highest diagnostic yield (n = 3/5, 60%), followed by the cryptogenic CP category (n = 7/24, 29%). Fifteen percent of patients with non‐cryptogenic CP (n = 3/20) had a Mendelian disorder on WES. Interpretation WES demonstrated a significant prevalence of Mendelian disorders in individuals clinically diagnosed with CP, including in individuals with known CP risk factors.

Two new proteins of approximately 70 amino acids in length, corresponding to an unnaturally-linked N- and C-helix of the ectodomain of the gp41 protein from the human immunodeficiency virus (HIV) type 1, were designed and characterized. A designed tripeptide links the C-terminus of the C-helix with the N-terminus of the N-helix in a circular permutation so that the C-helix precedes the N-helix in sequence. In addition to the artificial peptide linkage, the C-helix is truncated at its N-terminus to expose a region of the N-helix known as the \"Trp-Trp-Ile\" binding pocket. Sedimentation, crystallographic, and nuclear magnetic resonance studies confirmed that the protein had the desired trimeric structure with an unoccupied binding site. Spectroscopic and centrifugation studies demonstrated that the engineered protein had ligand binding characteristics similar to previously reported constructs. Unlike previous constructs which expose additional, shallow, non-conserved, and undesired binding pockets, only the single deep and conserved Trp-Trp-Ile pocket is exposed in the proteins of this study. This engineered version of gp41 protein will be potentially useful in research programs aimed at discovery of new drugs for therapy of HIV-infection in humans.

Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel data type and a corresponding collection system that captures health data through guided questions using only a mobile/web application. The app facilitates the collection of an audio electronic health record (Voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context, potentially compensating for the typical limitations of unimodal clinical datasets. This report presents the application used for data collection, initial experiments on data quality, and case studies which demonstrate the potential of voice EHR to advance the scalability/diversity of audio AI.