Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by restrictive interests and/or repetitive behaviors and deficits in social interaction and communication. ASD is a multifactorial disease with a complex polygenic genetic architecture. Its genetic contributing factors are not yet fully understood, especially large structural variations (SVs). In this study, we aimed to assess the contribution of SVs, including copy number variants (CNVs), insertions, deletions, duplications, and mobile element insertions, to ASD and related language impairments in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Within the cohort, ~77% of the families contain SVs that followed expected segregation or de novo patterns and passed our filtering criteria. These SVs affected 344 brain-expressed genes and can potentially contribute to the genetic etiology of the disorders. Gene Ontology and protein-protein interaction network analysis suggested several clusters of genes in different functional categories, such as neuronal development and histone modification machinery. Genes and biological processes identified in this study contribute to the understanding of ASD and related neurodevelopment disorders.

The Rosetta Phenotype Harmonization Method Facilitates Finding a Relationship Quantitative Trait Locus for a Complex Cognitive Trait.

Genetics researchers increasingly combine data across many sources to increase power and to conduct analyses that cross multiple individual studies. However, there is often a lack of alignment on outcome measures when the same constructs are examined across studies. This inhibits comparison across individual studies and may impact the findings from meta-analysis. Using a well-characterized genotypic (brain-derived neurotrophic factor: BDNF) and phenotypic constructs (working memory and reading comprehension), we employ an approach called Rosetta, which allows for the simultaneous examination of primary studies that employ related but incompletely overlapping data. We examined four studies of BDNF, working memory, and reading comprehension with a combined sample size of 1711 participants. Although the correlation between working memory and reading comprehension over all participants was high, as expected (ρ = 0.45), the correlation between working memory and reading comprehension was attenuated in the BDNF Met/Met genotype group (ρ = 0.18, n.s.) but not in the Val/Val (ρ = 0.44) or Val/Met (ρ = 0.41) groups. These findings indicate that Met/Met carriers may be a unique and robustly defined subgroup in terms of memory and reading comprehension. This study demonstrates the utility of the Rosetta method when examining complex phenotypes across multiple studies, including psychiatric genetic studies, as shown here, and also for the mega-analysis of cohorts generally.

Thirty-Day readmissions among COVID-19 patients hospitalized during the early pandemic in the United States: Insights from the Nationwide Readmissions Database.

BACKGROUND: Hospital readmissions are core indicators of the quality of health care provision. OBJECTIVE: To understand factors associated with 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database. METHODS: This retrospective study characterized the 30-day, all-cause hospital readmission rate for patients with COVID-19 in the United States during the early pandemic by utilizing the Nationwide Readmissions Database. RESULTS: The 30-day, all-cause hospital readmission rate in this population was 3.2%. We found the most common diagnoses at readmission to be sepsis, acute kidney injury, and pneumonia. Chronic alcoholic liver cirrhosis and congestive heart failure were prominent predictors of readmission among patients with COVID-19. Moreover, we found that younger patients and patients from economically disadvantaged backgrounds were at higher risk of 30-day readmission. Acute complications during index hospitalization, including acute coronary syndrome, congestive heart failure, acute kidney injury, mechanical ventilation, and renal replacement therapy, also increased the risk of 30-day readmission for patients with COVID-19. CONCLUSION: Based on the results of our study, we advise clinicians to promptly recognize patients with COVID-19 who are at high risk of readmission, and to subsequently manage their underlying comorbidities, to institute timely discharge planning, and to allocate resources to underprivileged patients in order to decrease the risk of 30-day hospital readmissions.

EARLY CONSIDERATIONS OF GENETICS IN APHASIA REHABILITATION: A NARRATIVE REVIEW.

Background: Early investigations linking language and genetics were focused on the evolution of human communication in populations with developmental speech and language disorders. Recently, studies suggest that genes may also modulate recovery from post-stroke aphasia. Aims: Our goal is to review current literature related to the influence of genetics on post-stroke recovery, and the implications for aphasia rehabilitation. We describe candidate genes implicated by empirical findings and address additional clinical considerations. Main Contribution: We describe existing evidence and mechanisms supporting future investigations into how genetic factors may modulate aphasia recovery and propose that two candidate genes, brain derived neurotrophic factor (BDNF) and apolipoprotein E (APOE), may be important considerations for future research assessing response to aphasia treatment. Evidence suggests that BDNF is important for learning, memory, and neuroplasticity. APOE influences cognitive functioning and memory in older individuals and has also been implicated in neural repair. Moreover, recent data suggest an interaction between specific alleles of the BDNF and APOE genes in influencing episodic memory. Conclusions: Genetic influences on recovery from aphasia have been largely unexplored in the literature despite evidence that genetic factors influence behaviour and recovery from brain injury. As researchers continue to explore prognostic factors that may influence response to aphasia treatment, it is time for genetic factors to be considered as a source of variability. As the field moves in the direction of personalized medicine, eventually allied health professionals may utilize genetic profiles to inform treatment decisions and education for patients and care partners.

Invasive or More Direct Measurements Can Provide an Objective Early-Stopping Ceiling for Training Deep Neural Networks on Non-invasive or Less-Direct Biomedical Data.

Early stopping is an extremely common tool to minimize overfitting, which would otherwise be a cause of poor generalization of the model to novel data. However, early stopping is a heuristic that, while effective, primarily relies on ad hoc parameters and metrics. Optimizing when to stop remains a challenge. In this paper, we suggest that for some biomedical applications, a natural dichotomy of invasive/non-invasive measurements, or more generally proximal vs distal measurements of a biological system can be exploited to provide objective advice on early stopping. We discuss the conditions where invasive measurements of a biological process should provide better predictions than non-invasive measurements, or at best offer parity. Hence, if data from an invasive measurement are available locally, or from the literature, that information can be leveraged to know with high certainty whether a model of non-invasive data is overfitted. We present paired invasive/non-invasive cardiac and coronary artery measurements from two mouse strains, one of which spontaneously develops type 2 diabetes, posed as a classification problem. Examination of the various stopping rules shows that generalization is reduced with more training epochs and commonly applied stopping rules give widely different generalization error estimates. The use of an empirically derived training ceiling is demonstrated to be helpful as added information to leverage early stopping in order to reduce overfitting.

The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics.

Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.

Common genetic risk factors in ASD and ADHD co-occurring families.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two major neurodevelopmental disorders that frequently co-occur. However, the genetic mechanism of the co-occurrence remains unclear. The New Jersey Language and Autism Genetics Study (NJLAGS) collected more than 100 families with at least one member affected by ASD. NJLAGS families show a high prevalence of ADHD and provide a good opportunity to study shared genetic risk factors for ASD and ADHD. The linkage study of the NJLAGS families revealed regions on chromosomes 12 and 17 that are significantly associated with ADHD. Using whole-genome sequencing data on 272 samples from 73 NJLAGS families, we identified potential risk genes for ASD and ADHD. Within the linkage regions, we identified 36 genes that are associated with ADHD using a pedigree-based gene prioritization approach. KDM6B (Lysine Demethylase 6B) is the highest-ranking gene, which is a known risk gene for neurodevelopmental disorders, including ASD and ADHD. At the whole-genome level, we identified 207 candidate genes from the analysis of both small variants and structure variants, including both known and novel genes. Using enrichment and protein-protein interaction network analyses, we identified gene ontology terms and pathways enriched for ASD and ADHD candidate genes, such as cilia function and cation channel activity. Candidate genes and pathways identified in our study improve the understanding of the genetic etiology of ASD and ADHD and will lead to new diagnostic or therapeutic interventions for ASD and ADHD in the future.

MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders.

Autism spectrum disorder (ASD) is a childhood neurodevelopmental disorder with a complex and heterogeneous genetic etiology. MicroRNA (miRNA), a class of small non-coding RNAs, could regulate ASD risk genes post-transcriptionally and affect broad molecular pathways related to ASD and associated disorders. Using whole-genome sequencing, we analyzed 272 samples in 73 families in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Families with at least one ASD patient were recruited and were further assessed for language impairment, reading impairment, and other associated phenotypes. A total of 5104 miRNA variants and 1,181,148 3' untranslated region (3' UTR) variants were identified in the dataset. After applying several filtering criteria, including population allele frequency, brain expression, miRNA functional regions, and inheritance patterns, we identified high-confidence variants in five brain-expressed miRNAs (targeting 326 genes) and 3' UTR miRNA target regions of 152 genes. Some genes, such as SCP2 and UCGC, were identified in multiple families. Using Gene Ontology overrepresentation analysis and protein-protein interaction network analysis, we identified clusters of genes and pathways that are important for neurodevelopment. The miRNAs and miRNA target genes identified in this study are potentially involved in neurodevelopmental disorders and should be considered for further functional studies.

Improvement of automated analysis of coronary Doppler echocardiograms.

Coronary artery disease is the leading cause of heart disease, and while it can be assessed through transthoracic Doppler echocardiography (TTDE) by observing changes in coronary flow, manual analysis of TTDE is time consuming and subject to bias. In a previous study, a program was created to automatically analyze coronary flow patterns by parsing Doppler videos into a single continuous image, binarizing and separating the image into cardiac cycles, and extracting data values from each of these cycles. The program significantly reduced variability and time to complete TTDE analysis, but some obstacles such as interfering noise and varying video sizes left room to increase the program's accuracy. The goal of this current study was to refine the existing automation algorithm and heuristics by (1) moving the program to a Python environment, (2) increasing the program's ability to handle challenging cases and video variations, and (3) removing unrepresentative cardiac cycles from the final data set. With this improved analysis, examiners can use the automatic program to easily and accurately identify the early signs of serious heart diseases.

Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

COVID-19 global pandemic planning: Presence of SARS-CoV-2 fomites in a university hospital setting.

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has surged across the globe, great effort has been expended to understand mechanisms of transmission and spread. From a hospital perspective, this topic is critical to limit and prevent SARS-CoV-2 iatrogenic transmission within the healthcare environment. Currently, the virus is believed to be transmitted primarily through respiratory droplets, but a growing body of evidence suggests that spread is also possible through aerosolized particles and fomites. Amidst a growing volume of patients with coronavirus disease 2019 (COVID-19), the purpose of this study was to evaluate the potential for SARS-CoV-2 transmission through fomites. Samples collected from the exposed skin of clinicians (n = 42) and high-touch surfaces (n = 40) were collected before and after encounters with COVID-19 patients. Samples were analyzed using two assays: the CDC 2019-nCoV Real-Time Reverse Transcription polymerase chain reaction (RT-qPCR) assay, and a SYBR Green assay that targeted a 121 bp region within the S-gene of SARS-CoV-2. None of the samples tested positive with the CDC assay, while two high-touch surface areas tested positive for SARS-CoV-2 using the Spike assay. However, viral culture did not reveal viable SARS-CoV-2 from the positive samples. Overall, the results from this study suggest that SARS-CoV-2 RNA were not widely present either on exposed skin flora or high-touch surface areas in the hospital locations tested. The inability to recover viable virus from samples that tested positive by the molecular assays, however, does not rule out the possibility of SARS-CoV-2 transmission through fomites.

Integrative survival analysis of breast cancer with gene expression and DNA methylation data.

MOTIVATION: Integrative multi-feature fusion analysis on biomedical data has gained much attention recently. In breast cancer, existing studies have demonstrated that combining genomic mRNA data and DNA methylation data can better stratify cancer patients with distinct prognosis than using single signature. However, those existing methods are simply combining these gene features in series and have ignored the correlations between separate omics dimensions over time. RESULTS: In the present study, we propose an adaptive multi-task learning method, which combines the Cox loss task with the ordinal loss task, for survival prediction of breast cancer patients using multi-modal learning instead of performing survival analysis on each feature dataset. First, we use local maximum quasi-clique merging (lmQCM) algorithm to reduce the mRNA and methylation feature dimensions and extract cluster eigengenes respectively. Then, we add an auxiliary ordinal loss to the original Cox model to improve the ability to optimize the learning process in training and regularization. The auxiliary loss helps to reduce the vanishing gradient problem for earlier layers and helps to decrease the loss of the primary task. Meanwhile, we use an adaptive weights approach to multi-task learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. Finally, we build an ordinal cox hazards model for survival analysis and use long short-term memory (LSTM) method to predict patients' survival risk. We use the cross-validation method and the concordance index (C-index) for assessing the prediction effect. Stringent cross-verification testing processes for the benchmark dataset and two additional datasets demonstrate that the developed approach is effective, achieving very competitive performance with existing approaches. AVAILABILITY AND IMPLEMENTATION: https://github.com/bhioswego/ML_ordCOX.

MiR-29b is associated with perinatal inflammation in extremely preterm infants.

BACKGROUND: Inflammation is strongly associated with premature birth and neonatal morbidities. Increases in infant haptoglobin, haptoglobin-related protein (Hp&HpRP), and interleukin-6 (IL-6) levels are indicators of intra-amniotic inflammation (IAI) and have been linked to poor neonatal outcomes. Inflammation causes epigenetic changes, specifically suppression of miR-29 expression. The current study sought to determine whether miR-29b levels in cord blood or neonatal venous blood are associated with IAI, identified by elevated IL-6 and Hp, and subsequent clinical morbidities in the infant. METHODS: We tested 92 cord blood samples from premature newborns and 18 venous blood samples at 36 weeks corrected gestational age. MiR-29b, Hp&HpRP, and IL-6 were measured by polymerase chain reaction and enzyme-linked immunosorbent assay, respectively. RESULTS: Decreased levels of miR-29b were observed in infants exposed to IAI with elevated Hp&HpRP and IL-6 levels and in infants delivered by spontaneous preterm birth. Lower miR-29 levels were also observed in women diagnosed with histological chorioamnionitis or funisitis and in infants with cerebral palsy. Higher levels of miR-29 were measured in infants small for gestational age and in venous samples from older infants. CONCLUSIONS: MiR-29 may be an additional biomarker of IAI and a potential therapeutic target for treating poor newborn outcomes resulting from antenatal exposure to IAI. IMPACT: Decreases in miR-29b are associated with intrauterine inflammation. Hp&HpRP increases are associated with decreased miR-29b. MiR-29b may be an additional biomarker for neonatal outcomes and a potential therapeutic target for intrauterine inflammation.

Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features.

Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordination across parties not-at-hand (i.e., informatics researchers in the future). Thus, incomplete concordance of measurements across datasets poses a major challenge for researchers seeking to combine public databases. In any given field, some measurements are fairly standard, but every organization collecting data makes unique decisions on instruments, protocols, and methods of processing the data. This typically denies literal concatenation of the raw data since constituent cohorts do not have the same measurements (i.e., columns of data). When measurements across datasets are similar prima facie, there is a desire to combine the data to increase power, but mixing non-identical measurements could greatly reduce the sensitivity of the downstream analysis. Here, we discuss a statistical method that is applicable when certain patterns of missing data are found; namely, it is possible to combine datasets that measure the same underlying constructs (or latent traits) when there is only partial overlap of measurements across the constituent datasets. Our method, ROSETTA empirically derives a set of common latent trait metrics for each related measurement domain using a novel variation of factor analysis to ensure equivalence across the constituent datasets. The advantage of combining datasets this way is the simplicity, statistical power, and modeling flexibility of a single joint analysis of all the data. Three simulation studies show the performance of ROSETTA on datasets with only partially overlapping measurements (i.e., systematically missing information), benchmarked to a condition of perfectly overlapped data (i.e., full information). The first study examined a range of correlations, while the second study was modeled after the observed correlations in a well-characterized clinical, behavioral cohort. Both studies consistently show significant correlations >0.94, often >0.96, indicating the robustness of the method and validating the general approach. The third study varied within and between domain correlations and compared ROSETTA to multiple imputation and meta-analysis as two commonly used methods that ostensibly solve the same data integration problem. We provide one alternative to meta-analysis and multiple imputation by developing a method that statistically equates similar but distinct manifest metrics into a set of empirically derived metrics that can be used for analysis across all datasets.

The predominance of nucleotidyl activation in bacterial phosphonate biosynthesis.

Phosphonates are rare and unusually bioactive natural products. However, most bacterial phosphonate biosynthetic capacity is dedicated to tailoring cell surfaces with molecules like 2-aminoethylphosphonate (AEP). Although phosphoenolpyruvate mutase (Ppm)-catalyzed installation of C-P bonds is known, subsequent phosphonyl tailoring (Pnt) pathway steps remain enigmatic. Here we identify nucleotidyltransferases in over two-thirds of phosphonate biosynthetic gene clusters, including direct fusions to ~60% of Ppm enzymes. We characterize two putative phosphonyl tailoring cytidylyltransferases (PntCs) that prefer AEP over phosphocholine (P-Cho) - a similar substrate used by the related enzyme LicC, which is a virulence factor in Streptococcus pneumoniae. PntC structural analyses reveal steric discrimination against phosphocholine. These findings highlight nucleotidyl activation as a predominant chemical logic in phosphonate biosynthesis and set the stage for probing diverse phosphonyl tailoring pathways.

Response of sulfate-reducing bacteria and supporting microbial community to persulfate exposure in a continuous flow system.

Coupling of chemical oxidation using persulfate with bioremediation has been proposed as a method to increase remedial efficacy at petroleum hydrocarbon contaminated sites. To support this integrated treatment approach, an understanding of persulfate impact on the indigenous microbial community is necessary for system design. As sulfate-reducing bacteria (SRB) are active in most aquifer systems and can utilize the sulfate generated from the degradation of persulfate, this study assessed the impact on SRB and the supporting anaerobic microbial community when exposed to persulfate in a continuous flow system. A series of bioreactors (1000 L) packed with anaerobic aquifer material were operated for an 8 month acclimatization period before being continuously subjected to benzene, toluene, ethylbenzene and xylenes (total BTEX 3 mg L-1). After 2 months, the bioreactors were then exposed to an unactivated persulfate solution (20 g L-1), or an alkaline-activated persulfate solution (20 g L-1, pH 12) then effluent-sampled for 60 days following. A combination of culture and molecular-based techniques were used to monitor SRB presence and structural profiles in the anaerobic SRB-specific and broader microbial community. Post-exposure, the rate of BTEX mass removal remained below pre-exposure values; however, trends suggest that full recovery would be expected. Rebound of SRB-specific and the associated microbial community to pre-exposure levels were observed in all exposed bioreactors. Structural community profiles identified recovery in both microbial species and diversity indices. Findings from this investigation demonstrate robustness of SRB in the presence of a supporting microbial community and, thus, are suitable organisms for target use during bioremediation in an integrated system with persulfate.

Outcomes following a comprehensive versus a selective approach for infants born at 22 weeks of gestation.

OBJECTIVE: To examine outcomes at two institutions with different approaches to care among infants born at 22 weeks of gestation. STUDY DESIGN: Retrospective, cohort study (2006-2015). Enrollment was limited to mother-infant dyads at 22 weeks of gestation. Proactive care was defined as provision of antenatal corticosteroids and neonatal resuscitation and intensive care. One center (Uppsala, Sweden; UUCH) provided proactive care to all mother-infant dyads (comprehensive center); the other center (Nationwide Children's Hospital, USA; NCH) initiated or withheld treatment based on physician and family preferences (selective center). Differences in outcomes between the two centers were evaluated. RESULT: Among 112 live-born infants at 22 weeks of gestation, those treated at UUCH had in-hospital survival rates higher than those at NCH (21/40, 53% vs. 6/72, 8%; P < 0.01). Among the subgroup of infants receiving proactive care (UUCH: 40/40, 100%; NCH: 16/72, 22%) survival was higher at UUCH than at NCH (21/40, 53% vs. 3/16, 19%; P < 0.05). CONCLUSION: Even when mother-infant dyads were provided proactive care at NCH (selective center), survival was lower than infants provided proactive care at UUCH (comprehensive center). Differences between the approaches to care at the two centers at 22 weeks of gestation merits further investigation.

Visualisation and analysis of hepatitis C virus non-structural proteins using super-resolution microscopy.

Hepatitis C virus (HCV) RNA replication occurs in the cytosol of infected cells within a specialised membranous compartment. How the viral non-structural (NS) proteins are associated and organised within these structures remains poorly defined. We employed a super-resolution microscopy approach to visualise NS3 and NS5A in HCV infected cells. Using single molecule localisation microscopy, both NS proteins were resolved as clusters of localisations smaller than the diffraction-limited volume observed by wide-field. Analysis of the protein clusters identified a significant difference in size between the NS proteins. We also observed a reduction in NS5A cluster size following inhibition of RNA replication using daclatasvir, a phenotype which was maintained in the presence of the Y93H resistance associated substitution and not observed for NS3 clusters. These results provide insight into the NS protein organisation within hepatitis C virus RNA replication complexes and the mode of action of NS5A inhibitors.

Understanding developmental language disorder - the Helsinki longitudinal SLI study (HelSLI): a study protocol.

BACKGROUND: Developmental language disorder (DLD, also called specific language impairment, SLI) is a common developmental disorder comprising the largest disability group in pre-school-aged children. Approximately 7% of the population is expected to have developmental language difficulties. However, the specific etiological factors leading to DLD are not yet known and even the typical linguistic features appear to vary by language. We present here a project that investigates DLD at multiple levels of analysis and aims to make the reliable prediction and early identification of the difficulties possible. Following the multiple deficit model of developmental disorders, we investigate the DLD phenomenon at the etiological, neural, cognitive, behavioral, and psychosocial levels, in a longitudinal study of preschool children. METHODS: In January 2013, we launched the Helsinki Longitudinal SLI study (HelSLI) at the Helsinki University Hospital ( http://tiny.cc/HelSLI ). We will study 227 children aged 3-6 years with suspected DLD and their 160 typically developing peers. Five subprojects will determine how the child's psychological characteristics and environment correlate with DLD and how the child's well-being relates to DLD, the characteristics of DLD in monolingual versus bilingual children, nonlinguistic cognitive correlates of DLD, electrophysiological underpinnings of DLD, and the role of genetic risk factors. Methods include saliva samples, EEG, computerized cognitive tasks, neuropsychological and speech and language assessments, video-observations, and questionnaires. DISCUSSION: The project aims to increase our understanding of the multiple interactive risk and protective factors that affect the developing heterogeneous cognitive and behavioral profile of DLD, including factors affecting literacy development. This accumulated knowledge will form a heuristic basis for the development of new interventions targeting linguistic and non-linguistic aspects of DLD.

Creating Change: An Experiential Quality Improvement and Patient Safety Curriculum for Medical Students.

Introduction: Medical students are the future drivers of change in health care. The AAMC encourages quality improvement and patient safety (QI/PS) education. Unfortunately, many schools do not have a formal QI/PS curriculum. To offer the patient-centered, safe, evidence-based, and high-value care patients deserve, students will be expected to have both knowledge of and experience in QI/PS. This extracurricular experiential QI/PS curriculum is designed to prepare medical students for this role. Methods: The curriculum includes six monthly didactic and work-group sessions that cover QI/PS fundamentals and facilitate the design and implementation of student projects. Results: Twenty-two medical students, with representation from academic years 1-4, completed the curriculum. The average Quality Improvement Knowledge Application Tool-Revised score increased from 5.61 to 7.75 (p < .01). Six projects were undertaken, with teams completing an average of 2.83 plan-do-study-act cycles. Projects decreased Clostridium difficile ordering, reduced discordance between documented and true intraoperative wound classification, and increased the quantity and quality of patient sleep. Responding "Agree" or "Strongly Agree," 80.9% of students felt their practice would change due to this experience, and 96.5% planned on participating in QI/PS in the future. Four students volunteered to continue as student leaders. Many students (96.5%) felt their experience was good or very good. Discussion: This ready-to-implement curriculum offers medical students an opportunity to obtain the knowledge and experience necessary to participate meaningfully in QI/PS now and throughout their careers.