Macrophage mitochondrial bioenergetics and tissue invasion are boosted by an Atossa-Porthos axis in Drosophila.

Cellular metabolism must adapt to changing demands to enable homeostasis. During immune responses or cancer metastasis, cells leading migration into challenging environments require an energy boost, but what controls this capacity is unclear. Here, we study a previously uncharacterized nuclear protein, Atossa (encoded by CG9005), which supports macrophage invasion into the germband of Drosophila by controlling cellular metabolism. First, nuclear Atossa increases mRNA levels of Porthos, a DEAD-box protein, and of two metabolic enzymes, lysine-α-ketoglutarate reductase (LKR/SDH) and NADPH glyoxylate reductase (GR/HPR), thus enhancing mitochondrial bioenergetics. Then Porthos supports ribosome assembly and thereby raises the translational efficiency of a subset of mRNAs, including those affecting mitochondrial functions, the electron transport chain, and metabolism. Mitochondrial respiration measurements, metabolomics, and live imaging indicate that Atossa and Porthos power up OxPhos and energy production to promote the forging of a path into tissues by leading macrophages. Since many crucial physiological responses require increases in mitochondrial energy output, this previously undescribed genetic program may modulate a wide range of cellular behaviors.

Msl3 promotes germline stem cell differentiation in female Drosophila.

Gamete formation from germline stem cells (GSCs) is essential for sexual reproduction. However, the regulation of GSC differentiation is incompletely understood. Set2, which deposits H3K36me3 modifications, is required for GSC differentiation during Drosophila oogenesis. We discovered that the H3K36me3 reader Male-specific lethal 3 (Msl3) and histone acetyltransferase complex Ada2a-containing (ATAC) cooperate with Set2 to regulate GSC differentiation in female Drosophila. Msl3, acting independently of the rest of the male-specific lethal complex, promotes transcription of genes, including a germline-enriched ribosomal protein S19 paralog RpS19b. RpS19b upregulation is required for translation of RNA-binding Fox protein 1 (Rbfox1), a known meiotic cell cycle entry factor. Thus, Msl3 regulates GSC differentiation by modulating translation of a key factor that promotes transition to an oocyte fate.

Enhancing ICD-Code-Based Case Definition for Heart Failure Using Electronic Medical Record Data.

BACKGROUND: Surveillance and outcome studies for heart failure (HF) require accurate identification of patients with HF. Algorithms based on International Classification of Diseases (ICD) codes to identify HF from administrative data are inadequate owing to their relatively low sensitivity. Detailed clinical information from electronic medical records (EMRs) is potentially useful for improving ICD algorithms. This study aimed to enhance the ICD algorithm for HF definition by incorporating comprehensive information from EMRs. METHODS: The study included 2106 inpatients in Calgary, Alberta, Canada. Medical chart review was used as the reference gold standard for evaluating developed algorithms. The commonly used ICD codes for defining HF were used (namely, the ICD algorithm). The performance of different algorithms using the free text discharge summaries from a population-based EMR were compared with the ICD algorithm. These algorithms included a keyword search algorithm looking for HF-specific terms, a machine learning-based HF concept (HFC) algorithm, an EMR structured data based algorithm, and combined algorithms (the ICD and HFC combined algorithm). RESULTS: Of 2106 patients, 296 (14.1%) were patients with HF as determined by chart review. The ICD algorithm had 92.4% positive predictive value (PPV) but low sensitivity (57.4%). The EMR keyword search algorithm achieved a higher sensitivity (65.5%) than the ICD algorithm, but with a lower PPV (77.6%). The HFC algorithm achieved a better sensitivity (80.0%) and maintained a reasonable PPV (88.9%) compared with the ICD algorithm and the keyword algorithm. An even higher sensitivity (83.3%) was reached by combining the HFC and ICD algorithms, with a lower PPV (83.3%). The structured EMR data algorithm reached a sensitivity of 78% and a PPV of 54.2%. The combined EMR structured data and ICD algorithm had a higher sensitivity (82.4%), but the PPV remained low at 54.8%. All algorithms had a specificity ranging from 87.5% to 99.2%. CONCLUSIONS: Applying natural language processing and machine learning on the discharge summaries of inpatient EMR data can improve the capture of cases of HF compared with the widely used ICD algorithm. The utility of the HFC algorithm is straightforward, making it easily applied for HF case identification.

Automated extraction of weight, height, and obesity in electronic medical records are highly valid.

Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs). Methods: In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. A rule-based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed. Results: The study cohort consisted of 1904 patients with 50.8% females and 43.3% > 64 years of age. The final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and F1 score of 94.8%. Conclusions: This study developed a highly valid rule-based EMR algorithm that detects height and weight. This could allow large-scale analyses using obesity that were previously not possible.

BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study.

BACKGROUND: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls. OBJECTIVE: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model. METHODS: A cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture. RESULTS: To address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F1-score model (F1=64.4). Our proposed method outperformed 3 classical ML algorithms and an International Classification of Diseases code-based algorithm for fall detection, showing its potential for improved performance in diverse clinical settings. CONCLUSIONS: The developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals.

Hypertension identification using inpatient clinical notes from electronic medical records: an explainable, data-driven algorithm study.

BACKGROUND: Case identification is important for health services research, measuring health system performance and risk adjustment, but existing methods based on manual chart review or diagnosis codes can be expensive, time consuming or of limited validity. We aimed to develop a hypertension case definition in electronic medical records (EMRs) for inpatient clinical notes using machine learning. METHODS: A cohort of patients 18 years of age or older who were discharged from 1 of 3 Calgary acute care facilities (1 academic hospital and 2 community hospitals) between Jan. 1 and June 30, 2015, were randomly selected, and we compared the performance of EMR phenotype algorithms developed using machine learning with an algorithm based on the Canadian version of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD), in identifying patients with hypertension. Hypertension status was determined by chart review, the machine-learning algorithms used EMR notes and the ICD algorithm used the Discharge Abstract Database (Canadian Institute for Health Information). RESULTS: Of our study sample (n = 3040), 1475 (48.5%) patients had hypertension. The group with hypertension was older (median age of 71.0 yr v. 52.5 yr for those patients without hypertension) and had fewer females (710 [48.2%] v. 764 [52.3%]). Our final EMR-based models had higher sensitivity than the ICD algorithm (> 90% v. 47%), while maintaining high positive predictive values (> 90% v. 97%). INTERPRETATION: We found that hypertension tends to have clear documentation in EMRs and is well classified by concept search on free text. Machine learning can provide insights into how and where conditions are documented in EMRs and suggest nonmachine-learning phenotypes to implement.

Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing.

BACKGROUND: Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders' abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes. METHODS: CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients' chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULT: Of the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical documents from the EMR, four document types were identified as the most influential text sources for identifying CeVD disease ("nursing transfer report," "discharge summary," "nursing notes," and "inpatient consultation."). The best performing NLP model was XGBoost, combining the Unified Medical Language System concepts extracted by cTAKES (e.g., top-ranked concepts, "Cerebrovascular accident" and "Transient ischemic attack"), and the term frequency-inverse document frequency vectorizer. Compared with ICD codes, the model achieved higher validity overall, such as sensitivity (25.0% vs 70.0%), specificity (99.3% vs 99.1%), PPV (82.6 vs. 87.8%), and NPV (90.8% vs 97.1%). CONCLUSION: The NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied for future studies such as surveillance, and longitudinal studies.

A feedback loop between heterochromatin and the nucleopore complex controls germ-cell-to-oocyte transition during Drosophila oogenesis.

Germ cells differentiate into oocytes that launch the next generation upon fertilization. How the highly specialized oocyte acquires this distinct cell fate is poorly understood. During Drosophila oogenesis, H3K9me3 histone methyltransferase SETDB1 translocates from the cytoplasm to the nucleus of germ cells concurrently with oocyte specification. Here, we discovered that nuclear SETDB1 is required for silencing a cohort of differentiation-promoting genes by mediating their heterochromatinization. Intriguingly, SETDB1 is also required for upregulating 18 of the ∼30 nucleoporins (Nups) that compose the nucleopore complex (NPC), promoting NPC formation. NPCs anchor SETDB1-dependent heterochromatin at the nuclear periphery to maintain H3K9me3 and gene silencing in the egg chambers. Aberrant gene expression due to the loss of SETDB1 or Nups results in the loss of oocyte identity, cell death, and sterility. Thus, a feedback loop between heterochromatin and NPCs promotes transcriptional reprogramming at the onset of oocyte specification, which is critical for establishing oocyte identity.

Exploring the reliability of inpatient EMR algorithms for diabetes identification.

INTRODUCTION: Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms. MATERIALS AND METHODS: A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV). RESULTS: The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99. DISCUSSION: Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery.

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models.

BACKGROUND: Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs. OBJECTIVE: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes. METHODS: Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model's performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score. RESULTS: Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms. CONCLUSIONS: The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

BACKGROUND: Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection. METHODS: This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. RESULTS: There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835-0.978), PR AUC of 0.637 (95% CI 0.528-0.746), and F1 score of 0.79 (0.67-0.90). CONCLUSIONS: Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.

The incidence rates of surgical site infections following total hip and knee arthroplasty were 0.5 and 0.52 per 100 surgical procedures. The incidence of SSIs varied significantly between care facilities (ranging from 0.53 to 1.71 per 100 procedures). The optimal machine learning model achieved a ROC AUC of 0.906 (95% CI 0.835­0.978), PR AUC of 0.637 (95% CI 0.528­0.746), and F1 score of 0.79 (0.67­0.90).

H/ACA snRNP-dependent ribosome biogenesis regulates translation of polyglutamine proteins.

Stem cells in many systems, including Drosophila germline stem cells (GSCs), increase ribosome biogenesis and translation during terminal differentiation. Here, we show that the H/ACA small nuclear ribonucleoprotein (snRNP) complex that promotes pseudouridylation of ribosomal RNA (rRNA) and ribosome biogenesis is required for oocyte specification. Reducing ribosome levels during differentiation decreased the translation of a subset of messenger RNAs that are enriched for CAG trinucleotide repeats and encode polyglutamine-containing proteins, including differentiation factors such as RNA-binding Fox protein 1. Moreover, ribosomes were enriched at CAG repeats within transcripts during oogenesis. Increasing target of rapamycin (TOR) activity to elevate ribosome levels in H/ACA snRNP complex-depleted germlines suppressed the GSC differentiation defects, whereas germlines treated with the TOR inhibitor rapamycin had reduced levels of polyglutamine-containing proteins. Thus, ribosome biogenesis and ribosome levels can control stem cell differentiation via selective translation of CAG repeat-containing transcripts.

Oo-site: A dashboard to visualize gene expression during Drosophila oogenesis suggests meiotic entry is regulated post-transcriptionally.

Determining how stem cell differentiation is controlled has important implications for understanding the etiology of degenerative disease and designing regenerative therapies. In vivo analyses of stem cell model systems have revealed regulatory paradigms for stem cell self-renewal and differentiation. The germarium of the female Drosophila gonad, which houses both germline and somatic stem cells, is one such model system. Bulk mRNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), and bulk translation efficiency (polysome-seq) of mRNAs are available for stem cells and their differentiating progeny within the Drosophila germarium. However, visualizing those data is hampered by the lack of a tool to spatially map gene expression and translational data in the germarium. Here, we have developed Oo-site (https://www.ranganlab.com/Oo-site), a tool for visualizing bulk RNA-seq, scRNA-seq, and translational efficiency data during different stages of germline differentiation, which makes these data accessible to non-bioinformaticians. Using this tool, we recapitulated previously reported expression patterns of developmentally regulated genes and discovered that meiotic genes, such as those that regulate the synaptonemal complex, are regulated at the level of translation.

A translation control module coordinates germline stem cell differentiation with ribosome biogenesis during Drosophila oogenesis.

Ribosomal defects perturb stem cell differentiation, and this is the cause of ribosomopathies. How ribosome levels control stem cell differentiation is not fully known. Here, we discover that three DExD/H-box proteins govern ribosome biogenesis (RiBi) and Drosophila oogenesis. Loss of these DExD/H-box proteins, which we name Aramis, Athos, and Porthos, aberrantly stabilizes p53, arrests the cell cycle, and stalls germline stem cell (GSC) differentiation. Aramis controls cell-cycle progression by regulating translation of mRNAs that contain a terminal oligo pyrimidine (TOP) motif in their 5' UTRs. We find that TOP motifs confer sensitivity to ribosome levels that are mediated by La-related protein (Larp). One such TOP-containing mRNA codes for novel nucleolar protein 1 (Non1), a conserved p53 destabilizing protein. Upon a sufficient ribosome concentration, Non1 is expressed, and it promotes GSC cell-cycle progression via p53 degradation. Thus, a previously unappreciated TOP motif in Drosophila responds to reduced RiBi to co-regulate the translation of ribosomal proteins and a p53 repressor, coupling RiBi to GSC differentiation.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.

BACKGROUND: Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. OBJECTIVE: This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. METHODS: A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. RESULTS: A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule-based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. CONCLUSIONS: Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

CREATE: A New Data Resource to Support Cardiac Precision Health.

BACKGROUND: The initiatives of precision medicine and learning health systems require databases with rich and accurately captured data on patient characteristics. We introduce the Clinical Registry, AdminisTrative Data and Electronic Medical Records (CREATE) database, which includes linked data from 4 population databases: Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH; a national clinical registry), Sunrise Clinical Manager (SCM) electronic medical record (city-wide), the Discharge Abstract Database (DAD), and the National Ambulatory Care Reporting System (NACRS). The intent of this work is to introduce a cardiovascular-specific database for pursuing precision health activities using big data analytics. METHODS: We used deterministic data linkage to link SCM electronic medical record data to APPROACH clinical registry data using patient identifier variables. The APPROACH-SCM data set was subsequently linked to DAD and NACRS to obtain inpatient and outpatient cohort data. We further validated the quality of the linkage, where applicable, in these databases by comparing against the Alberta Health Insurance Care Plan registry database. RESULTS: We achieved 99.96% linkage across these 4 databases. Currently, there are 30,984 patients with 35,753 catheterizations in the CREATE database. The inpatient cohort contained 65.75% (20,373/30,984) of the patient sample, whereas the outpatient cohort contained 29.78% (9226/30,984). The infrastructure and the process to update and expand the database has been established. CONCLUSIONS: CREATE is intended to serve as a database for supporting big data analytics activities surrounding cardiac precision health. The CREATE database will be managed by the Centre for Health Informatics at the University of Calgary, and housed in a secure high-performance computing environment.

CONTEXTE: Les initiatives en matière de médecine de précision et les systèmes de santé apprenants ont besoin de bases de données riches et exactes sur les caractéristiques des patients. Nous présentons ici la base de données CREATE ( C linical Re gistry, A dminis t rative Data and E lectronic Medical Records), qui regroupe les données couplées de quatre bases de données populationnelles : le registre clinique national APPROACH ( A lberta P rovincial Pr oject for O utcome A ssessment in C oronary H eart Disease), le système de gestion des dossiers médicaux électroniques SCM (Sunrise Clinical Manager, utilisé à l'échelle municipale), la Base de données sur les congés des patients (BDCP), et le Système national d'information sur les soins ambulatoires (SNISA). Notre objectif est d'offrir une base de données portant précisément sur les maladies cardiovasculaires, afin de soutenir les activités en santé de précision nécessitant l'analyse de mégadonnées. MÉTHODOLOGIE: Nous avons utilisé une méthode de couplage déterministe pour apparier les données du système SCM à celles du registre APPROACH à l'aide de variables d'identification des patients. L'ensemble de données SCM-APPROACH a ensuite été couplé aux données de la BDCP et du SNISA, afin d'obtenir les données des cohortes des patients hospitalisés et des patients ambulatoires. Lorsque c'était possible, nous avons en outre validé la qualité du couplage en comparant les données à celles de la base de données du Régime d'assurance maladie de l'Alberta. RÉSULTATS: Nous avons obtenu un taux de couplage de 99,96 % pour les quatre bases de données. À l'heure actuelle, la base de données CREATE compte 30 984 patients ayant subi 35 753 cathétérismes. La cohorte des patients hospitalisés représente 65,75 % (20 373/30 984) de l'échantillon, tandis que la cohorte des patients ambulatoires représente 29,78 % (9226/30 984). L'infrastructure et le processus de mise à jour et d'expansion de la base de données ont été définis. CONCLUSIONS: La base de données CREATE est destinée à soutenir les activités d'analyse de mégadonnées nécessaires à la santé cardiaque de précision. Elle sera gérée par le Centre for Health Informatics de l'Université de Calgary et hébergée dans un environnement informatique à haut rendement sécurisé.

Prognostic Value of Electrocardiographic QRS Diminution in Patients Hospitalized With COVID-19 or Influenza.

During the clinical care of hospitalized patients with COVID-19, diminished QRS amplitude on the surface electrocardiogram (ECG) was observed to precede clinical decompensation, culminating in death. This prompted investigation into the prognostic utility and specificity of low QRS complex amplitude (LoQRS) in COVID-19. We retrospectively analyzed consecutive adults admitted to a telemetry service with SARS-CoV-2 (n = 140) or influenza (n = 281) infection with a final disposition-death or discharge. LoQRS was defined as a composite of QRS amplitude <5 mm or <10 mm in the limb or precordial leads, respectively, or a ≥50% decrease in QRS amplitude on follow-up ECG during hospitalization. LoQRS was more prevalent in patients with COVID-19 than influenza (24.3% vs 11.7%, p = 0.001), and in patients who died than survived with either COVID-19 (48.1% vs 10.2%, p <0.001) or influenza (38.9% vs 9.9%, p <0.001). LoQRS was independently associated with mortality in patients with COVID-19 when adjusted for baseline clinical variables (odds ratio [OR] 11.5, 95% confidence interval [CI] 3.9 to 33.8, p <0.001), presenting and peak troponin, D-dimer, C-reactive protein, albumin, intubation, and vasopressor requirement (OR 13.8, 95% CI 1.3 to 145.5, p = 0.029). The median time to death in COVID-19 from the first ECG with LoQRS was 52 hours (interquartile range 18 to 130). Dynamic QRS amplitude diminution is a strong independent predictor of death over not only the course of COVID-19 infection, but also influenza infection. In conclusion, this finding may serve as a pragmatic prognostication tool reflecting evolving clinical changes during hospitalization, over a potentially actionable time interval for clinical reassessment.

Post-transcriptional gene regulation regulates germline stem cell to oocyte transition during Drosophila oogenesis.

During oogenesis, several developmental processes must be traversed to ensure effective completion of gametogenesis including, stem cell maintenance and asymmetric division, differentiation, mitosis and meiosis, and production of maternally contributed mRNAs, making the germline a salient model for understanding how cell fate transitions are mediated. Due to silencing of the genome during meiotic divisions, there is little instructive transcription, barring a few examples, to mediate these critical transitions. In Drosophila, several layers of post-transcriptional regulation ensure that the mRNAs required for these processes are expressed in a timely manner and as needed during germline differentiation. These layers of regulation include alternative splicing, RNA modification, ribosome production, and translational repression. Many of the molecules and pathways involved in these regulatory activities are conserved from Drosophila to humans making the Drosophila germline an elegant model for studying the role of post-transcriptional regulation during stem cell differentiation and meiosis.

Ribosomal protein RACK1 enhances translation of poliovirus and other viral IRESs.

Viruses have evolved strategies to ensure efficient translation using host cell ribosomes and translation factors. In addition to cleaving translation initiation factors required for host cell translation, poliovirus (PV) uses an internal ribosome entry site (IRES). Recent studies suggest that viruses exploit specific ribosomal proteins to enhance translation of their viral proteins. The ribosomal protein receptor for activated C kinase 1 (RACK1), a protein of the 40S ribosomal subunit, was previously shown to mediate translation from the 5' cricket paralysis virus and hepatitis C virus IRESs. Here we found that translation of a PV dual-luciferase reporter shows a moderate dependence on RACK1. However, in the context of a viral infection we observed significantly reduced poliovirus plaque size and titers and delayed host cell translational shut-off. Our findings further illustrate the involvement of the cellular translational machinery during PV infection and how viruses usurp the function of specific ribosomal proteins.

Insulin modulates norepinephrine-mediated melatonin synthesis in cultured rat pineal gland.

The mammalian pineal gland synthesizes melatonin in a circadian manner, peaking during the dark phase. This synthesis is primarily regulated by sympathetic innervations via noradrenergic fibers, but is also modulated by many peptidergic and hormonal systems. A growing number of studies reveal a complex role for melatonin in influencing various physiological processes, including modulation of insulin secretion and action. In contrast, a role for insulin as a modulator of melatonin synthesis has not been investigated previously. The aim of the current study was to determine whether insulin modulates norepinephrine (NE)-mediated melatonin synthesis. The results demonstrate that insulin (10(- 8)M) potentiated norepinephrine-mediated melatonin synthesis and tryptophan hydroxylase (TPOH) activity in ex vivo incubated pineal glands. When ex vivo incubated pineal glands were synchronized (12h NE-stimulation, followed by 12h incubation in the absence of NE), insulin potentiated NE-mediated melatonin synthesis and arylalkylamine-N-acetyltransferase (AANAT) activity. Insulin did not affect the activity of hydroxyindole-O-methyltranferase (HIOMT), nor the gene expression of tpoh, aanat, or hiomt, under any of the conditions investigated. We conclude that insulin potentiates NE-mediated melatonin synthesis in cultured rat pineal gland, potentially through post-transcriptional events.