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Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a high multimorbidity burden. However, existing risk assessment instruments prioritise physiological measures while overlooking systemic comorbidities. We aim to develop and validate an electronic health record (EHR)-embedded artificial intelligence (AI) model—AiRES (AI in patients with RESpiratory disease)—to predict the 30-day, 90-day and 180-day risks of all-cause and index-disease hospitalisations. This model represents a first step towards a clinical decision support tool for personalised multimorbidity management in patients with CRD.
Patients aged ≥18 years with a validated case definition of asthma and COPD will be identified from Singapore health administrative data (2012–2020). Candidate predictors will include age, sex, ethnicity, housing type, and comorbidities, measured across multiple care settings as visit frequency, grouped at quarterly intervals in Year 1 and annually for Years 2 and 3 over a 3-year lookback window. We will predict 30-day, 90-day, and 180-day risks of (1) all-cause and (2) asthma/COPD-specific hospital admissions using up to five randomly selected index dates per individual. Three machine learning algorithms—logistic regression (LR) with Lasso regularisation, eXtreme Gradient Boosting, and Categorical Boosting—will be trained using 10-fold cross-validation (CV) with an ensemble feature selection strategy. The optimal model, selected based on performance and feature importance, will be benchmarked against two reference models: a full LR and a Zero-Inflated Negative Binomial regression with hospitalisation history as the sole predictor. Discrimination and calibration will be assessed using internal-external cluster-based and temporal CV. Clinical utility will be evaluated using decision curve analysis.
This study obtained ethics approval from the National University of Singapore (NUS-IRB-2024-849). Results will be published in international peer-reviewed journals.
by Birhan Berihun Abebe, Abebe Girma Demissie, Habtie Bassie Felatie, Aderajew Adgo Tesema, Baye Wodajo, Wondye Ayaliew Shiferaw, Sualih Gobeze Hailu
Tomato (Solanum lycopersicum) is a widely used vegetable in Ethiopia, but its production is severely affected by late blight, early blight and bacterial wilt. This study aims to isolate Pseudomonas fluorescens as a bio-control agent against Alternaria solani. Biological control using Pseudomonas fluorescens offers a potential alternative to chemical fungicides. Rhizosphere soil and healthy tomato roots were sampled from three Kebeles in North Wollo, Ethiopia. P. fluorescens was isolated on Pseudomonas Isolation Agar, while A. solani isolated from infected leaves on Potato Dextrose Agar and confirmed pathogenic on tomato seedlings. Three isolates of P. fluorescens (Pfs12, Pfk13, Pfsa31) were screened in vitro using the dual culture method, and their efficacy was further tested in vivo under greenhouse conditions. Isolates Pfs12 and Pfk13 showed moderate effectiveness against the radial growth of A. solani, achieving percent growth inhibitions of 56.04% and 55.04%, respectively. The standard chemical treatment (mancozeb) resulted in a 54.84% growth inhibition. The control group (Pseudomonas fluorescens) also demonstrated a moderate growth inhibition of 57.65% against A. solani. Data were gathered regarding disease parameters. The day after transplanting, the percent disease index was significantly lower in all treated groups compared to the control (water). The isolate Pfsa31 achieved the lowest disease index of 24.733%, which was comparable to the standard chemical treatment at 28.467%. Both treatments were significantly different from the control (water) at 60.333%. The findings showed the bio-control potential of selected P. fluorescens isolates as effective and environmentally sustainable alternatives to synthetic fungicides for the management of early blight disease in tomato cultivation, emphasizing the importance of utilizing indigenous strains for optimal performance.Assess US registered nurse genomic competency.
Administered the Genetics and Genomics Nursing Practice Survey (GGNPS).
GGNPS assesses genomic knowledge, skills, attitudes, confidence, and utilization in nursing practice. Distributed by the American Nurses Association via email and online to US registered nurses. Results are analyzed using descriptive statistics and compared to 2010 data.
1065 registered nurses responded. Most (41%) were Master's prepared, actively seeing patients (51%) and 66% considered it very important to learn more about genomics. Most (55%) reported their genomic knowledge was poor yet 51% reported a patient initiated a genetic discussion with them in the past 3 months. 66% completed all knowledge score items with a median score of 9/12, no change from 2010. Only 26% had heard of the Essential Competencies. Most reported no genomic curricular content (64%); had not attended a genomic course since licensure (64%); intended to learn more about genomics (70%); and would attend a course on their own time (79%).
Nurses felt genomics was important but have capacity deficits. Despite genomic discoveries and evidence-based practice guidelines that impact healthcare quality and safety, 20 years after the Genomic Competencies were established (2005) nursing genomic practice capacity remains low.
Genomics is critical to the safe, quality nursing practice regardless of the level of academic training, clinical role, or specialty.
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