Assessing the Efficacy of Active Learning to Support Student Performance Across Undergraduate Programmes in Biomedical Science

British Journal of Biomedical Science · 2024-03-04 · 11 citations

Introduction: Active learning is a useful tool to enhance student engagement and support learning in diverse educational situations. We aimed to assess the efficacy of an active learning approach within a large interprofessional first year Medical Cell Biology module taken by six healthcare programmes across the School of Biomedical Sciences at Ulster University, United Kingdom. Materials and methods: An active learning approach was developed for weekly formative assessment using Smartwork to design a weekly interactive multiple-choice quiz to reinforce key concepts specifically for each lecture. We tracked and assessed student performance in the module overall and in each element of course work and exam for 2 years prior to and following the introduction of an active learning strategy to engage and support learning for students from all academic backgrounds and abilities. Results: Full engagement with active learning was significantly associated with an increased overall module performance as well as a significantly increased performance in each element of class test (No engagement vs. Full engagement, p < 0.001), exam (No Engagement vs. Full engagement, p < 0.05) and coursework (No engagement vs. Full engagement, p < 0.001) within this overall total (No Engagement vs. Full engagement, p < 0.01). Partial engagement with active learning was associated significantly improved class test (No engagement vs. partially engaged, p < 0.001) and coursework (No engagement vs. partially engaged, p < 0.05) performance. While a trend toward increased performance in exam and overall module mark was observed, these were not significant. Discussion: Active learning is a useful tool to support student learning across a range of healthcare programmes taken by students with differing backgrounds and academic abilities in an interprofessional and widening participation setting. Student engagement in active learning was highlighted as a key contributory factor to enhanced student performance in all aspects of assessment.

Fluid Optimisation in Emergency Laparotomy (FLO-ELA) Trial: study protocol for a multi-centre randomised trial of cardiac output-guided fluid therapy compared to usual care in patients undergoing major emergency gastrointestinal surgery

Trials · 2023 · 10 citations

• Medicine
• Emergency medicine
• Intensive care medicine

INTRODUCTION: Postoperative morbidity and mortality in patients undergoing major emergency gastrointestinal surgery are a major burden on healthcare systems. Optimal management of perioperative intravenous fluids may reduce mortality rates and improve outcomes from surgery. Previous small trials of cardiac-output guided haemodynamic therapy algorithms in patients undergoing gastrointestinal surgery have suggested this intervention results in reduced complications and a modest reduction in mortality. However, this existing evidence is based mainly on elective (planned) surgery, with little evaluation in the emergency setting. There are fundamental clinical and pathophysiological differences between the planned and emergency surgical setting which may influence the effects of this intervention. A large definitive trial in emergency surgery is needed to confirm or refute the potential benefits observed in elective surgery and to inform widespread clinical practice. METHODS: The FLO-ELA trial is a multi-centre, parallel-group, open, randomised controlled trial. 3138 patients aged 50 and over undergoing major emergency gastrointestinal surgery will be randomly allocated in a 1:1 ratio using minimisation to minimally invasive cardiac output monitoring to guide protocolised administration of intra-venous fluid, or usual care without cardiac output monitoring. The trial intervention will be carried out during surgery and for up to 6 h postoperatively. The trial is funded through an efficient design call by the National Institute for Health and Care Research Health Technology Assessment (NIHR HTA) programme and uses existing routinely collected datasets for the majority of data collection. The primary outcome is the number of days alive and out of hospital within 90 days of randomisation. Participants and those delivering the intervention will not be blinded to treatment allocation. Participant recruitment started in September 2017 with a 1-year internal pilot phase and is ongoing at the time of publication. DISCUSSION: This will be the largest contemporary randomised trial examining the effectiveness of perioperative cardiac output-guided haemodynamic therapy in patients undergoing major emergency gastrointestinal surgery. The multi-centre design and broad inclusion criteria support the external validity of the trial. Although the clinical teams delivering the trial interventions will not be blinded, significant trial outcome measures are objective and not subject to detection bias. TRIAL REGISTRATION: ISRCTN 14729158. Registered on 02 May 2017.

Developing a Climate Change Risk Perception Model in the Philippines and Fiji: Posttraumatic Growth Plays Central Role

International Journal of Environmental Research and Public Health · 2023-01-13 · 4 citations

BACKGROUND: This two-study paper developed a climate change risk perception model that considers the role of posttraumatic growth (i.e., a reappraisal of life priorities and deeper appreciation of life), resource loss, posttraumatic stress, coping, and social support. METHOD: In Study 1, participants were 332 persons in the Philippines who experienced Super Typhoon Haiyan. In Study 2, participants were 709 persons in Fiji who experienced Cyclone Winston. Climate change can increase the size and destructive potential of cyclones and typhoons as a result of warming ocean temperatures, which provides fuel for these storms. Participants completed measures assessing resource loss, posttraumatic stress, coping, social support, posttraumatic growth, and climate change risk perception. RESULTS: Structural equation modeling was used to develop a climate change risk perception model with data collected in the Philippines and to confirm the model with data collected in Fiji. The model showed that climate change risk perception was influenced by resource loss, posttraumatic stress, coping activation, and posttraumatic growth. The model developed in the Philippines was confirmed with data collected in Fiji. CONCLUSIONS: Posttraumatic growth played a central role in climate change risk perception. Public health educational efforts should focus on vividly showing how climate change threatens life priorities and that which gives life meaning and can result in loss, stress, and hardship. Disaster response organizations may also use this approach to promote preparedness for disaster threats.

Corn and Currency; in An Address to the Land Owners

2021-03-24

James Graham was the son and heir of Sir James Graham, a Cumbrian baronet, and Lady Catherine Stewart, daughter of John, 7th Earl of Galloway. In 1821 Graham began to manage the family estate in Netherby which consisted of 26,000 acres including 19,000 acres of arable land. Graham’s biographer, J.T. Ward cites John Christian Curwen (1756-1828), whose estate became the center of Cumbrian agricultural improvements, and Lord Brougham (1778-1868) as probable influences on Graham’s views on the corn law. Every landlord who grants a lease, delivers over his valuable property for a term of years, relying on the confident assurance, which the laws confirm, that he shall be permitted to receive the stipulated equivalent in value throughout the duration of the term. Any alteration in the value of the currency destroys the equity of the agreement; if it be an act of the Legislature, the injustice is accomplished by an ex post facto law.

Climate change threatens nomadic herding in Mongolia: A model of climate change risk perception and behavioral adaptation

Journal of Environmental Psychology · 2021-04-30 · 27 citations

Temperate-zone cultivation of <i>Oedogonium</i> in municipal wastewater effluent to produce cellulose and oxygen

Journal of Industrial Microbiology & Biotechnology · 2020-01-24 · 11 citations

Abstract Cultivation of the filamentous chlorophyte Oedogonium in municipal wastewater effluent is known to improve water quality and yield lipid- and protein-rich biomass for industrial applications. Chlorophyte celluloses, whose molecular organization and physical traits differ from those of plants, represent yet another valuable extractive, and algal oxygen production is of economic value in wastewater treatment. Consequently, we explored cellulose and oxygen production from Oedogonium biomass batch-cultivated in treated secondary municipal wastewater effluent. We compared biomass, cellulose, and oxygen production outside and within an adjacent greenhouse, under differing dissolved CO2 and pH conditions, and during temperate-zone seasonal change from summer through fall. Overall production did not differ within or outside the greenhouse, but outside production was higher in summer and lower in fall as air temperatures declined. Batch cultivation offered advantages, but high levels of mixing and CO2 were essential to maintain neutral pH for optimal algal growth and oxygen production.

Prediction model and risk scores of ICU admission and mortality in COVID-19

PLoS ONE · 2020 · 323 citations

• Medicine
• Emergency medicine
• Intensive care medicine

This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.

Climate Change in Tonga: Risk Perception and Behavioral Adaptation

Climate change management · 2020-01-01 · 8 citations

Peer Review #2 of "Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables (v0.2)"

2020-11-06

Background: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients.Methods: This retrospective study consisted of 5766 persons-under-investigation for COVID-19 between February 7, 2020, and May 4, 2020.Demographics, chronic comorbidities, vital signs, symptoms, and laboratory tests at admission were collected.A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality.Prediction performance used the receiver operating characteristic area under the curve (AUC).Results: The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin, and oxygen saturation.The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein, and oxygen saturation.Age and troponin were unique top predictors for mortality but not ICU admission.The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 [95% CI:0.760-0.785]and 0.844 [95% CI:0.839-0.848],respectively.The corresponding risk scores yielded an AUC of 0.728 [95% CI:0.726-0.729]and 0.848 [95% CI:0.847-0.849],respectively.Conclusions: Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.

STAT3 and GR Cooperate to Drive Gene Expression and Growth of Basal-Like Triple-Negative Breast Cancer

Cancer Research · 2020-08-19 · 30 citations

Breast cancers are divided into subtypes with different prognoses and treatment responses based on global differences in gene expression. Luminal breast cancer gene expression and proliferation are driven by estrogen receptor alpha, and targeting this transcription factor is the most effective therapy for this subtype. By contrast, it remains unclear which transcription factors drive the gene expression signature that defines basal-like triple-negative breast cancer, and there are no targeted therapies approved to treat this aggressive subtype. In this study, we utilized integrated genomic analysis of DNA methylation, chromatin accessibility, transcription factor binding, and gene expression in large collections of breast cancer cell lines and patient tumors to identify transcription factors responsible for the basal-like gene expression program. Glucocorticoid receptor (GR) and STAT3 bind to the same genomic regulatory regions, which were specifically open and unmethylated in basal-like breast cancer. These transcription factors cooperated to regulate expression of hundreds of genes in the basal-like gene expression signature, which were associated with poor prognosis. Combination treatment with small-molecule inhibitors of both transcription factors resulted in synergistic decreases in cell growth in cell lines and patient-derived organoid models. This study demonstrates that GR and STAT3 cooperate to regulate the basal-like breast cancer gene expression program and provides the basis for improved therapy for basal-like triple-negative breast cancer through rational combination of STAT3 and GR inhibitors. SIGNIFICANCE: This study demonstrates that GR and STAT3 cooperate to activate the canonical gene expression signature of basal-like triple-negative breast cancer and that combination treatment with STAT3 and GR inhibitors could provide synergistic therapeutic efficacy.