Embedding Assessment Literacy Can Enhance Graduate Attribute Development in a Biomedical Sciences Curriculum.

This paper describes the successful implementation of an assessment literacy strategy within a Biomedical Sciences degree. Teaching was aligned with an assessment literacy framework and aimed to prepare undergraduates for a literature comprehension assessment. Students were introduced to the assessment purpose and an adapted Miller's pyramid model illustrated how the assessment contributed to competency development during their degree. Students read primary research papers and answered questions relating to the publications. They were then introduced to the processes of assessment and collaboratively graded answers of different standards. Finally, student and faculty grades were compared, differences considered, and key characteristics of answers discussed. Most students reported that they understood more about assessment standards than prior to the intervention [139/159 (87.4%)] and felt it had helped prepare them for their exam [138/159 (86.8%)]. The majority also reported they had increased confidence in evaluating data [118/159 (74%)], communicating their reasoning [113/159 (71%)] and considering what a reader needs to know [127/159 (79.9%)]. Students were asked to state the most important thing they had learned from the assessment literacy teaching. Notably, no responses referred to domain-specific knowledge. 129 free text responses were mapped to the University of Edinburgh graduate attribute framework. 93 (72%) statements mapped to the graduate attribute category "Research and Enquiry," 66 (51.16%) mapped to "Communication" and 21 (16.27%) mapped to "Personal and Intellectual Autonomy." To explore any longer-term impact of the assessment literacy teaching, a focus group was held with students from the same cohort, 2 years after the original intervention. Themes from this part of the study included that teaching had provided insights into standards and expectations for the assessment and the benefits of domain specific knowledge. A variety of aspects related to graduate attributes were also identified. Here, assessment literacy as a vehicle for graduate attribute development was an unexpected outcome. We propose that by explicitly engaging students with purpose, process, standards, and expectations, assessment literacy strategies may be used to successfully raise awareness of developmental progression, and enhance skills, aptitudes, and dispositions beneficial to Biomedical Sciences academic achievement and life after university.

Compact and cost-effective laser-powered speckle contrast optical spectroscopy fiber-free device for measuring cerebral blood flow.

Significance: In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF noninvasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. Aim: Our study aims to introduce a compact speckle contrast optical spectroscopy device for noninvasive CBF measurements at long source-to-detector distances, offering cost-effectiveness, and scalability while tracking blood flow (BF) with remarkable sensitivity and temporal resolution. Approach: The wearable sensor module consists solely of a laser diode and a board camera. It can be easily placed on a subject's head to measure BF at a sampling rate of 80 Hz. Results: Compared to the single-fiber-based version, the proposed device achieved a signal gain of about 70 times, showed superior stability, reproducibility, and signal-to-noise ratio for measuring BF at long source-to-detector distances. The device can be distributed in multiple configurations around the head. Conclusions: Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing noninvasive cerebral monitoring technologies.

Clinical Outcome Assessments and Digital Health Technologies Supporting Clinical Trial Endpoints in Early Parkinson's Disease: Roundtable Proceedings and Roadmap for Research.

This article summarizes the Parkinson's Disease (PD) Endpoints Roundtable, which was held in Washington, D.C., on November 2-3, 2022, and hosted by The Michael J. Fox Foundation for Parkinson's Research, Parkinson's UK, and Parkinson Canada. This event brought representatives from academia and industry together with those from regulatory agencies, community partners, and research funders to discuss challenges in clinical outcome assessment development for treatments in early PD and to identify priorities for the field and opportunities for collaboration. This article provides a summary of the presentations given and topics discussed at the roundtable and synthesizes the discussions about the development of clinical outcome assessments and the use of digital health technologies for developing clinical trial endpoints.

Skin-interfacing wearable biosensors for smart health monitoring of infants and neonates.

Health monitoring of infant patients in intensive care can be especially strenuous for both the patient and their caregiver, as testing setups involve a tangle of electrodes, probes, and catheters that keep the patient bedridden. This has typically involved expensive and imposing machines, to track physiological metrics such as heart rate, respiration rate, temperature, blood oxygen saturation, blood pressure, and ion concentrations. However, in the past couple of decades, research advancements have propelled a world of soft, wearable, and non-invasive systems to supersede current practices. This paper summarizes the latest advancements in neonatal wearable systems and the different approaches to each branch of physiological monitoring, with an emphasis on smart skin-interfaced wearables. Weaknesses and shortfalls are also addressed, with some guidelines provided to help drive the further research needed.

The BJA Editorial Fellowship 2024: a barometer for the state of academic anaesthesiology, perioperative, pain, and critical care medicine.

Throughout its 100-yr history, a key ambition of the British Journal of Anaesthesia has been to foster our academic community by addressing the needs of individuals in the early stages of their independent clinical and research careers. Longitudinal mentoring and peer networking are critical for establishing a community of like-minded peers and mentor-advisors required to navigate the challenges of academic medicine. In 2019, the Journal launched an Editorial Fellowship scheme, aimed at comprehensively demystifying the process of peer review, editing, and publishing through guided mentorship and experiential learning.

Assessment of the Quality of Patient-Oriented Internet Information on Fluorescence Imaging in Surgery.

BACKGROUND: In the digital age, patients are increasingly turning to the Internet to seek medical information to aid in their decision-making process before undergoing medical treatments. Fluorescence imaging is an emerging technological tool that holds promise in enhancing intra-operative decision-making during surgical procedures. This study aims to evaluate the quality of patient information available online regarding fluorescence imaging in surgery and assesses whether it adequately supports informed decision-making. METHOD: The term "patient information on fluorescence imaging in surgery" was searched on Google. The websites that fulfilled the inclusion criteria were assessed using 2 scoring instruments. DISCERN was used to evaluate the reliability of consumer health information. QUEST was used to assess authorship, tone, conflict of interest and complementarity. RESULTS: Out of the 50 websites identified from the initial search, 10 fulfilled the inclusion criteria. Only two of these websites were updated in the last two years. The definition of fluorescence imaging was stated in only 50% of the websites. Although all websites mentioned the benefits of fluorescence imaging, none mentioned potential risks. Assessment by DISCERN showed that 30% of the websites were rated low and 70% were rated moderate. With QUEST, the websites demonstrated an average score of 62.5%. CONCLUSION: This study highlights the importance of providing patients with accurate and balanced information about medical technologies and procedures they may undergo. Fluorescence imaging in surgery is a promising technology that can potentially improve surgical outcomes. However, patients need to be well-informed about its benefits and limitations in order to make informed decisions about their healthcare.

Enhancing equity in access to automated insulin delivery systems in an ethnically and socioeconomically diverse group of children with type 1 diabetes.

INTRODUCTION: Manufacturer-supported didactic teaching programmes offer effective automated insulin delivery (AID) systems onboarding in children and young people (CYP) with type 1 diabetes (T1D). However, this approach has limited flexibility to accommodate the needs of families requiring additional support. RESEARCH DESIGN AND METHODS: Evaluate the efficacy of an inperson manufacturer-supported didactic teaching programme (Group A), in comparison to a flexible flipped learning approach delivered virtually or inperson (Group B). Retrospective analysis of CYP with T1D using continuous glucose monitoring (CGM), who were initiated on AID systems between 2021 and 2023. Compare CGM metrics from baseline to 90 days for both groups A and B. Additionally, compare the two groups for change in CGM metrics over the 90-day period (∆), patient demographics and onboarding time. RESULTS: Group A consisted of 74 CYP (53% male) with median age of 13.9 years and Group B 91 CYP (54% male) with median age of 12.7 years. From baseline to 90 days, Group A lowered mean (±SD) time above range (TAR, >10.0 mmol/L) from 47.6% (±15.0) to 33.2% (±15.0) (p<0.001), increased time in range (TIR, 3.9-10.0 mmol/L) from 50.4% (±14.0) to 64.7% (±10.2) (p<0.001). From baseline to 90 days, Group B lowered TAR from 51.3% (±15.1) to 34.5% (±11.3) (p<0.001) and increased TIR from 46.5% (±14.5) to 63.7% (±11.0) (p<0.001). There was no difference from baseline to 90 days for time below range (TBR, <3.9 mmol/L) for Group A and Group B. ∆ TAR, TIR and TBR for both groups were comparable. Group B consisted of CYP with higher socioeconomic deprivation, greater ethnic diversity and lower carer education achievement (p<0.05). The majority of Group B (n=79, 87%) chose virtual flipped learning, halving diabetes educator time and increasing onboarding cadence by fivefold. CONCLUSIONS: A flexible virtual flipped learning programme increases onboarding cadence and capacity to offer equitable AID system onboarding.

The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies.

The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as self-driving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years.

Datawiz-IN: Summer Research Experience for Health Data Science Training.

Background: Good science necessitates diverse perspectives to guide its progress. This study introduces Datawiz-IN, an educational initiative that fosters diversity and inclusion in AI skills training and research. Supported by a National Institutes of Health R25 grant from the National Library of Medicine, Datawiz-IN provided a comprehensive data science and machine learning research experience to students from underrepresented minority groups in medicine and computing. Methods: The program evaluation triangulated quantitative and qualitative data to measure representation, innovation, and experience. Diversity gains were quantified using demographic data analysis. Computational projects were systematically reviewed for research productivity. A mixed-methods survey gauged participant perspectives on skills gained, support quality, challenges faced, and overall sentiments. Results: The first cohort of 14 students in Summer 2023 demonstrated quantifiable increases in representation, with greater participation of women and minorities, evidencing the efficacy of proactive efforts to engage talent typically excluded from these fields. The student interns conducted innovative projects that elucidated disease mechanisms, enhanced clinical decision support systems, and analyzed health disparities. Conclusion: By illustrating how purposeful inclusion catalyzes innovation, Datawiz-IN offers a model for developing AI systems and research that reflect true diversity. Realizing the full societal benefits of AI requires sustaining pathways for historically excluded voices to help shape the field.

Electronic Decision-Making Tool for Smoking Cessation (Pare de Fumar Conosco) Versus Standard of Care: A Cost-Effectiveness Analysis.

OBJECTIVES: The study aimed to evaluate the cost-effectiveness of the Pare de Fumar Conosco software compared with the standard of care adopted in Brazil for the treatment of smoking cessation. METHODS: In the cohort of smokers with multiple chronic conditions, we developed an decision tree model for the benefit measures of smoking cessation. We adopted the perspectives of the Brazilian Unified Health System and the service provider. Resources and costs were measured by primary and secondary sources and effectiveness by a randomized clinical trial. The incremental cost-effectiveness ratio (ICER) was calculated, followed by deterministic and probabilistic sensitivity analyses and deterministic and probabilistic sensitivity analyses. No willingness to pay threshold was adopted. RESULTS: The software had a lower cost and greater effectiveness than its comparator. The ICER was dominant in all of the benefits examined (-R$2 585 178.29 to -R$325 001.20). The cost of the standard of care followed by that of the electronic tool affected the ICER of the benefit measures. In all probabilistic analyses, the software was superior to the standard of care (53.6%-82.5%). CONCLUSION: The Pare de Fumar Conosco software is a technology that results in cost savings in treating smoking cessation.

Design of Bio-Optical Transceiver for In Vivo Biomedical Sensor Applications.

This paper presents an enhanced version of our previously developed bio-optical transceiver, presenting a significant advancement in nanosensor technology. Using self-assembled polymers, this nanodevice is capable of electron detection while maintaining biocompatibility, an essential feature for in vivo medical biosensors. This enhancement finds significance in the field of infectious disease control, particularly in the early detection of respiratory viruses, including high-threat pathogens such as SARS-CoV-2. The proposed system harnesses bioluminescence by converting electric signaling to visible blue light, effectively opening the path of linking nano-sized mechanisms to larger-scale systems, thereby pushing the boundaries of in vivo biomedical sensing. The performance evaluation of our technology is analytical and is based on the use of Markov chains, through which we assess the bit error probability. The calculated improvements indicate that this technology qualifies as a forerunner in terms of supporting the communication needs of smaller, safer, and more efficient manufactured sensor technologies for in vivo medical applications.

Retrospective study and implementation of a low-cost LAMP-turbidimetric assay for screening α0-thalassemia (SEA deletion): preventing and controlling Hb Bart's hydrops fetalis syndrome in Thailand.

Homozygous α0-thalassemia (SEA deletion) or Hb Bart's hydrops fetalis syndrome is a significant public health issue in Thailand and Southeast Asia. A prevention and control program has been implemented in this region. This study focuses on retrospective laboratory data collected between January 2021 and April 2023 at a single center. Additionally, we developed a low-cost LAMP-turbidimetric assay to propose in the screening strategy. A total of 3,623 samples underwent screening tests (MCV, MCH, and DCIP), including 1,658 couple screenings (84.25%) and 310 single pregnant screenings (15.75%). Negative screenings, which did not require further investigation, were found in 75.51% for couple screenings and 46.58% for single pregnant screenings. At hemoglobin (Hb) analysis identified 129 couples which had fetuses at risk of severe thalassemia, whereas molecular analysis during the retrospective period revealed 210 samples with different genotypes. These remaining samples were validated using the low-cost LAMP-turbidimetric assay to detect α0-thalassemia (SEA deletion). The developed LAMP turbidimetric assay demonstrated a sensitivity and specificity of 100% (36/36 × 100) and 97.7% (170/174 × 100), respectively, when compared with gap-PCR. Furthermore, we propose a strategy involving the addition of the low-cost LAMP-turbidimetric assay before performing the gold standard. This strategy represents a cost-saving of USD 2,608 based on 210 samples that required DNA analysis. Finally, the developed LAMP turbidimetric assays offer advantages such as reduced time, workload, cost savings, no need for highly developed instruments, and a straightforward interpreting process. Therefore, implementation of LAMP assays into routine settings would be improve the efficiency of prevention and control program for severe thalassemia disease in this region.

Assessment of hemostatic ability of biomaterial based on chitosan andEclipta prostrataL. extract.

The biomaterials based on chitosan andEclipta prostrataL. extract have been prepared by microemulsion method and solution method (with and without sodium tripolyphosphate (STPP) as a cross-linking agent). The main component inEclipta prostrataL. extract is flavonoid groups. The structure of the chitosan/extract biomaterials was studied by infrared spectroscopy. The chitosan/extract biomaterial using STPP cross-linker appeared an absorption band at 1152 cm-1attributed to the vibrations of C-O-P bonds, which proved that chitosan has crosslinked with STPP. The morphology of the biomaterials was investigated by the dynamic light scattering technique and field emission scanning electron microscopy. The obtained results showed that the particle size of the chitosan/extract biomaterials prepared by microemulsion method and solution method with STPP ranged from 68.06 nm to 1484 nm, with an average particle size of 304.9-1019 nm. The microemulsion method produced biomaterials with much smaller average particle size than the solution method using cross-linkers. The hemostatic ability of the biomaterials was better than that of the control sample based on the time of blood clotting formation and glomerular aggregation ability. The sample with the ratio ofE. prostrataL. extract: chitosan of 1:30 had the lowest hemostasis time (6 min 46 s) and its glomerular aggregation rate after 5 min was 13.05%. This indicated that the biomaterials based on chitosan andE. prostrataL. extract are promising for application in biomedicine as hemostatic materials.

A QALY is [still] a QALY is [still] a QALY? : Evaluating proportional shortfall as the answer to the problem of equity in healthcare allocations.

Despite clinical evidence of drug superiority, therapeutic modalities, like combination immunotherapy, are mostly considered cost-ineffective due to their high costs per life year(s) gained. This paper, taking an ethical stand, reevaluates the standard cost-effectiveness analysis with that of the more recent justice-enhanced methods and concludes by pointing out the shortcomings of the current methodologies.

ACORN SDOH survey: Terminological representation for use with NLP and CDS.

Objective: Social Determinants of Health (SDOH) greatly influence health outcomes. SDOH surveys, such as the Assessing Circumstances & Offering Resources for Needs (ACORN) survey, have been developed to screen for SDOH in Veterans. The purpose of this study is to determine the terminological representation of the ACORN survey, to aid in natural language processing (NLP). Methods: Each ACORN survey question was read to determine its concepts. Next, Solor was searched for each of the concepts and for the appropriate attributes. If no attributes or concepts existed, they were proposed. Then, each question's concepts and attributes were arranged into subject-relation-object triples. Results: Eleven unique attributes and 18 unique concepts were proposed. These results demonstrate a gap in representing SDOH with terminologies. We believe that using these new concepts and relations will improve NLP, and thus, the care provided to Veterans.

Diversity supplements: An underutilized opportunity to improve the diversity of the health sciences research workforce.

Purpose: This paper describes the process developed at the University of Pittsburgh to increase the number of NIH-funded Diversity Supplements. Method: The authors formed a Diversity in Academia Workgroup where we created the infrastructure and process to increase the number of Diversity Supplements. Each year, the Office of Sponsored Programs provided a list of grants that would be eligible to submit a Diversity Supplement. We surveyed the Principal Investigators inquiring about their interest in working with a trainee on a Diversity Supplement. If yes, we included their information in a database we built so that trainees could search for eligible research studies. The Diversity Deans then identified underrepresented faculty and postdoctoral researchers. We invited Program Officers from NIH to participate in a panel presentation for trainees, which was well attended. Results: The number of Diversity Supplements awarded to Pitt researchers has significantly increased from 7 in 2020 to 10 in 2021 and to 15 in 2022. Six more have been awarded in the first half of 2023. Conclusions: We created the Diversity in Academia Workgroup with the goal to increase the number of Diversity Supplements at the University of Pittsburgh and in so doing, increase the diversity in the biomedical research workforce. While challenging, we know the critical importance and benefits of increased diversity at the University, and we have made significant strides toward this goal.

Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors.

BACKGROUND: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data. METHODS: Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal-Wallis test and Dunn post hoc analysis. RESULTS: Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p < 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00-01:00), and in step count, more at measurement days 6 and 7 (p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. CONCLUSIONS: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.

Quantification and assessment of hazardous mercury emission from industrial process and other unattended sectors in India: A step towards mitigation.

Hazardous pollutants like Mercury (Hg) have emerged as a pressing challenge in recent times where the expanding industrial sector is regarded as the major source in developing country India. In this study, we are trying to identify all possible industrial sectors at district level to quantify Hg emission load across India for the year 2019 using IPCC methodology where the country-specific technological emission factors are used. We have included 5 major sectors out of which emission from coal combustion in thermal power plants accounts for 186.5 t/yr of Hg emission followed by non-ferrous metal production (88.3 t/yr), captive power plants (65.5 t/yr) and fly ash generation from various manufacturing industries (45.9 t/yr). A total of 459.4 t/yr of Hg is released into the ecosystem in 2019 with an uncertainty of ± 48%. This study also estimated that about 233 million people living in and around 10 km periphery of major industrial zones with as many as 17 million people residing near the 10 major hotspots are susceptible to hazardous Hg emissions directly or indirectly. This information would be quite useful in formulating future Hg emission control strategies in India. ENVIRONMENTAL IMPLICATIONS: Present study is the first-of-its-kind quantification of Hg emission load from the Industrial process and many unattended sectors over India, which will not only give an insight into potential hotspots regions across the country but also assess the population exposed to it. It will provide aid in tracking the mercury burden to match the international conventions. The findings suggest that about 233 million people are likely to be exposed to hazardous Hg emissions. It will also enlighten the government, policymakers, stakeholders and people about their mercury footprint and envision protecting the biomes and formulating future control strategies in India.

Predicting the onset of end-stage knee osteoarthritis over two- and five-years using machine learning.

OBJECTIVE: Identifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous factors influencing TKR decisions. Acknowledging these limitations, our study identifies a critical need for a more robust metric to assess severe KOA. The end-stage KOA (esKOA) measure, which combines symptomatic and radiographic criteria, serves as a solid indicator. To enhance future trials that use esKOA as an endpoint, our study focuses on developing and validating a machine-learning tool to identify individuals likely to develop esKOA within 2 to 5 years. DESIGN: Utilizing the Osteoarthritis Initiative (OAI) data, we trained models on 3,114 participants and validated them with 606 participants for the right knee, and similarly for the left knee, with external validation from the Multicentre Osteoarthritis Study (MOST) involving 1,602 participants. We aimed to predict esKOA onset at 2-to-2.5 years and 4-to-5 years, defining esKOA by severe radiographic KOA with moderate/severe symptoms or mild/moderate radiographic KOA with persistent/intense symptoms. Our analysis considered 51 candidate predictors, including demographics, clinical history, physical examination, and X-ray evaluations. An online tool predicting esKOA progression, based on models with ten and nine predictors for the right and left knees, respectively, was developed. RESULTS: External validation (MOST) for the right knee at 2.5 years yielded an Area Under Curve (AUC) of 0.847 (95 % CI 0.811 to 0.882), and at 5 years, 0.853 (95 % CI 0.823 to 0.881); for the left knee at 2.5 years, AUC was 0.824 (95 % CI 0.782 to 0.857), and at 5 years, 0.807 (95 % CI 0.768 to 0.843). Models with fewer predictors demonstrated comparable performance. The online tool is available at: https://eskoa.shinyapps.io/webapp/. CONCLUSION: Our study unveils a robust, externally validated machine learning tool proficient in predicting the onset of esKOA over the next 2 to 5 years. Our tool can lead to more efficient KOA trials.