SUMMARY ANSWER: High-throughput flagellar waveform tracking and analysis enable measurement of experimentally intractable quantities such as energy dissipation, disturbance of the surrounding medium and viscous stresses, which are not possible by tracking the sperm head alone.
WHAT IS KNOWN ALREADY: The clinical gold standard for sperm motility analysis comprises a manual analysis by a trained professional, with existing automated sperm diagnostics [computer-aided sperm analysis (CASA)] relying on tracking the sperm head and extrapolating measures. It is not currently possible with either of these approaches to track the sperm flagellar waveform for large numbers of cells in order to unlock the potential wealth of information enclosed within.
STUDY DESIGN, SIZE, DURATION: The software tool in this manuscript has been developed to enable high-throughput, repeatable, accurate and verifiable analysis of the sperm flagellar beat.
PARTICIPANTS/MATERIALS, SETTING, METHODS: Using the software tool [Flagellar Analysis and Sperm Tracking (FAST)] described in this manuscript, we have analysed 176 experimental microscopy videos and have tracked the head and flagellum of 205 progressive cells in diluted semen (DSM), 119 progressive cells in a high-viscosity medium (HVM) and 42 stuck cells in a low-viscosity medium. Unscreened donors were recruited at Birmingham Women's and Children's NHS Foundation Trust after giving informed consent.
MAIN RESULTS AND THE ROLE OF CHANCE: We describe fully automated tracking and analysis of flagellar movement for large cell numbers. The analysis is demonstrated on freely motile cells in low- and high-viscosity fluids and validated on published data of tethered cells undergoing pharmacological hyperactivation. Direct analysis of the flagellar beat reveals that the CASA measure 'beat cross frequency' does not measure beat frequency; attempting to fit a straight line between the two measures gives ${\mathrm{R}}^2$ values of 0.042 and 0.00054 for cells in DSM and HVM, respectively. A new measurement, track centroid speed, is validated as an accurate differentiator of progressive motility. Coupled with fluid mechanics codes, waveform data enable extraction of experimentally intractable quantities such as energy dissipation, disturbance of the surrounding medium and viscous stresses. We provide a powerful and accessible research tool, enabling connection of the mechanical activity of the sperm to its motility and effect on its environment.
LARGE SCALE DATA: The FAST software package and all documentation can be downloaded from www.flagellarCapture.com.
LIMITATIONS, REASONS FOR CAUTION: The FAST software package has only been tested for use with negative phase contrast microscopy. Other imaging modalities, with bright cells on a dark background, have not been tested but may work. FAST is not designed to analyse raw semen; it is specifically for precise analysis of flagellar kinematics, as that is the promising area for computer use. Flagellar capture will always require that cells are at a dilution where their paths do not frequently cross.
WIDER IMPLICATIONS OF THE FINDINGS: Combining tracked flagella with mathematical modelling has the potential to reveal new mechanistic insight. By providing the capability as a free-to-use software package, we hope that this ability to accurately quantify the flagellar waveform in large populations of motile cells will enable an abundant array of diagnostic, toxicological and therapeutic possibilities, as well as creating new opportunities for assessing and treating male subfertility.
STUDY FUNDING/COMPETING INTEREST(S): M.T.G., G.C., J.C.K-B. and D.J.S. gratefully acknowledge funding from the Engineering and Physical Sciences Research Council, Healthcare Technologies Challenge Award (Rapid Sperm Capture EP/N021096/1). J.C.K-B. is funded by a National Institute of Health Research (NIHR) and Health Education England, Senior Clinical Lectureship Grant: The role of the human sperm in healthy live birth (NIHRDH-HCS SCL-2014-05-001). This article presents independent research funded in part by the NIHR and Health Education England. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The data for experimental set (2) were funded through a Wellcome Trust-University of Birmingham Value in People Fellowship Bridging Award (E.H.O.).The authors declare no competing interests.
RESEARCH DESIGN AND METHODS: This observational study included all episodes of DKA from April 2014 to September 2020 in a UK tertiary care hospital. Data were collected on diabetes type, demographics, biochemical and clinical features at presentation, and DKA management.
RESULTS: From 786 consecutive DKA, 583 (75.9%) type 1 diabetes and 185 (24.1%) type 2 diabetes episodes were included in the final analysis. Those with type 2 diabetes were older and had more ethnic minority representation than those with type 1 diabetes. Intercurrent illness (39.8%) and suboptimal compliance (26.8%) were the two most common precipitating causes of DKA in both cohorts. Severity of DKA as assessed by pH, glucose and lactate at presentation was similar in both groups. Total insulin requirements and total DKA duration were the same (type 1 diabetes 13.9 units (9.1-21.9); type 2 diabetes 13.9 units (7.7-21.1); p=0.4638). However, people with type 2 diabetes had significantly longer hospital stay (type 1 diabetes: 3.0 days (1.7-6.1); type 2 diabetes: 11.0 days (5.0-23.1); p<0.0001).
CONCLUSIONS: In this population, a quarter of DKA episodes occurred in people with type 2 diabetes. DKA in type 2 diabetes presents at an older age and with greater representation from ethnic minorities. However, severity of presentation and DKA duration are similar in both type 1 and type 2 diabetes, suggesting that the same clinical management protocol is equally effective. People with type 2 diabetes have longer hospital admission.
METHODS: Two SIMBA sessions-thyroid and pituitary-were conducted in July-August 2020. Each session included simulation of various real-life cases and interactive discussion. Participants' self-reported confidence, acceptance, and knowledge were measured using surveys and multiple-choice questions pre- and post-simulation and in a 6- to 12-week follow-up period. The evaluation surveys were designed using Moore's 7 Levels of CME Outcomes Framework.
RESULTS: A total of 116 participants were included in the analysis. Significant improvement was observed in participants' self-reported confidence in approach to simulated cases (thyroid, n = 37, P
METHODS: Five SIMBA sessions were conducted between May and August 2020. Each session included simulation of scenarios and interactive discussion. Participants' self-reported confidence, acceptance, and relevance of the simulated cases were measured.
RESULTS: Significant improvement was observed in participants' self-reported confidence (overall n = 204, p<0.001; adrenal n = 33, p<0.001; thyroid n = 37, p<0.001; pituitary n = 79, p<0.001; inflammatory bowel disease n = 17, p<0.001; acute medicine n = 38, p<0.001). Participants reported improvements in clinical competencies: patient care 52.0% (n = 106/204), professionalism 30.9% (n = 63/204), knowledge on patient management 84.8% (n = 173/204), systems-based practice 48.0% (n = 98/204), practice-based learning 69.6% (n = 142/204) and communication skills 25.5% (n = 52/204).
CONCLUSION: SIMBA is a novel pedagogical virtual simulation-based learning model that improves clinicians' confidence in managing conditions across various specialties.
METHODS: This study was done as part of the continuous professional development for Health Education England West Midlands speciality trainees in diabetes and Endocrinology. Standardized transcripts of anonymized real-life endocrinology (endocrine session) and diabetes cases (diabetes session) were used in the simulation model. Trainees interacted with moderators through WhatsApp® in this model. All cases were then discussed in detail by a consultant endocrinologist with reference to local, national and international guidelines. Trainee acceptance rate and improvement in their self-reported confidence levels post-simulation were assessed.
RESULTS: 70.8% (n = 17/24) and 75% (n = 18/24) strongly agreed the simulation session accommodated their personal learning style and the session was engaging. 66.7% (n = 16/24) strongly felt that the simulation was worth their time. In the endocrine session, there was a significant improvement in trainees' confidence in the management of craniopharyngioma (p = 0.0179) and acromegaly (p = 0.0025). There was a trend towards improved confidence levels to manage Cushing's disease and macroprolactinoma. In diabetes session, there was a significant improvement in trainees' confidence to interpret continuous glucose monitor readings (p = 0.01). There was a trend towards improvement for managing monogenic diabetes, hypoglycaemic unawareness and interpreting Libre readings. Overall, there was a significant improvement in trainees' confidence in managing cases that were discussed post-simulation.
CONCLUSION: SIMBA is an effective learning model to improve trainees' confidence to manage various diabetes and endocrine case scenarios. More sessions with a variety of other speciality case scenarios are needed to further assess SIMBA's effectiveness and application in other areas of medical training.