A clinically relevant sheep model of orthotopic heart transplantation 24 h after donor brainstem death

BACKGROUND: Heart transplantation (HTx) from brainstem dead (BSD) donors is the gold-standard therapy for severe/end-stage cardiac disease, but is limited by a global donor heart shortage. Consequently, innovative solutions to increase donor heart availability and utilisation are rapidly expanding. Clinically relevant preclinical models are essential for evaluating interventions for human translation, yet few exist that accurately mimic all key HTx components, incorporating injuries beginning in the donor, through to the recipient. To enable future assessment of novel perfusion technologies in our research program, we thus aimed to develop a clinically relevant sheep model of HTx following 24 h of donor BSD. METHODS: BSD donors (vs. sham neurological injury, 4/group) were hemodynamically supported and monitored for 24 h, followed by heart preservation with cold static storage. Bicaval orthotopic HTx was performed in matched recipients, who were weaned from cardiopulmonary bypass (CPB), and monitored for 6 h. Donor and recipient blood were assayed for inflammatory and cardiac injury markers, and cardiac function was assessed using echocardiography. Repeated measurements between the two different groups during the study observation period were assessed by mixed ANOVA for repeated measures. RESULTS: Brainstem death caused an immediate catecholaminergic hemodynamic response (mean arterial pressure, p = 0.09), systemic inflammation (IL-6 - p = 0.025, IL-8 - p = 0.002) and cardiac injury (cardiac troponin I, p = 0.048), requiring vasopressor support (vasopressor dependency index, VDI, p = 0.023), with normalisation of biomarkers and physiology over 24 h. All hearts were weaned from CPB and monitored for 6 h post-HTx, except one (sham) recipient that died 2 h post-HTx. Hemodynamic (VDI - p = 0.592, heart rate - p = 0.747) and metabolic (blood lactate, p = 0.546) parameters post-HTx were comparable between groups, despite the observed physiological perturbations that occurred during donor BSD. All p values denote interaction among groups and time in the ANOVA for repeated measures. CONCLUSIONS: We have successfully developed an ovine HTx model following 24 h of donor BSD. After 6 h of critical care management post-HTx, there were no differences between groups, despite evident hemodynamic perturbations, systemic inflammation, and cardiac injury observed during donor BSD. This preclinical model provides a platform for critical assessment of injury development pre- and post-HTx, and novel therapeutic evaluation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40635-021-00425-4

An Ovine Model of Haemorrhagic Shock and Resuscitation, to Assess Recovery of Tissue Oxygen Delivery and Oxygen Debt, and Inform Patient Blood Management

BACKGROUND: Aggressive fluid or blood component transfusion for severe hemorrhagic shock may restore macrocirculatory parameters, but not always improve microcirculatory perfusion and tissue oxygen delivery. We established an ovine model of hemorrhagic shock to systematically assess tissue oxygen delivery and repayment of oxygen debt; appropriate outcomes to guide Patient Blood Management. METHODS: Female Dorset-cross sheep were anesthetized, intubated, and subjected to comprehensive macrohemodynamic, regional tissue oxygen saturation (StO2), sublingual capillary imaging, and arterial lactate monitoring confirmed by invasive organ-specific microvascular perfusion, oxygen pressure, and lactate/pyruvate levels in brain, kidney, liver, and skeletal muscle. Shock was induced by stepwise withdrawal of venous blood until MAP was 30 mm Hg, mixed venous oxygen saturation (SvO2) 4 mM. Resuscitation with PlasmaLyte® was dosed to achieve MAP > 65 mm Hg. RESULTS: Hemorrhage impacted primary outcomes between baseline and development of shock: MAP 89 ± 5 to 31 ± 5 mm Hg (P < 0.01), SvO2 70 ± 7 to 23 ± 8% (P < 0.05), cerebral regional tissue StO2 77 ± 11 to 65 ± 9% (P < 0.01), peripheral muscle StO2 66 ± 8 to 16 ± 9% (P < 0.01), arterial lactate 1.5 ± 1.0 to 5.1 ± 0.8 mM (P < 0.01), and base excess 1.1 ± 2.2 to -3.6 ± 1.7 mM (P < 0.05). Invasive organ-specific monitoring confirmed reduced tissue oxygen delivery; oxygen tension decreased and lactate increased in all tissues, but moderately in brain. Blood volume replacement with PlasmaLyte® improved primary outcome measures toward baseline, confirmed by organ-specific measures, despite hemoglobin reduced from baseline 10.8 ± 1.2 to 5.9 ± 1.1 g/dL post-resuscitation (P < 0.01). CONCLUSION: Non-invasive measures of tissue oxygen delivery and oxygen debt repayment are suitable outcomes to inform Patient Blood Management of hemorrhagic shock, translatable for pre-clinical assessment of novel resuscitation strategies.</p

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.</p

Surface Modifications of Detonation Nanodiamonds Probed by Multiwavelength Raman Spectroscopy

International audienceDetonation nanodiamonds (DND) with different surface chemical and structural features were characterized using multiwavelength Raman analysis, from deep UV to more conventional visible wavelengths. In particular, effects of DND surface modifications induced by annealing (air or vacuum) or by hydrogen microwave plasma on the line shape of their corresponding Raman spectra were investigated. Indeed, the different surface treatments led to specific surface terminations and/or different shell structures or surface reconstructions. In addition, annealing treatments in air or hydrogen have also been performed along with in situ Raman spectroscopy measurements. This original combination allows a better understanding of the corresponding surface modifications. The effects of the different surface terminations on the line shape of the spectra as well as the choice of the Raman experimental conditions are discussed. The line shape of the Raman spectrum of DND is also discussed in detail