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Controlled reperfusion by monitoring the blood pressure, blood flow, and specific blood parameters during extracorporeal reperfusion after cardiac arrest has the potential to limit ischemia–reperfusion injury. The intracellular calcium overload as part of the ischemia–reperfusion injury provides the possibility for the injury to be counteracted by the early suppression of serum calcium with the aim of improving survival and the neurological outcome. We investigated the effects of prolonged serum calcium suppression via sodium citrate during extracorporeal resuscitation using the CARL protocol (CARL—controlled automated reperfusion of the whole body) compared to a single-dose approach in a porcine model after prolonged cardiac arrest. A control group (N = 10) was resuscitated after a 20 min cardiac arrest, initially lowering the intravascular calcium with the help of a single dose of sodium citrate as part of the priming solution. Animals in the intervention group (N = 13) received additional sodium citrate for the first 15 min of reperfusion. In the control group, 9/10 (90.0%) animals survived until day 7 and 7/13 (53.8%) survived in the intervention group (p = 0.09). A favorable neurological outcome on day 7 after the cardiac arrest was observed in all the surviving animals using a species-specific neurological deficit score. The coronary perfusion pressure was significantly lower with a tendency towards more cardiac arrhythmias in the intervention group. In conclusion, a prolonged reduction in serum calcium levels over the first 15 min of reperfusion after prolonged cardiac arrest tended to be unfavorable regarding survival and hemodynamic variables compared to a single-dose approach in this animal model. [ABSTRACT FROM AUTHOR]

Nonspecific back pain (NSBP) contributes greatly to the overall burden of disease from musculoskeletal conditions. Digital therapeutics (DTx) aims to address the excess demand for movement and exercise therapy resulting from this spectrum of conditions. This study aims to investigate the differential therapeutic response of NSBP to different use profiles of a digital home exercise program. Methods: This study used a PSM model to comparatively assess the achievement of a clinically relevant pain improvement among patients who exhibit a high use (HU), intermediate use (IU), low use (LU), or sub-LU use profile. Sensitivity analyses with commonly accepted thresholds for clinically relevant improvements were conducted. Results: Higher use profiles show a higher probability of achieving a clinically relevant improvement of self-reported pain intensities. Additionally, the achievement of any higher use level is associated with a significant increase in the probability of achieving a clinically relevant improvement. Conclusion: To enable the optimal effectiveness of DTx home exercise programs, an HU use profile should be pursued. This finding is in line with earlier guidance for the achievement of optimal therapeutic benefit from conventional movement and exercise therapy and underscores the importance of a cross-disciplinary effort from patients, healthcare professionals and system stakeholders alike to maximize the therapeutic effect from DTx. [ABSTRACT FROM AUTHOR]

Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care.