¿Un europeo olvidado? Salvador de Madariaga y la integración europea

En este artículo me ocupo de Salvador de Madariaga (1886-1978), quien, como erudito, diplomático y político, acuñó el “principio de la integración europea”. El texto analiza sus ideas a favor de la integración y su actuación en unas instituciones en las que entonces existía un intenso debate entre una integración global o una integración en el nivel europeo. La investigación se ocupa, concretamente, de dos tramos temporales claramente diferenciados por la historia: antes y después de la II Guerra Mundial. El contraste de estos dos tramos temporales coincide con el cambio de las teorías de Madariaga a favor de la integración supranacional. En el debate acerca de la integración europea, en síntesis, Madariaga se encuentra en el área conflictiva situada entre las ideas del idealismo y del realismo. Por lo demás, su compromiso con la integración de Europa ilustra la transición de una idea abstracta de una Europa unificada a la acción de la “política real”

Functional definition of the mutation cluster region of adenomatous polyposis coli in colorectal tumours

The mutation cluster region (MCR) of adenomatous polyposis coli (APC) is located within the central part of the open reading frame, overlapping with the region encoding the 20 amino acid repeats (20R) that are β-catenin-binding sites. Each mutation in the MCR leads to the synthesis of a truncated APC product expressed in a colorectal tumour. The MCR extends from the 3′ border of the first 20R coding region to approximately the middle of the third 20R coding region, reflecting both positive and negative selections of the N- and C-terminal halves of the APC protein in colon cancer cells, respectively. In contrast, the second 20R escapes selection and can be either included or excluded from the truncated APC products found in colon cancer cells. To specify the functional outcome of the selection of the mutations, we investigated the β-catenin binding capacity of the first three 20R in N-terminal APC fragments. We found in co-immunoprecipitation and intracellular co-localization experiments that the second 20R is lacking any β-catenin binding activity. Similarly, we also show that the tumour-associated truncations abolish the interaction of β-catenin with the third 20R. Thus, our data provide a functional definition of the MCR: the APC fragments typical of colon cancer are selected for the presence of a single functional 20R, the first one, and are therefore equivalent relative to β-catenin bindin

Symptoms, toxicities, and analytical results for a patient after smoking herbs containing the novel synthetic cannabinoid MAM-2201

We report a case of intoxication by the synthetic cannabinoid MAM-2201 ([1-(5-fluoropentyl)-1H-indol-3-yl](4-methyl-1-naphthalenyl)-methanone). A 31-year-old man smoked about 300mg of a herbal blend. He experienced an acute transient psychotic state with agitation, aggression, anxiety, and vomiting associated with a sympathomimetic syndrome. MAM-2201 was detected and quantified in a plasma sample using liquid chromatography-tandem mass spectrometry (LC-MS-MS). The level was 49ng/ml 1h after smoking. The use of other drugs was analytically excluded. The presence of MAM-2201 was confirmed in the herbal blend using gas chromatography-mass spectrometry (GC-MS) and LC-high resolution MS. This is the first description of an analytically confirmed intoxication and of the determination of MAM-2201 in human blood plasm

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed

Therapeutic drug monitoring of once daily aminoglycoside dosing: comparison of two methods and investigation of the optimal blood sampling strategy

Purpose: Therapeutic drug monitoring of patients receiving once daily aminoglycoside therapy can be performed using pharmacokinetic (PK) formulas or Bayesian calculations. While these methods produced comparable results, their performance has never been checked against full PK profiles. We performed a PK study in order to compare both methods and to determine the best time-points to estimate AUC0-24 and peak concentrations (C max). Methods: We obtained full PK profiles in 14 patients receiving a once daily aminoglycoside therapy. PK parameters were calculated with PKSolver using non-compartmental methods. The calculated PK parameters were then compared with parameters estimated using an algorithm based on two serum concentrations (two-point method) or the software TCIWorks (Bayesian method). Results: For tobramycin and gentamicin, AUC0-24 and C max could be reliably estimated using a first serum concentration obtained at 1h and a second one between 8 and 10h after start of the infusion. The two-point and the Bayesian method produced similar results. For amikacin, AUC0-24 could reliably be estimated by both methods. C max was underestimated by 10-20% by the two-point method and by up to 30% with a large variation by the Bayesian method. Conclusions: The ideal time-points for therapeutic drug monitoring of once daily administered aminoglycosides are 1h after start of a 30-min infusion for the first time-point and 8-10h after start of the infusion for the second time-point. Duration of the infusion and accurate registration of the time-points of blood drawing are essential for obtaining precise predictions

Physical Activity Comparison Between Body Sides in Hemiparetic Patients Using Wearable Motion Sensors in Free-Living and Therapy: A Case Series

Background: Physical activity (PA) is essential in stroke rehabilitation of hemiparetic patients to avoid health risks, and moderate to vigorous PA could promote patients' recovery. However, PA assessments are limited to clinical environments. Little is known about PA in unguided free-living. Wearable sensors could reveal patients' PA during rehabilitation, and day-long long-term measurements over several weeks might reveal recovery trends of affected and less-affected body sides.Methods: We investigated PA in an observation study during outpatient rehabilitation in a day-care center. PA of affected and less-affected body sides, including upper and lower limbs were derived using wearable motion sensors. In this analysis we focused on PA during free-living and clinician guided therapies, and investigated differences between body-sides. Linear regressions were used to estimate metabolic equivalents for each limb at comparable scale. Non-parametric statistics were derived to quantify PA differences between body sides.Results: We analyzed 102 full-day movement data recordings from eleven hemiparetic patients during individual rehabilitation periods up to 79 days. The comparison between free-living and clinician guided therapy showed on average 16.1 % higher PA in the affected arm during therapy and 5.3 % higher PA in the affected leg during therapy. Average differences between free-living and therapy in the less-affected side were below 4.5 %.Conclusion: We analyzed PA of patients with a hemiparesis in two distinct rehabilitation settings, including free-living and clinician guided therapies over several weeks and compared MET values of affected and less-affected body sides. In particular, we investigated PA using individual regression models for each limb. We demonstrated that wearable motion sensors provide insights in patient's PA during rehabilitation. Although, no clear PA trends were found, our analysis showed patients' tendency to sedentary behavior, confirming previous lab study results. Our PA analysis approach could be used beyond clinical rehabilitation to devise personalized patient and limb-specific exercise recommendations in future remote rehabilitation

Bewegungsüberwachung und Evaluierung von Patienten nach Schlaganfall im Alltag mit körpergetragenen Sensoren und digitalen Biomarkern

Patients after stroke often face long-term disability due to hemiparesis and thus require rehabilitation. With ageing societies, the stroke incidence is expected to increase, even among people who are in the workforce. Hence, costs for healthcare systems will rise. The current situation in stroke rehabilitation could intensify, more patients require treatment while at the same time a shortage of clinical personnel1 becomes apparent. Wearable motion sensors, including inertial measurement units (IMUs), have the potential to mitigate challenges in stroke rehabilitation and offer great potential for reshaping healthcare. With digital biomarkers derived from wearable sensors, e.g. to describe gait parameters or motion intensity, clinicians and patients could be supported during the rehabilitation. For example, objective movement quantification might help clinicians adapting therapies to the individual needs of a patient after stroke. State-of-the-art performance monitoring and evaluation is restricted to guided short-term measurements that follow defined assessment tasks in clinical environments, which are subjectively assessed by clinicians. Remote monitoring and evaluation of patients after stroke in free-living and the potential of wearable sensors is insufficiently addressed in research. Therefore, solutions for continuous and objective performance monitoring and evaluation using wearable motion sensors and algorithms are sought. The aim of this thesis is to devise and evaluate new solutions for longitudinal performance monitoring and evaluation in patients after stroke using algorithms and digital biomarkers, which could be used in freeliving. We test the following hypotheses: 1. IMUs, (machine learning) algorithms, and digital biomarkers are viable for longitudinal performance monitoring in patients after stroke. 2. Motion performance differences in the affected and less-affected upper and lower body-sides can be evaluated during therapies and free-living using IMU data and digital biomarkers. To test the hypotheses, a six month, longitudinal clinical observation study with eleven hemiparetic patients after stroke was implemented. In a novel study design, outpatients were followed by the examiner and more than 620 hours of motion data were recorded and annotated using a smartphone application. In full-day recordings, patients followed their therapy and performed various activities of daily living while wearing six body-worn IMUs. In addition, we used digital twins for personalised movement analyses in two case studies, including athletes and patients after stroke. This thesis includes eight peer-reviewed scientific publications, addressing four specific goals: (1) to review wearable motion sensors and machine learning algorithms for clinical assessment score estimation, (2) to implement activity primitive extraction algorithms for clinical score estimation and trend analysis, (3) to develop and evaluate digital biomarkers for performance analysis, and (4) to investigate digital twins for movement analysis and the evaluation of wearable sensor systems and algorithms. Wearable motion sensors and machine learning algorithms for clinical score estimation were reviewed. The review showed that mainly accelerometers for measurements were deployed and that score estimation algorithms included classification or regression-based machine learning techniques. Rule-based algorithms for activity primitive extraction from continuous sensor data were implemented. We showed that the Extended Barthel Index (EBI) can be estimated with approx. 12% relative error on average using support vector regression and leave-one-participant-out cross-validation. Further, the analysis of activity primitives revealed patient-specific recovery trends. The convergence point (CP), a newly developed digital biomarker for longitudinal, bilateral trend analysis revealed patient-specific recovery trends. In addition, the physical activity (PA) and functional range of motion (fROM) was analysed. The CP, PA, and fROM confirmed that differences in affected and lessaffected upper and lower body can be quantified during rehabilitation, including therapies and free-living. Finally, we present a novel methodology based on biomechanical simulations and motion data synthesis for the systematic evaluation of wearable sensor systems, algorithms, and digital biomarkers using personalised digital twins.Patienten nach einem Schlaganfall sind in vielen Fällen in Ihren Aktivitäten aufgrund einer halbseitigen Körperlähmung eingeschränkt und deshalb auf Rehabilitation angewiesen. Als eine weltweit führende Ursache für Aktivitätseinschränkungen, belasten Schlaganfälle das Gesundheitssystem und generieren steigende Kosten. Durch den demographischen Wandel ist eine Häufung von Schlaganfällen, auch bei Arbeitnehmenden, wahrscheinlich. Die Situation in der Rehabilitation könnte sich verschärfen, da mehr Patienten auf Therapien angewiesen sind, gleichzeitig ein Therapeutenmangel vorliegt. Körpergetragene Bewegungssensoren, sogenannteWearables und digitale Biomarker, die zum Beispiel den Gang oder die Bewegungsintensität beschreiben, haben das Potential für personalisierte Bewegungsanalysen in der Rehabilitation. Wearables liefern objektive Messdaten, welche mit Algorithmen verarbeitet werden können, um Therapieempfehlungen für Patienten nach einem Schlaganfall individuell anzupassen. Aktuelle Ansätze zur Bewegungsanalyse nach einem Schlaganfall mitWearables und Algorithmen sind limitiert auf kurze Messungen welche in Kliniken durchgeführt werden. Darüber hinaus müssen Patienten definierten Bewegungsabläufen folgen, welche subjektiv von Therapeuten bewertet werden. Aktuelle Ansätze sind daher für den Einsatz im Alltag nicht geeignet. In dieser Arbeit untersuchen wir neue Lösungsansätze für die Langzeit-Beobachtung und Bewegungsanalyse nach einem Schlaganfall im Alltag und testen folgende Hypothesen: 1. Wearables sind geeignet, um Patienten nach einem Schlaganfall zu überwachen und deren Bewegung mittels Algorithmen and digitalen Biomarkern zu identifizieren und zu quantifizieren. 2. Wearables sind geeignet, um Bewegungen basierend auf Alltagsaktivitäten zu evaluieren und Unterschiede der betroffenen und weniger-betroffenen Körperseite sowie oberer und unterer Extremitäten zu quantifizieren. Um die Hypothesen zu testen, haben wir eine sechsmonatige, longitudinale Beobachtungsstudie mit elf hemiparetischen Patienten nach einem Schlaganfall durchgeführt. In einem neuartigen Studiendesign wurden ambulante Patienten während mehr als 620 Stunden bei der Rehabilitation vom Versuchsleiter begleitet. So konnten Aktivitäten mit Hilfe einer Smartphone-Applikation annotiert werden. In Tagesaufnahmen folgten Patienten Ihren individuellen Therapieprogrammen und Alltagsaktivitäten. Zusätzlich untersuchten wir in dieser Arbeit digitale Zwillinge für die personalisierte Bewegungsanalyse in zwei Fall- Studien, inklusive Athleten und Patienten nach Schlaganfall. Diese Arbeit besteht aus acht begutachteten wissenschaftlichen Publikationen, und verfolgt vier Ziele: 1. Literatur-Recherche zur Evaluierung von körpergetragenen Sensoren für die Bewegungsmessung, sowie Analyse von Algorithmen des Maschinellen Lernens für die Schätzung klinisch relevanter Parameter. 2. Entwicklung von regelbasierten Algorithmen zur Extraktion und Analyse von Aktivitätsprimitiven aus kontinuierlichen Sensordaten. 3. Entwicklung und Evaluierung neuer digitaler Biomarker für Gangund Trendanalysen der betroffenen und weniger-betroffenen Körperseite, inklusive Konvergenzpunkte, physischer Aktivität und Intensität, sowie des funktionalen Bewegungsumfang der Oberarme. 4. Digitalisierung der personalisierten Bewegungsanalyse sowie die systematische Modellierung und Evaluierung von Wearables und Algorithmen mittels biomechanischer Simulationen und Datensynthese. Klassische Bewegungsmessungen in der Rehabilitation beruhen meist auf Beschleunigungssensoren. Für die Schätzung klinischer Parameter wurden häufig Klassifikations- und Regressions-Algorithmen genutzt. Diese Arbeit zeigt, dass Aktivitätsprimitiven aus kontinuierlichen Sensordaten mit regelbasierten Algorithmen extrahiert werden können. Mit maschinellen Lernmethoden, hier Regressions-Algorithmen, lässt sich der Erweiterte Barthel Index schätzen. Zudem zeigten sich patientenspezifische Trends. Konvergenzpunkte basierend auf Gangparameter, wurden als neuartige digitale Biomarker für die bilaterale Gang- und Trendanalyse eingeführt. Zusätzlich konnte mittels statistischer Auswertungen der physischen Therapie- und Alltagsaktivitäten sowie der Beschreibung des Bewegungsumfangs der Oberarme bestätigt werden, dass Unterschiede in beiden Körperseiten quantifiziert werden können. Schliesslich zeigt diese Arbeit neuartige Methoden basierend auf digitalen Zwillingen. Mit Hilfe personalisierten biomechanischen Simulationen und Datensynthese konnte demonstriert werden, dass die Bewegungsanalyse sowie die Evaluierung von Wearables und Algorithmen digitalisiert werden kann

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis

Abstract We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders