HLA Linked Regional Differences in Donor Selection: A 9-Year Study of the Swiss Registry.

Introduction: The large HLA diversity in worldwide populations is a major challenge for matched unrelated haematopoietic stem cell (HSC) donor searches. The impact of regional diversity on the effective HSC donor selection has not been documented so far for national registries. Methods: The aim of the study was to analyse the 532 consecutive work-up (WU) requests received by Swiss Blood Stem Cells (SBSC), over a 9-year period (2011-2019) with respect to criteria including the geographical origin of the donors as derived from the postal codes, countries requesting SBSC donors, HLA-matching parameters, and patients' HLA haplotype frequencies. Results: Highly matched donors (10/10) represented 73.5% of the WU, whereas 8-9/10 mismatched donors accounted for 24.0%. The remaining donors were 7-8/8 matched (1.7%) or had an unknown matching grade (0.8%). Among the 10/10 matched patient/donor pairs with full HLA-DPB1 typing information, the rate of 11-12/12 matched donors was 73.3%. Of the 532 WU requests, 47.6% were for patients of the four neighbouring countries and for national patients. The ratio of WU requests was directly proportional to the total number of donors registered in each region (Pearson's r = 0.977). However, for two regions (lemanic and north-eastern areas of Switzerland (CH)), the proportion of selected donors was slightly above the min-max ratio of registered donors throughout the study period. The number of WU requests differed between countries when considering donors from the northern and southern parts of the country delineated by the alpine barrier. Conclusion: This study shows the value of the SBSC registry for both national and international patients. Two countries (USA and Germany) which operate the two worldwide largest registries (>19 million donors) requested 30% of SBSC registered donors, while the Swiss transplant centres accounted for 13% of the WU requests. When considering the geographic origin of SBSC donors, we observe a correlation of WU requests with the total number of registered donors in each subregion. This finding thus supports recruitment efforts throughout all regions. Interestingly, donors from three regions (lemanic area, Zurich and Ticino) are slightly over-represented, which is possibly related to higher HLA haplotypic diversity. A focus on planning recruitment in these regions might contribute to more successful donor searches.

Surface electromyography for analysis of heart rate variability in preterm infants.

OBJECTIVE: Characterizing heart rate variability (HRV) in neonates has gained increased attention and is helpful in quantifying maturation and risk of sepsis in preterm infants. Raw data used to derive HRV in a clinical setting commonly contain noise from motion artifacts. Thoracic surface electromyography (sEMG) potentially allows for pre-emptive removal of motion artifacts and subsequent detection of interbeat interval (IBI) of heart rate to calculate HRV. We tested the feasibility of sEMG in preterm infants to exclude noisy raw data and to derive IBI for HRV analysis. We hypothesized that a stepwise quality control algorithm can identify motion artifacts which influence IBI values, their distribution in the time domain, and outcomes of nonlinear time series analysis. APPROACH: This is a prospective observational study in preterm infants <6 days of age. We used 100 sEMG measurements from 24 infants to develop a semi-automatic quality control algorithm including synchronized video recording, threshold-based sEMG envelope curve, optimized QRS-complex detection, and final targeted visual inspection of raw data. MAIN RESULTS: Analysis of HRV from sEMG data in preterm infants is feasible. A stepwise algorithm to exclude motion artifacts and improve QRS detection significantly influenced data quality (34% of raw data excluded), distribution of IBI values in the time domain, and nonlinear time series analysis. The majority of unsuitable data (94%) were excluded by automated steps of the algorithm. SIGNIFICANCE: Thoracic sEMG is a promising method to assess motion artifacts and calculate HRV in preterm neonates.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers.

BACKGROUND: Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data.

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.