Heterogeneity in families with ATTRV30M amyloidosis: a historical and longitudinal Portuguese case study impact for genetic counselling.

BACKGROUND: Hereditary transthyretin amyloidosis (ATTRv amyloidosis) is an inherited disease, where the study of family history holds importance. This study evaluates the changes of age-of-onset (AOO) and other age-related clinical factors within and among families affected by ATTRv amyloidosis. METHODS: We analysed information from 934 trees, focusing on family, parents, probands and siblings relationships. We focused on 1494 female and 1712 male symptomatic ATTRV30M patients. Results are presented alongside a comparison of current with historical records. Clinical and genealogical indicators identify major changes. RESULTS: Overall, analysis of familial data shows the existence of families with both early and late patients (1/6). It identifies long familial follow-up times since patient families tend to be diagnosed over several years. Finally, results show a large difference between parent-child and proband-patient relationships (20-30 years). CONCLUSIONS: This study reveals that there has been a shift in patient profile, with a recent increase in male elderly cases, especially regarding probands. It shows that symptomatic patients exhibit less variability towards siblings, when compared to other family members, namely the transmitting ancestors' age of onset. This can influence genetic counselling guidelines.

Clinical model for Hereditary Transthyretin Amyloidosis age of onset prediction.

Introduction: Hereditary transthyretin amyloidosis (ATTRv amyloidosis) is a rare neurological hereditary disease clinically characterized as severe, progressive, and life-threatening while the age of onset represents the moment in time when the first symptoms are felt. In this study, we present and discuss our results on the study, development, and evaluation of an approach that allows for time-to-event prediction of the age of onset, while focusing on genealogical feature construction. Materials and methods: This research was triggered by the need to answer the medical problem of when will an asymptomatic ATTRv patient show symptoms of the disease. To do so, we defined and studied the impact of 77 features (ranging from demographic and genealogical to familial disease history) we studied and compared a pool of prediction algorithms, namely, linear regression (LR), elastic net (EN), lasso (LA), ridge (RI), support vector machines (SV), decision tree (DT), random forest (RF), and XGboost (XG), both in a classification as well as a regression setting; we assembled a baseline (BL) which corresponds to the current medical knowledge of the disease; we studied the problem of predicting the age of onset of ATTRv patients; we assessed the viability of predicting age of onset on short term horizons, with a classification framing, on localized sets of patients (currently symptomatic and asymptomatic carriers, with and without genealogical information); and we compared the results with an out-of-bag evaluation set and assembled in a different time-frame than the original data in order to account for data leakage. Results: Currently, we observe that our approach outperforms the BL model, which follows a set of clinical heuristics and represents current medical practice. Overall, our results show the supremacy of SV and XG for both the prediction tasks although impacted by data characteristics, namely, the existence of missing values, complex data, and small-sized available inputs. Discussion: With this study, we defined a predictive model approach capable to be well-understood by medical professionals, compared with the current practice, namely, the baseline approach (BL), and successfully showed the improvement achieved to the current medical knowledge.

Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders.

The increasingly aging society in developed countries has raised attention to the role of technology in seniors' lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition in free-living conditions for triggering smart reminders to autonomously living seniors, keeping system design considerations, namely usability and senior-acceptance criteria, in the loop. To that end, we conceived a new dataset featuring accelerometer and gyroscope wrist data to conduct the experiments. We assessed the performance of a single multi-class classification model when compared against several binary classification models, one for each activity of interest (eating vs. non-eating; drinking vs. non-drinking). Binary classification models performed consistently better for all tested classifiers (k-NN, Naive Bayes, Decision Tree, Multilayer Perceptron, Random Forests, HMM). This evidence supported the proposal of a semi-hierarchical activity recognition algorithm that enabled the implementation of two distinct data stream segmentation techniques, the customization of the classification models of each activity of interest and the establishment of a set of restrictions to apply on top of the classification output, based on daily evidence. An F1-score of 97% was finally attained for the simultaneous recognition of eating and drinking in an all-day acquisition from one young user, and 93% in a test set with 31 h of data from 5 different unseen users, 2 of which were seniors. These results were deemed very promising towards solving the problem of food and fluids intake monitoring with practical systems which shall maximize user-acceptance.

Randomized Trial of Machine Perfusion Versus Cold Storage in Recipients of Deceased Donor Kidney Transplants With High Incidence of Delayed Graft Function.

BACKGROUND: This study compared the use of static cold storage versus continuous hypothermic machine perfusion in a cohort of kidney transplant recipients at high risk for delayed graft function (DGF). METHODS: In this national, multicenter, and controlled trial, 80 pairs of kidneys recovered from brain-dead deceased donors were randomized to cold storage or machine perfusion, transplanted, and followed up for 12 months. The primary endpoint was the incidence of DGF. Secondary endpoints included the duration of DGF, hospital stay, primary nonfunction, estimated glomerular filtration rate, acute rejection, and allograft and patient survivals. RESULTS: Mean cold ischemia time was high but not different between the 2 groups (25.6 ± 6.6 hours vs 25.05 ± 6.3 hours, 0.937). The incidence of DGF was lower in the machine perfusion compared with cold storage group (61% vs. 45%, P = 0.031). Machine perfusion was independently associated with a reduced risk of DGF (odds ratio, 0.49; 95% confidence interval, 0.26-0.95). Mean estimated glomerular filtration rate tended to be higher at day 28 (40.6 ± 19.9 mL/min per 1.73 m2 vs 49.0 ± 26.9 mL/min per 1.73 m2; P = 0.262) and 1 year (48.3 ± 19.8 mL/min per 1.73 m2 vs 54.4 ± 28.6 mL/min per 1.73 m2; P = 0.201) in the machine perfusion group. No differences in the incidence of acute rejection, primary nonfunction (0% vs 2.5%), graft loss (7.5% vs 10%), or death (8.8% vs 6.3%) were observed. CONCLUSIONS: In this cohort of recipients of deceased donor kidneys with high mean cold ischemia time and high incidence of DGF, the use of continuous machine perfusion was associated with a reduced risk of DGF compared with the traditional cold storage preservation method.

An ORCID based synchronization framework for a national CRIS ecosystem.

PTCRIS (Portuguese Current Research Information System) is a program aiming at the creation and sustained development of a national integrated information ecosystem, to support research management according to the best international standards and practices. This paper reports on the experience of designing and prototyping a synchronization framework for PTCRIS based on ORCID (Open Researcher and Contributor ID). This framework embraces the "input once, re-use often" principle, and will enable a substantial reduction of the research output management burden by allowing automatic information exchange between the various national systems. The design of the framework followed best practices in rigorous software engineering, namely well-established principles in the research field of consistency management, and relied on formal analysis techniques and tools for its validation and verification. The notion of consistency between the services was formally specified and discussed with the stakeholders before the technical aspects on how to preserve said consistency were explored. Formal specification languages and automated verification tools were used to analyze the specifications and generate usage scenarios, useful for validation with the stakeholder and essential to certificate compliant services.