Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia

Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic device mimicking the slits of the spleen red pulp area and video data analysis are combined for the characterization of RBCs in RHHA. This microfluidic unit is designed to evaluate RBC deformability by maintaining them fixed in planar orientation, allowing the visual inspection of RBC’s capacity to restore their original shape after crossing microconstrictions. Then, two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%

Protocol for a multicentre and prospective follow-up cohort study of early detection of atrial fibrillation, silent stroke and cognitive impairment in high-risk primary care patients: the PREFA-TE study

Background Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Future estimations suggest an increase in global burden of AF greater than 60% by 2050. Numerous studies provide growing evidence that AF is not only associated with stroke but also with cognitive impairment and dementia.Aim The main goal is to assess the impact of the combined use of cardiac rhythm monitoring devices, echocardiography, biomarkers and neuroimaging on the early diagnosis of AF, silent strokes and cognitive decline, in subjects at high risk of AF.Methods and analysis Two-year follow-up of a cohort of individuals aged 65–85 years at high risk for AF, with no prior diagnosis of either stroke or dementia. The study involves baseline echocardiography, biomarkers, and neuroimaging, yearly cardiac monitoring, and semiannual clinical assessments. Different parameters from these tests will be analysed as independent variables. Throughout the study period, primary outcomes: new diagnoses of AF, stroke and cognitive impairment, along with any clinical and therapeutic changes, will be registered. A first descriptive and bivariate statistical analysis, appropriate to the types of variables, will be done. The information obtained from the data analysis will encompass adjusted risk estimates along with 95% confidence intervals. Event risk predictions will rely on multivariate Cox proportional hazards regression models. The predictive value of the model will be evaluated through the utilisation of receiver operating characteristic curves for area under the curve calculation. Additionally, time-to-event analysis will be performed using Kaplan-Meier curves.Ethics and dissemination This study protocol has been reviewed and approved by the Independent Ethics Committee of the Foundation University Institute for Primary Health Care Research-IDIAP Jordi Gol (expedient file 22/090-P). The authors plan to disseminate the study results to the general public through various scientific events. Publication in open-access journals and presentations at scientific congresses, seminars and meetings is also foreseen.Trial registration number NCT05772806