Corrigendum: Personalized monitoring of circulating tumor DNA with a specific signature of trackable mutations after chimeric antigen receptor T-cell therapy in follicular lymphoma patients.

[This corrects the article DOI: 10.3389/fimmu.2023.

Corrigendum: Personalized monitoring of circulating tumor DNA with a specific signature of trackable mutations after chimeric antigen receptor T-cell therapy in follicular lymphoma patients.

[This corrects the article DOI: 10.3389/fimmu.2023.

Personalized monitoring of circulating tumor DNA with a specific signature of trackable mutations after chimeric antigen receptor T-cell therapy in follicular lymphoma patients.

Background: CART therapy has produced a paradigm shift in the treatment of relapsing FL patients. Strategies to optimize disease surveillance after these therapies are increasingly necessary. This study explores the potential value of ctDNA monitoring with an innovative signature of personalized trackable mutations.

Real-life disease monitoring in follicular lymphoma patients using liquid biopsy ultra-deep sequencing and PET/CT.

In the present study, we screened 84 Follicular Lymphoma patients for somatic mutations suitable as liquid biopsy MRD biomarkers using a targeted next-generation sequencing (NGS) panel. We found trackable mutations in 95% of the lymph node samples and 80% of the liquid biopsy baseline samples. Then, we used an ultra-deep sequencing approach with 2 · 10 sensitivity (LiqBio-MRD) to track those mutations on 151 follow-up liquid biopsy samples from 54 treated patients.

Extensive pharmacokinetic variability of Levetiracetam. ¿Are doctors aware?

Introduction: Levetiracetam was presented as a drug with linear pharmacokinetics. There is currently evidence on its extensive pharmacokinetic variability in real clinical practice.

Objective: To describe levetiracetam pharmacokinetic variability in patients with epilepsy in real clinical practice.

Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning.

Chronicity is a problem that is affecting quality of life and increasing healthcare costs worldwide. Predictive tools can help mitigate these effects by encouraging the patients' and healthcare system's proactivity. This research work uses supervised learning techniques to build a predictive model of the healthcare status of a chronic patient, using Clinical Risk Groups (CRGs) as a measure of chronicity and prescription and diagnosis data as predictors.

Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets.

This work proposes the use of Process Mining methodologies on healthcare datasets containing diagnosis information as a means to identify the course of a disease across organizations. Datasets containing diagnosis information for administrative purposes are a good candidate due to its standardized format, widespread availability and coverage. We present a methodology to preprocess, cluster and mine diagnosis information and the results of a preliminary use case with diabetes type II.

Usability and Interoperability in Wireless Sensor Networks for Patient Telemonitoring in Chronic Disease Management.

This paper addresses two key technological barriers to the wider adoption of patient telemonitoring systems for chronic disease management, namely, usability and sensor device interoperability. As a great percentage of chronic patients are elderly patients as well, usability of the system has to be adapted to their needs. This paper identifies (from previous research) a set of design criteria to address these challenges, and describes the resulting system based on a wireless sensor network, and including a node as a custom-made interface that follows usability design criteria stated.

Activity recognition using hybrid generative/discriminative models on home environments using binary sensors.

Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach.

Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques.

Background: Outcome prediction for subarachnoid hemorrhage (SAH) helps guide care and compare global management strategies. Logistic regression models for outcome prediction may be cumbersome to apply in clinical practice.

Objective: To use machine learning techniques to build a model of outcome prediction that makes the knowledge discovered from the data explicit and communicable to domain experts.

[Quantitative prevalence of Anoplocephala sp. and egg shedding consistency in horses].

The objective of this work was to determine the quantitative prevalence of Anoplocephala sp. in thoroughbred horses raised in São José dos Pinhais, PR using the modified centrifugal-flotation technique. Repeatability values for the eggs per gram (EPG) were evaluated at 28-day intervals.

Standards for medical device communication: X73 PoC-MDC.

When using a number of medical devices from very different manufacturers with different proprietary formats the problem of a lack of interoperability emerges. Connectivity and communications are then limited and the systems and users can not exploit all the possibilities that Information and Communication Technologies offer today. The use and application of standards can be the solution to bring light to this confusion of languages in this Tower of Babel.

Telemedicine experience for chronic care in COPD.

Information and telecommunication technologies are called to play a major role in the changes that healthcare systems have to face to cope with chronic disease. This paper reports a telemedicine experience for the home care of chronic patients suffering from chronic obstructive pulmonary disease (COPD) and an integrated system designed to carry out this experience. To determine the impact on health, the chronic care telemedicine system was used during one year (2002) with 157 COPD patients in a clinical experiment; endpoints were readmissions and mortality.

A telemedicine system to support a new model for care of chronically ill patients.

We have developed a new model for the care of chronically ill patients, based on home care supported by remote monitoring technology and telemedicine. The variables monitored included non-invasive blood pressure, blood oxygen saturation, threelead electrocardiogram, spirometry (including flow-volume curve) and respiratory rate. The telemedicine system consisted of a home-based patient unit and a management centre that received information from the home units.