Long-term safety and efficacy of givinostat in polycythemia vera: 4-year mean follow up of three phase 1/2 studies and a compassionate use program.

Polycythemia vera (PV) is a BCR-ABL1-negative myeloproliferative neoplasm (MPN) characterized by excessive proliferation of erythroid, myeloid, and megakaryocytic components in the bone marrow, mainly due to a Janus kinase 2 gene mutation (JAK2V617F). Givinostat, a histone-deacetylase inhibitor that selectively targets JAK2V617F cell growth, has demonstrated good efficacy and safety in three phase 1/2 studies in patients with PV. This manuscript focuses on the 4-year mean (2.8 year median) follow-up of an open-label, long-term study that enrolled 51 patients with PV (out of a total of 54 with MPN) who received clinical benefit from givinostat in these previous studies or on compassionate use, and who continued to receive givinostat at the last effective and tolerated dose. The primary objectives are to determine givinostat's long-term safety and tolerability, and efficacy evaluated by the investigators according to internationally recognized response criteria. During follow-up, only 10% of PV patients reported Grade 3 treatment-related adverse events (AEs), while none had Grade 4 or 5 treatment-related AEs. The overall response rate for the duration of follow-up was always greater than 80% in patients with PV. In conclusion, givinostat demonstrated a good safety and efficacy profile in patients with PV, data supporting long-term use in this population.

Models of community-based primary care for epilepsy in low- and middle-income countries.

OBJECTIVE: To review systematically community-based primary care interventions for epilepsy in low- and middle-income countries to rationalize approaches and outcome measures in relation to epilepsy care in these countries. METHODS: A systematic search of PubMed, EMBASE, Global Index Medicus, CINAHL, and Web of Science was undertaken to identify trials and implementation of provision of antiseizure medications, adherence reinforcement, and/or health care provider or community education in community-based samples of epilepsy. Data on populations addressed, interventions, and outcomes were extracted from eligible articles. RESULTS: The 24 reports identified comprise mostly care programs addressing active convulsive epilepsy. Phenobarbital has been used most frequently, although other conventional antiseizure medications (ASMs) have also been used, but none of the newer. Tolerability rates in these studies are high, but overall attrition is considerable. Other approaches include updating primary health care providers, reinforcing treatment adherence in clinics, and raising community awareness. In these programs, the coverage of existing treatment gap in the community, epilepsy-related mortality, and comorbidity burden are only fleetingly addressed. None, however, explicitly describe sustainability plans. CONCLUSIONS: Cost-free provision, mostly of phenobarbital, has resulted in short-term seizure freedom in roughly half of the people with epilepsy in low- and middle-income countries. Future programs should include a range of ASMs. These should cover apart from seizure control and treatment adherence, primary health care provider education, community awareness, and referral protocols for specialist care. Programs should incorporate impact assessment at the local level. Sustainability in the long term as much as resilience and scalability should be addressed in future initiatives.

Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial.

BACKGROUND: The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods. METHODS: Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation. RESULTS: A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency. CONCLUSIONS: The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness.

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.

Corrigendum: Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness.

[This corrects the article DOI: 10.3389/fnins.2019.00807.].