Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence

Artificial neural network applied to fragile X-associated tremor/ataxia syndrome stage diagnosis based on peripheral mitochondrial bioenergetics and brain imaging outcomes

No proven prognosis is available for the neurodegenerative disorder fragile X-associated tremor/ataxia syndrome (FXTAS). Artificial neural network analyses (ANN) were used to predict FXTAS progression using data from 127 adults (noncarriers and FMR1 premutation carriers with and without FXTAS) with five outcomes from brain MRI imaging and 22 peripheral bioenergetic outcomes from two cell types. Diagnosis accuracy by ANN predictions ranged from 41.7 to 86.3% (depending on the algorithm used), and those misclassified usually presented a higher FXTAS stage. ANN prediction of FXTAS stages was based on a combination of two imaging findings (white matter hyperintensity and whole-brain volumes adjusted for intracranial volume) and four bioenergetic outcomes. Those at Stage 3 vs. 0-2 showed lower mitochondrial mass, higher oxidative stress, and an altered electron transfer consistent with mitochondrial unfolded protein response activation. Those at Stages 4-5 vs. 3 had higher oxidative stress and glycerol-3-phosphate-linked ATP production, suggesting that targeting mGPDH activity may prevent a worse prognosis. This was confirmed by the bioenergetic improvement of inhibiting mGPDH with metformin in affected fibroblasts. ANN supports the prospect of an unbiased molecular definition in diagnosing FXTAS stages while identifying potential targets for personalized medicine.

Cardiac involvement in Wilson's disease: a retrospective cohort study

BACKGROUND AND OBJECTIVES

Wilson's disease (WD) is an inherited disorder with perturbations in copper metabolism and can cause multiorgan damage. This study aims to explore cardiac findings mainly based on electrocardiography (ECG) in WD patients.

METHODS

We retrospectively enrolled adult patients who were diagnosed with WD between January 2011 and December 2020. Demographic and clinical data were collected and reevaluated.

RESULTS

A total of 126 patients were included. There were 71 men and 55 women. The mean age was 27.2 years. Ninety-nine had hepatic presentation as the initial symptom and 27 had neuropsychiatric presentation as the initial symptom. Thirty-seven patients (29.4%) had cardiac manifestations. Of these patients, nine presented apparent cardiac symptoms (three with discontinuous chest tightness, three with dizziness, two with palpitation and one with atypical chest pain) and 28 had asymptomatic electrocardiography (ECG) abnormalities. Among the nine patients, four had second- or third-degree atrioventricular block, three had ST-segment change and two had ventricular tachycardia. ECG abnormalities in the 28 patients included increase in the width of the QRS complex in 8, atrial premature beats in 8, T-wave inversion in 5, P-wave inversion in 2, sinus bradycardia in 2, ST-segment change in 2, and coexistence of sinus bradycardia and T-wave inversion in 1. No statistical difference (P = 0.32) existed in the occurrence of ECG abnormalities between patients with hepatic presentation (27/99) and those with neuropsychiatric presentation (10/27).

CONCLUSION

Cardiac involvement is not rare in adult WD patients. We suggest that cardiac evaluation should be routinely performed in the population.

Effect of COVID-19 on Selected Characteristics of Life Satisfaction Reflected in a Fuzzy Model

The general goal of the research in this article is to devise an algorithm for assessing overall life satisfaction—a term often referred to as Quality of Life (QoL). It is aggregated to its own proposition, called personal life usual satisfaction (PLUS). An important assumption here is that the model is based on already known and commonly used solutions, such as medical (psychological and physiotherapeutic) questionnaires. Thanks to this, the developed solution allows us to obtain a synergy effect from the existing knowledge, without the need to design new, complicated procedures. Fuzzy multivariate characterization of life satisfaction presents a challenge for a complete analysis of the phenomenon. The complexity of description using multiple scales, including linguistic, requires additional computational solutions, as presented in this paper. The detailed aim of this study is twofold: (1) to develop a fuzzy model reflecting changes in life satisfaction test scores as influenced by the corona virus disease 2019 (COVID-19) pandemic, and (2) to develop guidelines for further research on more advanced models that are clinically useful. Two groups affected by professional burnout to different degrees were analyzed toward life satisfaction twice (pre- and during pandemy): a study group (physiotherapists, n=25) and a reference group (computer scientists, n=25). The Perceived Stress Score (PSS10), Maslach Burnout Inventory (MBI), Satisfaction with Life Scale (SWLS), and Nordic Musculoskeletal Questionnaire (NMQ) were used. The resultant model is based on a hierarchical fuzzy system. The novelty of the proposed approach lies in the combination of the use of data from validated clinimetric tests with the collection of data from characteristic time points and the way in which they are analyzed using fuzzy logic through transparent and scalable hierarchical models. To date, this approach is unique and has no equivalent in the literature. Thanks to the hierarchical structure, the evaluation process can be defined as a modular construction, which increases transparency and makes the whole procedure more flexible.