Amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disease characterized by a permanent degeneration of both upper and lower motor neurons. Many different genes and pathophysiological processes contribute to this disease, however its exact cause remains unclear. Therefore, it is necessary to understand this heterogeneity to find effective treatments. In this review, we focus on selected environmental and genetic risk factors predisposing to ALS and highlight emerging treatments in ALS therapy. Of numerous defective genes associated with ALS, we focus on four principal genes that have been identified as definite causes of ALS: the SOD1 gene, C9orf72, TDP-43, as well as the recently identified TBK1. We also provide up-to-date information on selected environmental factors that have historically been considered as key players in ALS development and pathogenesis. In parallel to our survey of known risk factors, we also discuss emerging ALS stem cell therapies and experimental medicines currently undergoing phase II and III clinical trials.

Background. The aim of this article is to present a short review of noninvasive, nonpharmacological treatment methods used in somatic illnesses that fall under the umbrella of approach called behavioral medicine. Methods. The narrative review method was applied in the study. Science paper databases, including PubMed, had been used to retrieve papers on therapeutic methods used in clinical setting that meet the broad criteria of behavioral medicine definition as stated in the Charter of International Behavioral Medicine Society Results. Main groups of methods, disorders in which they are being employed and their effectiveness, have been identified. Conclusions. Behavioral medicine is grouping treatment methods and interventions that hold large potential for clinical setting. Two groups of methods can be distinguished by the scrutiny and level of evidence gathered in their effectiveness assessment; for biofeedback, guided imagery, and hypnosis techniques, comprehensive evidence reports in the framework of U.S. Evidence Synthesis Program exist. Meditation techniques, disclosure therapies, and relaxation methods are less well assessed. Broader employment of behavioral medicine therapies in clinical setting is possible after addressing two major problems in the field, which are deficiencies in quality evidence of effectiveness for many of the methods and their insufficiencies in underlying therapeutic mechanism knowledge.

Objective: This study introduces the ATTARI-12-PL, a Polish adaptation of the ATTARI-12 questionnaire designed to measure general attitudes toward artificial intelligence (AI) across cognitive, affective, and behavioral facets. Method: An online sample of 332 participants was used for the psychometric evaluation of the ATTARI-12-PL scale. The internal structure and consistency of the scale were assessed, and the validity of the questionnaire was evaluated through correlation analyses. Results: Single-factor model did not provide adequate fit in confirmatory factor analysis (CFA). In contrast, a bifactor model incorporating a general attitude factor alongside specific orthogonal factors (attitude facets and item wording) demonstrated very good fit indices and accounted well for the data structure. The scale showed high internal consistency (Cronbach's α = 0.94). Strong negative correlations were found between AI attitude and negative attitude toward interacting with robots, moderate negative correlations with AI anxiety and negative attitude toward robots with human traits, and a mild negative association with belief in human nature uniqueness (BHNU). The scale also showed a strong positive correlation with other AI measurement tool: AIAS-4-PL. Conclusions: The ATTARI- 12-PL is a reliable and valid tool for measuring general attitudes toward AI in the Polish language context. The scale's properties and significant correlations with related constructs support its use in future research on AI attitudes.

Psychology students face limitations in opportunities for supervised practice, constrained by access to clients, the costs of standardized patient programs, and ethical barriers. Novel teaching instruments in the form of multimodal avatars and human simulations have been proposed as innovative solutions, yet their effectiveness remains unclear. This review aimed to answer the question: to what extent do existing systems support skill acquisition in psychology training? A structured search of nine databases (2015–2025) identified seven eligible studies. Solutions from the studies were classified into five categories: basic virtual patient simulations, chatbot-based avatars, mixed reality avatars, virtual reality avatars, and virtual world avatars. Results show that digital simulation solutions are feasible, engaging, and accepted by students. There is evidence of improved training in diagnostic reasoning, questioning strategies, and self-efficacy. However, traditional actor-based simulations are still more effective for developing interpersonal skills. Technological issues, such as limited interactivity and the Uncanny Valley effects, hurt realism and reduce training gains. In conclusion, these technologies have promise as supplements to traditional training. Yet, the field remains underdeveloped and will require further design-focused improvements to reach its full potential.

Background/Objectives: COVID-19, caused by the SARS-CoV-2 virus, emerged in late 2019 and rapidly became a global pandemic, affecting over 200 countries. The pandemic has had profound impacts on global health, the economy, and mental health, leading to increased anxiety and more cases of posttraumatic stress disorder in the general population. This study aimed to evaluate the factors influencing the long-term psychological effects, both positive and negative, seen in the late stages of the COVID-19 pandemic in Poland (starting in the second half of 2021). It combined specific COVID-19 anxiety factors with a broader general mental health assessment to identify significant associations. Methods: Data were collected from 416 participants through paper-based and online questionnaires, with 235 valid responses gathered in total. This study utilized the Coronavirus Anxiety Scale; General Health Questionnaire (GHQ-28); and, to study Positive Trauma Effects (PTEs) and Negative Trauma Effects (NTEs), the Changes in Outlook Questionnaire. Data analysis was performed using the R language and a Generalized Additive Model analysis was also performed. Results: The study found generally low levels of COVID-19 anxiety and mental distress among participants. Significant predictors of NTEs included COVID-19 anxiety and general mental health status, which explained 47% of the variance. PTEs were significantly associated with gender, with women experiencing higher PTE levels relative to men. Conclusions: The findings indicate that combining specific ailment anxiety measurements with general mental health assessments enhances our ability to predict Negative Trauma Effects. Addressing mental health symptomatology and well-being during mass health crises is crucial to mitigate long-lasting psychological damage.