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ABSTRACT The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others.

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and commitment therapy (ACT) holds promise for enhancing motivation and well-being among the elderly, although delivering such psychological interventions is hindered by limited access to services, prompting exploration of remote delivery options like mobile applications. In this paper, we introduce the BrainHeart App (v.1.1.8), a mobile application tailored to improve physical and mental well-being in seniors. The app features a 10-day ACT program and other sections promoting healthy lifestyle. In a pilot study involving twenty participants, individuals engaged in daily mental exercises for 10 days using the app. Clinical evaluations, including assessments of psychological flexibility, overall cognitive profile, mindfulness disposition, cognitive fusion, and heart rate collected with Polar H10, were conducted at baseline (T0) and one month post-intervention (T1). Analysis revealed significant improvements in almost all neuropsychological scores, with high usability reported (system usability scale average score: 82.3 ± 9.31). Additionally, a negative correlation was found between usability and experiential avoidance (r = −0.51; p = 0.026), and a notable difference in heart rate was observed between baseline and post-intervention (F-value = 3.06; p-value = 0.09). These findings suggest that mindfulness-ACT exercises delivered via the BrainHeart App can enhance the well-being of elderly individuals, highlighting the potential of remote interventions in addressing mental health needs in this population.

Cardiovascular and neurological diseases are a major cause of mortality and morbidity worldwide. Such diseases require careful monitoring to effectively manage their progression. Artificial intelligence (AI) offers valuable tools for this purpose through its ability to analyse data and identify predictive patterns. This review evaluated the application of AI in cardiac and neurological diseases for their clinical impact on the general population. We reviewed studies on the application of AI in the neurological and cardiological fields. Our search was performed on the PubMed, Web of Science, Embase and Cochrane library databases. Of the initial 5862 studies, 23 studies met the inclusion criteria. The studies showed that the most commonly used algorithms in these clinical fields are Random Forest and Artificial Neural Network, followed by logistic regression and Support-Vector Machines. In addition, an ECG-AI algorithm based on convolutional neural networks has been developed and has been widely used in several studies for the detection of atrial fibrillation with good accuracy. AI has great potential to support physicians in interpretation, diagnosis, risk assessment and disease management.

Heart failure (HF) is a growing epidemic, affecting millions of people worldwide, and is a major cause of mortality, morbidity, and impaired quality of life. Traditional cardiac rehabilitation is a valuable approach to the physical and quality-of-life recovery of patients with cardiovascular disease. The innovative approach of remote monitoring through telemedicine offers a solution based on modern technologies, enabling continuous collection of health data outside the hospital environment. Remote monitoring devices present challenges that could adversely affect patient adherence, resulting in the risk of dropout. By applying a cognitive-behavioral model, we aim to identify the antecedents of dropout behavior among patients adhering to traditional cardiac rehabilitation programs and remote monitoring in order to improve the latter. Our study was conducted from October 2023 to January 2024. In the first stage, we used data from literature consultation. Subsequently, data were collected from the direct experience of 49 health workers related to both remote monitoring and traditional treatment, recruited from the authors’ workplace. Results indicate that patients with cardiovascular disease tend to abandon remote monitoring programs more frequently than traditional cardiac rehabilitation therapies. It is critical to design approaches that take these barriers into account to improve adherence and patient satisfaction. This analysis identified specific antecedents to address, helping to improve current monitoring models. This is crucial to promote care continuity and to achieve self-management by patients in the future.

The implementation of cognitive health apps in patients with mild cognitive impairment (MCI) is challenging because of their cognitive, age, and other clinical characteristics. In this project, we aimed to evaluate the usability and feasibility of the Rehastart app tested in MCI patients. Eighteen subjects affected by MCI due to neurodegenerative disorders (including Parkinson’s disease, multiple sclerosis, and amnestic/multidomain MCI) and eighteen healthcare professionals were recruited to this study. Patients were registered on the app by clinicians and they were assigned a protocol of specific cognitive exercises. The recruitment was conducted in the period between March and June 2023. The trial testing of the app consisted of three sessions per week for three weeks, with each session lasting about 30 min. After three weeks, the participants as well as medical personnel were invited to rate the usability and feasibility of the Rehastart mobile application. The instruments employed to evaluate the usability and feasibility of the app were the System Usability Scale (SUS), The Intrinsic Motivation Inventory (IMI) and the Client Satisfaction Questionnaire (CSQ). We did not find statistically significant differences on the SUS (p = 0.07) between healthcare professionals and patients. In addition, we found promising results on subscales of the Intrinsic Motivation Inventory, suggesting high levels of interest and enjoyment when using the Rehastart app. Our study demonstrated that smartphone-based telerehabilitation could be a suitable tool for people with MCI due to neurodegenerative disorders, since the Rehastart app was easy to use and motivating for both patients and healthy people.

Objective Emergency psychological interventions are needed in patients with COVID-19. During the pandemic, psychological counseling services have been provided using online platforms to address adverse psychological impacts and symptoms in patients and the general population. We investigated the effects of telepsychotherapy on emotional well-being and psychological distress in patients affected by COVID-19. Methods Forty-five Sicilian patients who had contracted COVID-19 joined “Telecovid Sicilia” from March to June 2020. Participants completed self-assessment questionnaires and psychological testing to measure levels of anxiety, presence of depressive symptoms, and altered circadian rhythm with consequent sleep disorders and psychological distress. Individual telepsychotherapy services were provided for 1 hour, twice a week, for 16 sessions in total. Results We enrolled 45 patients (42.2% women). We found significant changes between baseline and the end of follow-up in all outcome measures, especially depression (χ 2 (1) = 30.1; effect size [ES] = 0.82), anxiety (χ 2 (1) = 37.4; ES = 0.91), and paranoid ideation (χ 2 (1) = 5.6; ES = 0.35). The proportion of participants with sleep disorders decreased to 84.1% after intervention (χ 2 (1) = 58.6; ES = 1.14). Conclusion A telepsychotherapeutic approach showed promising effects on psychological symptoms, with significantly reduced patient anxiety and depression.

Background and Objectives: In March 2020, COVID-19 pandemic affected the world. All countries, to limit viral transmission, imposed quarantine. This emergency exerted personal, social, economic, and psychological impact on people. For health systems, was needed to create alternative care pathways. Telemedicine can be helpful to reduce isolation, provide health care services, and monitor virus infections. Italian regions, including Sicily, have activated telemedicine services for management of patients with COVID-19. Objective: The purpose of study is to describe a Sicilian telemedicine model for management of COVID-19 patients, showing results on feasibility, usability and quality of service and patient satisfaction. Materials and Methods: This is a descriptive exploratory study on a telemedicine service for residents in Messina infected by COVID-19. It included monitoring of vital signs and specialist consultations (i.e., doctor, psychologists, social workers, and nutritionist biologists). Results: More than twenty percent (23.8%) of participants used tele-monitoring and tele-counselling services; 14.3% were only telemonitored. Participants judged positively telemedicine service (30% were quiet and 50% were very satisfied), as well as tool (70% were quiet and 10% were very satisfied). Telemonitoring had a low agreement (10% were slightly satisfied and 50% were neutral); tele-counselling had a high rate of satisfaction (40% quiet and 60% were very satisfied). Conclusions: This study showed that telemedicine model for Sicilian population affected by COVID-19 was feasible, easy to use and appreciated by patients. Our promising results allow us to assume that if in Sicily there was a return of the emergency, we would be ready to manage it This system can be the solution to remote management of these patients, to reduce isolation, provide health services, and monitor virus infections. The use of this technology should encourage future research to change the health care system and provides opportunities to ensure health and care for oneself and others.

This review examines the factors that affect the decision-making process of parental couples evaluating prenatal screening and diagnostic tests. A systematic search was performed using PubMed and PsycInfo databases. The 46 included studies had to: investigate the decision-making process about prenatal testing; focus on tests detecting trisomy 21, 18, 13, and abnormalities of sex chromosomes; be published in English peer-reviewed journals. The decision-making process seems composed of different levels: an individual level with demographic, clinical, and psychological aspects; a contextual level related to the technical features of the test and the information received; a relational level involving family and society.

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and commitment therapy (ACT) holds promise for enhancing motivation and well-being among the elderly, although delivering such psychological interventions is hindered by limited access to services, prompting exploration of remote delivery options like mobile applications. In this paper, we introduce the BrainHeart App (v.1.1.8), a mobile application tailored to improve physical and mental well-being in seniors. The app features a 10-day ACT program and other sections promoting healthy lifestyle. In a pilot study involving twenty participants, individuals engaged in daily mental exercises for 10 days using the app. Clinical evaluations, including assessments of psychological flexibility, overall cognitive profile, mindfulness disposition, cognitive fusion, and heart rate collected with Polar H10, were conducted at baseline (T0) and one month post-intervention (T1). Analysis revealed significant improvements in almost all neuropsychological scores, with high usability reported (system usability scale average score: 82.3 ± 9.31). Additionally, a negative correlation was found between usability and experiential avoidance (r = −0.51; p = 0.026), and a notable difference in heart rate was observed between baseline and post-intervention (F-value = 3.06; p-value = 0.09). These findings suggest that mindfulness-ACT exercises delivered via the BrainHeart App can enhance the well-being of elderly individuals, highlighting the potential of remote interventions in addressing mental health needs in this population. [ABSTRACT FROM AUTHOR]

Cardiovascular and neurological diseases are a major cause of mortality and morbidity worldwide. Such diseases require careful monitoring to effectively manage their progression. Artificial intelligence (AI) offers valuable tools for this purpose through its ability to analyse data and identify predictive patterns. This review evaluated the application of AI in cardiac and neurological diseases for their clinical impact on the general population. We reviewed studies on the application of AI in the neurological and cardiological fields. Our search was performed on the PubMed, Web of Science, Embase and Cochrane library databases. Of the initial 5862 studies, 23 studies met the inclusion criteria. The studies showed that the most commonly used algorithms in these clinical fields are Random Forest and Artificial Neural Network, followed by logistic regression and Support-Vector Machines. In addition, an ECG-AI algorithm based on convolutional neural networks has been developed and has been widely used in several studies for the detection of atrial fibrillation with good accuracy. AI has great potential to support physicians in interpretation, diagnosis, risk assessment and disease management. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is a growing epidemic, affecting millions of people worldwide, and is a major cause of mortality, morbidity, and impaired quality of life. Traditional cardiac rehabilitation is a valuable approach to the physical and quality-of-life recovery of patients with cardiovascular disease. The innovative approach of remote monitoring through telemedicine offers a solution based on modern technologies, enabling continuous collection of health data outside the hospital environment. Remote monitoring devices present challenges that could adversely affect patient adherence, resulting in the risk of dropout. By applying a cognitive-behavioral model, we aim to identify the antecedents of dropout behavior among patients adhering to traditional cardiac rehabilitation programs and remote monitoring in order to improve the latter. Our study was conducted from October 2023 to January 2024. In the first stage, we used data from literature consultation. Subsequently, data were collected from the direct experience of 49 health workers related to both remote monitoring and traditional treatment, recruited from the authors' workplace. Results indicate that patients with cardiovascular disease tend to abandon remote monitoring programs more frequently than traditional cardiac rehabilitation therapies. It is critical to design approaches that take these barriers into account to improve adherence and patient satisfaction. This analysis identified specific antecedents to address, helping to improve current monitoring models. This is crucial to promote care continuity and to achieve self-management by patients in the future. [ABSTRACT FROM AUTHOR]

The basic equations, governing the one-dimensional motion in an isothermal no-slip drift flux model, are considered. Using the invariance group properties of the original system, the new autonomous system is found. The propagation of weak discontinuities is considered in the known particular solution of the autonomous system. The critical time, when these discontinuities culminate into a shock, is also determined. [ABSTRACT FROM AUTHOR]