PsyCARE study: assessing impact, cost-effectiveness, and transdiagnostic factors of the Italian ministry of health’s “psychological bonus” policy

Background: The prevalence of anxiety and depression disorders is surging worldwide, prompting a pressing demand for psychological interventions, especially in less severe cases. Responding to this need, the Italian government implemented the “Psychological Bonus” (PB) policy, allotting 25 million euros for mental health support. This policy entitles individuals to a minimum of four to twelve psychological sessions. In collaboration with the National Board of Italian Psychologists, our study assesses this policy’s effectiveness. Indeed, the PsyCARE study aims to examine the utilization of the Psychological Bonus, evaluate its impact on adult and adolescent participants’ psychological well-being through pre- and post-intervention assessments and six-month follow-up, and conduct a longitudinal cost-effectiveness analysis of this policy. A secondary aim is to investigate the influence of these interventions on transdiagnostic factors, including emotion regulation and epistemic trust. Methods: The study involves licensed psychotherapists and their patients, both adults and adolescents, benefiting from the Psychological Bonus. Data collection is underway and set to conclude in December 2023. Psychotherapists will provide diagnostic information and assess patient functioning. In addition, patients will be evaluated on mental health aspects such as clinical symptoms, emotion regulation, epistemic trust, and quality of life. We will employ linear mixed-effects models to analyze the outcomes, accounting for both fixed and random effects to capture the hierarchical structure of the data. Discussion: We anticipate the study’s findings will highlight reduced psychological distress and improved quality of life for participants and demonstrate the Psychological Bonus policy’s cost-effectiveness. The study will gather data on the role of specific versus nonspecific therapeutic factors in psychotherapy while adopting a patient-tailored approach to identify effective therapeutic elements and examine transdiagnostic factors. Overall, this study’s findings will guide future measures within the Italian healthcare system, fostering a psychological health culture and providing valuable insights to the broader public. Study registration: https://osf.io/6zk2j

Dispositional optimism as a correlate of decision-making styles in adolescence

Despite the numerous psychological areas in which optimism has been studied, including career planning, only a small amount of research has been done to investigate the relationship between optimism and decision-making styles. Consequently, we have investigated the role of dispositional optimism as a correlate of different decision-making styles, in particular, positive for effective styles and negative for ineffective ones (doubtfulness, procrastination, and delegation). Data were gathered through questionnaires administered to 803 Italian adolescents in their last 2 years of high schools with different fields of study, each at the beginning stages of planning for their professional future. A paper questionnaire was completed containing measures of dispositional optimism and career-related decision styles, during a vocational guidance intervention conducted at school. Data were analyzed using stepwise multiple regression. Results supported the proposed model by showing optimism to be a strong correlate of decision-making styles, thereby offering important intervention guidelines aimed at modifying unrealistically negative expectations regarding their future and helping students learn adaptive decision-making skills

Lateral specialization in unilateral spatial neglect : a cognitive robotics model

In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans

Loss of miR-204 expression is a key event in melanoma

Cutaneous melanoma (CM) is a malignancy with increasing occurrence. Its microRNA repertoire has been defined in a number studies, leading to candidates for biological and clinical relevance: miR-200a/b/c, miR-203, miR-205, miR-204, miR-211, miR-23b and miR-26a/b. Our work was aimed to validate the role of these candidate miRNAs in melanoma, using additional patients cohorts and in vitro cultures. miR-26a, miR-204 and miR-211 were more expressed in normal melanocytes, while miR-23b, miR-200b/c, miR-203 and miR-205 in epidermis and keratinocytes. None of the keratinocyte-related miRNAs was associated with any known mutation or with clinical covariates in melanoma. On the other hand, the loss of miR-204 was enriched in melanomas with NRAS sole mutation (Fisher exact test, P = 0.001, Log Odds = 1.67), and less frequent than expected in those harbouring CDKN2A mutations (Fisher exact test, P = 0.001, Log Odds − 1.09). Additionally, miR-204 was associated with better prognosis in two independent melanoma cohorts and its exogenous expression led to growth impairment in melanoma cell lines. Thus, miR-204 represents a relevant mechanism in melanoma, with potential prognostic value and its loss seems to act in the CDKN2A pathway, in cooperation with NRAS

Evaluation of MCM-2 Expression in TMA Cervical Specimens

Background:Minichromosome maintenance proteins (MCM) are highly expressed in actively replicating cells. The need for biological markers for cervical carcinoma and its precursor lesions is emerging. Our main aim was to determine the immunohistochemical expression of MCM-2 in HIV-positive and -negative dysplastic cervical specimens. Methods:Immunohistochemical analysis of MCM-2 was performed in a total of 352 cervical TMA specimens of normal control, low-grade CIN, high-grade CIN and invasive tumor. 38 specimens were from HIV-positive women. A receiver operating characteristic (ROC) curve was constructed to determine the best cutoff to diagnose high-grade CIN and invasive cervical cancer. Results:In the progression from normal epithelium to high-grade CIN and invasive tumor we found significant differences in the MCM-2 expression (p,0.05). Based on the ROC curve of 80% with an area under the curve (AUC) of 0.78, expression of MCM-2 to diagnose high-grade CIN and invasive tumor resulted in sensitivity of 81%, specificity of 66%, a positive predictive value (PPV) of 86% and a negative predictive value (NPV) of 57%. HIV-positive cervices revealed a decreasing expression of MCM-2 in both LGCIN and HGCIN compared with HIV-negative specimens (p,0.0001). Conclusions:The present study suggests that immunohistochemical MCM-2 may not be a promising biomarker for diagnosing high-grade CIN and invasive cance

A network study to differentiate suicide attempt risk profiles in male and female patients with major depressive disorder

Suicide attempts are a possible consequence of Major Depressive Disorder (MDD), although their prevalence varies across different epidemiological studies. Suicide attempt is a significant predictor of death by suicide, highlighting its importance in understanding and preventing tragic outcomes. Researchers are increasingly recognizing the need to study the differences between males and females, as several distinctions emerge in terms of the characteristics, types and motivations of suicide attempts. These differences emphasize the importance of considering gender-specific factors in the study of suicide attempts and developing tailored prevention strategies. We conducted a network analysis to represent and investigate which among multiple neurocognitive, psychosocial, demographic and affective variables may prove to be a reliable predictor for identifying the 'suicide attempt risk' (SAR) in a sample of 81 adults who met DSM-5 criteria for MDD. Network analysis resulted in differences between males and females regarding the variables that were going to interact and predict the SAR; in particular, for males, there is a stronger link toward psychosocial aspects, while for females, the neurocognitive domain is more relevant in its mnestic subcomponents. Network analysis allowed us to describe otherwise less obvious differences in the risk profiles of males and females that attempted to take their own lives. Different neurocognitive and psychosocial variables and different interactions between them predict the probability of suicide attempt unique to male and female patients

Abstract concept learning in cognitive robots

Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior

The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis

Introduction: The Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD). Methods: To better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment. Results: At baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node. Conclusion: NIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections