Background and Objectives: Although nonsuicidal self-injury (NSSI), by definition, excludes suicidal intent, numerous studies show associations between NSSI and suicidal phenomena in clinical and outpatient adolescent samples. Given the growing interest in the relationship between NSSI and suicidal phenomena, the present study aimed to investigate the relationship between NSSI and suicidal beliefs in adolescent psychiatric inpatients. Materials and Methods: The study sample included 50 adolescent inpatients at a specialized facility, with a mean age of 15.44 ± 1.39, who fulfilled DSM-5 criteria for NSSI. For study purposes, we use the Ottawa Self-Injury Inventory (OSI) and Brief Suicide Cognitions Scale (B-SCS). Statistical data processing was performed in the R software 4.3.0 (R Core Team, Vienna, Austria). Results: Of all NSSI functions, the Internal ER function score was the highest (18.72 ± 7.08), followed by External ER (8.10 ± 3.11), Social Influence (5.88 ± 5.37), and Sensation Seeking (3.44 ± 2.98). The mean Craving (C) score was 14.06 ± 7.51. The mean value of the B-SCS score was 19.54 ± 5.24. It was found that the B-SCS score is significantly related to Internal ER (r = 0.441, p < 0.001) and Craving (r = 0.297, p = 0.036). The multivariable model shows that internal ER function and participants’ age are significantly related to the B-SCS score. Conclusion: Despite the limitations of the study, it is emphasized that cognitions occurring across the fluid suicidal belief system alone do not fully capture the complexity of suicide, but assessing the suicidal belief system in NSSI inpatient adolescents could nevertheless provide helpful information for identifying individuals who may have an elevated vulnerability to experiencing suicidal ideas and behaviors over time.

Background: Depression and anxiety disorders remain among the leading contributors to global disability and represent a major public health challenge. Although evidence-based psychotherapies are available, access to treatment remains limited due to structural, economic, geographical, and workforce-related barriers. Digital mental health interventions have emerged as scalable approaches to reducing this treatment gap, with artificial intelligence (AI)-guided cognitive behavioral therapy (CBT) representing a rapidly developing and clinically relevant extension of digital psychotherapy. Objective: This review aims to synthesize current evidence on digital and AI-guided CBT interventions for depression and anxiety, with a focus on clinical utility, scalability, mechanisms of change, safety considerations, and public health relevance. In addition, the review proposes a clinically oriented conceptual framework for understanding the role of AI-guided CBT within contemporary digital psychiatry. Methods: A focused narrative review was conducted using PubMed, Scopus, and Google Scholar databases, covering publications from 2010 to 2025. Relevant peer-reviewed studies, systematic reviews, meta-analyses, and conceptual papers addressing digital CBT, AI-assisted CBT, conversational agents, symptom monitoring, and digital mental health implementation were identified and analyzed qualitatively. Results: Existing evidence suggests that internet-delivered CBT, mobile applications, and AI-based conversational agents may reduce depressive and anxiety symptoms, particularly in individuals with mild to moderate conditions. However, the evidence base remains heterogeneous, with limitations including short follow-up periods, variability in intervention quality, reliance on self-reported outcomes, and insufficient data on long-term effectiveness, safety, and real-world implementation. Emerging concepts such as digital therapeutic alliance, continuous symptom monitoring, adaptive intervention delivery, and AI-driven personalization may represent key factors influencing engagement and clinical outcomes. Conclusions: AI-guided CBT represents a promising but still evolving component of modern mental health care. These technologies have the potential to improve accessibility, optimize resource allocation, and support stepped-care and hybrid models of treatment. Future research should prioritize rigorous clinical validation, long-term outcome evaluation, transparent safety protocols, ethical governance, and integration into real-world health systems. AI-guided CBT should not be understood as a replacement for clinicians, but as a complementary and scalable extension of evidence-based psychotherapy.

Introduction/Objective: Autism spectrum disorder (ASD) is a complex neuro-developmental condition with a multifactorial and not yet fully understood etiology. Following the publication of Andrew Wakefield's 1998 paper suggesting a possible association between the measles, mumps and rubella (MMR) vaccine and ASD, public confidence in vaccination declined substantially. This review aimed to examine and summarise the current scientific evidence on the association between MMR vaccination and ASD. Methods: A narrative review of the available scientific literature was conducted, focusing on epidemiological studies, cohort studies, systematic reviews, and meta-analyses evaluating the potential association between MMR vaccination and ASD. Results: The reviewed evidence consistently shows no causal association between MMR vaccination and ASD development. Conversely, declining vaccination coverage has contributed to the re-emergence of measles outbreaks in several countries. Current evidence strongly supports the safety, effectiveness, and favourable benefit-risk profile of the MMR vaccine. Conclusion: Available scientific evidence confirms that the MMR vaccine does not increase the risk of ASD. Promoting evidence-based communication and maintaining high vaccination coverage remain essential to prevent vaccine-preventable diseases and address vaccine hesitancy.

Artificial intelligence (AI)-assisted digital mental health interventions are being increasingly discussed as a potential strategy for improving access to psychological support among adolescents with anxiety symptoms and anxiety disorders. Their possible advantages include flexible availability, reduced barriers to help-seeking, psychoeducation, symptom monitoring, CBT-informed exercises, conversational interaction, and personalized feedback. However, their clinical role remains insufficiently defined, particularly regarding efficacy, safety, ethical governance, and the distinction between digital support and psychotherapy in the traditional clinical sense. This narrative review examines current evidence and conceptual debates regarding AI-assisted digital interventions relevant to adolescent anxiety disorders, with a focus on usability, acceptability, preliminary efficacy, safety, ethical and legal considerations, and human clinical oversight. Available literature suggests that conversational agents and CBT-informed digital tools may provide low-threshold support and may reduce the symptoms of anxiety, depression, or general emotional distress in some users. Nevertheless, existing evidence remains heterogeneous and preliminary, often relying on young adult, mixed-age, non-clinical, or transdiagnostic samples, short follow-up periods, and self-report outcomes. Direct evidence for AI-assisted interventions specifically in adolescents with formally diagnosed anxiety disorders remains limited, and findings from broader youth, young adult, mixed-age, or transdiagnostic samples should not be directly generalized to this clinical population. It should not be considered a replacement for therapist-delivered psychotherapy, clinical assessment, or specialist child and adolescent mental health care. Their most appropriate current role is as adjunctive or supportive tools within supervised, stepped-care, or blended-care models. Future research should include adolescent-specific clinical trials, anxiety-focused outcomes, longer follow-up, systematic safety reporting, and clear evaluation of human oversight models. AI-assisted digital interventions may help reduce parts of the adolescent mental health treatment gap; however, their use must remain evidence-informed, developmentally sensitive, ethically regulated, and clinically supervised.