Search Results (29)

Attachment styles and defense mechanisms are widely recognized as central components in personality development. However, few empirical studies have examined their combined contribution to maladaptive personality traits within the dimensional framework of the DSM-5 Alternative Model for Personality Disorders (AMPD). This study investigated the extent to which adult attachment styles and defense mechanisms predict the five AMPD maladaptive personality domains: negative affectivity, detachment, antagonism, disinhibition, and psychoticism. Data were collected from a community sample of 400 adults (190 males, 47.5%), aged 18 to 69 years (M = 36.96; SD = 11.59). Hierarchical multiple regression analyses were conducted for each maladaptive personality domain to examine the predictive roles of attachment styles and defense mechanisms. Our findings indicate that each maladaptive personality domain is associated with specific configurations of attachment styles and defense mechanisms. In conclusion, the findings suggest the relevance of assessing adult attachment styles and defensive functioning in clinical contexts in order to deepen the understanding of the individuals’ personality profiles. Full article

Background: Systemic Lupus Erythematosus (SLE) and Antiphospholipid Syndrome (APS) are two common autoimmune disorders for which the choice of drug regimen is clinically crucial. However, due to drug-drug interactions and individual differences, the therapeutic process faces greater risks. Methods: In this study, we propose a drug recommendation model that combines drug combination frequency, risk assessment, and genetic interaction information with the aim of providing personalized, low-risk treatment options for patients with lupus erythematosus and antiphospholipid syndrome. We extracted drug combination frequencies and drug-gene interaction information from data sources, such as the MIMIC-III clinical database, Drug Bank, and Gene Expression Omnibus. The model comprehensively evaluates the frequency of drug combinations, the risk level, and the gene interaction information through a greedy algorithm to recommend the optimal drug alternatives. Results: The experimental results show that the model is able to effectively reduce the potential risk between drugs while ensuring the drug treatment effect. In addition, the performance evaluation of the drug recommendation model shows that the model performs well under different drug combinations and clinical scenarios, and can provide clinicians with effective drug substitution suggestions. Conclusions: This study provides an important theoretical basis and technical support for advancing the realization of personalized therapy and precision medicine. Full article

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that progressively impairs motor and communication abilities. Globally, the prevalence of ALS was estimated at approximately 222,800 cases in 2015 and is projected to increase by nearly 70% to 376,700 cases by 2040, primarily driven by demographic shifts in aging populations, and the lifetime risk of developing ALS is 1 in 350–420. Despite international advancements in assistive technologies, a recent national survey in Saudi Arabia revealed that 100% of ALS care providers lack access to eye-tracking communication tools, and 92% reported communication aids as inconsistently available. While assistive technologies such as speech-generating devices and gaze-based control systems have made strides in recent decades, they primarily support English speakers, leaving Arabic-speaking ALS patients underserved. This paper presents SOUTY, a cost-effective, mobile-based application that empowers ALS patients to communicate using gaze-controlled interfaces combined with a text-to-speech (TTS) feature in Arabic language, which is one of the five most widely spoken languages in the world. SOUTY (i.e., “my voice”) utilizes a personalized, pre-recorded voice bank of the ALS patient and integrated eye-tracking technology to support the formation and vocalization of custom phrases in Arabic. This study describes the full development life cycle of SOUTY from conceptualization and requirements gathering to system architecture, implementation, evaluation, and refinement. Validation included expert interviews with Human–Computer Interaction (HCI) expertise and speech pathology specialty, as well as a public survey assessing awareness and technological readiness. The results support SOUTY as a culturally and linguistically relevant innovation that enhances autonomy and quality of life for Arabic-speaking ALS patients. This approach may serve as a replicable model for developing inclusive Augmentative and Alternative Communication (AAC) tools in other underrepresented languages. The system achieved 100% task completion during internal walkthroughs, with mean phrase selection times under 5 s and audio playback latency below 0.3 s. Full article

Obstructive sleep apnea (OSA) is a prevalent and heterogeneous sleep disorder associated with significant health and societal burdens. While continuous positive airway pressure (CPAP) remains the gold standard treatment, its limitations in adherence and patient tolerance have highlighted the need for alternative therapies. Positional therapy (PT), which targets apneas that occur predominantly in the supine position, has emerged as a promising option for individuals with positional OSA (POSA). This narrative review synthesizes the current literature on PT, examining its clinical indications, typologies, comparative efficacy with CPAP, oral appliances, and hypoglossal nerve stimulation, as well as data on adherence and barriers to long-term use. Traditional methods such as the tennis ball technique have largely been replaced by modern vibrotactile devices, which demonstrate improved comfort, adherence, and comparable short-term outcomes in selected POSA subjects. While PT remains inferior to CPAP in reducing overall AHI and oxygen desaturation, it performs favorably in terms of mean disease alleviation (MDA) and sleep continuity. Importantly, treatment effectiveness is influenced by both anatomical and non-anatomical traits, underscoring the need for accurate phenotyping and individualized care. PT should be considered within a broader patient-centered model that incorporates preferences, lifestyle, and motivational factors. Further research is needed to validate long-term efficacy, optimize selection criteria, and integrate PT into personalized OSA management strategies. Full article

We present a novel method to estimate blood cortisol concentration from sweat cortisol measurements, incorporating a kinetic model to simulate cortisol transport dynamics. Cortisol dysregulation is observed in conditions like Cushing’s syndrome, characterized by excessive cortisol production, and stress-related disorders, which can lead to metabolic disturbances, anxiety, and impaired overall health. Sweat-sensing technology offers a non-invasive and continuous alternative to blood sampling. However, the limited research exploring the sweat–blood cortisol relationship in patients shows a moderate correlation ($R<0.6$), hindering its clinical application for long-term monitoring. In this paper, we propose a novel kinetic model describing cortisol transport from blood to sweat. The model was validated using data from 44 patients before and after cardiac surgery. A high Pearson correlation coefficient of 0.95 (95% CI: 0.92–0.97) was observed between our model’s estimated and experimental blood cortisol concentrations. Moreover, the method enables personalized estimation of physiological parameters, accurately reflecting patients’ status under varying clinical conditions. The method paves the way for the clinical application of long-term, non-invasive monitoring of cortisol using sweat-sensing technology. Enabling the personalized estimation of physiological parameters could potentially support clinical decision-making, helping doctors diagnose and monitor patients with health conditions involving cortisol dysregulation. Full article

Obstructive sleep apnea syndrome (OSAS) in children and adolescents is a prevalent and multifactorial disorder associated with significant short- and long-term health consequences. While adenotonsillectomy (AT) remains the first-line treatment, a substantial proportion of patients—especially those with obesity, craniofacial anomalies, or comorbid conditions—exhibit persistent or recurrent symptoms, underscoring the need for individualized and multimodal approaches. This review provides an updated and comprehensive overview of current and emerging treatments for pediatric OSAS, with a focus on both surgical and non-surgical options, including pharmacological, orthodontic, and myofunctional therapies. A narrative synthesis of recent literature was conducted, including systematic reviews, randomized controlled trials, and large cohort studies published in the last 10 years. The review emphasizes evidence-based indications, mechanisms of action, efficacy outcomes, safety profiles, and limitations of each therapeutic modality. Adjunctive and alternative treatments such as rapid maxillary expansion, mandibular advancement devices, myofunctional therapy, intranasal corticosteroids, leukotriene receptor antagonists, and hypoglossal nerve stimulation show promising results in selected patient populations. Personalized treatment plans based on anatomical, functional, and developmental characteristics are essential to optimize outcomes. Combination therapies appear particularly effective in children with residual disease after AT or with specific phenotypes such as Down syndrome or maxillary constriction. Pediatric OSAS requires a tailored, multidisciplinary approach that evolves with the child’s growth and clinical profile. Understanding the full spectrum of available therapies allows clinicians to move beyond a one-size-fits-all model, offering more precise and durable treatment pathways. Emerging strategies may further redefine the therapeutic landscape in the coming years. Full article

Background/Objectives: Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, often persisting into adolescence and adulthood, with significant impacts on social, academic, and occupational functioning. Emerging research highlights the role of personality traits in ADHD, suggesting their influence on symptom presentation, functional impairments, and treatment adherence. This study aims to investigate maladaptive personality domains and traits in adolescents with ADHD using the Alternative Model of Personality Disorders (AMPD) framework and the Personality Inventory for DSM-5 Short Form (PID-5-SF), with a particular focus on the differences among same-sex peers and differences from typically developing peers. Methods: This study included 30 ADHD and 25 TD adolescents aged 11–17 (12.6 ± 2.1 vs. 14.9 ± 1.7; p < 0.001), recruited at IRCCS Centro Neurolesi “Bonino Pulejo”. Participants underwent clinical assessments, cognitive evaluation, and standardized rating scales, with maladaptive personality traits measured using the PID-5-SF. Chi-squared and Mann–Whitney U tests were used to compare the two groups, as appropriate. Results: ADHD adolescents showed significantly higher scores than TD peers in restricted affectivity (p = 0.007), distractibility (p = 0.001), impulsivity (p = 0.049), irresponsibility (p = 0.036), hostility (p = 0.046), perseveration (p = 0.010), submissiveness (p = 0.023), and risk-taking (p = 0.032). Among personality domains, disinhibition was significantly higher in the ADHD group (p = 0.002), while detachment approached significance. Female ADHD participants scored higher than TD females in restricted affectivity, distractibility, and risk-taking. Conclusions: These findings suggest that maladaptive personality traits play a key role in ADHD during adolescence, highlighting the need for tailored interventions. Integrating personality assessment into clinical practice may enhance diagnostic accuracy and support more effective, individualized treatment strategies. Full article

Background/Objectives: The Level of Personality Functioning Scale—Brief Form 2.0 (LPFS-BF 2.0) is a self-report screening measure of personality impairments according to the DSM-5 Alternative Model for Personality Disorders and the ICD-11 classification of personality disorders. Nevertheless, reliable cut-off scores that could help in clinical decision making are still lacking for many populations. The aim of this study was to develop severity benchmarks of the LPFS-BF 2.0 for a Polish population based on the item response theory (IRT) approach. Methods: A sample of Polish adults (n = 530) took part in the study. The participants assessed their personality functioning and pathological personality traits and provided information about psychiatric diagnosis and psychotherapy seeking. The severity benchmarks were developed using IRT and validated using mean and frequency comparisons between groups of different personality impairments according to the developed cut-offs. Results: Confirmatory factor analysis (CFA) supported a unidimensional model of the LPFS-BF 2.0. The graded IRT model indicated satisfactory item functioning for all LPFS-BF 2.0 items. The normative observed score thresholds at different latent severity levels of personality impairments were developed, and significant overall differences were found between the LPFS-BF 2.0 norm-based severity benchmarks in pathological personality traits and psychotherapy seeking. Conclusions: The IRT-based cut-offs for the LPFS-BF 2.0 identified individuals high on pathological personality traits (particularly disinhibition) and were predictive of psychotherapy seeking. The developed severity benchmarks allow for the interpretation of LPFS-BF 2.0 scores, supporting clinical diagnosis and relevant decision making in the Polish population. Practical implications for healthcare practice and research are being discussed. Full article

Background/Objectives: Depressive disorders are diagnosed using categorical definitions provided by DSM-5 and ICD-11. However, categorization for diagnostic purposes fails to account for the inherently dimensional nature of depression. Artificial categorization may impede research and obstruct the achievement of optimal treatment outcomes. Methods: The current study utilized a Canadian historical dataset called the National Population Health Survey (NPHS) to explore a simple alternative approach that does not depend on categorization. The NPHS collected complete data from 5029 participants through biannual interviews conducted in 1994–2010. Data collection included the K6 Distress Scale as well as the Composite International Diagnostic Interview Short Form for Major Depression. Data from the National Population Health Survey (NPHS) were used to quantify vulnerability to depressive symptoms through longitudinal K6 Distress Scale assessments. Variability of symptoms across this dimension of apparent vulnerability was quantified using ordinal regression, adjusting for age and sex. Results: Predicted probabilities from these models were used in simulations to produce a visualization of the epidemiology and to explore clinical implications. Conclusions: Consideration of these two dimensional factors (estimated overall level of vulnerability to depression and variability over time) is already a component of clinical assessment and is also accessible to repeated measurement in settings adopting measurement-based care. More formal consideration of these elements may provide a complementary approach to categorical diagnostic assessment and an opportunity for greater personalization of care and improved clinical outcomes. Future studies should validate these findings in diverse clinical settings to ensure their applicability in real-world contexts. Full article

Background/Objectives: In the new conceptualization of personality disorders (PD) in ICD-11 and Diagnostic and Statistical Manual 5 Alternative Model of Personality Disorders (DSM-5 AMPD), identity development in terms of impaired personality functioning plays a central role in diagnostic guidelines and determining PD severity. On the one hand, there is a temporary identity crisis while keeping an integrated sense of identity and, on the other hand, there is pathological identity diffusion, which is associated with a high risk of a current or emerging PD. The latter is characteristic not only of borderline PD but of all personality disorders and should be detected as early as possible to prevent chronic illness and critical life courses. Maternal psychopathology is linked to several areas of child psychopathology (e.g., eating disorders, depression). In the current study, its potential to predict a child’s impaired identity development is investigated. Methods: A total of 101 mothers were asked about their health status 2 weeks after the birth of their child and when their child was 6 weeks, 4 months, 14 months and 5.5 years of age. Specifically, physical and psychological symptoms were assessed with SCL-90-R. In addition, their children were assessed in young adulthood regarding their identity development with the AIDA (Assessment of Identity Development in Adolescence) questionnaire. Linear regression models were used to investigate the amount of explanation of children’s identity diffusion by maternal symptom burden. Results: Maternal psychopathology significantly predicted identity diffusion at all time points with small effect sizes, while after 14 months, the explanation model showed a medium effect size. Conclusions: The present data suggest a relevant influence of maternal psychopathological symptoms on their children’s identity development in terms of functioning that has not yet been empirically shown in a longitudinal study. This finding highlights the importance of including further factors (particularly on the part of the child) in longitudinal studies and of investigating this clinically highly relevant relationship in greater depth. Full article

To people living with Alzheimer’s Disease-Related Disorders (ADRD), timely and coordinated communication is essential between their informal caregivers and healthcare providers. In provider shortage areas, for example, the state of Nevada, telehealth can be an effective primary care delivery alternative to in-person visits. To evaluate the cost-effectiveness of telehealth visits for people living with ADRD in the state of Nevada, a decision-analytic Markov model was developed from healthcare system perspectives with a 10-year horizon/1-year cycle. To estimate the effects of demographic and geographic parameters on the Markov model, race parameters were divided into non-Hispanic White individuals vs. others and location parameters were divided into urban vs. rural. A 12-item short-version Zarit Burden Interview (ZBI-12) was applied to measure the informal caregiver burdens of non-institutionalized people living with ADRD. The values of mortality rate and healthcare utilization were obtained from healthcare systems’ publicly available payor administrative data and Nevada State Inpatient/Emergency Department datasets. Among urban-residing non-Hispanic White individuals, the Incremental Cost-Effectiveness Ratio (ICER) per modified ZBI-12 indicated a cost saving of USD 9.44 with telehealth visits; among urban-residing racial minorities, the ICER per modified ZBI-12 indicated a cost saving of USD 29.26 with in-person visits; and among rural residents, the ICER per modified ZBI-12 indicated a cost-saving of USD 320.93 with telehealth visits. Distributional differences in the cost-saving effects of telehealth primary care were noted in line with racial and geographic parameters. Workforce and caregiver training is necessary for reducing distributional differences, especially among urban-residing racial monitories living with ADRD in the provider shortage area of the state of Nevada. Full article

Background: There is ongoing research into the potential use of psychedelics and 3,4-methylenedioxy-methamphetamine (MDMA) as alternatives to commonly used medications for treating major depressive and anxiety disorders. Aims: We aimed to assess the efficacy of psychedelics and MDMA in managing depressive and anxiety symptoms and evaluate their safety profiles. Methods: We searched five databases for randomized controlled trials of psychedelics and MDMA targeting depressive and anxiety symptoms and conducted a meta-analysis using a random effects model when possible. The review protocol is registered in PROSPERO under CRD42022341325. Results: Psilocybin induced a rapid and sustained reduction in depressive and anxiety symptoms in patients with major depressive disorder and in patients with life-threatening cancer. MDMA induced a decrease in depressive symptoms in patients with life-threatening cancer, autism spectrum disorder, and post-traumatic stress disorder. MDMA’s effect size was either negligible or negative in reducing generalized anxiety symptoms, but MDMA reduced social anxiety symptoms. Ayahuasca induced a reduction in depressive symptoms in individuals with treatment-resistant major depressive and personality disorders. Lysergic acid diethylamide (LSD) induced a decrease in anxiety symptoms in individuals with life-threatening cancer. Psilocybin’s adverse effects were noticeable for elevated blood pressure, headaches, and panic attacks. For MDMA, elevated blood pressure, headaches, panic attacks, and feeling cold were noticeable. Conclusions: Psychedelics (psilocybin, ayahuasca, and LSD) and MDMA have the potential to induce a reduction in depressive and anxiety symptoms. Adverse effects are noticed. Rigorous randomized controlled studies with larger sample sizes utilizing instruments with better reliability and validity are warranted. Full article

Animal experimentation has long been a cornerstone of neurology research, but it faces growing scientific, ethical, and economic challenges. Advances in artificial intelligence (AI) are providing new opportunities to replace animal testing with more human-relevant and efficient methods. This article explores the potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models. These approaches can better recapitulate human brain physiology, predict drug responses, and uncover novel insights into neurological disorders. They also offer faster, cheaper, and more ethical alternatives to animal experiments. Case studies demonstrate AI’s ability to accelerate drug discovery for Alzheimer’s, predict neurotoxicity, personalize treatments for Parkinson’s, and restore movement in paralysis. While challenges remain in validating and integrating these technologies, the scientific, economic, practical, and moral advantages are driving a paradigm shift towards AI-based, animal-free research in neurology. With continued investment and collaboration across sectors, AI promises to accelerate the development of safer and more effective therapies for neurological conditions while significantly reducing animal use. The path forward requires the ongoing development and validation of these technologies, but a future in which they largely replace animal experiments in neurology appears increasingly likely. This transition heralds a new era of more humane, human-relevant, and innovative brain research. Full article

This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts. Full article

Bipolar Disorder is associated with high rates of suicidal thoughts, behaviors, and outcomes, yet the lived experience of suicidality and Bipolar Disorder is not particularly well understood. Understanding the role of psychosocial aetiologies in suicidality outcomes for those living with Bipolar Disorder is key for developing appropriately targeted interventions focusing on factors that are amenable to change. In line with PRISMA guidance, we conducted a scoping review to identify the types of psychosocial factors studied in relation to the experience of suicidality for people living with Bipolar Disorder diagnoses. Systematic literature searches identified a sample of 166 articles from which key study data were extracted and charted. A narrative synthesis of the reviewed literature is presented ordered by the factors investigated across studies, a frequency count of the types of psychological/social aetiologies studied, and a brief overview of the key findings for each aetiology. Most of the identified literature took the form of quantitative cross-sectional studies, with only one qualitative study and 18 quantitative prospective studies. The most studied aetiologies were trauma (specifically early adverse experiences and childhood traumas) and stressful life events, impulsivity (primarily subjective self-reported trait impulsivity), social support and functioning, and personality/temperament factors. Only six studies in the final sample reported basing their research questions and/or hypotheses on an explicit theoretical model of suicide. The literature was primarily focused on using self-report measurements of key aetiologies and on factors which lead to worsened suicidality rather than focusing on potentially protective or buffering factors. Future research needs to better justify the aetiologies investigated in relation to suicidality outcomes for people living with Bipolar Disorder, including a firmer basis in theory and hypothesis testing, more prospective designs, and the use of alternative assessments of psychosocial aetiologies in addition to self-report questionnaires. Full article