Search Results (1,548)

Background: Cognitive behavioral therapy (CBT) has shown consistent efficacy in individuals with psychosis, as supported by many trials. One classical distinction is that between affective and non-affective psychosis. Few studies have specifically examined the possible moderating role of substantial affective elements. In this systematic review and meta-regression analysis, we assess how CBT response differs across the affective spectrum in psychosis. Methods: We included studies assessing various CBT modalities, including third-wave therapies, administered in people with psychosis. The study protocol is published in the Open Science Framework. Meta-regression was conducted to assess whether the proportion of participants with affective psychosis (AP), as proxied by a documented diagnosis of schizoaffective (SZA) disorder, moderated CBT efficacy across positive, negative, and depressive symptom domains. Results: The literature search identified 4457 records, of which 39 studies were included. The median proportion of SZA disorder participants was 17%, with a total of 422 AP participants represented. Meta-regression showed a trend toward lower CBT efficacy for positive symptoms with a higher SZA disorder proportion (β = +0.10 SMD per 10% increase in AP; p = 0.12), though it was not statistically significant. No significant associations were found for negative (β = +0.05; p = 0.73) or depressive symptoms (β = −0.02; p = 0.78). Heterogeneity was substantial across all models (I² ranging from 54% to 80%), and funnel plot asymmetry was observed in negative and depressive symptoms, indicating possible publication bias. Risk of bias assessment showed the anticipated inherent difficulty of psychotherapies in blinding and possibly dropout rates affecting some studies. Conclusions: Affective symptoms may reduce the effectiveness of CBT for positive symptoms in psychotic disorders, although the findings did not reach statistical significance. Other patient-level characteristics in psychosis could indicate which patients can benefit most from CBT modalities. Full article

Background/Objectives: Feigning spectrum behavior (FSB) is the exaggeration, fabrication, or false imputation of symptoms. It occurs in compensable injury with great cost to society by way of loss of productivity and excessive costs. The aim of this study is to identify feigning by developing cut scores on the long and short forms (SF) of the Orebro Musculoskeletal Pain Screening Questionnaire (OMPSQ and OMPSQ-SF) and the Perceived Stress Scale (PSS and PSS-4). Methods: As part of pre-screening for a support program, 40 injured workers who had been certified unfit for work for more than 2 weeks were screened once with the OMPSQ and PSS by telephone by a mental health professional. A control sample comprised of 40 non-injured community members were screened by a mental health professional on four occasions under different aliases, twice responding genuinely and twice simulating an injury. Results: Differences between the workplace injured people and the community sample were compared using ANCOVA with age and gender as covariates, and then receiver operator characteristics (ROCs) were calculated. The OMPSQ and OMPSQ-SF discriminated (ρ < 0.001) between all conditions. All measures discriminated between the simulation condition and workplace injured people (ρ < 0.001). Intraclass correlation demonstrated the PSS, PSS-4, OMPSQ, and OMPSQ-SF were reliable (ρ < 0.001). Area Under the Curve (AUC) was 0.750 for OMPSQ and 0.835 for OMPSQ-SF for work-injured versus simulators. Conclusions: The measures discriminated between injured and non-injured people and non-injured people instructed to simulate injury. Non-injured simulators produced similar scores when they had multiple exposures to the test materials, showing the uniformity of feigning spectrum behavior on these measures. The OMPSQ-SF has adequate discriminant validity and sensitivity to feigning spectrum behavior, making it optimal for telephone screening in clinical practice. Full article

Background/Objectives: Pituitary tumors account for over 90% of all sellar region masses. However, a spectrum of rare neoplastic, inflammatory, infectious, and vascular lesions—benign and malignant—can arise in the intra- and parasellar compartments and clinically and radiologically mimic PitNETs. We report a cohort of 47 such rare and cystic midline intracranial lesions, emphasizing their distinctive morphological, clinical, and imaging features and the personalized treatment strategies applied. Methods: In this retrospective single-center study, we reviewed all patients treated for suspected PitNETs via transsphenoidal approach between 2015 and 2024. Of 529 surgical cases, we excluded confirmed PitNETs, meningiomas, and classical intradural craniopharyngiomas. Collected data encompassed patient demographics, tumor characteristics, presenting symptoms, extent of resection or medical therapy, endocrine outcomes, and follow-up information. Results: Among all 529 patients who underwent surgical treatment for sellar lesions from 2015 to 2024, 47 cases (8.9%) were identified as rare or cystic masses. Forty-six underwent transsphenoidal resection; one patient with hypophysitis received corticosteroid therapy alone. Presenting symptoms included headache (n = 16), dizziness (n = 5), oculomotor disturbances (n = 2), and visual impairment (n = 17). Endocrine dysfunction was found in 30 patients, 27 of whom required hydrocortisone replacement. Histopathological diagnoses were led by colloid cysts (n = 14) and Rathke’s cleft cysts (n = 11). The remaining 22 cases comprised plasmacytoma, germinoma, lymphoma, pituicytoma, inverted papilloma, metastatic carcinoma, chordoma, nasopharyngeal carcinoma, chloroma, and other rare entities. Preoperative imaging diagnosis proved incorrect in 38% (18/47) of cases, with several lesions initially misidentified as PitNETs. Conclusions: Nearly 9% of presumed PitNETs were rare, often benign or inflammatory lesions requiring distinct management. Most could be safely resected and demonstrated excellent long-term outcomes. Yet, despite advanced imaging techniques, accurate preoperative differentiation remains challenging, with over one-third misdiagnosed. Clinical red flags—such as early hormone deficits, rapid progression or atypical imaging findings—should prompt early interdisciplinary evaluation and, when indicated, image-guided biopsy to avoid unnecessary surgery and ensure tailored therapy. Full article

Background/Objectives: Cannabidiol (CBD) is a non-intoxicating cannabinoid touted for a variety of medical benefits, including alleviation of anxiety. While legalization of hemp-derived products in the United States (containing ≤0.3% delta-9-tetrahydrocannabinol [d9-THC] by weight) has led to a rapid increase in the commercialization of hemp-derived CBD products, most therapeutic claims have not been substantiated using clinical trials. This trial aimed to assess the impact of 6 weeks of treatment with a proprietary hemp-derived, full-spectrum, high-CBD sublingual solution similar to those available in the marketplace in patients with anxiety. Methods: An open-label pilot clinical trial (NCT04286594) was conducted in 12 patients with at least moderate levels of anxiety. Patients self-administered a hemp-derived, high-CBD sublingual solution twice daily during the 6-week trial (target daily dose: 30 mg/day CBD). Clinical change over time relative to baseline was assessed for anxiety, mood, sleep, and quality of life, as well as changes in cognitive performance on measures of executive function and memory. Safety and tolerability of the study product were also evaluated. Results: Patients reported significant reductions in anxiety symptoms over time. Concurrent improvements in mood, sleep, and relevant quality of life domains were also observed, along with stable or improved performance on all neurocognitive measures. Few side effects were reported, and no serious adverse events occurred. Conclusions: These pilot findings provide initial support for the efficacy and tolerability of the hemp-derived, high-CBD product in patients with moderate-to-severe levels of anxiety. Double-blind, placebo-controlled studies are indicated to obtain robust data regarding efficacy and tolerability of these types of products for anxiety. Full article

Diagnosing hypermobility disorders and Ehlers-Danlos syndrome (EDS) in children is challenging due to overlapping features with generalized joint hypermobility (GJH) and the lack of biomarkers. Background/Objectives: This study aims to describe the clinical and genetic features of pediatric EDS patients and identify key comorbidities and correlations. Methods: This is a single-center observational study of patients under 18 diagnosed with suspicion of EDS (2018–2024) at a tertiary pediatric hospital. Diagnoses were made using 2017 criteria. Results: Forty-one patients (46% female; mean age 11.1 ± 2.8 years) were included. Based on 2017 criteria, 61% had hypermobile EDS (hEDS)/hypermobility spectrum disorder (HSD), 22% classical EDS, 7.3% vascular, and 9.7% other subtypes. Musculoskeletal (90.2%), cutaneous (68.3%), and psychiatric (56.1%) symptoms were most frequent. Significant associations included older age with psychiatric symptoms (p = 0.029), Beighton score with dislocations (p = 0.026), and less atrophic scarring in hEDS (p < 0.008). Genetic testing (73% performed) confirmed pathogenic variants (11 novel) in EDS with a known molecular cause. Conclusions: This study proposes a clinically guided approach and diagnostic algorithm for youth hypermobility, emphasizing precision medicine principles, while highlighting the urgent need for further research to identify hEDS biomarkers. Full article

Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims to evaluate and compare five major LLMs (Grok, ChatGPT, Gemini, Claude.ai, and Meta AI) regarding dry eye syndrome information delivery across different demographic groups. Methods: LLMs were queried using standardized prompts simulating a 62-year-old patient with dry eye symptoms across four demographic categories (White, Black, East Asian, and Hispanic males and females). Responses were analyzed for word count, readability, cultural sensitivity scores (0–3 scale), keyword coverage, and response times. Results: Significant variations existed across LLMs. Word counts ranged from 32 to 346 words, with Gemini being the most comprehensive (653.8 ± 96.2 words) and Claude.ai being the most concise (207.6 ± 10.8 words). Cultural sensitivity scores revealed Grok demonstrated highest awareness for minority populations (scoring 3 for Black and Hispanic demographics), while Meta AI showed minimal cultural tailoring (0.5 ± 0.5). All models recommended specialist consultation, but medical term coverage varied significantly. Response times ranged from 7.41 s (Meta AI) to 25.32 s (Gemini). Conclusions: While all LLMs provided appropriate referral recommendations, substantial disparities exist in cultural sensitivity, content depth, and information delivery across demographic groups. No LLM consistently addressed the full spectrum of dry eye causes across all demographics. These findings underscore the importance for physician oversight and standardization in AI-generated healthcare information to ensure equitable access and prevent care delays. Full article

Sensorimotor integration along the gastrointestinal (GI) tract is crucial for normal gut function yet remains poorly understood in the context of neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD). The genetic tractability of zebrafish allows investigators to generate molecularly defined models that provide a means of studying the functional circuits of digestion in vivo. Optical transparency during development allows for the use of optogenetics and calcium imaging to elucidate the mechanisms underlying GI-related symptoms associated with ASD. The array of commonly reported symptoms implicates altered sensorimotor integration at various points along the GI tract, from the pharynx to the anus. We will examine the reflex arcs that facilitate swallowing, nutrient-sensing, absorption, peristalsis, and evacuation. The high level of conservation of these processes across vertebrates also enables us to explore potential therapeutic avenues to mitigate GI distress in ASD and other NDDs. Full article

Background/Objectives: Inborn errors of immunity (IEIs) represent a wide spectrum of diseases characterized by a predisposition to recurrent infections, as well as increased susceptibility to autoimmune, atopic, and autoinflammatory diseases and malignancies. The aim of this study was to report the registry-based frequency and describe the clinical characteristics of IEIs among patients in the Republic of Kazakhstan. Methods: We analyzed data from 269 patients belonging to 204 families who were either self-referred or referred by healthcare providers to the University Medical Center of Nazarbayev University with suspected IEIs. All patients resided in various regions across Kazakhstan. Results: A total of 269 diagnosed cases were identified in the national registry. The estimated prevalence was 1.3 per 100,000 population. The gender ratio was nearly equal, with 139 males and 130 females. The median age at diagnosis was 5 years (range: 1 month to 70 years), while the mean age was 11.3 years. The most common diagnosis was humoral immunodeficiency, observed in 120 individuals (44.6%), followed by complement deficiencies in 83 individuals (30.8%). Combined immunodeficiencies with syndromic features were found in 35 patients (13%), and phagocytic cell defects were identified in 12 patients (4.5%). The predominant clinical manifestations included severe recurrent infections and autoimmune cytopenias, while atopic and autoinflammatory symptoms were reported less frequently. Conclusions: These findings contribute to a better understanding of the registry-based distribution and clinical spectrum of IEIs in Kazakhstan and underscore the importance of early diagnosis and targeted care for affected individuals. Full article

Background/Objectives: Autism spectrum disorder (ASD) encompasses several neurodevelopmental disorders, whose onset is correlated to genetic and environmental factors. Although the etiopathogenesis is not entirely clear, the involvement of inflammatory processes, the endocannabinoid system, and alterations in the permeability and composition of the intestinal microbiota are known to occur. Methods: This review systematically explores the literature available to date on the most widely used murine models for the study of ASD, the main biomarkers investigated for the diagnosis of ASD, and the therapeutic potential of probiotics, with a particular focus on the use of strains of Lactiplantibacillus (Lpb.) plantarum in in vivo models and clinical trials for ASD. Results: Several studies have demonstrated that targeting multifactorial biomarkers in animal models and patients contributes to a more comprehensive understanding of the complex mechanisms underlying ASD. Moreover, accumulating evidence supports the beneficial effect of probiotics, including Lpb. plantarum, as a promising alternative therapeutic strategy, capable of modulating gut–brain axis communication. Conclusions: Probiotic supplementation, particularly with selected Lpb. plantarum strains, is emerging as a potential complementary approach for ameliorating ASD-related gastrointestinal and behavioral symptoms. However, further large-scale clinical studies are essential to validate their efficacy and determine optimal treatment protocols and dietary strategies. Full article

Background and Objectives: Toe walking (TW) is frequently observed in children with Autism Spectrum Disorder (ASD), yet its clinical significance and association with comorbid conditions remain poorly understood. This study aimed to examine the prevalence of TW in a large Italian cohort of children with ASD and to explore its association with ASD severity, sleep disturbances, feeding behaviors, and gastrointestinal symptoms. Materials and Methods: A total of 289 children with ASD and 289 typically developing controls (TDC), matched for age and sex, were evaluated in a multicentric observational study. TW was assessed during neurodevelopmental evaluations. Sleep quality was assessed using the Sleep Disturbance Scale for Children (SDSC), feeding behaviors via the Brief Autism Mealtime Behavior Inventory (BAMBI), and gastrointestinal symptoms through clinical reporting. Statistical analyses included Chi-square tests, Mann–Whitney U tests, Spearman correlations, and logistic regressions. Results: TW was significantly more prevalent in the ASD group (27.3%) than in TDC (5.5%, p < 0.0001). Within the ASD group, TW occurred in 50.5% of children with Level 3 severity but was absent in Levels 1 and 2 (p < 0.0001). Males exhibited TW more frequently than females. Children with TW had higher SDSC scores (ρ = 0.33, p < 0.0001), though no subscale independently predicted TW. Constipation was reported in 100% of children with Level 3 ASD and was strongly correlated with SDSC total scores (ρ = 0.58, p < 0.0001). The Disorders of Arousal (DA) subscale emerged as an independent predictor of constipation (β = 0.184, p = 0.019). Conclusions: TW in ASD appears to be a marker of greater neurodevelopmental severity and is associated with sleep disturbances and gastrointestinal dysfunction. These findings support the hypothesis that TW may reflect broader dysfunctions involving the gut–brain axis, sensory processing, and motor control. The routine clinical assessment of TW should include the evaluation of sleep and somatic symptoms to better understand the multisystemic nature of ASD phenotypes. Full article

Porcine epidemic diarrhea virus (PEDV) infection induces severe, often fatal, watery diarrhea and vomiting in neonatal piglets, characterized by profound dehydration, villus atrophy, and catastrophic mortality rates approaching 100% in unprotected herds. This study developed a composite probiotic from Min-pig-derived Lactobacillus crispatus LCM233, Ligilactobacillus salivarius LSM231, and Lactiplantibacillus plantarum LPM239, which exhibited synergistic growth, potent acid/bile salt tolerance, and broad-spectrum antimicrobial activity against pathogens. In vitro, the probiotic combination disrupted pathogen ultrastructure and inhibited PEDV replication in IPI-2I cells. In vivo, PEDV-infected piglets administered with the multi-strain probiotic exhibited decreased viral loads in anal and nasal swabs, as well as in intestinal tissues. This intervention was associated with the alleviation of diarrhea symptoms and improved weight gain. Furthermore, the multi-strain probiotic facilitated the repair of intestinal villi and tight junctions, increased the number of goblet cells, downregulated pro-inflammatory cytokines, enhanced the expression of barrier proteins, and upregulated antiviral interferon-stimulated genes. These findings demonstrate that the multi-strain probiotic mitigates PEDV-induced damage by restoring intestinal barrier homeostasis and modulating immune responses, providing a novel strategy for controlling PEDV infections. Full article

Background/Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. However, traditional diagnostic procedures for identifying autism spectrum disorder (ASD) typically involve lengthy clinical examinations, which can be both time-consuming and costly. This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. Methods: In this study, by collecting data from various rehabilitation centers across Pakistan, we applied a specific AUTOML tool known as Tree-based Pipeline Optimization Tool (TPOT) for ASD detection. Notably, this study marks one of the initial explorations into utilizing AUTOML for ASD detection. The experimentations indicate that the TPOT provided the best pipeline for the dataset, which was verified using a manual machine learning method. Results: The study contributes to the field of ASD diagnosis by using AUTOML to determine the likelihood of ASD in children at prompt stages of evolution. The study also provides an evaluation of precision, recall, and F1-score metrics to confirm the correctness of the diagnosis. The propose TPOT-based AUTOML framework attained an overall accuracy 78%, with a precision of 83%, a recall of 90%, and an F1-score of 86% for the autistic class. Conclusions: In summary, this research offers an encouraging approach to improve the detection of autism spectrum disorders (ASD) in children, which could lead to better results for affected individuals and their families. Full article

Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. Results: Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85–99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9–12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. Conclusions: AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD. Full article

Background/Objectives: Atypical sensory processing is increasingly recognized as a transdiagnostic dimension of neurodevelopmental disorders (NDDs), with critical implications for emotional and behavioral regulation. This study aimed to identify distinct sensory profiles in preschool children with NDDs and to examine their associations with emotional–behavioral and cognitive/developmental functioning. Methods: A total of 263 children (aged 21–71 months) diagnosed with autism spectrum disorder (ASD), language disorder (LD), or other NDDs (ONDD) were recruited. Sensory processing was assessed using the SPM-P, emotional–behavioral functioning was assessed via the CBCL 1½–5, and cognitive/developmental levels were assessed through standardized instruments. Latent profile analysis (LPA) was conducted to identify sensory subtypes. Group comparisons and multinomial logistic regression were used to examine profile characteristics and predictors of profile membership. Results: Three sensory profiles emerged: (1) Multisystemic Sensory Dysfunction (20.1%), characterized by pervasive sensory and emotional difficulties, primarily observed in ASD; (2) Typical Sensory Processing (44.9%), showing normative sensory and emotional functioning, predominantly LD; and (3) Mixed Subclinical Sensory Processing (35%), with subclinical-range scores across multiple sensory and emotional domains, spanning all diagnoses. Higher cognitive functioning and fewer internalizing symptoms significantly predicted membership in the typical profile. A gradient of symptom severity was observed across profiles, with the Multisystemic group showing the most pronounced emotional–behavioral impairments. Conclusions: Distinct sensory–emotional phenotypes were identified across diagnostic categories, supporting a dimensional model of neurodevelopment. Sensory profiles were strongly associated with emotional functioning, independently of diagnostic status. Early sensory assessment may therefore offer clinically meaningful insights into emotional vulnerability and inform targeted interventions in preschool populations with NDDs. Full article

Autism Spectrum Disorder (ASD) encompasses various neurological disorders with symptoms varying by age, development, genetics, and other factors. Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engagement issues. Diagnosing ASD poses challenges as signs can appear at early stages of life, leading to delayed diagnoses. Traditional diagnosis relies mainly on clinical observation, which is a subjective and time-consuming approach. However, AI-driven techniques, primarily those within machine learning and deep learning, are becoming increasingly prevalent for the efficient and objective detection and classification of ASD. In this work, we review and discuss the most relevant related literature between January 2016 and May 2024 by focusing on ASD detection or classification using diverse technologies, including magnetic resonance imaging, facial images, questionnaires, electroencephalogram, and eye tracking data. Our analysis encompasses works from major research repositories, including WoS, PubMed, Scopus, and IEEE. We discuss rehabilitation techniques, the structure of public and private datasets, and the challenges of automated ASD detection, classification, and therapy by highlighting emerging trends, gaps, and future research directions. Among the most interesting findings of this review are the relevance of questionnaires and genetics in the early detection of ASD, as well as the prevalence of datasets that are biased toward specific genders, ethnicities, or geographic locations, restricting their applicability. This document serves as a comprehensive resource for researchers, clinicians, and stakeholders, promoting a deeper understanding and advancement of AI applications in the evaluation and management of ASD. Full article