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PubMed2026

Comparison of clinical efficacy and gut microbiota characteristics in children with ASD treated with fecal microbiota transplantation and ketogenic diet.

OBJECTIVE: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and interaction, along with restricted, repetitive patterns of behavior. It is often accompanied by gastrointestinal dysfunction and gut microbiota dysbiosis. Fecal Microbiota Transplantation (FMT) and the Ketogenic Diet (KD) are interventions targeting the gut microbiota for ASD. METHODS: 30 participants were diagnosed with ASD according to DSM-5 and ADOS-2. ASD core symptoms were evaluated with CARS and ABC. Gut microbiota composition was analyzed by shotgun metagenomic sequencing. RESULTS: Both groups demonstrated significant improvements in core symptoms. In the FMT group, the mean CARS score significantly decreased from 34.87 to 33.53 (p&#x2009;<&#x2009;0.01); in the KD group, it declined from 35.13 to 33 (p&#x2009;<&#x2009;0.01). The mean ABC score reduced from 79.93 to 69.33 (p&#x2009;=&#x2009;0.064) in the FMT group and from 63.07 to 42.73 (p&#x2009;<&#x2009;0.01) in the KD group. Following the intervention, no statistically significant changes were observed in &#x3b1;-diversity or &#x3b2;-diversity within either group. LEfSe analysis revealed distinct post-intervention microbial signatures: FMT significantly enriched butyrate-producing taxa (Wujia chipingensis, Eubacterium sp. MSJ-33, and Butyrivibrio crossotus), while KD elevated Blautia massiliensis and decreased propionate metabolism -associated taxa (Veillonella sp. S12025-13 and Veillonella nakazawae). KEGG enrichment analysis revealed that KD enriched propionate metabolism (Fold enrichment&#x2009;=&#x2009;3.747, q&#x2009;=&#x2009;0.010) and aromatic compound degradation (Fold enrichment&#x2009;=&#x2009;3.591, q&#x2009;=&#x2009;0.010). CONCLUSIONS: Both interventions significantly improved clinical symptoms among children with ASD, potentially through distinct patterns of gut microbiota modulation. CLINICAL TRIALS NUMBER: NCT06348433 (03/21/2024).

Child↗
PubMed2026

Gene-environment interaction between perinatal oxytocin exposure and Pten mutation shapes epigenetic reprogramming of oxytocin signaling and behavior in mice.

Synthetic oxytocin (Pitocin) is the most commonly used pharmacologic agent for induction and augmentation of labor. Beyond its uterotonic effects, oxytocin plays a critical role in neurodevelopment and social behavior. Dysregulated oxytocin signaling has been implicated in autism spectrum disorder (ASD), raising concern that perinatal exposure to exogenous oxytocin may have lasting neurodevelopmental consequences. This study aimed to determine whether offspring harboring a genetic predisposition for ASD are differentially impacted by perinatal oxytocin exposures, with a focus on long-term oxytocin signaling and autism-like behavior. Pregnant mice carrying offspring with heterozygous mutations in phosphatase and tensin homolog deleted on chromosome ten (Pten), a well-established monogenic risk factor for ASD, received continuous oxytocin versus phosphate-buffered saline (PBS) control via micro-osmotic pumps during late gestation. Wild-type (WT) offspring exposed to each treatment served as a secondary control. Adult offspring were assessed for oxytocin receptor (Oxtr) methylation in the frontal cortex and hippocampus, oxytocin expression in the hypothalamus, serum oxytocin levels, and were subject to a battery of social and anxiety-related behavior tests. Perinatal oxytocin exposure produced genotype-dependent effects in offspring. Epigenetic analyses revealed bidirectional remodeling of Oxtr methylation in the frontal cortex and hippocampus, with increased exon 1 methylation in WT mice and decreased methylation in Pten-mutant mice, resulting in significant genotype-treatment interactions. Hypothalamic oxytocin expression increased following treatment regardless of genotype, though baseline levels were higher in Pten-mutant mice. Neither oxytocin treatment nor genotype impacted long-term serum oxytocin levels. Behavioral outcomes were modest but context-specific: repetitive behaviors and cognition performance were unchanged, but oxytocin-treated Pten-mutant mice exhibited increased anxiety-like behavior alongside improved social memory. In contrast, oxytocin-treated WT mice showed reduced social novelty preference. Exploratory analyses suggested potential sex-dependent trends. Our findings support a model in which genetic susceptibility shapes the epigenetic encoding of early-life hormonal signals, thereby recalibrating oxytocin system function and downstream behavioral outcomes. Together, these data highlight the context-dependent effects of perinatal oxytocin exposure and argue against uniformly beneficial or detrimental effects, emphasizing the importance of gene-environment interactions in neurodevelopmental trajectories.

Animals↗
PubMed2025

Examining Behavioral Interventions for Infancy and Early Toddlerhood: A Systematic Review of Intervention Effects, Parameters, and Participants.

Rapid advancement is paving the way to identify children who would likely benefit from early intervention during the first years of life, prior to the onset of significant delays in development. With the widely acknowledged benefits of early intervention, key questions arise: Does behavioral intervention targeted to infancy and early toddlerhood improve developmental outcomes? What procedures might be used, and under what circumstances? Who do these interventions work for? The current review comprehensively examined the literature on behavioral interventions based in operant learning, focused on key developmental areas with children in the first two years of life. We located and synthesized 69 studies with unique participant cohorts that included 1735 children. The search revealed many studies focused on the first year of life, of which a large proportion investigated approaches to increase communication. We provide implications, limitations, and future directions on how behavioral interventions for infants and young toddlers can inform current practice and future intervention research this population.

Humans↗
PubMed2026

Complexity in disguise: a systematic review of fractal analysis in psychiatric neuroimaging.

OBJECTIVES: Psychiatric diagnosis and fractal studies are complex processes that extend beyond clinical evaluation and require careful methodological considerations in neuroimaging. Over the years, fractals have helped reduce these complexities in research, but they still cannot grant clinical diagnoses. Thus, the main objective was a systematic review exploring the potential applications of fractal analysis in characterizing psychiatric conditions through neuroimaging techniques-including both functional and structural MRI. MATERIALS AND METHODS: A systematic literature review was conducted on PubMed, identifying thirty-nine original studies that met the inclusion criteria. Areas showing statistical significance (p&#x2009;<&#x2009;0.05) were reported. These studies were categorized according to DSM-V classification and examined for the description of psychiatric conditions through the fractal analysis. RESULTS: The review primarily focuses on young adults with psychiatric conditions compared to control groups. Schizophrenia and Autism Spectrum Disorder are major areas of investigation, and fractal dimension (FD) is the primary analysis method used to reflect brain patterns. Studies that calculated whole-brain FD may have underestimated local abnormalities due to the inclusion of a high percentage of tissue, potentially resulting in overlooked findings. Notably, abnormalities in the frontal cortex represent a common neurobiological feature across several psychiatric conditions. CONCLUSIONS: The findings from this systematic review shed light on the use of fractal analysis to quantify complex brain patterns in both psychiatric patients and healthy individuals. However, it is essential to recognize the need for further research to elucidate a fractal analysis protocol that allows for optimal extraction of psychiatric insights. KEY POINTS: Question Fractal analysis applied to structural and functional MRI help characterize brain alterations across psychiatric conditions. Findings This review shows consistent fractal patterns across multiple psychiatric disorders, especially in frontal regions. Despite heterogeneous methodologies, results highlight shared structural and functional abnormalities. Clinical relevance Fractal analysis may offer complementary characterization of subtle brain organization across psychiatric disorders. Its potential clinical utility-such as improving diagnostic characterization, earlier detection, among others-remains limited by the current absence of a standardized protocol.

Humans↗
PubMed2026

Impaired toilet training and bladder and bowel dysfunction in children with developmental coordination disorder - an underreported issue.

BACKGROUND & OBJECTIVE: Children with Developmental Coordination Disorder (DCD) face significant, yet often overlooked, challenges beyond motor impairments, including difficulties with toilet training and bladder and bowel dysfunctions (BBD). This study aims to explore whether children with DCD exhibit greater difficulties in these areas compared to typically developing children (TDC). METHOD: This cross-sectional case-control study included 84 children aged 5-8 years (42 DCD, 42 TDC), matched by school-grade and sex (33 boys and 9 girls per group). Parents completed the Vancouver Symptom Score for Dysfunctional Elimination Syndrome (VSSDES) to assess BBD symptoms, the Dutch DCD-questionnaire (DCD-Q) to evaluate motor coordination, and additional questions regarding toilet training, elimination diagnosis, and co-occurring conditions. Correlations and exploratory group differences were analysed. RESULTS: Parents of children with DCD reported significantly more toilet training difficulties than parents of controls (p < 0.001), including persistent accidents (47.6% vs. 9.5%), prolonged training (45.2% vs. 4.8%), and difficulties recognizing urination urgency (40.5% vs. 2.4%). The parental report of delayed attainment of bowel and bladder control (p < 0.05) and BBD rates (p < 0.001) was also significantly higher in children with DCD. Exploratory analyses indicated that the prevalence of these outcomes was not significantly different between children with DCD with and without co-occurring ADHD or autism, suggesting a possible primary association with DCD. Additionally, across both children with DCD and TDC, poorer motor skills were associated with more BBD symptoms (r = -0.474, p < 0.001). CONCLUSION: Children with DCD have a higher prevalence of toilet training difficulties and BBD compared to TDC, potentially affecting their psychosocial well-being. Greater awareness is crucial for timely and targeted care. Notably, the study's design, directly comparing children diagnosed with DCD to matched TDC, provides new insights into the prevalence of elimination difficulties in this population.

Humans↗
PubMed2026

Two-Year Outcomes of a 211 Care Coordination Trial.

BACKGROUND AND OBJECTIVES: Early screening for developmental concerns enables timely diagnosis and referral, yet many families face barriers accessing services. Prior work showed that early childhood care coordination could improve timely service connection. This study assessed developmental outcomes among children participating in a randomized controlled trial of Information and Referral Federation of Los Angeles County (211LA). METHODS: Participants, aged 21-42&#xa0;months, were randomized to usual care or the 211LA intervention. Developmental and behavioral measures, including the Parental Evaluation of Developmental Status Developmental Milestones Assessment Level (PEDS-DM-AL) and the Child Behavior Checklist (CBCL), were collected at baseline and 24&#xa0;months later. The sample included 499 participants, 250 in the 211LA intervention and 249 in usual care. Primary analyses examined changes in PEDS-DM-AL and CBCL scores over the 24-month period by study arm. Post hoc analyses compared family characteristics between intervention and control families who enrolled in services. RESULTS: Developmental and behavioral measures showed some clinically insignificant change over time, but these changes did not differ by condition (expressive/receptive language skills mastered: P&#x2009;>&#x2009;.9; autism, attention, aggression, and externalizing behavior T scores: P&#x2009;>&#x2009;.4). Post hoc analyses identified potentially relevant imbalances between the treatment arms at baseline as well as in the subgroup that enrolled in services, with families assigned to the 211LA intervention being more likely to have a non-US born parent and a parent with limited English proficiency compared with families assigned to usual care. Intervention families enrolled in services also used telehealth more frequently and received a lower duration of services than those receiving usual care. CONCLUSIONS: This study measured the indirect influence of service enrollment through 211LA care coordination on developmental outcomes. Although increased service enrollment through 211LA did not affect developmental outcomes, we hypothesize this may be because of several factors, including overrepresentation of a subset of historically underrepresented families in the 211LA intervention, suboptimal performance of our developmental assessment tool, and complexity of conducting a trial of this magnitude during the COVID-19 pandemic, which may have diminished the ability of this trial to demonstrate developmental benefits despite demonstrated service enrollment gains.

Humans↗
PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗
PubMed2026

A Systematic Review of Lived Experiences of Receiving a Diagnosis of ADHD in Adulthood.

OBJECTIVE: With rising numbers of adults seeking and receiving ADHD diagnoses, understanding their first-hand experiences of the diagnostic process is key for sensitive support and service design. This systematic review collates, evaluates and synthesises the existing evidence-base on lived experiences of adult ADHD diagnosis. METHOD: Keyword searches of six databases generated 10,357 citations, which were subjected to a systematic screening process that identified 21 relevant studies. Findings were analysed using thematic synthesis. RESULTS: Analysis generated three overarching themes, elaborating how diagnostic experiences are shaped by adults' Relationship with Self, Relationship with Others, and Relationship with Systems. Personally, diagnosis was widely experienced as a pivotal identity event, triggering biographical reflection that could foster greater self-compassion, but also grief, anger and identity confusion. Socially, diagnosis facilitated interpersonal understanding and communication, but also exposed adults to stigma and introduced dilemmas about diagnostic disclosure. Systemically, adults experienced the diagnostic process as beset by barriers and delays, and reported highly variable access to post-diagnosis supports or treatment. CONCLUSION: Results suggest receiving an ADHD diagnosis in adulthood is a complex relational process that can be both validating and destabilising, with variation in experiences resulting from individual biographies, interpersonal resources, stigma climates, and service structures.

Humans↗
PubMed2026

Sitosterolemia: evolving strategies for earlier diagnosis.

PURPOSE OF REVIEW: Sitosterolemia is a rare autosomal recessive lipid disorder caused by biallelic pathogenic variants in ABCG5 or ABCG8 , resulting in excessive intestinal absorption and impaired biliary excretion of plant sterols. Although historically considered exceptionally rare, recent genetic studies suggest the disorder is substantially underdiagnosed, with marked phenotypic heterogeneity ranging from xanthomas and premature atherosclerosis to hematologic abnormalities, and frequently mimics familial hypercholesterolemia. This review summarizes recent advances in the clinical, biological, and genetic diagnosis of sitosterolemia, with a focus on strategies that may facilitate earlier detection. RECENT FINDINGS: Phytosterol quantification, particularly sitosterol, campesterol, and stigmasterol, remains indispensable for accurate diagnosis. Hematologic abnormalities, including hemolytic anemia, stomatocytosis, and macrothrombocytopenia, are increasingly recognized as valuable diagnostic clues complementing the biochemical approach. Expanded variant catalogs for ABCG5/ABCG8 and genome-wide association studies have revealed potentially polygenic contributions to phytosterol metabolism extending beyond these two genes. However, no specific guidelines have yet been established for cascade screening. SUMMARY: Earlier diagnosis requires integration of clinical, biochemical, hematologic, and genetic data. Plasma phytosterol measurement remains the diagnostic cornerstone. Improved disease awareness, broader access to sterol testing, and expanded genetic screening may reduce diagnostic delays and enable timely management, including ezetimibe and dietary phytosterol restriction.

Humans↗
PubMed2026

Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

BACKGROUND: Skin cancer is one of the most prevalent malignancies worldwide, particularly within Caucasian populations. This systematic review and meta-analysis aimed to quantitatively review the current literature on non-invasive diagnosis of skin cancer and evaluate the current evidence to support the use of tools in addition to, or in replacement of clinician face-to-face assessment. METHODS: A literature search was conducted for publications in PubMed, Medline and Embase databases. Articles describing accuracy, sensitivity, specificity and outcomes of their mode of assessment were included. A total of 208 articles met the inclusion criteria. RESULTS AND CONCLUSION: This systematic review and meta-analysis showed that the diagnostic performance of artificial intelligence (AI) in the interpretation of dermatoscopic images was high for melanoma diagnosis, basal cell carcinoma or malignancy, in comparison to dermatoscopic assessment alone by clinicians and experts. Although AI interpretation of images demonstrated higher sensitivity for melanoma diagnosis in comparison to clinical assessment combined with dermatoscopic assessment, it is unclear if this is also the case for basal cell carcinoma and squamous cell carcinoma diagnosis. Reflectance confocal microscopy, a non-invasive high resolution imaging technique, is known to have a high sensitivity for diagnosing cutaneous malignancy, and this may have applications within secondary care. Therefore, AI could help reduce resource burden and aid in clinical assessment, particularly within primary care settings.

Humans↗
PubMed2026

Combining neuromelanin-sensitive MRI and quantitative susceptibility mapping for enhanced diagnosis and differentiation of parkinson's disease: A systematic review.

BACKGROUND: Loss of dopaminergic neurones and iron deposition in the substantia nigra pars compacta (SNpc) are two major pathological hallmarks of Parkinson's disease (PD). Such changes can be visualised by advanced techniques including neuromelanin-sensitive MRI (NM-MRI) and quantitative susceptibility mapping (QSM). This systematic review investigates the diagnostic performance and methodological development of the integrated use of NM-MRI and QSM in PD. METHODS: The systematic search was performed in four databases (Scopus, PubMed, ScienceDirect, and Web of Science) according to the PRISMA 2020 guidelines until July 2026. Bias was assessed using QUADAS-2 and certainty of evidence was assessed using GRADE. RESULTS: Seventeen studies with 2228 participants were included. Combined NM-MRI and QSM consistently showed reduced neuromelanin volume/contrast and increased iron deposition in the SNpc of PD patients compared to healthy controls. Multimodal integration yielded a significant improvement in diagnostic accuracy (AUC values 0.86-0.99), and was able to successfully differentiate PD. Recent methodological advances included simultaneous acquisition sequences (e.g. MTC-GRE, STAGE, setMag) and AI-driven automated segmentation, which led to significantly reduced scan times and improved reproducibility. CONCLUSION: The combination of NM-MRI and QSM has a synergistic effect and provides powerful complementary biomarkers for the diagnosis and differential diagnosis of PD.

Humans↗
PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗
PubMed2026

Prenatal exome sequencing of fetuses with central nervous system anomalies based on prenatal ultrasound and magnetic resonance imaging diagnosis: A retrospective cohort study with a systematic review and meta-analysis.

INTRODUCTION: Fetal central nervous system (CNS) abnormalities have diverse etiologies, with genetic factors as a major contributor. Prenatal exome sequencing (ES) is a powerful tool for precise molecular diagnosis of CNS anomalies, but its diagnostic yield varies among studies. This study aimed to evaluate the additional diagnostic yield of prenatal ES compared with chromosomal microarray analysis (CMA) in fetuses with CNS anomalies detected by prenatal imaging. MATERIAL AND METHODS: We collected ES results from fetuses diagnosed with CNS anomalies by prenatal imaging (2019-2024) who had negative results. Subgroup analyses assessed phenotype-specific ES diagnostic yield for associated genes and variants. A systematic review and meta-analysis incorporating our data and published studies further explored the association between phenotype and diagnostic yield. RESULTS: In the cohort study of 219 cases, ES identified pathogenic/likely pathogenic single nucleotide variations in 36 cases (16%). The highest diagnostic yield of ES was in cases with multisystem malformations (25%, 14/55), followed by multiple CNS anomalies (15%, 2/13) and isolated CNS anomalies (13%, 20/151). The most commonly identified isolated CNS anomaly was agenesis of the corpus callosum (31%, 5/16). Neural tube defects with urogenital anomalies were associated with a positive ES finding in 57% (4/7) of cases. The meta-analysis of 989 cases from 22 studies showed a pooled diagnostic yield of ES of 27% (95% CI, 21%-34%). The highest diagnostic yield of ES was in cases of corpus callosum anomalies with facial abnormalities (75%, 8/11) and neural tube defects with urogenital malformations (80%, 12/15). The diagnostic yield of ES for three or more CNS abnormalities was 43% (95% CI, 31%-58%), significantly higher than that for only two abnormalities (10%, 95% CI, 4%-18%). No significant difference in diagnostic yield was found between cases identified by prenatal MRI combined with ultrasound (27%, 95% CI, 20%-36%) and those identified by ultrasound alone (25%, 95% CI, 17%-35%). CONCLUSIONS: ES provided a significantly higher diagnostic yield than CMA for fetal CNS abnormalities, with diagnostic yields varying by phenotype. The systematic review and meta-analysis confirmed that the complexity and combination of malformations are key factors associated with differences in ES diagnostic yield.

Humans↗
PubMed2026

Non-motor symptoms and healthcare utilization before diagnosis of myasthenia gravis: a nationwide cohort study.

BACKGROUND: Non-motor symptoms have been reported prior to myasthenia gravis (MG) diagnosis. However, the temporal patterns of non-motor symptoms and healthcare utilization before MG diagnosis remain unclear. METHODS: We conducted a retrospective, population-based cohort study using the Korean National Health Insurance Service (KNHIS) database from 2011 to 2021. Incident MG cases were identified using the International Classification of Diseases, Tenth and Rare Intractable Disease codes. Individuals younger than 20&#xa0;&#xa0;years or with missing health screening data were excluded. Each MG case was matched 1:10 by age, sex, and index date to controls. Non-motor symptoms and healthcare utilization were defined using operational criteria derived from KNHIS claims data. Rate ratios (RRs) and 95&#xa0;% confidence intervals (CIs) were estimated across four prespecified intervals (0-1, 1-2, 2-5, and 5-10&#xa0;&#xa0;years) before MG diagnosis. RESULTS: We included 8,355 MG patients and 83,550 controls (mean age, 53.7&#xa0;&#xa0;years; male, 44&#xa0;%). MG patients had higher rates of any non-motor symptoms over 10&#xa0;&#xa0;years(RR 1.34; 95&#xa0;% CI 1.30-1.39), with the sharpest increase in the year before diagnosis. Depression, anxiety, migraine, constipation, and insomnia consistently showed higher RRs across all intervals. Hospitalizations (RR 1.66; 95&#xa0;% CI 1.61-1.71) and outpatient clinic visits (RR 1.10; 95&#xa0;% CI 1.04-1.17) were consistently higher across 10&#xa0;&#xa0;years, peaking during the 0-1 year before MG diagnosis. CONCLUSION: Non-motor symptoms and healthcare utilization increased years before MG diagnosis. Earlier recognition of these symptom patterns may facilitate timelier evaluation for MG and improve diagnostic pathways.

Humans↗
PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis.&#xa0;A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST&#x2009;+&#x2009;AI for prediction model studies.&#xa0;Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST&#x2009;+&#x2009;AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection.&#xa0;AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗
PubMed2026

The environmental impact of diagnosis and therapy in obstructive sleep Apnea: A systematic review.

Healthcare contributes significantly to global greenhouse gas (GHG) emissions, yet the environmental impact of sleep medicine, particularly the diagnosis and therapy of obstructive sleep apnea (OSA), remains poorly characterized. We systematically searched PubMed, Scopus, and Embase (2015-2025) for studies on OSA care reporting environmental metrics (carbon footprint, energy use, resource consumption) or healthcare resource utilization. Supplementary searches identified additional non-peer-reviewed sustainability-focused studies that have been presented at conferences. Of 19 primary peer-reviewed studies on OSA care and utilization, only one reported environmental metrics (telemedicine CO2 savings related to reduction in travel-related emissions). Supplementary sources revealed that OSA care has a measurable carbon footprint driven by disposable equipment, device electricity, and travel and that OSA diagnostics create significant solid waste with opportunities for waste reduction through the use of reusable equipment. This review shows that while the environmental impact of sleep medicine has been rarely studied to this date, available evidence suggests significant opportunities for sustainability through virtual care, home testing, and equipment optimization. Future research should incorporate environmental impact into the assessment of clinical pathways.

Humans↗
PubMed2026

Blood Bile Acids for Inflammatory Bowel Disease Diagnosis and Disease Activity Assessment: A Metabolomics Meta-Analysis.

Alterations in circulating bile acids (BAs) have been reported in inflammatory bowel disease (IBD), but the consistency of these changes across clinically relevant comparisons remains unclear. Our goal was to investigate systemic BA alterations in IBD using a metabolomics meta-analysis with an exploratory analysis of BA-related gene expression as a supporting context. A systematic review and meta-analysis of 28 metabolomics studies examined blood BA profiles associated with IBD, IBD diagnosis, and disease activity assessment. Univariate analysis and logistic regression modeling of two independent IBD cohorts explored the blood BA-related genes and IBD. Across 28 studies that comprised 5056 IBD patients, 1721 healthy controls, and 314 non-IBD patients, 131 BAs were reported. Eight predefined clinical comparisons were eligible for the meta-analysis. Lower secondary BA levels were consistently observed in IBD patients compared with controls, between UC and CD, and in active versus remission patients. Deoxycholic acid, glycodeoxycholic acid, and taurodeoxycholic acid were frequently decreased, whereas glycocholic acid was increased in certain comparisons. Transcriptomics analyses revealed differential expression of several BA-related genes in blood, including SLC51A, ABCB4, and ACOT8, across the comparisons. Our findings identify consistent circulating BA alterations in IBD and highlight the relevance of blood BA for future biomarker research in the diagnosis and disease activity assessment.

Humans↗