Although most children require medications only for occasional acute conditions, a significant proportion lives with chronic diseases that necessitate long-term, often multiple treatments, which negatively impact their health and quality of life, and may lead to adverse outcomes. Challenges related to prescribing and formulation further complicate pediatric treatment, underscoring the need for safe and effective innovative strategies. Autacoid Local Injury Antagonism amides (ALIAmides), particularly palmitoylethanolamide (PEA) and Adelmidrol, represent promising therapeutic options. This structured narrative review aims to collect and discuss all available literature on PEA and Adelmidrol use in pediatric patients.

A search of PubMed, Scopus, and the Cochrane Library identified 17 studies involving a total of 2,530 patients, of whom 1,772 received PEA (oral or topical), and 54 received Adelmidrol. Following a transdiagnostic approach, results were organized by symptom domains rather than by specific diagnoses, to capture potential benefits across multiple conditions and facilitate comparison with previous studies. Data extraction included study design, population characteristics, intervention type and formulation, outcomes assessed, and main findings.

Beneficial effects on behavioral, cognitive, motor, and visceral functions, along with improvements in respiratory and dermatological disorders emerged from studies administering oral PEA, mostly in micron-sized formulations. Topical application of both PEA and Adelmidrol also demonstrated benefits on respiratory and skin conditions.

Overall, the findings of this review suggest that PEA and Adelmidrol may represent promising, well-tolerated adjunctive strategies for managing pediatric symptoms across various clinical conditions, particularly those that are chronic or require polypharmacy.

To develop a risk prediction model for moderate-to-severe insomnia among adults with autism spectrum disorder (ASD) and to identify key predictive features using interpretable machine learning methods, to support exploratory risk estimation and stratification.

This study used data from the 2024 Psychological and Behavioral Survey Database of Adults with ASD and included 976 adults with ASD in the final analysis. Moderate-to-severe insomnia was defined as an Insomnia Severity Index (ISI) score ≥ 15. To avoid outcome leakage, all ISI items and the ISI total score were excluded from the predictor set, and sleep-related items were also removed from the depression and anxiety symptom scores. The dataset was first divided into training and test sets at a ratio of 7:3 using stratified random sampling. Candidate predictors were subsequently selected using least absolute shrinkage and selection operator regression based exclusively on the training set, and multicollinearity was assessed using the variance inflation factor (VIF). For models requiring feature scaling, standardization parameters were estimated from the training data only and subsequently applied unchanged to the test data. Nine machine learning models were developed using the training set and evaluated in the independent test set. The final model was interpreted using SHAP, and an online prediction tool was developed based on important variables.

Among the 976 adults with ASD, 265 participants (27.2%) were classified as having moderate-to-severe insomnia. After LASSO selection using the training set, 18 predictors were retained, and no substantial multicollinearity was observed. In the held-out test set, logistic regression achieved a ROC AUC of 0.7262 and a PR AUC of 0.4624, compared with a no-skill precision-recall baseline of approximately 0.273. At the operating threshold used for classification, sensitivity was 0.4875, specificity was 0.8216, and accuracy was 0.7304, which was similar to the no-information rate of 0.7270. SHAP analysis showed that the total score of the non-sleep items of the GAD-9, the total score of the non-sleep items of the PHQ-8, anxiety diagnosis, proportion of screen time spent watching short videos, age, and sex were among the predictors with higher contributions.

This study developed an interpretable prediction model for moderate-to-severe insomnia risk among adults with ASD. Anxiety- and depression-related symptoms were the main predictive features, while the proportion of screen time spent watching short videos also provided additional predictive value. The online tool developed based on key variables may provide an exploratory approach to individualized risk estimation and stratification, but further validation and threshold optimization are required before it can be considered for screening applications.

Electronic health records (EHRs) provide new opportunities for computational phenotyping in mental health, but routinely collected diagnostic data are often sparse, heterogeneous, and challenging to interpret. While numerous computational approaches have been applied to identify patient subgroups, clinically useful phenotyping requires workflows capable of transforming routine diagnostic information into interpretable patient profiles. The goal of this work is to develop and evaluate an end-to-end computational phenotyping pipeline for routine ICD-10 diagnostic data and demonstrate its feasibility in adults with autism spectrum disorder (ASD).

A retrospective observational study was conducted using routine EHR data from adults with ASD receiving care at a specialized mental health service. ICD-10 diagnoses were extracted, aggregated into clinically meaningful diagnostic categories, and transformed into binary patient-level representations. A self-organizing map (SOM) was used to organize patients according to diagnostic similarity, followed by hierarchical clustering to identify phenotype groups. Cluster solutions were evaluated using internal validity metrics, cluster size distributions, and interpretability. The resulting phenotypes were identified through diagnostic prevalence profiles and SOM-based visualizations.

The study included 927 adults with ASD, of whom 744 presented at least one comorbid diagnostic category and were included in the computational phenotyping analysis. The proposed pipeline identified four phenotypes characterized by distinct patterns of psychiatric and developmental comorbidity. The SOM structure provided an interpretable visualization of the diagnostic landscape, while hierarchical clustering enabled the identification of coherent phenotype groups. Internal validation metrics supported the selected solution while preserving informative subgroup sizes.

This study presents an end-to-end computational phenotyping pipeline that transforms routinely collected psychiatric EHR data into interpretable patient phenotypes. By integrating diagnostic extraction, clinically informed aggregation, representation learning, clustering, visualization, and characterization, the workflow provides a practical framework for analyzing complex diagnostic data in real-world mental health settings. Future studies should evaluate its applicability across other psychiatric and neurodevelopmental populations.

Perimenopause has been associated with mood worsening in some individuals, including increased depressive symptoms. Yet, the link between bipolar disorder (BD) and perimenopause remains understudied. This review aimed to provide an update on recent original studies and contextualize findings within the broader literature to guide future research and clinical practice.

A systematic search was conducted on Embase (January 1, 2018 to August 5, 2026) to identify studies on BD (I, II, or NOS/BD spectrum) that directly or indirectly examined perimenopause and BD. Pooled samples were included if they reported separate BD results. Results were narratively synthesized.

Fourteen studies were identified from Australia (n = 4), Italy (n = 3), UK (n = 2), Taiwan (n = 1), South Korea (n = 1), Sweden (n = 1), USA (n = 1), and one study of international trials. There is converging evidence showing that perimenopause is a period of higher risk of first onset BD. Among individuals with existing BD, increased depression and heightened anxiety during perimenopause were the most consistent illness-course findings, whereas findings for hypo/mania were mixed. Evidence concerning psychotherapy or pharmacological treatment, including hormone replacement therapy, was limited or absent.

This review provides an updated perspective on BD and perimenopause, extending existing knowledge beyond mood symptoms and identifying several clinical and research gaps. Clinicians should consider the potential impact of perimenopause in BD management. Research using standardized menopause staging criteria (e.g., STRAW+10) is encouraged to inform mechanisms, illness trajectories, treatments, and to better support individuals with BD through perimenopause.

Accurate documentation of sleep timing (enabling calculation of duration and variability) is imperative for research, health, and safety recommendations. Methods in real-world settings include one-time self-report, diaries, and wearables that vary by ease, expense, and accuracy. Here we highlight how programmable online daily diaries compare to gold-standard polysomnography for measuring sleep timing, should be preferred over one-time self-report, and complement wearables for longitudinal clinical and research studies and real-world applications.

Adopting Therapeutic Jurisprudence (TJ) as its theoretical framework and employing the methods of literature analysis and normative analysis, this paper conceptualizes the rules of recommendation algorithms, quantitative feedback, open social connectivity, and automated review procedures as structural arrangements that shape the psychological experiences of minors, and examines their potential mental health implications. The analysis suggests that these rules may affect the mental health of minors along four dimensions-autonomous will, self-perception, relational trust, and procedural trust-and that, in operation, these rules are interwoven and mutually reinforcing. However, China's current regulatory model places disproportionate emphasis on content governance and usage management, while the regulation of designated platforms and the Minor Mode likewise face obstacles in implementation, making it hard to respond effectively to the structural shaping of minors' psychological experiences by platform rules. By examining pioneering governance experiences at the international level, this paper compares the normative functions of different institutional instruments in the protection of minors' mental health and the conditions of their application. On this basis, the paper argues that the normative objectives of safeguarding minors' self-determination, maintaining reasonable trust, and realizing procedural justice should guide the construction of a psychologically friendly digital normative environment for minors, and that institutional arrangements covering the entire process of platform use should be established, encompassing psychological impact assessment, proactive identification and default protection, and restorative protection of rights.

Central Asia comprising Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, confronts a complex array of health challenges shaped by the post-Soviet transition, including fragmented health systems, a dual burden of communicable and non-communicable diseases, environmental degradation, healthcare inequality, and competing geopolitical influences. Public health diplomacy has emerged as a critical instrument for addressing these transnational challenges.

This scoping review aimed to map and synthesize the existing evidence to examine the role of public health diplomacy in strengthening health governance and regional and international health cooperation.

Following the Arksey and O'Malley scoping review framework, this paper used a systematic search of PubMed, and Google Scholar. Studies published between 2000 and 2025 addressing health diplomacy, health governance, cross-border cooperation, or health system reform in Central Asia were included. Data was extracted using a standardized charting form and synthesized through descriptive and narrative thematic analysis.

Seventeen studies met the inclusion criteria. Kazakhstan was the most extensively studied country (n = 4), followed by Uzbekistan (n = 2), Kyrgyzstan (n = 1), and Tajikistan (n = 1). No country-specific study addressed Turkmenistan. Key findings revealed: (i) all five countries continue to navigate the post-Soviet health transition with uneven progress toward UHC; (ii) significant intra-national healthcare inequalities persist, particularly in Kazakhstan's rural areas; (iii) environmental health threats including transboundary water pollution, industrial contamination, and household air pollution, demand coordinated diplomatic responses; (iv) the European Union uses health as a soft-power instrument, while China's Belt and Road Initiative and the SCO represent expanding but under-evaluated platforms for health cooperation; (v) Russia's influence persists through institutional legacies and migrant health governance; (vi) centralized, top-down health communication hinders public trust across the region; and (vii) no health diplomacy training program exists for Central Asia. The strongest SDG 3 alignment was with target 3.8 UHC, while maternal mortality, child health, and substance abuse targets remained unexamined through a PHD lens.

Public health diplomacy has significant potential to strengthen health governance and regional cooperation in Central Asia. Advancing this agenda requires stronger regional collaboration, context-specific capacity building, greater evidence generation, and systematic evaluation.

Neonatal macrosomia is a major perinatal complication associated with maternal metabolic disorders. This study aimed to assess whether hemoglobin concentration and gestational diabetes mediate the association between pre-pregnancy body mass index and neonatal macrosomia. A retrospective analysis was conducted among 700 singleton full-term pregnancies at El Idrissi Provincial Hospital, Kenitra, Morocco. Macrosomia was defined as birth weight ≥4000 g, and elevated hemoglobin as ≥12.5 g/dL. Gestational diabetes was diagnosed using fasting glucose or a 75-g oral glucose tolerance test. Mediation analysis using the Karlson-Holm-Breen method was adjusted for maternal age, parity, gestational hypertension, and infant sex. The prevalence of gestational diabetes was 11.9%, and macrosomia occurred in 14.7% of cases. Overweight and obesity were significantly associated with macrosomia, and 71.86% (overweight) and 67.52% (obesity) of these associations were jointly explained by hemoglobin concentration and gestational diabetes, mainly through gestational diabetes. Early metabolic screening during pregnancy may reduce the risk of adverse birth outcomes. La macrosomie néonatale est une complication périnatale majeure associée aux troubles métaboliques maternels. Cette étude visait à évaluer si la concentration d’hémoglobine et le diabète gestationnel médient l’association entre l’indice de masse corporelle préconceptionnel et la macrosomie néonatale. Une analyse rétrospective a été réalisée sur 700 grossesses uniques à terme à l’Hôpital Provincial El Idrissi de Kénitra, Maroc. La macrosomie a été définie par un poids de naissance ≥ 4000 g, et l’hémoglobine élevée par ≥ 12,5 g/dL. Le diabète gestationnel a été diagnostiqué à l’aide de la glycémie à jeun ou d’un test d’hyperglycémie provoquée par voie orale avec 75 g de glucose. L’analyse de médiation utilisant la méthode de Karlson–Holm–Breen a été ajustée sur l’âge maternel, la parité, l’hypertension gravidique et le sexe du nouveau-né. La prévalence du diabète gestationnel était de 11,9 %, et la macrosomie était observée dans 14,7 % des cas. Le surpoids et l’obésité étaient significativement associés à la macrosomie, et 71,86 % (surpoids) et 67,52 % (obésité) de ces associations étaient expliqués conjointement par la concentration d’hémoglobine et le diabète gestationnel, principalement par le diabète gestationnel.. Un dépistage métabolique précoce pendant la grossesse pourrait réduire le risque d’issues néonatales défavorables.