Psoriasis is a chronic inflammatory skin disease that significantly impacts patients’ quality of life. Although biological therapies are effective, they are associated with high costs and potential side effects, necessitating strategies for dose reduction. Pharmacological conditioning, using placebo mechanisms through associative learning, presents a promising approach to maintain therapeutic efficacy with lower doses of medication.

The single-centre, randomised controlled trial aims to investigate pharmacological conditioning with secukinumab in patients with moderate-to-severe psoriasis (N=168). Participants will be randomly allocated to a treatment-as-usual group or one of two experimental groups receiving partial or continuous reinforcement schedules with reduced secukinumab doses combined with a distinctive gustatory stimulus. Primary outcomes include changes in itch intensity, skin-related quality of life and objective disease severity. Secondary outcomes encompass psychological variables, side effects and biological markers. Results may contribute to optimised long-term psoriasis management, reducing medication burden while maintaining treatment efficacy.

The study protocol was approved by the ethics committee of the University Hospital Essen (19–8636 BO) on 20 November 2023. Written informed consent will be obtained from all participants. Participant confidentiality will be ensured through pseudonymised data handling and secure storage. The results will be disseminated through peer-reviewed publications.

DRKS00034977.

Long-term brain health profiles following exposure to repetitive head impacts and/or concussions in contact sports are a public health focus and the subject of a national debate. The true prevalence rates of mild cognitive impairment (MCI) or neurobehavioural dysregulation are unknown in the nearly 20 000 current/living former professional football players. Here, we describe the procedures and methodology of the prevalence study of cognitive function in former professional football players from the Brain Health Initiative at the University of Pittsburgh. The objective is to define the prevalence of normal cognitive function versus neurodegeneration in former professional football players through clinical, neuroimaging and biomarker assessments.

Participants include former professional football players aged 29–59 years at study onset who played a minimum of three professional football games in three professional seasons and non-exposed controls. Participants are recruited by two mechanisms, a random and non-random sample. The full study protocol includes a 3–4-day, multidomain assessment (eg, neurological, neurocognitive, psychiatric, sleep, vestibular, orthopaedic and cardiovascular) for neurodegenerative disease and overall health and function, including MRI, positron emission tomography scans, analysis of blood plasma and cerebrospinal fluid, neurocognitive assessments, applanation tonometry, overnight sleep study and informant interview. A multidisciplinary clinical panel conducts a blinded diagnostic consensus conference to adjudicate the presence of MCI and/or traumatic encephalopathy syndrome, which serve as the study’s primary and secondary outcomes, respectively. Point prevalence of these for both the exposed and unexposed cohorts will be calculated as the primary statistical analysis.

The University of Pittsburgh Institutional Review Board approved the study prior to recruiting human subjects (protocol numbers STUDY19010008: sIRB - Brain Health Initiative (Part 1) and STUDY19030211: sIRB - Brain Health Initiative (Part 2)). The results will be disseminated in peer-reviewed journals and as presentations at national and international scientific conferences.

Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential. Traditional measures fail to fully capture the risks associated with a child’s development outcomes. Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.

To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. The review focuses on applied ML techniques, data types, feature sets, outcomes, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and reported limitations.

Scoping review using the Arksey and O‘Malley framework with enhancements by Levac et al.

A systematic search was conducted on 16 June 2024 across PubMed, Web of Science, IEEE Xplore and PsycINFO, supplemented by grey literature (OpenGrey) and reference hand-searching. No publication date limits were applied.

Included studies applied ML or its variants (eg, deep learning (DL), natural language processing) to developmental outcomes in children aged 0–8 years. Studies were in English and addressed cognitive, language, motor or social-emotional development. Excluded were studies focusing on robotics; neurodevelopmental disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder and communication disorders; disease or medical conditions; and review articles.

Three reviewers independently extracted data using a structured MS Excel template, covering study ML techniques, data types, feature sets, outcomes, outcome measures, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and limitations. A narrative synthesis was conducted, supported by descriptive statistics and visualisations.

Of the 759 articles retrieved, 27 met the inclusion criteria. Most studies (78%) originated from high-income countries, with none from sub-Saharan Africa. Supervised ML classifiers (40.7%) and DL techniques (22.2%) were the most used approaches. Cognitive development was the most frequently targeted outcome (33.3%), often measured using the Bayley Scales of Infant and Toddler Development-III (33.3%). Data types varied, with image, video and sensor-based data being most prevalent. Key predictive features were grouped into six categories: brain features; anthropometric and clinical/biological markers; socio-demographic and environmental factors; medical history and nutritional indicators; linguistic and expressive features; and motor indicators. Most studies (74.1%) focused solely on prediction, with the majority conducting predictions at age 2 years and above. Only 41% of studies employed explainability methods, and validation strategies varied widely. Few studies (7.4%) conducted external validation, and only one had progressed to a clinical trial. Common limitations included small sample sizes, lack of external validation and imbalanced datasets.

There is growing interest in using ML for ECD research, but current research lacks geographical diversity, external validation, explainability and practical implementation. Future work should focus on developing inclusive, interpretable and externally validated models that are integrated into real-world implementation.