Advances in the study of perfectionism in sport

Interest in perfectionism in sport psychology has steadily increased over the last twenty-five years. The last 10 years in particular has seen a dramatic increase in research dedicated to the topic. As a result, we have learned a great deal about perfectionism in this domain. However, it is also an area of work in which there has been considerable disagreement on key issues; most notably, the degree to which perfectionism is helpful or a hindrance to athletes. A number of new concepts have recently emerged that may help navigate some of the issues that have historically hampered the study of perfectionism: combined and total unique effects, perfectionistic tipping points, and perfectionistic climate. In this short overview some of the latest advances in this area are introduced, explained, and discussed. Each concept offers interesting opportunities for advancing the study of perfectionism in sport. They also each provide avenues for novel research, as well as impetus to revisit previous research and existing data to yield new insights. Most importantly, the concepts offer the promise of taking us closer to our aim of understanding the effects of perfectionism in sport, and better identifying and supporting athletes at risk to its negative effects

Ethical oversight of Artificial Intelligence in Nigerian Healthcare: A qualitative analysis of ethics committee members’ perspectives on integration and regulation

Background The adoption of artificial intelligence (AI) in healthcare has the potential to improve diagnostic accuracy, streamline processes, and address resource shortages, particularly in low- and middle-income countries (LMICs) like Nigeria. However, challenges related to knowledge, ethics, and regulation hinder its implementation. Aim This study aimed to explore ethics committee members’ perspectives on AI integration in healthcare across public teaching hospitals in southwest Nigeria, examining their knowledge, perceived benefits, challenges, and regulatory considerations surrounding AI adoption in healthcare settings. Methods A qualitative study design was used, involving semi-structured interviews with 10 ethics committee members from five public teaching hospitals across southwest Nigeria. Thematic analysis was conducted using NVivo software to identify key themes regarding knowledge, benefits, challenges, risks, and regulatory needs associated with AI in healthcare. Results Participants acknowledged AI’s potential to improve efficiency and accuracy in healthcare. However, they expressed concerns about limited knowledge and training, financial barriers, and data privacy issues. Ethical concerns included potential AI errors and overreliance on technology. Participants highlighted the need for comprehensive regulatory frameworks and emphasized a collaborative approach to AI regulation, involving multiple stakeholders. Trust in AI was found to be contingent upon demonstrated accuracy and reliability. Conclusions While participants recognized the benefits of AI in addressing healthcare challenges, significant knowledge gaps, ethical concerns, and regulatory deficiencies present barriers to AI’s successful implementation. Addressing these challenges through training, investment, and multi-stakeholder regulatory efforts could facilitate the responsible and effective integration of AI into Nigeria’s healthcare sector

Hybridized artificial intelligence system for reducing neonatal mortality in Nigeria

Background Neonatal diseases represent the leading cause of death in Nigeria, ranking the country second globally in neonatal mortality rates. Early and accurate diagnosis remains challenging, leading to delayed interventions and increased mortality. Aim To develop an artificial intelligence system capable of detecting multiple neonatal diseases using local datasets and advanced machine learning techniques to facilitate early intervention and reduce neonatal mortality in Southwest Nigeria. Methods Clinical records from 4,027 previously treated neonatal patients were collected from five tertiary hospitals across three Southwest Nigerian states. The dataset underwent comprehensive analysis, balancing using Synthetic Minority Over-sampling Technique (SMOTE), and preprocessing before training three deep learning models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and a novel hybrid LSTM-ANN architecture. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with rigorous subject-wise validation and statistical testing. Results The hybrid LSTM-ANN model demonstrated superior performance with 82 % accuracy, 88 % precision, 82 % recall, and 86 % F1-score, significantly outperforming both standalone ANN (80 % accuracy) and LSTM (77 % accuracy). Disease-specific classification revealed exceptional performance for sepsis (precision: 0.90, F1-score: 0.88), birth asphyxia (0.88, 0.85), jaundice (0.86, 0.83), and prematurity (0.82, 0.80). McNemar’s test confirmed significant hybrid superiority over ANN (χ2 = 12.45, p < 0.001) and LSTM (χ2 = 15.67, p < 0.001), whilst Friedman test (χ2 = 18.42, p < 0.001) validated the 5–6 % accuracy improvement. Conclusion The hybrid LSTM-ANN model establishes a valuable diagnostic tool for early neonatal disease detection. However, external validation and prospective clinical trials are necessary before clinical deployment

Recent Occurrence of Microplastics in Freshwater and Efficiency of Available Treatment Technologies- A Review

This review assesses microplastic occurrence in freshwater systems globally between 2018 and 2024, examining spatial distribution patterns across rivers, lakes, groundwater, and wastewater treatment plants, alongside treatment technology efficiency. Studies were selected following PRISMA guidelines, with inclusion criteria requiring spectroscopic confirmation using ATR-FTIR or Raman spectroscopy and compliance with ISO/TR 21960 and GESAMP quality control protocols. Microplastics were detected across five continents with notable spatial variations: riverine systems showed mean concentrations of 0.5-5 particles/L, lakes exhibited 0.1-2.5 particles/L, whilst groundwater demonstrated significantly lower levels at 0.01-0.5 particles/L. The most prevalent polymers were polyethylene and polypropylene, primarily linked to secondary microplastic formation from consumer packaging degradation and agricultural film, whilst fibres (predominantly polyester and polyamide) originated from textile washing effluents, representing primary microplastic sources. Conventional drinking water treatment plants achieved 85-95% removal efficiency for particles >50 μm but declined to 40-60% for smaller fractions, with analytical limitations persisting below 5 μm. Emerging technologies including photocatalytic degradation demonstrated up to 70% polypropylene removal, though scalability challenges include high energy requirements (2-5 kWh/m³) and potential toxic intermediate formation. Health implications include endocrine disruption, inflammatory responses, and oxidative stress, with nanoplastics (<1 μm) potentially 10-100 times more prevalent than microplastics, though detection capabilities remain critically limited. Legislative frameworks, including the EU Single-Use Plastics Directive, have shown measurable reductions (20-40%) in targeted polymer types, though enforcement gaps and limited scope continue hampering comprehensive pollution control. Standardised international monitoring protocols remain integral for effective contamination assessment

Comparative Genomic Hybridization (CGH) in Genotoxicology: From the Basics to Modern Approaches.

Over the past two decades, comparative genomic hybridization (CGH) and array CGH have become essential tools in clinical diagnostics, oncology, and toxicological risk assessment. Initially developed to identify chromosomal imbalances like copy number variations (CNVs) in tumor cells, these technologies have expanded into genotoxicology and toxicogenomics, exploring gene responses to toxic agents and their molecular mechanisms. As of 2024, new developments include integrating array CGH with next-generation sequencing (NGS), machine learning, and CRISPR-Cas9 genome editing, greatly improving precision. High-density CGH arrays now offer single-cell resolution, enabling the detection of cellular heterogeneity in toxic responses, while long-read sequencing facilitates the identification of complex genomic rearrangements. Recent innovations include combining CGH and toxicogenomics with organ-on-chip models for real-time, tissue-specific toxicological assessment. This has significantly improved the relevance of toxicological data for human health. However, while these advances are promising, array CGH remains costly and requires substantial data processing, driving the need for advanced bioinformatics tools. AI-driven predictive toxicology models are also gaining traction, correlating toxicogenomic profiles with clinical outcomes. Despite these advancements, the field still faces challenges, such as evolving regulatory guidelines and complex data interpretation, which hinder broader adoption and the full realization of CGH's potential in toxicology and risk assessment. [Abstract copyright: © 2026. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.