Introduction:Despite the growing use of systematic reviews of animal studies, it remains unclear how often systematic review methodology - such as detailed search strategies, critical appraisal, and protocol registration - is applied in regulatory contexts. We aimed to assess the use and reporting quality of systematic reviews in the European Food Safety Authority's (EFSA) Scientific Opinions on animal health and welfare. Methods:We conducted an exploratory study comprising of a retrospective analysis of 151 EFSA Scientific Opinions. We classified the types of studies underpinning these reports, including systematic reviews, literature reviews, and other study designs. Additionally, we assessed the reporting rigor of key systematic review elements. Results:Literature reviews were the most common study type, present in 126 reports (83%), with 40 reports (27%) applying systematic review methods such as searching multiple databases and reporting clear research questions, inclusion criteria, and study counts. Eleven studies (27%) were explicitly labelled as systematic reviews, with their use increasing over time. Reporting quality varied: 64% listed more than one reviewer, 45% reported a risk of bias assessment, and 36% registered a study protocol. Discussion:Systematic review methodology is increasingly applied in EFSA's Scientific Opinions on animal health and welfare. However, methodological rigor and reporting standards remain inconsistent, underscoring the need for improvement to strengthen the reliability and transparency of EFSA's evidence base.

Abstract Background Large-scale estimates of animal-to-human drug translation and the study characteristics associated with successful translation remain limited. The expanding preclinical literature also challenges manual evidence synthesis. We developed a natural language processing (NLP) pipeline to structure and link preclinical and clinical evidence at scale. Methods In this retrospective meta-research study, we analysed more than 500,000 neuroscience-related animal drug studies from PubMed and linked them to clinical trial and regulatory approval data. NLP methods extracted drug, disease, and experimental design characteristics from abstracts and full texts. Translation was defined as progression to completed phase III/IV trials or regulatory approval. Logistic regression assessed associations between preclinical study characteristics and successful translation. Findings Among 291,624 drug entities identified in animal studies, 6·7% entered clinical development and 3·1% reached phase III/IV trials or regulatory approval. At the drug–disease level, 4·4% entered clinical development and 1·9% achieved translation. Restricting analyses to successfully linked ontology entities increased estimates to 11·3% and 4·1%, respectively. Male-only animal studies predominated, whereas reporting of randomisation, blinding, and sample size calculations remained limited. Testing across multiple species and reporting blinding were associated with higher odds of successful translation. Interpretation Only a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and blinding were associated with improved translational success. NLP-based evidence synthesis may support scalable evaluation of translational research and identification of potentially modifiable research practices. Funding Swiss National Science Foundation, UZH Digital Entrepreneurship Fellowship, Universities Federation for Animal Welfare. Research in context Evidence before this study We searched the literature for studies quantifying large-scale animal-to-human translation and factors associated with successful translation. Existing work was mainly limited to specific diseases, interventions, or manually curated datasets, and large-scale linkage of animal and clinical evidence remained limited. Added value of this study We developed a natural language processing pipeline linking more than 500,000 animal studies to clinical trial and regulatory approval data. The study provides large-scale estimates of translation and identifies experimental characteristics associated with successful translation. Implications of all the available evidence The findings suggest that only a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and reporting of blinding were associated with improved translation. Automated evidence synthesis may support more systematic evaluation of translational research practices.

'Brain age' is a numerical estimate of the biological age of the brain and an overall effort to measure neurodegeneration, regardless of disease type. In multiple sclerosis, accelerated brain ageing has been linked to disability accrual. Artificial intelligence has emerged as a promising tool for the assessment and quantification of the impact of neurodegenerative diseases. Despite the existence of numerous AI models, there is a noticeable lack of comparative imaging data for traditional machine learning versus deep learning in conditions such as multiple sclerosis. A retrospective observational study was initiated to analyse clinical and MRI data (4584 MRIs) from various scanners in a large longitudinal cohort (n = 1516) of people with multiple sclerosis collected from two institutions (Karolinska Institute and Oslo University Hospital) using a uniform data post-processing pipeline. We conducted a comparative assessment of brain age using a deep learning simple fully convolutional network and a well-established traditional machine learning model. This study was primarily aimed to validate the deep learning brain age model in multiple sclerosis. The correlation between estimated brain age and chronological age was stronger for the deep learning estimates (r = 0.90, P < 0.001) than the traditional machine learning estimates (r = 0.75, P < 0.001). An increase in brain age was significantly associated with higher expanded disability status scale scores (traditional machine learning: t = 5.3, P < 0.001; deep learning: t = 3.7, P < 0.001) and longer disease duration (traditional machine learning: t = 6.5, P < 0.001; deep learning: t = 5.8, P < 0.001). No significant inter-model difference in clinical correlation or effect measure was found, but significant differences for traditional machine learning-derived brain age estimates were found between several scanners. Our study suggests that the deep learning-derived brain age is significantly associated with clinical disability, performed equally well to the traditional machine learning-derived brain age measures, and may counteract scanner variability.

Animal research, sometimes referred to as preclinical research, plays a vital role in bridging the gap between basic science and clinical applications. However, the rapid increase in publications and the complexity of reported findings make it increasingly difficult for researchers to extract and assess relevant information. While automation through natural language processing (NLP) holds great potential for addressing this challenge, progress is hindered by the absence of high-quality, comprehensive annotated resources specific to preclinical studies. To fill this gap, we introduce PreClinIE, a fully open manually annotated dataset. The corpus consists of abstracts and methods sections from 725 publications, annotated for study rigor indicators (e.g., random allocation) and other study characteristics (e.g., species). We describe the data collection and annotation process, outlining the challenges of working with preclinical literature. By providing this resource, we aim to accelerate the development of NLP tools that enhance literature mining in preclinical research.

Abstract Background Animal systematic reviews are critical to inform translational research. Despite their growing popularity, there is a notable lack of information on their quality, scope, and geographical distribution over time. Addressing this gap is important to maintain their effectiveness in fostering medical advancements. Objective This study aimed to assess the quality and demographic trends of animal systematic reviews in neuroscience, including changes over time. Methods We performed an umbrella review of animal systematic reviews, searching Medline and Embase for reviews until January 27, 2023. A data mining method was developed and validated to automatically evaluate the quality of these reviews. Results From 18‘065 records identified, we included 1‘358 animal systematic reviews in our study. These reviews commonly focus on translational research but with notable topical gaps such as schizophrenia, other psychiatric disorders, and brain tumours. They originate from 64 countries, with the United States, China, the UK, Brazil, and Iran being the most prolific. The automated quality assessment indicated high reliability, with F1-scores over 80% for most criteria. Overall, the reviews were of high quality and the quality improved over time. However, many systematic reviews did not report a pre-registered study protocol. Reviews with a pre-registered protocol generally scored higher in quality. No significant differences in quality were observed between countries. Conclusion Animal systematic reviews in neuroscience are of overall of high quality. Our study highlights specific areas for enhancement such as the recommended pre-publication of study protocols. It also identifies under-represented topics that could benefit from further investigation to inform translational research. Such measures can contribute to the effective translation of animal research findings to clinical applications.

INTRODUCTION:Conducting a rigorous systematic review of animal studies requires a priori registration of a study protocol. However, it remains unknown how many of these registered studies culminate in publication and how long it takes to complete such a systematic review. Thus, this study had two objectives: (1) to assess the proportion of registered protocols that result in publication, and (2) to determine the time required to complete and publish systematic reviews of animal studies after protocol registration. METHODS:All available systematic reviews protocols of animal study were manually downloaded from PROSPERO, the international registry of systematic review protocols. Start and completion date as well as topical and demographic data were extracted, complemented by a web-scraping approach. Assessment of publication status was achieved through a systematic literature search. RESULTS:From a total of 1,771 protocols, 406 were excluded due to recent start dates. This left 1,365 protocols eligible for the final analysis. Among these, 694 (51%) resulted in a published systematic review. Median time to complete and publish a systematic review was 11.5 months (range: 0.13-44.9 months) and 16.2 months (range: 1.0-49.7 months), respectively. This time was 69% more until submission than anticipated by the authors (6.8 months [range: 0.9-48.0]). CONCLUSION:Only half of registered protocols resulted in publication, suggesting possible publication bias. Authors can expect to complete and publish an animal systematic review within approximately one year.

PURPOSE:To quantitatively map the myelin lipid-protein bilayer in the live human brain. METHODS:This goal was pursued by integrating a multi-TE acquisition approach targeting ultrashort T2 signals with voxel-wise fitting to a three-component signal model. Imaging was performed at 3 T in two healthy volunteers using high-performance RF and gradient hardware and the HYFI sequence. The design of a suitable imaging protocol faced substantial constraints concerning SNR, imaging volume, scan time, and RF power deposition. Model fitting to data acquired using the proposed protocol was made feasible through simulation-based optimization, and filtering was used to condition noise presentation and overall depiction fidelity. RESULTS:A multi-TE protocol (11 TEs of 20-780 μs) for in vivo brain imaging was developed in adherence with applicable safety regulations and practical scan time limits. Data acquired using this protocol produced accurate model fitting results, validating the suitability of the protocol for this purpose. Structured, grainy texture of myelin bilayer maps was observed and determined to be a manifestation of correlated image noise resulting from the employed acquisition strategy. Map quality was significantly improved by filtering to uniformize the k-space noise distribution and simultaneously extending the k-space support. The final myelin bilayer maps provided selective depiction of myelin, reconciling competitive resolution (1.4 mm) with adequate SNR and benign noise texture. CONCLUSION:Using the proposed technique, quantitative maps of the myelin bilayer can be obtained in vivo. These maps offer unique information content with potential applications in basic research, diagnosis, disease monitoring, and drug development.

Extracting and aggregating information from clinical trial registries could provide invaluable insights into the drug development landscape and advance the treatment of neurologic diseases. However, achieving this at scale is hampered by the volume of available data and the lack of an annotated corpus to assist in the development of automation tools. Thus, we introduce NeuroTrialNER, a new and fully open corpus for named entity recognition (NER). It comprises 1093 clinical trial summaries sourced from ClinicalTrials.gov, annotated for neurological diseases, therapeutic interventions, and control treatments. We describe our data collection process and the corpus in detail. We demonstrate its utility for NER using large language models and achieve a close-to-human performance. By bridging the gap in data resources, we hope to foster the development of text processing tools that help researchers navigate clinical trials data more easily.

Introduction: Understanding the prevalence of multiple sclerosis (MS) provides information for healthcare planning and helps identify trends and patterns of disease occurrence. For Switzerland, the number of persons with MS (pwMS) was last estimated at approximately 15,000 in 2016. The study's objectives were to update estimates of MS prevalence and characterise the change in MS prevalence in Switzerland between 2016 and 2021, the last year with complete administrative data. Methods: The Swiss MS Registry (SMSR) is an ongoing, longitudinal study in Switzerland. It has previously established a methodology to assess the epidemiology of MS in Switzerland by integrating SMSR data with administrative data on reimbursement approvals for disease-modifying therapies (DMTs). Subsequently, the benchmark-multiplier method is applied to the combined data. Using the same methodology, we calculated overall and sex- and age-specific prevalence rates for 2021. Furthermore, we descriptively analysed changes since 2016 by comparing the prevalence figures and demographic and clinical characteristics of pwMS in both years. Results: We estimated the population of pwMS in Switzerland at 18,140 (95% simulation interval: 17,550-18,750), corresponding to a period prevalence of 200.8-214.5/100,000 inhabitants. Peak prevalence was observed in the 50- to 55-year age group. Compared to 2016, the 2021 estimate corresponds to a 20% increase (n = 3,000). Extrapolating from Swiss population growth, we estimated that one-fifth of the observed prevalence increase may be attributed to a rising population. The proportion of pwMS in the age range from 50 to 64 (32.5% vs. 35.9%) and above 65 (8.0% vs. 11.1%) years increased. Consequently, the median (interquartile range) age increased from 47 (37-55) to 49 (38-57) years. The median age at diagnosis (36 [28-45] years) and the female-to-male ratio (2.7:1) remained stable since 2016. The proportion of pwMS treated with DMTs increased from 62.1% to 69.0%, with the largest change observed in infusion therapies (15.7% vs. 23.3%). Conclusion: The estimated MS prevalence in Switzerland has increased since the previous estimate in 2016, with a shift in peak prevalence towards older ages. Population growth explained around one-fifth of this increase, thus leaving room for contributions by additional factors, which require further investigation. The rising MS prevalence has several implications for healthcare, research, and society.

Background While potential risk factors for multiple sclerosis (MS) have been extensively researched, it remains unclear how persons with MS theorize about their MS. Such theories may affect mental health and treatment adherence. Using natural language processing techniques, we investigated large-scale text data about theories that persons with MS have about the causes of their disease. We examined the topics into which their theories could be grouped and the prevalence of each theory topic.Methods A total of 486 participants of the Swiss MS Registry longitudinal citizen science project provided text data on their theories about the etiology of MS. We used the transformer-based BERTopic Python library for topic modeling to identify underlying topics. We then conducted an in-depth characterization of the topics and assessed their prevalence.Results The topic modeling analysis identifies 19 distinct topics that participants theorize as causal for their MS. The topics most frequently cited are Mental Distress (31.5%), Stress (Exhaustion, Work) (29.8%), Heredity/Familial Aggregation (27.4%), and Diet, Obesity (16.0%). The 19 theory topics can be grouped into four high-level categories: physical health (mentioned by 56.2% of all participants), mental health (mentioned by 53.7%), risk factors established in the scientific literature (genetics, Epstein-Barr virus, smoking, vitamin D deficiency/low sunlight exposure; mentioned by 47.7%), and fate/coincidence (mentioned by 3.1%). Our study highlights the importance of mental health issues for theories participants have about the causes of their MS.Conclusions Our findings emphasize the importance of communication between healthcare professionals and persons with MS about the pathogenesis of MS, the scientific evidence base and mental health. Multiple sclerosis (MS) is a disease that affects the brain and spinal cord, causing a wide range of symptoms. Our study investigated what people living with the disease think causes MS. We analyzed the replies given by 486 people who were questioned about their MS to look for patterns in the responses. We identified 19 distinct themes, notably mental and work-related stress, genetics, and dietary factors, which we grouped into 4 categories: physical health, mental health, established scientific risk factors, and chance. We found that mental health problems were viewed as a key factor for MS. Our work highlights the need for healthcare professionals to have transparent conversations with people with MS about what is known about the disease course and potential causes. In addition, it highlights the importance of fully informing and supporting people with MS regarding their mental health. Haag et al. examine the theories of persons with multiple sclerosis (MS) about the causes of MS using natural language processing techniques. Their study identifies unique topics and highlights the role of mental health and the need for an open dialogue about the scientific and mental health aspects of MS.

Background Improving health-related quality of life (HRQoL) is an important disease management goal in persons with Multiple Sclerosis (PwMS). HRQoL decreases with increasing age and prolonged disease duration; other factors remain less understood. Objective To identify associations of multiple sclerosis (MS) disease characteristics and symptom burden with low HRQoL. Methods Using the Swiss MS Registry, we applied quantile regression adjusted for age and MS disease duration to determine 25th (low HRQoL) and 75th (high HRQoL) percentiles of the EuroQol-5-Dimension (EQ-5D) distribution for PwMS. We compared PwMS across HRQoL groups by analyzing differences in sociodemographics, symptom burden, MS risk factors, gait impairment, and the MS Severity Score (MSSS), all measured at the same time as HRQoL. The analyses included descriptive methods, multivariable multinomial regression, and simultaneous quantile regression as a sensitivity analysis. Results We included 1697 PwMS with median age and time-to-diagnosis of 49 and 9 years. Multivariable regression revealed low HRQoL to be associated with receiving invalidity insurance benefits, reporting depression, muscle weakness, memory problems, pain, and severe gait impairment. The analysis for individuals with available MSSS (n = 937) showed an increasing probability of low HRQoL with higher MSSS. Conclusion Our segmentation method identified symptom burden and MS severity as factors associated with low HRQoL. Pharmacological and non-pharmacological MS symptom management, especially for depression, fatigue, pain, and muscle weakness, may warrant increased attention to preserve or improve HRQoL.

OBJECTIVE:This study was undertaken to provide a comprehensive review of neuroimaging characteristics and corresponding clinical phenotypes of autoimmune glial fibrillary acidic protein astrocytopathy (GFAP-A), a rare but severe neuroinflammatory disorder, to facilitate early diagnosis and appropriate treatment. METHODS:A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis)-conforming systematic review and meta-analysis was performed on all available data from January 2016 to June 2023. Clinical and neuroimaging phenotypes were extracted for both adult and paediatric forms. RESULTS:A total of 93 studies with 681 cases (55% males; median age = 46, range = 1-103 years) were included. Of these, 13 studies with a total of 535 cases were eligible for the meta-analysis. Clinically, GFAP-A was often preceded by a viral prodromal state (45% of cases) and manifested as meningitis, encephalitis, and/or myelitis. The most common symptoms were headache, fever, and movement disturbances. Coexisting autoantibodies (45%) and neoplasms (18%) were relatively frequent. Corticosteroid treatment resulted in partial/complete remission in a majority of cases (83%). Neuroimaging often revealed T2/fluid-attenuated inversion recovery (FLAIR) hyperintensities (74%) as well as perivascular (45%) and/or leptomeningeal (30%) enhancement. Spinal cord abnormalities were also frequent (49%), most commonly manifesting as longitudinally extensive myelitis. There were 88 paediatric cases; they had less prominent neuroimaging findings with lower frequencies of both T2/FLAIR hyperintensities (38%) and contrast enhancement (19%). CONCLUSIONS:This systematic review and meta-analysis provide high-level evidence for clinical and imaging phenotypes of GFAP-A, which will benefit the identification and clinical workup of suspected cases. Differential diagnostic cues to distinguish GFAP-A from common clinical and imaging mimics are provided as well as suitable magnetic resonance imaging protocol recommendations.

There is an ongoing debate about the value of animal experiments to inform medical practice, yet there are limited data on how well therapies developed in animal studies translate to humans. We aimed to assess 2 measures of translation across various biomedical fields: (1) The proportion of therapies which transition from animal studies to human application, including involved timeframes; and (2) the consistency between animal and human study results. Thus, we conducted an umbrella review, including English systematic reviews that evaluated the translation of therapies from animals to humans. Medline, Embase, and Web of Science Core Collection were searched from inception until August 1, 2023. We assessed the proportion of therapeutic interventions advancing to any human study, a randomized controlled trial (RCT), and regulatory approval. We meta-analyzed the concordance between animal and human studies. The risk of bias was probed using a 10-item checklist for systematic reviews. We included 122 articles, describing 54 distinct human diseases and 367 therapeutic interventions. Neurological diseases were the focus of 32% of reviews. The overall proportion of therapies progressing from animal studies was 50% to human studies, 40% to RCTs, and 5% to regulatory approval. Notably, our meta-analysis showed an 86% concordance between positive results in animal and clinical studies. The median transition times from animal studies were 5, 7, and 10 years to reach any human study, an RCT, and regulatory approval, respectively. We conclude that, contrary to widespread assertions, the rate of successful animal-to-human translation may be higher than previously reported. Nonetheless, the low rate of final approval indicates potential deficiencies in the design of both animal studies and early clinical trials. To ameliorate the efficacy of translating therapies from bench to bedside, we advocate for enhanced study design robustness and the reinforcement of generalizability.

Purpose:People with multiple sclerosis (pwMS) experience autoimmunity-mediated inflammation and neurodegeneration throughout the central nervous system. There remains a need for clinically accessible, reliable functional markers of neurodegeneration in MS. Previous research has described changes to electroretinography (ERG)-derived measures of retinal bipolar cell function in pwMS early in the disease course. We, therefore, investigated ERG as a potential outcome measure in individuals with more advanced disease. Methods:This cross-sectional observational study included pwMS with Expanded Disability Status Scale (EDSS) scores of ≥3.0 and healthy control (HC) participants who underwent ERG, optical coherence tomography, high- and low-contrast visual acuity measurement, and an ophthalmological examination. ERG findings in MS eyes with and without previous optic neuritis (MS +ON; MS -ON) were compared with those in HC eyes. Effects of EDSS, disease duration, ON, and treatment status on selected ERG outcomes were measured. Additional exploratory analyses assessed potential influences of MS phenotype and disease status (clinically active, radiologically active, and disease progression). Results:Delays to two ERG peak times (dark-adapted 3.0 b-wave; light-adapted flicker) were recorded in MS +ON and MS -ON eyes. No influences of EDSS score, disease duration, previous ON, or treatment status were observed. Exploratory analyses were consistent with no effects of MS phenotype or disease status. Conclusions:ERG findings are abnormal in individuals with moderate-severe disability caused by MS; however, these findings are not distinct from those observed earlier in the disease course. Although bipolar dysfunction appears to be common in pwMS throughout the disease course, ERG is likely not useful in monitoring or prognostication of MS.

Background Despite successes in multiple sclerosis (MS) drug development, the effectiveness of animal studies in predicting successful bench-to-bedside translation is uncertain. Our goal was to identify predictors of successful animal-to-human translation for MS by systematically comparing animal studies of approved disease-modifying therapies (DMTs) with those that failed in clinical trials due to efficacy or safety concerns. Methods Systematic review of animal studies testing MS DMTs, identified from searches in PubMed and EMBASE. A random effect meta-analysis was fitted to the data to compare outcome effect sizes for approved versus failed DMTs. Effect sizes and testing under diverse experimental conditions were assessed as potential predictors for successful translation. Findings We included 497 animal studies, covering 15 approved and 11 failed DMTs, tested in approximately 30′000 animals. DMTs were tested in a small repertoire of experimental parameters: about 86% of studies used experimental autoimmune encephalomyelitis (EAE), 80% used mice, and 76% used female animals. There was no association between animal study outcomes or testing DMTs under varied conditions (e.g., different laboratories or models) and successful approval. Surprisingly, 91% of animal studies were published after first-in-MS trial and 91% after official regulatory approval. Interpretation Our findings emphasize the complexity in carrying drugs from animals to clinical practice. Specific challenges include limited experimental methods in animal research and a disconnect between preclinical and clinical research. We advocate for efforts to streamline drug development for MS to improve animal research's relevance for patients. Funding NIH, Swiss National Science Foundation, Universities Federation for Animal Welfare.

The preclinical research community faces an ever-expanding corpus of biomedical literature, making it challenging to keep abreast with the latest findings. This hampers evidence-based research and informed decision-making. Thus, reliable tools are warranted to manage this evidence and maximize the global investment in research. Systematic reviews, syntheses of existing scientific evidence that address a focused question in an unbiased manner and using explicit methods, have gained momentum as an effective solution. Systematic reviews have an important role in uncovering problems in preclinical research, informing best practice guidelines, reducing research waste, promoting reproducibility and guiding translational research. Systematic reviews of preclinical studies also promote ethical animal use by maximizing the use of existing animal studies, thereby fostering animal welfare. However, poorly performed systematic reviews can produce unreliable results, leading to incorrect conclusions about the underlying literature. This Primer presents guidance for conducting a rigorous systematic review with or without meta-analysis of preclinical studies including animal and in vitro studies. It also discusses the limitations of systematic reviews and outlines current developments such as systematic review automation. By following this Primer, researchers can ensure the rigour and usefulness of their systematic reviews, ultimately benefiting decision-making and research outcomes in preclinical research. Preclinical systematic reviews look at scientific evidence addressing focused questions from animal research studies to inform future clinical research. In this Primer, Ineichen et al. discuss the best practices for conducting preclinical systematic reviews, promoting reproducibility and guiding translational research.

The advent of large language models (LLMs) such as BERT and, more recently, GPT, is transforming our approach of analyzing and understanding biomedical texts. To stay informed about the latest advancements in this area, there is a need for up-to-date summaries on the role of LLM in Natural Language Processing (NLP) of biomedical texts. Thus, this scoping review aims to provide a detailed overview of the current state of biomedical NLP research and its applications, with a special focus on the evolving role of LLMs. We conducted a systematic search of PubMed, EMBASE, and Google Scholar for studies and conference proceedings published from 2017 to December 19, 2023, that develop or utilize LLMs for NLP tasks in biomedicine. We evaluated the risk of bias in these studies using a 3-item checklist. From 13,823 references, we selected 199 publications and conference proceedings for our review. LLMs are being applied to a wide array of tasks in the biomedical field, including knowledge management, text mining, drug discovery, and evidence synthesis. Prominent among these tasks are text classification, relation extraction, and named entity recognition. Although BERT-based models remain prevalent, the use of GPT-based models has substantially increased since 2023. We conclude that, despite offering opportunities to manage the growing volume of biomedical data, LLMs also present challenges, particularly in clinical medicine and evidence synthesis, such as issues with transparency and privacy concerns.