Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review

Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: A systematic scoping review

Exposure assessment is a crucial component of environmental health research, providing essential information on the potential risks associated with various chemicals. A systematic scoping review was conducted to acquire an overview of accessible human exposure assessment methods and computational tools to support and ultimately improve risk assessment. The systematic scoping review was performed in Sysrev, a web platform that introduces machine learning techniques into the review process aiming for increased accuracy and efficiency. Included publications were restricted to a publication date after the year 2000, where exposure methods were properly described. Exposure assessments methods were found to be used for a broad range of environmental chemicals including pesticides, metals, persistent chemicals, volatile organic compounds, and other chemical classes. Our results show that after the year 2000, for all the types of exposure routes, probabilistic analysis, and computational methods to calculate human exposure have increased. Sixty-three mathematical models and toolboxes were identified that have been developed in Europe, North America, and globally. However, only twelve occur frequently and their usefulness were associated with exposure route, chemical classes and input parameters used to estimate exposure. The outcome of the combined associations can function as a basis and/or guide for decision making for the selection of most appropriate method and tool to be used for environmental chemical human exposure assessments in Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment (ONTOX) project and elsewhere. Finally, the choice of input parameters used in each mathematical model and toolbox shown by our analysis can contribute to the harmonization process of the exposure models and tools increasing the prospect for comparison between studies and consistency in the regulatory process in the future.

A quantitative weight-of-evidence method for confidence assessment of adverse outcome pathway networks: A case study on chemical-induced liver steatosis

The field of chemical toxicity testing is undergoing a transition to overcome the limitations of in vivo experiments. This evolution involves implementing innovative non-animal approaches to improve predictability and provide a more precise understanding of toxicity mechanisms. Adverse outcome pathway (AOP) networks are pivotal in organizing existing mechanistic knowledge related to toxicological processes. However, these AOP networks are dynamic and require regular updates to incorporate the latest data. Regulatory challenges also persist due to concerns about the reliability of the information they offer. This study introduces a generic Weight-of-Evidence (WoE) scoring method, aligned with the tailored Bradford-Hill criteria, to quantitatively assess the confidence levels in key event relationships (KERs) within AOP networks. We use the previously published AOP network on chemical-induced liver steatosis, a prevalent form of human liver injury, as a case study. Initially, the existing AOP network is optimized with the latest scientific information extracted from PubMed using the free SysRev platform for artificial intelligence (AI)-based abstract inclusion and standardized data collection. The resulting optimized AOP network, constructed using Cytoscape, visually represents confidence levels through node size (key event, KE) and edge thickness (KERs). Additionally, a Shiny application is developed to facilitate user interaction with the dataset, promoting future updates. Our analysis of 173 research papers yielded 100 unique KEs and 221 KERs among which 72 KEs and 170 KERs, respectively, have not been previously documented in the prior AOP network or AOP-wiki. Notably, modifications in de novo lipogenesis, fatty acid uptake and mitochondrial beta-oxidation, leading to lipid accumulation and liver steatosis, garnered the highest KER confidence scores. In conclusion, our study delivers a generic methodology for developing and assessing AOP networks. The quantitative WoE scoring method facilitates in determining the level of support for KERs within the optimized AOP network, offering valuable insights into its utility in both scientific research and regulatory contexts. KERs supported by robust evidence represent promising candidates for inclusion in an in vitro test battery for reliably predicting chemical-induced liver steatosis within regulatory frameworks.

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support

BACKGROUND

Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs.

METHODS

DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed.

RESULTS

Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs.

CONCLUSIONS

We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.

Meta-analysis reveals that the effects of precipitation change on soil and litter fauna in forests depend on body size

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

Rationalising development of classification systems describing livestock production systems for disease burden analysis within the Global Burden of Animal Diseases programme

The heterogeneity that exists across the global spectrum of livestock production means that livestock productivity, efficiency, health expenditure and health outcomes vary across production systems. To ensure that burden of disease estimates are specific to the represented livestock population and people reliant upon them, livestock populations need to be systematically classified into different types of production system, reflective of the heterogeneity across production systems. This paper explores the data currently available of livestock production system classifications and animal health through a scoping review as a foundation for the development of a framework that facilitates more specific estimates of livestock disease burdens. A top-down framework to classification is outlined based on a systematic review of existing classification methods and provides a basis for simple grouping of livestock at global scale. The proposed top-down classification framework, which is dominated by commodity focus of production along with intensity of resource use, may have less relevance at the sub-national level in some jurisdictions and will need to be informed and adapted with information on how countries themselves categorize livestock and their production systems. The findings in this study provide a foundation for analysing animal health burdens across a broad level of production systems. The developed framework will fill a major gap in how livestock production and health are currently approached and analysed.

Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine)

The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

Strategies to reduce antimicrobials in livestock and aquaculture, and their impact under field conditions: a structured scoping literature review

Antimicrobial resistance is a pandemic problem, causing substantial health and economic burdens. Antimicrobials are extensively used in livestock and aquaculture, exacerbating this global threat. Fostering the prudent use of antimicrobials will safeguard animal and human health. A lack of knowledge about alternatives to replace antimicrobials, and their effectiveness under field conditions, hampers changes in farming practices. This work aimed to understand the impact of strategies to reduce antimicrobial usage (AMU) in livestock and aquaculture, under field conditions, using a structured scoping literature review. The Extension for Scoping Reviews of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (PRISMA-ScR) were followed and the Patient, Intervention, Comparison, Outcome, Time and Setting (PICOTS) framework used. Articles were identified from CAB Abstracts, MEDLINE and Scopus. A total of 7505 unique research articles were identified, 926 of which were eligible for full-text assessment; 203 articles were included in data extraction. Given heterogeneity across articles in the way alternatives to antimicrobials or interventions against their usage were described, there was a need to standardize these by grouping them in categories. There were differences in the impacts of the strategies between and within species; this highlights the absence of a 'one-size-fits-all' solution. Nevertheless, some options seem more promising than others, as their impacts were consistently equivalent or positive when compared with animal performance using antimicrobials. This was particularly the case for bioactive protein and peptides, and feed/water management. The outcomes of this work provide data to inform cost-effectiveness assessments of strategies to reduce AMU.

An adverse outcome pathway for chemical-induced Parkinson's disease: Calcium is key

Exposure to pesticides is associated with an increased risk of developing Parkinson's disease (PD). Currently, rodent-based risk assessment studies cannot adequately capture neurodegenerative effects of pesticides due to a lack of human-relevant endpoints targeted at neurodegeneration. Thus, there is a need for improvement of the risk assessment guidelines. Specifically, a mechanistic assessment strategy, based on human physiology and (patho)biology is needed, which can be applied in next generation risk assessment. The Adverse Outcome Pathway (AOP) framework is particularly well-suited to provide the mechanistic basis for such a strategy. Here, we conducted a semi-systematic review in Embase and MEDLINE, focused on neurodegeneration and pesticides, to develop an AOP network for parkinsonian motor symptoms. Articles were labelled and included/excluded using the online platform Sysrev. Only primary articles, written in English, focused on effects of pesticides or PD model compounds in models for the brain were included. A total of 66 articles, out of the 1700 screened, was included. PD symptoms are caused by loss of function and ultimately death of dopaminergic neurons in the substantia nigra (SN). Our literature review highlights that a unique feature of these cells that increases their vulnerability is their reliance on continuous low-level influx of calcium. As such, excess intracellular calcium was identified as a central early Key Event (KE). This KE can lead to death of dopaminergic neurons of the SN, and eventually parkinsonian motor symptoms, via four distinct pathways: 1) activation of calpains, 2) endoplasmic reticulum stress, 3) impairment of protein degradation, and 4) oxidative damage. Several receptors have been identified that may serve as molecular initiating events (MIEs) to trigger one or more of these pathways. The proposed AOP network provides the biological basis that can be used to develop a mechanistic testing strategy that captures neurodegenerative effects of pesticides.

Does microfluidic sperm selection improve clinical pregnancy and miscarriage outcomes in assisted reproductive treatments? A systematic review and meta-analysis

BACKGROUND

The microfluidic sperm selection (MFSS) device has emerged as a promising adjunct in assisted reproduction treatments (ART). It employs mechanisms of biomimicry based on the microanatomy of the female reproductive tract through strategies like chemotaxis and rheotaxis. Numerous studies assert improvements in ART outcomes with the use of MFSS, often attributed to the theoretical reduction in sperm DNA damage compared to other techniques. However, these attributed benefits lack validation through large-scale clinical trials, and there is no significant evidence of enhanced assisted reproductive treatments (ART) outcomes.

OBJECTIVE

To evaluate whether the utilization of MFSS enhances clinical pregnancy results and abortion outcomes in couples undergoing ART compared to standard sperm selection techniques for Intracytoplasmic Sperm Injection (ICSI). We also assessed laboratory outcomes as a supplementary analysis.

SEARCH METHODS

We conducted searches across databases including PubMed, NIH, LILACS, CENTRAL, Crossref, Scopus, and OpenAlex. A total of 1,255 records were identified. From these, 284 duplicate records were eliminated, and an additional 895 records were excluded due to their association with patent applications, diagnostic tests, forensic analyses, or irrelevance to the research focus. Among the initially eligible 76 studies, 63 were excluded, encompassing abstracts, studies lacking adequate control groups, and ongoing clinical trials. Ultimately, 13 studies were selected for inclusion in the ensuing meta-analysis.

RESULTS

Regarding clinical pregnancy, we assessed a total of 868 instances of clinical pregnancies out of 1,646 embryo transfers. Regarding miscarriage, we examined 95 cases of pregnancy loss among the 598 confirmed clinical pregnancies in these studies.

CONCLUSION

The utilization of MFSS demonstrates marginal positive outcomes compared to standard sperm selection techniques, without statistical significance in any of the analyses.

BROADER IMPLICATIONS

This study conducted the first meta-analysis to evaluate clinical pregnancy rates, miscarriage rates, and laboratory results associated with the use of MFSS compared to standard sperm selection techniques. We have also listed potentially eligible studies for future inclusion. It's important to emphasize the need for multicenter studies with standardized parameters to attain a more robust clarification of this issue.

Approaches for disease prioritization and decision-making in animal health, 2000-2021: a structured scoping review

This scoping review identifies and describes the methods used to prioritize diseases for resource allocation across disease control, surveillance, and research and the methods used generally in decision-making on animal health policy. Three electronic databases (Medline/PubMed, Embase, and CAB Abstracts) were searched for articles from 2000 to 2021. Searches identified 6, 395 articles after de-duplication, with an additional 64 articles added manually. A total of 6, 460 articles were imported to online document review management software (sysrev.com) for screening. Based on inclusion and exclusion criteria, 532 articles passed the first screening, and after a second round of screening, 336 articles were recommended for full review. A total of 40 articles were removed after data extraction. Another 11 articles were added, having been obtained from cross-citations of already identified articles, providing a total of 307 articles to be considered in the scoping review. The results show that the main methods used for disease prioritization were based on economic analysis, multi-criteria evaluation, risk assessment, simple ranking, spatial risk mapping, and simulation modeling. Disease prioritization was performed to aid in decision-making related to various categories: (1) disease control, prevention, or eradication strategies, (2) general organizational strategy, (3) identification of high-risk areas or populations, (4) assessment of risk of disease introduction or occurrence, (5) disease surveillance, and (6) research priority setting. Of the articles included in data extraction, 50.5% had a national focus, 12.3% were local, 11.9% were regional, 6.5% were sub-national, and 3.9% were global. In 15.2% of the articles, the geographic focus was not specified. The scoping review revealed the lack of comprehensive, integrated, and mutually compatible approaches to disease prioritization and decision support tools for animal health. We recommend that future studies should focus on creating comprehensive and harmonized frameworks describing methods for disease prioritization and decision-making tools in animal health.

Mapping the key characteristics of carcinogens for glyphosate and its formulations: A systematic review

Glyphosate was classified as a probable human carcinogen (Group 2A) by the International Agency for Research on Cancer (IARC) partially due to strong mechanistic evidence in 2015. Since then, numerous studies of glyphosate and its formulations (GBF) have emerged. These studies can be evaluated for cancer hazard identification with the newly described ten key characteristics (KC) of carcinogens approach. Our objective was to assess all in vivo, ex vivo, and in vitro mechanistic studies of human and experimental animals (mammals) that compared exposure to glyphosate/GBF with low/no exposure counterparts for evidence of the ten KCs. A protocol with our methods adhering to PRISMA guidelines was registered a priori (INPLASY202180045). Two blinded reviewers screened all in vivo, ex vivo, and in vitro studies of glyphosate/GBF exposure in humans/mammals reporting any KC-related outcome available in PubMed before August 2021. Studies that met inclusion criteria underwent data extraction conducted in duplicate for each KC outcome reported along with key aspects of internal/external validity, results, and reference information. These data were used to construct a matrix that was subsequently analyzed in the program R to conduct strength of evidence and quality assessments. Of the 2537 articles screened, 175 articles met inclusion criteria, from which we extracted >50,000 data points related to KC outcomes. Data analysis revealed strong evidence for KC2, KC4, KC5, KC6, KC8, limited evidence for KC1 and KC3, and inadequate evidence for KC7, KC9, and KC10. Notably, our in-depth quality analyses of genotoxicity (KC2) and endocrine disruption (KC8) revealed strong and consistent positive findings. For KC2, we found: 1) studies conducted in humans and human cells provided stronger positive evidence than counterpart animal models; 2) GBF elicited a stronger effect in both human and animal systems when compared to glyphosate alone; and 3) the highest quality studies in humans and human cells consistently revealed strong evidence of genotoxicity. Our analysis of KC8 indicated that glyphosate's ability to modulate hormone levels and estrogen receptor activity is sensitive to both exposure concentration and formulation. The modulations observed provide clear evidence that glyphosate interacts with receptors, alters receptor activation, and modulates the levels and effects of endogenous ligands (including hormones). Our findings strengthen the mechanistic evidence that glyphosate is a probable human carcinogen and provide biological plausibility for previously reported cancer associations in humans, such as non-Hodgkin lymphoma. We identified potential molecular interactions and subsequent key events that were used to generate a probable pathway to lymphomagenesis.

A Systematic Review of Methods Used to Determine the Work of Breathing during Exercise

INTRODUCTION

Measurement of the work of breathing (Wb) during exercise provides useful insights into the energetics and mechanics of the respiratory muscles across a wide range of minute ventilations. The methods and analytical procedures used to calculate the Wb during exercise have yet to be critically appraised in the literature.

PURPOSE

The aim of this systematic review was to evaluate the quality of methods used to measure the Wb during exercise in the available literature.

METHODS

We conducted an extensive search of three databases for studies that measured the Wb during exercise in adult humans. Data were extracted on participant characteristics, flow/volume and pressure devices, esophageal pressure (P oes ) catheters, and methods of Wb analysis.

RESULTS

A total of 120 articles were included. Flow/volume sensors used were primarily pneumotachographs ( n = 85, 70.8%), whereas the most common pressure transducer was of the variable reluctance type ( n = 63, 52.5%). Esophageal pressure was frequently obtained via balloon-tipped catheters ( n = 114, 95.0%). Few studies mentioned calibration, frequency responses, and dynamic compensation of their measurement devices. The most popular method of measuring the Wb was pressure-volume integration ( n = 51, 42.5%), followed by the modified Campbell ( n = 28, 23.3%) and Dean & Visscher diagrams ( n = 26, 21.7%). Over one-third of studies did not report the methods used to process their pressure-volume data, and the majority (60.8%) of studies used the incorrect Wb units and/or failed to discuss the limitations of their Wb measurements.

CONCLUSIONS

The findings of this systematic review highlight the need for the development of a standardized approach for measuring Wb, which is informative, practical, and accessible for future researchers.

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

BACKGROUND

Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships.

METHODS

Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship.

RESULTS

This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis.

CONCLUSIONS

This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.

Animal sources of antimicrobial-resistant bacterial infections in humans: a systematic review

Bacterial antimicrobial resistance (AMR) is among the leading global health challenges of the century. Animals and their products are known contributors to the human AMR burden, but the extent of this contribution is not clear. This systematic literature review aimed to identify studies investigating the direct impact of animal sources, defined as livestock, aquaculture, pets, and animal-based food, on human AMR. We searched four scientific databases and identified 31 relevant publications, including 12 risk assessments, 16 source attribution studies, and three other studies. Most studies were published between 2012 and 2022, and most came from Europe and North America, but we also identified five articles from South and South-East Asia. The studies differed in their methodologies, conceptual approaches (bottom-up, top-down, and complex), definitions of the AMR hazard and outcome, the number and type of sources they addressed, and the outcome measures they reported. The most frequently addressed animal source was chicken, followed by cattle and pigs. Most studies investigated bacteria-resistance combinations. Overall, studies on the direct contribution of animal sources of AMR are rare but increasing. More recent publications tailor their methodologies increasingly towards the AMR hazard as a whole, providing grounds for future research to build on.

Main topics in assisted reproductive market: A scoping review

BACKGROUND

Infertility affects around 12% of couples, and this proportion has been gradually increasing. In this context, the global assisted reproductive technologies (ART) market shows significant expansion, hovering around USD 26 billion in 2019 and is expected to reach USD 45 billion by 2025.

OBJECTIVES

We realized a scoping review of the ART market from academic publications, market reports, and specialized media news, to identify the main terms and characterize them into the main topics in the area.

DESIGN

We apply an LDA topic modeling process to identify the main terms, and clustered them into semantic synonymous topics. We extracted the patterns and information to these topics and purposed a factor/consequence correlation to them.

RESULTS

We found 2,232 academic papers and selected 632 to include in the automatic term detection. We also included 34 market reports and seven notices produced by specialized enterprises. Were identified 121 most relevant cited terms covering 7,806 citations. These terms were manually aggregated into 10 topics based on semantic similarity: neutral terms (37.2%), economic aspects (17.6%), in vitro fertilization (IVF) commodities & cross-border reproductive care (CBRC) (10.6%), geographic distribution (9.5%), social aspects (7%), regulation (6%), trends & concerns (3.9%), accessibility (3.4%), internet influence (2.9%), and fertility preservation for non-medical reasons (2%).

DISCUSSION

The analysis indicates a market with expressive complexity. Most terms were associated with more than one topic, indicating the synergism of this market's behavior. Only seven terms related to economic aspects, surrogacy and donation represent around 50% of the citations. Except for the topic formed by generic terms, the topic of the economic aspects was the most represented, reflecting macro perspectives such as a-la-carte standard of treatments, many clinics operating on a small/medium scale, and the recent formation of conglomerates. The IVF commodities & CBRC topic brings an overview of gametes pricing and transnational surrogacy, and its regulation. The topic of geographic distribution indicates that that the Asia-Pacific (APAC) market has the most significant growth potential in all fields. Despite the increase in supply and demand for infertility treatments and technological advances in recent decades, the success rate of IVF cycles remains at around 30%. Terms referring to research and development or technical improvement were not identified in a significant way in this review.

CONCLUSIONS

The formation of topics by semantic similarity proved to be an initial path for the elaboration of in-depth studies on the dynamics between several factors, for this, we present the panel classifying main terms into factors (demand, pent-up demand, or distributive) or ART market consequences. Through this approach, it was possible to observe that most of the works addresses economic aspects, regulation and geographic aspects and that topics related to research and improvement have not been addressed. In this way, we highlight the need to deepen the analysis of market elements that may be related to increased efficiency of IVF in the technical field.

Meta-analysis of macrophage nanoparticle targeting across blood and solid tumors using an eLDA Topic modeling Machine Learning approach

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generation of nanoparticle targeting technologies. With the large amount of literature and the speed at which it is generated it is difficult to remain current with the most up-to-date literature. In this study we performed a topic modeling analysis of the most common usages of nanoparticle targeting of macrophages in solid tumors. The data spans 20 years of literature, providing an extensive meta-analysis of the nanoparticle strategies. Our topic model found 6 distinct topics: Immune and TAMs, Nanoparticles, Imaging, Gene Delivery and Exosomes, Vaccines, and Multi-modal Therapies. We also found distinct nanoparticle usage, tumor types, and therapeutic trends across these topics. Moreover, we established that the topic model could be used to assign new papers into the existing topics, thereby creating a Living Review. This type of meta-analysis provides a useful assessment tool for aggregating data about a large field.

The use of artificial intelligence for automating or semi-automating biomedical literature analyses: a scoping review

OBJECTIVE

Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely exceeds the capacity of human-only analysis. In this context, artificial intelligence (AI) and its branches such as machine learning (ML) can be used to facilitate human efforts in analyzing the literature to foster EBM. The present scoping review aimed to examine the use of AI in the automation of biomedical literature survey and analysis with a view to establishing the state-of-the-art and identifying knowledge gaps.

MATERIALS AND METHODS

Comprehensive searches of the main databases were performed for articles published up to June 2022 and studies were selected according to inclusion and exclusion criteria. Data were extracted from the included articles and the findings categorized.

RESULTS

The total number of records retrieved from the databases was 12,145, of which 273 were included in the review. Classification of the studies according to the use of AI in evaluating the biomedical literature revealed three main application groups, namely assembly of scientific evidence (n=127; 47%), mining the biomedical literature (n=112; 41%) and quality analysis (n=34; 12%). Most studies addressed the preparation of systematic reviews, while articles focusing on the development of guidelines and evidence synthesis were the least frequent. The biggest knowledge gap was identified within the quality analysis group, particularly regarding methods and tools that assess the strength of recommendation and consistency of evidence.

CONCLUSION

Our review shows that, despite significant progress in the automation of biomedical literature surveys and analyses in recent years, intense research is needed to fill knowledge gaps on more difficult aspects of ML, deep learning and natural language processing, and to consolidate the use of automation by end-users (biomedical researchers and healthcare professionals).

Central and peripheral mechanisms of pain in fibromyalgia: scoping review protocol

Fibromyalgia is characterised by widespread musculoskeletal pain, which may present with fatigue, depression, anxiety, sleep and cognitive disturbances. It is the second most prevalent rheumatic disease. An accurate diagnosis is challenging, since its symptoms may resemble diverse conditions such as carpal tunnel syndrome, Raynaud syndrome, Sjögren syndrome, amongst others. Neuropathic pain and autonomic dysfunction in fibromyalgia suggest the involvement of the nervous system. Ion channels, neurotransmitters and neuromodulators may play a role. Small fibre neuropathy (SFN) may also cause chronic widespread pain. SFN may occur in 50% of fibromyalgia patients, but its role in the disease is unknown. Despite several efforts to synthesise the evidence on the mechanisms for pain in fibromyalgia, there are few studies applying an integrative perspective of neurochemical, immunological, and neuroanatomical characteristics, and their relevance to the disease. This protocol aims to clarify the mechanisms of the central and peripheral nervous system associated with pain in fibromyalgia. We will retrieve published studies from Web of Science, MEDLINE, Scopus, EBSCOhost, Ovid and Google Scholar. All clinical studies or experimental models of fibromyalgia reporting imaging, neurophysiological, anatomical, structural, neurochemical, or immunological characteristics of the central or peripheral nervous systems associated with pain will be included. Exclusion criteria will eliminate studies evaluating pain without a standardised measure, studies written in languages different from Spanish or English that could not be appropriately translated, and studies whose full-text files could not be retrieved after all efforts made. A narrative synthesis will be performed.

Applications for open access normalized synthesis in metastatic prostate cancer trials

Recent metastatic castration-resistant prostate cancer (mCRPC) clinical trials have integrated homologous recombination and DNA repair deficiency (HRD/DRD) biomarkers into eligibility criteria and secondary objectives. These trials led to the approval of some PARP inhibitors for mCRPC with HRD/DRD indications. Unfortunately, biomarker-trial outcome data is only discovered by reviewing publications, a process that is error-prone, time-consuming, and laborious. While prostate cancer researchers have written systematic evidence reviews (SERs) on this topic, given the time involved from the last search to publication, an SER is often outdated even before publication. The difficulty in reusing previous review data has resulted in multiple reviews of the same trials. Thus, it will be useful to create a normalized evidence base from recently published/presented biomarker-trial outcome data that one can quickly update. We present a new approach to semi-automating normalized, open-access data tables from published clinical trials of metastatic prostate cancer using a data curation and SER platform. Clinicaltrials.gov and Pubmed.gov were used to collect mCRPC clinical trial publications with HRD/DRD biomarkers. We extracted data from 13 publications covering ten trials that started before 22nd Apr 2021. We extracted 585 hazard ratios, response rates, duration metrics, and 543 adverse events. Across 334 patients, we also extracted 8,180 patient-level survival and biomarker values. Data tables were populated with survival metrics, raw patient data, eligibility criteria, adverse events, and timelines. A repeated strong association between HRD and improved PARP inhibitor response was observed. Several use cases for the extracted data are demonstrated via analyses of trial methods, comparison of treatment hazard ratios, and association of treatments with adverse events. Machine learning models are also built on combined and normalized patient data to demonstrate automated discovery of therapy/biomarker relationships. Overall, we demonstrate the value of systematically extracted and normalized data. We have also made our code open-source with simple instructions on updating the analyses as new data becomes available, which anyone can use even with limited programming knowledge. Finally, while we present a novel method of SER for mCRPC trials, one can also implement such semi-automated methods in other clinical trial domains to advance precision medicine.

Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.