Current Applications and Future Implications of Artificial Intelligence in Spine Surgery and Research: A Narrative Review and Commentary.

STUDY DESIGN: Narrative review. OBJECTIVES: Artificial intelligence (AI) is being increasingly applied to the domain of spine surgery. We present a review of AI in spine surgery, including its use across all stages of the perioperative process and applications for research. We also provide commentary regarding future ethical considerations of AI use and how it may affect surgeon-industry relations. METHODS: We conducted a comprehensive literature review of peer-reviewed articles that examined applications of AI during the pre-, intra-, or postoperative spine surgery process. We also discussed the relationship among AI, spine industry partners, and surgeons. RESULTS: Preoperatively, AI has been mainly applied to image analysis, patient diagnosis and stratification, decision-making. Intraoperatively, AI has been used to aid image guidance and navigation. Postoperatively, AI has been used for outcomes prediction and analysis. AI can enable curation and analysis of huge datasets that can enhance research efforts. Large amounts of data are being accrued by industry sources for use by their AI platforms, though the inner workings of these datasets or algorithms are not well known. CONCLUSIONS: AI has found numerous uses in the pre-, intra-, or postoperative spine surgery process, and the applications of AI continue to grow. The clinical applications and benefits of AI will continue to be more fully realized, but so will certain ethical considerations. Making industry-sponsored databases open source, or at least somehow available to the public, will help alleviate potential biases and obscurities between surgeons and industry and will benefit patient care.

Contextualizing toxic elements in the diet: a case for integration of toxic element data into food databases.

Food composition data plays a key role in the practice of nutrition. However, nutrition professionals may currently lack the resources they need to integrate information about toxic elements - such as arsenic, cadmium, and lead - in food into the advice they give consumers. Geographic, sociocultural, and individual factors may impact not only the toxic element content of food, but also how the balance between potentially toxic and health-promoting components of food must be weighed. Better integration and contextualization of toxic element data into key food databases could allow for more nuanced, comprehensive nutrition guidance.

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health. MATERIALS AND METHODS: This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting. The calculations used the AAindex matrices in parallel to the Hamming distance. The calculations were carried out with different types and values of antigenic escape thresholds, on four data sets. The results were compared using common binary classification metrics. RESULTS: Significant differentiation is shown depending on the data sets used. The best results were demonstrated by all three models for the forecast autumn season of 2022, which were preliminary trained on the February season of the same year (Auroc 0.934; 0.958; 0.956, respectively). The lowest results were obtained for the entire forecast year 2023, they were set up on data from two seasons of 2022 (Aucroc 0.614; 0.658; 0.775). The dependence of the results on the types of thresholds used and their values turned out to be insignificant. The additional use of AAindex matrices did not significantly improve the results of the models without introducing significant deterioration. CONCLUSION: More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.

Checklist and key to species of stink bugs (Hemiptera, Heteroptera, Pentatomidae) of Kentucky, United States of America.

Stink bugs (Heteroptera: Pentatomidae) have received a lot of attention as there are many economically important pest species. However, the status of species richness, distribution, and taxonomy remain overlooked and outdated in Kentucky (USA). Having such information at a regional scale is crucial to allow the development of suitable pest management and conservation programs. Here, the stink bug fauna of Kentucky was examined from museum specimens, literature, and public online repositories. Overall, 42 species in 28 genera and three subfamilies (Asopinae, Podopinae, and Pentatominae) are listed from Kentucky. Thirteen species are new records for Kentucky, 10 species are considered to be of economic importance and eight are strict predators. Pictures of species are provided along with the first key for the identification of the stink bug species of Kentucky.

Large-Scale Construction and Analysis of Amorphous Porous Polymer Network Materials.

In recent decades, data-driven methodologies have emerged as irreplaceable tools in materials science, particularly for elucidating structure-property relationships and facilitating the discovery of novel materials. However, despite the rapid development witnessed in other domains, amorphous materials have received relatively less attention in this context. The disordered atomic structure of amorphous materials resulting from irreversible reactions between building blocks has posed a difficulty in structural modeling, leading to a lack of databases that accurately reflect the amorphous nature of these materials. In this work, a database composed of 10,237 porous polymer networks (PPNs) was constructed from self-assembly simulations, resulting in the largest database of PPNs considering their amorphous characteristics. Through the distinct differences observed in comparison with existing databases, we emphasize that carefully considering the structural disorder of PPNs is essential for accurately characterizing their chemical behaviors. Machine learning models trained on the constructed database have confirmed that the macroscopic properties of amorphous PPNs can be predicted solely from the atomic structures of their monomers, implying that the characteristics of previously unseen PPNs can be assessed without the need for additional self-assembly simulations.

The power of citizen science to advance fungal conservation.

Fungal conservation is gaining momentum globally, but many challenges remain. To advance further, more data are needed on fungal diversity across space and time. Fundamental information regarding population sizes, trends, and geographic ranges is also critical to accurately assess the extinction risk of individual species. However, obtaining these data is particularly difficult for fungi due to their immense diversity, complex and problematic taxonomy, and cryptic nature. This paper explores how citizen science (CS) projects can be lever-aged to advance fungal conservation efforts. We present several examples of past and ongoing CS-based projects to record and monitor fungal diversity. These include projects that are part of broad collecting schemes, those that provide participants with targeted sampling methods, and those whereby participants collect environmental samples from which fungi can be obtained. We also examine challenges and solutions for how such projects can capture fungal diversity, estimate species absences, broaden participation, improve data curation, and translate resulting data into actionable conservation measures. Finally, we close the paper with a call for professional mycologists to engage with amateurs and local communities, presenting a framework to determine whether a given project would likely benefit from participation by citizen scientists.

Trends and Most-Cited Articles on Hemispheric Surgery: A Bibliometric Analysis and Historical Review.

INTRODUCTION: Hemispheric surgery is a multistep, highly effective, and radical surgical procedure in the treatment of drug-resistant epilepsy due to extensive unilateral hemispheric disease. The procedure ranges from a resective procedure (hemispherectomy) to disconnection (hemispherotomy) and has developed substantially over the last century from morbid to elegant, minimally invasive, and routinely practiced procedures. Bearing in mind the numerous articles that have been published on hemispherectomy and hemispherotomy, we aimed to highlight the top 100 cited and impactful articles to create familiarity with the topic. We anticipate that this will be a helpful guide for clinicians and academics navigating the literature on this subject. METHODS: A Scopus title-based search on the top 100 most-cited articles on "hemispherectomy" and "hemispherotomy" was performed in September 2023 with no restrictions. The top 100 most-cited articles were then retrieved. The article title, first author, first author's specialty, country of origin, first author's institution at the time of publication, journal of publication, year of publication, citation count, and citations per year were collected. The Google Scholar database citation count for each paper was added for correlation and comprehensive coverage. RESULTS: The top 100 most-cited articles were cited 92 times per paper on average. The publication dates ranged from 1949 to 2016. The most frequently cited article "Clinical outcomes of hemispherectomy for epilepsy in childhood and adolescence" with 307 citations was published by A.M. Devlin et al. (2003) in the journal Brain. The USA was the highest publishing country (41 articles). The highest-publishing journal was Neurology. The most prolific first authors were A. Smith, J. Schramm, and J. Villemure, each with four publications. The institution with the most contributions was McGill University and its affiliated Health Centers, with nine publications in total. Neurosurgery was the most common specialty among the first authors. Most of the included studies were cohort studies or case series. CONCLUSION: We identified the top 100 cited articles on hemispherectomy and hemispherotomy using the Scopus database and supplemented our results with Google Scholar. We highlighted the most prominent authors, institutions, countries, journals, and study designs and illuminated the historical development of hemispherectomy and hemispherotomy procedures, in addition to landmark and currently trending papers.

4th DNA Forensic Symposium: Challenges and future directions in Africa.

The 4th Forensic DNA Symposium in Africa underscored the critical role of regional collaboration in advancing forensic sciences, with a particular focus on forensic DNA examinations, databases, and humanitarian initiatives. The symposium aimed to assess the current forensic DNA capabilities across African countries and develop strategies to expand and better utilize DNA platforms. Key findings from the symposium highlight the necessity for enhanced cooperation among African nations to build robust forensic DNA databases and improve data-sharing mechanisms. The symposium also identified significant gaps in current capabilities and the need to develop legal frameworks, infrastructure, and expertise to support forensic initiatives. Moving forward, these findings suggest a strategic focus on capacity building, establishing standardized procedures, and implementing sustainable forensic practices across the continent. Champions were nominated by attending delegates to lead their respective countries in the implementation of these strategies, marking a critical step towards strengthening forensic science in Africa and addressing the pressing challenges related to crime and humanitarian efforts.

User Requirements for an Electronic Patient Recruitment System: Semistructured Interview Analysis After First Implementation in 3 German University Hospitals.

BACKGROUND: Clinical trials are essential for medical research and medical progress. Nevertheless, trials often fail to reach their recruitment goals. Patient recruitment systems aim to support clinical trials by providing an automated search for eligible patients in the databases of health care institutions like university hospitals. To integrate patient recruitment systems into existing workflows, previous works have assessed user requirements for these tools. In this study, we tested patient recruitment systems KAS+ and recruIT as part of the MIRACUM (Medical Informatics in Research and Care in University Medicine) project. OBJECTIVE: Our goal was to investigate whether and to what extent the 2 different evaluated tools can meet the requirements resulting from the first requirements analysis, which was performed in 2018-2019. A user survey was conducted to determine whether the tools are usable in practice and helpful for the trial staff. Furthermore, we investigated whether the test phase revealed further requirements for recruitment tools that were not considered in the first place. METHODS: We performed semistructured interviews with 10 participants in 3 German university hospitals who used the patient recruitment tools KAS+ or recruIT for at least 1 month with currently recruiting trials. Thereafter, the interviews were transcribed and analyzed by Meyring method. The identified statements of the interviewees were categorized into 5 groups of requirements and sorted by their frequency. RESULTS: The evaluated recruIT and KAS+ tools fulfilled 7 and 11 requirements of the 12 previously identified requirements, respectively. The interviewed participants mentioned the need for different notification schedules, integration into their workflow, different patient characteristics, and pseudonymized screening lists. This resulted in a list of new requirements for the implementation or enhancement of patient recruitment systems. CONCLUSIONS: Trial staff report a huge need of support for the identification of eligible trial participants. Moreover, the workflows in patient recruitment differ across trials. For better suitability of the recruitment systems in the workflow of different kinds of trials, we recommend the implementation of an adjustable notification schedule for screening lists, a detailed workflow analysis, broad patient filtering options, and the display of all information needed to identify the persons on the list. Despite criticisms, all participants confirmed to use the patient recruitment systems again.

Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research.

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

Concomitant Administration of Psychotropic and Prostate Cancer Drugs: A Pharmacoepidemiologic Study Using Drug-Drug Interaction Databases.

Prostate cancer (PC) represents the second most common diagnosed cancer in men. The burden of diagnosis and long-term treatment may frequently cause psychiatric disorders in patients, particularly depression. The most common PC treatment option is androgen deprivation therapy (ADT), which may be associated with taxane chemotherapy. In patients with both PC and psychiatric disorders, polypharmacy is frequently present, which increases the risk of drug-drug interactions (DDIs) and drug-related adverse effects. Therefore, this study aimed to conduct a pharmacoepidemiologic study of the concomitant administration of PC drugs and psychotropics using three drug interaction databases (Lexicomp®, drugs.com®, and Medscape®). This study assayed 4320 drug-drug combinations (DDCs) and identified 814 DDIs, out of which 405 (49.63%) were pharmacokinetic (PK) interactions and 411 (50.37%) were pharmacodynamic (PD) interactions. The most common PK interactions were based on CYP3A4 induction (n = 275, 67.90%), while the most common PD interactions were based on additive torsadogenicity (n = 391, 95.13%). Proposed measures for managing the identified DDIs included dose adjustments, drug substitutions, supplementary agents, parameters monitoring, or simply the avoidance of a given DDC. A significant heterogenicity was observed between the selected drug interaction databases, which can be mitigated by cross-referencing multiple databases in clinical practice.

Cyber catalogue and revision of the nematode genus Enchodelus (Dorylaimida, Nordiidae).

Background: The genus Enchodelus is an intriguing free-living dorylaimid nematode taxon. Its representatives display a distinct distributional pattern as they are mainly spread in high altitudinal enclaves of the Northern Hemisphere, being often associated with mosses and cliff vegetation. Although their feeding habits have not been studied with experimental protocols, it is traditionally assumed that they are omnivorous.The genus Enchodelus has not been recently revised; descriptions of many 'old species' (that have been described long ago and have not been reported since their original discovery) are of poor quality, hardly discoverable and do not conform to the nowadays taxonomical standards. Thus, a comprehensive compilation and analysis of their literature data is indispensable to provide new insights into the taxonomy of the genus and to elucidate its evolutionary relationships. New information: This contribution provides a cyber catalogue of all Enchodelus species, 28 in total. It compiles available information from the key European Research Infrastructures, such as TreatmentBank, Swiss Institute of Bioinformatics Literature Services (SIBiLS), the Catalogue of Life (CoL), Global Biodiversity Information Facility (GBIF), European Nucleotide Archive (ENA) and Biodiversity Literature Repository (BLR). Data about their distribution (geographical records and habitats) are incorporated too and all brought together. It is completed with discussion and notes for some species, along with information on species distributions and microhabitats. Here, all available information on Enchodelus species is brought together. This will contribute to a more complete assessment of species diversity and distribution and support further biogeographical and ecological research.Besides, type material Enchodelusvestibulifer Altherr, 1952, deposited in the Museo Cantonale di Storia Naturale di Lugano (Switzerland), is re-examined and the species is considered as incertae sedis. Further, a new species of the genus found in Caucasus, Georgia is described after its morphological and molecular study; also morphological and molecular data for E.macrodorus (de Man, 1880) Thorne, 1939, the type species of the genus, collected from Spain are provided.

The Molecular Comorbidity Network of Periodontal Disease.

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene-disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities.

Global Research Trends in the Links between Periodontal Disease and Cancer: A Bibliometric Analysis.

Both periodontal disease and cancer are prevalent conditions with significant impacts on individuals and society. Extensive research has suggested a potential link between these two diseases. This study conducted a bibliometric analysis using the Thomson Reuters Web of Science Core Collection database, focusing on publications from 2014 to 2023. The analysis included data extraction and examination of authors, affiliations, publication dates, journals, countries, citation counts, keywords, and the H-index. A total of 253 relevant articles were identified, showing an increasing trend in both publications and citations over the years. The analysis highlighted the most productive authors, institutions, and countries/regions, with Michaud DS and Abnet CC leading in the number of publications. Highly cited articles emphasized the role of specific oral microbiota, particularly F. nucleatum and P. gingivalis, in various cancers, suggesting their potential as diagnostic markers and therapeutic targets. Four key thematic clusters emerged from the keyword analysis: the broader health implications of periodontal disease, the microbiome's role in carcinogenesis, inflammation, and specific bacteria in cancer, and epidemiological methods in studying the disease-cancer association. This bibliometric analysis underscores the growing interest in the connection between periodontal disease and cancer. Future research should adopt interdisciplinary approaches, focus on large-scale microbiome studies and longitudinal research to understand the systemic effects of periodontal disease, identify cancer-associated bacterial profiles, and investigate the molecular mechanisms of bacterial carcinogenesis. Additionally, public health interventions aimed at improving oral hygiene and reducing cancer risk factors are recommended.

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model.

Background: Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis. Methods: LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power. Results: Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses. Conclusions: This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

The consistently up-regulated expression of NLRP3 in severe asthma patients from mRNA microarray and ovalbumin-induced mouse model of asthma.

Background: Severe asthma (SA) is a chronic lung disease, resistant to current treatments, symbolized by repeated symptoms of reversible airflow obstruction, airway hyper-responsiveness, and inflammation. The aim of this study was to identify genes exhibiting differential expression in individuals without asthma and SA patients. We aimed to pinpoint hub differentially expressed genes (DEGs) by utilizing a mouse model of asthma sensitized to ovalbumin (OVA). Methods: Microarray data for SA were acquired from the Gene Expression Omnibus (GEO) databases. DEGs were identified, and functional enrichment analyses were carried out. STRING and Cytoscape were utilized to design a protein-protein interaction (PPI) network and conduct module analysis. An OVA-induced asthma mice model was established. Lung tissue from the mice was collected for quantitative reverse transcription polymerase chain reaction (qRT-PCR), Western blot, and immunohistochemistry (IHC) to assess the expression of DEGs. Results: A total of 545 DEGs were identified, among which 172 genes were upregulated in SA patients compared to healthy controls. The nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3) was significantly up-regulated in SA patients [adjusted P value (Padj) =0.001]. Analysis of lung tissue using qRT-PCR, western blot, and IHC revealed higher expression of NLRP3 in OVA-induced asthma mice compared to the control group. Enrichment analysis suggests the involvement of NLRP3 in pathways related to pyroptosis, c-type lectin receptor signaling, and NOD-like receptor signaling. Conclusions: Through bioinformatics analysis, we identified a multitude of DEGs that could potentially contribute significantly to the development of SA. Notably, our findings highlight NLRP3 as a potential pivotal player in asthma pathogenesis, underscoring its prospective utility as a biomarker for SA.

O-GlcNAc informatics: advances and trends.

As a post-translational modification, protein glycosylation is critical in health and disease. O-Linked ß-N-acetylglucosamine (O-GlcNAc) modification (O-GlcNAcylation), as an intracellular monosaccharide modification on proteins, was discovered 40 years ago. Thanks to technological advances, the physiological and pathological significance of O-GlcNAcylation has been gradually revealed and widely appreciated, especially in recent years. O-GlcNAc informatics has been quickly evolving. Clearly, O-GlcNAc informatics tools have not only facilitated O-GlcNAc functional studies, but also provided us a unique perspective on protein O-GlcNAcylation. In this article, we review O-GlcNAc-focused software tools and servers that have been developed for O-GlcNAc research over the past four decades. Specifically, we will (1) survey bioinformatics tools that have facilitated O-GlcNAc proteomics data analysis, (2) introduce databases/servers for O-GlcNAc proteins/sites that have been experimentally identified by individual research labs, (3) describe software tools that have been developed to predict O-GlcNAc sites, and (4) introduce platforms cataloging proteins that interact with the O-GlcNAc cycling enzymes (i.e., O-GlcNAc transferase and O-GlcNAcase). We hope these resources will provide useful information to both experienced researchers and new incomers to the O-GlcNAc field. We anticipate that this review provides a framework to stimulate the future development of more sophisticated informatic tools for O-GlcNAc research.