Salivary and serum levels of lactate dehydrogenase in oral submucous fibrosis: A meta-analysis.

BACKGROUND: The occurrence of oral submucous fibrosis (OSF) is often accompanied by an increase in lactate dehydrogenase (LDH) levels. In this meta-analysis, we compared the salivary and serum levels of LDH levels between OSF patients and controls. MATERIAL AND METHODS: A comprehensive search was conducted in PubMed, Embase, Web of Science, and Cochrane Library from the establishment of the database to June 2023, and the quality of the studies was checked by the Newcastle-Ottawa Quality Assessment scale. The mean difference (MD) and 95% confidence interval (CI) were calculated using RevMan 5.4 software. RESULTS: A total of 28 studies were retrieved from the database, and we included 5 studies in this meta-analysis. The salivary LDH level of OSF patients was higher than healthy controls (MD: 423.10 pg/L 95%CI: 276.42-569.77 pg/mL, P < .00001), the serum LDH level of OSF patients was also higher than that of healthy controls (MD: 226.20 pg/mL, 95%CI: 147.71-304.69 pg/mL, P < .00001). CONCLUSIONS: This meta-analysis showed that salivary and serum LDH levels were higher in OSF patients than in healthy controls, suggesting that LDH may be a potential biomarker for OSF.

Deciphering the intricate linkage between the gut microbiota and Alzheimer's disease: Elucidating mechanistic pathways promising therapeutic strategies.

BACKGROUND: The gut microbiome is composed of various microorganisms such as bacteria, fungi, and protozoa, and constitutes an important part of the human gut. Its composition is closely related to human health and disease. Alzheimer's disease (AD) is a neurodegenerative disease whose underlying mechanism has not been fully elucidated. Recent research has shown that there are significant differences in the gut microbiota between AD patients and healthy individuals. Changes in the composition of gut microbiota may lead to the development of harmful factors associated with AD. In addition, the gut microbiota may play a role in the development and progression of AD through the gut-brain axis. However, the exact nature of this relationship has not been fully understood. AIMS: This review will elucidate the types and functions of gut microbiota and their relationship with AD and explore in depth the potential mechanisms of gut microbiota in the occurrence of AD and the prospects for treatment strategies. METHODS: Reviewed literature from PubMed and Web of Science using key terminologies related to AD and the gut microbiome. RESULTS: Research indicates that the gut microbiota can directly or indirectly influence the occurrence and progression of AD through metabolites, endotoxins, and the vagus nerve. DISCUSSION: This review discusses the future challenges and research directions regarding the gut microbiota in AD. CONCLUSION: While many unresolved issues remain regarding the gut microbiota and AD, the feasibility and immense potential of treating AD by modulating the gut microbiota are evident.

Assessment of transparency indicators in space medicine.

Space medicine is a vital discipline with often time-intensive and costly projects and constrained opportunities for studying various elements such as space missions, astronauts, and simulated environments. Moreover, private interests gain increasing influence in this discipline. In scientific disciplines with these features, transparent and rigorous methods are essential. Here, we undertook an evaluation of transparency indicators in publications within the field of space medicine. A meta-epidemiological assessment of PubMed Central Open Access (PMC OA) eligible articles within the field of space medicine was performed for prevalence of code sharing, data sharing, pre-registration, conflicts of interest, and funding. Text mining was performed with the rtransparent text mining algorithms with manual validation of 200 random articles to obtain corrected estimates. Across 1215 included articles, 39 (3%) shared code, 258 (21%) shared data, 10 (1%) were registered, 110 (90%) contained a conflict-of-interest statement, and 1141 (93%) included a funding statement. After manual validation, the corrected estimates for code sharing, data sharing, and registration were 5%, 27%, and 1%, respectively. Data sharing was 32% when limited to original articles and highest in space/parabolic flights (46%). Overall, across space medicine we observed modest rates of data sharing, rare sharing of code and almost non-existent protocol registration. Enhancing transparency in space medicine research is imperative for safeguarding its scientific rigor and reproducibility.

Text mining and portal development for gene-specific publications on Alzheimer's disease and other neurodegenerative diseases.

BACKGROUND: Tremendous research efforts have been made in the Alzheimer's disease (AD) field to understand the disease etiology, progression and discover treatments for AD. Many mechanistic hypotheses, therapeutic targets and treatment strategies have been proposed in the last few decades. Reviewing previous work and staying current on this ever-growing body of AD publications is an essential yet difficult task for AD researchers. METHODS: In this study, we designed and implemented a natural language processing (NLP) pipeline to extract gene-specific neurodegenerative disease (ND) -focused information from the PubMed database. The collected publication information was filtered and cleaned to construct AD-related gene-specific publication profiles. Six categories of AD-related information are extracted from the processed publication data: publication trend by year, dementia type occurrence, brain region occurrence, mouse model information, keywords occurrence, and co-occurring genes. A user-friendly web portal is then developed using Django framework to provide gene query functions and data visualizations for the generalized and summarized publication information. RESULTS: By implementing the NLP pipeline, we extracted gene-specific ND-related publication information from the abstracts of the publications in the PubMed database. The results are summarized and visualized through an interactive web query portal. Multiple visualization windows display the ND publication trends, mouse models used, dementia types, involved brain regions, keywords to major AD-related biological processes, and co-occurring genes. Direct links to PubMed sites are provided for all recorded publications on the query result page of the web portal. CONCLUSION: The resulting portal is a valuable tool and data source for quick querying and displaying AD publications tailored to users' interested research areas and gene targets, which is especially convenient for users without informatic mining skills. Our study will not only keep AD field researchers updated with the progress of AD research, assist them in conducting preliminary examinations efficiently, but also offers additional support for hypothesis generation and validation which will contribute significantly to the communication, dissemination, and progress of AD research.

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria and chemical compounds. We obtained more than 7 million relationships between more than 74 000 terms, and many types of relationships were not available in any other resource. As the terms were obtained from widely used resources and ontologies, the relationships are given using the standard identifiers provided by them and hence can be linked to other data. A web interface allows users to browse these associations, searching for relationships for a set of terms of interests provided as input, such as between a disease and their associated symptoms, underlying molecular processes or affected tissues. The results are presented in an interactive interface where the user can explore the reported relationships in different ways and follow links to other resources. Database URL: https://csbg.cnb.csic.es/CoMentG/.

GPDminer: a tool for extracting named entities and analyzing relations in biological literature.

PURPOSE: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. METHODS: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. RESULTS: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. CONCLUSION: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management.

Glycemic Changes Related to Arsenic Exposure: An Overview of Animal and Human Studies.

BACKGROUND: Arsenic (As) is a risk factor associated with glycemic alterations. However, the mechanisms of action and metabolic aspects associated with changes in glycemic profiles have not yet been completely elucidated. Therefore, in this review, we aimed to investigate the metabolic aspects of As and its mechanism of action associated with glycemic changes. METHODS: We searched the PubMed (MEDLINE) and Google Scholar databases for relevant articles published in English. A combination of free text and medical subject heading keywords and search terms was used to construct search equations. The search yielded 466 articles; however, only 50 were included in the review. RESULTS: We observed that the relationship between As exposure and glycemic alterations in humans may be associated with sex, smoking status, body mass index, age, occupation, and genetic factors. The main mechanisms of action associated with changes induced by exposure to As in the glycemic profile identified in animals are increased oxidative stress, reduced expression of glucose transporter type 4, induction of inflammatory factor expression and dysfunction of pancreatic ß cells. CONCLUSIONS: Therefore, As exposure may be associated with glycemic alterations according to inter-individual differences.

The association between zinc and prostate cancer development: A systematic review and meta-analysis.

BACKGROUND: Prostate cancer is affecting males globally, with several complications. Zinc can play roles in cancers. We aimed to clarify the association between zinc levels or intake with prostate cancer development. METHODS: We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science until May 1, 2023. We included case-controls and cross-sectionals that measured zinc level and/or intake in patients with prostate cancer or cohorts that evaluated the association between zinc and prostate cancer development. Studies that did not have a healthy control group were excluded. Joanna Briggs Institute was used for quality assessment. Publication bias was evaluated using Egger's and Begg's tests and funnel plot. RESULTS: Overall, 52 studies (n = 44 case controls, n = 4 cohorts, and n = 4 cross sectionals) with a total number of 163909 participants were included. Serum (standardized mean difference (SMD): -1.11; 95% confidence interval (CI): -1.67, -0.56), hair (SMD: -1.31; 95% CI: -2.19, -0.44), and prostatic fluid or tissue zinc levels (SMD: -3.70; 95% CI: -4.90, -2.49) were significantly lower in prostate cancer patients. There were no significant differences in nail zinc level and zinc intake between those with prostate cancer and healthy controls. There was no publication bias except for serum and hair zinc levels based on Begg's and Egger's tests, respectively. The mean risk of bias scores were 4.61 in case-controls, eight in cohorts, and seven in cross-sectionals. CONCLUSIONS: Overall, high zinc levels might have a protective role in prostate cancer, which can be used as a therapeutic or preventive intervention. Future large-scale studies are needed to confirm the association.

Inter-reviewer reliability of human literature reviewing and implications for the introduction of machine-assisted systematic reviews: a mixed-methods review.

OBJECTIVES: Our main objective is to assess the inter-reviewer reliability (IRR) reported in published systematic literature reviews (SLRs). Our secondary objective is to determine the expected IRR by authors of SLRs for both human and machine-assisted reviews. METHODS: We performed a review of SLRs of randomised controlled trials using the PubMed and Embase databases. Data were extracted on IRR by means of Cohen's kappa score of abstract/title screening, full-text screening and data extraction in combination with review team size, items screened and the quality of the review was assessed with the A MeaSurement Tool to Assess systematic Reviews 2. In addition, we performed a survey of authors of SLRs on their expectations of machine learning automation and human performed IRR in SLRs. RESULTS: After removal of duplicates, 836 articles were screened for abstract, and 413 were screened full text. In total, 45 eligible articles were included. The average Cohen's kappa score reported was 0.82 (SD=0.11, n=12) for abstract screening, 0.77 (SD=0.18, n=14) for full-text screening, 0.86 (SD=0.07, n=15) for the whole screening process and 0.88 (SD=0.08, n=16) for data extraction. No association was observed between the IRR reported and review team size, items screened and quality of the SLR. The survey (n=37) showed overlapping expected Cohen's kappa values ranging between approximately 0.6-0.9 for either human or machine learning-assisted SLRs. No trend was observed between reviewer experience and expected IRR. Authors expect a higher-than-average IRR for machine learning-assisted SLR compared with human based SLR in both screening and data extraction. CONCLUSION: Currently, it is not common to report on IRR in the scientific literature for either human and machine learning-assisted SLRs. This mixed-methods review gives first guidance on the human IRR benchmark, which could be used as a minimal threshold for IRR in machine learning-assisted SLRs. PROSPERO REGISTRATION NUMBER: CRD42023386706.

MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.

BACKGROUND: The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools. RESULTS: We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances. CONCLUSIONS: MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats-PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.

Emerging roles of SIRT1 activator, SRT2104, in disease treatment.

Silent information regulator 1 (SIRT1) is a NAD+-dependent class III deacetylase that plays important roles in the pathogenesis of numerous diseases, positioning it as a prime candidate for therapeutic intervention. Among its modulators, SRT2104 emerges as the most specific small molecule activator of SIRT1, currently advancing into the clinical translation phase. The primary objective of this review is to evaluate the emerging roles of SRT2104, and to explore its potential as a therapeutic agent in various diseases. In the present review, we systematically summarized the findings from an extensive array of literature sources including the progress of its application in disease treatment and its potential molecular mechanisms by reviewing the literature published in databases such as PubMed, Web of Science, and the World Health Organization International Clinical Trials Registry Platform. We focuses on the strides made in employing SRT2104 for disease treatment, elucidating its potential molecular underpinnings based on preclinical and clinical research data. The findings reveal that SRT2104, as a potent SIRT1 activator, holds considerable therapeutic potential, particularly in modulating metabolic and longevity-related pathways. This review establishes SRT2104 as a leading SIRT1 activator with significant therapeutic promise.

Is it more effective for anhedonia and avolition? A systematic review and meta-analysis of non-invasive brain stimulation interventions for negative symptoms in schizophrenia.

BACKGROUND: Noninvasive brain stimulation (NIBS) techniques are a promising tool for treating the negative symptoms of schizophrenia. Growing evidence suggests that different dimensions of negative symptoms have partly distinct underlying pathophysiological mechanisms. Previous randomized controlled trials (RCTs) have shown inconsistent impacts of NIBS across dimensions. OBJECTIVE: This systematic review and meta-analysis evaluated the effects of NIBS on general negative symptoms, and on specific domains, including blunted affect, alogia, asociality, anhedonia, and avolition. DATA SOURCES: PubMed, Web of Science, Embase, Cochrane CENTRAL, PsycINFO, OpenGrey, and Clinicaltrials.gov from the first date available to October, 2023. RESULTS: Among 1049 studies, we identified eight high-quality RCTs. NIBS significantly affects general negative symptoms (SMD = -0.54, 95% CI [-0.88, -0.21]) and all five domains (SMD = -0.32 to -0.63). Among dimensions, better effects have been shown for improvement of avolition (SMD = -0.47, 95% CI [-0.81, -0.13]) and anhedonia (SMD = -0.63, 95% CI [-0.98, -0.28]). Subgroup analyses of studies that applied once daily stimulation or >10 sessions showed significantly reduced negative symptom severity. CONCLUSION: NIBS exerts distinct effects across multiple dimensions of negative symptom, with treatment effects related to stimulation frequency and total sessions. These results need to be confirmed in dedicated studies.

Utilization of EHRs for clinical trials: a systematic review.

BACKGROUND AND OBJECTIVE: Clinical trials are of high importance for medical progress. This study conducted a systematic review to identify the applications of EHRs in supporting and enhancing clinical trials. MATERIALS AND METHODS: A systematic search of PubMed was conducted on 12/3/2023 to identify relevant studies on the use of EHRs in clinical trials. Studies were included if they (1) were full-text journal articles, (2) were written in English, (3) examined applications of EHR data to support clinical trial processes (e.g. recruitment, screening, data collection). A standardized form was used by two reviewers to extract data on: study design, EHR-enabled process(es), related outcomes, and limitations. RESULTS: Following full-text review, 19 studies met the predefined eligibility criteria and were included. Overall, included studies consistently demonstrated that EHR data integration improves clinical trial feasibility and efficiency in recruitment, screening, data collection, and trial design. CONCLUSIONS: According to the results of the present study, the use of Electronic Health Records in conducting clinical trials is very helpful. Therefore, it is better for researchers to use EHR in their studies for easy access to more accurate and comprehensive data. EHRs collects all individual data, including demographic, clinical, diagnostic, and therapeutic data. Moreover, all data is available seamlessly in EHR. In future studies, it is better to consider the cost-effectiveness of using EHR in clinical trials.

Extracting adverse drug events from clinical Notes: A systematic review of approaches used.

BACKGROUND: An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and patient medication studies. OBJECTIVE: From the considerable amount of clinical narrative text, natural language processing (NLP) researchers have developed methods for extracting ADEs and their related attributes. This work presents a systematic review of current methods. METHODOLOGY: Two biomedical databases have been searched from June 2022 until December 2023 for relevant publications regarding this review, namely the databases PubMed and Medline. Similarly, we searched the multi-disciplinary databases IEEE Xplore, Scopus, ScienceDirect, and the ACL Anthology. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement guidelines and recommendations for reporting systematic reviews in conducting this review. Initially, we obtained 5,537 articles from the search results from the various databases between 2015 and 2023. Based on predefined inclusion and exclusion criteria for article selection, 100 publications have undergone full-text review, of which we consider 82 for our analysis. RESULTS: We determined the general pattern for extracting ADEs from clinical notes, with named entity recognition (NER) and relation extraction (RE) being the dual tasks considered. Researchers that tackled both NER and RE simultaneously have approached ADE extraction as a "pipeline extraction" problem (n = 22), as a "joint task extraction" problem (n = 7), and as a "multi-task learning" problem (n = 6), while others have tackled only NER (n = 27) or RE (n = 20). We further grouped the reviews based on the approaches for data extraction, namely rule-based (n = 8), machine learning (n = 11), deep learning (n = 32), comparison of two or more approaches (n = 11), hybrid (n = 12) and large language models (n = 8). The most used datasets are MADE 1.0, TAC 2017 and n2c2 2018. CONCLUSION: Extracting ADEs is crucial, especially for pharmacovigilance studies and patient medications. This survey showcases advances in ADE extraction research, approaches, datasets, and state-of-the-art performance in them. Challenges and future research directions are highlighted. We hope this review will guide researchers in gaining background knowledge and developing more innovative ways to address the challenges.

Temporal attention networks for biomedical hypothesis generation.

OBJECTIVES: Hypothesis Generation (HG) is a task that aims to uncover hidden associations between disjoint scientific terms, which influences innovations in prevention, treatment, and overall public health. Several recent studies strive to use Recurrent Neural Network (RNN) to learn evolutional embeddings for HG. However, the complex spatiotemporal dependencies of term-pair relations will be difficult to depict due to the inherent recurrent structure. This paper aims to accurately model the temporal evolution of term-pair relations using only attention mechanisms, for capturing crucial information on inferring the future connectivities. METHODS: This paper proposes a Temporal Attention Networks (TAN) to produce powerful spatiotemporal embeddings for Biomedical Hypothesis Generation. Specifically, we formulate HG problem as a future connectivity prediction task in a temporal attributed graph. Our TAN develops a Temporal Spatial Attention Module (TSAM) to establish temporal dependencies of node-pair (term-pair) embeddings between any two time-steps for smoothing spatiotemporal node-pair embeddings. Meanwhile, a Temporal Difference Attention Module (TDAM) is proposed to sharpen temporal differences of spatiotemporal embeddings for highlighting the historical changes of node-pair relations. As such, TAN can adaptively calibrate spatiotemporal embeddings by considering both continuity and difference of node-pair embeddings. RESULTS: Three real-world biomedical term relationship datasets are constructed from PubMed papers. TAN significantly outperforms the best baseline with 12.03%, 4.59 and 2.34% Micro-F1 Score improvement in Immunotherapy, Virology and Neurology, respectively. Extensive experiments demonstrate that TAN can model complex spatiotemporal dependencies of term-pairs for explicitly capturing the temporal evolution of relation, significantly outperforming existing state-of-the-art methods. CONCLUSION: We proposed a novel TAN to learn spatiotemporal embeddings based on pure attention mechanisms for HG. TAN learns the evolution of relationships by modeling both the continuity and difference of temporal term-pair embeddings. The important spatiotemporal dependencies of term-pair relations are extracted based solely on attention mechanism for generating hypotheses.

Antipsychotic agents in anxiety disorders: An umbrella review.

BACKGROUND: Although not approved for the treatment of anxiety disorders (except trifluoperazine) there is ongoing off-label, unapproved use of first-generation antipsychotics (FGAs) and second-generation antipsychotics (SGAs) for anxiety disorders. There have been systematic reviews and meta-analyses on the use of antipsychotics in anxiety disorders, most of which focused on SGAs. OBJECTIVE: The specific aims of this umbrella review are to: (1) Evaluate the evidence of efficacy of FGAs and SGAs in anxiety disorders as an adjunctive treatment to traditional antidepressant treatments and other nonantipsychotic medications; (2) Compare monotherapy with antipsychotics to first-line treatments for anxiety disorders in terms of effectiveness, risks, and side effects. The review protocol is registered on PROSPERO (CRD42021237436). METHODS: An initial search was undertaken to identify systematic reviews and meta-analyses from inception until 2020, with an updated search completed August 2021 and January 2023. The searches were conducted in PubMed, MEDLINE (Ovid), EMBASE (Ovid), APA PsycInfo (Ovid), CINAHL Complete (EBSCOhost), and the Cochrane Library through hand searches of references of included articles. Review quality was measured using the AMSTAR-2 (A MeaSurement Tool to Assess Systematic Reviews) scale. RESULTS: The original and updated searches yielded 1796 and 3744 articles respectively, of which 45 were eligible. After final review, 25 systematic reviews and meta-analyses were included in the analysis. Most of the systematic reviews and meta-analyses were deemed low-quality through AMSTAR-2 with only one review being deemed high-quality. In evaluating the monotherapies with antipsychotics compared with first-line treatments for anxiety disorder there was insufficient evidence due to flawed study designs (such as problems with randomization) and small sample sizes within studies. There was limited evidence suggesting efficacy of antipsychotic agents in anxiety disorders other than quetiapine in generalized anxiety disorder (GAD). CONCLUSIONS: This umbrella review indicates a lack of high-quality studies of antipsychotics in anxiety disorders outside of the use of quetiapine in GAD. Although potentially effective for anxiety disorders, FGAs and SGAs may have risks and side effects that outweigh their efficacy, although there were limited data. Further long-term and larger-scale studies of antipsychotics in anxiety disorders are needed.

PubMed and beyond: biomedical literature search in the age of artificial intelligence.

Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.

Reporting guidelines for traditional Chinese medicine could be improved: a cross-sectional study.

OBJECTIVES: The aim of this study is to identify available reporting guidelines for traditional Chinese medicine (TCM), delineate their fundamental characteristics, assess the scientific rigor of their development process, and evaluate their dissemination. STUDY DESIGN AND SETTING: A search was conducted in Medline (via PubMed), China National Knowledge Infrastructure (CNKI), SinoMed, WANFANG DATA, and the EQUATOR Network to identify TCM reporting guidelines. A preprepared Excel database was used to extract information on the basic characteristics, development process, and dissemination information. The development process quality of TCM reporting guidelines was assessed by evaluating their compliance with the Guidance for Developers of Health Research Reporting Guidelines (GDHRRG). The extent of dissemination of these guidelines was analyzed by examining the number of citations received. RESULTS: A total of 26 reporting guidelines for TCM were obtained from 20 academic journals, with 61.5% of them published in English journals. Among the guidelines, 14 (53.8%) were registered in the EQUATOR Network. On average, the compliance rate of GDHRRG guidelines was reported to be 63.3% ranging from 22.2% to 94.4%. Three steps showed poor compliance, namely guideline endorsement (23.1%), translated guidelines (19.2%), and developing a publication strategy (19.2%). Furthermore, the compliance rate of GDHRRG guidelines published in English journals was higher than that in Chinese journals. In terms of the dissemination, 15.4% of the guidelines had been cited over 100 times, while 73.1% had been cited less than 50 times. CONCLUSION: The development of TCM reporting guidelines still has limitations in terms of regarding scientific rigor and follow-up dissemination. Therefore, it is important to ensure adherence to the scientific process in the development of TCM reporting guidelines and to strengthen their promotion, dissemination, and implementation.

Question answering systems for health professionals at the point of care-a systematic review.

OBJECTIVES: Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. MATERIALS AND METHODS: We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems. RESULTS: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians' questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. DISCUSSION: While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.

Mapping the concept of health care integration: A lexicographic analysis of scientific literature.

BACKGROUND: Systems fragmentation is a major challenge for an efficient organization, integration being a potential solution also proposed in health care field, including pharmacy as a player. However, the use of different terms and definitions in the literature hinders the comparison of different integration initiatives. OBJECTIVE: To identify and map the terms used in scientific literature regarding integration in health care and to characterize each emerging topic. METHODS: A lexicographic analysis of the integration of healthcare systems literature indexed in PubMed was conducted. Ten different systematic searches, four using only Medical Subject Headings (MeSH) and six using text words, were conducted in March 2023. Journal scattering was analyzed following Bradford's distribution using the Leimkuhler model. An overall text corpus was created with titles and abstracts of all the records retrieved. The corpus was lemmatized, and the most used bigrams were tokenized as single strings. To perform a topic modeling, the lemmatized corpus text was analyzed using IRaMuTeQ, producing descending hierarchic classification and a correspondence analysis. The 50 words with higher chi-square statistics in each class were considered as representative of the class. RESULTS: A total of 42,479 articles published from 1943 to 2023 in 4469 different journals were retrieved. The MeSH "Delivery of Health Care, Integrated", created in the 1996 MeSH update, was the most productive retrieving 33.7 % of the total articles but also retrieving 22.6 % of articles not retrieved in any other search. The text word "Integration" appeared in 15,357 (36.2 %) records. The lexicographic analysis resulted in 7 classes, named as: Evidence and implementation, Quantitative research, Professional education, Qualitative research, Governance and leadership, Clinical research, and Financial resources. Association between the classes and the searches or the text-words used ranged from moderate to weak demonstrating the lack of a standard pattern of use of terms in literature regarding healthcare integration. CONCLUSIONS: The term "integration" and the MeSH "Delivery of Health Care, Integrated" are the most used to represent the concept of integration in healthcare and should be the preferred terms in the literature.