The automatic recognition of biomedical relationships is an important step in the semantic understanding of the information contained in the unstructured text of the published literature. The BioRED track at BioCreative VIII aimed to foster the development of such methods by providing the participants the BioRED-BC8 corpus, a collection of 1000 PubMed documents manually curated for diseases, gene/proteins, chemicals, cell lines, gene variants, and species, as well as pairwise relationships between them which are disease-gene, chemical-gene, disease-variant, gene-gene, chemical-disease, chemical-chemical, chemical-variant, and variant-variant. Furthermore, relationships are categorized into the following semantic categories: positive correlation, negative correlation, binding, conversion, drug interaction, comparison, cotreatment, and association. Unlike most of the previous publicly available corpora, all relationships are expressed at the document level as opposed to the sentence level, and as such, the entities are normalized to the corresponding concept identifiers of the standardized vocabularies, namely, diseases and chemicals are normalized to MeSH, genes (and proteins) to National Center for Biotechnology Information (NCBI) Gene, species to NCBI Taxonomy, cell lines to Cellosaurus, and gene/protein variants to Single Nucleotide Polymorphism Database. Finally, each annotated relationship is categorized as \'novel\' depending on whether it is a novel finding or experimental verification in the publication it is expressed in. This distinction helps differentiate novel findings from other relationships in the same text that provides known facts and/or background knowledge. The BioRED-BC8 corpus uses the previous BioRED corpus of 600 PubMed articles as the training dataset and includes a set of newly published 400 articles to serve as the test data for the challenge. All test articles were manually annotated for the BioCreative VIII challenge by expert biocurators at the National Library of Medicine, using the original annotation guidelines, where each article is doubly annotated in a three-round annotation process until full agreement is reached between all curators. This manuscript details the characteristics of the BioRED-BC8 corpus as a critical resource for biomedical named entity recognition and relation extraction. Using this new resource, we have demonstrated advancements in biomedical text-mining algorithm development. Database URL: https://codalab.lisn.upsaclay.fr/competitions/16381.

BACKGROUND: Despite advances in research on psychopathology and social media use, no comprehensive review has examined published papers on this type of research and considered how it was affected by the coronavirus disease 2019 (COVID-19) outbreak.
OBJECTIVE: To explore the status of research on psychopathology and social media use before and after the COVID-19 outbreak.
METHODS: We used Bibliometrix (an R software package) to conduct a scientometric analysis of 4588 relevant studies drawn from the Web of Science Core Collection, PubMed, and Scopus databases.
RESULTS: Such research output was scarce before COVID-19, but exploded after the pandemic with the publication of a number of high-impact articles. Key authors and institutions, located primarily in developed countries, maintained their core positions, largely uninfluenced by COVID-19; however, research production and collaboration in developing countries increased significantly after COVID-19. Through the analysis of keywords, we identified commonly used methods in this field, together with specific populations, psychopathological conditions, and clinical treatments. Researchers have devoted increasing attention to gender differences in psychopathological states and linked COVID-19 strongly to depression, with depression detection becoming a new trend. Developments in research on psychopathology and social media use are unbalanced and uncoordinated across countries/regions, and more in-depth clinical studies should be conducted in the future.
CONCLUSIONS: After COVID-19, there was an increased level of concern about mental health issues and a changing emphasis on social media use and the impact of public health emergencies.

OBJECTIVE: Synthesizing and evaluating inconsistent medical evidence is essential in evidence-based medicine. This study aimed to employ ChatGPT as a sophisticated scientific reasoning engine to identify conflicting clinical evidence and summarize unresolved questions to inform further research.
METHODS: We evaluated ChatGPT\'s effectiveness in identifying conflicting evidence and investigated its principles of logical reasoning. An automated framework was developed to generate a PubMed dataset focused on controversial clinical topics. ChatGPT analyzed this dataset to identify consensus and controversy, and to formulate unsolved research questions. Expert evaluations were conducted 1) on the consensus and controversy for factual consistency, comprehensiveness, and potential harm and, 2) on the research questions for relevance, innovation, clarity, and specificity.
RESULTS: The gpt-4-1106-preview model achieved a 90% recall rate in detecting inconsistent claim pairs within a ternary assertions setup. Notably, without explicit reasoning prompts, ChatGPT provided sound reasoning for the assertions between claims and hypotheses, based on an analysis grounded in relevance, specificity, and certainty. ChatGPT\'s conclusions of consensus and controversies in clinical literature were comprehensive and factually consistent. The research questions proposed by ChatGPT received high expert ratings.
CONCLUSIONS: Our experiment implies that, in evaluating the relationship between evidence and claims, ChatGPT considered more detailed information beyond a straightforward assessment of sentimental orientation. This ability to process intricate information and conduct scientific reasoning regarding sentiment is noteworthy, particularly as this pattern emerged without explicit guidance or directives in prompts, highlighting ChatGPT\'s inherent logical reasoning capabilities.
CONCLUSIONS: This study demonstrated ChatGPT\'s capacity to evaluate and interpret scientific claims. Such proficiency can be generalized to broader clinical research literature. ChatGPT effectively aids in facilitating clinical studies by proposing unresolved challenges based on analysis of existing studies. However, caution is advised as ChatGPT\'s outputs are inferences drawn from the input literature and could be harmful to clinical practice.

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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.
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.
Reviewed literature from PubMed and Web of Science using key terminologies related to AD and the gut microbiome.
Research indicates that the gut microbiota can directly or indirectly influence the occurrence and progression of AD through metabolites, endotoxins, and the vagus nerve.
This review discusses the future challenges and research directions regarding the gut microbiota in AD.
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.

BACKGROUND: Nobiletin is a natural polymethoxylated flavonoid widely present in citrus fruit peels. It has been demonstrated to exert the effects of anti-tumor, anti-inflammation, anti-oxidative, anti-apoptotic and improve cardiovascular function. Increasing evidences suggest that nobiletin plays an important role in respiratory diseases (RDs) treatment.
OBJECTIVE: This review aimed to investigate the therapeutic potential of nobiletin against RDs, such as lung cancer, COPD, pulmonary fibrosis, asthma, pulmonary infection, acute lung injury, coronavirus disease 2019, and pulmonary arterial hypertension.
METHODS: We retrieved extensive literature of relevant literatures in English until June 26, 2023 from the database of PubMed, Web of Science, and Scopus databases. The keywords of \"nobiletin and lung\", \"nobiletin and respiratory disease\", \"nobiletin and chronic respiratory diseases\", \"nobiletin and metabolites\", \"nobiletin and pharmacokinetics\", \"nobiletin and toxicity\" were searched in pairs. A total of 298 literatures were retrieved from the above database. After excluding the duplicates and reviews, 53 were included in the current review.
RESULTS: We found that the therapeutic mechanisms are based on different signaling pathways. Firstly, nobiletin inhibited the proliferation and suppressed the invasion and migration of cancer cells by regulating the related pathway or key target, like Bcl-2, PD-L1, PARP, and Akt/GSK3β/β-catenin in lung cancer treatment. Secondly, nobiletin treats COPD and ALI by targeting classical signaling pathway mediating inflammation. Besides, the available findings show that nobiletin exerts the effect of PF treatment via regulating mTOR pathway.
CONCLUSIONS: With the wide range of pharmacological activities, high efficiency and low toxicity, nobiletin can be used as a potential agent for preventing and treating RDs. These findings will contribute to further research on the molecular mechanisms of nobiletin and facilitate in-depth studies on nobiletin at both preclinical and clinical levels for the treatment of RDs.

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.

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.
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.
PubMed, Web of Science, Embase, Cochrane CENTRAL, PsycINFO, OpenGrey, and Clinicaltrials.gov from the first date available to October, 2023.
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.
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.

OBJECTIVE: 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.
METHODS: 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.
CONCLUSIONS: 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.

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.
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.
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.
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.