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PubMed2026

Knowledge on the Haemophilia Care Among Healthcare Providers in Tanzania: A Multicenter Cross-Sectional Study.

BACKGROUND: Haemophilia is a rare inherited bleeding disorder associated with recurrent bleeding, disability, and mortality when diagnosis and management are delayed. In low- and middle-income countries, limited diagnostic capacity, access to treatment and gaps in Healthcare Providers' (HCPs') knowledge are major contributors to morbidity and mortality. In Tanzania, the recent improvement in haemophilia services highlights the need for systematic evaluation of HCPs' knowledge and clinical practices. OBJECTIVE: The study assessed the knowledge of haemophilia care among healthcare providers in Tanzania. METHODS: A multicenter hospital-based cross-sectional study was conducted among HCPs in tertiary and regional hospitals in Tanzania. A structured self-administered questionnaire assessed knowledge on haemophilia, including the pathophysiology, clinical features, diagnosis, treatment, and complications. Data were analyzed using IBM SPSS statistics version 27. RESULTS: Among 799 HCPs assessed (50.9%) aged 20-29 and (59.2%) males. Nurses were the majority (31.8%), and 75.2% had ≤5 years' experience. Overall haemophilia knowledge was high (median 83.3%, IQR: 75.9-88.9), strongest performance in general knowledge and weakest in treatment (68.2%, IQR: 54.5-77.3). Most respondents identified haemophilia as inherited (95.6%), non-infectious (93.7%), and recognized prolonged bleeding after injury or circumcision as key-symptoms (>90%). Knowledge varied by cadre, department, and experience (p<0.05); physicians and specialists scored higher than nurses, while health attendants scored lower. CONCLUSION: Healthcare providers demonstrated fairly adequate general knowledge of haemophilia. However, gaps remain in understanding genetic inheritance, acquired haemophilia, and modern treatment strategies, with knowledge variation by cadre, department, and experience, highlighting the need for targeted education across all HCPs groups.

Tanzania↗

PubMed2025

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica , an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

biomedical knowledge graph↗

PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗

PubMed2026

A flipped classroom approach compared with low-interactive online learning for pediatric pain management knowledge and instructional motivation in nursing students: A randomized controlled study.

AIM: This study aimed to compare a flipped classroom approach with low-interactive online learning in terms of nursing students' questionnaire-assessed pediatric pain management knowledge and instructional motivation. BACKGROUND: Pain management in children is a critical and multidimensional nursing responsibility. However, limited curricular time and opportunities for applied learning may restrict nursing students' preparedness in this area. Structured and interactive instructional formats, such as the flipped classroom, may support knowledge acquisition and motivation in pediatric nursing education. METHODS: This study employed a parallel-group randomized controlled trial design with a 1:1 allocation ratio. Eighty-eight third-year prelicensure nursing students were randomized to either the flipped classroom group (n = 44) or the low-interactive online learning group (n = 44). Due to attrition (2 intervention, 2 control), analyses included 42 participants per group (n = 84 in total). Data were collected between February and July 2022 using the Pediatric Pain Management Knowledge Scale for Nursing Students and the Instructional Materials Motivation Survey. This study was prospectively registered at ClinicalTrials.gov (Identifier: NCT07129044). RESULTS: At baseline, the groups were comparable in terms of knowledge and learning motivation. Following the intervention, the flipped classroom group demonstrated greater improvements in questionnaire-assessed pediatric pain management knowledge and instructional motivation than the low-interactive online learning group. Although scores declined from post-test to the three-month follow-up, they remained above baseline in the flipped classroom group. CONCLUSIONS: Within the context of this course, the flipped classroom approach was associated with greater improvement in questionnaire-assessed pediatric pain management knowledge and instructional motivation than low-interactive online learning. The findings should be interpreted as proximal educational outcomes rather than evidence of improved clinical competence or durable long-term effectiveness. Further studies using objective performance-based outcomes and longer follow-up periods are needed.

Humans↗

PubMed2026

Attitudes, knowledge, and behavioral control regarding child abuse reporting in middle eastern nursing professionals: A systematic review and meta-analysis.

INTRODUCTION: Child abuse is critically underreported in the Middle East, with up to 90% of cases going unidentified. Nurses play a central role in child protection systems, yet factors influencing their reporting practices in this region have not been comprehensively synthesized. This study aims to review systematically and meta-analyze evidence on Middle Eastern nurses' knowledge, attitudes, and perceived behavioral control regarding child abuse reporting. METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. Five databases (PubMed, Scopus, Web of Science, Embase, Cochrane Library) were searched through November 2025. Studies targeting nursing professionals or students in Middle Eastern countries assessing knowledge, attitudes, intention to report, subjective norms, or perceived behavioral control were included. Random-effects meta-analyses were performed with subgroup analyses comparing students and practicing nurses. RESULTS: Fourteen studies comprising 4018 participants across six countries met the inclusion criteria. The pooled knowledge score was 61.64% (95% CI: 50.14-73.14), with nursing students scoring higher than practicing nurses (75.28% vs. 54.79%, p = 0.03). The pooled attitude score was 60.69% (95% CI: 44.82-76.57). Educational interventions significantly improved knowledge (SMD = 1.54, 95% CI: 0.70-2.38, p = 0.0003). The pooled proportion of nurses intending to report suspected abuse was 75% (95% CI: 0.58-0.92). The pooled subjective norm score was 8.02 (95% CI: 7.75-8.29), and the pooled perceived behavioral control score was 24.34 (95% CI: 22.20-26.46). High heterogeneity (I2 > 95%) reflects substantial variability in educational and legal contexts across the region. CONCLUSIONS: Middle Eastern nurses show moderate knowledge and attitudes toward child abuse reporting, with notable gaps among practicing professionals. Educational interventions are effective but should be integrated with systemic reforms, including mandatory reporting legislation and clear protocols.

Humans↗

PubMed2026

Female genital mutilation knowledge, attitudes and training needs among health professionals in non-practicing countries: A literature review.

BACKGROUND: With increasing globalization and migration, the number of women affected by female genital mutilation who reside in countries where the practice is not traditionally performed is constantly increasing. Healthcare providers in these settings are required to address the complex health needs of this vulnerable population. We aimed to synthesize recent literature on their knowledge, preparedness, and educational background. METHODS: We conducted a systematic review across PubMed, Scopus and Embase, identifying papers published from January 2015 onwards, examining providers' knowledge, education and attitudes toward female genital mutilation in non-practicing countries. Both quantitative and qualitative observational studies were eligible. Given heterogeneity in study populations, outcome definitions, and assessment tools, findings were synthesized narratively. The review protocol was registered with the International Prospective Register of Systematic Reviews (CRD420251044761). FINDINGS: 1046 records were screened by title and abstract, and 140 full-text articles were assessed for eligibility. 31 studies met the inclusion criteria (23 quantitative, 8 qualitative). Many providers reported clinical experience with women affected by female genital mutilation, yet substantial variability was observed in knowledge, training, and attitudes. Gaps were particularly evident regarding legislation, World Health Organization classification, clinical guidelines, referral pathways, workplace protocols. Midwives and younger professionals tended to demonstrate higher knowledge levels. Training exposure ranged from 5% to 91%, and many participants perceived it as insufficient. Qualitative findings echoed these patterns, highlighting challenges in female genital mutilation classification, legal awareness, documentation systems, the impact of providers' cultural beliefs on care delivery. CONCLUSION: Considerable efforts are needed to equip healthcare providers to deliver high-quality, culturally competent care to women affected by female genital mutilation. Research should develop validated tools to assess preparedness, adopt mixed-methods strategies to capture patient and provider perspectives, and guide standardized, up-to-date training programs, strengthening knowledge in managing female genital mutilation.

Humans↗

PubMed2025

Men's Knowledge, Attitudes, Practices, Cultural Beliefs, and Perceived Risk and Susceptibility Regarding Prostate Cancer in the Vhembe District, Limpopo Province, South Africa.

BACKGROUND: Prostate cancer (PCa) awareness and knowledge among men in Vhembe District, Limpopo Province, South Africa, remain inadequately studied despite the high local burden of the disease. This study investigates the knowledge, attitudes, practices, cultural beliefs, and perceived risk of PCa among men aged 40 and above in selected villages under the Mphaphuli and Niani tribal authorities. METHODS: A quantitative survey was conducted with 431 men, utilizing a questionnaire adapted from the African Women Awareness of Cancer (AWACAN) tool. The questionnaire, translated into Tshivenda, assessed socio-demographic data, awareness, knowledge of risk factors and symptoms, health-seeking behavior, and barriers to seeking medical help. RESULTS: The study revealed that 51.3% of participants had heard of PCa, while 48.7% had not. Awareness varied significantly with age, relationship status, education level, and language. Older men and those with higher education levels were more knowledgeable about PCa. Clinics, hospitals, and media were the primary sources of information. Misconceptions about risk factors were prevalent, with 24.0% of men indicating a preference for traditional healers for PCa symptoms. Barriers to medical help included fear of the disease, procedural fears, and cultural taboos. Multivariate analysis identified significant factors associated with PCa knowledge, including age, language, access to tap water, and cell phone ownership. CONCLUSION: These findings underscore the importance of targeted educational interventions considering sociodemographic and cultural contexts. Future public health initiatives should focus on bridging the gap between traditional and modern medical practices to enhance health outcomes in the Vhembe District and similar settings.

Humans↗

PubMed2025

Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.

MOTIVATION: Pinpointing the subcellular location of proteins is essential for studying protein function and related diseases. Advances in spatial proteomics have shown that automatic recognition of protein subcellular localization from images could highly facilitate protein translocation analysis and biomarker discovery, but existing machine-learning works have been mostly limited to processing 2D images. By contrast, 3D images have higher spatial resolution and allow researchers to observe cellular structures in their natural context, but currently, there are only a few studies of 3D image processing for protein distribution analysis due to the lack of data and complexity of modeling. RESULTS: We developed a knowledge-enhanced protein subcellular localization model, KE3DLoc, which could recognize distribution patterns in 3D fluorescence microscope images using deep learning methods. The model designs an image feature extraction module that incorporates information from 3D and 2D projected cells and implements asymmetric loss and confidence weights to address data imbalance and weak cell annotation issues. Besides, considering that the biological knowledge in the Gene Ontology (GO) database can provide valuable support for protein location understanding, the KE3DLoc model incorporates a novel knowledge enhancement module that optimizes the protein representation by related knowledge graphs derived from the GO. Since the image module and the knowledge module calculate features from different levels, KE3DLoc designs protein ID aggregation to enhance the consistency of protein features across different cells. Experimental results on three public datasets have demonstrated that the KE3DLoc significantly outperforms existing methods and provides valuable insights for spatial proteomics research. AVAILABILITY AND IMPLEMENTATION: All datasets and codes used in this study are available at GitHub: https://github.com/PRBioimages/KE3DLoc.

Microscopy, Fluorescence↗

PubMed2025

Large Language Model and Knowledge Graph-Driven AJCC Staging of Prostate Cancer Using Pathology Reports.

Background/Objectives: To develop an automated American Joint Committee on Cancer (AJCC) staging system for radical prostatectomy pathology reports using large language model-based information extraction and knowledge graph validation. Methods: Pathology reports from 152 radical prostatectomy patients were used. Five additional parameters (Prostate-specific antigen (PSA) level, metastasis stage (M-stage), extraprostatic extension, seminal vesicle invasion, and perineural invasion) were extracted using GPT-4.1 with zero-shot prompting. A knowledge graph was constructed to model pathological relationships and implement rule-based AJCC staging with consistency validation. Information extraction performance was evaluated using a local open-source large language model (LLM) (Mistral-Small-3.2-24B-Instruct) across 16 parameters. The LLM-extracted information was integrated into the knowledge graph for automated AJCC staging classification and data consistency validation. The developed system was further validated using pathology reports from 88 radical prostatectomy patients in The Cancer Genome Atlas (TCGA) dataset. Results: Information extraction achieved an accuracy of 0.973 and an F1-score of 0.986 on the internal dataset, and 0.938 and 0.968, respectively, on external validation. AJCC staging classification showed macro-averaged F1-scores of 0.930 and 0.833 for the internal and external datasets, respectively. Knowledge graph-based validation detected data inconsistencies in 5 of 150 cases (3.3%). Conclusions: This study demonstrates the feasibility of automated AJCC staging through the integration of large language model information extraction and knowledge graph-based validation. The resulting system enables privacy-protected clinical decision support for cancer staging applications with extensibility to broader oncologic domains.

artificial intelligence↗

PubMed2026

Power as equal ability, knowledge and resistance: Systematic review of experiences of adults with noncommunicable diseases.

PURPOSE: To analyse subjective experiences of power of adults with noncommunicable diseases in relationships with healthcare practitioners as well as underlying facilitators and barriers of these experiences. METHODS: Systematic review (4 databases) of experiences using reflexive thematic analysis underpinned by critical realist approach. The analysis was conducted with an abductive reasoning using previous theories on social power as well as retroduction. RESULTS: Based on 24 studies, we formed three themes, which depict experiences of power as 1) the position, equal ability and freedom to make one's own choices and (re)negotiate within shared dialogue, 2) the ability to use knowledge to claim one's rights, 3) resistance. Facilitators were connected to acknowledgement as an equally valuable individual, positive healthcare practitioner attitudes and actions towards patient activity and views, safety in the relationship as well as to sufficient, clear and varied information. Main barriers were experiences of dehumanisation, negative healthcare practitioner attitudes and actions, perceived or assumed practitioner domination in interactions, lack of or incomprehensible knowledge and testimonial smothering. CONCLUSION: Results suggest that adults with noncommunicable diseases may experience power primarily as a positive power: being acknowledged as having legitimate position to make decisions and being in possession of varied knowledge through which they can gain agency to protect and claim their rights, by resisting, if necessary. Healthcare practitioners are in key position to support these experiences through positive transforming actions, while knowledge asymmetries, persistent inequality and paternalistic structures continue to hinder it.

Humans↗

PubMed2026

Community-tailored One Health educational intervention to enhance knowledge and practices for zoonotic disease prevention in rural Thailand: A protocol for a prospective cluster randomised controlled Trial in Chanthaburi, Thailand (Saan Suk trial).

BACKGROUND: Zoonotic infectious disease risk arises at human-animal-environment interfaces where pathogen spillover can occur. Rural communities living in biodiverse settings may experience frequent contact with wildlife and shared environments through livelihoods, food practices, and economic activities. Reducing spillover risk and strengthening pandemic prevention requires both structural and individual-level change. Community-based interventions that promote awareness, risk perception, self-efficacy, pro-environmental behaviour, and safe coexistence with wildlife may support prevention by shifting behavioural determinants of zoonotic disease risk. The Saan Suk intervention was co-developed with rural communities in Thailand using a Human-Centred Design approach and is grounded in the Health Belief Model and One Health principles. The intervention is intended to be feasible, acceptable, and deliverable through Thailand's established Village Health Volunteer (VHV) system. METHODS: This protocol describes a parallel-arm, cluster-randomised controlled superiority trial that will be conducted during July - October 2026, in Chanthaburi Province, Thailand. 24 villages will be equally randomised to the Saan Suk intervention or the current practice (control). In intervention villages, trained VHVs will deliver, once a week over four weeks, a multimodal One Health educational intervention designed to improve knowledge of zoonotic spillover, promote protective behaviours, reduce risky wildlife-related contacts, and support respectful coexistence with wildlife. Trained outcome assessment teams will conduct structured interviews with 42 adult participants per village, yielding a total sample size of 1,008 participants. The sample size was calculated for the primary outcome, accounting for clustering, with 90% power to detect a medium effect size (6 points on the 0-100 knowledge scale) at a significance level of 0.05, accounting for a design effect with an ICC of 0.028. The primary outcome is knowledge of zoonotic spillover, transmission pathways, risk factors, protective and risky behaviours, and safe coexistence with wildlife. Secondary outcomes include attitudes, self-efficacy, preventive and risky behaviours, and reported contacts with major local reservoir hosts. A structured questionnaire was developed, expert-reviewed, and piloted for the outcome assessment. Outcomes will be analysed using mixed-effects regression models with random effects for village and adjustment for relevant pre-specified confounders. Primary analyses will follow the intention-to-treat principle. DISCUSSION: This trial will evaluate whether a co-designed, VHV-delivered One Health educational programme can improve knowledge of zoonotic disease prevention and behavioural determinants in rural communities living in close contact with wildlife and shared ecosystems. If effective and feasible, Saan Suk could inform integration into routine VHV training and community-based zoonotic disease and pandemic prevention strategies. TRIAL REGISTRATION: The Saan Suk trial is registered with the German Clinical Trials Register (DRKS). Registration ID: DRKS00038582; date of registration: 11 May 2026.

Zoonoses↗

PubMed2026

Evaluating the Effectiveness of an Intimate Partner Violence Training Intervention on Healthcare Providers' Preparedness, Knowledge, and Experiences: A Mixed-Methods Study From Nepal.

Intimate partner violence (IPV) places a considerable burden on health systems globally due to its profound effects on women's health, and women who experience violence often seek care from healthcare providers (HCPs). However, HCPs often lack the preparedness and confidence to respond effectively, resulting in missed opportunities for support and care. This study, conducted in Nepal, evaluated the impact of structured training intervention on HCPs' perceived preparedness, knowledge, and attitudes toward managing IPV and its mental health consequences, including self-harm and suicidal tendencies. The study was nested within a larger cluster randomized trial. A convergent mixed-methods design with a comparison group was conducted among 46 female HCPs in all public hospitals (except one) and 17 primary healthcare centers in Madhesh Province, Nepal. The intervention group (n = 24) received a 10-day intensive IPV and mental health training, while the control group (n = 22) completed 3-day training. Quantitative data were collected using a validated self-administered Physician's Readiness to Manage IPV questionnaire and IPV consequences scale. Paired and independent t-tests were applied to assess changes. Insights from key informant interviews were thematically analyzed to explore participant experiences and perceived impacts. At baseline, over 80% of participants had not received IPV management training. Post-intervention, significant improvements were observed in HCPs perceived preparedness (median change 2.1; 95% confidence interval (CI): 1.1- 2.9), knowledge (median change 2.7; 95% CI: 2.0-3.1), and awareness of IPV consequences (mean difference 1.2; 95% CI: 0.5-2.0), with greater gains in the intervention group. Qualitative findings revealed enhanced confidence in identifying IPV, addressing psychological impacts, and supporting survivors through safety planning and referral. The training significantly improved HCPs' knowledge, preparedness, and confidence to manage IPV and related mental health issues, underscoring the need to scale similar programs to frontline providers, particularly in rural and underserved settings, to strengthen health system's response to IPV.

Humans↗

PubMed2026

Operationalizing Local Ecological Knowledge for Aquatic Biodiversity Conservation: A Systematic Review and Management Framework.

Effective conservation and management of aquatic biodiversity is severely constrained by the absence of long-term ecological data in small-scale, tropical, and data-poor fisheries, where roughly one-quarter to one-third of freshwater fish species and 37.5% of elasmobranchs are threatened with extinction once Data Deficient species are accounted for. Conventional monitoring and stock-assessment tools are often financially and technically inaccessible in these systems, leaving managers without the evidence needed to prioritize conservation action or implement precautionary governance. Local Ecological Knowledge (LEK) is a largely underutilized resource for natural resource management that can provide temporal depth, spatial resolution, and species-specific ecological insights unavailable from scientific records. We conducted a systematic review and bibliometric synthesis of 60 peer-reviewed studies (1997-2025) applying LEK to assess fish conservation status, examining how, where, and through what methods this knowledge has been used. Our analysis identifies four complementary pathways through which LEK informs conservation management: reconstructing multi-decadal population changes, documenting spatial contraction and habitat loss, detecting extreme rarity and local extirpation, and characterizing intrinsic sensitivity to exploitation based on life-history traits. Despite growing methodological rigor, freshwater systems and African fisheries remain critically underrepresented, and formal integration of LEK into fisheries governance and biodiversity assessment remains the exception rather than the rule. We propose a practical three-stage framework to operationalize LEK within existing management and conservation systems. Recognizing fishing communities as legitimate co-producers of ecological knowledge is both scientifically necessary and an equity imperative for achieving global biodiversity commitments under the Kunming-Montreal Global Biodiversity Framework.

Biodiversity↗

PubMed2026

Voluntary Knowledge Brokering to Promote Evidence-Based Nursing Practice: A Qualitative Study.

Knowledge brokering is a process of connecting knowledge producers with users to facilitate evidence-based practice through relationship building and information sharing. This descriptive qualitative study aimed to clarify knowledge brokering by nurses in Japanese hospitals. Twelve registered nurses in Japanese hospitals participated. They had over 5 years' clinical experience, including experience in conducting research, particularly staff research, and education. Data were collected through semi-structured individual interviews and analyzed using qualitative content analysis. The analysis revealed a central theme: continuous efforts to foster empathy among colleagues and spontaneously promote evidence-based practice: multifaceted brokering activities by clinical nurses. Findings identified 10 categories categorized into four interconnected gears: establishing the foundational ground, assessing clinical needs and staff readiness, tailoring and diffusing evidence, and sustaining and evolving evidence-based practice. Even nurses without formal titles voluntarily bridged the research-practice gap, providing new insights into informal brokering. Brokers communicated considerately, balanced evidence with clinical context, negotiated practical compromises, and fostered staff research competency.

Humans↗

PubMed2026

Indigenous and local knowledge inclusion in forest fauna research: A systematic review in the tropics.

Indigenous and Local Knowledge (ILK) is an expression of biocultural diversity and is vital for inclusive and sustainable forest management and epistemic justice. We examine how researchers studying tropical forest fauna engage with ILK and the Indigenous Peoples and Local Communities (IPLC) who are holders of this knowledge. We conducted a systematic review of 62 articles that focus on tropical forest fauna and ILK. We used a category-based quantitative and qualitative content analysis on the types of forest fauna studied and how research engages with, defines and represents ILK. We also evaluated the varied forms of inclusion of IPLC in the research. We find that less than half of the reviewed studies (25) explicitly define ILK, and only four studies reported including IPLC in the decision-making processes. Our findings reveal that science has not fully acknowledged and understood the depth of ILK and we suggest ways to address this in future research.

Forests↗

PubMed2026

MetagenomicKG: a knowledge graph for metagenomic applications.

MOTIVATION: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for the genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities from different databases to standardized identifiers, allowing their interrelations to be captured into a cohesive network regardless of the naming conventions used in each source. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. RESULTS: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information on the human microbiome sourced from various databases, and further connects these with existing biomedical KGs to expand the biological network. Through various case studies involving the human microbiome, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. CODE AVAILABILITY: The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available on GitHub at https://github.com/KoslickiLab/MetagenomicKG. The data used in this manuscript, including the pre-built files and use case input data, are archived on Zenodo with DOI: 10.5281/zenodo.17546861.

Metagenomics↗

PubMed2026

Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.

BACKGROUND: Cell type annotation is essential for gaining biological insight from single-cell RNA sequencing data, yet manual labeling remains time-consuming and difficult to reproduce. Various computational approaches have been developed to automate this process, and recent studies suggest that large language models can infer cell types with promising accuracy in single-cell analysis. However, most workflows still rely on cluster-specific markers derived from gene expression alone or manual curation. As a result, marker selection can be sensitive to statistical criteria and dataset-dependent bias, which may lead to the selection of less informative genes or missing important markers, while providing limited biological context. RESULTS: To address this limitation, we introduce CELLIA, an LLM-based workflow for automated and robust cell type annotation. CELLIA employs an integrative evidence-knowledge marker selection strategy that combines statistical differential expression criteria with curated tissue-specific marker resources to identify informative marker genes. In benchmarking analyses of 102 cell types, this approach improved agreement with manual annotations. In addition, CELLIA achieved higher agreement in subtype-level analyses of closely related immune populations and was further evaluated in a non-immune stromal subtype setting, covering 25 cell types in total. CONCLUSION: By integrating evidence-knowledge from gene expression with curated biological prior knowledge, CELLIA provides a more stable marker selection and improves the reliability of LLM-cell type annotation.

Cell type annotation↗

PubMed2025

BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases.

Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base. BiomedSQL comprises 68,000 question/SQL query/answer triples generated from templates and grounded in a harmonized BigQuery knowledge base that integrates gene-disease associations, causal inference from omics data, and drug approval records. Each question requires models to infer domain-specific criteria, such as genome-wide significance thresholds, effect directionality, or trial phase filtering, rather than rely on syntactic translation alone. We evaluate a range of open- and closed-source LLMs across prompting strategies and interaction paradigms. Our results reveal a substantial performance gap: GPT-o3-mini achieves 59.0% execution accuracy, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%. BiomedSQL provides a new foundation for advancing text-to-SQL systems capable of supporting scientific discovery through robust reasoning over structured biomedical knowledge bases. Our dataset is publicly available at https://huggingface.co/datasets/NIH-CARD/BiomedSQL, and our code is open-source at https://github.com/NIH-CARD/biomedsql.

Journal Article↗