Medicine Research Research Articles

Where do we intervene to optimize sports systems? Leverage Points the way

ABSTRACT Sport and sports research are inherently complex systems. This appears to be somewhat at odds with the current research paradigm in sport in which interventions are aimed are fixing or solving singular broken components within the system. In any complex system, such as sport, there are places where we can intervene to change behaviour and, ideally, system outcomes. Meadows influential work describes 12 different points with which to intervene in complex systems (termed “Leverage Points”), which are ordered from shallow to deeper based on their potential effectiveness to influence transformational change. Whether research in sport is aimed at shallow or deeper Leverage Points is unknown. This study aimed to assess highly impactful research in sports science, sports nutrition/metabolism, sports medicine, sport and exercise psychology, sports management, motor control, sports biomechanics and sports policy/law through a Leverage Points lens. The 10 most highly cited original-research manuscripts from each journal representing these fields were analysed for the Leverage Point with which the intervention described in the manuscript was focused. The results indicate that highly impactful research in sports science, sports nutrition/metabolism, sports biomechanics and sports medicine is predominantly focused at the shallow end of the Leverage Points hierarchy. Conversely, the interventions drawn from journals representing sports management and sports policy/law were focused on the deeper end. Other journals analysed had a mixed profile. Explanations for these findings include the dual practitioner/academic needing to “think fast” to solve immediate questions in sports science/medicine/nutrition, limited engagement with “working slow” systems and method experts and differences in incremental vs. non-incremental research strategies

Publication Trends and Hot Spots of ChatGPT's Application in the Medicine.

This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which "digital health"was not only the largest in scale but also had the most citations. The study highlights ChatGPT's cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT's specific technical uses in medical practice.

O12 Macrophages enhance angiogenesis in an induced pluripotent stem cell-derived skin organoid model

Abstract Introduction and aims Hair-bearing skin organoids (SkOs) are a complex three-dimensional (3D) model that faithfully recapitulates human skin as a multicellular organ with appendage structures. The development of such models provides unique potential for investigating human skin development and translational dermatology. Although this model answers many demands of an investigative tool, one obvious limitation is the absence of immune cells in SkOs. Our aim is to generate coculture systems of SkOs with macrophages to investigate how macrophages influence skin development. Methods Our group, and others, generated single-cell (sc)RNA-Seq and spatial transcriptomic datasets to thoroughly characterize human skin cell populations across the human lifespan. We used these datasets to compare the dynamic tempo of induced pluripotent stem cell (iPSC)-derived SkO (n = 84) differentiation with human prenatal (n = 15) and adult skin. For functional assays, we generated 2D angiogenesis coculture assays with macrophages and endothelial cells (n = 3) and 3D coculture of SkOs with macrophages (n = 3). Macrophages, endothelial cells and SkOs were all derived from the same iPSC line following established protocols. SkO vasculature was analysed after immunowholemount staining of CD31+ endothelial cells. Results Analysis of scRNA-Seq data highlighted a paucity in endothelial cell heterogeneity in SkOs compared with prenatal skin. We hypothesized that this could be explained by the absence of immune cells, as spatial transcriptomics microenvironment analysis of prenatal skin demonstrated that endothelial cells collocate with macrophages. With this in mind, we generated functional assays to investigate interactions of macrophages and endothelial cells in skin development. The 2D and 3D assays revealed that presence of macrophages significantly increased endothelial cell sprouting and network formation. Conclusions Our findings suggest a vital role of macrophages in skin morphogenesis, specifically in relation to the guidance of tip cells during angiogenesis. In addition, we present the SkO as an excellent complex model for investigating skin development, cell–cell interactions, and disease modelling for translational research in regenerative medicine and beyond.

Global trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study

ObjectiveWith the rapid advancement of Chat Generative Pre-Trained Transformer (ChatGPT) in medical research, our study aimed to identify global trends and focal points in this domain.MethodAll publications on ChatGPT in medical research were retrieved from the Web of Science Core Collection (WoSCC) by Clarivate Analytics from January 1, 2023, to January 31, 2024. The research trends and focal points were visualized and analyzed using VOSviewer and CiteSpace.ResultsA total of 1,239 publications were collected and analyzed. The USA contributed the largest number of publications (458, 37.145%) with the highest total citation frequencies (2,461) and the largest H-index. Harvard University contributed the highest number of publications (33) among all full-time institutions. The Cureus Journal of Medical Science published the most ChatGPT-related research (127, 10.30%). Additionally, Wiwanitkit V contributed the majority of publications in this field (20). “Artificial Intelligence (AI) and Machine Learning (ML),” “Education and Training,” “Healthcare Applications,” and “Data Analysis and Technology” emerged as the primary clusters of keywords. These areas are predicted to remain hotspots in future research in this field.ConclusionOverall, this study signifies the interdisciplinary nature of ChatGPT research in medicine, encompassing AI and ML technologies, education and training initiatives, diverse healthcare applications, and data analysis and technology advancements. These areas are expected to remain at the forefront of future research, driving continued innovation and progress in the field of ChatGPT in medical research.

Abutilon indicum: Bioactive Compounds and Diverse Therapeutic Applications

: A large variety of secondary metabolites are found in medicinal plants, and the majority are significant in medicine. One of the well-known plant species used to cure a wide range of human illnesses is Abutilon indicum. The plant Abutilon indicum has a wide range of therapeutic uses, and its varied parts have been employed to cure a wide range of ailments. Numerous bioactive substances, such as polyphenols, flavonoids, alkaloids, saponins, tannins, and glycosides are present in the plant. Potent antioxidant, anti-inflammatory, antibacterial, hepatoprotective, hypoglycaemic, analgesic, sedative, and diuretic activities have been demonstrated in these compounds. Additionally, the plant has been shown to have cytoprotective properties and to guard against DNA damage brought on by oxidative stress. Future research is required to pinpoint the specific bioactive chemicals responsible for the pharmacological effects of Abutilon indicum and the molecular mechanisms underlying the plant's therapeutic uses. Overall, Abutilon indicum has demonstrated great therapeutic potential and deserves further study as a possible source of homeopathic treatments for various illnesses. The present study covers a summary of the phytochemical and ethnomedicinal applications of Abutilon Indicum that would further help in exploring the research in natural medicine.

Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics

Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70–80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine.

Ortho-Halobenzyl Halides as Precursors for the Synthesis of Five- to Nine-Membered Ring Structures Employing Transition Metals as Catalysts

AbstractThis review highlights the multifaceted usefulness of o-halobenzyl halides as pivotal substrates for the construction of five- to nine-membered cyclic structures with the aid of transition metals as catalysts. These privileged entities engage dual active sites, enabling the combination of both intermolecular benzylation and intramolecular arylation strategies that directs the formation of a diverse repository of cyclic structures. The introduction of transition-metal catalysis in cross-coupling transformations sparked a revolution in forging aryl–heteroatom bonds, culminating in the evolution of more potent methodologies for the synthesis of a wide spectrum of valuable compounds. Furthermore, the associated pharmaceutical and biological attributes of these cyclic structures augment their significance in medicinal chemistry research. This review aims to showcase the importance of this synthetic methodology and its far-reaching applications in synthesis.1 Introduction2 Synthesis of Five-Membered Rings3 Synthesis of Six-Membered Rings4 Synthesis of Seven-Membered Rings5 Synthesis of Eight- and Nine-Membered Rings6 Conclusion

Essential oils as promising treatments for treating Candida albicans infections: research progress, mechanisms, and clinical applications

Candida albicans: (C. albicans) is a prevalent opportunistic pathogen that can cause severe mucosal and systemic fungal infections, leading to high morbidity and mortality rates. Traditional chemical drug treatments for C. albicans infection have limitations, including the potential for the development of drug resistance. Essential oils, which are secondary metabolites extracted from plants, have gained significant attention due to their antibacterial activity and intestinal regulatory effects. It makes them an ideal focus for eco-friendly antifungal research. This review was aimed to comprehensively evaluate the research progress, mechanisms, and clinical application prospects of essential oils in treating C. albicans infections through their antibacterial and intestinal regulatory effects. We delve into how essential oils exert antibacterial effects against C. albicans infections through these effects and provide a comprehensive analysis of related experimental studies and clinical trials. Additionally, we offer insights into the future application prospects of essential oils in antifungal therapy, aiming to provide new ideas and methods for the development of safer and more effective antifungal drugs. Through a systematic literature review and data analysis, we hope to provide insights supporting the application of essential oils in antifungal therapy while also contributing to the research and development of natural medicines. In the face of increasingly severe fungal infections, essential oils might emerge as a potent method in our arsenal, aiding in the effective protection of human and animal health.

The promises and limitations of artificial intelligence for quality improvement, patient safety, and research in hospital medicine.

The promises and limitations of artificial intelligence for quality improvement, patient safety, and research in hospital medicine.

Development and Validation of the Jawline Subject Satisfaction Scale

Introduction: Research in aesthetic medicine commonly includes evaluations of subject satisfaction with treatment results. However, conventional analytic methods typically generate statistically imprecise ordinal scores. To overcome this limitation, researchers have begun employing the Rasch model, an analytical framework grounded in item response theory. The Rasch model permits scale modifications capable of enhancing measurement accuracy. This study focuses on using the Rasch model to evaluate a scale measuring subject satisfaction following aesthetic treatments to the jawline. Objective: To develop and validate a multiitem, self-administered questionnaire measuring patient satisfaction with aesthetic treatment of the jawline. Methods: A 10-item questionnaire [The Jawline Subject Satisfaction Scale (JS3)] was devised to measure subject satisfaction following aesthetic treatments of the jawline. Each question was responded to using a 5-point Likert scale, with response selections ranging from “very much satisfied” to “very much dissatisfied” or “strongly agree” to “strongly disagree.” The scale's psychometric properties (reliability and separation for items and persons, item and person fit statistics, and unidimensionality and local independence) were validated using a Rasch model based on a dataset collected from a sample of forty subjects. Results: The results of the Rasch analysis revealed high internal consistency of the JS3, with a person reliability estimate of 0.86 and an item reliability estimate of 0.96. The separation estimates for persons and items were 2.50 and 4.72, respectively, demonstrating the scale's ability to differentiate between high and low responders and validating the instrument's construct. All infit and outfit values fell within the established range (0.5-1.5), and the data fit the model of unidimensionality and local independence. Raw score transformations into logits were conducted, which were then converted to Rasch measurements. These measurements are available for use in practice for conducting standard statistical analyses evaluating treatment and/or group effects. Conclusions: The application of the Rasch model produced a valid and reliable scale (ie, JS3) for measuring satisfaction with the appearance of the jawline following aesthetic treatments.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60

The role of bipolar disorder and family wealth in choosing creative occupations

Research in psychology and medicine has linked mental health disorders, and particularly bipolar disorder (BD), to employment in creative professions. Little is known, however, about the mechanisms for this link, which could be due to biology (primarily through a person’s genes) or environmental (through socioeconomic status). Using administrative data on mental health diagnoses and occupations for the population of Denmark, we find that people with BD are more likely to be musicians than the population, but less likely to hold other creative jobs. Yet, we also show that healthy siblings of people with BD are significantly more likely to work in creative professions. Notably, people from wealthy families are consistently more likely to work in creative professions, and access to family wealth amplifies the likelihood that siblings of people with BD pursue creative occupations. Nevertheless, family wealth explains only a small share of the correlation between BD and creative employment.

Establishment of XRD fourier fingerprint identification method of realgar decoction pieces and its anti-tumor activity in tumor-in-situ transplanted mice

Ethnopharmacological relevanceRealgar, a traditional mineral Chinese medicine, has been used in China for more than 2000 years. It has been recorded in many ancient and modern works that it has anti-cancer and anti-tumor effects. Of course, colon cancer is also within the scope of its treatment. Realgar needs to be processed into realgar decoction pieces by water grinding before being used for medicine. To ensure the consistency of efficacy and quality of realgar decoction pieces, modern methods need to be used for further quality control. Aim of the studyThe research of traditional mineral Chinese medicine is relatively difficult, and the related research is less. The purpose of this study is to control the quality of realgar decoction pieces by modern analytical technology and analyze its components. On this basis, its anti-colon cancer activity was discussed. Materials and methodsSeveral batches of realgar decoction pieces were analyzed by XRD, and the components of realgar decoction pieces were obtained. The quality control fingerprints of realgar decoction pieces were established by processing XRD spectra and similarity evaluation. Then, the effects of realgar decoction pieces on apoptosis of CT26 and HTC-116 cells were observed in vitro by Hoechst 33258 staining, flow cytometry, measurement of mitochondrial membrane potential and Western blot; In vivo, the mouse model of tumor-in-situ transplantation of colon cancer was established, and the related indexes were observed. ResultThe explorations showed that the XRD Fourier fingerprints of realgar decoction pieces samples that had the same phase revealed 10 common peaks, respectively. The similarity evaluation of the established XRD Fourier fingerprint was greater than 0.900. We also demonstrated that realgar decoction pieces can promote apoptosis and inhibit tumor growth in colon cancer cells, its activating effect on p53 protein, and its safety when used within reasonable limits. ConclusionThe quality control of realgar decoction pieces by XRD is scientific and has the inhibitory effect on colon cancer, which has the development potential.

Medical research production in native languages: A descriptive analysis of PubMed database.

Language barriers in medicine can hinder effective communication, comprehension, and patient care. While English has emerged as the dominant language in global medicine, the importance of native languages should not be overlooked. This article aims to examine the extent of publishing in native languages by analyzing the PubMed database literature to gain further insights into the usage of native languages in medicine and medical research. In December 2023, a comprehensive examination of the PubMed literature was conducted for each of the 55 registered languages. We searched for records published in each language (e.g., German[lang]) by applying language filters. Ethnologue provided data on the number of worldwide native speakers for each language, facilitating a comparative analysis. By December 2023, PubMed contained over 36 million publications, with 86.5% of them published in English. German, French, and Russian came after English, with over 700 thousand publications each. Among the languages analyzed, fourteen had fewer than 50 publications, nineteen had fewer than 100, twenty-two had fewer than 500, and twenty-five had fewer than one thousand publications. European languages were well-represented with thousands of publications each, while widely spoken languages such as Hindi and Arabic had limited representation. The production of medical research in native languages reflects the attention given to native languages in medicine and medical education within each country. It is crucial to provide due attention to these language-related issues and explore strategies for including native languages in medicine to bridge the gaps in language and medicine.

Open science practices in traditional, complementary, and integrative medicine research: A path to enhanced transparency and collaboration

This educational article explores the convergence of open science practices and traditional, complementary, and integrative medicine (TCIM), shedding light on the potential benefits and challenges of open science for the development, dissemination, and implementation of evidence-based TCIM. We emphasize the transformative shift in medical science towards open and collaborative practices, highlighting the limited application of open science in TCIM research despite its growing acceptance among patients. We define open science practices and discuss those that are applicable to TCIM, including: study registration; reporting guidelines; data, code and material sharing; preprinting; publishing open access; and reproducibility/replication studies. We explore the benefits of open science in TCIM, spanning improved research quality, increased public trust, accelerated innovation, and enhanced evidence-based decision-making. We also acknowledge challenges such as data privacy concerns, limited resources, and resistance to cultural change. We propose strategies to overcome these challenges, including ethical guidelines, education programs, funding advocacy, interdisciplinary dialogue, and patient engagement. Looking to the future, we envision the maturation of open science in TCIM, the development of TCIM-specific guidelines for open science practices, advancements in data sharing platforms, the integration of open data and artificial intelligence in TCIM research, and changes in the context of policy and regulation. We foresee a future where open science in TCIM leads to a better evidence base, informed decision-making, interdisciplinary collaboration, and transformative impacts on healthcare and research methodologies, highlighting the promising synergy between open science and TCIM for holistic, evidence-based healthcare solutions.

Centralized and Federated Models for the Analysis of Clinical Data.

The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.

Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pre-trained language models

Background: The research and utilization of ancient traditional Chinese medicine (TCM) books are relatively limited at present. With the rapid development of artificial intelligence (AI), knowledge graph related technology has brought light on this field. Objective: To construct the knowledge graph of Shen Nong Ben Cao Jing, analyze basic knowledge of materia medica, explore implicit knowledge, and conduct visualization display, as well to provide methodological references for the study of ancient TCM books. Methods: The types of knowledge entities and relationships between entities in Shen Nong Ben Cao Jing were analyzed. A training corpus dataset was produced by using the BIO sequence labeling method; a self-developed CNLP text labeling system was used for text labeling; the BERT model was used to recognize named entities; the relationships between entities were set based on rules and semantic associations; the data into the Neo4j-community 4.4.9 graph database was imported by using Cypher language for storage and visualization display after knowledge fusion; finally, a knowledge graph was constructed. Results: The knowledge graph of Shen Nong Ben Cao Jing included 5, 273 nodes and 11, 064 relationships. The schema layer contained 14 entity types and 15 relationship types. Through the query, knowledge can be visualized from the aspects of classification, property, and seven mutual relationships of herbal combination. Conclusion: The knowledge graph constructed in this study directly reflects the knowledge recorded in Shen Nong Ben Cao Jing and the relationship between them, which is suitable for knowledge mining and intuitive multi-dimensional display of ancient TCM books.

Current progress in the synthesis of imidazoles and their derivatives via the use of green tools

Abstract Imidazoles have a unique position in heterocyclic chemistry as these constitute the basic framework of several bio-molecules. Thus, increasing research is being carried out on the synthesis of imidazoles and their derivatives, mainly because of the application of imidazoles in pharmaceutical and medicinal research. Keeping sustainability in mind, researchers are developing synthetic pathways for the synthesis of imidazoles and their derivatives by employing techniques involving green tools, thus leading to sustainable pathways. In this review, we aim to compile such synthetic methodologies involving green tools for the synthesis of imidazoles. The review will cover the synthetic reactions that involve green tools such as microwave irradiation, ultrasound irradiation, and ball milling. We aim to highlight the scope and relevance of such green tools in today’s synthetic research. Through this review, we wish to contribute towards the synthesis of imidazoles that serve as a useful class of heterocyclic compounds involved in the development of pharmaceutically active molecules. We sincerely hope that this review will serve as a relevant guide for future sustainable research in the synthesis of imidazoles and their derivatives.

Chemical Constituents of the Artemisia ciniformis Aerial Parts Grown in the Northeast of Iran and Their Chemotaxonomic Significance

Background: The Artemisia genus is one of the largest genera of the Asteraceae family, with various traditional and modern reported uses. Within this group, Artemisia ciniformis (Dermane Talaei) is an endemic plant of Iran with antioxidant, antimalarial, anticancer, cytoprotective, and neuroprotective properties. Due to these potential benefits, further phytochemical research needs to be conducted to understand its chemotaxonomic and pharmaceutical effects. Objectives: The main purpose of this study is to investigate the dichloromethane fraction of A. ciniformis from Iran for phytochemical and chemotaxonomic aspects. Methods: Artemisia ciniformis aerial parts were collected, dried, and extracted by the maceration method using dichloromethane after defatting. The prepared extract was subjected to chromatographic techniques, including column chromatography, vacuum liquid chromatography, and HPLC, to isolate purified compounds, which were identified using spectroscopic analyses such as NMR and EI-MS. Results: Two tetrahydrofuran-type sesquiterpenoids: (1) Hydroxydavanone and (2) arteincultone; two acetophenone derivatives: (3) Xanthoxylin and (4) 2,4-dihydroxy-6-methoxy acetophenone; and a flavonoid, (5) jaceosidin, were isolated. Additionally, a polymethoxylated flavone was obtained in small amounts, and its structure was tentatively assigned as (6) 5,6-dihydroxy-2-(3-hydroxy-4,5-dimethoxyphenyl)-7-methoxy-4H-chromen-4-one (Ciniformon). Conclusions: Artemisia ciniformis is a rich source of phytochemicals from various categories, such as sesquiterpenes and phenolic compounds, that can be used in systematic studies and medicinal research to treat human disorders by discovering new biologically active as well as lead compounds.

A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.