Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers

Bibliometric and visualized analysis of the applications of exosomes based drug delivery

Exosomes, endogenous vesicles secreted by cells, possess unique properties like high biocompatibility, low immunogenicity, targeting ability, long half-life, and blood-brain barrier permeability. They serve as crucial intercellular communication vectors in physiological processes and disease occurrence. Our comprehensive analysis of exosome-based drug delivery research from 2013 to 2023 revealed 2,476 authors from 717 institutions across 33 countries. Keyword clustering identified five research areas: drug delivery, mesenchymal stem cells, cancer immunotherapy, targeting ligands, surface modifications, and macrophages. The combination of exosome drug delivery technology with a proven clinical model enables the precise targeting of tumors with chemotherapy or radiosensitising agents, as well as facilitating gene therapy. This bibliometric analysis aims to characterize the current state and advance the clinical application of exosome-based drug delivery systems.

BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging

Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.

Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis

This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.

Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers

AIM

To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend.

METHODS

On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms.

RESULTS

Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification.

CONCLUSION

The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.

Bibliometric and visualized analysis of metal-organic frameworks in biomedical application

Background: Metal-organic frameworks (MOFs) are hybrid materials composed of metal ions or clusters and organic ligands that spontaneously assemble via coordination bonds to create intramolecular pores, which have recently been widely used in biomedicine due to their porosity, structural, and functional diversity. They are used in biomedical applications, including biosensing, drug delivery, bioimaging, and antimicrobial activities. Our study aims to provide scholars with a comprehensive overview of the research situations, trends, and hotspots in biomedical applications of MOFs through a bibliometric analysis of publications from 2002 to 2022. Methods: On 19 January 2023, the Web of Science Core Collection was searched to review and analyze MOFs applications in the biomedical field. A total of 3,408 studies published between 2002 and 2022 were retrieved and examined, with information such as publication year, country/region, institution, author, journal, references, and keywords. Research hotspots were extracted and analyzed using the Bibliometrix R-package, VOSviewer, and CiteSpace. Results: We showed that researchers from 72 countries published articles on MOFs in biomedical applications, with China producing the most publications. The Chinese Academy of Science was the most prolific contributor to these publications among 2,209 institutions that made contributions. Reference co-citation analysis classifies references into 8 clusters: synergistic cancer therapy, efficient photodynamic therapy, metal-organic framework encapsulation, selective fluorescence, luminescent probes, drug delivery, enhanced photodynamic therapy, and metal-organic framework-based nanozymes. Keyword co-occurrence analysis divided keywords into 6 clusters: biosensors, photodynamic therapy, drug delivery, cancer therapy and bioimaging, nanoparticles, and antibacterial applications. Research frontier keywords were represented by chemodynamic therapy (2020-2022) and hydrogen peroxide (2020-2022). Conclusion: Using bibliometric methods and manual review, this review provides a systematic overview of research on MOFs in biomedical applications, filling an existing gap. The burst keyword analysis revealed that chemodynamic therapy and hydrogen peroxide are the prominent research frontiers and hot spots. MOFs can catalyze Fenton or Fenton-like reactions to generate hydroxyl radicals, making them promising materials for chemodynamic therapy. MOF-based biosensors can detect hydrogen peroxide in various biological samples for diagnosing diseases. MOFs have a wide range of research prospects for biomedical applications.