Background: Polyhydroxyalkanoates are bio-based, biodegradable naturally occurring polymers produced by a wide range of organisms, from bacteria to higher mammals. The properties and biocompatibility of PHA make it possible for a wide spectrum of applications. In this context, we analyze the potential applications of PHA in biomedical science by exploring the global trend through the patent survey. The survey suggests that PHA is an attractive candidate in such a way that their applications are widely distributed in the medical industry, drug delivery system, dental material, tissue engineering, packaging material as well as other useful products.
Objective: In our present study, we explored patents associated with various biomedical applications of polyhydroxyalkanoates.
Method: Patent databases of European Patent Office, United States Patent and Trademark Office and World Intellectual Property Organization were mined. We developed an intensive exploration approach to eliminate overlapping patents and sort out significant patents.We demarcated the keywords and search criterions and established search patterns for the database request. We retrieved documents within the recent 6 years, 2010 to 2016 and sort out the collected data stepwise to gather the most appropriate documents in patent families for further scrutiny.
Results: By this approach, we retrieved 23,368 patent documents from all the three databases and the patent titles were further analyzed for the relevance of polyhydroxyalkanoates in biomedical applications. This ensued in the documentation of approximately 226 significant patents associated with biomedical applications of polyhydroxyalkanoates and the information was classified into six major groups. Polyhydroxyalkanoates has been patented in such a way that their applications are widely distributed in the medical industry, drug delivery system, dental material, tissue engineering, packagingmaterial as well as other useful products.
Conclusion: There are many avenues through which PHA & PHB could be used. Our analysis shows patent information can be used to identify various applications of PHA and its representatives in the biomedical field. Upcoming studies can focus on the application of PHA in the different field to discover the related topics and associate to this study.We believe that this approach of analysis and findings can initiate new researchers to undertake similar kind of studies in their represented field to fill the gap between the patent articles and research publications.
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http://dx.doi.org/10.2174/1872208312666180131114125 | DOI Listing |
Nature
January 2025
Machine Learning Lab, University of Freiburg, Freiburg, Germany.
Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.
View Article and Find Full Text PDFNat Med
January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.
View Article and Find Full Text PDFNat Med
January 2025
Google Research, Mountain View, CA, USA.
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval.
View Article and Find Full Text PDFNature
January 2025
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models.
View Article and Find Full Text PDFSci Rep
January 2025
Divisions of Physical Therapy and Rehabilitation Science, Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, MN, 55455, USA.
OrthoFusion, an intuitive super-resolution algorithm, is presented in this study to enhance the spatial resolution of clinical CT volumes. The efficacy of OrthoFusion is evaluated, relative to high-resolution CT volumes (ground truth), by assessing image volume and derived bone morphological similarity, as well as its performance in specific applications in 2D-3D registration tasks. Results demonstrate that OrthoFusion significantly reduced segmentation time, while improving structural similarity of bone images and relative accuracy of derived bone model geometries.
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