Best practices for data management and sharing in experimental biomedical research.

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.

How studies in developmental epithelial-mesenchymal transition and mesenchymal-epithelial transition inspired new research paradigms in biomedicine.

Epithelial-mesenchymal transition (EMT) and its reverse mechanism, mesenchymal-epithelial transition (MET), are evolutionarily conserved mechanisms initially identified in studies of early metazoan development. EMT may even have been established in choanoflagellates, the closest unicellular relative of Metazoa. These crucial morphological transitions operate during body plan formation and subsequently in organogenesis. These findings have prompted an increasing number of investigators in biomedicine to assess the importance of such mechanisms that drive epithelial cell plasticity in multiple diseases associated with congenital disabilities and fibrosis, and, most importantly, in the progression of carcinoma. EMT and MET also play crucial roles in regenerative medicine, notably by contributing epigenetic changes in somatic cells to initiate reprogramming into stem cells and their subsequent differentiation into distinct lineages.

A comprehensive review on knowledge graphs for complex diseases.

In recent years, knowledge graphs (KGs) have gained a great deal of popularity as a tool for storing relationships between entities and for performing higher level reasoning. KGs in biomedicine and clinical practice aim to provide an elegant solution for diagnosing and treating complex diseases more efficiently and flexibly. Here, we provide a systematic review to characterize the state-of-the-art of KGs in the area of complex disease research. We cover the following topics: (1) knowledge sources, (2) entity extraction methods, (3) relation extraction methods and (4) the application of KGs in complex diseases. As a result, we offer a complete picture of the domain. Finally, we discuss the challenges in the field by identifying gaps and opportunities for further research and propose potential research directions of KGs for complex disease diagnosis and treatment.

Opportunities and challenges for ChatGPT and large language models in biomedicine and health.

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.

Challenges in computational discovery of bioactive peptides in 'omics data.

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Molecular Basis of Yeasts Antimicrobial Activity-Developing Innovative Strategies for Biomedicine and Biocontrol.

In the context of the growing concern regarding the appearance and spread of emerging pathogens with high resistance to chemically synthetized biocides, the development of new agents for crops and human protection has become an emergency. In this context, the yeasts present a huge potential as eco-friendly agents due to their widespread nature in various habitats and to their wide range of antagonistic mechanisms. The present review focuses on some of the major yeast antimicrobial mechanisms, their molecular basis and practical applications in biocontrol and biomedicine. The synthesis of killer toxins, encoded by dsRNA virus-like particles, dsDNA plasmids or chromosomal genes, is encountered in a wide range of yeast species from nature and industry and can affect the development of phytopathogenic fungi and other yeast strains, as well as human pathogenic bacteria. The group of the "red yeasts" is gaining more interest over the last years, not only as natural producers of carotenoids and rhodotorulic acid with active role in cell protection against the oxidative stress, but also due to their ability to inhibit the growth of pathogenic yeasts, fungi and bacteria using these compounds and the mechanism of competition for nutritive substrate. Finally, the biosurfactants produced by yeasts characterized by high stability, specificity and biodegrability have proven abilities to inhibit phytopathogenic fungi growth and mycelia formation and to act as efficient antibacterial and antibiofilm formation agents for biomedicine. In conclusion, the antimicrobial activity of yeasts represents a direction of research with numerous possibilities of bioeconomic valorization as innovative strategies to combat pathogenic microorganisms.

Green Synthesis and Biosafety Assessment of MXene.

The rise of MXene-based materials with fascinating physical and chemical properties has attracted wide attention in the field of biomedicine, because it can be exploited to regulate a variety of biological processes. The biomedical applications of MXene are still in its infancy, nevertheless, the comprehensive evaluation of MXene's biosafety is desperately needed. In this review, the composition and the synthetic methods of MXene materials are first introduced from the view of biosafety. The evaluation of the interaction between MXene and cells, as well as the safety of different forms of MXene applied in vivo are then discussed. This review provides a basic understanding of MXene biosafety and may bring new inspirations to the future applications of MXene-based materials in biomedicine.

The HEPS synchrotron unleashes new medical frontiers.

The High Energy Photon Source (HEPS) in Beijing achieved a beam energy of 6 GeV. This milestone enables groundbreaking advances in health sciences and various research fields, promising new insights into biological and quantum processes.

A review of the reproductive biology of mormyroid fishes: An emerging model for biomedical research.

Mormyroidea is a superfamily of weakly electric African fishes with great potential as a model in a variety of biomedical research areas including systems neuroscience, muscle cell and craniofacial development, ion channel biophysics, and flagellar/ciliary biology. However, they are currently difficult to breed in the laboratory setting, which is essential for any tractable model organism. As such, there is a need to better understand the reproductive biology of mormyroids to breed them more reliably in the laboratory to effectively use them as a biomedical research model. This review seeks to (1) briefly highlight the biomedically relevant phenotypes of mormyroids and (2) compile information about mormyroid reproduction including sex differences, breeding season, sexual maturity, gonads, gametes, and courtship/spawning behaviors. We also highlight areas of mormyroid reproductive biology that are currently unexplored and/or have the potential for further investigation that may provide insights into more successful mormyroid laboratory breeding methods.

Exploring pathways toward open-hardware ecosystems to safeguard genetic resources for biomedical research communities using aquatic model species.

Development of reliable germplasm repositories is critical for preservation of genetic resources of aquatic species, which are widely utilized to support biomedical innovation by providing a foundational source for naturally occurring variation and development of new variants through genetic manipulations. A significant barrier in repository development is the lack of cryopreservation capability and reproducibility across the research community, posing great risks of losing advances developed from billions of dollars of research investment. The emergence of open scientific hardware has fueled a new movement across biomedical research communities. With the increasing accessibility of consumer-level fabrication technologies, such as three-dimensional printers, open hardware devices can be custom designed, and design files distributed to community members for enhancing rigor, reproducibility, and standardization. The overall goal of this review is to explore pathways to create open-hardware ecosystems among the communities using aquatic model resources for biomedical research. To gain feedback and insights from community members, an interactive workshop focusing on open-hardware applications in germplasm repository development was held at the 2022 Aquatic Models for Human Disease Conference, Woods Hole, Massachusetts. This work integrates conceptual strategies with practical insights derived from workshop interactions using examples of germplasm repository development. These insights can be generalized for establishment of open-hardware ecosystems for a broad biomedical research community. The specific objectives were to: (1) introduce an open-hardware ecosystem concept to support biomedical research; (2) explore pathways toward open-hardware ecosystems through four major areas, and (3) identify opportunities and future directions.

Recent Progress in Synthesis and Application of Chiral Organoselenium Compounds.

Chiral organoselenium compounds have shown an important role as intermediates in many areas, such as drug discovery, organic catalysis, and nanomaterials. A lot of different methods have been developed to synthesize chiral compounds which contain selenium, because they have interesting properties and can be used in real life. Over the last few decades, a lot of progress has been made in synthesizing chiral organoselenium compounds. This work gives an overview of the progress made in creating new ways to synthesize chiral organoselenium compounds by categorizing them into groups based on the reactions they undergo. In addition, the use of chiral organoselenium compounds is also discussed.

Methodological advice for the young at heart investigator: Triangulation to build better foundations.

In medicine and science, one is typically taught the main theories in a discipline or field along with standard models before receiving more instructions on how to apply certain methods. The aim of this work is not to address one method, but rather methodology, the study and evaluation of methods, by taking a philosophy of science detour. In this, a critique of biomedicine will be used as a starting point to address some positions regarding reductionism, specifying notions such as systems and mechanisms, as well as regarding the mind-body problem discussing psychosomatic medicine and psychoneuroimmunology. Some recommendations to make science more pluralistic, robust and translationally-relevant will then be made as a way to foster constructive debates on reductionism and the mind-body problem and, in turn, favor more interdisciplinary research.

A survey of recent methods for addressing AI fairness and bias in biomedicine.

OBJECTIVES: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias. METHODS: We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness. RESULTS: The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.

Interrogating the microenvironmental landscape of tumors with computational image analysis approaches.

The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.

Algae in Biomedicine.

Algae, which live in marine or freshwater, are photosynthetic organisms. They vary greatly in size, morphology, and degree of complexity of their body structures. Algae are generally divided into two main groups, microalgae, which are small in size, and macroalgae, which are larger in size. These aquatic organisms have rich and valuable compounds including sterols, polysaccharides, pigments, fatty acids, proteins, enzymes, minerals, and vitamins that could be used in different application fields due to their bioactive functions. In recent years, algae and their components have attracted interest in biomedicine and health applications as their bioactive components could show antioxidant, anticancer, anti-inflammatory, antiviral, antiangiogenic, antidiabetic, antiobesity, immunostimulatory, vaccine adjuvant, and hypolipidemic activities. In this chapter, these activities and bioactive components underlying these properties are reviewed.

Exploring valuation practices in diagnosis-as-category: The rising dominance of clinical practice in the categorisation of Sepsis, 1991-2016.

This article contributes to the sociology of diagnosis by exploring how an acute medical condition, sepsis, was categorised over a 25-year period. We focus on publications reporting the outcomes of three consensus conferences that were convened to stabilise definitions of sepsis and on the failure of a controversial drug. We also focus on the category of severe sepsis by exploring why it was considered useful when introduced in 1991 but redundant by 2016. Drawing on insights from the sociology of valuation, our analysis of pivotal events within this period involving actors from clinical practice, biomedical science, regulation and industry identifies numerous and often contesting evaluative frameworks that came to bear on the categorisation of sepsis. Our analysis further reveals that the evaluative framework of clinical practice, mobilised by the professional specialty of intensive and critical care, became dominant in this categorisation, and that this did not reflect a dominance of biomedicine despite concerning an acute medical condition. Our contributions are to posit the ontological purpose of diagnostic categories and the role of valuation practices and strategic agency in diagnosis-as-category.

Overwork as a concept to understand health inequities for ethnicised patients in health care.

Health inequities for ethnically minoritised patients are well-documented. In this ethnographic study, we follow thirteen patients categorised as 'ethnic minorities' in Danish health care during hospitalisation in three orthopaedic wards across two hospitals. The categorisation of 'ethnic minority patient' has been problematised for its Eurocentric origin and practices within Westernised health care. We use ethnicised to emphasise the process of becoming minoritised based on markers of physical appearance, religious symbols, language or names. Access to health care also rely on perceived legitimacy as health-care recipients which requires work by patients. We demonstrate the workings patients categorised as 'ethnic minorities' engage in by (re)producing othering ideas about non-Danishness, including distancing from other patients perceived as problematic. These were then (counter)produced by positioning oneself as the opposite, as deserving health-care receivers by displaying welfare reciprocity, supporting egalitarian ideas by discounting discriminatory experiences, showing gratitude and identifying staff with good vibes. We propose these doings as creating overwork. This theoretical approach enables a sensitivity towards subtle and covert workings for patients placed in the margins of health care. In this study, overwork is closely related to notions of Danishness and takes on specific forms within a modernised and universalised Danish health-care system.

Flexural-Mode Piezoelectric Resonators: Structure, Performance, and Emerging Applications in Physical Sensing Technology, Micropower Systems, and Biomedicine.

Piezoelectric material-based devices have garnered considerable attention from scientists and engineers due to their unique physical characteristics, resulting in numerous intriguing and practical applications. Among these, flexural-mode piezoelectric resonators (FMPRs) are progressively gaining prominence due to their compact, precise, and efficient performance in diverse applications. FMPRs, resonators that utilize one- or two-dimensional piezoelectric materials as their resonant structure, vibrate in a flexural mode. The resonant properties of the resonator directly influence its performance, making in-depth research into the resonant characteristics of FMPRs practically significant for optimizing their design and enhancing their performance. With the swift advancement of micro-nano electronic technology, the application range of FMPRs continues to broaden. These resonators, representing a domain of piezoelectric material application in micro-nanoelectromechanical systems, have found extensive use in the field of physical sensing and are starting to be used in micropower systems and biomedicine. This paper reviews the structure, working principle, resonance characteristics, applications, and future prospects of FMPRs.

Chitosan-coated nanoparticles in innovative cancer bio-medicine.

In the recent decade, nanoparticles (NPs) have had enormous implications in cancer biomedicine, including research, diagnosis, and therapy. However, their broad application still faces obstacles due to some practical limitations and requires further development. Recently, there has been more interest in the coated class of nanoparticles to address those challenges. Chitosan-coated NPs are simple to produce, biodegradable, biocompatible, exhibit antibacterial activity, and have less cytotoxicity. This study provides an updated and comprehensive overview of the application of chitosan-coated NPs as a promising class of NPs in cancer biomedicine. Additionally, we discussed chitosan-coated lipid, metal, and polymer-based nanoparticles in biomedical applications. Furthermore, different coating methods and production/characterization procedures were reviewed. Moreover, the biological and physicochemical advantages of chitosan-coated NPs, including facilitated controlled release, greater physicochemical stability, improved cell/tissue interaction, and enhanced bioavailability of medications, were highlighted. Finally, the prospects of chitosan-coated NPs in cancer biomedicine were discussed.

Recent Advances in Light-Driven Semiconductor-Based Micro/Nanomotors: Optimization Strategies and Emerging Applications.

Micro/nanomotors represent a burgeoning field of research featuring small devices capable of autonomous movement in liquid environments through catalytic reactions and/or external stimuli. This review delves into recent advancements in light-driven semiconductor-based micro/nanomotors (LDSM), focusing on optimized syntheses, enhanced motion mechanisms, and emerging applications in the environmental and biomedical domains. The survey commences with a theoretical introduction to micromotors and their propulsion mechanisms, followed by an exploration of commonly studied LDSM, emphasizing their advantages. Critical properties affecting propulsion, such as surface features, morphology, and size, are presented alongside discussions on external conditions related to light sources and intensity, which are crucial for optimizing the propulsion speed. Each property is accompanied by a theoretical background and conclusions drawn up to 2018. The review further investigates recent adaptations of LDSM, uncovering underlying mechanisms and associated benefits. A brief discussion is included on potential synergistic effects between different external conditions, aiming to enhance efficiency-a relatively underexplored topic. In conclusion, the review outlines emerging applications in biomedicine and environmental monitoring/remediation resulting from recent LDSM research, highlighting the growing significance of this field. The comprehensive exploration of LDSM advancements provides valuable insights for researchers and practitioners seeking to leverage these innovative micro/nanomotors in diverse applications.