Phenotypically plastic responses of animals to adjust to environmental variation are pervasive. Reversible plasticity , where adult phenotypes can be reversibly altered according to prevailing environmental conditions, allow for better matching of phenotypes to the environment and can generate fitness benefits but may also be associated with costs that trade-off with capacity for flexibility. Here, we review the literature on avian metabolic and muscle plasticity in response to season, temperature, migration and experimental manipulation of flight costs, and employ an integrative approach to explore the phenotypic flexibility of metabolic rates and skeletal muscle in wild birds. Basal (minimum maintenance metabolic rate) and summit (maximum cold-induced metabolic rate) metabolic rates are flexible traits in birds, typically increasing with increasing energy demands. Because skeletal muscles are important for energy use at the organismal level, especially to maximum rates of energy use during exercise or shivering thermogenesis, we consider flexibility of skeletal muscle at the tissue and ultrastructural levels in response to variations in the thermal environment and in workloads due to flight exercise. We also examine two major muscle remodeling regulatory pathways: myostatin and insulin-like growth factor -1 (IGF-1). Changes in myostatin and IGF-1 pathways are sometimes, but not always, regulated in a manner consistent with metabolic rate and muscle mass flexibility in response to changing energy demands in wild birds, but few studies have examined such variation so additional study is needed to fully understand roles for these pathways in regulating metabolic flexibility in birds. Muscle ultrastrutural variation in terms of muscle fiber diameter and associated myonuclear domain (MND) in birds is plastic and highly responsive to thermal variation and increases in workload, however, only a few studies have examined ultrastructural flexibility in avian muscle. Additionally, the relationship between myostatin, IGF-1, and satellite cell (SC) proliferation as it relates to avian muscle flexibility has not been addressed in birds and represents a promising avenue for future study. Phenotypically plastic responses of morphology, behavior and physiology to environmental variation are ubiquitous among living organisms . GeneticThe primary functions of skeletal muscle in birds are locomotion and thermogenesis. Powered flight is supported by large flight muscles, which comprise 10%–25% of total body mass in volant birds . For morsum), in winter  from the relatively mild winter climate of southeastern China. BMR may also be associated with overwinter survival in birds, but on a fluctuating basis, with high BMR favored in cold winters and low BMR favored in warm winters , little bunting (Emberiza pusilla), and Eurasian tree sparrow (P. montanus). The overall generalization from recent work on flexibility in pectoralis mass and metabolic capacities in birds in response to winter or cold exposure is well-stated by In contrast to these studies, a number of other recent studies suggest that seasonal or cold-induced changes in muscle size do not necessarily accompany changes in metabolic capacities in birds . In addiCalidris canutus,Limosa lapponica) showed increases in lean dry pectoralis mass of 21 and 27% in males and females, respectively, between early and late stopover phases (Zonotrichia leucophrys) in central California with birds just arriving from migration in May in Alaska and found that flight muscle mass was greater upon arrival in Alaska than during winter or just prior to departure in April in California. Flight training also typically results in increases in pectoralis muscle mass in birds. Fight-trained European starlings (Sturnus vulgaris) increased pectoralis mass by 8% compared to untrained birds (Taeniopygia guttata) to hover while feeding, thus increasing flight costs, which resulted in a 9% increase in pectoralis mass for females, but not for males.In addition to winter acclimatization or cold acclimation influences on muscle masses and metabolic rates in birds, development of migratory disposition is also typically associated with increases in flight muscle mass of up to 35% . Such inr phases . More rer phases . Concurred birds . Zhang esum was positively correlated with breast muscle size measured by ultrasonography in migratory and cold acclimated red knots  elevated Msum by 10.9% between spring and fall migration, although female kinglets showed statistically stable Msum between spring and fall, consistent with a more rapid pace of migration in spring , also known as growth differentiation factor 8 (GDF-8), is a TGF-β superfamily growth factor . Myostatactivity . Metalloactivity . Becauseactivity . Both maactivity , allowinActive mature myostatin binds to the type II activin receptors A and B (ActRIIA and ActRIIB) , which rMutations in the myostatin gene and blockage of myostatin action result in dramatic increases in muscle growth . In mammS. tristis) or black-capped chickadees (Poecile atricapillus) between summer and winter were not consistent with house sparrows. sum  (Setophaga petechia), warbling vireos (Vireo gilvus), and yellow-rumped warblers (Setophaga coronata), either between migratory seasons  or compared to summer  compared to the non-migratory season (winter) in white-throated sparrows (icollis) . Moreoveo summer . In conto summer . Similarvs. long-day) and temperature (3°C or 24°C) treatments did not significantly alter gene expression of myostatin or the TLLs nor myostatin protein levels in dark-eyed juncos   . Photopeyemalis) . In addiyemalis)  by eitheyemalis) . ConsequT. guttata), accomplished by forcing birds to forage while hovering, resulted in female, but not male, finches showing lower myostatin protein levels than controls (S. vulgaris)  . HoweverPygoscelis adeliae) expressed high IGF-1 mRNA in pectoralis muscle during a rapid growth period, indicating that it plays an important role in the development of an enhanced muscle phenotype (Zonotrichia leucophrys gambelii), but decreased in the gastrocnemius muscle at pre-departure stage  axis stimulates growth and accounts for up to 83% of postnatal growth and skeletal muscle hypertrophy in young mammals . Locallyhenotype . Gene exre stage . On the re stage . Flight-re stage . Taken tmTOR and genes in the BMP-signaling cascade (TOB1 and BMPR2) in pectoralis muscle, suggesting possible involvement of these pathways in seasonal phenotypic flexibility.In addition, transcriptomic analyses on dark-eyed juncos under different temperature and photoperiod treatments indicated that very few genes associated with the mammalian target of rapamycin (mTOR) signaling pathway that governs protein synthesis, and with which myostatin and IGF-1 interact , were chSkeletal muscle is a multinucleated syncitium. As such, nuclei are each supported by linker of nucleoskeleton and cytoskeleton (LINC) complexes that preserve nuclear positioning , so as tDrosophila models , and IGF-1, among others . Additioa models . Howevera models .MND regulation is much more extensively studied in mammals than in birds, so an exploration of mammalian regulation of MND can provide the necessary background to examine similar MND regulation in birds. In mammals, hypertrophy can be initiated and sustained for a time in the absence of SCs in adult mice, but hypertrophy maintained for 8 weeks or more is dampened without additional SCs , and larPost-natal development of muscle fibers involves an increase in fiber size and a concomitant increase in the number of nuclei (derived from SCs) . In adulA further implication of these data is that, whereas avian muscle seems more phenotypically flexible than mammalian muscle, the biological processes surrounding myonuclear function may be more closely related to those of mammals . SCs havChanging environmental temperatures can affect chick muscle development , which iMyoD expression , which demonstrated a pectoralis muscle fiber population including very small and very large muscle fibers (MND-related questions in the avian study model are still in a primitive state, with only a few studies addressing this concept. Regulation of MND in birds appears to be dominated by some of the same mechanisms as mammals, however, its ability to adjust to thermal or workload changes may be faster, potentially due to avian SCs ability to proliferate quickly . Future e fibers . These se fibers .


Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain. In fact, some have purported that the modern AI age can be traced to the convolutional neural network AlexNet winning the ImageNet challenge in 2012;3 clearly, this task could not have been accomplished without the careful construction and curation of the ImageNet platform several years earlier.4 During the same interval, the “Big Cancer Data” era was arguably initiated by the availability of large-scale public cancer databases. Specifically, The Cancer Genome Atlas (TCGA) has had a transformative effect on oncologic research.5 Crucial for the oncologic imaging community, and consequently the radiation oncology community, a subsidiary effort, The Cancer Imaging Archive (TCIA),6 was originally developed as a repository for matched image data from TCGA cases; however, TCIA has become a leader in its own right for distributing reliable and high-quality datasets independent of TCGA. Subsequently, radiation oncology has now entered a new era for AI applications due to an increasing deluge of data afforded by resources such as TCIA. In the following sections, we review and discuss the role of data sharing in the radiation oncology ecosystem, important regulatory and ethical considerations, and contemporary databases available for public investigation.The rise of artificial intelligence (AI) and machine learning in recent years has seen an explosion in technical approaches to statistical modeling, driven mostly by innovation on large datasets in the computational science community. These groundbreaking efforts have benefitted immensely from the availability of large-scale highly annotated datasets, such as the ImageNet visual recognition task dataset,7 To ensure the usefulness of large datasets and foster data re-usability, it is fundamental to follow the Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management.8 These principles emphasize machine-actionability, that is, the capacity of computational systems to find, access, interoperate, and reuse data with minimal human intervention. To foster the widespread implementation of FAIR scientific principles in day-to-day scientific workflows, several steps can and should be taken by practitioners of open-science practices in the radiation oncology community. These practices should include the utilization of study preregistration, manuscript preregistration, open-access journals, and code/data sharing ,15 Drawing inspiration from the well-known physics preprint server arXiv, in 2019 a clinical medicine preprint repository, medRxiv, was established.16 Since its inception, medRxiv has quickly become a leading figure in medically related preprint dissemination.17 The benefits of publishing preprints for radiation oncology are numerous, chief among which include near-immediate distribution of scientific findings, ease of use, circumvention of journal politics and dominant narratives, and complete access by the general public. Naturally, the use of preprints raises the potential for faulty analysis. However, these concerns seem minor compared to the relative benefit of rapid dissemination of information which could then be verified through indicators of internal validity and wider scientific community evaluation. Closely related to concepts regarding preprints is the use of open-access options for scientific manuscripts. By allowing wider distribution of scientific findings, open-access options may lead to a greater return on investment for scientific innovation.18 As of 2008, the National Institutes of Health (NIH) has mandated publicly funded research be made available through PubMed Central.19 Moreover, as of 2021, the Plan S initiative will require that all EU-funded efforts be published through open access journals or platforms.20 These geopolitical pushes highlight the importance of open access models for manuscript dissemination and will likely be a core staple of future radiation oncology research.After study completion, the utilization of preprints, a manuscript that precedes formal peer review, allows for rapid dissemination of study findings to the general public.21 Examples of journals publishing data descriptors include Medical Physics and Nature Scientific Data.22 Finally, code repositories, such as GitHub, have allowed for the rapid and dynamic development of code related to scientific studies in the medical domain, and fostered community engagement for future developments.23Finally, data and code accessibility, which are particularly germane to AI-driven analyses in radiation oncology, form a more recently established central tenant for disseminating scientific information. An in-depth discussion of data repositories is described in other sections of this review see “”. MoreovWhile the current components of scientific dissemination remain relatively independent, in the future, one could envision a modular framework where automated processes link these components in an integrated fashion . Corresp24 For ease of distribution, DICOM data is often transformed into Neuroimaging Informatics Technology Initiative format, which is increasingly seen as a standard for reproducible imaging research.25 While image data is often standardized and distributed relatively easily due to established nomenclature and customs, unfortunately, image annotations are not often stored in a single common format.26 For example, image annotations can be stored in picture archiving and communication system and other systems as DICOM presentation state objects which may vary from vendor to vendor. The recently developed DICOM structured reporting (DICOM-SR) standard has allowed to somewhat combat this lack of standardization in annotation but there still remains problems with intersite variability of annotation templates.27 For non-graphic annotations, the annotation and image markup format was recently developed and incorporated into DICOM-SR.28 Within radiation oncology, the DICOM radiotherapy (DICOM-RT) standard has allowed for a systematic characterization of radiotherapy-related data.29 However, DICOM-RT was not developed with AI/machine-learning applications in mind, thus annotations derived from these files can at times be challenging to incorporate directly into models; community-driven undertakings have developed some solutions to these problems.31 Thus, ongoing efforts will be needed to reach a consensus on the best practices to standardize image annotation data for radiation oncology applications.Broadly, there are two main types of data relevant to AI model development using public datasets in radiation oncology - image data formats and image annotation formats. The majority of hospital picture archiving and communication system infrastructure stores medical images in Digital Imaging and Communications in Medicine (DICOM) format, which is the recognized international standard for image data storage.33 Among the many accomplishments of AAPM Task Group 263, the group was able to standardize: (1) structure names across image processing and treatment planning system platforms, (2) nomenclature for dosimetric data, (3) templates for clinical trial groups, and (4) formalisms for nomenclature schema which could accommodate the addition of other structures defined in the future. Through these guidelines, annotation processes for radiation oncology datasets and corresponding public data deposition should be more easily standardized for use in reproducible and robust research applications.Outside of file objects and corresponding metadata standardization, there are additional considerations for radiation oncology annotation data that are crucial for public distribution. Specifically, nomenclature conventions can often vary across institutions. For example, nomenclature for target/non-target structures is often highly variable . Subsequ34  which was unveiled in May 2018. GDPR’s main goal is to protect data subjects by ensuring that organizations respect and protect the personal data associated with an individual. While GDPR’s main goal is aligned with research ethics to enable full data protection of the data subject, its strict guidelines have however been perceived at times by the scientific community as an impediment to scientific progress.37 The current conundrum of having FAIR data that should also adhere to GDPR is potentially solvable with improved techniques for de-identification as well as other approaches like differential privacy and federated analysis.38In research integrity and ethics, it is accepted across public, health, and research institutions around the world that the privacy of the study participants must always be respected and never compromised to avoid concrete risks such as identity theft, blackmailing, or any other possible adverse consequences that a subject might face if their identity is disclosed with the data. Institutional review boards and ethical committees ensure that scientific research follows the highest standards to protect the safety of human subjects. However, ethics is not law, and while the ethical review process is comparable across countries, the legal principles and interpretation of data protection vary considerably and even deter FAIR data reuse. Currently in the USA, the Health Insurance Portability and Accountability Act (HIPAA) advises the removal of 18 pieces of protected health information (PHI) when sharing de-identified data34 . By remo39 Often, data are said to be anonymized when PHI has been completely removed such that the data can no longer be associated with an individual in any manner, while de-identification refers to the general removal of PHI.40 More technical definitions include de-identification referring to rule-based techniques to remove PHI, with anonymization referring to statistical/probabilistic techniques to remove PHI.39 For the purposes of this review, we will refer to the terms de-identification and anonymization interchangeably.Chief among concerns for utilizing patient-derived medical data in public datasets is proper anonymization and de-identification of PHI. The terms de-identification and anonymization, when applied to medical data can encompass a wide array of definitions, and at times can be vague, inconsistent, or even contradictory.41 In cases where meta-data is available in DICOM or similar files, these data can be stripped using commonly available tools, such as the Radiological Society of North America Clinical Trial Processor,42 though care should still be taken to ensure all data has been properly modified. Moreover, PHI may be “burned” into existing images, such as when considering radiographs directly scanned into electronic medical records. These embedded pieces of information can often be removed using optical character recognition techniques or similar methods; however, they can provide additional challenges in ensuring the full removal of text-based PHI.43For obvious pieces of medical information, such as patient name, medical record number, date of birth, and other demographics, the anonymization process is often straightforward in removing this data or meta-data from associated files. DICOM formatted files typically contain metadata that links images to information regarding patient demographics and image acquisition parameters, among other information.46 For example, using facial recognition software paired to MRI-derived facial reconstructions, 83% of research participants were able to be identified from their MRI scan.46 Within the realm of radiation oncology, perhaps the avenue where the greatest amount of concern for facial identification is in the generation of public medical imaging datasets of head and neck cancer. While brain images are often processed such that obvious facial features are removed , these crude pre-processing techniques will likely remove important information for building predictive models with head and neck cancer imaging data, prohibiting their use in data resharing strategies. “De-facing” tools, where voxels that correspond to the areas of the patient’s facial features are either removed or altered are one solution. However, they encounter the problem of information loss in important areas needed for predictive modeling or treatment planning. While several studies have investigated the effects of de-facing for applications in neuroimaging,50 to our knowledge only one study has investigated the effects of de-facing tools for radiation oncology applications in head and neck cancer.51 As an example use case, we demonstrate organs at risk are obscured with 4 state-of-the-art de-facing tools54  to generate similar “comparable” visualizations of a patient’s face with relative ease. Several studies have shown the potential danger in releasing unaltered medical images containing facial features as they can often be easily recognized by humans or machines. tools54 , which w56 As of 2003, the NIH has implemented a Data Sharing Policy which encourages data to “be made as widely and freely available as possible while safeguarding the privacy of participants, and protecting con-fidential and proprietary data.” Specifically, investigators were required to specify data sharing protocols within NIH grant applications. These protocols for data sharing could be accomplished through the use of data archives, data enclaves, or under the auspices of the principal investigator. Congruent with the increasing ability to generate, store, share, and combine data, in 2015 the NIH initiated a more comprehensive data sharing policy in tandem with efforts to modernize data sharing infrastructure in its Plan for Increasing Access to Scientific Publications and Digital Scientific Data from NIH Funded Scientific Research.57 Effective in 2023, the NIH has issued a new Final NIH Policy for Data Management and Sharing which will require NIH funded researchers to prospectively submit a plan detailing how scientific data and metadata will be managed and shared taking into account potential restrictions or limitations58; this plan will replace the 2003 NIH Data Sharing Policy. These policies underscore the importance of data stewardship and management for nationally-funded research, and have influenced data sharing practices to funders of radiation oncology research, particularly the National Cancer Institute (NCI).Broadly, the sharing of biomedical data generated through research studies allows the scientific community to expedite the translation of findings into knowledge, products, and procedures to improve health.https://www.nlm.nih.gov/NIHbmic/domain_specific_repositories.html; a subset of radiation oncology related repositories is shown in 59Within the Final NIH Policy for Data Management and Sharing, detailed documentation has been provided to help researchers select specific repositories for data deposition (“Selecting a Repository for Data Resulting from NIH-Supported Research”). Ideally, where applicable, data should be deposited in discipline or data-type specific repositories. The NIH has provided a list of approved specific repositories at 60 The NCI CRDC includes cloud-based domain-specific data repositories and analysis-focused cloud resources to facilitate collaborative and standardized research practices using diverse data types. Within the CRDC, several data repositories have been established, such as the Genomic Data Commons61 and the Imaging Data Commons.62 As opposed to previously described data repositories, the cloud-based infrastructure of the CRDC allows researchers to utilize data in real-time without the need for local downloading of data files. Moreover, through these data repositories, disparate data sources  can be combined and investigated with compute resources provided by cloud environments, thereby providing researchers with the ability to perform robust harmonized analysis. An example of the IDC user portal interface is shown in Driven by improvements and innovations in cloud-computing paradigms for use in big data research, the NCI has created the Cancer Research Data Commons (CRDC) as a component of a national cancer data ecosystem.6 this integrated database has offered the imaging community large volumes of curated data for exploratory image analysis, computational model development, and model validation.63 Provided human data originates from various sources, ranging from small-scale calibration studies to large-scale clinical trials. These imaging data have been crucial to developing contemporary medical AI models and catapulted TCIA as a de-facto leader of medical image data dissemination . Before publication, datasets are rigorously curated to ensure acceptable image quality and data integrity. Currently, TCIA utilizes the Posda open-source framework65 to aid in the curation process and remove any identifying information in metadata. Collection contents are described through “wiki pages”, which also list relevant publications and instructions for data use. Increasingly, focused “data descriptors,” in-depth manuscripts detailing individual datasets, such as those published through Nature Scientific Data,22 are also generated for TCIA collections to engender greater transparency in data generation, collection protocols, and intended use-cases. For end-users, TCIA provides web interfaces and software  to easily retrieve and catalog collections on local computing infrastructure.The overarching structure of the TCIA is stratified into individual collections defined by a common disease , imaging modality , and/or research focus. Collections are assigned persistent digital object identifiers (DOIs), thereby allowing researchers to reference and acquire datasets.24 Importantly, TCIA has formed a corpus for not only raw imaging data, but also corresponding supporting data, such as region of interest segmentations through DICOM radiotherapy structure set (RTSTRUCT) files and clinical outcome data. Therefore, TCIA houses a rich stream of information for supervised machine learning segmentation and classification models. For radiation oncology applications, DICOM radiotherapy plan (RTPLAN) and DICOM radiotherapy dose (RTDOSE) are often also included in collections which can be used for model development germane to radiation therapy planning. A list of currently available TCIA collections that include RTPLAN and/or RTDOSE data is shown in TCIA data is most often available through standardized imaging formats such as DICOM.66 A similar trend has emerged in the medical domain, particularly for medical imaging, where anonymized data is provided to challenge participants to solve important healthcare problems.68 The field of radiation oncology, which is heavily centered on image-based workflows, is no exception to this increasing trend, with several radiotherapy-related data challenges emerging in recent years. Here, we summarize a few key data challenges that have been particularly impactful for radiation oncology applications.Data “challenges,” that is, competitions where datasets are publicly provided to interested participants to solve a specific problem, have been a staple in developing modern-day cutting-edge AI algorithms. For example, the ImageNet Large Scale Visual Recognition Challenge, a competition where participants are tasked with classifying photographic images of common objects, was the impetus for the rise of deep learning approaches for computer vision applications.69 the University of Texas MD Anderson Cancer Center and the Medical Image Computing and Computer Assisted Intervention Society organized two public radiomics challenges in the head and neck radiation oncology domain.70 Through the Kaggle InClass commercial educationally-oriented platform,71 the organizers tasked participants to develop predictive models to: (1) classify patients based on human papillomavirus (HPV) status, and (2) predict local tumor recurrence status. A large number of contrast-enhanced CT images of oropharyngeal cancer patients and corresponding clinical data72 were provided to participants to build models to solve the 2 tasks through evaluation of independent test data , but participants were still able to generate segmentation results of reasonable quality on the independent pre-AAPM challenge test set (n = 10) and online challenge test set (n = 10). These datasets have become publicly available in their entirety through TCIA,73 allowing for the community to continue improving upon methods for radiotherapy planning segmentation. Moreover, given the rapidly increasing interest in MRI-guided radiotherapy,74 it is foreseeable that analogous datasets could be released in the near future to aid in adaptive radiotherapy auto-segmentation applications.In 2019, the AAPM hosted a 2-part competition, titled the “RT-MAC” challenge, for auto-segmentation of radiotherapy-related structures using MRI scans.e levels . The cha75 The challenge utilized data from multiple Canadian and European medical institutions to provide participants with highly curated imaging and segmentation data. Participants were able to develop AI models, predominantly based on deep learning, to generate high-quality primary tumor segmentations on unseen test data. In the 2021 edition of the challenge, additional imaging data from a greater number of institutions were added to the training and testing datasets. Moreover, new tasks based on the prediction of progression-free survival were integrated into the challenge. While the official post-challenge analysis for the 2021 edition has not been made public yet, participants were shown to improve upon segmentation from the previous year’s challenge76 and demonstrate encouraging results to predict prognosis.78 A new edition of the challenge has been planned for 2022, which will include incorporating metastatic cervical lymph node segmentations and additional datasets from more institutions. The HECKTOR challenge is increasingly seen as a leader in current day radiation oncology related data challenges; we anticipate it will lead to important clinical innovations for translational AI approaches in coming years as its corresponding datasets continue to mature.More recently, the HEad and neCK TumOR (HECKTOR) challenge was established to benchmark the utility of computational methods using PET/CT imaging for head and neck cancer radiotherapy-related applications. Initiated in 2020 through the Medical Image Computing and Computer Assisted Intervention Society, the first edition of the challenge sought to develop automatic methods to segment primary gross tumor volumes in patients with oropharyngeal cancer .75 The c79 Subsequently, it is crucial to capture the biases in AI systems before they can be deployed in large-scale clinical settings. Unfortunately, most guiding principles for machine learning in healthcare applications do not directly address model fairness in detail.80 However, there is growing widespread interest in racial, gender, and socioeconomic disparities of AI-based healthcare algorithms.While AI holds immense promise in improving the radiation oncology workflow through public medical imaging datasets, a thorough understanding of the current limitations of existing AI approaches is crucial before their widespread implementation. One such limitation is algorithmic bias/fairness, which if severe enough could further inequity and disparities in patient care. Algorithmic bias is not unique to advanced machine learning approaches. For example, a landmark paper by Obermeyer et al. demonstrated a widely used commercial algorithmic risk score based on simple demographic factors severely underestimated the health needs of the sickest marginalized groups (black patients) by focusing on financial costs.81 and systemically.82 It is well documented that marginalized racial groups often receive inferior care compared to wealthy or white patients,83 so the potential amplification of inequality caused by AI software in radiation oncology is a significant concern. While racial disparities have been modestly investigated for healthcare in AI generally, specific mechanisms for the existence of these disparities in imaging data/models remains relatively unexplored. However, a recent study by Banerjee et al.84 demonstrated that standard deep learning models could predict self-identified race from medical images with high performance. Importantly, they showed that this ability was not due to imaging-related surrogate covariates for race. Moreover, the performance of models persisted over a wide spectrum of clinical applications and image modalities, suggesting a significant and prevalent problem that warrants further investigation. These results are important since they suggest AI can trivially predict race where clinical experts cannot, thereby limiting human oversight and leading to potential downstream disparities, particularly for minorities.Radiation oncology is not immune to biases in patient care, both at the level of the individuals,85In an ideal setting, machine learning models should be trained and evaluated on data that accurately represent real-world data. In designing public data challenges, these concepts should be considered in the curation of training and test sets. Since data-driven methods inherently recognize patterns in training data, any bias already present in the data will be propagated to downstream models. Naturally, all datasets at some level will contain biases inherently tied to the sampling procedures. Importantly, biased sampling may lead to inaccurate predictions in unseen evaluation data, as illustrated in 80 and these approaches should be subsequently implemented in imaging data for radiation oncology applications/data challenges. When curating datasets for data challenges or public dissemination, regardless of the target application, data on race, ethnicity, and socioeconomic status should also be collected and made available in order to assess their relationship to the underlying models where appropriate. Methods to circumvent algorithmic bias would include increases in model interpretability/explainability, either through inherently interpretable models or post-hoc techniques.86 Moreover, when developing and conceptualizing new AI models, it stands to reason that individuals with a vested interest in combating inequalities should be included in discussions to address potential sources and consequences of bias.87Recommendations for operationalizing fairness for AI in medical data have been previously suggested,In summary, we describe how radiation oncology has benefited from FAIR scientific data distribution principles and will continue to benefit in the coming years. Given increasing attention by governing institutions and collaborative efforts, dissemination of radiation oncology data through structured repositories and public data challenges have led to algorithmic development and advancement, particularly with respect to AI-driven clinical decision support tools. A variety of concerns still plague the public dissemination of radiation oncology data, namely proper protection of patient PHI, ensuring standardized data objects and nomenclature, addressing health equity concerns, and consolidation of individual components of scientific dissemination. However, the future of public data distribution remains bright, and is certain to lead to continued innovation and clinical impact within the radiation oncology community.


Because mitochondria play a crucial role in cellular homeostasis and energy balance, mitochondrial dysfunction is critically implicated in the pathobiology of various human diseases/conditions. Redox imbalance, abnormal mitochondrial biogenesis, dysregulated mitophagy, and mitochondrial DNA damage are important causes of mitochondrial dysfunction. Novel pharmacological interventions to improve mitochondrial function hold great promise for the development of future therapies. Research groups from different corners are paying their efforts to address this issue. In this special topic, research and review articles introduce some new insights and strategies which can be capitalized for future development in this field.Montecino-Garrido et al. investigated the impact of three triphenylphosphonium (TPP)-based compounds, including honokiol, lonidamine, and atovaquone on platelet function in vitro. They observed that TPP derivatives exhibited an insignificant effect on platelet activation and aggregation, making them a potential antitumor agent with low bleeding risk. However, further studies are imperative to validate whether the findings can be extrapolated to animal models and humans for future clinical use.While targeting mitochondrial dysfunction in cancer therapy, the use of mitochondria-targeted drugs has some risks, such as abnormal bleeding tendency, since platelet function relies on mitochondrial activity. To address this issue, in vitro has been explored by Xin et al. They found that MA-5 inhibited IL-1β-induced oxidative stress and protected chondrocytes by upregulating the SIRT3/Parkin-related autophagy pathway, suggesting that MA-5 could be further developed as a potential agent against osteoarthritis.Mitochondrial dysfunction is crucially implicated in the pathogenesis of osteoarthritis and regulation of mitochondrial homeostasis through pharmacological intervention may benefit patients with this disease. Provided that mitochonic acid-5 (MA-5) plays an important role in regulating mitochondrial energy metabolism and protecting against mitochondrial damage and activating mitophagy, the potential of MA-5 against IL-1β-induced inflammation in chondrocytes Rahman et al. provide an overview of the nephroprotective properties of SNPs, including berberine, betulinic acid, celastrol, curcumin, polydatin, resveratrol, and salidroside. SNPs have been demonstrated to be effective in treating kidney damage from oxidative stress and mitochondrial DNA damage as well as restoring mitochondrial biogenesis and dynamics in response to a variety of injury stimuli. Thus, such agents need to be acknowledged as multi-target treatments and prospective drugs to slow down the pathogenesis of renal complications, specifically those caused by mitochondrial dysfunction.Moreover, the development of kidney diseases, which has become a public health concern worldwide and is associated with serious clinical complications is also intrinsically linked to mitochondrial dysfunction. Despite the difficulties in drug discovery for kidney diseases, bioactive natural products and their sources have been pursued as a complementary therapeutic approach. Numerous studies have found that small-molecule natural products (SNPs) improve renal function and slow down the course of kidney diseases. In this review, Li et al. summarize that the natural substances isolated from herbal medicine are effective for the prevention or treatment of neurodegenerative disorders through the management of mitochondrial dysfunction with reduction of other side effects. Therefore, it is important to concentrate on the potential herbal therapeutics that can treat neurodegenerative diseases via inhibiting apoptosis by ameliorating mitochondrial dysfunction. Eventually, this will allow us to lay the groundwork for the development of herbal medicines that can treat neurodegenerative diseases targeting mitochondrial dysfunction.Neurodegenerative diseases are a group of neurological conditions that cause structural and functional disorders of the peripheral and the central nervous system which slowly break down over time. The three most frequently diagnosed neurodegenerative disorders are Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. Unfortunately, the number of people affected by neurodegenerative diseases is growing at an alarming rate. Despite this situation, the drugs currently on the market can rarely interfere with the progression of the disease, in addition to causing significant side effects, which have a heavy impact not only on patients but also on society as a whole. Mounting evidence shows that the emergence of neurodegenerative diseases is directly associated with mitochondrial dysfunction. Apoptosis can take place in nerve cells to varying degrees whenever control of mitochondrial homeostasis breaks down. In summary, we anticipate that this Research Topic will offer detailed settings of mitochondrial dysfunction-related disorders along with recent advances in mitochondria-targeted pharmacological options. Combining these studies will significantly aid in the development of effective therapeutic strategies for treating various diseases associated with mitochondrial dysfunction.


The facile formation of drug nanoparticles in injectable/ingestible oils, of water-soluble antiretroviral tenofovir disoproxil fumarate, using a novel nanoprecipitation is presented with studies showing drug release into relevant aqueous media. The facile formation of drug nanoparticles in injectable/ingestible oils, of water-soluble antiretroviral tenofovir disoproxil fumarate, using a novel nanoprecipitation is presented with studies showing drug release into relevant aqueous media. For regimens that require repeated dosing over very long timescales, daily oral doses of tablets, capsules or liquids serve as a reminder of disease and erode quality of life.3 In recent years, the benefits of LAIs for HIV have been demonstrated in therapy and pre-exposure prophylaxis. These formulations comprise nano-milled dispersions4 of poorly water-soluble antiretrovirals (e.g. cabotegravir or rilpivirine)5,6 that are intramuscularly administered and provide systemic drug exposure for >1 month; in the case of cabotegravir, up to three months of drug exposure within the therapeutic window has been achieved.Over the last 50 years, the use of long-acting depot injections (LAIs), delivered subcutaneously or intramuscularly, has improved patient adherence, clinical outcomes and maintenance of chronic conditions including schizophrenia, alcohol dependence, androgen ablation and contraception.7 The NRTI “backbone” drugs are water-soluble and, therefore, are not readily compatible with conventional nano-milling; depot injections of cabotegravir or rilpivirine would require daily supplementation of orally dosed NRTI tablets which effectively negates the benefits of their LAI formulations. Bimonthly rilpivirine/cabotegravir combination injections are in late stage development and have shown to be as effective as orally dosed combinations of cabotegravir, abacavir and lamivudine in suppression of HIV-1 viral replication;8 however, two-drug combinations are not currently a standard of care. Unfortunately, water-soluble NRTIs such as emtricitabine, tenofovir disoproxil fumarate (TDF), lamivudine and abacavir are not currently available as LAI formulations to combine with ongoing clinical opportunities such as the poorly soluble cabotegravir (integrase inhibitor) or rilpivirine (non-nucleoside reverse transcriptase inhibitor).LAIs for diseases such as HIV can change a patient's life by offering a small number of regular injections rather than daily combinations of different drug classes. There is, however, the need for combination therapies within HIV management that has added to the complexity for LAI approaches. Typically, HIV requires a minimum of two nucleoside reverse transcriptase inhibitors (NRTIs) plus an additional antiretroviral drug such as a protease inhibitor.i.e. release the desired drug compound) in vivo, has enabled injectable oil-solutions9 to be administered clinically and nano-milled dispersions to be formed in aqueous media when relatively high melting point solid prodrugs are synthesised;10 recently we have shown the benefits of long acting aqueous nanoparticulate antimalarial depot injections to prevent malarial infection in pre-clinical species11 and the use of emulsion templated freeze drying (ETFD)12,13 to form semi-solid prodrug nanoparticles where the resulting prodrugs have low melting points.14The formation of LAI formulations from water-soluble drugs is more challenging without significant chemical modification or added materials to alter solubility; both strategies lead to increased injection volumes to achieve adequate drug dosing. Injections of aqueous solutions may lead to very high local drug concentrations and “dose dumping” can lead to highly attenuated drug delivery durations. The formation of hydrophobic prodrugs, that are activated to parent compounds ,19,20 and controlled addition to water results in a dilution of the water-miscible “good” solvent regime and the formation of a “bad” solvent, or antisolvent, environment. During this process, dissolved materials nucleate, precipitate and form colloidal structures; in the presence of appropriate stabilisation, macro-phase separation may be avoided, and colloidal stability can be induced whilst the particles are still within the nanoscale. This has been successfully exploited at scale to form a range of products including β-carotene nanoparticles for health, food and veterinarian products.21 Recent reports of “inverse nanoprecipitation”22 have utilised the addition of aqueous solutions into water-miscible organic solvents, such as acetone, with subsequent crosslinking leading to hydrophilic nanoparticles. “Inverse flash nanoprecipitation” to encapsulate biologics has also been reported; in this process, an aqueous/solvent mixture (e.g. dimethyl sulfoxide) of the biologic and a charged amphiphilic block copolymer is rapidly mixed with an antisolvent such as dichloromethane (DCM) to precipitate the hydrophilic biologic with stabilisation derived from the hydrophobic segments of the block copolymer.23 To the best of our knowledge, no reported nanoprecipitations of organic compounds are completely anhydrous, and all of them employ water in either the solvent or antisolvent phase.Low-energy formation of nanoparticles has been successfully achieved by nanoprecipitation processes for many years.Several key considerations must be addressed to achieve the nanoprecipitation of water-soluble antiretroviral salts such as TDF  using an−1 and found to be substantially insoluble in peanut oil, sesame oil, soy bean oil and castor oil; sesame and peanut oils were selected due to their widespread use in depot injections and to introduce diversity of oil chemistry to this study. Dichloromethane was selected as the antisolvent due to its good miscibility with methanol, and a screening approach was used to study 52 potential commercial lipophilic stabilisers, such as glycerol monolinoleate , 24 The form 1 crystal structure of TDF shows an interaction of the protonated amino functionality with the associated fumarate, with significant changes occurring on heating.25 The potential for ion-exchange reactions to drive nanoprecipitation under anhydrous conditions was therefore studied in an attempt to establish conditions for precipitation during ion-exchange of TDF and AOT. As with recent reports of isothermal titration calorimetry (ITC) of aqueous mixtures of ammonium and sulfonate surfactants,26 complex formation was observed between AOT and TDF in water, with subsequent nanoparticle formation as determined by dynamic light scattering (DLS). After full titration of the interaction, a particle dispersion with a z-average diameter (Dz) of approximately 270 nm was observed (TDF was found to be soluble in methanol (MeOH) to a concentration of >80 mg mLobserved .27 suggested a multiple site mechanism and was possibly complicated by the measured enthalpies being derived from simultaneous complexation and nanoprecipitation.The ITC studies were highly reproducible and clearly indicated interactions occurring during mixing of AOT and TDF in water; however, model fitting .Having observed nanoparticle formation in water, a strategy for nanoprecipitating TDF in the presence of AOT under anhydrous, but polar, solvent conditions and readily resulting in an injectable oil dispersion of the water-soluble NRTI was devised . TDF wase needle  and oil Dz values of approximately 540 nm . With the caveat that these values are potentially not fully representative, the presence of the different oils appeared to have a relatively minor influence on the observed Dz values, even though nanoparticle formation occurs prior to oil addition  such as sesamin, sesamolin, episesamin, sesamol and sesaminol , or the use of oil carriers. The approach presented here offers a rapid and facile method to create injectable oil dispersions that release TDF. This is a critical drug that is widely used orally across the world and systemic pharmacokinetics and pharmacodynamics have been clinically established. As such, repurposing to long acting depot injections has a clear development and regulatory pathway. Importantly, the strategy presented here creates a new administration format without the need for new chemical entity synthesis and the clinical and financial complexities this would create. Additionally, sterile freeze drying36 (often termed lyophilisation) is a well-known and common manufacturing process for injectable products (including vaccines) and the approach here should be able to be scaled using standard procedures as a wide range of solvents can be removed using freeze-drying to prescribed acceptable limits.37 Many mechanisms may dictate the actual observed release rates in vivo, including the formation of a granuloma after macrophage infiltration,38 and “flip-flop” kinetics (circulating drug concentrations determined by release from the injection site and not clearance/elimination mechanisms) that have been observed in antipsychotic LA medication.39 We understand that this demonstration is the first step towards the establishment of clinical value and further work is required, including determination of appropriate in vivo release kinetics, studying long term stability, tolerability, in vivo pharmacokinetics and injection site toxicology; however, we have demonstrated a route which may unlock future developments and stimulate the progression of novel long acting NRTI therapies.In conclusion, a considerable clinical need to adopt long-acting therapeutics within global HIV management exists; however, the lack of a wide range of long-acting candidates which encompass well established drug classes, such as NRTIs, may delay full realisation of patient benefits and the creation of a pipeline of combination options for therapy. Water-soluble drugs are not readily converted to long-acting depot formulations without either prodrug approaches, polymer-controlled release strategies (The authors are inventors on patents that seek to protect TDF nanoprecipitation to allow charitable access. C. F. reports serving as a paid consultant for Cipla Pharmaceuticals, Janssen Pharmaceuticals, Merck Laboratories, Mylan Pharmaceuticals, and ViiV Healthcare, and received research grant support from Gilead Sciences paid to his University. AO and SR are directors of Tandem Nano Ltd. AO and SR have received research funding from ViiV Healthcare and AstraZeneca; AO has acted as a personal consultant to Gilead, ViiV Healthcare and Merck. The authors declare no competing interests.NA-001-C9NA00529C-s001


Metabolic reprogramming is one of the hallmarks of cancer. As nutrients are scarce in the tumor microenvironment (TME), tumor cells adopt multiple metabolic adaptations to meet their growth requirements. Metabolic reprogramming is not only present in tumor cells, but exosomal cargos mediates intercellular communication between tumor cells and non-tumor cells in the TME, inducing metabolic remodeling to create an outpost of microvascular enrichment and immune escape. Here, we highlight the composition and characteristics of TME, meanwhile summarize the components of exosomal cargos and their corresponding sorting mode. Functionally, these exosomal cargos-mediated metabolic reprogramming improves the "soil" for tumor growth and metastasis. Moreover, we discuss the abnormal tumor metabolism targeted by exosomal cargos and its potential antitumor therapy. In conclusion, this review updates the current role of exosomal cargos in TME metabolic reprogramming and enriches the future application scenarios of exosomes. Extracellular vesicles (EVs) are nanoscale cellular secretions that act as key mediators in many pathological/physiological processes , 2. AccoExosomes are nanoscale bilayered vesicles that are actively released by cells into the extracellular fluid and carry a variety of genetic materials . They arExosomes carry host cell-derived proteins, lipids, non-coding RNA (ncRNA) and metabolites. These bioactive substances can be involved in regulating intercellular communication between tumor cells and TME and mediating TME heterogeneity , 18. ExoTME is a "sanctuary" for tumor cells. Previously, the primary goal of human tumor treatment was the direct elimination of tumor cells. With the introduction of the TME concept, tumor is no longer composed of aggregates of tumor cells, but of surrounding cells and non-cellular components . TME notTME consist of tumor cells, resident and recruited host cells (mainly CAFs (cancer-associated fibroblasts) and immune cells), secreted products of the above cells , vasculature and ECM (extracellular matrix) , 25. SpeCAFs are one of the main cell types involved in ECM remodeling in TME, with highly heterogeneity . It is nA previous report has proved the heterogeneity of CAFs. Researchers have characterized four CAF subtypes with different characteristics and activation levels in breast cancer. Costa et al. distinguished CAF into different subtypes  based on the expression levels of CD29, FAP, α-SMA, PDGFRb, FSP1 and CAV1, among which CAF-S1 and CAF-S4 fibroblasts expressed a-SMA and were regarded as myofibroblasts . In addiTumor-associated macrophages (TAMs) are the major infiltrating immune cells in TME, with high heterogeneity and plasticity . In the T cells recognize antigens on MHC molecules on the surface of antigen-presenting cells (APCs) to regulate tumor immune responses via the T cell receptor (TCR) . T cellsGenerally speaking, in view of the cytotoxicity of CD8 + T cells, the increased number of CD8 + T cells in TME is often associated with a good prognosis –53. HoweTumor growth and metastasis are inseparable from adequate oxygen and nutrient supply, which all depend on the tumor vasculature. Compared with normal tissues, the tumor vasculature exhibits tortuosity and dysfunction, which is reflected in the heterogeneous permeability . NormallTumor vasculature mainly includes vascular sprouting, angiogenesis and vascular mimicy , 65. ThrTumor lymphatic vessels belong to the tumor vasculature, and their main function is to remove the interstitial fluid (ISF) formed by capillary filtrate and tissue immune surveillance . LymphatThe role of tumor lymphatic vessels in tumor progression has advantages and disadvantages. Certain metastatic tumors can penetrate into the lymphatic vessels and cause distant metastasis, such as breast cancer, nasopharyngeal cancer, and prostate cancer –79. HoweAdipocytes are one of the stromal cells in the TME. As a storage site for triglycerides, adipocytes not only participate in the process of energy metabolism, but also secrete various metabolites to regulate TME . There aAdipocytes in TME can be transformed into cancer-associated adipocytes (CAAs) after communicating with tumor cells. Compared with mature adipocytes, CAAs have smaller size, irregular shape and dispersed lipid droplets . In addiStellate cells originate from mesothelial and submesothelial cells with highly plasticity. The common stellate cells in the TME include hepatic stellate cells (HSCs) and pancreatic stellate cells (PSCs). Resting stellate cells are rich in vitamin A, accompanied by highly expression of desmi and glial fibrillary acidic protein (GFAP). Conversely, activated stellate cells lost vitamin A, accompanied by highly expression of α-SMA , 98. ExoECM is composed of a variety of proteins and macromolecules, including collagen, glycoprotein, elastin, fibronectin and proteoglycan, which are mainly secreted by CAFs . ECM hasECM has complex mechanical behavior, and the viscoelasticity and tension of the matrix can promote the stemness, metastasis and drug resistance of tumor cells , 106. ThHypoxia is a hallmark of TME. Hypoxia or inadequate oxygenation is a key factor in the difficulty of eradicating tumors and also predisposes tumor cells to treatment resistance . Causes Hypoxia can regulate exosome release. Tumor cells are more likely to release exosomes in the hypoxic TME, which is related to the activation of the small GTPase Rab27a, a major regulator of exosomal synthesis, regulated by HIF-1α . It has Exosomes are small extracellular vesicles (sEVs) with a diameter between 40-160 nm . It origThe specific mechanism of exosomes formation is still unclear, and the endosomal sorting complex (ESCRT) required for transportation is currently recognized as a classic pathway. The exosomes biogenesis pass through the stages of early sorting endosome, late sorting endosome, ILV and multivesicular body (MVB). Finally MVB is subsequently degraded by lysosomes or fused with the plasma membrane to release its contents, including exosomes . The sorSeveral proteins are related to the biogenesis of exosomes, such as Rab GTPase and ESCRT protein. Exosome surface proteins including transmembrane 4 superfamily , lipid raft protein (flotillin-1), and Ceramide are often used as biomarkers for exosomes . Some prBased on the potentials of exosomes in the diagnosis and treatment of tumors, there is an urgent need for high-efficiency isolation of exosomes . DensityExosome cargos are the core components that confer biological effects on exosomes. Nearly 100,000 proteins and over 1,000 lipids have been reported to be associated with exosomes . NucleicThe role of exosomal RNAs in tumors has been widely reported, mainly including mRNAs and ncRNAs. Regarding functional exosomal mRNAs, an early study reported that exosomal mRNAs can complete translation in receptor cells . RecentlExosomal DNA mainly includes gDNA and mtDNA . The TMEThe lipid cargos of exosomes mainly include sphingolipids, cholesterol, phosphatidylserine, saturated fatty acids, and ceramides, which are mainly associated with exosome biogenesis . The bilSome biologically active lipids are important cargos for exosomes to perform their biological functions. LTB4 is packaged and released in exosomes . This LTProteins are important cargos of exosomes. Protein cargos from different sources of exosomes are heterogeneous, but the proteins involved in exosome biogenesis, exosome content sorting and exosome release are invariant. These protein cargos are as described in Sect. 2.2.2. Exosomal heterogeneity is mainly reflected in protein cargos with signal transduction function and enzyme cargos with biological activity . The maiIn general, nucleic acids can be sorted to exosomes through the interaction of RNA-binding proteins or lipid rafts in MVB . Some exInitially, four mechanisms of miRNA sorting to exosomes were proposed. The neural sphingomyelinase 2 (nSMase2), heterogeneous nuclear ribonucleoprotein (hnRNP), uridylation at 3’ends and argonaute 2 (Ago2) are involved in this process –176. RecSome lncRNAs are also found in exosomes, probably by forming lncRNA–RBP complexes . AlthougCircRNA is a component of exosomal ncRNAs, and different circRNAs were found in exosomes originating from different cells, indicating that the sorting process of exosomal circRNAs is selective . SNF8, aThe sorting mechanism of exosomal DNA is still unclear. Certain physiological pathways may be involved in this sorting process. MtDNA sorting to exosomes may be related to the endosomal pathway , and gDNTypically, proteins can enter cells together with cell surface proteins by endocytosis and invagination of the plasma membrane . The proSome non-canonical mechanisms are also involved in the regulation of exosomal protein sorting. A recent study shows that proteins containing the KFERQ pentapeptide sequence can be sorted to exosomes by a process dependent on the membrane protein LAMP2A, a novel mechanism independent of ESCRT , 184. EpExosomes contain intact metabolites (mainly lipid metabolites) with characteristic of the host cells . For exaMetabolic abnormalities are one of the hallmarkers of tumor cells, which are metabolically reprogrammed to meet their rapid proliferation requirements . Given tGlucose is the main source of energy for cellular metabolism and biosynthesis. Glucose metabolism includes the glycolytic pathway, the pentose phosphate pathway (PPP), the serine synthesis pathway (SSP) and the tricarboxylic acid (TCA) cycle pathway . Tumor cHeterogeneity of TME is associated with glucose metabolism. It was shown that hypoxic conditions in TME promoted HIF-1-induced glycolysis . HIF-1 pAbnormal fatty acid (FA) metabolism is particularly noteworthy, as it is structural components of the membrane matrix and important secondary messengers that can serve as a fuel source for energy production . In mammGlucose or glutamine in tumor cells is oxidized or reversibly carboxylated by pyruvate in the TCA cycle to generate citrate, respectively . CitrateFA contributes to the remodeling of TME. Arachidonate is an important class of bioactive lipid molecules, such as prostaglandins, leukotrienes and ω-hydroxylase , 218. ThAmino acids are one of the raw materials for cellular synthesis of biomolecules, such as proteins, lipids, and nucleic acids. In contrast to normal cells, tumor cells require large uptake of amino acids for malignant development, as well as other amino acids to provide a source of carbon and nitrogen . ReprogrGlutamine (Gln) is one of the most abundant non-essential amino acids in the body, a recent study has shown that it is Gln that is the highest nutrient intake by cancer cells . GlycolyDisturbance in amino acid metabolism can remodel the immune microenvironment of tumors. It has been shown that Glutamine metabolism can regulate the immunosuppressive function of myeloid-derived suppressor cells (MDSCs) . GlutamiTumor occurrence not only requires the metabolic reprogramming of cancer cells, but also the metabolic reprogramming of non-cancer cells in TME also participates in tumor progression. Exosomal cargos, as a type of intercellular communication messenger, mediate the metabolic regulation of different types of cells in TME , 270. AfCAFs are the most common cell type in TME and the major cells producing ECM. The metabolic reprogramming of CAFs is beneficial to the growth and metastasis of tumor cells. Studies have reported that exosomal miRNAs are involved in the regulation of glucose and lipid metabolism of CAFs , 238. ThExosomal cargos are involved in the abnormal metabolism of ECs to promote tumor angiogenesis. A study has shown that endothelial progenitor cell-derived (EPC-derived) exosomal miR-210 can reduce ROS production and promote ATP production in ECs, contributing to the maintenance of ECs stability . Acute mTEX-mediated metabolic reprogramming is associated with tumor immunity. It was found that purine metabolites (adenosine and inosine) enriched in TEXs may promote immune escape, and the levels of purine metabolites in circulating exosomes may suggest clinical stage and lymph node metastasis in HNSCC patients –277. T l2BR-dependent signal transduction [Tumor antigen presentation by dendritic cells (DCs) is the initiating step of the immune response in vivo . A studysduction . Hypoxiasduction . PKM2 issduction . ConsideAdipocytes in the TME can participate in tumor progression through their secretory functions, and exosomal cargos-mediated metabolic reprogramming of adipocytes can induce adipocytes to assume the CAAs phenotype. It has been shown that pancreatic cancer-derived exosomes can modulate lipid metabolism in adipocytes, in which TG is significantly down-regulated. This may be related to the increased expression of IL-6 and the promotion of lipolysis . Breast Activation of stellate cells is commonly seen in liver metastasis of colorectal cancer (CRLM) and pancreatic ductal adenocarcinoma (PDAC). As part of the stroma in TME, activated stellate cells tend to have the characteristics of CAFs that facilitate the composition of the pre-metastatic niche . A numbeIt is well known that CAFs and CAAs are the main cells that produce ECM. Exosome-mediated metabolic reprogramming can activate CAFs and CAAs in TME, which inevitably leads to an impact on ECM. Colorectal cancer (CRC) cell-derived exosomes enhance the secretion of ECM  by CAFs based on the regulation of metabolic reprogramming . In addiExosomal cargos-mediated metabolic reprogramming is bidirectional between tumor cells and TME. Tex induces non-tumor cells in TME to exhibit a malignant phenotype, and these cells can secrete exosomal cargos to regulate metabolic reprogramming of tumor cells and accelerate tumor progression. In this way, a malignant positive feedback regulation pattern is formed between TME and tumor cells.Fibroblasts in the TME are activated by stimulatory signals into CAFs and remodel the TME through their secretory functions  . The exoExosomal miR-522 secreted by CAFs targets ALOX15 to activate the USP7/hnRNPA1 pathway, blocking lipid-ROS accumulation and inhibiting the ferroptosis of gastric cancer cells , 283. ExMacrophages are the most numerous white blood cells in TME, and TAMs play a key regulatory role in the occurrence and development of tumors. Exosomal ncRNAs secreted by TAMs can affect the metabolic state of tumor cells . In recePrevious studies on exosomes and stromal cells in TME have mostly focused on CAF, but little attention has been paid to CAA, especially the exosomes secreted by CAAs and their functions. In fact, adipocytes, as a kind of secretory cells, can not only secrete metabolites such as leptin and fatty acids directly, but also secrete bioactive cargos in the form of exosomes, which can modify the metabolism of tumor cells. Advanced melanoma cells are in direct contact with subcutaneous adipocytes. Adipocytes can directly transfer fatty acids into melanocytes, resulting in increased lipid content and abnormal lipid droplet formation. . In addiAs oncology researches have become more detailed, the understanding of tumors has transitioned from genetic diseases to chronic metabolic diseases. The aim of anti-tumor therapy has also gradually evolved from targeting molecular biomarkers to targeting tumor metabolic pathways. The development of single-cell sequencing technology has led to a more comprehensive understanding of TME and the discovery that metabolic reprogramming exists not only in tumor cells, but also in other components of TME  , 288. ThThe most concise way to target exosomal cargos-mediated reprogramming of TME metabolism for tumor treatment is to inhibit exosomal secretion. PKM2 is a key enzyme in glycolysis and is involved in the secretion of exosomes in addition to directly reshaping cellular metabolism through the OXPHOS and Warburg effects . During Exosomal ncRNAs, as common cargos in TME, play an important role in the metabolic reprogramming of TME. It was found that oxaliplatin-resistant CRC cells-derived exosomal circRNA ciRS-122 was delivered to sensitive cells, which enhanced glycolysis and chemoresistance in sensitive cells via miR-122/PKM2 signaling axis . DevelopBenefiting from the targeting and biocompatibility of exosomes, exploitation of exosomes as carriers for drug delivery targeting tumor metabolism has a bright future. Although there are currently no engineered exosomes to directly target various metabolic pathways in the TME, exosomes can be combined with classical drugs or modalities as an adjuvant therapy to improve the efficacy of anti-tumor therapy. It has been shown that combination of exosome-mediated cPLA2 siRNA and metformin reduces the growth of glioblastoma xenografts by impairing the energy metabolism of mitochondria . PhotodyThis review highlights the multiple roles and molecular mechanisms of exosome-mediated metabolic reprogramming in TME reprogramming. The field of exosomes (or EVs) has made great progress in recent years benefiting from technological breakthroughs in isolation, purification, in vivo tracking and content analysis . This haWe describe the cell–cell communication mediated by exosomal cargos in TME and how these cargos are sorted to exosomes. Along with technological advances, the way of sorting various types of cargos into exosomes (or specific subtypes of EVs) is the bottleneck for further development in the field of exosomes. The bioactive cargos are the key to the function of exosomes. In addition to the cell-derived exosomal cargos in human TME, milk exosomes have a bright future as an oral drug delivery system, due to the biocompatibility of milk exosomes with exogenous cargos .The heterogeneity of TME promotes tumor proliferation, metastasis, stemness and drug resistance. We summarized the main components and characteristics of TME, and highlighted the role and mechanism of exosomal cargos-mediated metabolic reprogramming in the heterogeneity of TME. Improving TME becomes an emerging strategy for anti-tumor treatment. The plasticity of tumor metabolism is both promising and challenging. Given the complex composition of TME, targeting one component for metabolic remodeling is difficult, and we need to consider more whether altered metabolism has the same therapeutic effects on multiple components of TME. Application of tumor organoid platforms to exosomes may be used to simulate the effect of exosomes on TME.Exosomal cargos-mediated abnormalities metabolism in TME remains to be extensively studied. Considering the widespread of exosomal cargos, exploring the molecular mechanisms of exosomal cargos-induced metabolic reprogramming is beneficial for tumor precision treatment. As more and more biologic companies are entering the exosome field, the development of exosome-based drug delivery modalities to reshape metabolism in TME is promising. Combining chemotherapy, radiotherapy or targeted therapy with novel metabolic therapies may be the future trend in tumor treatment.


Rotaviruses (RVs) are endemic in swine populations, and all swine herds certainly have a history of RV infection and circulation. Rotavirus A (RVA) and C (RVC) are the most common among all RV species reported in swine. RVA was considered most prevalent and pathogenic in swine; however, RVC has been emerging as a significant cause of enteritis in newborn piglets. RV eradication from swine herds is not practically achievable, hence producers’ mainly focus on minimizing the production impact of RV infections by reducing mortality and diarrhea. Since no intra-uterine passage of immunoglobulins occur in swine during gestation, newborn piglets are highly susceptible to RV infection at birth. Boosting lactogenic immunity in gilts by using vaccines and natural planned exposure (NPE) is currently the only way to prevent RV infections in piglets. RVs are highly diverse and multiple RV species have been reported from swine, which also contributes to the difficulties in preventing RV diarrhea in swine herds. Human RV-gut microbiome studies support a link between microbiome composition and oral RV immunogenicity. Such information is completely lacking for RVs in swine. It is not known how RV infection affects the functionality or structure of gut microbiome in swine. In this review, we provide a detailed overview of genotypic diversity of swine RVs, host-ranges, innate and adaptive immune responses to RVs, homotypic and heterotypic immunity to RVs, current methods used for RV management in swine herds, role of maternal immunity in piglet protection, and prospects of investigating swine gut microbiota in providing immunity against rotaviruses. Rotavirus genus in the Reoviridae family. The RV genome is approximately 18,500 bp in size and consists of 11 segments of dsRNA encoding six structural proteins  and five non-structural proteins (NSP 1- NSP5/6)  genotype (previously identified as a G4P[7] strain) significantly neutralizes different human G9 strains in vitro, including 116E, R44, R143, US1205, INL1, and BD524 originating from different countries . Antisern = 32) to neutralize RVs in human IECs, including ileal enteroids and HT-29 cells [A study reported that infection-induced heterotypic immunoglobulins (Igs) mainly target VP5* region of VP4 . Heterot29 cells . Most  to RVs in swine stems from the studies where gnotobiotic piglets were used as a model to study immune responses to human rotaviruses. There is a necessity to generate adaptive immunity data against porcine RVs to understand the dynamics of antibody-based and T-cell-based immunity against RVs, correlates of protection against RVs, the effects of co-infection of different RV species in mediating immunity against each other, and the level of cross protection conferred by different RV G and P-types.Since piglets get infected with RV at birth, it is impractical to vaccinate the piglets to the RV field strains. The best approach is to immunize gilts before farrowing to boost their antibody levels, which can be passively transferred to the piglets through lacteal secretions. Boosting the lactogenic immunity appears to be the most efficient way of providing RV immunity to the piglets until the piglets reach an age at which they are less susceptible to rotaviral infections. Passive immune protection occurs in the form of high IgG antibodies in colostrum and high secretory IgA (sIgA) antibodies in colostrum and milk. In particular, sIgA antibodies play a major role in preventing RV infection at the gut mucosal level . IgG andIgA is the longest-lasting Ig present in lactating sows, but antibodies are typically strongly protective for only two weeks after farrowing . Levels Two modified live virus (MLV) vaccines, ProSytstems Rota and ProSystems RCE, developed by Merck Animal Health, are commercially available for use against RVA in swine. However, field testing and usage data for both of these vaccines is scarce and hence a true estimate of their efficacy cannot be established. Diversity of RVA strains other than the vaccine strains co-circulating in swine farms may assist RVAs to escape vaccine-derived immunity. Despite being the most common cause of rotaviral diarrhea in piglets less than 1 weeks of age, no vaccine is available for RVC due to challenges of adapting RVC to cell culture. However, recently a vectored vaccine platform known as Sequivity has been introduced by Merck animal health for use in pre-farrow gilts/sows against RVs. Sequivity is an RNA particle (RP) vaccine based on farm-specific VP7 sequences of RVA and RVC. Early trials of the Sequivity RP vaccine for RVs showed that RP vaccine yielded lower mortality and higher weight gain than natural planned exposure (NPE) , but morLack of updated strains in the current RVA vaccine and absence of modified live virus (MLV) vaccines against RVC have prompted swine producers to mimic natural RV infection in gilts or sows in the form of “feedback” or “natural planned exposure (NPE)”, which contains RV-infected material. Gilts or sows are fed NPE to stimulate maternal immunity and to provide lactogenic immunity to piglets. NPE method involves feeding live farm-specific RV strains mixed with feed a few weeks prior to farrowing. Using NPE precludes the need to generate RVB and RVC in vitro and hence is currently the only method available to prevent RVB and RVC infections in swine farms. However, there are some disadvantages of using NPE at swine farms. One of the major concerns is the possibility of introducing unwanted viral and bacterial pathogens in the farrowing room. Stimulating high levels of passive immunity without introducing RV particles into the farrowing room is difficult since sows can have subclinical RV infections yet shed high amounts of virus into the environment. An optimal NPE dosing strategy has not been determined or standardized across the industry, making it difficult to know what strategies may be most effective in production settings. Another major challenge to create NPE material with high RVC viral load. Attempts have been made to determine the best time for exposing sows to the feedback material. NPE at 5, 4, and 3 weeks prior to farrowing is considered most successful in preventing pre-weaning RVA shedding in piglets. Three doses of NPE induced the highest level of RVA and RVC shedding in gilts following NPE and led to the least shedding and best performance in piglets . HoweverThere are several areas of future research in the field of immunity against porcine rotaviruses, which include: (1). Use of swine enteroids to propagate RVC and other difficult to grow RVs. Successful propagation of RVC will pave the way for the development of modified live virus (MLV) vaccines against RVC; (2). Although NPE is the most widely practiced method to stimulate maternal against RVA and RVC, lack of serological assays to assess antibody response to NPE or natural RV infection makes it difficult to assess true efficacy of NPE protocols. Hence, it is recommended to develop genotype-specific RVA and RVC-specific indirect ELISAs to measure antibody response to RVs in swine; (3). Finally, NPE material administered to gilts prior to farrowing contains both RVA and RVC genotypes. However, we do not know whether both of these viruses affect each other’s ability to colonize swine gut, and hence results in varied immune response. It would be interesting to investigate the differences in gut colonization capability and antibody responses to RVA and RVC in an individual and co-infection model.The gut microbiota helps maintain normal functioning of the intestinal mucosal barrier and stimulate host immune response. Recent studies suggest that gut microbiota also play a crucial role in the regulation, elimination, and potentiation of infectious diseases. In pigs, Bacteroidetes and Firmicutes are predominant phyla of gut microbiota regardless of age and breed . Normal Escherichia coli and Serratia) in Pakistan and an increased abundance of Streptococcus bovis and decreased abundance of Bacteroidetes in Ghana [Recently, changes in gut microbiome composition have been correlated with improved protection against viral diseases such as porcine epidemic diarrhea virus (PEDv) , human Rin Ghana ,152. In in Ghana . The pooBifidobacterium and Lactobacillus spp. were found to significantly reduce the duration of RV induced diarrhea in infants [Lactobacillus rhamnosus GG (LGG) and Escherichia coli Nissle (EcN) colonized gnotobiotic pigs [E. coli Nissle 1917 (EcN) was reported to mediate enhanced protection against human rotavirus (HRV) compared to Lactobacillus rhamnosus GG (LGG) in a gnotobiotic piglet model [Bifidobacterium lactis and Streptococcus thermophilus failed to decrease the duration of RV-induced diarrhea in infants [Probiotics have been used to improve immune response to RV vaccines in humans with varied success . Bifidob infants . Both pr infants . Rice brtic pigs . Rice brtic pigs . E. coliet model . In thiset model . A combiet model . In contet model . In anot infants . The the infants . Few studies have investigated an association between Vitamin A and immune response to human rotavirus vaccines in gnotobiotic piglet model ,161,162.The probiotic based studies have failed to address the key question of whether there is a causal association between gut microbiome and RV vaccine immune response. Recently, malnutrition was suggested to reduce the protective efficacy of oral live attenuated human RV vaccine (attHRV) in human infant fecal microbiota (HIFM) gnotobiotic piglet challenge model . Four grGut microbiome modulation using narrow spectrum antibiotics has been reported to influence the response to oral RV vaccine in humans . In thisTo conclude, human data supports a link between microbiome composition and oral RV immunogenicity. However, the association between gut microbiome and porcine rotaviruses has not been studied. Gnotobiotic piglets, because of their close anatomical and physiological resemblance to human infants, have been utilized as models to investigate the effects of nutritional deficiencies, RV infection, and vaccine efficacy in humans ,166,167.Rotavirus infections are endemic in swine herds, and their elimination from swine population is not pragmatic. Moreover, huge genetic diversity and high potential for reassortment adds to the difficulty in developing effective prevention and control strategies. This review mainly focuses on genetic diversity among porcine RVs, immune response against RVs, current methods to stimulate maternal immunity in soon to farrow sows, and the role of the gut microbiome in modulating immune response to RVs. Based on the literature reviewed, the following areas of research have been identified as most important to swine RVs: (1). Routine surveillance of clinical samples from swine herds is needed to identify the effectiveness of current RVA vaccines and emergence of new genotypes due to immune pressure created by use of RVA vaccines. Surveillance data will also help in developing vaccines which are based on prevalent G and P-types in a particular area; (2). The potential of swine enteroids cell cultures to study RVC replication needs to be investigated. Lack of an established cell culture system for RVC hampers the development of an effective RVC vaccine; (3). There is a lack of serological assays to assess antibody response to NPE or natural RV infection which makes it difficult to assess true efficacy of NPE protocols. Earlier, real-time PCR-based assays were used to determine fecal RV shedding as an indirect measure to estimate RV replication and immune response in swine. Hence, it is imperative to develop genotype-specific RVA and RVC specific indirect ELISAs to measure antibody response to RVs in swine; (4). The possibility of a viral vector-based vaccines simultaneously expressing both RVA and RVC protective antigens (VP7 and VP4) needs to be explored for use in swine. This approach could circumvent the need of adapting RVC to cell culture; (5). Currently, NPE material administered to gilts contain both RVA and RVC, but no information is available regarding differences in their capability to colonize swine gut. Hence, it would be interesting to study gut colonization and antibody responses to RVA and RVC in an individual and co-infection swine model; 6. Another area of importance is to characterize gut microbiome changes in swine due to RVs particularly against RVC. Probiotics which reduce the gut colonization by RVCs could be fed to the neonatal piglets to prevent RVC infection in piglets immediately after birth.


Chronic low-grade inflammation can be associated with obesity or subclinical mastitis (SCM), which is associated with poor infant growth in low- to middle-income country settings. It is unknown what physiological mechanisms are involved in low milk supply, but our research group has shown that mothers with low milk supply have higher inflammatory markers. Studies investigating oxylipin signaling have the potential to help explain mechanisms that mediate the impacts of inflammation on milk production. Animal studies have reported various elevated oxylipins during postpartum inflammation, mastitis, and mammary involution in ruminant models. Several investigations have quantified oxylipins in human milk, but very few studies have reported circulating oxylipin concentrations during lactation. In addition, there are technical considerations that must be addressed when reporting oxylipin concentrations in human milk. First, the majority of milk oxylipins are esterified in the triglyceride pool, which is not routinely measured. Second, total milk fat should be considered as a covariate when using milk oxylipins to predict outcomes. Finally, storage and handling conditions of milk samples must be carefully controlled to ensure accurate milk oxylipin quantitation, which may be affected by highly active lipases in human milk. The numerous benefits of breastfeeding for infant health have been well-documented and include decreased risk of necrotizing enterocolitis in preterm infants as well as decreased risk of otitis media, acute diarrheal disease, and asthma in all infants ,2. GrowiDespite the known benefits of breastfeeding, many families struggle to meet their breastfeeding goals, and only 25% of American mothers meet the recommendations of exclusive breastfeeding for 6 months and continued breastfeeding through 1–2 years of age . In the Oxylipins are a diverse class of enzymatically produced specialized signaling molecules derived from polyunsaturated fatty acids (PUFA). Both omega-3 and omega-6 oxylipins are involved in the initiation and resolution of inflammatory processes  and haveStaphylococcus bacterial species. One of the most profound outcomes of mastitis is a significant drop in milk production, leading many mothers to supplement with infant formula or stop breastfeeding earlier than intended [Inflammatory breast conditions during lactation are common, especially in the first few weeks after birth, and can include milk stasis, mastitis, and breast abscess. Mastitis occurs in up to 33% of lactating women in some populations , and is intended .Traditionally, mastitis is considered an acute condition to be treated by improved emptying of milk from the breast, rest, fluid intake, non-steroidal anti-inflammatory medications, and sometimes antibiotic treatment . HoweverIn high-resource settings, repeated infection resulting in chronic inflammation is rare, but another source of low-grade chronic inflammation, obesity, is common. Mothers with obesity are less likely to meet breastfeeding recommendations and achieve their breastfeeding goals ,36. ObesOur group has developed a conceptual model linking inflammation to low milk supply . In thisEp) pathways are involved in the inflammatory response in complex and diverse ways [Ep pathway have multiple beneficial effects including anti-inflammatory, anti-hypertensive, and analgesic effects [Ep pathways, since these pathways represent the primary knowledge gaps in the literature and produce the precursors of SPMs. Oxylipins referred to in this narrative review are listed by parent fatty acid in Given this conceptual model, proper resolution of the inflammatory response following injury or infection is vital to ensure energy and substrate availability for milk synthesis. Oxylipin products of the cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P 450 epoxygenase -DiHOME showed the opposite pattern and 13-oxo-ODE decreased steadily across lactation in dairy cows . In contEp pathway oxylipins associated with involution during the dry-off period in dairy cows [Ep pathway oxylipin, 9(10)-DiHOME [The linoleic acid product of the 15-LOX activity, 13-HpODE is elevated in mastitis and can stimulate apoptosis in bovine mammary epithelial cells, while 13-HODE and 13-oxo-ODE have a neutral or possibly anti-inflammatory effect ,74. Putmiry cows . Mavangiiry cows . Postpar)-DiHOME . Taken t)-DiHOME . In addi)-DiHOME ,23, but )-DiHOME .Both obesity and SCM are examples of commonly occurring conditions associated with chronic low-grade inflammation. Currently, there is a lack of evidence for the expected role of circulating and mammary oxylipin signaling in mediating the effects of low-grade inflammation on lactation. However, at least one study has reported oxylipin profile differences with obesity during pregnancy , and milTotal milk fat content does not appear to be altered with SCM , but sevThe current evidence suggests that oxylipins have a strong potential to be potent mediators of inflammatory processes in the human mammary gland during lactation. However, several important large gaps remain in the current literature. As outlined before, there is a lack of studies that report circulating maternal oxylipins during lactation, which could elucidate the role of systemic inflammatory signals in mammary gland inflammation and milk synthesis. In addition, there is a lack of mechanistic studies linking chronic low-grade inflammation to milk synthesis. For example, although LPL is highly expressed in the mammary gland , data arAnother gap in the current literature available on milk oxylipins is the lack of data in the esterified pool. Milk contains abundant esterified fatty acids and oxylipins in the milk fat globule triglycerides ,53. HoweThe total milk fat concentration;Potential matrix effects of milk affecting the extraction efficiency;High lipase activity in human milk.There are several important technical factors to consider when measuring oxylipins in milk, including: When reporting milk fatty acid profile, data are usually presented as the weight% of total fatty acids. This is partly due to the fact that absolute concentration is mostly determined by the total milk fat content of milk, which is highly variable . It is lA second technical concern is low extraction efficiency, as observed by Gouveia-Figueira and colleagues. In their report, they found that using protocols optimized for detecting oxylipins in plasma resulted in significantly lower extraction efficiencies in human milk compared with other types of biological samples . Some poAt least two studies have reported that sampling and processing conditions  of human milk change oxylipin concentrations profoundly ,57, evenOur research group had access to milk samples from a case–control study  of low mIt is possible that increased oxylipin concentration with thawing is due to autoxidation of the milk PUFA. We examined if there was a difference between the number of freeze–thaw cycles and 9-HETE concentrations, a marker of autoxidation. In our data, there was no difference in 9-HETE concentrations by freeze–thaw cycles, suggesting that autoxidation is not the most likely explanation H. An altEp enzymes incorporate oxygen atoms into non-esterified PUFA. Therefore, lipases increase the potential substrate for these oxylipin-producing enzymes to act upon. Human milk is very high in lipase enzymes, particularly LPL, leading to elevated non-esterified fatty acid content with prolonged storage [Ep enzyme pathways would have profound impacts on the concentrations of these molecules.There is evidence for the theory that elevated oxylipins could be the result of lipolysis during storage and processing. Oxylipins are produced when COX, LOX, and CYP450 storage ,86,87. T storage . LipolysPasteurization also alters oxylipin concentrations, with different patterns when using different pasteurization techniques . IncreasThere is strong potential to use oxylipins during lactation, both in milk and circulation, to identify mechanisms linking inflammation to lactation and breastfeeding outcomes. Specifically, mechanistic studies are needed to clarify the physiological link between obesity and inflammation with low milk supply. Oxylipins play important roles in initiating and resolving inflammation, and milk oxylipin concentrations may represent biomarkers of the inflammatory state of the mammary gland. DHA- and EPA-derived oxylipins may have particular potential in mediating the inflammatory resolution needed to return low-grade inflammation back to baseline. However, studies reporting oxylipin concentrations in milk must consider several technical complexities in their study design and interpretation. First, the oxylipin compartment measured should be considered. Although the non-esterified oxylipin pool is considered the most bioactive, the majority of oxylipins in milk are contained in the esterified pool as part of the milk fat globule triglycerides and phospholipids. Therefore, the esterified pool should be considered, especially when using oxylipins to predict infant outcomes, since esterified oxylipins will be made available to the infant during digestion and absorption of the milk fat. Second, milk oxylipins are strongly correlated with total milk fat. Therefore, when using milk oxylipins to predict and study specific outcomes, researchers must consider if the outcomes could be explained by total milk fat concentration and consider using milk fat content as a covariate in statistical models. Third, studies have shown that concentrations of milk oxylipins increase significantly with extended storage, especially when unfrozen. Preliminary evidence from our research group also shows that samples with multiple freeze–thaw cycles have significantly higher oxylipin concentrations compared with unthawed samples. Therefore, sample handling is an important variable that must be controlled in study designs using milk oxylipins. Future carefully designed studies are needed in this important topic area and have strong potential to improve outcomes for lactating mothers and their infants.


A cancer susceptibility gene (CSG) is a gene whose variants can increase the cancer risk of those bearing such variants.The methods used for discovery of CSGs have evolved from single candidate gene analysis to population-based next-generation sequencing. The syndrome of increased familial susceptibility to neoplastic diseases was described in 1969 by Li and Fraumeni, and by gene linkage within affected families followed by association analysis of candidate genes in the linkage region, the causative gene, p53, was identified. Such studies are expensive, difficult in terms of patient recruitment, and time consuming, so few CSGs had been identified by 2000.Population-based CSG candidate discovery was facilitated by the breakthrough of widespread genotyping, allowing genome-wide association studies. Further advances in whole-exome and whole-genome sequencing allowed identification of genetic variants by comparing their frequency in population-based analyses of patients with cancer and control subjects. However, these methods are costly and limited in practical use. Compared with whole-genome and whole-exome sequencing, panel sequencing by reducing genome-wide genes to cancer-associated or customized target genes is less costly and more widespread in clinical genetic testing. Although these studies permit significantly expanded sample sizes, they are limited to studying known genes.https://csgs.sequenxe.com/), including those from literature review,Most CSGs discovered by the above methods have not been proven to cause cancer and have not yet been applied clinically, but a comprehensive summary of the current CSGs is helpful to acknowledge the progress of the field, to provide a reference gene list for genetic consulting, and for mining potential key cancer-prevention targets. Bioinformatic analysis has an emerging role in CSG discovery. In this overview, efforts have been made to summarize the CSG list associated with the risk of cancer , followed by the digestive and immune systems. , some were sex-differentially expressed only within specific tissues, such as GSTP1 and CASP8 in brain (To our knowledge, the sex-specific CSG expression profile across human tissues has not been reported. By mapping CSGs to the recent sex-differential transcriptome,TP53 and BRCA2 were enriched in were “response to an organic substance,” “enzyme binding,” and “membrane-enclosed lumen.” This suggests that the malfunction within cells of the molecular binding ability of external factors could be a general susceptibility mechanism. By mapping the CSG list to any cancer-causing signaling pathways, such as the MAPK signaling pathway, we could identify which genetic node in tumorigenic pathways could mediate cancer susceptibility.Studies of common pathways in which CSGs are involved may improve our mechanistic insights into cancer origin. Here, we provide a different aspect of common pathways affected by CSGs with a background of all genes compared with previous findings. In a Kyoto Encyclopedia of Genes and Genomes analysis, the evidence of CSGs affecting “pathways in cancer” was solid E. FurtheStudies of CSGs continue, and the number of CSGs is expanding. With advanced computational tools applied in medical research such as machine-learning and deep neural networks, more CSGs will be identified. Our knowledge of how individual CSGs contribute to susceptibility in people bearing risk alleles will improve our understanding of inherited cancer susceptibility and eventually lead to personalized cancer prevention and treatments.