Alternaria pathotypes, the recent discovery of accessory chromosome-associated biosynthetic genes in Fusarium species offer fresh insights into the evolution of biosynthetic enzymes such as non-ribosomal peptide synthetases (NRPSs), polyketide synthases (PKSs) and regulatory mechanisms governing their expression.Accessory chromosomes are strain- or pathotype-specific chromosomes that exist in addition to the core chromosomes of a species and are generally not considered essential to the survival of the organism. Among pathogenic fungal species, accessory chromosomes harbor pathogenicity or virulence factor genes, several of which are known to encode for secondary metabolites that are involved in plant tissue invasion. Accessory chromosomes are of particular interest due to their capacity for horizontal transfer between strains and their dynamic “crosstalk” with core chromosomes. This review focuses exclusively on secondary metabolism (including mycotoxin biosynthesis) associated with accessory chromosomes in filamentous fungi and the role accessory chromosomes play in the evolution of secondary metabolite gene clusters. Untargeted metabolomics profiling in conjunction with genome sequencing provides an effective means of linking secondary metabolite products with their respective biosynthetic gene clusters that reside on accessory chromosomes. While the majority of literature describing accessory chromosome-associated toxin biosynthesis comes from studies of  Large scale screening and characterization of fungal extracts has been carried out for over half a century by various industries  and as a result, a considerable breadth of the chemical space associated with activity . Howeveractivity . The funFusarium and Alternaria), produce a broad diversity of secondary metabolites. However, secondary metabolism can vary within species populations of some plant pathogens. One reason for observed intra-species variations in secondary metabolism arises from the presence of lineage-specific accessory chromosomes (ACs) within the genome of select strains that encode secondary metabolite biosynthetic gene clusters.In agriculture, fungal secondary metabolites produced by plant pathogens and spoilage fungi can be mycotoxins acting as virulence factors that directly impact crop yields and market suitability. Fungal plant pathogens with biotrophic/necrotrophic and saprophytic lifestyles  and recent genomic studies of Fusarium species have also detected biosynthetic enzymes on accessory chromosomes. Enzymes of interest include non-ribosomal peptide synthetases (NRPSs), polyketide synthases (PKSs), terpene synthases and the regulatory mechanisms governing their expression. Relationships between ACs and secondary metabolite biosynthesis are discussed, including evidence for horizontal chromosome transfer of ACs between fungi, the relevance of ACs providing a niche for rapid multiplication and diversification of secondary metabolite biosynthesis genes, the implications of regulatory “cross talk” between core chromosomes and ACs, and the role of “repeat-induced point mutation” in detecting and eliminating duplicated genes in the context of AC-associated gene duplications. The utility of long-read sequencing technology to elucidate the structure of genomes through “closed” or “telomere to telomere” chromosomal assemblies is highlighted as essential for the study of ACs, greatly improving analyses of secondary metabolite gene clusters. Finally, a “top down—bottom up” screening strategy coupling genomics based approaches with untargeted metabolomics is presented, where the production of unique secondary metabolite products within species populations can be linked with biosynthetic gene clusters associated with ACs.This review focuses exclusively on secondary metabolism (including mycotoxin biosynthesis) associated with ACs in filamentous fungi. We fill in historical gaps and provide a current overview of both the pathways and associated gene clusters involved in the biosynthesis of host specific toxins and other secondary metabolite virulence factors associated with ACs to date. Examples of AC-associated toxin biosynthesis are numerous among Alternaria species; formae speciales or pathotypes, causal agents of host specific plant diseases.The term “accessory chromosome” (or AC) has been adopted by researchers working on a variety of different Ascomycete genera and conceptually encompasses the terms “type B,” “supernumerary,” or “lineage-specific” chromosomes used in the study of plants, animals and fungal species . We haveFungal ACs display key structural and genetic differences compared to core chromosomes. ACs are distinguished in some species by their low gene density, high level of transposable element (TE) distribution, epigenetic markers, and non-Mendelian inheritance patterns . ACs vaFusarium oxysporum   genes in xysporum , detoxifsp. pisi , and toxmestica) . As moreAlternaria alternata cause disease on niche plant hosts due the production of host specific toxins (described below). The taxonomy of small spored Alternaria species is notoriously convoluted, in part due to strains that are morphologically indistinguishable but produce different host specific toxins and thus have a unique host range. Differing usages of either the term pathotype or formae speciales (or combinations of the two) within the genus Alternaria exist , which causes Alternaria stem canker on tomato, produces 5 regioisomeric pairs of AAL-toxins: TA, TB, TC, TD, and TE -configured tricarballyl-enzyme intermediate that is then transferred, via the condensation domain of ALT12b, onto the polyketide chain at either the C14 or C15 alcohol , is the causal agent of Alternaria leaf spot of rough lemon (Citrus jambhiri Lush.) and rangpur lime (C. limonia Osbeck), and causes the formation of lesions on, and the abscission of, leaves and immature fruit  that affects grapefruit, hybrids of grapefruit and tangerine, tangerine and sweet orange  due to the production of the host specific AK-toxin  affects both strawberry and pear due to the production of several host specific toxins   . ACT-, Aand III) .These toxins all share a 9,10-epoxy-8-hydroxy-9-methyl-decatrienoic acid (EDA) moiety  and diffAFTS1, which encodes a 2-ketoisovalerate reductase and thus should eliminate 2-hydroxyisovalerate formation, did not abolish AF-toxin II biosynthesis  causing Alternaria blotch on a narrow range of apple cultivars world-wide , and two alanines . Furthermore, the AMT1 gene is absent in non-pathogenic strains  can effectively resolve genomic regions of low complexity and/or highly repetitive content, producing “closed” or “telomere-to-telomere” assemblies. The quality of fungal genome assemblies is often improved by inclusion of massively parallelized, high-fidelity shorter read sequences (such as those derived from Illumina platforms) in assembly pipelines. The resulting closed genomes are highly desirable for the study of rearrangements, regulation of clustered genes, gene duplications, transposable element proliferation and other forms of gene migration between genome compartments such as ACs, accessory regions and core chromosomes .Alternaria, Colletotrichum, Fusarium, Magnaporthe, and Zymoseptoria.To explore relationships between secondary metabolite biosynthesis and ACs, we compiled 15 recently published fungal genomes containing sequences identified as ACs and scanned them using the fungal version of antiSMASH  to detecFusarium and Alternaria, an unsurprising fact as these are genera associated with diverse secondary metabolism products and are likely over-represented in available databases of high-quality genomes. Published genomes derived from isolates of Zymoseptoria tritici, Magnaporthe oryzae, Colletotrichum graminicola, Fusarium oxysporum f. sp. lini and Fusarium solani (anamorph of Nectria haematococca) all lacked AC-associated secondary metabolite biosynthetic gene clusters, whereas Alternaria and Fusarium genomes contained numerous secondary metabolite biosynthetic gene multiplications in AC-associated sequences. The gene multiplication exhibited on ACs suggests that ACs are fertile grounds for secondary metabolite biosynthetic gene amplification and neofunctionalization (discussed below).Most of the biosynthetic gene clusters associated with ACs in this subset of fungi are encoded by isolates of A. brassicae strain J3 and F. poae strain Fp157, respectively   . The pospicidins . Examinati clade . A recen(F. poae .C. carbonum strains contained a unique genetic locus (collectively designated as TOX2) that spans a 500kb region and is characterized by highly repetitive sequences in which multiple, functional copies of TOX2 reside  likely has 3 or 4 copies of AMT2, AMT3 and AMT4, and observed that mutants containing single and double copies of AMT2 and AMT3 produced less AM-toxin than the wild-type strain. The single and double-mutants of AMT gene knockouts were confirmed as less virulent in plant infection trials compared to wild-type. These observations underline the importance of gene duplication as a potential mechanism to increase host specific toxin production in aggressive pathotypes. Other duplicated Alternaria secondary metabolite biosynthetic genes include those involved in AK-toxin production in A. alternata pear and apple pathotypes , and PKS22 , and is also consistent to F. poae genomes, which have less evidence for RIP in ACs than in core chromosomes  is relevant. Comparative genomic analysis of al decay . In asexal decay , and othnsposons . These rnsposons . Interesomosomes . UnderstIn planta, we hypothesize that anastomosis may arise when intracellular hyphae of two strains interact in close proximity in the apoplast of host cells . The ability to maintain ACs with virulence factors in a subset of a fungal population could permit the maintenance of different trophic phases within the species, providing an adaptive advantage in periods of time when plant populations develop countermeasures to infection.Horizontal transference of an AC between fungal strains can turn an endophyte or saprophyte into a pathogen , suggestEBR1 is involved with the onset of pathogenicity (as a master regulator of primary and secondary metabolism expression) and typically occurs as a single copy on a core chromosome; however, multiple duplications of EBR1 occur on ACs of F. poae and most F. oxysporum f.sp. pathogenic strains  can promote possible inference regarding the role that AC-acquired secondary metabolite expression may have on the evolution of strain pathogenicity or virulence within the species population.This “multi-omics” strategy is currently being applied in studies of agriculturally relevant crop pathogens, making use of strain population libraries generated from annual disease surveys on various crops and geographical locations. In such a strategy, results from metabolomics profiling work are used to make selections of the population for full genome sequencing (long and short-read). For pathogens that are known to have ACs, metabolomes derived from extracts of single spore isolates cultured under axenic conditions on multiple media can be combined to generate “consensus” chemical phenotypes (representing the breadth in secondary metabolism expressed by a given strain; Alternaria pathotypes/f.sp., ACs act as a chromosomal “engine” for the horizontal transfer, duplication, and rearrangement of secondary metabolite biosynthetic gene clusters. AC-associated gene cluster expression expands both the diversity and quantity of mycotoxins and other virulence factors produced, enabling isolates to invade and adapt to specific plant hosts. Untargeted metabolomics is a promising tool for exploring the evolving chemical space associated with these adaptations. Metabolomic profiling of Alternaria and Fusarium spp. populations will likely lead to the discovery of more biosynthetically active ACs with potential roles in pathogenicity. Such biosynthetic gene clusters on ACs can also provide insights into the evolution of fungal natural products, as the dynamic nature of ACs can promote diversification of natural product families. Although their effects on pathogenicity are currently unexplored, further study into secondary metabolite biosynthetic gene clusters on Fusarium ACs and their resulting products should provide insight into the evolutionary trajectories of closely-related fungal species with divergent lifestyles and genomic architectures, such as F. poae, F. oxysporum and F. graminearum. The differences between Alternaria and Fusarium genomes reviewed here suggest there are a multiplicity of relationships between ACs and core chromosomes in fungi, and this diversity will likely expand as more fungal taxa are discovered and sequenced.The study of the accessory genome is the study of the genomic space in which fungi develop novel invasion strategies and adapt to evolving plant defenses. In some TW, NV, CB, and DO wrote the manuscript and approved its final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Over the past two decades, the Notch signaling pathway has been investigated as a therapeutic target for the treatment of cancers, and more recently in the context of immune and inflammatory disorders. Notch is an evolutionary conserved pathway found in all metazoans that is critical for proper embryonic development and for the postnatal maintenance of selected tissues. Through cell-to-cell contacts, Notch orchestrates cell fate decisions and differentiation in non-hematopoietic and hematopoietic cell types, regulates immune cell development, and is integral to shaping the amplitude as well as the quality of different types of immune responses. Depriving some cancer types of Notch signals has been shown in preclinical studies to stunt tumor growth, consistent with an oncogenic function of Notch signaling. In addition, therapeutically antagonizing Notch signals showed preclinical potential to prevent or reverse inflammatory disorders, including autoimmune diseases, allergic inflammation and immune complications of life-saving procedures such allogeneic bone marrow and solid organ transplantation (graft-versus-host disease and graft rejection). In this review, we discuss some of these unique approaches, along with the successes and challenges encountered so far to target Notch signaling in preclinical and early clinical studies. Our goal is to emphasize lessons learned to provide guidance about emerging strategies of Notch-based therapeutics that could be deployed safely and efficiently in patients with immune and inflammatory disorders. Inflammation is a dynamic process mobilizing multiple cell types and mediators in response to stimuli that are perceived as harmful. The Notch signaling pathway is emerging as a critical regulator of inflammation, with pathogenic roles in several inflammatory and immune disorders including autoimmunity and allergic airway inflammation. In addition, Notch critically regulates graft-versus-host disease (GVHD) and graft rejection, the major complications mediated by immune responses to foreign tissue antigens after life-saving transplantation procedures, such as transplantation of allogeneic bone marrow or solid organ allografts. Therapeutic strategies to inhibit Notch signaling have first been developed preclinically and in early phase clinical trials to target oncogenic functions of the Notch pathway in tumor cells, or in tumor angiogenesis. However, many of these strategies are now also actively investigated in preclinical settings for their therapeutic value in non-malignant inflammatory disorders.In this review, we discuss emerging concepts about the effects of Notch signaling in the regulation of mature immune cell function, beyond the role of the Notch pathway that was first established in immune cell development . First, Drosophila melanogaster fruit flies , referred to as Notch ligand or receptor “paralogs” . Agonistin vivo, and some have translational potential. Conditional inactivation of Notch ligand and receptor genes in specific cell types requires knowledge of the expression pattern and relative importance of Notch ligand and receptor paralogs  fused to GFP that exerts potent dominant negative activity downstream of all Notch receptors ; Pofut1 ; Adam10 ; and genes encoding subunits of the γ-secretase complex (such as Psen1/2).The unique mechanisms of Notch activation have inspired genetic and pharmacological strategies of Notch inhibition . These lparalogs . For exact Tregs . In contic cells . For Notic cells . Howeveric cells . Other geceptors . AlthougComplementing genetic approaches, pharmacological strategies with translational potential are depicted in NOTCH1 point mutations or other genetic events activating Notch signaling  based on a rare t chromosomal translocation forcing expression of a constitutively active form of NOTCH1 . Later iignaling . While Nin T-ALL . Other sin T-ALL . In addiogenesis . These eThe first drugs used to target Notch in the clinic originated from groundbreaking work designed to target the γ-secretase complex in Alzheimer’s disease. In Alzheimer’s, γ-secretase plays a critical role in the cleavage of the amyloid precursor protein, subsequently forming aggregates of amyloid-beta peptides in the brain as a major contributor to disease progression . GSIs taRbpj inactivation in +Villin-Cre intestinal cells or the use of GSIs induced the differentiation of intestinal crypt cells into post-mitotic goblet cells, leading to severe diarrhea . These findings were consistent with critical effects of Notch signaling in CD4+ T cells, which play a key role in EAE pathogenesis both through Th1 and Th17 differentiation. The T cell-intrinsic pathogenic functions of Notch during EAE were subsequently confirmed via T cell-specific Rbpj inactivation or expression of the pan-Notch inhibitor DNMAML, which provided high protection from EAE induced by polyclonal T cells or by myelin-specific 2D2 T cell receptor transgenic T cells  in mice, a model that shares many features with human Multiple Sclerosis (MS) . Using G T cells . The imp T cells . Finally T cells .Altogether, Notch signaling is emerging as a central regulator of EAE pathogenesis, with T cell-intrinsic functions playing a major role. Additional investigations combining genetic and pharmacological approaches are needed to carefully dissect the impact of Notch signaling in T cells vs. other cell types involved in EAE progression. From a translational perspective, it will be essential to evaluate the role of Dll4 as opposed to other Notch ligands; map the cellular source of Notch ligands as well as their spatial and temporal interactions with T cells; and define the critical time windows during which systemic Notch inhibition induces maximum benefits. For example, it would be interesting to define if and when transient systemic Notch ligand inhibition can abort disease flares by itself or in combination with other interventions.Allogeneic bone marrow or hematopoietic cell transplantation  is a potentially life-saving therapeutic modality for patients with benign and malignant hematological disorders, including leukemias and lymphomas. However, acute and chronic graft-versus-host disease (GVHD) remain major immune complications of the procedure that limit its success and curtail its widespread use. GVHD is triggered by donor-derived T cells in the hematopoietic graft that recognize foreign tissue antigens in the recipient, leading to target organ damage. Unmet clinical needs include the occurrence of GVHD in a high fraction of patients despite universal use of prophylactic immunosuppression; severe acute GVHD, especially when resistant to corticosteroids; extensive forms of chronic GVHD, which induce serious life-long morbidity; and the need to control GVHD without eliminating the beneficial graft-versus-tumor effects of allo-HCT in cancer patients.Notch1/2 inactivation only in regulatory T cells (Tregs)  . In term (Tregs) . Mechani (Tregs) . Notch i (Tregs) . Importa (Tregs) . Thus, NCcl19-Cre transgene in secondary lymphoid organs  phenotype under the control of the master transcription factor GATA3 is central to disease pathogenesis via secretion of Th2 cytokines , although Th17 differentiation also takes place. Ex vivo work using coculture with antigen-presenting cells first identified the potential for Jagged ligands to drive CD4+ Th2 polarization (Gata3 and Il4 transcription, the latter through ICN binding at the Il4 CNS2 enhancer element (Asthma is characterized by bronchial hyperreactivity and airway infiltration by T lymphocytes, innate lymphoid cells, macrophages, neutrophils, mast cells and eosinophils, with important inflammatory roles for both innate and adaptive immune cells. CD4rization . In seve element . Other g element . Collect element . Other i element . In a ho element . Antibod element . Systemi element . Thus, tNotch1 and Notch2 in T cells. Interestingly, transgenic Gata3 expression in Notch1/2-deficient or Rbpj-deficient T cells only had a limited impact on their phenotype, suggesting Gata3-independent effects of canonical Notch signals. Instead, Notch was found to promote lymph node egress and trafficking of CD4+ T cells into the lung, possibly via a KLF2/S1PR1 axis. Recently, Chatila’s group described a Jagged1-Notch4 signaling axis at the core of asthma pathogenesis in mice, with correlative data suggesting its relevance to human patients (Jag1 expression when exposed to ultrafine ambient particles through a mechanism dependent on Aryl Hydrocarbon Receptor, in turn engaging Notch receptors in CD4+ cells (+ T lymphocytes. Roles for Notch signaling have been reported in the regulation of lung-infiltrating effector CD8+ T cells, lung-resident memory CD8+ T cells as well as innate lymphoid cell differentiation and function, all of which could impact asthma pathogenesis (Recent work is expanding our understanding of Notch signaling in asthma, revealing new mechanisms and unexpected players. patients . Alveola4+ cells . Surpris4+ cells . Functioogenesis . Finallyogenesis .Altogether, these findings reveal the interesting and complex biology of Notch signaling in airway inflammation. The mechanisms of Notch action and all the cellular partners involved need to be investigated further, including immune and non-immune cell types. These considerations are especially relevant when systemic rather than cell-specific targeted genetic interventions are being considered in translational investigations.Although space limitations prevent us from comprehensively including all work reported in the field, a role for Notch signaling has been suggested in other immune and inflammatory disorders, with various degrees of evidence. An interesting common denominator is that investigations of inflammatory disorders identify complex interactions of immune cells with non-hematopoietic partners in their environment, and new functions of Notch signaling in unexpected cell types.in vivo antibody-mediated Dll4 blockade attenuated the progression of atherosclerotic plaques, as well as macrophage accumulation and M1 differentiation (In inflammatory aspects of atherosclerosis, Notch may be involved in the regulation of both endothelial function and infiltrating leukocytes. Endothelial Notch1 expression was suppressed in mice and humans on high fat diets, which correlated with increased atherosclerosis progression . In the ntiation . More woNotch3-expressing sublining fibroblasts with evidence of Notch activation in rheumatoid arthritis patients (Jagged1 expression being induced as a Notch transcriptional target as part of a positive feedback loop (In rheumatoid arthritis, early work suggested the presence of activated Notch1 receptors in synovial cells, especially in vascular and perivascular regions . In a mopatients . Single-ack loop . In a moack loop . These eMuch can be learned already from the rich biology of Notch signaling in cancer and inflammatory disorders, and from preclinical and early clinical investigations to target the Notch pathway therapeutically .In cancer, Notch behaves as an oncogenic pathway in a diverse range of tumors through gene translocations, mutational activation or natural interactions of the receptors with Notch ligands in the microenvironment. Notch also controls tumor angiogenesis via a crosstalk with the VEGF pathway. Yet, early clinical interventions attempted so far to target Notch signaling in cancer have been disappointing. Key issues include selection for resistant cells as well as on-target side effects that result from prolonged systemic inhibition of the Notch pathway or its components. On-target toxicities rooted in the physiological role of Notch in normal tissues have been dose-limiting and have prevented the deployment of maximally effective inhibition schedules, thus in turn likely contributing to decreased anti-tumor activity. Moving forward, it will be essential to better select patients with Notch-sensitive tumors and consider synergistic effects of combination therapies. In addition, all efforts need to be made to identify more specific strategies to target Notch signaling or its consequences in tumor cells while sparing or protecting normal tissues, especially since prolonged inhibition remains desirable in cancer therapy.In contrast, the role of Notch signaling in immune and inflammatory disorders represents a more recent discovery leading to new therapeutic opportunities. Of note, all information available so far stems from preclinical disease models, thus the benefits of Notch inhibition in human inflammatory disorders remains to be established. To maximize chances of success, we believe that it will be important to consider rules that differ from those applying to Notch signaling in cancer. Prolonged Notch inhibition is not always necessary to achieve long-term therapeutic benefits in immune disorders. Instead, pulses of Notch inhibition applied at critical times in the disease course can reprogram immune cells to a less pathogenic state, or decrease immune cell trafficking to target organs, while expanding and reinforcing the function of regulatory T cells. In addition, selective targeting of individual Notch ligands and receptors can open a therapeutic window that does not exist with systemic pan-Notch inhibition. In some contexts, the discovery of new roles for understudied Notch pathway members, such as Notch3 and Notch4, may provide therapeutic opportunities even with prolonged inhibition, as the side effects of targeting these receptors are not predicted to be severe. Finally, combination therapies could provide other avenues, building on deeper molecular understanding of Notch signaling in immune cells.FA and IM wrote and edited the manuscript. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Healthcare workers are exposed to both physical and mental demands in the hospital environment; recently intensified by overstretching staff and resources during the current COVID-19 pandemic. Despite healthcare workers banding together, physician burnout is more prevalent than ever before due to emotional, physical and mental exhaustion. Firstly, this poster aims to expose the prevalence of burnout among healthcare workers during the COVID-19 pandemic. Secondly, to highlight the interventions and strategies to help minimise burnout among healthcare workers.I will focus on reviewing clinical trials with a particular focus on healthcare workers affected by burnout within the COVID-19 pandemic timeframe. Therefore, narrowing my search to 20 trials within the past 12 months using the following restricted search criteria: ‘burnout’, ‘covid-19’ and ‘healthcare’. Furthermore, commenting on strategies and interventions to minimise burnout by stretching my criteria to interventions trialled within the last 24 months. This is due to limited data and trial evidence for burnout strategies within the last 12 months of the COVID-19 pandemic.Burnout is on the rise among healthcare workers across the globe, 47% of healthcare staff are expressing an element of burnout worldwide. With growing concerns of healthcare staff developing long term mental health implications as a result of work-related stress. At present, one third of frontline staff are experiencing depression and distress; which must be addressed. Reviewing recent trials has highlighted a number of successful strategies for approaching burnout including: app technology, talking therapy, staff support and internet-based resources. App-related technology and web resources have shown to be particularly beneficial among recent trials, with limited participation and engagement for support groups/talking therapy.A significant rise in physician burnout and distress during COVID-19 has been noted in various trials. Interventions specifically related to burnout within COVID-19 are limited due to a low yield of completed trials; however, a couple of trials have found an improvement in COVID-19 related stress among healthcare workers using app-related technology. Internet based intervention is cheap, widely accessible and a non-judgemental method for seeking help, especially within a profession where burnout is heavily stigmatized.


Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets. Semantic segmentation partitions raw image data into structured and meaningful regions and thus enables further image analysis and quantification, which are critical for various applications, including anatomy research, disease diagnosis, treatment planning, and prognosis monitoring5. With the global expansion of medical imaging and the advancement of imaging techniques, the volume of acquired medical image data is increasing at a pace much faster than available human experts can interpret. Thus, automated segmentation algorithms are needed to assist physicians in realizing accurate and timely imaging-based diagnosis7.Medical imaging contributes significantly to progress in scientific discoveries and medicine13. Nevertheless, the applicability of deep-learning methods to clinical practice is limited because of the heavy reliance on training data, especially training annotations15. Large curated datasets are necessary, but annotating medical images is a time-consuming, labor-intensive, and expensive process. Depending on the complexity of the regions of interest to segment and the local anatomical structures, minutes to hours may be required to annotate a single image. Furthermore, label noise is inevitable in real-world applications of deep-learning models16. Such noise can result from systematic errors of the annotator, as well as inter-annotator variation. More than three domain experts are typically needed to generate trustworthy annotations17. Any biases in the data can be transferred to the outcomes of the learned models18. Consequently, the lack of large and high-quality labeled datasets has been identified as the primary limitation of the application of supervised deep learning for medical imaging tasks21. Learning with imperfect datasets having limited annotations , lacking target domain annotations , or containing noisy annotations  are three of the most frequently encountered challenges in clinical applications22.In the past decade, deep learning has made considerable progress in automatic medical image segmentation by demonstrating promising performance in various breakthrough studies24 that works by training two classifiers for two complementary views using the labeled data, generating pseudo-labels for unlabeled data by enforcing agreement between the classifier predictions, and combining the labeled and pseudo-labeled data for further training. Co-training has been employed mainly to semi-supervised classification tasks. Only recently has co-training been extended to semi-supervised image segmentation25 and UDA of segmentation models26. Despite the achieved inspiring performance, the direct employment of co-training methods to NLL is problematic, as they do not possess the ability to distinguish between accurate and noisy labels27. Co-teaching was developed based on co-training to specifically address the challenge of NLL by dropping suspected highly noisy samples during network optimization28. Nevertheless, data filtering by dropping samples is an inefficient approach that might lead to model learning from a pseudo-realistic data distribution; thus, co-teaching methods are more applicable to natural image classification tasks when sufficient large datasets are available to cover the full range of different situations, even after data dropping.Co-training is one of the most prevalent methods for SSLIn this study, we develop an annotation-efficient deep-learning framework for medical image segmentation, which we call AIDE, to handle different types of imperfect datasets. AIDE is designed to address all three challenges of SSL, UDA, and NLL. With AIDE, SSL and UDA are transformed into NLL by generating low-quality noisy labels for the unlabeled training data utilizing models trained either on the limited annotated data (SSL) or on the annotated source domain data (UDA). A cross-model self-correction method is proposed to achieve annotation-efficient network learning. Specifically, cross-model co-optimization learning is realized by training two networks in parallel and conducting cross-model information exchange. With the exchanged information, self-label filtering and correction of inexpensive noisy labels proceed with cascaded local and global steps progressively in an elaborately designed schedule according to an observed small loss criterion. The framework is flexible regarding the deep neural network (DNN) models to be utilized.Methodological analyses are performed to evaluate the effectiveness of AIDE for handling imperfect training datasets. In order to fairly evaluate the method without severe ground-truth label biases, we conduct extensive experiments on a variety of public datasets which have widely accepted data and labels. Specifically, extensive experiments on open datasets with limited, no target domain, and noisy annotations are performed. Better results are achieved with AIDE compared to the respective fully supervised baselines optimized with the available noisy annotations or model-generated low-quality labels. Furthermore, to test the applicability of our method in real-world applications, three datasets for breast tumor segmentation  collected from three medical centers are experimented with. The annotations of these data are carefully curated by three experienced radiologists with more than 10 years of experience in breast MR image interpretation. Comparable segmentation results to those obtained by fully supervised models with access to 100% training data annotations and those provided by independent radiologists are achieved with AIDE by utilizing only 10% of the annotations. Our results indicate that DNNs are capable of exploring the image contents of large datasets under proper guidance without the necessity of high-quality annotations. We believe that our proposed framework has the potential to improve the medical image diagnosis workflow in an efficient manner and at a low cost.AIDE is a deep-learning framework that achieves accurate image segmentation with imperfect training datasets. A cross-model self-label correction mechanism is proposed to efficiently exploit inexpensive noisy training data labels, which are either generated by pretrained low-performance deep-learning models or provided by individual annotators without quality controls.An overview of AIDE and example images of the datasets we utilized are depicted in Fig. 30, should multimodal inputs be available.For AIDE, two networks are trained in parallel to conduct cross-model co-optimization Fig. . In eachMultiple evaluation metrics are reported to characterize the segmentation performance, namely, the Dice score , relative area/volume difference (RAVD), average symmetric surface distance (ASSD), and maximum symmetric surface distance (MSSD). Higher DSC values and lower RAVD, ASSD, and MSSD indicate more accurate segmentation results.31. This design can prevent the negative effects of low-quality annotations and exploit as much useful information as possible from these inputs instead of dropping them. Compared to conventional fully supervised learning, which trains the network only by minimizing the discrepancies between the predictions and the ground-truth labels, the consistency constraint of AIDE can force the network to learn latent features from inputs that are transformation invariant; in this way, the network concentrates more on the image contents instead of focusing on only the regression of ground-truth labels. Second, the global label-correction step after each epoch progressively shifts the networks towards predicting consistent results at different time points. In other words, the networks are expected to have small variations, and those samples that lead to large variations are considered low-quality annotated samples since low-quality labels, to a certain extent, contradict the information provided in the images. These suspected low-quality annotated samples should be corrected. Direct label correction to replace the old labels, which are predicted by the framework in previous steps, aims to find useful information from the framework’s own outputs; thus, it offers the framework a self-evolving capability. Since in most cases, medical images of the same region appear roughly similar among different patients, we believe that this evolving capability of updating a portion of suspected low-quality annotations is reasonable and applicable. The remaining non-updated samples can guarantee the stable training of the network. Third, the proposed cross-model co-optimization learning can prevent error propagation and accumulation in one network. By building two networks and letting them exchange information, we reduce the risk of network over-fitting to its own predicted pseudo-labels.The effectiveness of AIDE in handling low-quality annotated data can be attributed to the following advantages. First, the local label-filtering step in each iteration enforces a constraint on suspected low-quality annotated samples to generate predictions that are consistent with those of the augmented inputs, which is a form of data distillation that enforces a transformation-consistent constraint on the model predictionsThe CHAOS dataset, which is built for liver segmentation, is introduced to investigate the effectiveness of AIDE for SSL Fig. . DiffereP = 0.0133 from a two-sided paired t test between the DSCs of Exp. 6 and Exp. 7 in Supplementary Table 32 and co-teaching27 in Table P = 0.0791 from a two-sided paired t test).To enlarge the training dataset, low-quality noisy labels are generated by models trained with the minimal annotated training data (1 case with 30 image samples). Then, networks are trained with the enlarged dataset. In this work, low-quality labels or noisy labels are counted in an image-based manner. Different levels of noisy labels are experimented with . Considering the small labeled image data utilized, our method achieves a reasonably good performance compared to these fully supervised ones.Results generated by AIDE, which is trained with ten training cases (one labeled case and nine unlabeled cases), are submitted for evaluation online, and an average DSC of 83.1% is achieved on the test data. This DSC value is within the range of the values achieved by fully supervised methods trained on 20 labeled cases  of prostate datasets with different image acquisition protocols are utilized to inspect the framework performance when no target domain annotations are provided during model training. Domain 3 is a combined dataset with different image acquisition parameters. When training with a single domain dataset, the networks become biased to the domain properties, and the performance on data from other domains is compromised. Table 34. Besides, better performance is achieved by AIDE than that of the two existing methods, pseudo-label32 and co-teaching27  are conducted to investigate the feasibility of the proposed framework for processing raw clinical data. Dynamic contrast-enhanced MR (DCE-MR) images are acquired. The experimental results validate the effectiveness of AIDE in analyzing clinical samples with decreased manual efforts Fig. . Under t08) Fig. . For GPP27) Fig. . For HPP45) Fig. . The vis45) Fig. . We furt45) Fig. , which cAdditional radiologists were employed to segment the breast tumors in the central slices of the three test sets Fig. –10. For 35 achieved a mean DSC of 72% for breast tumor segmentation in DCE-MR images with a hierarchical convolutional neural network framework, and Qiao et al.36 obtained a value of 78% utilizing 3D U-Net and single-phase DCE-MR images. Our results (73.1% on the HPPH dataset) are comparable to these literature-reported values achieved by fully supervised learning with hundreds of patient cases. Considering the small number of training annotations utilized (25 cases for the HPPH dataset), our proposed method can be a valuable tool in clinical practice to assist radiologists in achieving fast and reliable breast tumor segmentation.Breast tumor segmentation in DCE-MR images is a challenging task due to the severe class imbalance issue  and the high confounding background signals (organs in the chest and dense glandular tissues). As a result, the DSC values obtained for breast tumor segmentation are not as high as those for other tasks (such as prostate segmentation). Nevertheless, our method is not intended to replace radiologists in the disease diagnosis or treatment planning workflow but serves as an automated computer-aided system. Recently, Zhang et al.40. Despite the reported successful applications, the performance of DNNs depends heavily on the quality of the utilized training datasets. Frameworks that can optimize DNNs without strict reliance on large and high-quality annotated training datasets can significantly narrow the gap between research and clinical applications. In this study, we propose an annotation-efficient deep-learning framework, AIDE, for medical image segmentation network learning with imperfect datasets to address three challenges: SSL, UDA, and NLL. Our target is not to build a more sophisticated model for fully supervised learning, but to build a framework that can work properly without sufficient labeled data, so as to alleviate the reliance on the time-consuming and expensive manual annotations when applying AI to medical imaging. Extensive experiments have been conducted with open datasets. The results show that AIDE performs better than conventional fully supervised models under the same training conditions and even comparable to models trained with corresponding perfect datasets, thereby validating the effectiveness of AIDE. Additional experiments on clinical breast tumor segmentation datasets from three medical centers further prove the robustness and generalization ability of AIDE when processing real clinical data. On all three independent datasets, AIDE generates satisfactory segmentation results by utilizing only 10% of the training annotations, which indicates that AIDE can alleviate radiologists’ manual efforts compared to conventional fully supervised DNNs.Image segmentation plays an important role in medical imaging applications. In recent studies, DNNs have been widely employed to automate and accelerate the segmentation process45 but have not yet shown general applicability by addressing all three types. Methods such as data augmentation46, transfer learning43, semi-supervised learning47, and self-supervised learning49 have been extensively investigated to handle cases with limited training annotations or no target domain annotations. By contrast, much less attention has been given to noisy label learning in medical imaging50. Most existing studies concentrate on designing new loss weighting strategies52 or new loss functions53. Since various issues with the datasets may exist in reality, the effectiveness of these methods is compromised26. The available methods most closely related to ours are pseudo-labels32 and co-teaching27. Compared to existing pseudo-label studies that update the pseudo-labels of all unlabeled data simultaneously during network learning55, label updating in AIDE is conducted in an orderly  and selective (only a defined percentage of noisy labels are updated) manner according to an observed small loss criterion, which has also been noted and confirmed for natural images56. Moreover, AIDE trains the model with the segmentation loss on filtered labels to avoid the negative effects of highly noisy labels, and an additional consistency loss is introduced to enforce consistent predictions with augmented inputs for the remaining suspected highly noisy data. AIDE also differs from the two-model co-teaching method28: AIDE generates pseudo-labels for suspected low-quality annotated data in an effective and progressive manner. During network training, a hyper-parameter that increases from 0 to 1 within defined initial training epochs is introduced. This hyper-parameter controls the contribution of the consistency loss; thus, the consistency loss becomes increasingly important for network optimization in the training process. Additionally, the local label-filtering and global label-correction design progressively places more emphasis on pseudo-labels. These properties of AIDE contribute to the full utilization of valuable medical image data and improve the model performance.Recent deep-learning works have presented encouraging performance in handling certain types of imperfect datasets in isolation17. Employing a large number of domain experts to annotate large medical image datasets requires massive financial and logistical resources that are difficult to obtain in many applications. Our proposed AIDE has the potential to solve these issues effectively. It achieves promising performance by utilizing only 10% of the training annotations used by its fully supervised counterparts. Nevertheless, label-free or unsupervised learning that can eliminate manual annotation entirely is more appealing57. We are still working on improving the methodology to further reduce the reliance on annotations. In our following study, we will seek to address the challenge of unsupervised deep learning for large-scale and automatic medical image segmentation. Furthermore, although only medical image segmentation is considered in this work, AIDE might be applicable to other medical image analysis tasks, for example, image classification. The flexibility of AIDE in this perspective will also be evaluated in the future work.Constructing large datasets with high-quality annotations is particularly challenging in medical imaging. The process of medical image annotation, especially dense annotation for image segmentation, is highly resource intensive. Different from natural images, only domain experts have the knowledge to annotate medical images. In some cases, such as our brain tumor segmentation task, large variations exist between experts Fig. , which rIn summary, we have analyzed and empirically demonstrated that our proposed framework, AIDE, can achieve accurate medical image segmentation to address the three challenges of semi-supervised learning, unsupervised domain adaptation, and noisy label learning. Therefore, AIDE provides another perspective for DNNs to handle imperfect training datasets. In the real-life case study, compared to conventional DNN training, AIDE can save almost 90% of the manual effort required to annotate the training data. With further development and clinical trials, such a ten-fold improvement in efficiency in utilizing expert labels is expected to promote a wide range of biomedical applications. Our proposed framework has the potential to improve the medical image diagnosis workflow in an efficient manner and at a low cost.59 , the test set (10 cases), and the leaderboard set (10 cases). All are provided with high-quality annotations. Annotation was performed by two groups separately, one with two experts and the other with three experts. We combine the test set and leaderboard set to form our enlarged test set (20 cases) and divide the dataset into two datasets according to the data acquisition sites to form two domain samples. The domain 1 dataset contains training and testing data collected with 1.5 T MRI systems from Boston Medical Center (30 training cases and 10 test cases), and the domain 2 dataset contains data collected with 3.0 T MRI systems from Radboud University Nijmegen Medical Centre (30 training cases and 10 test cases). PROMISE12 provides data from 50 patients with high-quality annotations. Annotation was conducted by an experienced reader and then checked by a second expert. These samples were collected from different centers using different MRI machines with different acquisition protocols. Deleting 13 cases that are the same as those in domain 2, 37 cases collected from Haukeland University Hospital (12 cases), Beth Israel Deaconess Medical Center (12 cases), and University College London (13 cases) are obtained. Therefore, PROMISE12 can be treated as a combined dataset from different domains and is referred to as the domain 3 dataset in our experiments. Of the 37 cases, 27 are randomly selected as the training set, and the remaining 10 form the test set. UDA is constructed via model learning with labeled training data from the source domain and unlabeled training data from the target domain. Pseudo-labels are generated for the target domain training data, and the high-quality labeled source domain training data and low-quality noisily labeled target domain training data form the combined dataset to facilitate the domain transfer of the models. Performance is evaluated on the target domain testing data.T2-weighted MR images of the prostate from two challenges Fig. , NCI-ISBThe QUBIQ challenge provides four datasets with annotations from multiple experts, including a prostate image dataset with 55 cases (6 sets of annotations), a brain growth image dataset with 39 cases (7 sets of annotations), a brain tumor image dataset with 32 cases (3 sets of annotations), and a kidney image dataset with 24 cases (3 sets of annotations). In total, there are seven binary segmentation tasks: two for prostate segmentation, one for brain growth segmentation, three for brain tumor segmentation, and one for kidney segmentation. In this study, we conduct four tasks Fig. , one forThis retrospective study was approved by the institutional review board of each participating hospital with the written informed consent requirement waived. All patient records were de-identified before analysis and were reviewed by the institutional review boards to guarantee no potential risk to patients. The researchers who conducted the segmentation tasks have no link to the patients to prevent any possible breach of confidentiality.Dynamic contrast-enhanced breast MR data from three medical centers, Guangdong General Hospital (GGH), Guizhou Provincial People’s Hospital (GPPH), and Henan Provincial People’s Hospital (HPPH), were investigated Fig. . FDA-appFigure K augmented inputs are calculated and are regarded as pseudo-labels after temperature sharpening62. Consistency loss, which is implemented as the mean square error (MSE) loss  with the smallest DSCs are selected, and their labels are updated when certain criteria are met. In this work, we update the labels if the training epoch number is smaller than the defined warm-up epoch number and every ten epochs thereafter.64. Here, we find a similar network memorization pattern for medical image segmentation. By training a neural network utilizing the conventional fully supervised learning method with noisy labels, an overall positive correlation between the DSCs calculated by comparing network outputs to the noisy labels and the DSCs calculated by comparing the noisy labels to the high-quality labels exists for the training data during the initial training epochs  is adopted to process the different image inputs65. The extracted features are combined in a multilayer fusion manner. The features from all downsampling blocks are correspondingly concatenated to fuse information from different modalities. During decoding, the extracted image features pass through four upsampling blocks, each followed by a bilinear upsampling operation, to generate segmentation outputs with the same size as the inputs. Downsampling and upsampling blocks have similar components: two convolutional layers with a 3 × 3 convolution, batch normalization, and ReLU activation. The low-level features of the encoder are introduced to the decoder through skip connections and feature concatenation. Finally, a 1 × 1 convolution is appended to generate two features corresponding to the background and target segmentation maps, and a softmax activation is included to generate the segmentation probability maps.Similar encoder−decoder network architectures are utilized to handle single- and multimodal image datasets. During encoding, input images pass through five downsampling blocks sequentially with four max-pooling operations in between to extract multilayer image features. For multimodal inputs, multiple downsampling streams are utilized to extract features from the different modalitiesS(yʹ) and S(y) indicate the boundary points on the predicted segmentations and reference segmentations.Different metrics can be used to characterize the segmentation results. For our experiments, we choose the commonly utilized Dice score , relative area/volume difference (RAVD), average symmetric surface distance (ASSD), and maximum symmetric surface distance (MSSD).t tests, withSignificant differences between the different experiments and between the model results and human annotations are determined using two-sided paired The code used for training the deep-learning models are made publicly available for the reproducibility purpose. Statistical analysis has been given as well. Specifically, we run the code three times with different random initializations for the CHAOS dataset. For the domain adaptation task on prostate segmentation, six independent experiments were performed. On the QUBIQ datasets, we repeated 6, 7, 3, and 3 times respectively for the four different subtasks according to their dataset properties. For our breast datasets, data from three hospitals were utilized. So the experiments were performed independently for three times.Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


Network degree  and nodal efficiency  showed a trend toward a positive association with CVR. The results indicate remote abnormalities in functional connectivity contralateral to the diseased hemispheres in patients with unilateral SOD, despite the absence of macrovascular disease or demonstrable hemodynamic impairment. The clinical impact of remote functional disruptions requires dedicated investigation but may portend far reaching consequence for even putatively unilateral cerebrovascular disease.The purpose of this study was to assess spatially remote effects of hemodynamic impairment on functional network topology contralateral to unilateral anterior circulation steno-occlusive disease (SOD) using resting-state blood oxygen level-dependent (BOLD) imaging, and to investigate the relationships between network connectivity and cerebrovascular reactivity (CVR), a measure of hemodynamic stress. Twenty patients with unilateral, chronic anterior circulation SOD and 20 age-matched healthy controls underwent resting-state BOLD imaging. Five-minute standardized baseline BOLD acquisition was followed by acetazolamide infusion to measure CVR. The BOLD baseline was used to analyze network connectivity contralateral to the diseased hemispheres of SOD patients. Compared to healthy controls, reduced network degree ( Patients with chronic steno-occlusive disease (SOD) of the cerebrovascular system are at risk of ischemic stroke , 2. CereObservations of remote and widespread network dysfunction  have comA challenge to such paradigms, however, rests in the nature of BOLD functional network connectivity, which is influenced both by changes in neurovascular coupling and true neural connections , 28. In In the present study, we aimed to investigate remote effects of hemodynamic compromise on BOLD functional connectivity of the contralateral hemispheres in unilateral, chronic cerebral SOD, hypothesizing the presence of measurable disturbances in functional connectivity remote and even contralateral to SOD in patients with unilateral macrovascular disease on cross-sectional or catheter angiographic cerebrovascular imaging. Graph-theory metrics of functional networks were estimated using resting-state BOLD, addressing two questions: (i) As compared to healthy controls, do observable network alterations exist within the contralateral, normal hemispheres in SOD patients? (ii) Does network connectivity correlate with cerebral hemodynamics as measured by CVR in the normal-appearing hemispheres of the patients?N = 20), as well as on CT angiography (N = 7) or digital subtraction angiography (N = 13) when available. All imaging was assessed by a vascular neurologist (F.N.) and one of neuroradiologists (J.W.A. and S.D.), each with >10 years of experience.Twenty patients with unilateral chronic SOD of the anterior circulation were studied , including eight patients with occlusion or high-grade (>75%) stenosis of the cervical internal carotid artery (ICA), 6 patients with occlusion or high-grade (>75%) stenosis of the intracranial ICA, 1 patient with occlusion of the middle cerebral artery, and five patients with idiopathic intracranial SOD. For each patient, the diseased hemisphere was determined on 3D time-of-flight MR angiography  were enrolled for comparison. This study was approved by the Institutional Review Board at Emory University School of Medicine. Informed consent was obtained from healthy controls and retrospective review of patient data was performed.2, matrix = 64 × 64, slice thickness = 4 mm, 30 slices). After 5 min of baseline BOLD acquisition, acetazolamide (ACZ)  was slowly infused intravenously over 3–5 min, followed by normal saline flush. The healthy controls underwent resting-state BOLD imaging without ACZ enhanced BOLD measurement for 13.33 min (400 volumes) using the same scan protocol with the same MRI scanner and coil models (Siemens Tim Trio with 32-channel head array coil). To avoid bias, the same duration of BOLD measurement from both patients and healthy controls was used for network analysis, as detailed below. In both patient and control groups, a high-resolution 3D anatomical image was also acquired using an axial T1-weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) sequence .MR imaging was performed on a Siemens Tim Trio 3-Tesla scanner  equipped with a 32-channel head array coil in all patients with SOD. Twenty-minute (600 volumes) continuous BOLD data were collected using a gradient-echo echo-planar imaging (EPI) sequence  and MATLAB . Anatomical MPRAGE image was co-registered to BOLD images, and segmented to create gray matter, white matter (WM) and cerebrospinal fluid (CSF) masks. BOLD data preprocessing comprised removal of the first 10 volumes (20 s), slice-timing correction, motion correction, normalization to Montreal Neurological Institute space, and spatial smoothing with a 6-mm Gaussian kernel. For network analysis, the first 5 min of data were used, and underwent linear detrending, regression of nuisance signals and temporal band-pass filtering (0.01–0.1 Hz). The nuisance signals included head-motion profiles as well as WM and CSF signals. WM and CSF signals were extracted from contralateral hemispheres in the patients to eliminate contamination from lesions  and after (BOLDpost−ACZ) ACZ administration were generated by averaging the first and the last 30 volumes  of BOLD data, respectively. CVR maps were then determined as CVR = (BOLDpost−ACZ – BOLDpre−ACZ) / BOLDpre−ACZ × 100%  in Anatomical Automatic Labeling atlas  comprising ng atlas  that divng atlas , 37.http://www.brain-connectivity-toolbox.net). We calculated node-based network metrics including node degree, nodal efficiency Enodal, local efficiency Elocal and betweenness centrality, and global network metrics including global efficiency Eglobal, modularity and assortativity coefficient. Degree is the sum of all edge weights connected to a node. Nodal efficiency Enodal is defined as the reciprocal of the harmonic mean of shortest path lengths between a given node and all other nodes. The average Enodal across all nodes can be used to evaluate global efficiency Eglobal of parallel information processing in a network. Local efficiency Elocal is the global efficiency calculated in node neighborhoods. Elocal is an indicator of fault tolerance, measuring how well the information is transferred between the immediate neighbors of a given node when it is eliminated. Betweenness centrality is determined as the fraction of all shortest paths in the network that pass through a given node, thereby used to detect important nodes for information transfer. Modularity is defined as the ability of a network to be decomposed into subnetworks that are more linked within modules than between modules. Given that the optimal modular structure may be slightly different from run to run owing to heuristics in the algorithm  method was employed to correct for multiple comparisons.In the healthy controls, the mean and standard deviation (SD) of network measures were calculated for both left and right hemispheres separately as a reference standard for the patients. To evaluate abnormalities in the hemispheres contralateral to SOD in the patient population, network parameters were scored in terms of ed above . SpecifiElocal, nodal efficiency Enodal, and betweenness centrality in both hemispheres of the healthy controls and in the contralateral hemispheres of the patients. In general, degree, Elocal and Enodal were lower in the patients as compared to the healthy controls. These observations were confirmed by z-scores of network parameters in the contralateral hemispheres of the patients relative to the respective hemispheres of the controls , Elocal , and Egobal  were lower than zero, suggesting compromised network strength and efficiency in the patient cohort. The z-score of modularity was higher than zero , which suggests that the brain networks are subdivided into more clearly delineated groups in the patients. Similar results were obtained at connectivity sparsity = 20 and 30% , and between Enodal and CVR , while significance was not observed between Elocal and CVR , and between betweenness centrality and CVR  , and between Enodal and CVR  became significant.rrected) . For theThis study reveals altered organization of BOLD functional brain networks contralateral to unilateral anterior circulation steno-occlusive disease. Reduced degree and efficiency, and enhanced modularity of functional networks were observed in normal-appearing hemispheres contralateral to SOD in patients as compared to healthy controls, indicating potentially long-range consequence to hemodynamic impairment manifesting in network disruptions or reorganization even remote from areas of apparent disease. The network degree and nodal efficiency furthermore showed a close relationship with CVR, suggesting that cerebral hemodynamics may be predictive of BOLD network metrics.post-hoc analyses for estimating intrinsic functional connectivity can be applied on resting-state data to explore multiple functional networks, thus resting-state imaging is potentially well-suited to clinical settings, including patients unable to perform complex tasks.fMRI has been widely utilized to explore brain function  includinWhile most fMRI studies have interpreted BOLD signal as a measure of neural activity, based in the assumption that neurovascular coupling is relatively consistent , neurovaBOLD functional networks likely reflect underlying physiology. Recent fMRI studies in healthy participants have reported relationships between network efficiency and cerebral blood flow (CBF) , and betCerebrovascular disease may produce not only territorial, lacunar, and borderzone ischemia, but also demonstrable silent infarctions or even subclinical damage below the resolution of conventional imaging. It should be noted that neurophysiological changes occur even in anatomically intact areas . Remote 3) resolution was employed for BOLD imaging in this study. A recently developed multi-band multi-echo EPI  and spatial .SD — UNRELATED: Unpaid scientific consultation and collaboration with Ischemaview; Past travel support from Ischemaview as an individual; Scientific consulting with Regeneron as an individual; Domestic and international patent filed by institution for inventor's disclosure in the development of device to measure brain electrical properties; Grant funding paid to institution from undisclosed donor for study on the development of remote stroke detection devices. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Recent studies have identified characteristic changes in fatty acid metabolism in idiopathic pulmonary fibrosis (IPF) lungs, which implicates its dysregulation in the pathogenesis of this disorder. Here, we review the evidence for how fatty acid metabolism contributes to the development of pulmonary fibrosis, focusing on the profibrotic processes associated with specific types of lung cells, including epithelial cells, macrophages, and fibroblasts. We also summarize the potential therapeutics that target this metabolic pathway in treating IPF.Fatty acid metabolism, including the  Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic disorder of unknown etiology. It is usually associated with worsening respiratory symptoms, lung function decline, and limited responses to therapies. The worldwide incidence of IPF has risen steadily over time , and disAlthough the underlying mechanisms of these dysregulated fibrotic responses are not completely understood, recent evidence indicates that metabolic abnormalities play a critical role. For example, altered glycolysis and glutamine metabolism were found in human lungs with severe IPF . AdditioIn this brief review, we describe the general concepts in fatty acid (FA) metabolism and the pathology of IPF. We analyze the roles of this metabolic pathway in regulating the function of specific cells and the relevant pathologic cellular responses in IPF, including pro-fibrotic phenotype changes of alveolar epithelial cells and macrophages, and fibroblast/myofibroblast activation. We also discuss potential pulmonary fibrosis therapeutics that target this pathway.via the de novo FA synthesis pathway, supporting cell adaption to environmental changes and generating large amounts of adenosine triphosphate (ATP) through β-oxidation (Fatty acids (FAs) contain a terminal carboxyl group and a hydrocarbon chain, and mostly contain an even number of carbons; they can be saturated or unsaturated. The understanding of the role of FA metabolism in both healthy and disease physiology has recently been greatly advanced . Fatty axidation . As wellxidation  such as xidation .de novo FA synthesis. The FA biosynthesis pathway is highly conserved and occurs in the cytoplasm  in cells derive either from exogenous sources or ytoplasm . The TCAytoplasm . Subsequytoplasm . For exaytoplasm . Fatty a as CD36 , which ivia carnitine palmitoyl transferase 1 (CPT1) activity to form acylcarnitine which is subsequently transported into the mitochondrial matrix by carnitine acylcarnitine translocase (CAT). Acylcarnitine is then converted back to acyl-CoA and carnitine by CPT2 in the mitochondria. Acyl-CoA undergoes β-oxidation, which is a series of enzyme-mediated reactions that yield large amounts of intermediator metabolites that are subsequently utilized in the TCA cycle; carnitines are recycled by being transported out of the mitochondrial matrix (FA oxidation (FAO) is a major pathway for the utilization of FAs to generate biological energy. Free FAs in the cytosol are activated by acyl-CoA synthase to generate acyl-CoA. Acyl-CoA is conjugated to carnitine l matrix .Besides anabolism and catabolism pathways in the cytoplasm, some FAs serve as substrates for enzymatic conversation to lipid-derived mediators that are bioactive in tissue inflammation and organ injury. Arachidonic acid is incorporated into cellular phospholipids and is rapidly released from cell membranes by phospholipase A2 enzymes for enzymatic conversion to prostaglandins (PGs) and leukotrienes  as well An altered content and profile of saturated and unsaturated FAs have been identified in IPF patients and animal models of lung fibrosis; however, there is no consensus on the specific changes. One study observed a 63% increase in serum total FA levels in IPF patients compared with control subjects . Others The chemotherapeutic drug bleomycin is widely used as a means of inducing experimental lung fibrosis in animal models , includiExtensive changes in FA metabolism have been observed in IPF, suggesting its potential critical role in disease pathophysiology. Various FA metabolism pathways are intricately intertwined, and a perturbation of any of these in the lung may contribute to the development of pro-fibrotic phenotypes in epithelial cells, macrophages, and fibroblasts/myofibroblasts .Epithelial cell dysfunction is a central component in IPF pathophysiology. Alveolar type (AT)2 cells are the most active cell of lung lipid metabolism, with an important role in alveolar homeostasis involving surfactant biosynthesis, and as progenitor cells to both self-renew and transdifferentiate into AT1 cells . AT2 celFatty acid (FA) synthesis and composition are known to be involved in ER stress . Lipids in vivo and reduced the generation of TGF-β1  in IPF remains controversial, recent evidence indicates that multiple inflammatory mediators and essential FA-derived SPMs involved in EMT also function in lung fibrosis. For example, maresin 1 (MaR1), an SPM derived from docosahexaenoic acid, was found to inhibit TGF-β1-induced EMT and prevent the activation of Smad2/3, Akt, and the transcription factor Snail. Additionally, MaR1 treatment attenuated bleomycin-induced lung fibrosis f TGF-β1 . Similarf TGF-β1 . Other wf TGF-β1 .As the most abundant immune cell in the lung, macrophages play a vital role in the pathogenesis of pulmonary fibrosis. Most resident macrophages originate from progenitors in the bone marrow and migrate into different tissues where the local environment and signal cues shape the macrophage phenotype . Macrophvia the simultaneous escalation of FA uptake and a cycle of self-augmented profibrotic activation. The loss of CD36 was reported to inhibit lung fibrosis and reduce levels of pro-fibrotic Th2 cytokines including IL-9, IL-4, and IL-13 (There appears to be a strong relationship between macrophage polarization and its demand for FAs. Fatty acids provide energy to support macrophage polarization and activate signaling pathways to shape it, while FAO generates large quantities of ATP that is thought to promote M2 polarization. Indeed, increased levels of FAO were found in IPF lungs , suggestnd IL-13 . Lipoxinnd IL-13 .Dysfunctional epithelial cells and polarized macrophages generate large quantities of profibrotic cytokines that induce fibroblast differentiation into myofibroblasts. Myofibroblasts within IPF lungs have a pathologic phenotype , includiAdditional characteristic phenotypes of fibroblasts/myofibroblasts that contribute to the development of lung fibrosis include increased proliferation, resistance to apoptosis, and the acquisition of invasive activity . During de novo FA synthesis because the inhibition of FASN attenuated the invasive activity of TGFβ1-treated fibroblasts (-SMA promoter that contributed to its upregulation (Unlike normal fibroblasts, IPF fibroblasts invade the surrounding ECM much like metastatic cancer cells . The undroblasts . Anothergulation .via PPAR-γ activation and inhibiting collagen production via AMP-activated protein kinase activation (Therapies targeting FA metabolism have been tested in pre-clinical models of lung fibrosis . Promotitivation . Based otivation , while itivation .Abnormalities of FA metabolism in pulmonary fibrosis have received increasing attention in recent years. This review describes how the FA metabolism regulates the profibrotic phenotype of alveolar epithelial cells and macrophages, as well as fibroblasts/myofibroblasts activation in the IPF lungs and the lungs of mice with experimental pulmonary fibrosis. Understanding the mechanism of these metabolic abnormalities in IPF will open a new avenue of novel therapeutics.All authors contributed to the writing of the article and the development of the figures.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Whether all types of inhaled corticosteroids (ICSs) would increase the pneumonia risk in patients with chronic obstructive pulmonary disease (COPD) remains controversial. We aimed to assess the association between ICSs treatment and pneumonia risk in COPD patients, and the impact of medication details and baseline characteristics of patients on the association.Methods: Four databases  were searched to identify eligible randomized controlled trials (RCTs) comparing ICSs treatment with non-ICSs treatment on the pneumonia risk in COPD patients. Pooled results were calculated using Peto odds ratios (Peto ORs) with corresponding 95% confidence intervals (CIs).Results: A total of 59 RCTs enrolling 103,477 patients were analyzed. All types of ICSs significantly increased the pneumonia risk . Subgroup analysis showed that there was a dose-response relationship between ICSs treatment and pneumonia risk . Subgroup analyses based on treatment durations and baseline characteristics  of patients were consistant with the above results. Subgroup analysis based on severity of pneumonia showed that fluticasone  increased the risk of serious pneumonia, while budesonide and beclomethasone did not.Conclusions: ICSs treatment significantly increased the risk of pneumonia in COPD patients. There was a dose-response relationship between ICSs treatment and pneumonia risk. The pneumonia risk was related with COPD severity. Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death in the world, and acute exacerbations contribute substantially to this . TreatmeSome recent studies have raised concerns about increased pneumonia risk associated with long-term use of ICSs . HoweverThe aim of this meta-analysis was to objectively reappraise the pneumonia risk and serious pneumonia associated with various types of ICSs in COPD patients through all available RCTs. We also aimed to assess the impact of medication details (including dosage level and treatment duration) and demographic characteristics  of patients on this association.This meta-analysis was carried out according to the Preferred Reporting Items for Systematic review and Meta-Analysis . Ethics Trials.gov from inception until February 2021, using the following terms:  AND . Articles in English were included. Disagreements regarding eligibility were resolved by discussion by two investigators and, if necessary, consultation with a third investigator (Hao Yan).Two reviewers (Hong Chen and Jian Sun) independently searched the databases of PubMed, Embase, Cochrane Library, and Clinical 1) Participants: patients aged 40 yr or over, with stable, moderate (GOLD stage II) to very severe (GOLD stage IV) COPD. Patients with other respiratory diseases, such as asthma, bronchiectasis were excluded. 2) Interventions: various types and doses of ICSs as the intervention treatment. 3) Comparisons: non-ICSs treatment as a control treatment. 4) Outcome: Pneumonia for this meta-analysis was defined as an adverse event based on the Medical Dictionary for Regulatory Activities  pneumonia-related preferred terms, including “pneumonia,” “lobar pneumonia,” “bronchopneumonia,” “pneumonia pneumococcal,” or “pneumonia staphylococcal” (5) Study design: only RCTs were included. Non-RCTs, such as retrospective studies, reviews, case reports and case-control studie, were excluded.We included eligible studies based on the PICOS  criteria : 1) Partococcal” . One or ococcal” . 5) StudTwo reviewers (Qiang Huang and Yongqi Liu) independently identified references and extracted data from eligible RCTs. Any disagreements would be resolved by discussion to reach a consensus, and consulted a third reviewer if necessary. The risk of bias of the included RCTs was assessed by two independent reviewers (Hong Chen and Mengxin Yuan) using the Cochrane risk of bias tool . Any dis1) types of ICSs ; 2) doses of ICSs ; 4) treatment durations (short-term ICSs treatment [defined as ≤6 mo] and long-term ICSs treatment [defined as >6 mo]); 5) severity ; 6) age of patients (<65 yr old and ≥65 yr old); 7) body mass index (BMI) of patients (≥25 and <25).Subgroup analyses were conducted based on: p-value <0.05 was considered to be statistically significant. Statistical heterogeneity was further measured using the I2 test, and I2 ≥50% indicated a substantial heterogeneity  could provide the best confidence interval (CI) when events are rare , the pooogeneity .Our search identified 4,595 citations. After evaluating these citations, we included 59 RCTs . These tAll included studies were assessed using the Cochrane Collaboration risk of bias assessment tool. The results are presented in 2 = 16.4%) indicated that there was no significant difference in the pneumonia risk associated with different types of ICSs . Subgroup analysis based on types of ICSs showed that all types of ICSs increased the pneumonia risk . Test for subgroup differences (I of ICSs .2 = 74%) indicated that there was a significant difference in the pneumonia risk associated with different doses of ICSs , 26 RCTs , and 27 RCTs  assessed high-dose, medium-dose, and low-dose ICSs and pneumonia risk, respectively. Subgroup analysis showed that there was a dose-response relationship between ICSs treatment and pneumonia risk. Low-dose , medium-dose , and high-dose  ICSs all significantly increased the pneumonia risk. Test for subgroup differences (I of ICSs .2 = 0%) indicated that there was no significant difference in the pneumonia risk associated with different treatment durations of ICSs , and 28 RCTs  assessed short-term ICSs treatment and long-term ICSs treatment and pneumonia risk. Subgroup analysis showed that both short-term ICSs treatment  and long-term ICSs treatment  significantly increased the pneumonia risk. Test for subgroup differences (I of ICSs .2 = 90.1%) indicated that there was a significant difference in the pneumonia risk in patients with different severity.Eighteen RCTs , 34 RCTs  and two RCTs  assessed ICSs associated pneumonia in moderate, severe, and very severe COPD patients, respectively. Subgroup analysis showed that the pneumonia risk was related with COPD severity. ICSs treatment significantly increased the pneumonia risk in all severity subgroups of COPD patients:  . Test fo2 = 0%) indicated that there was no significant difference in the pneumonia risk in patients with different age  and 19 RCTs  assessed ICSs associated pneumonia in patients with different age. Subgroup analysis showed that ICSs treatment significantly increased the pneumonia risk in patients both age subgroups: . Test for subgroup differences (Irent age .2 = 0%) indicated that there was no significant difference in the pneumonia risk in patients with different BMI  and 23 RCTs  assessed ICSs associated pneumonia in patients with different BMI. Subgroup analysis showed that ICSs treatment significantly increased the pneumonia risk in patients both BMI subgroups: . Test for subgroup differences (Irent BMI .2 = 61.9%) indicated that there was a significant difference in the serious pneumonia risk associated with different types of ICSs  offered data on serious pneumonia associated with ICSs treatment. Compared with non-ICSs treatment, ICSs treatment significantly increased the serious pneumonia risk . Of the included RCTs, seven RCTs  assessed fluticasone and serious pneumonia risk, six RCTs  assessed budesonide and two RCTs  assessed beclomethasone, respectively. Subgroup analysis showed that only fluticasone significantly increased the serious pneumonia risk  while budesonide  and beclomethasone  did not. Test for subgroup differences  with high risk of bias, the pooled results were similar in magnitude and direction to those (pooled results of association between various types of ICSs and pneumonia risk) obtained from all included RCTs .In this meta-analysis of 59 RCTs , all types of ICSs, not only fluticasone, increased the pneumonia risk in COPD patients in a dose-dependent manner, and the risk was particularly evident in more severe COPD patients. Moreover, fluticasone increased the risk of serious pneumonia, while budesonide and beclomethasone did not. To our knowledge, this study was the first meta-analysis which revealed the pneumonia risk associated ICSs treatment was related with COPD severity. In addition, there was a dose-response relationship between the pneumonia risk and ICSs treatment.At present, ICSs are widely used in the maintenance treatment of COPD patients. Since numerous COPD patients use ICSs every day, both its efficacy and safety should be considered. Although some studies have reported that fluticasone increases the pneumonia risk in COPD patients, whether other types of ICSs would increase the pneumonia risk in COPD patients remains controversial . In addiOur results first revealed that all types of ICSs significantly increased the pneumonia risk in COPD patients regardless of treatment duration. The dose-response relationship further confirmed the causality of ICSs treatment and increased pneumonia risk in COPD patients. Moreover, our results revealed that the pneumonia risk was related with COPD severity. However, age and BMI may not be the determinants of ICSs associated pneumonia. In addition, we found that COPD patients receiving different types of ICSs may have different risk of serious pneumonia. Only fluticasone increased the risk of serious pneumonia, while other types of ICSs did not. We speculated that this may be due to the different pharmacodynamics and pharmacokinetic characteristics of different types of ICSs. Previous studies reported that fluticasone could exhibit a longer retention in the airway mucosa and thus have a more prolonged suppression of local immunity of patients .Trials.gov. In particular, we systematically searched the online supplementary documents of relevant RCTs. Indeed, pneumonia risk was not the primary outcome in most RCTs, some researchers provided data on pneumonia risk in the online supplementary documents rather than in the text. Second, compared with the previous meta-analyses, we conducted more subgroup analyses based on the baseline demographic characteristics of the patients  to clarify possible varied pneumonia risk in different patients receiving ICSs treatment. Third, our results indicated that all types of ICSs, not only fluticasone, increase the pneumonia risk in COPD patients in a dose-dependent manner, and the risk is particularly evident in more severe patients.Several previous meta-analyses  also assIn 2009, Singh et al.  performeThe major strength of our study was that we conducted a comprehensive literature search including all currently available RCTs, thus ensured the generalizability of the conclusions. Moreover, the multiple subgroup analyses based on the medication details (dose and treatment duration) and baseline of patients  enhanced the reliability of the conclusions, and also provided implications for the clinical practice. As far as we know, our study is the first meta-analysis which systematically assesses the association between various types of ICSs and the pneumonia risk based on baseline characteristics of patients.This meta-analysis had several limitations. First, none of the included RCTs were specifically designed to monitor pneumonia event, therefore, there may be underreporting of pneumonia incidence. However, the underestimate of the pneumonia risk could not substantially impact the pooled results of this meta-analysis, since underreporting of pneumonia incidence might occur equally in ICSs treatment groups and non-ICSs treatment groups. Moreover, in the sensitivity analysis, after removing four non double-blind RCTs, the results were consistent with the previous pooled results. Second, the pooled results of momethasone  and beclomethasone  may weakened by the relatively small sample size. Third, some studies were excluded because of incomplete data or non-English literature, which may lead to inevitable selection bias.ICSs treatment significantly increased the risk of pneumonia in COPD patients. There was a dose-response relationship between ICSs treatment and pneumonia risk. The pneumonia risk was related with COPD severity.