Understanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies and exploring accurate treatment strategies. miRNA–mRNA interactions have a major effect on many biological processes that are important in the formation and progression of cancer. In this study, we introduced a computational pipeline to extract tissue- and cohort-specific miRNA–mRNA regulatory modules of multiple cancer types from the same origin using miRNA and mRNA expression profiles of primary tumors. Our model identified regulatory modules of underlying cancer types  and shared regulatory modules between cohorts .MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA–mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA–mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA–mRNA regulatory modules separately. We tested the model’s ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts . We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA–mRNA signatures. Cancer is one of the most leading causes of death globally. Despite the remarkable improvements in cancer therapies, cancer patients remain undiagnosed or mistakenly diagnosed in many cases. This mainly happens when cancer therapy cannot match a specific disease due to insufficient knowledge of molecular mechanisms . There iMicroRNAs (miRNAs) are important non-protein-coding RNA regulators of gene expression by directly or indirectly targeting messenger RNAs (mRNAs), and they are also known to be involved in biological processes that impact the formation, progression and treatment of various cancer types . HoweverThe evidence presented thus far, besides similarities in the molecular mechanism of different cancers from the same tissue and the highly correlated nature of genomic data , clearlyIdentifying interactions between miRNAs and mRNAs has been improved in recent years due to several proposed computational techniques ,11,12. EPreviously, we established a single-task algorithm to identify miRNA–mRNA regulatory modules in cancer . Here, wIn this project, we established a multitask learning sparse regularized factor regression (MSRFR) model to increase the power and consistency of biomarker identification by targeting sample size disparity in different cancer cohorts of the underlying tissue.MSRFR efficiently extracts tissue- and cohort-specific miRNA–mRNA regulatory modules of multiple cancer types from a similar origin  using miRNA and mRNA expression profiles of primary tumors. MSRFR was able to simultaneously estimate the effective number of modules for each cancer type (cohort-specific) and for shared modules , and extract regulatory modules by imposing a low-rank structure and by grouping correlated mechanisms. We applied our algorithm on three sets of cancer cohorts of the same tissue .The predictive performance of MSRFR and the percentage of regulatory modules with significant survival analysis identified by MSRFR was significantly higher than the single-task algorithm , which ihttps://portal.gdc.cancer.gov, accessed on 10 August 2022). TCGA provided genomic characterizations (including miRNA and mRNA expression profiles) and clinical information of cancer patients. Our computational analyses did not include metastatic tumors since their underlying biology is generally different to primary tumors.In this work, we developed a predictive model that incorporates expression profiles and clinical phenotypes of multiple cancer cohorts into a unified learning framework to identify tissue- and cohort-specific miRNA–mRNA regulatory modules. We used Lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), Acute myeloid leukemia (LAML), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Lung adenocarcinoma (LUAD), and Lung squamous cell carcinoma (LUSC) data sets which are publicly available by the Cancer Genome Atlas .K cohorts indexed by k. For each cohort, we are given a training set i for cancer k, respectively. All symbols used in our model  must to be estimated in the learning process. With this low-rank assumption, instead of learning It is believed that the relationship between miRNAs and mRNAs is sparse . Hence, We explored a multitask variant of multivariate regression between miRNA and mRNA expression profiles of multiple cancer cohorts under the assumption that the columns of N, but the number of important factors is typically smaller than N, it is a credible assumption that the relationship between predictors and responses is sparse. To fit the model on such data, regularized or penalized methods are needed to perform dimensionality reduction and feature extraction. Moreover, we intend to extract similarities of regulatory modules in different tasks (cancer cohorts) which will be interpreted as tissue-specific miRNA–mRNA regulatory modules. To capture this similarity, a multitask learning formulation needs to be applied. Consequently, we proposed MSRFR to find tissue- and cohort-specific miRNA–mRNA regulatory modules of multiple cancer cohorts from their miRNA and mRNA expression profiles and estimate the effective number of regulatory modules as follows:K cohort-specific regulatory modules, respectively. k, respectively, k, respectively. Note that number of regulatory modules are chosen a priori before optimization such that Considering that the total number of responses (mRNAs) and predictors (miRNAs) are much larger than By imposing elastic penalty which linearly combines Selecting a large or small number of latent factors  in  in (3a)WOur optimization strategy is described in Algorithm A1 with more details The algorithm’s pseudocode is presented in R. After finding For the sake of simplicity, we refer to analyses , since iSimilar to our previous strategy , we deteTo pick the key regulatory modules with biological relevance among all identified regulatory modules, we filtered the regulatory modules by performing functional survival and functional gene set enrichment analyses .k-means clustering based on the mRNA expression profiles. We then examined survival rates in the clinical parameters by checking whether there is a statistically significant difference between the two groups of patients using the log-rank test. Significant survival difference refers to a module with different mRNA expression levels of patients of the underlying cohort. The main motivation of this process was exploring the functional importance of identified regulatory modules.First, to check if the MSRFR-identified mRNAs are related to the survival rate of all patients, we classified patients into two groups using In the second step of filtering, regulatory modules that seem to have biological relevance from filtering step one were further evaluated in functional gene set enrichment analysis .A regulatory module is categorized as a key regulatory module if “survival analysis of the corresponding module reports a significant difference between patient groups” and “underlying module is enriched in either tissue-specific or cohort-specific gene sets”.Following the biological validation strategy, we attempted to investigate the identifying transcription factors (TFs) in miRNA–mRNA signatures. To determine whether or not TF regulation is affected by cancer-specific miRNA–mRNA signatures, we assessed the currently known human TFs  in the rTo test the applicability of our MSFRF model, we applied it on three pairs of cancer cohorts where each originated from the same tissue .Specifically, Lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) and Acute myeloid leukemia (LAML) cohorts, originated from blood, whilst kidney renal clear cell carcinoma (KIRC) and Kidney renal papillary cell carcinoma (KIRP) occur in kidney, and Lung adenocarcinoma (LUAD) and Lung squamous cell carcinoma (LUSC) derive from lung tissue.To set reasonable values to hyper-parameters of Problem , i.e., λBlood Kidney Lung We trained the algorithm with all combinations of predefined parameters using four-fold cross-validation. Then, we calculated the average root mean square error (RMSE) of four folds between predicted and actual mRNA expression levels for all parameter combinations. For each experiment, we picked the set of parameters with the minimum average RMSE. Finally, the parameters of the three experiments are set as follows:We considered k indexes the cancer cohorts.We tested the performance of MSRFR by calculating the normalized root mean squared error (NRMSE) of our algorithm between observed and predicted values of mRNA expression levels against the single-task algorithm  usingY^The predictive performance comparison showed that MSRFR algorithm explained a higher proportion of variance than the single-task algorithm in five out of six cohorts using fewer regulatory modules . By applk-means clustering on the expression values of the selected mRNAs. Next, we acquired the expression values of selected miRNAs and mRNAs and used the expression profiles of both cancers in each tissue to cluster the patients and to address their survival rates , we clustered patients into two groups by applying al rates a,b. SignFor tissue-specific analysis, we selected the overlap of regulatory modules with significant differences in both cancer cohorts of the same tissue. Interestingly, we find that the differences in expression levels of the identified miRNA–mRNA signatures seem to relate to survival differences in the different cancer patient groups . These fp-value < 0.05 in the log-rank test). To demonstrate the detection power of clinical characterization using the proposed method, we compared the percentage of regulatory modules with significant survival differences in this study against another algorithm [In 27 of 75 identified regulatory modules in total (36%), we observed a significant survival difference between the two groups . We found 118 tissue-specific modules and 220 cohort-specific modules in a total of 12 experiments. Of note, only 4/118 (3.39%) of tissue-specific regulatory modules presented significant survival differences, while 64/220 (29.10%) of cohort-specific regulatory modules are significant in survival analysis A. The raAccordingly, survival analysis based on clinical information in non-relevant cohort pairs was significantly lower than identified regulatory modules with significant survival differences in tissue-specific regulatory modules. However, there is no significant difference in cohort-specific regulatory modules B. These We conclude that MSRFR increases the detection power of the model by multitask formulation, enhances the capacity of the model in grouping genes that participate in the same processes while extracting fewer modules, effectively determines biologically related miRNA–mRNA regulatory modules by inferring information from other tasks and it distinguishes tissue- and cohort-specific regulatory modules from each other to extract tissue-specific information from different cohorts of disease-related tissue.We also applied MSRFR on healthy samples of kidney and lung that are deposited in TCGA and compared the findings to the two cohort-specific and their shared tissue-specific first regulatory modules. We found marginal overlaps between healthy and disease samples , indicatWe investigated the relevance of the identified cohort-specific regulatory modules underlying cancer using a literature survey validation by checking whether identified miRNAs are important for formation or progression of the corresponding cancer type. For 68 selected miRNAs out of 161 identified miRNAs (42.24%) in the first regulatory module of six different cohorts, we found published records in PubMed. The ratio of selected miRNAs with literature support in LUAD and LUSC cohorts was higher than in the other cohorts . DetaileMoreover, to see if identified miRNA–mRNA signatures are known in the literature (PubMed), we investigated all possible duplex combinations of the top 2 and top 10 selected miRNAs and mRNAs of each cohort/tissue. Specifically, we looked for direct or indirect associations as biomarkers in the formation, progression, or treatment of cancer. For example, mir-152 and mir-30e, the top two selected miRNAs by MSRFR for LUSC cohort, have been shown to improve the non-invasive diagnosis of renal cell carcinoma . mir-142To check if TF expression could be affected in the investigated primary tumors and their specific miRNA–mRNA signatures, we assessed the currently known 1639 human TFs  in our np-value = 0.004) than in random sets (To test if the number of overlapping TFs is meaningful, we developed a permutation test and compared our result with random sets of genes. To this end, we randomly picked 1030 genes 10,000× and compared the percentage of TFs among random experiments and MSRFR results. The maximum average of randomly identified TFs was 10.49%. In 6 out of 9 cohorts/tissues the average number of TFs identified by MSRFR deterministically dominates the average number of TFs in random genes A. We alsdom sets B.The detailed information on detected TFs in regulatory modules are listed in Understanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies. miRNAs’ interactions with mRNAs have a major effect on many biological processes that are important in the formation and progression of cancer. Therefore, identifying both cohort- and tissue-specific miRNA–mRNA regulatory modules of cancers have received considerable interest due to its importance in cancer biology.This study introduces a pipeline to extract tissue- and cohort-specific miRNA–mRNA regulatory modules of multiple cancer types from the same origin using matched miRNA and mRNA expression profiles of primary tumors. We generated a multitask sparse regularized factor regression model which was able to successfully extract and distinguish tissue- and cohort-specific regulatory modules and estimate the effective numbers of cohort-specific and tissue-specific regulatory modules.Out of all six considered cohorts, MSRFR model outperformed single-task regression method in 5/6 cohorts see . We wereCollectively, these results show that the proposed model is highly effective in identifying key miRNA–mRNA regulatory modules and distinguishing cohort-specific and tissue-specific regulatory modules.There is an abundant room for further progress in determining similarities in molecular patho-mechanisms. Extensions of this study can be applied to investigate other diseases where similar primary cells are involved to find cohort-specific mechanisms together with the mechanisms shared among underlying conditions. Moreover, further work is required to establish complementary research on other RNAs in the non-coding genome, such as long non-coding RNAs, to decode their functional similarities in different conditions.


Plants constantly face a diversity of pathogens and insects that affect food production. Synthetic agrochemicals are often use to overcome these challenges. However, current demands for stringent worldwide regulatory policies led to the development of sustainable agriculture strategies, including naturally-derived molecules that elicit plant defense responses . The comThis Research Topic highlights emerging trends in plant defense response induced by elicitors, focusing on the progression from molecules to plant communities and crops, and interdisciplinary approaches to decipher the complexity of the mechanisms underlying plant-pathogen and plant-insect interactions.Klemm et al. sheds light on the evolution and regulation of a large protein family and provides a framework for a more detailed understanding of the molecular functions of Kiwellins. The biological function of Kiwellins is apparently linked to plant defense. Klemm et al. found that Kiwellins are evolutionarily conserved in various plant species and emerged in land plants (embryophyta) and are absent from fungi. They also introduce a systematic Kiwellin nomenclature based on a detailed evolutionary reconstruction of this protein family. Hwang et al. showed that an apoplastic effector Pat-1Cm of the gram-positive bacterium Clavibacter michiganensis acts as both a pathogenicity factor and an immunity elicitor in plants. The results indicate that Pat-1Cm is a distinct secreted protein carrying a functional catalytic triad for serine protease and this enzymatic activity might be critical for both pathogenicity and HR-eliciting activities of Pat-1Cm in plants.Several publications herein bring important news about plant immunity. Agisha et al. studied the sugarcane molecular defense mechanism in response to two Sporisorium scitamineum isolates with varying degrees of virulence to facilitate the development of smut-resistant sugarcane varieties. Functional analysis of differentially expressed genes (DEGs) revealed that most of them were associated with hormone signaling and the synthesis of defense-related metabolites, suggesting a complex network of defense mechanisms triggered in response to specific isolates of the smut pathogen. In turn, Zhang et al. explored how cucumbers can regulate photosynthesis, protective enzyme activity, and basic metabolism to resist fungal disease (Sphaerotheca fuliginea) and aphids (Aphis gossypii). The combination of powdery mildew infection and aphid infestation reduced photosynthesis and basal metabolism in cucumber plants, although the activities of several protective enzymes increased. The results showed that cucumber could enhance its pest/pathogen resistance by changing physiological metabolism when exposed to a complex infection system of pathogenic microorganisms and insects.Wang et al. showed that the allotetraploid Nicotiana benthamiana recognizes two types of microbe-associated molecular patterns (MAMPs) by two homologous but diverged receptor-like proteins (RLPs), sugesting that an allopolyploid plant exhibits defense hybrid vigor by acquiring divergent immune receptors from different ancestor. Basu et al. introduced a novel therapy of Huanglongbing (HLB) and fire blight by enhancing the innate immunity of the host plant. Specifically, they constructed an in silico library of chimeras containing two different host peptides with observed or predicted antibacterial activity. Finally, they conducted ex planta studies to show that chimeras not only clear the causative bacteria from citrus leaves with HLB and from apple leaves with fire blight, but also augment host innate immunity during infection.Mao et al. broadened the understanding of how potato plants integrate responses to a multitude of stimuli upon herbivory by the potato tuber moth  and evidenced that insect-associated microbes greatly modulated the plants response to insect herbivory. Dejana et al. using the tomato-Funneliformis mosseae mycorrhizal system, analyzed the effect of moderate differences in P fertilization on plant and pest performance, and on Mycorrhiza induced resistance (MIR) against biotic stressors including the fungal pathogen Botrytis cinerea and the insect herbivore Spodoptera exigua. The results showed that P influences mycorrhizal priming of plant defenses and the resulting induced-resistance is dependent on P availability, and suggest that mycorrhiza fine-tunes the plant growth vs. defense prioritization depending on P availability. Finally, their results highlight how MIR is context dependent, thus unraveling molecular mechanism based on plant defense that will contribute to improved efficacy of mycorrhizal inoculants for crop protection.Plants rely on the perception of a multitude of herbivory-associated signals to activate their defenses to insect herbivores. These stimuli are mainly derived from three functional components, namely, mechanical damage, insect-associated microbe, and insect’s chemical cues . In thisThe editorial board of this special Research Topic - Eliciting Plant Defense Responses: from Basic to Applied Science for Sustainable Agriculture thanks the contributing authors for adding their knowledge to the field of plant immunity.JM, AM-M, KS-E, and MS contributed equally to the production of the Research Topic. All authors contributed to the production of the editorial.


Traumatic brain injury (TBI) induces instant activation of innate immunity in brain tissue, followed by a systematization of the inflammatory response. The subsequent response, evolved to limit an overwhelming systemic inflammatory response and to induce healing, involves the autonomic nervous system, hormonal systems, and the regulation of immune cells. This physiological response induces an immunosuppression and tolerance state that promotes to the occurrence of secondary infections. This review describes the immunological consequences of TBI and highlights potential novel therapeutic approaches using immune modulation to restore homeostasis between the nervous system and innate immunity. Traumatic brain injury (TBI) is a significant cause of morbidity and mortality, mainly in young patients . In addiThe susceptibility of patients with TBI to nosocomial infections is correlated with the development of a state of immunosuppression that sets in in the direct aftermath of the trauma  some chaAfter TBI, meningeal contusion, axonal shearing, or cerebrovascular injury alter the functions of glial cells for days . The relAt the same time, trauma-activated platelets and the subsequent coagulation cascade release pro-inflammatory mediators interacting with the immune system  and geneOther pathophysiological consequences include blood-brain barrier disruption, cellular swelling and vasogenic edema. HMGB-1 released by necrotic neurons induces the production of IL-6 by microglia resulting in aquaporin water channel expression in astrocytes and cytotoxic swelling . In TBI,The important release of neurotransmitters  in the extracellular space induces changes in the anti- and pro-inflammatory functions of glial cells and modulate neuroinflammation via numerous signaling pathways  is a complex neuroendocrine system controlling stress responses and regulating many vital functions. In response to stress the hypothalamic release of corticotropin-releasing hormone (CRH) is increased which leads to the secretion of the pituitary adrenéno-coticotropic-hormone (ACTH). In turn, the adrenal cortex synthesizes and releases cortisol into the circulation . CortisoPost-traumatic activation of the HPA axis usually results in the simultaneous release of dehydroepiandrosterone (DHEA). However, in the immediate aftermath of TBI, cortisol levels remain highly predominant over DHEA levels . The incRapidly after a TBI, there is a maladaptive secretion and abnormal cortisol response, so-called critical illness-related corticosteroid insufficiency (CIRCI). CIRCI is the impairment of the HPA axis secondary to inflammation which cannot respond adequately to secondary stimulations . CorticoThree major pathophysiological events account for CIRCI: anatomical disconnection of the HPA axis, alteration of the cortisol metabolism with decreased cortisol degradation , and tisTBI induces alterations in immune cells and each plays an important role in establishing an immunosuppression and tolerance state .bright) circulates in the peripheral blood and trauma is accompanied by a significant release of immature neutrophils (CD16dim/) into the circulation , neutrophils from TBI patients do not upregulate the CD11b and FcγRII receptors  persisting for several hours and also induces a defective phagosomal maturation thus decreasing the anti-bacterial capacities of neutrophils  count and maturation status are affected in ICU patients . A reducFor example, our team has shown that circulating DC from trauma patients express a tolerogenic transcription factor, Blimp1, characteristic of tolerogenic functions . In a moDim NK cells decreases significantly at the expense of immunosuppressive CD56Bight cells. On day four after TBI, the percentage of perforin-positive NK cells is also reduced compared to healthy controls  cells. These subsets include Th1, Th2, Th17 and regulatory T (Treg) cells. Each of these subtype’s functions differently in the aftermath of TBI.When activated after TBI, CD4+ Th1 cells require IL- 12 and transcription factor T- bet in order to produce IL- 2, IFN-γ and TNF-α  making their use impossible. From 2 weeks after the TBI, molecular imaging of microglia and brain macrophages like positron emission tomography (PET) and magnetic resonance (MR) imaging can be interesting. PET using radioligands specific of microglia activation has shown good sensibility and specificity regarding cerebral inflammation evaluation . AdvanceTo correct posttraumatic immunosuppression and prevent nosocomial infections or to limit chronic neuroinflammation, many therapies have been evaluated in recent years. They aimed either to limit initial and chronic inflammation, including the use of low-dose glucocorticoids , 194, orIn TBI patients, early administration of corticosteroids seems to be an attractive therapeutic approach. The administration of corticosteroids in this context of relative post-traumatic immunosuppression may be a paradox, but the effects of corticosteroids, especially at low doses, are pleiotropic. Indeed, in TBI patients, corticosteroids may decrease immunosuppression and the occurrence of nosocomial infections by two mechanisms:•During the inflammatory phase, steroids decrease the secretion of proinflammatory cytokines, the expression of PRRs, and the adhesion and recruitment of T cells and neutrophils . The int•Glucocorticoids modulate dendritic cells during and after inflammation to restore their central role in the immune response , therebyAll these arguments suggest that in patients with severe TBI and an exacerbated overcompensated inflammatory response, glucocorticoids may be beneficial in preventing nosocomial infections. The challenge remains to find biomarkers to identify these highest-risk patients who may benefit from this corticosteroid therapy.IL1 β is a pleiotropic cytokine secreted immediately after TBI. In the brain, IL1 β is secreted by microglia and induces the inflammatory cascade by stimulation of astrocytes and other brain cells. IL-1β can activate both the MAPK and NF-κB pathways. Activation of these pathways results in the transcription of proinflammatory genes and amplification of the cerebral inflammatory response. Recombinant interleukin-1 receptor antagonist (IL1ra) is an attractive therapeutic option to reduce early brain inflammation and limit secondary damages. Experimental studies demonstrated that blockade of IL-1β through treatment with IL-1Ra results in inhibition of inflammatory cascades, microglial activation, and proinflammatory cytokine expression . In miceTNF-α is a ubiquitarian proinflammatory cytokine localized in the plasma membrane as transmembrane TNF-α and is highly expressed in brain cells . TNF actTBI induces immediate vasogenic edema which requires an increased BBB permeability to serum proteins and immune cells. Increased BBB permeability after TBI with subsequent edema formation has been recently linked to substance P (SP) release . NeurokiFinally, authors hypothesize that the increase in SP-mediated protein transcytosis increases vascular permeability, contributing significantly to the development of increased intracranial pressure and a major influx of circulating immune cells via the BBB. Administration of NK1 antagonists reduces this protein transcytosis, decreasing vasogenic edema and reducing BBB permeability .The coagulation and immune systems interact following the TB mainly through the activation of complements. Tranexamic acid competitively inhibits the conversion of plasminogen to plasmin, thereby inhibiting fibrinolysis. Its efficacy was demonstrated in the CRASH 2 trial  in polytAs seen previously, TBI induces an inability of the antigen-presenting cells to present antigen to the effector T cells, notably via the human leukocyte antigen DR (HLA DR). Numerous studies, both in the laboratory and in the clinical setting, have focused on HLA-DR, as its expression on monocytes has been shown to reflect the host’s ability to present antigens , 220. Poin vivo in trauma. GC-SF which stimulates stem cells to produce granulocytes was tested in 61 adults with GCS score ≤ 8, showed a dose-dependent increase in neutrophil count and a significant decrease in bacteremia incidence but no effect on strong ICU outcomes (Acquired neutrophil dysfunction is central to immune system failures after TBI. Among them, impairment of neutrophil phagocytosis is associated with occurrence of nosocomial infections.  and resuoutcomes .via improving mTOR signaling, GLUT1 surface expression, and glucose uptake by T cells (Recent advances in cellular metabolism allow us to consider new therapies for inflammatory diseases. Immunometabolism is the study of immune cell metabolism and aim to develop therapies which modulate metabolism and regulate endogenous metabolites with anti-inflammatory effects . By modu T cells  and targ T cells . In TBI  T cells . Metform T cells . Metform T cells . Rapamyc T cells . The tar T cells . Treatme T cells . There a T cells .In recent years, new therapeutic approaches have emerged to limit the chronicity of neuroinflammation linking to chronic neurodegenerative disease such as Parkinson’s or Alzheimer’s . LimitinTBI initiates both local and systemic inflammatory responses which aim at enabling brain-tissue healing while limiting the risk of overwhelming inflammation. Given the strong interplay between the brain and peripheric organs in health conditions, the demonstration that the time course of the inflammatory responses in the brain and systemic compartment are highly correlated was almost expected, but still suggests that immune interventions have the potential to enhance neurological outcomes after severe brain injury. While several approaches can be proposed for pilot evaluations in humans, it will be crucial to deeply characterize both the systemic and brain effects of such approaches to understand potential unexpected effects and define subgroups of responders and non-responders.MB and AR wrote the draft. KA extensively reviewed the manuscript. All authors approved the manuscript before the submission.


Immunotherapy is crucial in fighting cancer and achieving successful remission. Many novel strategies have recently developed, but there are still some obstacles to overcome before we can effectively attack the cancer cells and decimate the cancer environment by inducing a cascade of immune responses. To successfully demonstrate antitumor activity, immune cells must be delivered to cancer cells and exposed to the immune system. Such cutting-edge technology necessitates meticulously designed delivery methods with no loss or superior homing onto cancer environments, as well as high therapeutic efficacy and fewer adverse events. In this paper, we discuss recent advances in cancer immunotherapy delivery techniques, as well as their future prospects. Many strategies for inducing an antitumor response against cancers have been developed. Conventional treatment modalities include surgery, radiotherapy, and chemotherapy, each of which has had advantages and disadvantages with limited success in improving clinical outcomes. A thorough understanding of how tumors interact with the host immune system will aid in development of cancer therapeutics . The tumCancer employs a variety of strategies to evade the immune system, including delaying or even stopping antitumor activities. These immune-evading mechanisms overpower the natural immune system’s antitumor activity, and promote tumor formation, and metastasis. These mechanisms continue to evolve as cancer progresses, becoming more diverse and complex in late-stage malignancies. Various host factors that make up the immune system influence treatment outcomes, which can lead to disease progression or regression. Blocking these immune evasion strategies had led to the discovery of new strategies for strengthening the immune response against cancer . Recent Various strategies are employed by tumors to evade the immune system, such as downregulating antigen processing or presenting machinery  and LMP 7, transport associated with antigen processing (TAP) protein and tapasin) as to not be recognized by T cells, which is the triggering point of recognizing and attacking tumor. Another strategy employed by tumors is to downregulate IFN signaling. which may evade antigen presentation and subsequently result in evasion from the immune system. Cancer cells also cause T cell exhaustion by increasing the PD-1 and PD-L1 expression by various inflammatory and oncogenic signals leading to immune evasion. Other immune suppressive modulators such as TGF-γ, IL-8, IL-10, VEGF can be secreted into the TME by tumor cells, which in turn suppress dendritic cell maturation and T cell functions. Tumor cells may suppress T cell function by manipulating the metabolic composition in the TME to wither its activity effectively .The cancer–immunity cycle is a schematic representation of the principles of cancer immunotherapy. This cycle begins with the release of tumor antigens, which are taken up, processed, and presented to naive T cells (APCs). As a result, cytotoxic T cells are produced that can specifically recognize and kill cancer cells. Lysed cancer cells then release antigens and costimulatory signals, triggering another round of the immune response cascade. Tumors can disrupt critical elements of the cancer–immunity cycle via a variety of negative feedback immune regulatory pathways, which are increasingly becoming cancer immunotherapy targets . These tBacteroides fragilis with Bacteroides thetaiotaomicron or Burkholderia cepacian increased Th1 response and DC maturation, subsequently enhancing the efficacy of anti-CTLA-4 therapy [Clostidiales, Ruminococcacease, and Faecalibacterium are increased. Studies have found that the correlation between gut microbiota with respect to immunological profiling in the tumor microenvironment has demonstrated that cytotoxic T cell marker expression was augmented with antigen presentation and processing in patients having favorable gut microorganisms when compared to patients with unfavorable gut microbes [The relationship between intestinal microbes and the immune system is mutual in developing tolerance against symbiotic bacteria and antigens present in the food. This makes the immune system prepare innate and acquired immunity against invading microorganism. There needs to be a balance between recognizing gut microbiota and invading pathogenic microbes. The metabolites produced by the microorganism in the gut can alter the balance of inflammatory cytokines in the body and disrupt the production of T cell subsets . Recent  therapy . Similar therapy ,12,13. Imicrobes . FindingImmunotherapy is fundamentally changing the clinical cancer treatment landscape. It outperformed standard-of-care therapy in several cancer types, including malignant melanoma and lung cancer, resulting in a number of cases with remarkable outcomes, such as total regression of advanced-stage (metastasized) tumors and prolonged disease-free survival . In addiCheck point inhibitors are the most extensively studied cancer immunotherapy modalities. CTLA4 inhibition and PD-1/PD-L1 blockade are the two most commonly used check point inhibitors. Check point inhibitors regulate the immune response to abnormal cells while protecting healthy tissues from immune attack . T cellsCancer immunotherapy clinical trials are being planned in conjunction with check point inhibitors and chemotherapies or other agents . Still, + and CD8+ cells. There is a number of interleukins that have pro and antitumor activity (IL-4) depending on the tumor type, stage and location and type of cells which produce them. IL-2 is important for T cell proliferation, whereas IL-10 inhibits T cells activation [Recombinant IFN was the first cytokine to be approved for immunotherapy in 1986. Since then, interferons, interleukins, and granulocyte macrophage colony-stimulating factor (GM-CSF) have been studied clinically for their immunotherapy potential . Interfetivation . GM-CSF tivation . Currenttivation .Tumor cell lysate, dendritic cells, and nucleic acids are examples of cancer vaccines ,34,35. DNucleic acid therapeutics such as DNA- or RNA-based vaccines are emerging as alternatives to conventional vaccines . NucleicVaccines are based on neoantigens that can boost the immune response against cancer cells ,43. BecaAgonistic antibodies are specifically designed to bind to T cell receptors and activate intracellular signaling pathways in order to effectively combat cancer cells. Monoclonal antibodies (mAbs) targeting immune checkpoints such as CTLA-4 and PD1/PD-L1 have recently been developed for antitumor activity . AgonistFollowing successful clinical trials and FDA approval, T cell engineering has recently gained attention. Autologous T cells were collected from cancer patients’ blood and genetically engineered to express chimeric antigen receptors found on tumor cells but not on healthy cells in the CAR-T cell approach. CAR-T cells recognize the target antigen on tumors and induce tumor cell death when re-engineered T cells are infused back into the patient ,53. The Since 2017, the US FDA has approved six CAR-T therapies for blood cancers such as lymphoma, certain leukemias, and, most recently, multiple myeloma. CD-19 is a target antigen for B cell acute lymphoblastic leukemia (ALL), B cell non-Hodgkin lymphoma, follicular lymphoma, mantle cell lymphoma (MCL), and B cell maturation antigen (BCMA) targeting against multiple myeloma . T cell Oncolytic viruses have shown promise in the treatment of cancer. Specific viruses that can replicate in the cancer cells induce antitumor immune attacks in the tumor ,62. ViruThe route of administration (ROA) of a drug can affect its therapeutic efficiency during the delivery process ,74,75 ROSuccessful cancer immunotherapy necessitates efficient and effective delivery methods, as well as drug efficacy that is specific and less toxic to host cells . The difNanomaterials are advantageous in several parameters, including surface-to-volume ratio, photo dynamics, magnetic and electrical conductivity, optical absorption, and fluorescent behavior properties, which make them an effective additive in cancer immunotherapy . Recent Nanoparticle-based approaches to drug delivery drugs to tumors have attracted the interest of researchers because they are cancer cell target specific . NanoparDeng et al. used NK cell-masked nanoparticles, which can be activated by photodynamic therapy to attack the cells and induce immunogenic cell death (ICD), releasing damage-associated molecular patterns, as well as NK-coated cells targeting M1 macrophages, which eventually promotes antigen-presenting cells’ (APCs) maturation, leading to T cell activation and elimination . Cancer 3 nanocomplexes were capable of activating immune cells and neutralizing tumors in a 4T1 breast cancer mouse model [Copolymer micelles have been shown to have EPR effects when used to target tumors. Grafting polylactic acid onto carboxymethyl cellulose as a copolymer and functionalizing with anti-EpCAM antibody can be used for doxorubicin chemotherapeutic drug delivery against hepatic cells (HepG2). Doxorubicin drug release was in specifically at the tumor site, and functionalized drug-loaded micelles exhibited antitumor effects in both in vitro and in vivo conditions . Chiang se model . Badrinase model . Anotherse model .Nano vaccines are intended to contain tumor-specific antigens as well as TAA in order to suppress the tumor. Nano vaccines target antigens or components found exclusively in tumors or expressed in tumors. APCs such as macrophages and dendritic cells will come into contact with vaccine antigens . Cell-, Jin et al. demonstrated an in situ cancer vaccine-based approach; in their study, they designed in situ vaccines by combining two synergetic approaches. First, ferrimagnetic nano cubes were encapsulated into an amphiphilic polymer, which generates the antigens by a magnetic field and destroys the primary tumor, and another polymeric nanoparticle coated with adjuvant R848 (resiquimod) delivers the formed antigens to the lymph node, activates the APCs and creates an antitumor immune response to distant tumors . Li et aIn another study, fluoropolymer combined with antigen ovalbumin aided dendritic cell maturation and antigen presentation, leading to tumor suppression. When these fluoropolymers were combined with antigens from resected cell membranes from primary tumors, it resulted in inhibition of tumor recurrence and metastasis . Luo et Organic and inorganic nanoparticles were combined in various ways to create effective photothermal agents to debilitate cancer cells ,144. BecMany strategies have been employed in attacking the TME using nanoparticles; one such strategy is to target the fibroblast cells associated within the tumor environment. Cancer-associated fibroblasts (CAF) were targeted using various nanoparticle delivery methods, and were able to be delivered into the deeper stroma reducing α-SMA (smooth muscle actin) levels around the tumor tissue and subsequently destroying the cancer cells ,149,150.Extracellular vesicles are small membrane vesicles formed by fusion of the plasma membrane and endosomes that are secreted by cells . As everDendritic cell-derived exosomes increased NK cell antitumor activity in a clinical trial against non-small cell lung cancer (NSCLC) as maintenance immunotherapy for patients undergoing chemotherapy ,169. WanImplantable functional scaffolds are frequently used in cancer immunotherapy to reprogram the biological responses by delivering bioactive chemicals or cells in a controlled manner . BiomateLong-term stimulation of APCs was achieved by constructing a 3D microporous alginate-reduced graphene oxide (rGO) scaffold loaded with GM-CSF, ovalbumin, and cytosine–phosphate–guanine oligonucleotides. The rGO component of the implantable scaffold’s large surface area and hydrophobic surface allow for significant loading and a very gradual release of a loaded antigen. In a B16 melanoma tumor model in mice, the scaffold recruited dendritic cells, which then activated T cells, effectively suppressing the tumor . AnotherA cancer vaccine composed of whole tumor lysate-based antigens and nanoadjuvants expressing Toll-like receptor (TLR) 3 agonists, as well as gemcitabine as an MDSC-depleting agent, was shown to improve antitumor immunity by lowering immune suppression in the tumor microenvironment . SimilarBecause each type of biomaterial has distinct advantages in certain contexts, the choice of biomaterial design, whether injectable or implantable, is fundamentally driven by application requirements. Injecting implantable materials directly into the organs or tissues is a much less invasive procedure than surgical implantation, and it reduces the risk of tissue damage and the inflammatory response associated with wounds . To makeIn another study, they created an intelligent drug delivery system with controlled and sustained drug release. They created in an injectable nanofiber hydrogel by combining betamethasone phosphate and calcium ion with anti-programmed cell death protein ligand 1 antibody (αPDL1), which results in cross-linking filamentous assemblies. By blocking the NF-B signaling pathway, the anti-inflammatory steroid betamethasone phosphate has been incorporated into an injectable nanofiber hydrogel to reprogram the protumoral immunosuppressive TME, and the sustained release of PDL1 from the hydrogel stimulates the T cells to synergistically increase the immunological response of tumor cells . AnotherThe advancement of clinical grade bench-to-bedside technology for isolating, genetically engineering, and ex vivo expansion of T cells from one’s own patient blood has brought T cell-based therapies to the forefront of cancer immunotherapy . Tumor-iAdoptive cell therapy is one such T cell therapy that is effective in treating blood cancers. Chimeric antigen receptor-T cells (CAR-T) are one such therapy that has recently received several FDA approvals, and products in the US, for which patient blood is collected and T cells are engineered to treat a variety of B-cell malignancies. Although this technology is effective for blood cancers, its limited effectiveness against solid tumors due to poor infiltration against the complex tumor microenvironment has prompted researchers to look for a delivery system that will allow CAR-T cells easier access to cancerous cells . Using iCombination therapy in one or more modalities can have a synergistic effect on the treatment of solid tumors. Hu et al. carried out one such study where they used CAR-T cells in combination with immune checkpoint inhibitors in a melanoma mouse model. Biodegradable hydrogel encapsulates CAR-T cells, targeting human chondroitin sulfate proteoglycan 4 (CSPG4.CAR) with nanoparticle-coated IL-15 and anti-PDL1 conjugated with human platelets in combination, allowing IL-15 to activate and proliferate CAR-T cells while blocking the PD1/PDL1 pathway to eradicate tumor cells . VariousWith new delivery technologies emerging for cancer immunotherapy, one has to carefully choose the appropriate method by which drug efficiency can be improved. Some of the delivery technologies are listed below with their advantages and disadvantages for cancer immunotherapy .Clinical trials are conducted using new delivery systems for cancers. To analyze, clinically translate and market personalized medication, rigorous research is required. Some of the clinical trials that have been studied to determine their effectiveness and safety are shown in below in Cancer drugs require novel delivery systems to make them effective and safe therapies. Recent anti-tumor therapies are designed in such a way they are efficacious in dealing complex tumor environment. Listed below in New developments in understanding the cancer prognosis and novel therapeutic approaches have called for innovative delivery methods in administering anticancer drugs. Immunotherapy-based drugs are currently studied in various types of cancers; their effect on solid tumors is meager because the low infiltration of immune cells makes lower tumor immunogenicity, leading to an immunosuppressive tumor environment. Developing unique and novel drug delivery systems in combination with multiple cancer therapies would allow the treatment of solid tumors. The key issues such as the controlled release of drugs at the specific site, techniques to assess these delivery mechanisms and their effect on the cellular or molecular level are some of the constraints in developing a robust delivery system. In the past thirty years, cancer nanomedicines-based approaches have achieved progress in tackling the tumor microenvironment by understanding the enhanced permeability and retention, but still certain hurdles in clinically driven transition in developing and approving are needed to be addressed .Many cancer nano formulations have certain drawbacks like off-target accumulation, stability, in vitro to in vivo correlation and fulfilling regulatory norms in bringing clinical translation is of some major issues . Major cA multicentric approach in developing oncological therapeutic research using novel drugs and delivery systems has gained popularity with the advancement of 3D-printing and personalized delivery digital devices. In the future, there will be significant progress in the development of nanorobots or implantable microchips that can deliver drugs and control tumor progress. The future onco-medicine developments require intelligent and robust multi-disciplinary approaches, where computer-based artificial intelligence and biotechnology should go hand in hand in developing intelligent nanorobotic-based drug carriers for delivering nanomedicines . One hasImmunotherapy was developed in response to the ever-increasing research on cancer and understanding and the use of technologies to find an effective treatment for cancer. Because cancer is a complex disease, smart and intelligent delivery technologies must be developed to overcome the challenges of controlling its growth and elimination. To achieve successful cancer remission, novel strategies and therapy regimens will be tested in preclinical and clinical research. In this review, we discuss the various cancer treatment approaches that use drugs and biomaterials to exploit the immunological cascades against the tumor microenvironment. Despite limitations and challenges in developing technologies to increase drug delivery or efficacious results, combining one or more therapies with improved delivery technologies can result in an effective clinical translation. Precision targeting approaches with immunologically effective, low-toxicity technologies in cancer immunotherapy and delivery should translate to clinical implications and eventually benefit patients.


Glioblastoma (GBM) is an aggressive primary brain tumor that is associated with a poor prognosis and quality of life. The standard of care has changed minimally over the past two decades and currently consists of surgery followed by radiotherapy (RT), concomitant and adjuvant temozolomide, and tumor treating fields (TTF). Factors such as tumor hypoxia and the presence of glioma stem cells contribute to the radioresistant nature of GBM. In this review, we discuss the current treatment modalities, mechanisms of radioresistance, and studies that have evaluated promising radiosensitizers. Specifically, we highlight small molecules and immunotherapy agents that have been studied in conjunction with RT in clinical trials. Recent preclinical studies involving GBM radiosensitizers are also discussed. Glioblastoma (GBM) is the most common malignant primary brain tumor with a dismal five-year relative survival rate of 6.8% . DespiteGBM is among the most challenging cancers to treat because of tumor location, tumor heterogeneity, and the infiltrating growth pattern . For patNumerous studies have found aggressive, maximal tumor resection is associated with better survival outcomes ,6. UnforTMZ has replaced nitrosoureas  as the standard chemotherapy for patients with GBM  9]. TMZ. TMZ9]. 2 of body-surface area per day) . Th. Th59]. 2/min on a weekly basis for 6 weeks 24 to 72 h prior to concomitant RT (60 Gy in 30 fractions) with the identification of dose-limiting toxicity and maximum tolerated dose as the primary end-points. Based on this study, 175 mg/m2/weekly was recommended for further evaluation in a phase II study. Twenty-three patients were enrolled in their phase II study and found concomitant RT and gemcitabine were well-tolerated with few severe adverse events . D. D109]. An analogue of tirapazamine is SN30000 with more favorable diffusion properties and is currently under development ,112. OthHistone deacetylases (HDACs) are enzymes that regulate chromatin structure and gene expression via deacetylation of histones and other cytoplasmic and nuclear proteins . ValproiAtaxia-telangiectasia-mutated (ATM) serine/threonine protein kinase plays a role in the repair of DNA double-strand breaks . ATM actRecently, medicinal chemists have developed a novel series of ATM inhibitors that demonstrate excellent efficacy and good pharmacokinetic properties . AZD0156Phenoxyacetic acid compounds were initially utilized as lipid-lowering drugs but later were found to stabilize the T state of hemoglobin . In a ph2/day for 6 weeks) in patients with newly diagnosed GBM [p < 0.004). The authors also noted a slight increase in adverse events and, over time, a decreased quality of life and neurocognitive function in the bevacizumab group.VEGF inhibitors such as bevacizumab have been explored with the hope of targeting angiogenesis . Chinot osed GBM . Althougosed GBM . Their sCancer immunotherapy is based on the concept of immunosurveillance where the immune system can actively detect and eliminate cancer cells, but some tumor cells are able to develop the ability to evade the immune system through immunoediting . ImmunoeImmunotherapy aims to overcome this immunoresistance with immune checkpoint inhibitors (ICIs) . Immune Although radiation is known to create DNA damage, several studies have suggested the immune system may impact the efficacy of radiation . The exaImmune checkpoint inhibitors were believed to affect the tumor microenvironment by enhancing the expression of cytokine and chemokine release, which increases immune cell infiltration ,142. AntUnfortunately, the addition of ICIs to GBM treatment has led to disappointing initial results. The first major clinical trial evaluating ICIs was CheckMate 143 . In thisThe combination of anti-PD-1 and RT with and without TMZ has also been explored and has been found to be well tolerated. These combinations were then studied in two phase III clinical trials: CheckMate 498 and CheckMate 548. CheckMate 498 evaluated anti-PD-1 as an alternative to TMZ in combination with RT while CheckMate 548 evaluated the addition of anti-PD-1 in addition to TMZ plus RT. In both trials, RT in combination with nivolumab was found to not improve survival .To date, GBM ICI phase III clinical trials have yielded disappointing results. Researchers believe the heterogeneity of GBM tumors may contribute to immunotherapy resistance ,148. ThiThere is a growing body of literature suggesting purine synthesis contributes to the aggressive nature of GBM . GICs haTumor cells predominately utilize glycolysis even in the presence of sufficient oxygen, also known as the Warburg effect . As withCurcumin has also been explored as a radiosensitizer for GBM . There iN-vinylpyrrolidone (NVP)-AUY922 enhanced radiosensitivity in CD133-positive GBM cells [Hsp90 is a molecular chaperone that has been associated with protection against radiation-induced cell death . InhibitBM cells .MDM2 has been shown to downregulate p53 activity via ubiquitin-mediated degradation and is amplified or overexpressed in certain GBM patients . MDM2 inFollowing the success of CAR T cell therapy in patients with leukemia and lymphoma, researchers have turned their attention towards developing CAR T cells directed toward solid tumors . O’ RourTo date, GBM accounts for a disproportionately high percentage of cancer morbidity and mortality. Extensive research efforts over the past two decades have improved our understanding of the mechanisms driving the treatment resistance seen in GBM. Methods for overcoming radioresistance have been of particular interest, and researchers have been exploring various agents  as radiosensitizers. Although clinical trials have thus far yielded negative results, recent preclinical results have been promising. Developing small molecules to target GBM-specific features such as increased GTP synthesis or amplification of p53 has the potential for selectively radiosensitizing tumor cells. Additionally, RT has the ability to potentiate the efficacy of immunotherapy, suggesting ICIs and CAR T cell therapy in combination with RT may lead to a synergistic effect. Furthermore, the successful implementation of radiosensitizers in other disease sites have gleaned valuable information and may facilitate the rational design of a GBM radiosensitizer.


In recent decades, repeated minor head impacts resulting in mild traumatic brain injury (mTBI) or sub-concussion due to its cumulative nature, has attracted attention from clinicians and scientists. Sub-concussive head impacts are common and can arise from participation in popular sports, such as soccer, Australian football, American football, and rugby, from accidents , and from injuries during military service. Although acute clinical signs are not present, chronic symptoms such as neurocognitive deficits and psychomotor dysfunction years and decades following repeated sub-concussive head impacts have been reported. This indicates the serious consequences of “silent” mTBI.Frontiers in Neurology Research Topic brings together novel research on sub-concussion and its acute and chronic effects through animal models and clinical studies. Seven articles provide an insight into the pathophysiological underpinning, assessment, and treatment of such injuries.This Leaston et al.). This study examined the feasibility of “qualitative ultrashort time-to-echo contrast enhanced” or QUTE-CE for monitoring early pathology and vulnerability of the BBB following a single or repeated mTBI. Structural damage and BBB permeability in rats were assessed at baseline and within an hour after the head impact using anatomical images and QUTE-CE biomarkers. A change in BBB permeability was reported following the second and third impacts, affecting multiple brain regions supporting the concerns among healthcare professionals of repeated mTBI.An important consequence of mTBI is change to brain anatomy and physiology at the cellular level. Structural damage to the blood brain barrier (BBB), due to a single head impact, can occur. Optimizing tools and developing new technologies for investigating changes in the brain resulting from such head impacts are crucial for advancing our understanding of functional changes in brain activity induced by mTBI . The BRAINBox TBI Test incorporates proteomics with clinical assessments to identify and define pathologic evidence of neurocognitive impairments. This has important clinical applications in diagnosing brain injury and providing an objective assessment of the patient's risk of developing post-concussive symptoms.Acute stage assessment of mTBI injury severity and sub-clinical head impacts are vital as these may result in persistent neuropsychiatric effects, especially, when impacts are repeated. This suggests a need for objective assessments to support acute clinical diagnosis and inform predictions of long-term outcomes. Neurocognitive testing combined with biomarker assessment can be used to diagnose brain injury when imaging is not sufficient. A study assessed the use of the “HEAD injury Serum markers and Multi-modalities for Assessing Response to Trauma” (Head SMART II) to develop a blood biomarker for evaluating acute traumatic encephalopathy . Repeated mild traumatic injury (rmTBI) impacts were designed to replicate an athletes' experience, such as soccer heading or tackling in rugby through matches or practice sessions, in a mouse model. Behavioral measures, serum biomarkers, cortical protein quantification, and immunohistochemistry were assessed following rmTBI at various frequencies. Serum biomarker analysis showed that GFAP and NF-L did not change, and decreases in IL-1β and IL-6 were observed in serum and cortical tissue, indicating a downregulation of inflammatory pathways. These findings provided insights in potential mechanisms of repeated sub-concussion.Changes in behavior and cognition have been observed following repeated sub-concussion  and eicosapentaenoic acid (EPA) supplementation against repetitive sub-concussive head impacts (RSHI). American football players were randomized into a treatment group who received DHA and EPA or a placebo group receiving oleic acid and linoleic acid. Neuroimaging data were obtained at baseline and post-season. Deterministic tractography using quantitative anisotropy showed an increase in structural connectivity in ascending cirticostriatal fibers and a decrease in long association and commissural fibers in the DHA+EPA group compared with the placebo group, indicating protective effects against axonal damage.An important research endeavor is to find ways to ameliorate injuries in athletes to improve safety and recovery in sport. The article by Vorn et al. investigates the plasma proteomic profiling related to RTS following a sport-related concussion. This study collected blood samples from 140 athletes within 48 h of injury, and a highly multiplexed proteomic technique was applied to target 1,305 proteins in plasma samples. Thirty two proteins were upregulated while 55 were downregulated in those who took more than 14 days to recover compared with those who recovered within 14 days, suggesting that large-scale plasma proteomic profiling across acute to subacute time periods may predict long-term outcome.A greater awareness of sport-related concussion has led researchers to investigate ways to optimize diagnosis and monitoring for safe return-to-sport (RTS). The study by Smirl et al. assessed heart rate variability (HRV) and cardiac baroreceptor sensitivity (BRS) metrics in elite soccer players following 40 headers, compared with body contact control, and non-contact sham conditions. ECG and finger photoplethysmography characterized acute HRV/cardiac BRS, before and after each condition. The SCAT3 was also applied. While heart rate increased from pre to post heading, HRV metrics in the time domain and frequency domain as well as cardiac BRS remained unchanged. Interestingly, SCAT3 score increased compared with the control and sham conditions. The study showed that, while SCAT3 scores were affected by a bout of controlled heading, no changes were detected in autonomic function.While animal studies are important, translating the knowledge into human research is a vital next step. The study by Lavender et al.). This definition addresses the clinical outcomes of the incident. It has also been defined as “repetitive subconcussive impacts (RSHI)” which adds the aspect of repetition as an important component. A lack of precision in describing sub-concussion has clinical and diagnostic implications. The article by Lavender et al. attempts to clarify the discussion by introducing the term “Mechanically Induced Neurophysiological Disruption (MIND)” to make the distinction between the incident of an impact and the resultant physiological responses. MIND is described as a sudden or ballistic low magnitude movement of the brain that disrupts neurophysiological function and connectivity resulting in asymptomatic changes with potential to affect neural pathway function if performed repeatedly for long periods. This definition encompasses the repeated nature of the events that lead to anatomical and physiological modulation of brain function.“Sub-concussion” has been described as “a cranial impact that does not result in a known or diagnosed concussion on clinical grounds” (Collectively, the articles in this Research Topic make a significant contribution toward better understanding of the effects of repeated sub-concussive head impacts. This Research Topic can impact grass roots through to elite sporting levels and military exercises, making sport, and military training safer.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging. Magnetic resonance imaging (MRI) is a non-invasive in vivo biomedical imaging modality that underpins many recent breakthroughs in biology and medicine. Compared with other imaging modalities, MRI is superior in providing excellent soft-tissue contrast. MRI can be applied to a diverse range of clinical and research applications to visualize anatomical structures, measure biophysical functions and metabolism, as well as quantify perfusion and diffusion weighted microstructures in soft tissues and organs.With the ever-increasing demand for shorter imaging time and higher image resolution, MRI increasingly suffers from low signal to noise ratio (SNR) and is prone to image artefacts arising from subject motion and image distortion. These pose crucial challenges to accurately and efficiently post-process MR images. Conventional image enhancement and artefact correction techniques have proven to be useful for improving image quality in MRI including denoising , geometrDeep learning has proven to be useful in various steps of the clinical imaging workflow including patient scheduling, data acquisition and reconstruction, image enhancement and correction, and interpretation of results see Fig. . SignifiIn this paper, we will first provide an overview of the recent development of deep learning for MRI post-processing including (i) image artefact reduction, (ii) denoising for different MRI contrasts, and (iii) improvement for image resolution. We aim to provide researchers with an overview of deep learning approaches for post-processing MR images and discuss future perspectives in these applications.Many MR post-processing tasks can be formulated as image-to-image transformation problems where deep learning models are used to capture the nonlinear relationships between image inputs and outputs. In both rigid and non-rigid motion correction tasks, motion-corrupted images are fed into deep neural networks (DNNs) which produce the corresponding motion-free images. To correct other artefacts, such as Gibbs ringing, image bias due to B0 inhomogeneity, ghosting, and distortion artefacts, DNNs take images with artefacts as the inputs and output artefact-free images. Similarly, various neural networks are implemented to reduce noise or enhance image resolution in MRI images with supervised or unsupervised training.Among various types of deep neural networks, convolutional neural networks (CNNs) are the most commonly used neural network architecture in medical image processing . The conCNN architectures, such as U-Net and its extensions, are the widely used models for MR image quality enhancement and artefact correction. Table Similar to U-Net architecture, some works applied autoencoders (AE) with residual connections to enhance medical images , 15. As Training a neural network is the process of optimizing its parameters to minimize the loss between the predicted and target images or patches. Loss functions determine which image characteristics will be used by the neural network to match with the ground truth data. In the scope of MR image enhancement and artefact correction, the mean absolute error (MAE) using L1 norm and mean square error (MSE) using L2 norm are the two most widely used to compute the pixel-wise residual component between neural network outputs and ground truth images or patches , 19–21. In the context of MRI image quality enhancement, the process of optimizing model parameters is mostly formed as a fully supervised or adversarial training framework. In a fully supervised setup, the models are trained with explicitly defined pairs of input and output. For example, to correct artefacts, models are optimized by using the loss functions between network outputs and artefact-free ground truth images or patches as the supervisory signals, through the process of gradient descent. The fully supervised learning framework was widely used for motion correction , 29, noiIn contrast to fully supervised learning frameworks, some methods applied unsupervised learning techniques to train neural network models without explicitly labelled training pairs. Among those, generative models such as the generative adversarial network (GAN) framework, as shown in Fig. To quantitatively evaluate the model performance, multiple metrics are used to measure the similarity between the enhanced images by the DNNs and the ground-truth reference images. Mean squared error (MSE) and root mean square error (RMSE) measures the pixel-wise difference between the artefact-free reference images and the artefact-corrected network output images , 24, 41 Artefacts in MR images emerge from different sources such as human physiology and instruments. In this section, we have categorized common MR imaging artefacts as shown in Fig. Patient motion during MRI acquisition frequently occurs due to either involuntary motion of the patient or physiologically related tissue movements. Motion during the scan has been a long-lasting issue in MR imaging , 50 and Motion types can be separated into rigid motion  and non-rigid motion .The use of deep learning to correct motion artefacts was first explored by Sood et al. , GodenscMotion simulation is an important aspect to develop DL motion correction models. Pawar et al.  developeBilgic et al. and Brown et al. , 62 furtMany works , 70–75 hModelling non-rigid motion such as respiratory- or cardiac-related movements is more challenging than rigid motion. Tamada et al.  developeAlthough most of the current literature focuses on the development of novel methods for motion correction in MRI, several methods have evaluated the effect of DL motion correction in clinical and research applications.Two studies , 72 valiTo evaluate motion correction for segmentation tasks, Maclaren et al.  comparedDiffusion parameter estimation can be affected by misalignment of echo planar image (EPI) volumes. Conventionally, volumes with motion greater than a predefined threshold are discarded retrospectively to avoid bias in estimation of the diffusion parameters. Terpstra et al.  proposedA major source of artefacts in MR images stem from the choice of acquisition parameters at the MR hardware operational limit, including at high slew rates and high gradients, and because of the non-uniform B0 and B1 fields. In this section, we categories artefacts based on the hardware limitations, choice of acquisition sequence, sequence parameters, and scanner non-uniformity.Truncation of the high-frequency k-space data is frequently employed to reduce scan time and often results in Gibbs ringing artefact. Loktyushin et al.  and WangMagnetic field B0 inhomogeneity results in varying Larmor frequency spatially and thus the artefacts. B0 inhomogeneities can produce image biases, blurring, shading, curved slice profiles, and banding artefacts. B0 inhomogeneity artefacts are more pronounced in gradient echo and echo planar imaging sequences due to lack of refocusing RF pulses and long read out. Another aspect of B0 inhomogeneity is poor fat saturation since the difference between fat and water frequency is not fixed in the presence of B0 inhomogeneity. Non-uniform intensity biases can also be caused by improper coupling of the receiver coils and non-uniform B1 due to inhomogeneity of the radiofrequency excitation and receive coils. Sommer et al.  proposedEPI is a fast-imaging sequence that acquires the whole slice in one excitation by traversing the full k-space in a predefined manner. For a rasterized zig-zag trajectory, each consecutive phase encode (PE) is acquired by traversing k-space in the opposite frequency encoding direction. This can result in two major artefacts in EPI images: (i) ghosting in the reconstructed images due to eddy currents generated during the reversal of frequency encoding direction and (ii) phase errors arising from slight B0 inhomogeneities that accumulate during the readout time and cause geometric distortions in the reconstructed images.Ghosting in the phase encoding direction can be corrected using two readouts in the opposite frequency encoding direction that calculate the phase errors due to the fast switching gradients. Lee et al.  proposedVariations in scanner configuration and protocol make the analysis of data from different imaging sites highly challenging. Images from a subject scanned at two different sites can result in images with highly variable quality. Characterization and removal of such variability is of critical clinical and research importance that can be done using data normalization methods.Grigorescu et al.  proposedIn , Tong etSynthetic MRI is quantitative and free of vendor-specific characteristics which is an ideal form of normalization. In , 87, mulImage noise is a long-standing issue in MRI. Improving SNR is increasingly challenging because of accelerated data acquisition and ill-posed image reconstruction methods. Noise in MR magnitude images is Rician distributed . For accConventional post-processing-based denoising methods are classified into filtering methods, transform domain methods, and statistical methods . WhFrom the generic image processing perspective, CNNs have been very effective in the reduction of image noise , a shrinkage convolutional neural network (SCNN), and deep learning-based reduction (dDLR) . The perFurthermore, several works reported the incorporation of peripheral deep learning concepts including adversarial training and transfer learning to overcome noise in brain MRI. Ran et al. used a residual encoder–decoder Wasserstein GAN for simultaneous improvement of noise suppression and preservation of anatomical details  and compSeveral groups have applied attention-based mechanisms and leveraged long-range dependencies to improve brain MR image denoising. Inspired by the attention-guided CNN networks, Hong et al. proposed a model to incorporate feature fusion and attention mechanisms to separate noise from observed MRI images  and prodIn an application to imaging prostate, Hong et al. presented a Bayes shrinkage-based fused wavelet transform (BSbFWT) and Block-based autoencoder network (BBAuto-Net) for removal of noise from prostate MR images . The metLow-field MRI, in particular, has many benefits including affordability, compact footprint, and reduced shielding. SNR is linearly proportional to the main magnetic field (B0); hence, low-field MRI systems (< 1 T) inherently have significantly low SNR compared to conventional 1.5–3 T MRI scanners. Song et al.  proposedFunctional MRI (fMRI) is a prominent imaging technique for functional brain mapping and identification of functional networks. fMRI data is normally acquired using a fast EPI readout with T2*-weighted contrast to capture blood oxygenation level-dependent (BOLD) signals. Both spatial and temporal noise can hinder accurate identification of functional brain maps and function networks. Yang and colleagues applied a time-dependent deep neural network (DeNN) to denoise fMRI time series in individual brain regions . The autCompared with anatomical MRI, diffusion-weighted imaging suffers from low signal to noise ratio due to the application of diffusion gradients and fast EPI data readout. CNNs and deep image prior-based denoising methods have been developed in order to improve SNR in diffusion MRI. Lin et al. used a deep image prior (DIP) to simultaneously denoise all diffusion-weighted images. The method demonstrated superior performance when compared with the local principal component analysis method using both simulated and in vivo datasets . KawamurPerfusion-weighted MRI images acquired using the arterial spin labelling (ASL) protocols are also based on an EPI sequence and suffer from low SNR. Xie et al. applied CNNs with dilated convolution kernels and wide activation residual blocks to preserve image resolution while suppressing noise . The resMRI flow measurements are vulnerable to acquisition noise, velocity aliasing, and phase offset artefacts in clinical applications. These complications represent significant challenges for the analysis of small vascular structures including identification of intracranial aneurysms and treatment for near-wall regions. Several studies have attempted to use deep learning to reduce noise in flow MRI. Sun and co-workers proposed a physics-constrained deep learning approach that effectively reduced the measurement noise . The metSpatial resolution is a key data acquisition parameter that impacts diagnostic accuracy and decision of subsequent clinical workflows. However, scans with higher spatial resolution often leads to longer data acquisition time and poorer signal to noise ratio and can be prone to motion artefacts. Post-processing resolution enhancement algorithms, such as zero-padding to increase the matrix size of the final image, have been widely applied in medical imaging. Further, methods such as B-splines and cubic interpolation can provide image resolution improvements without the use of prior models. Van Reeth and colleagues reviewed a general forward model for MR image resolution including geometric transformation, instrument point spread function, and downsampling during data acquisition  as well For the past decade, deep learning-based image resolution enhancement has been widely adopted in the computer vision literature . Dong etPartial volume effect (PVE) is key consideration in MRI super resolution, especially in volumetric and multi-slice MRI acquisitions where multiple tissue types are present in a single voxel. In such a scenario, the intensity of the resultant image depends on the collective contribution of each tissue. As a result, with high section thickness  and slice gaps, the risk of missing subtle anatomical features and lesions is high. To overcome this, Chaudhari et al.  proposedAnatomical MR scans with high in-plane resolution and minimal through-plane resolution can successfully reduce imaging time and improve image SNR. However, the resultant images have poor through-plane resolution and large partial volume errors. To address this problem, Zhao et al. exploited a deep learning approach called Synthetic Multi-Orientation Resolution Enhancement (SMORE)  for bothThe brain has fine anatomical structures and often suffers from partial volume errors from low resolution data acquisitions. Compared with conventional MRI resolution enhancement methods, deep learning models have shown superior performance.Pham and colleagues studied multiscale trained 3D networks with knowledge transferred from different acquisition protocols to improve spatial resolution of brain images . Chen etUsing residual learning, Shi and colleagues developed two algorithms for brain MR images. Their multi-scale residual learning network for image super resolution combined both multi-scale global residual learning (GRL) and shallow network block-based local residual learning (LRL) . The LRLBy leveraging high-resolution images from 7 T MRI, Zhang et al. developed a parameter-efficient butterfly network that employed a dual spatial and frequency domain DL model for mapping between 3 and 7 T image pairs that produced improved image resolution at 3 T . The resSince their introduction , GANs haLocalization of pathology is extremely challenging when data acquisition is constrained due to physiological motion and other practical limitations. Cardiac MRI is often acquired with a small data matrix that results in low spatial resolution. Masutani and co-workers evaluated the application of a CNN to enhance anatomical detail in comparison with zero padding and bicubic interpolation methods. In an evaluation of 400 MRI scans, the DL model significantly outperformed zero padding and bicubic interpolation methods in 99.2% of the image slices . MahapatThe low resolution in knee MR scans adversely affects the diagnosis of conditions such as knee osteoarthritis. To address this problem, Qiu et al.  used an Fetal MR images are generally acquired with low resolution to avoid motion artefacts. McDonagh et al. applied a context-sensitive super-resolution method on 145 fetal MRI scans . The modPark et al.  introducImaging the abdomen is important for many oncology applications such as prostate cancer. Thus, SR in prostate MR can facilitate early diagnosis and thereby influence the commencement of early treatments. Xu et al.  proposedWhile diffusion-weighted MRI allows high-resolution imaging at high magnetic fields and high gradient strength for long scan times, the image spatial resolution and number of diffusion directions are restricted. Several deep learning models have been developed to enhance the quality of diffusion images. Qin et al. developed a super-resolution q-space deep learning method  to estimFathi and co-workers developed a DL approach for super resolution and denoising of 4D-flow MRI. They used flow physics to regularize a DNN model to improve its convergence properties with limited training data . Flow veMR spectroscopy (MRS) aims to quantify tissue metabolism. However, due to the low concentration of most metabolites, their identification in proton MR spectra is a difficult task. Iqbal et al.  proposedDeep learning models have shown promising results for the reduction of image artefacts and noise and the improvement of image resolution. Compared with conventional machine learning methods, deep learning models show consistently better performance in these post-processing tasks. Furthermore, deep learning methods are also computationally efficient during inference in comparison with many conventional iterative algorithms. Significantly, the promising initial results have motivated imaging device manufacturers to increase the range of deep learning-based solutions in their product portfolios . HoweverMost deep learning models require a large amount of training data to avoid model over-fitting. However, good quality training sets of medical images in general are difficult to obtain due to privacy or other availability issues. Furthermore, MR image quality and contrasts are varied and highly dependent on anatomical regions, sequence parameters, and hardware configurations . These fExisting open MRI datasets that are available in the literature can add significant value in the development of deep learning methods. Example datasets include the IXI dataset (brain-development.org/ixi-dataset), fastMRI , mridataSimulated datasets can help to mitigate the lack of in vivo training datasets. For example, simulated head motion datasets can be used to train a network for application in clinical datasets with head motion , 182. DuOut-of-distribution (OOD) data refers to inputs that are drawn from a distribution different to that of the training dataset. OOD data is to be expected in medical imaging applications of deep learning models due to limited training datasets, scanning protocol variation, and the potential for an image to include uncommon or rare disease features. The performance of a deep learning model to OOD input is an important consideration for the assessment of its generalizability. To date, there is only a limited literature that assesses the reliability of deep learning models on clinical OOD data .Modelling data and parameter uncertainties can provide significant insight and assess risks for dealing with unseen datasets. Tanno and colleagues investigated uncertainty modelling for diffusion MRI super-resolution and sought to provide a high-level explanation of deep learning models with respect to variation in input datasets . A numbeUnsupervised training using unpaired datasets can learn to disentangle data and artefact representations in a latent space  to improAn important observation from the current literature is that most current deep learning methods have only been demonstrated in a small cohort of datasets at a single imaging site. A thorough evaluation of AI algorithms is essential before their full clinical utilization. In an opinion paper, Kelly and colleagues argued that clinical validation of artificial intelligence should be carefully considered in different test scenarios to gain insight for potential biases and variations . FurtherProspective and multi-site evaluation of deep learning models is important to identify unknown variations from datasets and models applied in real-world environments. In a prospective study, Rudie and colleagues have evaluated DL-based brain MRI enhancement for increase in SNR, anatomic conspicuity, overall image quality, imaging artefacts, and diagnostic confidence, assessed by four board-certified neuroradiologists . SimilarThe drive to develop explainable deep learning models is another important step for building trust in AI algorithms. The instability issue demonstrated during imaging and other potential bias during development are key aspects to address during clinical validation. Domain knowledge can be incorporated into deep learning models to improve their overall performance and reliability . SpecifiIn this review paper, we have provided an overview of deep learning methods for post-processing MR images including reduction of image artefacts, suppression of image noise, and enhancement of image resolution. Throughout the literature, there is consistent evidence that improved image quality can be achieved using deep learning methods in comparison with conventional techniques. However, the current challenges and future perspectives for data availability, generalizability, and clinical validation of deep learning algorithms highlight the requirement for a concerted and ongoing research effort in this rapidly evolving discipline.


Despite advances in cervical cancer screening and human papilloma virus (HPV) vaccines, cervical cancer remains a global health burden. The standard treatment of cervical cancer includes surgery, radiation therapy, and chemotherapy. Radiotherapy (RT) is the primary treatment for advanced-stage disease. However, due to radioresistance, most patients in the advanced stage have an adverse outcome. Recent studies have shown that long noncoding RNAs (lncRNAs) participate in the regulation of cancer radiosensitivity by regulating DNA damage repair, apoptosis, cancer stem cells (CSCs), and epithelial–mesenchymal transition (EMT). In this review, we summarize the molecular mechanisms of long noncoding RNAs in cervical cancer and radiosensitivity, hoping to provide a theoretical basis and a new molecular target for the cervical cancer RT in the clinic. Cervical cancer is the fourth most common female lower genital tract malignancy, and over 570000 women are diagnosed with cervical cancer each year . The stavia diverse mechanisms: 1) lncRNAs can regulate histone modification and chromatin status to affect gene transcription. 2) lncRNAs can attract transcription factors or repressors to the promoter of a particular gene  lncRlar gene . Many stlar gene . HoweverRadiation causes DNA damage, inhibits cell proliferation, and induces cellular apoptosis to treat cancer . The conNumerous studies have shown that lncRNAs are functionally involved in various critical cellular processes that regulate RT in cervical cancer. LncRNAs can modulate DNA damage repair, the cell cycle, and apoptosis and can also influence the redistribution of surviving cells by regulating EMT, cancer stem cells (CSCs) and the reoxygenation of cells by regulating aerobic glycolysis after RT in cervical cancer .Radiation-induced DNA damage includes base damage, single- and double-strand breaks, and DNA crosslinks caused by direct ionization or free oxygen radical effects , DNA douIn mammalian cells, ATM and ATR are two phosphatidylinositol kinases that are involved in DNA damage repair . When DNin vitro studies and found that the expression of activated ATM was high in Caski cells even before irradiation while in HeLa and SiHa cells was extremely low. And in comparison to HeLa and SiHa cells, Caski cells were significantly more resistant to irradiation. This result reveals us there is a positive correlation between baseline level of p-ATM and radioresistance. Then they proved ATM inhibition led to the destroy of G2/M arrest and finally sensitized tumour cells to radiation. The same result was also reported by. ATM has been proved to be connected with therapeutic effects of radiotherapy in cervical cancer. Many studies showing that lncRNAs can directly act on DNA damage repair-related protein to regulate DNA damage response. We retrieved related literature and found that lncRNAs can regulate the expression of ATM in various tumors, including cervical cancer , esophagbona fide heat shock protein (hsp) 90 client protein, is downregulated when hsp90 is inhibited. Additionally, they found that treatment with hsp90 inhibitors decreased both ATR and CHK1 levels, and the hsp90 inhibitor increased radiation-induced DNA damage as well as apoptosis of transformed cells by blocking ATR-CHK1-mediated DNA damage repair signaling. Kyungsoo Ha c  found invia miR-497-5p adsorption and upregulation of CDC25A. However they did not compare LINC00662 expression between different patients’ tumor tissues and collected clinical information to assess the therapeutic effects of radiotherapy in different expression level. Follow-up researchers can supplement the above points if they carry out experiments in this area, which can significantly improve the quality of the article.As we mentioned above CDC25 is required for DNA damage repair. LINC00662 is reported to be connected with CDC25A in cervical cancer. LncRNA in nonhomologous end joining pathway 1 (LINP1) is located on chromosome 10p14 and is associated with the NHEJ repair pathway . The NHEDNA damage induced by radiation induces DNA repair pathways and alters the expression of cell cycle checkpoint molecules. Affected cells are able to arrest cell cycle progression and repair damaged DNA or undergo apoptosis if the damage cannot be repaired. Radiosensitivity is closely associated with cell cycle arrest, since different phases of the cell cycle exhibit different levels of radiosensitivity, and the G2/M phase is the most radiosensitive phase of the cell cycle . LncRNAsAmong the mechanisms of irradiation-induced toxicity, apoptosis plays a significant role. Tumor cells that undergo apoptosis display a number of morphological features, including condensed chromatin, shrinkage and fragmentation, blebbing of the plasma membrane, and the formation of apoptotic bodies . ExtrinsTo determine the changes in signaling pathways in cervical cancer cells after radiation,  exposed By interacting with p53 pathway factors, LncRNAs regulate radiosensitivity. Under hypoxic conditions, p53 participates in the degradation of HIF-1α . Fu et ain vitro and in vivo. IER3 is necessary for cervical cancer cell apoptotic activity by inhibiting the expression of BCL-2 and BCL-xL , was upregulated, knockdown of SNHG6 can improve the radiosensitivity of cervical cancer cells by promoting radiation-induced apoptosis. SNHG6 can directly sponge miR-485-3p.Researchers have mentioned that in cervical cancer, miR-21 could facilitate cell proliferation by downregulating the expression of programmed cell death 4 (PDCD4)  as well Above all, lncRNAs are believed to affect cervical cancer cell radiosensitivity through their ability to induce apoptosis. Several mechanisms can be employed to achieve this effect, including affecting signaling pathways such as p53 or miRNA sponges that manipulate the expression of target genes . Hence, EMT plays an important role during tumor development. Tumor cells acquire an invasive phenotype when EMT programs aberrant activation. When tumor cells accept radiation, they may acquire stemness and oncogenic metabolism by inducing EMT . In tumoIn cervical cancer, lncRNAs can regulate EMT-related gene and transcription factors . For exaLncRNAs are involved in the EMT process of cervical cancer cells by different signaling pathways . LncRNA In vivo, knockdown of HOXA11-AS can decrease tumor growth. From this study, we predict that LncRNA HOXA11-AS plays an important role in the establishment of EMT/CSCs in cervical cancer cells  is another rate-limiting enzyme during aerobic glycolysis. LncRNA urothelial cancer associated 1 (UCA1) was reported to be connected with HK2 in cervical cancer. LncRNA UCA1 first discovered and researched in bladder cancer as an oncogene . Fan et Human telomerase reverse transcriptase (hTERT) is a subunit of telomerase capable of preserving telomere integrity. Cancer cells contain a highly active promoter of hTERT, whereas most normal cells contain a weak promoter . BarczakRadiotherapy is a key treatment modality for cervical cancer, but the efficacy is limited by radioresistance. Emerging research has demonstrated that lncRNAs are associated with radioresistance, and the relationship between them has gradually become a research hotspot. Studies have shown that lncRNAs have potential for use as biomarkers of the response to RT and can modulate the RT response of cervical cancer.The radiation response of cervical cancer differs greatly among different patients; thus, it is important to predict the response to radiation treatment for each patient. In addition, the overall RT treatment time for cervical cancer patients is usually several weeks. Thus, early identification of patients who are not responding to therapy would provide an opportunity to consider alternative treatment options. This would benefit individualized treatment. However, it is difficult to distinguish different lncRNAs that correlate with only the RT response between responders and nonresponders because in clinical studies, patients usually undergo combined chemoradiotherapy. Fortunately, a limited number of recent cervical cancer studies have shown that lncRNAs indeed exhibit differences in the setting of RT alone. At present, there are few studies on the ability to predict the RT effect of cervical cancer based on lncRNAs, but this will be a hot topic that requires large-scale clinical research.via basic experiments. However, the molecular mechanisms underlying radioresistance have not yet been completely elucidated. In this review, we summarized the potential mechanisms of lncRNAs that are associated with radiosensitivity in cervical cancer (Studies have focused on the effect of the expression level of lncRNAs on the radiosensitivity of cervical cancer l cancer . The rolin vitro; thus, clinical trials are lacking. Moreover, the particular molecular biological mechanisms by which lncRNAs regulate radiosensitivity have not yet been completely described.The study of lncRNAs in RT in cervical cancer remains in the initial stage, and most existing studies have only been carried out in vivo experiments to verify these conjectures. We believe that research on lncRNAs in the response to RT will provide new RT sensitization strategies for cervical cancer. Studies on lncRNAs that affect the cervical cancer RT response will provide new sensitization strategies for cervical cancer RT and lay the foundation for the clinical application of lncRNAs in the future.Understanding the molecular pathways regulated by lncRNAs should greatly improve the effectiveness of RT in cervical cancer and reveal better treatment strategies. Through summarizing the existing research articles, it was found that lncRNAs mainly affect RT sensitivity by affecting the DNA damage repair, cell cycle, apoptosis, EMT, CSCs, and glycolysis in cervical cancer RT. Recent studies have reported that human telomerase reverse transcriptase may also be related to the effect of RT, which is a very important finding and needs to be verified by more experiments. After recognizing the role of lncRNAs in RT of cervical cancer, we need to conduct