Abdominal aortic aneurysm (AAA) is a prevalent vascular disease with high mortality rates upon rupture. Despite its prevalence in elderly populations, there remain limited treatment options; invasive surgical repair, while risky, is the only therapeutic intervention with proven clinical benefits. Dietary factors have long been suggested to be closely associated with AAA risks, and dietary therapies recently emerged as promising avenues to achieve non-invasive management of a wide spectrum of diseases. However, the role of dietary therapies in AAA remains elusive. In this article, we will summarize the recent clinical and pre-clinical efforts in understanding the therapeutic and mechanistic implications of various dietary patterns and therapeutic approaches in AAA. Abdominal aortic aneurysm (AAA) is a localized, progressive weakening and dilation of the aortic wall, primarily in the infrarenal segment. It is a common vascular disease in elderly populations i.e., >65-year old), and up to 8% of males and 6% of females are estimated to develop AAA over a lifetime . While m-year oldStrategies to counter AAA's modifiable risk factors have long been recommended as secondary preventative measures to reduce overall cardiovascular mortality risks . Aside fThe lack of definitive clinical benefits in the foregoing risk reduction strategies necessitates further efforts toward the first non-surgical treatment of AAA. Over the past two decades, numerous FDA-approved drugs have been repurposed for treating AAAs, yet none have yielded any clinical success . The mosIn light of the aforementioned challenges and obstacles, recent years have witnessed a surging interest in pursuing lifestyle changes for AAA management. While smoking cessation and physical exercise both have shown promising benefits in reducing AAA risks, there is a scarcity of studies concerning the role of healthy dietary patterns, at both clinical and preclinical levels , 17, 18.In contrast to surgical repairs and drug treatments, dietary therapies are uniquely advantageous in multiple aspects, ranging from accessibility to the lack of invasiveness. For the management of cardiovascular diseases and metabolic syndromes, healthy dietary patterns have long been recommended to patients with proven benefits –22. HoweThe first epidemiological evidence concerning dietary elements came from the Life Line Screening study in the US. In this retrospective cohort of 3.1 million patients, Kent et al. reported that consumption of nuts, vegetables, and fruits at a frequency of >three times per week were associated with reduced risk of AAA, as was the case for exercise (≥1 time per week) and smoking cessation . HoweverBuilt upon the seminal work from Kent et al., Stackelberg et al. continued to investigate dietary factors, particularly fruit and vegetable consumption, using the prospective Cohort of Swedish Men and Mammography Cohort . SurprisThe role of dietary fiber and vegetable intake in AAA was not established until the reports from Harring et al. in 2018 and Bergwall et al. in 2019. In the former study, the ARIC study cohort was investigated regarding the association between AAA risk and their adherence to the Dietary Approaches To Stop Hypertension (DASH) dietary patterns . In addiSimilar to the DASH diet studied in the ARIC cohort, other similar dietary patterns have also been studied, including the anti-inflammatory diet and Mediterranean diet , 30. SimNotwithstanding the growing interest and epidemiological evidence, experimental studies concerning dietary therapies are very limited. Based on our literature search, three types of dietary regimens have been identified with preclinical therapeutic efficacies in murine models of AAA, i.e., calorie restriction, dietary phytoestrogen intake, and consumption of long-chain Omega-3 polyunsaturated fatty acids (PUFAs).Calorie restriction is a dietary regimen that consists of decreased calorie intake without causing malnutrition and is often prescribed to achieve weight loss. Experimental data derived from rodent and non-human primate models support a pleiotropic role of calorie-restricted diets in mediating a myriad of health benefits, most notably in prolonging lifespan , 33. DatPhytoestrogens are a group of plant-derived chemicals with structural and functional similarities to 17-β-estradiol, an estrogen with a presumed protective role against AAA. The primary sources of phytoestrogen intake are through consumption of soy and many other legumes, which have been previously linked to reduced AAA risk. Using a murine model with topical elastase application, Lu et al. showed that a phytoestrogen-rich diet could effectively ameliorate AAA development in male but not in female mice . In lineIncreased consumption of the PUFAs  — often deemed as the healthy fats — is a key component of the aforementioned diets  featuring high consumption of fish and nuts. Indeed, numerous Omega-3 PUFAs have been studied in murine models, in which dietary supplementation could reduce AAA development –43. WhilIn stark contrast to the paucity of experimental studies concerning dietary therapies, a plethora of literature are available, detailing the pathophysiological/phenotypic impacts upon experimental AAA pathogenesis. Most notable examples include dietary patterns that recapitulate AAA's established risk factors, such as high-fat diets (atherosclerosis/hypercholesterolemia) , 47, higSimilar to calorie restriction, protein restriction has also been shown to help prolong lifespan in fruit flies and mice , 52. In Unlike calorie or protein restriction, limiting the intake of amino acid(s) has been shown to produce similar health benefits in preclinical studies without significant risks of malnutrition. Clinical studies further established the safety profile as well as the early efficacy of a methionine-restricted diet in healthy and cancer patients, with additional randomized clinical trials currently ongoing , 58. WhiIntermittent fasting is a dietary pattern that features time-restricted eating with or without reducing calorie intake. Due to its reduced risk of malnutrition and ease of adherence, intermittent fasting has garnered significant popularity during the past decade as an alternative regimen. Clinical studies have suggested beneficial associations of intermittent fasting with cardiometabolic risks, albeit long-term outcomes are still pending , 65. In There is no pathway or molecular basis that could completely account for the health benefits of a given diet. Past studies have unveiled various mechanisms implicated in the aforementioned dietary regimens, such as overall energy expenditure reduction, antioxidant supplementation, increased production of hepatokines  –71, etc.Studies from other disciplines have uncovered a series of sensor proteins and pathways for cellular metabolism, energy expenditure, and certain nutrient cues. Amongst the sensors known thus far, the NAD+-dependent protein deacetylase sirtuin family member SIRT1 is the most notable example. Not only has SIRT1 been established to negatively modulate AAA pathogenesis , 72, expmTOR and general control non-derepressible 2 (GCN2) constitute the only two identified sensors for amino acids . While mvia cultivating distinct microbiome compositions and hence driving a shift toward beneficial microbiota-derived metabolic compositions. In fact, some microbiota-derived metabolites that are profoundly inhibited upon dietary restrictions  grant R01HL162895 (to BW).Author BW is the corresponding author, the coordinator for this research topic, and has recused from the peer-review and editorial processes. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Although the basic approaches are general, for illustrative purposes we describe two recent examples from cardiovascular medicine—tissue engineered heart valves (TEHVs) and tissue engineered vascular grafts (TEVGs)—to highlight current methods of approach as well as continuing needs.Continuing advances in genomics, molecular and cellular mechanobiology and immunobiology, including transcriptomics and proteomics, and biomechanics increasingly reveal the complexity underlying native tissue and organ structure and function. Identifying methods to repair, regenerate, or replace vital tissues and organs remains one of the greatest challenges of modern biomedical engineering, one that deserves our very best effort. Notwithstanding the continuing need for improving standard methods of investigation, including cell, organoid, and tissue culture, biomaterials development and fabrication, animal models, and clinical research, it is increasingly evident that modern computational methods should play increasingly greater roles in advancing the basic science, bioengineering, and clinical application of regenerative medicine. This brief review focuses on the development and application of computational models of tissue and organ mechanobiology and mechanics for purposes of designing tissue engineered constructs and understanding their development  There is, therefore, a pressing need both to improve and to accelerate the workflow from concept to clinical utility. In this brief review, we suggest that computational modeling can play important roles in this regard. Toward this end, we illustrate past accomplishments and emphasize future needs primarily via two illustrative examples from cardiovascular medicine and surgery—the design and use of tissue engineered heart valves (TEHVs) and TEVGs. Moreover, we focus on how the intrinsic response of living tissues to mechanical stimuli can be leveraged to guide neotissue development and adaptation toward successful outcomes in Regenerative Medicine.Tissue engineering seeks to repair, regenerate, or replace diseased or damaged tissues and organs using advances in molecular, cellular, and matrix biology as well as biomaterials and bioengineering. Early foundations upon which this growing field rest include work in the late 1970s and early 1980s wherein it was shown 10 Similarly, smooth muscle cells, fibroblasts, and valve interstitial cells respond to increases in pressure-induced tissue stresses or strains by altering gene expression, often resulting in changes in production (synthesis) and removal (degradation) of extracellular matrix constituents29 to return the mechanical state toward normal. It is, of course, the extracellular matrix that endows the tissues and organs of the cardiovascular system with appropriate stiffness and strength, hence mechanical stimuli-driven changes often lead to changes in composition and properties that affect function. As an example, increased cyclic stretching of vascular smooth muscle cells tends to increase local expression of angiotensin II and transforming growth factor-beta,55 which through separate but complementary cell signaling pathways  drive altered rates of extracellular matrix production and removal, with cyclic stretching also affecting the production, activation, and efficacy of matrix metalloproteinases. Importantly, this mechano-sensing mediated synthesis of matrix is complemented by a mechano-regulated assembly of the newly deposited matrix that depends on actomyosin activity, which allows the cells not only to organize new matrix but also to reorganize extant matrix.56 Cross-linking of the matrix via lysyl oxidase or transglutaminases, respectively, ensures mechanical functionality.Many cell types are sensitive to changes in their mechanical environment, often changing gene expression transiently to facilitate adaptive responses. Of particular importance in cardiovascular tissue engineering, endothelial cells are exquisitely sensitive to changes in blood flow-induced wall shear stress. For example, they upregulate their production of nitric oxide in response to increases in wall shear stress and upregulate endothelin-1 in response to decreases in wall shear stress. In both cases, these potent vasoregulatory molecules diffuse into the vascular wall and relax or contract, respectively, the smooth muscle cells that drive the vasodilation or vasoconstriction that is needed to return the wall shear stress toward normal values.40 Importantly, similar processes appear to be operative in tissue engineered constructs, both in vitro and in vivo.46 Conceptually, this homeostatic tendency is illustrated easily for a straight segment of a blood vessel subjected to a steady pressure-driven flow. Consider cell-sensed normalized differences . The parameters o), and if one considers simple steady-state / equilibrium solutions  and CRIMSON (crimson.software) enable CFD and fluid–solid interaction (FSI) solutions of unsteady, three-dimensional blood flow and pressure within complex, patient-specific domains. These models are also coupled easily with reduced-order models  to generate computationally efficient multi-domain models of networks of blood vessels, including those within organs. Noting that most soft tissues and organs exhibit material and geometric nonlinearities, it is fortunate that even early advances in finite element analysis (FEA) allowed study of complex nonlinear materials undergoing finite deformations.67 Many advances thereafter enabled solutions of complex initial-boundary value problems relevant to biological tissues, again for patient-specific geometric domains. Among others, the specialized code FEBio (febio.org) is now widely used in biomechanics, though other common commercially available non-specialized codes  are used as well.Computational fluid dynamics (CFD) has emerged as a powerful tool for biomedical analysis and designNotwithstanding the importance of quantifying the complex mechanics experienced at any time by cardiovascular and cardiopulmonary tissues and organs, including their interactions with the flowing blood (cardiovascular) or air (pulmonary), it is the ability of these tissues to grow and remodel during morphogenesis, homeostasis, and pathogenesis in response to diverse stimuli that truly distinguishes them as complex living materials. Such stimuli include the implantation of polymeric materials, which typically elicit a foreign body immune response while also changing the local mechanical environment.74 and the constrained mixture theory.39 Briefly, in the kinematic growth approach, one writes the finite deformation gradient in terms of a multiplicative decomposition, with one part described by a growth tensor and one described by a traditional (elastic) deformation gradient  and removal  functions. Under the assumption that the many structurally significant constituents may possess different (evolving) natural configurations while yet being constrained to move together with the mixture, and that such motions are slow in G&R processes at the tissue level relative to cyclic applied loads, the mixture mass balance equation can be integrated directly to yield a heredity integral (fading memory) based formulation in terms of the true mass density production function N constituent-specific constitutive relations for stored energy, which when summed yield the total stored energy that is needed to solve initial-boundary value problems either in weak (finite element) or strong  form, though typically with considerable computational expense. For this reason, multiple simplifying assumptions have been invoked to yield computationally less expensive formulations of a constrained mixture, including so-called homogenized12 and mechanobiologically equilibrated53 formulations. Similar to the kinematic growth models, constituent-specific finite deformations in the constrained mixture models involve multiplicative decompositions  or within the intended location within the body (in situ). For additional details on these two methods of modeling G&R, the reader is referred to the following.80Of particular importance herein, mixture formulations can easily account for the different mechanical properties and degradation kinetics of polymeric scaffolds and the deposited neotissue, whether within a bioreactor  to resolve hemodynamics over a cardiac cycle whereas G&R simulations are typically on the order of days to weeks to months. These disparate times scales typically necessitate loose coupling (handshaking) schemes, ones that should be adaptive because periods of rapid G&R can be followed by a period of slower G&R. Whereas such FSG models are discussed further below, note that direct coupling of hemodynamics and G&R can be achieved using reduced-order models for the blood flow, as, for example, a control volume relation based on energy balance.54Melding FSI and G&R models leads naturally to FSG models, which are ultimately needed to address most problems within the cardiovascular system. Although the need for and feasibility of these FSG models was established more than a decade ago,To render these general concepts more tangible, in the following two sections, we consider two illustrative examples of computer model-based design and analysis of tissue engineered constructs, one that led to new insight into design requirements and one that aided clinical management.20 Both types of prostheses are non-living, and thus cannot grow, adapt, or repair when needed. This lack of adaptive capacity often leads to complications (e.g. limited durability or size mismatch due to patient growth) that require repeat surgeries. This deficiency translates into a significantly reduced life expectancy of recipients of valve replacements, particularly for young patients.69 By contrast, TEHVs are living tissues having regenerative capacity, which thereby have the potential to become superior valve replacements compared to contemporary valve replacements.43 To enable this transition, the regeneration and adaptation of TEHVs should be understood and well controlled such that proper, ideally native-like, valve function is established during tissue development and maintained over long periods through tissue adaptation and repair.Current treatment options for end-stage valvular heart disease are largely limited to surgical replacement of the valve with either a mechanical or biological  valve substitute.in vitro using bioreactor platforms before implantation. Both decellularized biological matrices and biocompatible, biodegradable synthetic materials have been used as scaffold materials. Decellularized allografts and xenografts benefit from a more native-like tissue architecture compared to synthetic materials, and promising clinical outcomes have been reported.14 Yet, concerns remain regarding limited cell infiltration into biological matrices, the limited availability of allografts, and the zoonotic risks associated with xenografts. Synthetic scaffolds offer improved control over material properties and allow more rapid cell infiltration compared to biological materials. Nevertheless, despite promising early results,76 long-term pre-clinical studies repeatedly report progressive leaflet retraction and thickening due to cell-mediated tissue remodeling after implantation.73The basic principle of heart valve tissue engineering is that a non-living, biodegradable, valve-shaped scaffold is infiltrated by cells, which subsequently transform the degrading scaffold into a living tissue having growth and remodeling capacity. Traditionally, TEHVs were created in situ heart valve tissue engineering. In the in situ approach, non-living biodegradable valve-shaped scaffolds are implanted directly at the functional site, where they gradually transform into living heart valves. Despite promising results, regeneration of in situ TEHVs has so far been unpredictable and variable, which restricts their safe clinical translation. Specifically, substantial spatial differences in tissue formation and scaffold resorption have been observed, potentially correlated with spatial differences in cell infiltration as well as mechanical stimuli.82 Similar to the in vitro approach, major complications include progressive leaflet retraction and thickening, which ultimately lead to a loss of valve functionality. Additionally, the potential for functional growth of in situ TEHVs has yet to be demonstrated.Due to the scientific and regulatory complexity associated with the implantation of living tissues, current efforts are now focused mainly on 16 how (oscillatory) fluid flow and deformation influence in vitro engineered tissue formation,75 and how cellular traction forces and collagen remodeling can lead to leaflet retraction.57 Ultimately, mechanistic computational models of tissue regeneration and adaptation should be combined with optimization algorithms to efficiently identify promising scaffold designs, ideally as a function of patient-specific parameters that drive the regeneration, functionality, and adaptative capacity of TEHVs. Such models promise to accelerate the clinical translation of TEHVs and to reduce the number of animal experiments since only the most promising TEHV designs need to be tested experimentally.The main issues underlying many of these challenges are a poor understanding of progressive tissue regeneration and adaptation as well as limited predictive capabilities. We submit that computational models, particularly when integrated with experimental measurements, can help to overcome these issues. For example, computational models have been developed to understand how mechanical stresses and strains direct collagen remodeling in heart valves,in situ TEHVs, as this has been one of the most common failure modes of TEHVs over the past 10–15 years.78 Computational analysis of in vivo leaflet strains as a function of valve geometry and collagen anisotropy57 suggested that the often-used Thubrikar design was prone to adverse tissue remodeling leading to leaflet retraction in TEHVs. A more curved valve geometry was predicted computationally to yield more favorable leaflet strains with respect to valve functionality, and we hypothesized that this could also improve the strain-mediated remodeling of the TEHV. We subsequently investigated in situ the remodeling of TEHVs featuring this more curved leaflet geometry by implanting them as pulmonary valve replacements in sheep for 1 year. The results of this pre-clinical study confirmed substantially reduced leaflet retraction and preserved functionality of most TEHVs throughout the complete follow-up period.18 Moreover, our computational models were able to predict the in situ remodeling of these TEHVs from the initial tissue properties at implantation, thus explaining differences in outcome due to variations in tissue properties or hemodynamic loading conditions. Altogether, this example demonstrates that computational models can significantly advance our understanding of TEHV remodeling, predict in vivo outcomes, and reveal novel design strategies for improving the adaptation and corresponding functionality of TEHVs.As an example, we recently showed that computational models can enable breakthroughs in improving TEHV adaptation and functionality. For this, we focused on understanding and eliminating the development of progressive leaflet retraction in 64Disease and injury are responsible for many clinical interventions designed to support, repair, or replace blood vessels. Examples include use of prosthetic grafts in the repair of an aneurysmal aorta, use of autologous vein grafts in coronary artery bypass surgery, and use of prosthetic grafts in the Fontan completion surgery for palliative treatment of children with congenital heart defects. Grossly damaged blood vessels resulting from traumatic injury can similarly be replaced or supported by such grafts, and shunts can be introduced surgically to facilitate important clinical care, including hemodialysis. Despite the life-saving emergence of standard-of-care vascular grafts fabricated from Dacron , a polyester) or GoreTex™ (poly(tetrafluoroethylene), or PTFE), synthetic grafts yet associate with complications and failures that are responsible for continuing morbidity and mortality. Such complications include infections and thrombosis, but also longer term dilatation or stenosis, the latter sometimes including calcification. Indeed, thrombosis remains a continuing barrier to the use of synthetic grafts in the case of small vessels. A particular limitation of synthetic grafts in pediatric applications is their lack of growth potential. For these and other reasons, many have turned to the promise of tissue engineering in the search for improved vascular conduits, particularly since publication of the seminal paper by Dr. Laura Niklason and colleagues more than 20 years ago.in vitro development in specially designed bioreactors and in situ development at the intended site within the body. Regardless, the basic requirement is again that cells must infiltrate an initial scaffold and begin to form neotissue via deposition of extracellular matrix constituents. These initial scaffolds can consist of biodegradable polymers, such as poly(glycolic acid) and poly(ε-caprolactone), reconstituted extracellular matrix, often type I collagen or fibrin, or decellularized native vessels in the hope of retaining much of the complexity of normal tissues. A native blood vessel consists primarily of elastic fibers  and fibrillar collagens  in addition to three primary cell types , but nevertheless contains ~ 100 different proteins, glycoproteins, and glycosaminoglycans.47 It is unimaginable that such matrix complexity could be reproduced in the laboratory, hence one seeks to use cells to produce and organize the matrix in which they need to reside in vivo.As in the development of TEHVs, there are two basic approaches to constructing TEVGs: 30 In particular, they first modulated gelatin-based, fibrinogen-mediated electrospun tubular constructs to mimic the compliance of the abdominal aorta in a rat interposition model. The intrinsic stiffness of the fibers was altered via different periods of cross-linking , with interpolated values used to identify that period of cross-linking that yielded the desired overall compliance given near aorta-matching diameter and wall thickness. A subsequent study showed that a similar computational identification of a TEVG having a preferred compliance at implant yielded excellent short-term (4 weeks) in vivo results.23We and others have used computational models to advance the development of TEVGs. Noting that vessel-level compliance depends on both the geometry and intrinsic material properties of the TEVG, Vande Geest and colleagues used computational methods to design and fabricate compliance-matching grafts.in vitro and in situ TEVG development,65 the latter based on extensive data from a mouse model developed by Dr. Christopher K. Breuer. Briefly, for the mouse model, the small diameter scaffold consisted of a tubular poly(glycolic acid) felt with a 50–50 co-polymer sealant of poly(ε-caprolactone and L-lactide). The scaffolds were implanted as interposition grafts in the inferior vena cava of mice, some immuno-competent and some immuno-compromised to study immune factors, to simulate the lower blood pressures in the Fontan conduit (relative to systemic pressures). The vessels were imaged longitudinally in vivo and were harvested at times ranging from two weeks to two years, then subjected to biomechanical testing and immuno-histological examinations. The histo-mechanical data were then used to inform a constrained mixture based G&R model, which accounted for the geometry and mechanical properties of the initial polymeric scaffold and its degradation profile as well as subsequent cell-mediated deposition and degradation of collagen-dominated neotissue. Because of the initial strong foreign body response, largely by macrophages,70 the mass production term 59 which was found to predict well the evolution over a subsequent 18-month period.48 Importantly, this basic model also predicted a narrowing of the graft that should resolve spontaneously over a period of weeks; that is, an early exuberant immuno-driven production of matrix thickened the wall, which encroached on the lumen, but then reversed as the inflammation waned with polymer degradation. This unexpected computational prediction was validated in a pre-clinical sheep model over an 18-month study period, which confirmed an early narrowing of the TEVG (by 6 weeks of implantation) with a subsequent, spontaneous resolution of the narrowing within 18 weeks.15 This important finding resolved a previously identified clinical concern and thus enabled US Food and Drug Administration (FDA) approval of this promising technology for use as a Fontan conduit in children with congenital heart defects, which is now in clinical trials. In particular, consistent with model predictions, the patients are now followed more closely with medical imaging and interventional angioplasty is reserved for only symptomatic narrowing. Hence, computational models can provide increased insight into TEVG development and help to guide clinical management.Conversely, our group developed a computational G&R model of both in situ development of TEVGs promise yet another important application, rational design of the scaffold itself. Coupling methods of non-dimensionalization with micromechanical analyses of the properties of porous scaffold materials allowed parametric simulations of the in situ evolution of material properties of a TEVG as a function of the initial polymeric scaffold pore size, fiber diameter, and porosity.60 These simulations revealed interesting, initially non-intuitive results, including that an initially stiff, low-porosity scaffold could yield a more compliant neovessel at late times since an initial low porosity reduces early inflammatory cell infiltration and thus attenuates the immuno-driven deposition of matrix that is expected due to the implantation of a foreign body. It was later shown that formal methods of optimization, including use of the non-intrusive, derivative-free surrogate management framework, could be used to identify preferred values of particular scaffold parameters based on simulated long-term in situ development.79 Yet, as one would expect, the predicted optimization of the in situ development depended strongly on the objective function used, noting that there are many parameters to optimize, including initial scaffold dimensions, microstructure, and overall compliance. Perhaps more importantly, for the polymers simulated, it was clear that the high stiffness of these materials relative to native makes it very difficult to design an initial scaffold having appropriate compliance.Validated G&R models of 83 Toward this end, there is a need for advances along all fronts: a better understanding of the associated molecular and cell biology in native and diseased tissues and organs, development of advanced biomaterials that can better immuno-modulate the host response, perhaps via addition of biomimetic materials, and of particular importance here, advances in computational models that can be used to inform design and to predict outcomes. Computational models of the associated molecular and cell biology could also help identify short-term adjuvant pharmacotherapies,49 and so too computational models of the pharmacokinetics. See, for example, Fig. 62. In Europe, roadmaps have been defined toward realizing the Digital Patient  and defining strategies for in silico clinical trials .Much has been achieved, yet much remains to be accomplished for Regenerative Medicine to achieve its full potential and become a consistent contributor to improving human health-span and life-span.85 including chemical composition, porosity, fiber diameter and alignment, pore size, and degradation profile as well as graft-level geometry (layered or not), compliance, and strength. As noted above, much of scaffold design yet remains empirical, seeking systematically to control many of the key variables of interest. Computational models promise to reduce the parameter search space,35 thus reducing the experimental need and potentially accelerating the time to pre-clinical testing. Yet, to fully leverage the potential of computational models in Regenerative Medicine, several challenges still need to be addressed in the future.Fortunately, fabrication methods for building tissue engineering scaffolds have also advanced tremendously, now including traditional methods used to make textiles as well as electro-writing and 3D (bio)printing. With these advances comes the ability to control many different design parameters,in vivo, which may necessitate appropriate growth and adaptive capabilities in response to potentially changing loads. In many ways, the cardiovascular system serves well as an archetype for the need for multiphysics modeling to describe a complex in vivo environment. The primary function of the cardiovascular system is biotransport, leading to the transfer of nutrients, gases (O2 and CO2), and waste products to and from tissues throughout the body. Towards this end, complex nonlinear biosolid tissues facilitate the pressure-driven flow of a complex biofluid throughout the body. Hence, comprehensive models need to account for fluid–solid interactions that result in effective mass, momentum, and heat transfer. As noted above, FSG models19 thus become important because these tissues can grow and remodel (adapt) in response to changes in loading. There remains a pressing need to identify and implement an efficient FSG model.Tissue engineering scaffolds must be designed with the end in mind—effective performance over long periods via G&R mechanics), and cell-level behaviors  that are characterized by transcriptional changes (driving changes in gene products). The aforementioned constrained mixture G&R formulations naturally meld the continuum- and constituent-level behaviors, though there remains a need for advances in both constitutive formulations and computational implementations.37 Additionally, it has been shown that these same constrained mixture models can be linked with cellular automaton  models to account for cell-level behaviors31 and recently it has been shown that constrained mixture models can similarly be linked with logic-based cell signaling models, which enable transcriptional changes to affect tissue level responses.44 Thus, it is clear that true multiscale—tissue to constituent to cell to cell signaling—models can be built, and even coupled with hemodynamic models, yet the current implementations remain in their infancy.There continues to be a pressing need for computational biomechanical models from transcript-to-tissue, particularly given recent advances in transcriptomics and proteomics. In particular, although most engineered tissues and organs must function at a macroscale, it is the molecular mechanisms at the microscale that ultimately dictate success or failure. As noted earlier, even changes to mechanical loading in native tissues and organs drive myriad differentially expressed genes (DEGs). Multiscale models can and should account for tissue-level phenomena (continuum biomechanics), constituent-level mechanisms (e.g., turnover of particular structurally significant constituents in situ development of tissue engineered constructs. Indeed, fundamental to the continuing success of tissue engineering is improved methods of scaffold design. Given the broad design space, defined by so many free parameters, machine learning methods that enable identification of preferred microstructural properties offer considerable promise.86 Machine learning models can also complement the multiscale, multiphysics models68 that will be fundamental to advancing tissue engineering.Advances in scientific machine learning continue to emerge rapidly, with considerable promise to advance biomedical research and clinical care. With regard to Regenerative Medicine, there is an opportunity both to augment scaffold design and extend predictions of the 35 With regard to the future, we expect that computational models will play increasingly important roles in advancing tissue engineering in particular and Regenerative Medicine in general.In summary, we used two illustrative examples from cardiovascular medicine and surgery—TEHVs and TEVGs—to illustrate promising roles of computational modeling in understanding and advancing Regenerative Medicine. There are, of course, many other examples, including the earlier use of computational modeling to design scaffolds for bone regeneration.


Innate and acquired resistance towards the conventional therapeutic regimen imposes a significant challenge for the successful management of cancer for decades. In patients with advanced carcinomas, acquisition of drug resistance often leads to tumor recurrence and poor prognosis after the first therapeutic cycle. In this context, cancer stem cells (CSCs) are considered as the prime drivers of therapy resistance in cancer due to their ‘non-targetable’ nature. Drug resistance in cancer is immensely influenced by different properties of CSCs such as epithelial-to-mesenchymal transition (EMT), a profound expression of drug efflux pump genes, detoxification genes, quiescence, and evasion of apoptosis, has been highlighted in this review article. The crucial epigenetic alterations that are intricately associated with regulating different mechanisms of drug resistance, have been discussed thoroughly. Additionally, special attention is drawn towards the epigenetic mechanisms behind the interaction between the cancer cells and their microenvironment which assists in tumor progression and therapy resistance. Finally, we have provided a cumulative overview of the alternative treatment strategies and epigenome-modifying therapies that show the potential of sensitizing the resistant cells towards the conventional treatment strategies. Thus, this review summarizes the epigenetic and molecular background behind therapy resistance, the prime hindrance of present day anti-cancer therapies, and provides an account of the novel complementary epi-drug-based therapeutic strategies to combat drug resistance. In 1971, ‘National Cancer Act’ of the U.S. Congress declared a ‘War on cancer.’ Since then, billions of dollars have been invested in cancer research. Yet, despite decade-long studies and advancement in anti-cancer therapeutic regimens, cancer still poses as one of the major causes of global morbidity and mortality, with the diagnosis of millions of new cases every year. In addition, the development of drug resistance, consequent ineffectiveness of therapy, and successive tumor relapse lead to poor predictive outcomes in the majority of the cases and serve as the major limiting factors of the present day anti-cancer therapies. Studies have shown that despite substantial development in the early detection of breast cancer, more than one-third of patients do not respond to the primary chemotherapeutic regimens and develop therapy resistance .Moreover, the fact that chemotherapy is used as the first-line treatment strategy against the majority of the advanced carcinomas only worsens the scenario . DespiteThe concept of drug resistance was first reported in bacteria, based on antibiotic resistance. Studies on cancer and several other diseases showed similar, evolutionarily conserved mechanisms that impart resistance towards treatment stratagems . For exaThe recent era of cancer research has shifted a significant focus towards CSCs as they are being reported as one of the major culprits behind maintaining all the hallmarks of cancer. CSCs, a distinct subset of cells within the heterogeneous tumor mass, are adorned with the ability to self-renew and differentiate into multiple lineages . Over thThe EMT is the preliminary step in cancer metastasis whose induction is governed by epigenetic modifiers, like in the case of one of the transcription factors, Snail, which recruits multiple chromatin modifiers such as PRC2, histone deacetylase (HDAC)1/2, G9a, LSD1, to the promoter of E-cadherin, thereby promoting Snail-mediated E-cadherin repression, a hallmark of EMT .It has been reported that the induction of EMT promotes the acquisition of stem cell-like features in cancer cells. For instance, Twist, Slug, and Snail, the critical regulators of EMT, induce mesenchymal properties in breast cancer cells, triggering stem cell-like properties and mammosphere-forming ability . It has Interestingly, EMT and stemness share common signaling pathways like Wnt, Hedgehog, and Notch , which sABC transporters like ABCB1 (MDR1), ABCC1 (MRP1), ABCG2 are transmembrane proteins that pump toxins out of the cells using energy from ATP hydrolysis. CSCs of various solid tumors display significantly high expression of these drug efflux pumps, rendering them intrinsically resistant to most conventional therapeutic interventions ,31. ABCG0 phase and require a mitogen stimulus to enter into the division phase ..255].Three PRMT5 inhibitors, PF-06939999, GSK3326595 and JNJ-6461978, are currently under clinical trials in patients with solid and hematologic cancers. GSK3368715, the only developed inhibitor of PRMT1, has recently entered Phase I clinical trials in patients with various solid and hematologic malignancies (NCT03666988).Bromodomain (BRD) proteins are ‘readers’ of histone acetylation, which target transcription factors and chromatin-remodeling enzymes to target sites on the chromatin to regulate gene expression. BRD proteins are generally associated with the augmentation of target genes . Bromo- Consecutive research has led to the development of JQ1 analog TEN-010 (JQ2), which has completed Phase I clinical trial in various advanced solid tumors (NCT01987362), AML, and MDS patients NCT02308761) ,263 Tab. However761 ,263 Since CSCs are the major contributors behind drug resistance within the tumor mass, several studies have utilized epi-drugs to specifically target CSCs and sensitize them towards conventional therapies to achieve significant tumor regression . For insImmunotherapy or enhancing the patient’s immune response to eradicate cancer cells has become a breakthrough in anti-cancer therapeutics. But, a substantial percentage of nonresponding patients and systemic toxicities pose an obstacle to its therapeutic success . An immuEpigenetic reprogramming of tumor cells, TME, and tumor-associated immune cells play crucial roles in developing an antitumor immune response, patient’s response to immunotherapy, and overall prognostic outcome of the patient. For instance, derepression of endogenous retroviruses (ERVs) by LSD1 inhibitors have been reported to lead to accumulation of double-stranded RNAs (dsRNAs) in cancer cells, which simulates viral infection, in turn triggering antitumoral immunity through interferon response within the tumor milieu . AnotherAll these reports indicate that targeting epigenetic modulators can prompt significant anti-tumor immunity and thus can serve as a complementary therapeutic approach to current conventional therapies.Cancer is the principal cause of global morbidity and mortality, with billions of people succumbing to it each year. The most routinely used therapeutic regimen for most advanced cancers is chemotherapy, which has led to no significant improvement in the treatment scenario and mortality worldwide. Approximately 70–80% of patients with advanced carcinomas experience recurrence, which leads to an even more aggressive form of the disease and is mostly fatal. The development of the recurrent tumor is mainly attributed to drug resistance as the therapy-resistant cells remaining after first-line therapy, serve as the seed for future relapse. Drug resistance involves a complex architecture of molecular events, including heightened drug efflux, drug inactivation, inhibition of apoptosis, and enhanced DNA repair mechanisms. This resistance towards the conventional anti-cancer stratagems, be inherent or acquired, has hindered favorable prognosis since the emergence of the chemotherapy and seems to be a prime impediment in the effective management of breast cancer. Extensive research for the past decade has established that CSCs are the primary drug-resistant cells within the tumor mass, which remains unaffected even after the chemotherapeutic cycles and finally gives rise to recurrent tumors. This indicates the importance of developing CSC targeting therapies that could sensitize the drug-resistant tumor towards conventional chemotherapy and hence will be able to complement it. Combinatorial therapies involving the usage of various drugs or pathway inhibitors and chemotherapy have been highlighted for the past few years due to their efficacy in reducing tumor burden and attenuation of drug resistance.Epigenetic modifications play a crucial role in the acquisition of therapy resistance. However, even these combinatorial therapies often fall prey to the ingenious epigenetic reprogramming of the cancer cells, leading to treatment failure after a certain point in time. In this scenario, epigenome modifying drugs seem to be an attractive option to serve as a co-treatment procedure along with mainstream/combinatorial therapies. Hence, it is crucial to continue unravelling the intricate epigenetic regulatory mechanisms underlying the acquisition of drug resistance to keep developing more efficacious treatment possibilities. This review encompasses the currently known epigenetic landscape, which regulates the mechanisms of drug resistance in cancer. Further studies are required to enhance overall understanding and devise novel therapeutic strategies to target drug-resistant cancers.


Neuropathic pain is often chronic and can persist after overt tissue damage heals, suggesting that its underlying mechanism involves the alteration of neuronal function. Such an alteration can be a direct consequence of nerve damage or a result of neuroplasticity secondary to the damage to tissues or to neurons. Recent studies have shown that neuroplasticity is linked to causing neuropathic pain in response to nerve damage, which may occur adjacent to or remotely from the site of injury. Furthermore, studies have revealed that neuroplasticity relevant to chronic pain is modulated by microglia, resident immune cells of the central nervous system (CNS). Microglia may directly contribute to synaptic remodeling and altering pain circuits, or indirectly contribute to neuroplasticity through property changes, including the secretion of growth factors. We herein highlight the mechanisms underlying neuroplasticity that occur in the somatosensory circuit of the spinal dorsal horn, thalamus, and cortex associated with chronic pain following injury to the peripheral nervous system (PNS) or CNS. We also discuss the dynamic functions of microglia in shaping neuroplasticity related to chronic pain. We suggest further understanding of post-injury ectopic plasticity in the somatosensory circuits may shed light on the differential mechanisms underlying nociceptive, neuropathic, and nociplastic-type pain. While one of the prominent roles played by microglia appears to be the modulation of post-injury neuroplasticity. Therefore, future molecular- or genetics-based studies that address microglia-mediated post-injury neuroplasticity may contribute to the development of novel therapies for chronic pain. Injuries to neural circuits, whether in the peripheral or central somatosensory system, can result in neuropathic pain, an intractable chronic pain manifests as an array of symptoms and signs . The preThe dorsal horn of the spinal cord, organized into six Rexed laminae and composed broadly of excitatory and inhibitory neurons, gathers sensory information of the peripheral tissues through the afferent sensory neurons. Nociceptive information is transmitted to the superficial laminae I and II of the dorsal horn, and non-nociceptive information is transmitted to the deep laminae II–VI. Integrated sensory information is then relayed to several brain regions, including the thalamus. When a peripheral injury occurs to an extent that alters the somatosensory information processing system, it often leads to evoking pain sensation by normally non-noxious stimuli. Such an alteration in pain perception is often induced by structural and functional neuroplasticity of the spinal dorsal horn due to its closely related anatomical structures of each lamina. Pain evoked by non-noxious stimuli is called allodynia and it is one of the characteristics of neuropathic and nociplastic pain.iV/III border and partially in laminae I and deeper III, have been shown to mediate mechanical allodynia induced by neuropathic injuries. These pain mediations were shown to be through PKCγ neurons losing at least a partial synaptic connection with parvalbumin or with glycine inhibitory neurons [The excitatory and inhibitory neurons in the spinal dorsal horn have been morphologically and electrophysiologically analyzed, and their subtypes have also been identified by molecular and gene expression profiles –11. Thes neurons –17, and  neurons . This su neurons .Fig. 1To) send inhibitory gamma-aminobutyric acid (GABA) signals to projection neurons in lamina I. The excitability of Npy neurons decreased after a PNS injury, inducing touch-evoked pain-like behavior, which improved by increasing the excitability of the interneurons. Notably, the axons of Npy neurons project to lamina III/IV cells, including PKCγ neurons [Another well-studied neuroplastic change associated with allodynia occurs in neuropeptide Y promoter (Npy) inhibitory interneurons . The Npy neurons , the sub neurons . NeurophNoxious and non-noxious stimuli applied to peripheral tissues are conveyed to the brain via the spinal cord mainly by the projection neurons in lamina I and V of the spinal dorsal horn . The proGlu -S1Glu) is implicated in neuropathic pain induced by either CFA or SNI injury models. Another pathway of the parafascicular thalamic nucleus (PFGlu) to the anterior cingulate cortex is implicated in inducing allodynia with depression-like symptoms [Neuroplastic changes in S1 occur not only after injuries to the thalamus but also after PNS injuries. In the spared nerve injury (SNI) mouse model, which induces chronic pain, activities in S1 layer 5 (L5) pyramidal neurons persistently rise   [Myb transcription factor-dependent manner, microglia differentiate independently of the Myb transcription factor [Microglia are known as tissue-resident macrophages of the CNS in the parenchyma of the brain and spinal cord. A genetic fate mapping study using Runx1s (EMPs)  and thats (EMPs) , 37  . In addin factor , and then factor –45 , the brainstem, and the hippocampus –67. For Microglia can polarize from the form of ramified shape to the active form of amoeboid shape triggered by a damage to the nervous system, but cell proliferation and the release of various humoral factors by microglia can take place with or without distinctive morphological changes. Microglia without obvious morphological changes have been shown in the amygdala and the somatosensory cortex after nerve injury, and these microglia have been reported to regulate neuropathic pain , 68. HenMicroglia are said to be derived from the yolk sac; however, in the rise of pain occurrence, it has been shown that circulating monocytes infiltrate into the parenchyma of the spinal dorsal horn and differentiate into the functional microglia-like cells . ChronicRFP/+-CX3CR1GFP/ + mice have indicated that BM-derived microglia were not observed in the parenchyma of the CNS even after nerve injuries [Other more recent studies have shown, however, the origin of microglia after a nerve injury is unlikely to be derived from circulating monocytes. Studies using mild irradiation, parabiosis mice, and CCRinjuries , 71. ThuOne of the property changes in microglia is the release of brain-derived neurotrophic factor (BDNF) following PNS injury. Specifically, synaptic remodeling and pyramidal neuron hyperactivity in the S1 cortex induced by nerve injury can be altered by the depletion of microglial-specific BDNF . The speFurthermore, IL-10 secreted from microglia is shown to contribute to synapse formation , which iTogether, mounting evidence suggests that cerebral microglia participate in nociplastic changes in distant brain regions to an injury core. Identifying the detailed cellular or molecular microglial functions responsible for inducing neuroplasticity may yield a pharmacological target for treating chronic pain.Further elucidation of post-injury ectopic somatosensory circuits may help categorize the mechanisms underlying nociceptive, neuropathic, and nociplastic-type pain. Studies employing cellular and molecular approaches, including neuron type-specific pharmacological molecular inhibition or neuron type-specific genetic ablation, are expected to contribute to the development of a therapy targeting neuroplasticity that induces chronic pain in response to PNS or CNS injury. Among the various functions of microglia, one prominent function seems to be pain regulation in the PNS and CNS. Microglia may directly contribute to synaptic remodeling and altering pain circuits, or contribute indirectly to neuroplasticity through property changes, including the release of growth factors. Hence, further elucidation of the modulatory mechanisms of microglia, including a specific gene-regulating microglial function or cytokines and growth factors released to affect neuroplasticity, may lead to a therapeutic approach that addresses neuroplasticity-relevant chronic pain.


Increasing evidences show that unmodified extracellular vesicles (EVs) derived from various cells can effectively inhibit the malignant progression of different types of tumors by delivering the bioactive molecules. Therefore, EVs are expected to be developed as emerging anticancer drugs. Meanwhile, unmodified EVs as an advanced and promising nanocarrier that is frequently used in targeted delivery therapeutic cargos and personalized reagents for the treatment and diagnosis of cancer. To improve the efficacy of EV-based treatments, researchers are trying to engineering EVs as an emerging nanomedicine translational therapy platform through biological, physical and chemical approaches, which can be broaden and altered to enhance their therapeutic capability. EVs loaded with therapeutic components such as tumor suppressor drugs, siRNAs, proteins, peptides, and conjugates exhibit significantly enhanced anti-tumor effects. Moreover, the design and preparation of tumor-targeted modified EVs greatly enhance the specificity and effectiveness of tumor therapy, and these strategies are expected to become novel ideas for tumor precision medicine. This review will focus on reviewing the latest research progress of functionalized EVs, clarifying the superior biological functions and powerful therapeutic potential of EVs, for researchers to explore new design concepts based on EVs and build next-generation nanomedicine therapeutic platforms. Prokaryotes and eukaryotes release different vesicles into the extracellular space, these various types of secreted nanosized membrane vesicles with different size, density, composition and intracellular sources. EVs have diverse nomenclature, and the EVpedia provides a complete overview of vesicles nomenclature and classification. EVs contain many subtypes, including enveloped virus, exosomes, ectosomes, microvesicles, microparticles, large oncosomes, and apoptotic bodies. The latest research found that exosomes have different subtypes, which are named small exosomes  and large exosomes  respectively. These exosomes subsets have different biophysical and molecular characteristics. Meanwhile, the authors also discover a previously unknown class of nanoparticles, called exomere (30–50 nm). Compared with exosomes, exomere is a discrete and abnormally small nanoparticle with a diameter of about 35 nm that exhibits different proteins, lipids, RNAs and DNAs profiles  formation, fusion and efflux. Firstly, cells produce endocytic vesicles through lipid-mediated endocytosis, multiple endocytic vesicles fuse to form the early endosome. The early endosome again invaginate and encapsulates the intracellular material form the intraluminal vesicles (ILVs), and gradually evolving into late endosomes. Finally, MVBs directly fuse with the plasma membrane and release ILVs into the extracellular space known as exosomes. Other EVs  can be generated directly from the plasma membrane by outward membrane budding manner -dependent and independent pathways are responsible for the sorting of cargos into the MVBs and participate in regulating exosomes release   cells, and tumor cell lysates, the immune system can be mobilized cells to stimulate and enhance the antitumor immune response, and thus inhibit tumor growth, metastasis and recurrence  are the most common in tumor microenvironment, accounting for about 50% of the tumor microenvironment. Macrophages affected by tumor microenvironmental cytokines can differentiate into different types of TAM, which are mainly divided into tumor suppressor M1 type and tumor promote M2 type according to their functions. M1 polarized macrophage-derived nanovesicles (NVs) can repolarize M2 TAMs to M1 macrophages that release pro-inflammatory cytokines and induce antitumor immune responses . Moreove3.5.NK cells originated from hematopoietic stem cells, which have dual roles of effector and regulatory cells in innate immunity. NK cells can inhibit the malignant biological behavior of tumors through their direct cytolytic activity, and cytokine secretion. In recent studies, EVs, as a key component of NK cell therapy, have been proven to be effective in cancer therapy  may be an emerging therapeutic agent for anticancer therapy without obvious adverse responses. Specifically, single intravenous injection of OMVs not only could activate the immune system by boosting the secretion levels of anti-tumor related cytokines, but also could also lead to extravasation of red blood cells in the tumor  expressed T cells are a powerful and innovative therapeutic strategy for cancer patients. However, problems with serious side effects and toxicities caused by CAR T cells infusion 4.3.G12D siRNAs or shRNAs to achieve direct and specific targeting to oncogenic KRAS in pancreatic cancer  , multifunctional nanoparticles such as gold-carbon dots, gold nanostars, gold-iron oxide nanoparticles (GIONPs), and PMA/Au-BSA@Ce6, etc. can be decorated on the surface or inside of EVs for the early diagnosis and targeted treatment of tumors. The engineered EVs not only exhibit good tumor PTT and PDT effects, but also exhibit good biocompatibility, safety, long-circulation retention and endosomal escape capabilities.Photothermal therapy (PTT) and photodynamic therapy (PDT) are the two most common optical-based therapies. Under near-infrared laser irradiation, nanoparticles with photothermal conversion effect or photosensitizers can generate heat, singlet oxygen or reactive oxygen free radicals to kill tumor cells. Some studies have shown that EVs can be used as a promising delivery vehicle for targeted delivery of phototherapeutic substances, enhanced tissue penetration of tumor tissues, and real-time multimodal imaging 4.5.Radiotherapy has become one of the main methods in the treatment of malignant tumors. More than 70% of tumor patients require radiotherapy including comprehensive treatment and individual treatment. Previous research has shown that unmodified EVs combined with radiotherapy have a significant effect on promoting the treatment of tumors  and miR-21 inhibitor oligonucleotide to reverse drug resistance in colon cancer . These nanomaterial with one end coupled to the substance of interest can be embedded into EVs through hydrophobic insertion. Recently, researchers have been inspired by the accumulation of high levels of cholesterol, neurophospholipids and other components in the lipid bilayer of EVs, and have developed some novel lipidomimetic chain for the delivery of goods to EVs  fused to uracil phosphoribosyltransferase (UPRT) to inhibit schwannoma tumor growth  can simply and efficiently interfere with abnormally expressed genes in tumors to achieve effective treatment of tumors , and the presence of these endogenous miRNAs in EVs of different cell  is a new type of non-coding RNA with functions such as regulation and translation. It has a closed loop structure and exists in a large number of eukaryotic transcriptomes. Yang et al. generated rabies virus glycoprotein (RVG) decorated-EVs to targeted deliver circSCMH1 to ischemia-reperfusion injury of the brain, and engineered RVG-circSCMH1-EVs treatment promotes functional recovery in rodent and nonhuman primate ischemic stroke models -enriched EVs derived from HEK293 cells suppressed the growth of small cell lung cancer by inhibiting endothelial cell migration  have broad application prospects in a variety of biomedical fields . Based on the many advantages of EVs, researchers combine different types of gold-based nanomaterials with EVs for tumor treatment. Researchers decorated gold nanorods   is an emerging two-dimensional metal-free layered material. Due to its good biocompatibility, BP has been explored for potential biomedical applications. Wang et al. reported that bone-related functional cells derived Matrix vesicles (MVs) embedded with BP and functionalized with cell-specific aptamer for molecular recognition-guided biomineralization  and carbocyanine dyes . In addition, EVs can also be labeled with some membrane-permeable compounds . After nearly a decade of efforts and development, aggregation-induced emission (AIE) materials have been developed as a new photosensitizer, which has greatly promoted the development of tumor photodynamic therapy. Zhu et al. designed 4T1 tumor cell-derived EVs loaded with AIE luminogen, which facilitate efficient breast cancer penetration and photodynamic therapy (Zhu et al., 11.in vivo, many experiments did not conduct safety evaluations or only conducted short-term follow-up evaluations. These shortcomings will seriously hinder the safe application of engineered EVs in vivo in the future.Compared with traditional synthetic nanomaterials, EVs have a natural targeting properties, good biocompatibility, stability, plasticity, and safety (Ullah et al., 12.In the past 30 years, EVs have shown excellent efficacy and transformation prospects in preclinical studies and trials. With the development of materials science and the integration of interdisciplinary disciplines, new breakthroughs have been made in advanced separation technologies, detection platforms and engineering modification strategies for EVs. These EVs include unmodified, engineered, or in combination with other nanomaterials, as a new concept as a cancer treatment is an attractive and promising strategy. Here, the latest advances in the field of EV-based and its applications in the creation and customization of therapeutic nanomaterials, and focus on their underlying design principles. With the continuous breakthrough of EV key technical bottlenecks, engineered EVs will become a promising strategy for the treatment of complex and refractory tumors in clinical practice.


Over the millions of years-long co-evolution with their hosts, viruses have evolved plenty of mechanisms through which they are able to escape cellular anti-viral defenses and utilize cellular pathways and organelles for replication and production of infectious virions. In recent years, it has become clear that lipids play an important role during viral replication. Viruses use cellular lipids in a variety of ways throughout their life cycle. They not only physically interact with cellular membranes but also alter cellular lipid metabolic pathways and lipid composition to create an optimal replication environment. This review focuses on examples of how different viruses exploit cellular lipids in different cellular compartments during their life cycles. Viruses are classified as obligate intracellular parasites, which means they must complete their life cycle in live host cells. Upon entry into the cell, they use various mechanisms to rewire metabolic pathways and produce progeny. Over the past few decades, the role of lipids during viral replication has been intensively studied, and cellular lipids have been shown to play a crucial role in viral replication (reviewed in ). Since The plasma membrane, prevailingly made up of phospholipids, separates the intracellular environment from the extracellular and represents the first barrier for viral entry into the host cell. Most viruses overcome this barrier by binding to cellular receptors located within the plasma membrane. Membranous lipids facilitate virus entry by serving as viral receptors or as main constituents of platforms for gathering viral receptors. Receptors can be constitutively expressed in these platforms or translocated to them upon virus binding.One example of viruses that bind directly to plasma membrane lipids to initiate infection are polyomaviruses a. They cReceptors of some viruses are located in unique, detergent-insoluble membrane areas rich in cholesterol, sphingolipids, and GPI-anchored proteins termed lipid rafts. Lipid rafts make up approximately 15 to 20% of the total plasma membrane area and play an important role in various processes such as membrane signaling and signal transduction . Since tSome cellular receptors and co-receptors are constitutively expressed in lipid rafts b. PorcinIn other cases, cellular receptors and co-receptors are not expressed in the lipid rafts constitutively but are translocated to them upon virus binding to the cell surface c. One exAnother manner of raft involvement in the entry process represents the binding of SV40 to the major histocompatibility complex I (MHCI) that triggers the relocalization of viral particles into lipid rafts, where they bind to GM1 and induce entry through caveolae, a special type of lipid raft enriched in sphingolipids and cholesterol stabilized by cholesterol-binding caveolins ,37. The Lipid rafts are also crucial for the entry of the influenza A virus (IAV) since it enters the cell via raft-dependent endocytosis, and its receptor-binding glycoprotein hemagglutinin (HA) was found to interact with lipid rafts during entry into the cell ,40. The Some viruses require the presence of non-raft lipids during their entry d. One ofAnother example is the hepatitis C virus (HCV) that circulates in the bloodstream in complex with apolipoproteins and requires low-density lipoprotein receptors (LDL-R) in addition to other high-affinity receptors for entry into target cells. It seems that interaction with LDL-R brings the virus into the vicinity of other entry co-factors ,45.All the aforementioned examples show that viruses can enter a host cell using a variety of mechanisms, but plasma membrane lipids are crucial to each and every one of them.In addition to the initial stages of the viral life cycle, lipid rafts often play an important role during virion assembly and budding e. For exIAV virion assembly also takes place in lipid rafts since HA, an IAV surface protein, was found in association with these lipid microdomains during the budding of nascent virions . In addiSimilarly, the HIV Gag protein is associated with lipid rafts during virion budding , and as There are a few ways through which viruses are internalized into the cell. Although some enveloped viruses enter the cells by fusing with the cytoplasmic membrane, most non-enveloped and enveloped viruses enter the cell via endocytosis. After receptor binding, the cytoplasmic membrane encloses the virus, thus creating an endocytic vesicle that enters the cytoplasm. The endocytic vesicle fuses with an early endosome that later matures into a late endosome which differs in lipid and protein composition, as well as pH level . To starFlaviviridae family [Upon entering the endosome, Semliki Forest virus (SFV) glycoprotein E1 undergoes conformational changes that are dependent on low pH and result in the formation of E1 homotrimer and exposition of the fusion protein, crucial for the interaction of the viral and endosomal membrane. It has been demonstrated that E1 is preferably inserted in areas rich in cholesterol and sphingolipids and that it interacts with cholesterol during membrane fusion ,62. Anote family .Kobayashi and Hirabayashi discovered lipid rafts rich in a phospholipid known as lysobisphosphatidic acid (LBPA), termed LBPA microdomains, found in the endocytic pathway, particularly in the inner leaflet of late endosomes . Later, After internalization, viruses that replicate in the cytoplasm begin replicating their genome using various cellular compartments, such as the endoplasmic reticulum (ER), Golgi apparatus (GA), and lipid droplets (LDs), as the sites of replication. These areas are known as replication organelles and usually comprise the viral genome, viral RNA polymerases, and non-structural proteins required for replication. One of the most frequently used organelles is the ER, whose membranes get rearranged to create a safe environment, unapproachable for endonucleases and the host immune system . MoreoveFlaviviridae family. One of the examples comprises flaviviruses DENV and Zika virus that induce invaginations of the rough ER, which form invaginated vesicles known as vesicle packets (VPs) [This phenomenon is characteristic of infection with positive-sense RNA viruses and best described in the case of viruses belonging to the ts (VPs)   . Althougts (VPs) . In addits (VPs) . DMVs doFlaviviridae family [Similar structures were observed in cells infected with tick-borne encephalitis virus (TBEV), another member of the e family . While De family .Unlike other flaviviruses, replication of HCV causes the protrusion of the ER and creates organelle-like structures made of DMVs b, which Coronaviridae family [Another group of positive-strand viruses that induce the formation of DMVs is coronaviruses. Their replication–transcription complex (RTC), consisting of non-structural proteins, induces the rearrangement of ER membranes and initiates the formation of DMVs where RTCs then concentrate . In addie family .Flaviviruses and coronaviruses also induce changes in the smooth ER, which then rearranges and forms into clumps known as convoluted membranes (CMs) ,91. In fFlaviviridae family members are lipid droplets (LDs). LDs are cellular organelles whose core is composed of neutral lipids such as triacylglycerols (TAG) and steryl esters (SE), surrounded by a phospholipid monolayer and structural proteins, which separate them from hydrophilic cytosol [After genome replication and synthesis of viral proteins, mature virions are formed by assembling the proteins into viral capsids, inside which the replicated genome is then packaged. As in the case of viral genome replication, lipid structures also play a role during virion assembly. Noteworthy structures during the assembly of the  cytosol . LDs are cytosol . During  cytosol   ,106. SevIn addition to flaviviruses, LDs play a crucial role in the assembly of rotaviruses (RVs), whose replication takes place in discrete cytoplasmic inclusion bodies, termed viroplasms, which are found in association with LDs during RV infection. Apart from non-structural and structural RV proteins, viroplasms were found to contain LD components such as perilipin A and adipophilin. Colocalization of RV NSP5 protein, found in viroplasms with perilipins PLIN1 and PLIN2, markers of lipid droplets, confirmed the association between these two structures . A recenRecent findings show that newly emerged coronavirus SARS-CoV-2 utilizes LDs during virion assembly. The SARS-CoV-2 infection causes an upregulation in lipid metabolism and an increase in LD levels. Moreover, treatment of SARS-CoV-2-infected cells with A922500, a DGAT1 inhibitor, significantly reduced the production of infectious virions in a dose-dependent manner. The involvement of LDs in SARS-CoV-2 replication was confirmed by the detection of viral dsRNA in proximity to BODIPY-labeled LDs . RicciarIn addition to the storage of viral proteins, lipid droplets are occasionally used as a source of energy b. When eHeaton and Randall have demonstrated that DENV uses autophagy to regulate lipid metabolism during its replication . DENV inLipophagy was also observed during infection with PRRSV. The study by Wang and colleagues proposes that PRRSV induces autophagy through N-myc downstream-regulated gene 1 (NDRG1), which is thought to regulate autophagy through various signaling pathways . PRRSV iViruses have evolved mechanisms through which they are able to exploit host cells. With the increasing availability of tools, we have managed to broaden our understanding of virus–host interactions. Over the last two decades, virus interactions with cellular lipids have been intensively studied. Numerous studies have shown that viruses exploit lipids by altering lipid metabolism, signaling pathways that regulate lipid metabolism, or by remodeling lipid membranes. Even though almost all viruses utilize lipids in similar ways, every family also uses unique mechanisms, and therefore needs to be studied individually. It is not uncommon that one virus can use multiple mechanisms to exploit cellular lipids in the same cell, making their study more complicated. Moreover, the strategy of using cellular lipids and virus-induced changes in lipid metabolism may differ depending on viral strain and cell line, but the reasons for these differences are usually not clear. Although much more research is needed to fully understand virus–lipid interactions, current studies have already shown the importance of cellular lipids in the life cycle of many viruses. Detailed understanding of the viral requirements for cellular lipids may not only improve our knowledge of the virus–host interaction but may also provide new treatment options and opportunities to repurpose existing FDA-approved drugs targeting lipid metabolism as potential antiviral therapeutics. For example, as many viruses require active fatty acid biosynthesis, treatment with lipid-lowering drugs such as statins may have a beneficial effect on reducing viral infections. The advantage of such drugs is that they may be less prone to the development of viral drug resistance because they target host proteins. Therefore, an appealing outcome of research in this field is the prospect of broad-spectrum antiviral therapeutics that target lipid metabolism.


Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice. In recent years interest has focussed on the use of radiomics, the extraction of vast amounts of quantitative features from standard medical imaging  to improve the radiophenotyping of neurologic disease. The process of enabling computers to perform this task and indeed learn from such data to facilitate clinical decision-making is often referred to as artificial intelligence (AI). Such approaches have been employed in multiple applications in paediatric neuroradiology, particularly in regard to neuro-oncology . This arArtificial intelligence is the concept of making a computer system “clever” and, in a radiologic setting, able to make decisions based on the imaging data presented to it. Machine learning refers to how we afford computers the opportunity to do this. Rather than having to program a system to perform a decision-making task in paediatric neuroradiology, machine learning is where effective computer algorithms are developed that learn from experience (like a human) and create models, with associated decision-making skills, from a given body of supplied data. In the imaging domain, such data incorporate the imaging study itself.There has been rapid proliferation in the application of machine learning techniques in health care over recent years, secondary to the evolution of electronic health care records, increasing patient data accrual, and a growing interest in health informatics , 3. MachMachine learning models tend to follow two general schema, supervised or unsupervised approaches, with the former defined by whether they “learn” from a provided and characterised training dataset . UnsuperArtificial neural networks (ANNs) are so termed because of their resemblance to a biological neural network. They are composed of a network of nodes that are interlinked by connections, which are weighted. This connection weighting increases or decreases the strength of the connection between nodes and auto-adjusts as the model learns from its training data. Typically, ANNs consist of layers of node networks, with the output generated after data have traversed the network and reached the last layer , 13.Artificial neural networks have received significant exposure of late given their success with, for example, chest radiograph characterisation, often being described as “big-data” analysis. However, only moderate successes have been observed in brain tumour imaging, predominantly in the adult neuro-oncology sphere –18. A maSupport vector machines (SVMs) analyse and group labelled input data into classes, separated by the widest plane (support vector). They are often used in cases where there is a non-linear relationship between data, and, as such, a separation plane is not easily distinguishable. They tend to be used to categorise data into binary groups, e.g., “is this tumour a pilocytic astrocytoma or a medulloblastoma?” However they can be nested to allow for more complex decision-making.A significant proportion of paediatric neuroimaging relates to neonatal imaging and attempts to generate outcome prediction relating to possible neuro-trauma. The authors of this paper had some success in this regard using the thickness of the corpus callosum , to determine whether a child had suffered from hypoxic–ischaemic brain injury . PrincipDecision trees process observations about an object (branches) into conclusions about an item’s target value (leaves). Decision tree analysis is a decision modelling process with weighting of the branches at each step of the tree. This is an open process, in contrast to the black box ANNs discussed earlier, in that the branches and weights are visible and interpretable to end users. By way of example, in brain tumour assessment, the branches could consist of the anatomical location of a suspected tumour and advanced metric MRI features, with the final leaf representing the predicted tumour diagnosis. Decision trees also offer feature selection, in that features can be removed to improve the efficiency of the model. Decision trees are able to process large volumes of both numerical and categorical data, which is imperative in modern health care. However, decision trees are susceptible to small alterations in the training data set causing drastic changes in the final classification, while overly complex trees can lead to overfitting of training data and the inability to process novel data correctly , 30.Decision tree analysis has also been used in the field of neonatal imaging to aid in diagnosis and prognostication. Liu et al.  used sucDecision trees have also been used in another important role of machine learning in paediatric neuroimaging — that of data analysis to inform on neuroimaging in clinical trials. As more and more studies become reliant on multi-parametric data, we require more complex methodologies to analyse and model the resultant imaging findings. One example is the authors’ decision tree classifier, used in a randomised controlled trial in children with neurofibromatosis type 1 (NF1)-related autism who were being treated with simvastatin . In thisNaïve Bayesian classifiers are probabilistic classifiers that assume that all imaging variables available in a study are independent of one another, when generating the probability that a diagnosis is of a particular type. They have the advantage that they require relatively small training data sets, but in general terms they are currently thought to perform comparatively less well against other techniques.The diagnosis of autistic spectrum disorder is made based on clinical neuropsychological/psychiatric assessments in children. These children often have brain scans to look for underlying structural or associated germline conditions that might present with autistic spectrum disorder, e.g., single aberrant gene conditions such as NF1 or tuberous sclerosis. The majority of children with autistic spectrum disorder, however, do not have such underlying conditions and thereby have idiopathic autistic spectrum disorder. To date, such cases were not thought to have an underlying structural aetiology that was identifiable on imaging. Chen et al. , howeverNaïve Bayesian classifiers have also been successful when applied to MR imaging of another rare cohort with a small available imaging training data set: children with antenatally diagnosed fetal abnormalities. Naïve Bayesian classifiers were capable of classifying abnormalities including agenesis of the corpus callosum, Dandy-Walker variants, colpocephaly, mega-cisterna magna, cerebellar hypoplasia and polymicrogyria with accuracies of 63–91% .Linear discriminant analysis (LDA) classifies cases by identifying relationships between combinations of features that can discriminate two or more types using continuous independent variables, which are in turn analysed against a final categorical dependent variable. However, LDAs assume normal distribution of data with homogeneous variance; this can be an unsafe assumption to make in small cohorts of a rare disease process such as those in children’s neuroimaging. However, there have been some successful applications in the neonatal setting.Gui et al.  used an 1H) MR spectroscopy data to classify adult brain tumours [Any discussion regarding AI in paediatric neuroradiology must include a review of unsupervised learning algorithms, although, as mentioned, the relatively small number yet feature-rich datasets in this clinical domain result in limited success in this domain of radiologic practice. K nearest neighbours (kNNs) are pattern recognition systems that classify a new tumour into a previously recognised type within which the majority of its features would fall . Whilst  tumours , 37, theAs stated, machine learning limitations include overfitting of data and sensitivities to outliers. At an end-user level, clinicians might be reluctant to accept the output of a black box classifier system such as an advanced neural network because the decision-making process is not fully visible. This is perhaps reflected in the low translational uptake of decision support systems into routine clinical practice. There is also a lack of standardised legal regulation as exists for medical devices, and there are no clear legal guidelines about independent mathematical interrogation and validation of outputs generated by AI systems. These factors also hamper the acceptance of such systems into routine clinical practice . FinallyHaving reviewed the differing AI techniques that have been applied in paediatric brain imaging, we now examine the specific use case of paediatric neuro-oncology to inform the best suited roles for such methodologies in future clinical use.Brain tumours represent the most common solid tumour reported in children . DespiteThe diagnosis of a childhood brain tumour is largely standardised historically; conventional MR imaging is acquired at presentation before proceeding to surgery in the form of either biopsy, debulk or resection, with tissue samples undergoing morphological assessment by a histopathologist. Over recent years, the advent of molecular techniques such as genomic arrays, gene expression profiling, fluorescent in situ hybridisation (FISH), and genome sequencing and methylomics has refined our understanding of paediatric brain tumours beyond simple histological classifications, identifying inherent subgroups within pre-defined tumour entities based upon shared genetic aberrations and biological behaviour , 51–54.Noninvasive radiologic tumour assessment has also evolved in recent years through the advent and application of multi-parametric MRI, including diffusion-weighted imaging, perfusion-weighted imaging and MR spectroscopic sequences . Modern H3K27-mutated diffuse midline gliomas of the pons (WHO grade IV) can display minimal contrast enhancement [Routine contrast-enhanced diagnostic MRI, whilst providing adequate anatomical description of a lesion, does not describe its physiological or metabolic behaviour. In addition, there is radiologic diagnostic overlap when assessing many childhood brain tumours because they can display similar T1, T2 and contrast-enhanced appearances. Neither is the extent of contrast enhancement in paediatric brain tumours indicative of tumour grade, unlike their adult counterparts . Indeed,ancement , 59.It is hoped that multi-parametric MRI techniques will supplement conventional imaging data of brain tumours, providing more accurate, noninvasive in vivo radiophenotyping and radiologic determination of tumour molecular sub-groups where appropriate , 60–70. It is anticipated that through such techniques, improved tumour characterisation might better inform prognosis when compared with standard imaging metrics such as tumour dimensional size or volume , 71–73. 1H MR spectroscopy and DWI data from paediatric brain tumours so achieve successful categorisation of medulloblastomas, ependymomas, infiltrating gliomas and pilocytic astrocytomas [As discussed, there are instances where these machine learning techniques, now at the forefront of AI research in paediatric brain imaging, have been able to extract additional data from pre-existing imaging studies. Indeed, ANNs have had some success at predicting posterior fossa tumour types using such approaches to stratify pilocytic astrocytoma, medulloblastoma, ependymoma and diffuse midline glioma of the pons  using onocytomas –81.Support vector machines have been used in the field of MRI texture analysis in brain tumours, an application of machine learning that quantifies imaging data to generate an image texture, a feature usually imperceptible to the human eye , 83. TexBuilding upon the use of texture analysis, Orphanidou-Vlachou et al.  combinedSupport vector machines have also been successfully used to categorise tumour grade by combining conventional data with additional multi-parametric imaging information such as diffusion tensor imaging and PWI data . In addiWhilst the majority of neuro-oncological uses of AI in brain imaging have focused on tumour stratification, exciting opportunities are presenting themselves in regard to outcome prediction. An exemplar of such an approach that harnesses the power of human/computer interaction is the work by Liu et al.  in prediRare diseases such as paediatric brain tumours often present diagnostic challenges, particularly if the presenting radiologic phenotype is incongruent from any established radiologic diagnostic pattern or the local radiologic team lacks specialist experience within a particular field. A means of improving radiologic diagnosis of rare disease is through contribution of imaging features to shared databases, allowing comparison of newly presenting imaging features against a larger, previously unavailable reference library.OpenNEURO.org (sharing platform for MRI and electrocardiogram data), the UK Biobank , the Enhancing NeuroImaging Genetics through Meta-analysis (ENIGMA) Consortium  and the Human Connectome Project . These resources aim to increase available sample sizes beyond that of a single institution, generating data sets that might allow investigation of previously inscrutable questions. The breadth of data available on OpenNeuro.org has contributed to a wide range of studies, including human brain connectome, deductive reasoning function in children, white matter disease in schizophrenia and bipolar disease, and automatic prediction of MRI image quality [Open-access public datasets are now widely available, including  quality –101.When quality –106.1H MR spectroscopy features of brain tumours, a feature that not all radiologists could interpret, forming the basis of a linear-discriminant-analysis-driven decision-support system. The INTERPRET decision support system aimed to be a universally accessible to physicians and biochemists, allowing the input of new case data from any MRI scanner. Accurate tumour classification was achieved in 69–85% of test set analyses, although the authors recommended further evaluation of classification performance limits of the model and encountered issues with data quality control in early model iterations [Successful applications of specific paediatric radiologic databases include the INTERPRET (not an acronym) and Autism Brain Imaging Data Exchange (ABIDE) projects. The INTERPRET project successfully accrued erations , 108.The ABIDE aggregated more than 1,000 functional MRI scans of 539 children with autistic spectrum disease and case-matched controls, identifying common features in regions of known dysfunction in autism spectrum disease (mid- and posterior insular and posterior cingulate cortex) and also regions less frequently evaluated. This shared database demonstrates the potential benefits of pooling multiple source datasets in confirming established features, and in the discovery of novel features of rare diseases .Open-access shared data sets present data security and confidentiality issues, particularly in the context of rare diseases; re-anonymisation has been possible in datasets anonymised as per National Institutes of Health guidelines, leading to some collaborative efforts maintaining private-access databases –112.Multi-parametric MRI is becoming established within the imaging community for assessing paediatric brain tumours. The hope is that the feature-rich data generated will lead to improved radiomic feature extraction and, in certain instances, correlation with molecular subgroups. While radiogenomics and radiophenotyping studies are described, such data are not routinely integrated to enable up-front risk stratification and outcome prediction for children with brain tumours. Relapse and recurrence rates and time windows for such events have not been predicted using this approach. Artificial intelligence, underpinned by the machine learning techniques discussed here, offers a potential solution for enabling personalised medicine within this domain.Artificial intelligence provides us with useful tools for handling large volumes of high-dimensional data generated from modern medical imaging practice. Applying machine learning models to paediatric brain multi-parametric MRI data and integrating the accompanying genetic, histopathological, clinical and surgical data should enable better risk-stratification systems, better prognostic prediction and individualised surveillance protocols for children depending on their underlying diagnosis. The clinical translation of such systems is occurring through the implementation of the machine learning techniques described here and the AI underpinning clinical decision-support systems. A clinical decision-support system is defined as any piece of software that takes, as input data, the information about a clinical situation and produces, as output, the inferences regarding the clinical situation that can assist practitioners with their decision-making . ClinicaWe predict that validation and integration of neuroimaging AI applications into mainstream clinical practice will occur in two stages. First, classification and outcome prediction clinical decision support systems will be trialed in parallel to established clinical practices such as neuro-oncology multi-disciplinary team meetings, assessing performance in direct comparison to local expert clinical opinion. Second, models that demonstrate potential will then be prospectively trialed with unseen data under the conditions of a formal trial. With respect to paediatric neuroimaging applications, validation of applications might be a long-term process in view of small patient cohorts and sporadic presentations of certain conditions. There is also the challenge of integrating such systems into existing health informatics infrastructure; at present most National Health Service (NHS) systems are not capable of incorporating such models, nor are they conducive to automated data collection. The Topol Report has advised focused funding for digital transformation within the NHS to aid in development of such projects.Meaningful contributions to the field can be made at a local level through the contribution of well-organised data sets of rare disease imaging, particularly where advanced metric imaging has been performed. Data should ideally be contributed to secure open-access databases that encourage collaborative work in rare-disease fields. Collaborative initiatives should be encouraged between clinicians and data scientists to ensure application of appropriate data handling and machine learning algorithms, and the development of clinician-led AI projects within health care.In the next decade, we expect to see the gradual acceptance and deployment of such clinical diagnosis decision support systems into clinical paediatric neuroradiology as they are translated from the academic domains discussed here to the workstation and, most important, become valuable tools for use in routine clinical practice.