Extracellular vesicles (EVs) are small, membrane-based vesicles released by cells that play a critical role in various physiological and pathological processes. They act as vehicles for transporting a variety of endogenous cargo molecules, enabling intercellular communication. Due to their natural properties, EVs have emerged as a promising “cell-free therapy” strategy for treating various diseases, including cancer. They serve as excellent carriers for different therapeutics, including nucleic acids, proteins, small molecules, and other nanomaterials. Modifying or engineering EVs can improve the efficacy, targeting, specificity, and biocompatibility of EV-based therapeutics for cancer therapy. In this review, we comprehensively outline the biogenesis, isolation, and methodologies of EVs, as well as their biological functions. We then focus on specific applications of EVs as drug carriers in cancer therapy by citing prominent recent studies. Additionally, we discuss the opportunities and challenges for using EVs as pharmaceutical drug delivery vehicles. Ultimately, we aim to provide theoretical and technical support for the development of EV-based carriers for cancer treatment. Extracellular vesicles (EVs) are released from cells through paracrine or autocrine secretion under physiological and pathophysiological conditions and can be isolated from various body fluids . StructuIn the past few decades, despite substantial contributions to cancer therapy, cancer is still without question one of humanity’s biggest killers, and there is no effective treatment to surmount this challenge . Thus, sThis article aims at summarizing the latest research findings on EVs in recent years and reviews the research on Evs as therapeutic carriers for cancer treatment. First of all, the biological characteristics of Evs  are well summarized, especially their biogenesis and isolation techniques for Evs. We also discussed the working principles, advantages, and drawbacks of these separation methods. In addition, the general drug loading and functionalization of Evs are summarized pre- and post-separation individually. Furthermore, the application of EV-based carriers and their effects on cancer therapy in recent years are reviewed. It also addresses the progress of Evs in the efficacy of small-molecule drugs, nucleic acids, proteins, and other nanomaterials. Finally, we summarize the recent advances of Evs and put forward our prospective idea about Evs as nanovehicles for delivering therapeutic agents with an emphasis on their applications in cancer therapy, which will help us better understand the current research progress and future directions of EV-based carriers for targeted drug delivery in cancer treatment.In 1981, Trams et al. discovered a group of vesicle-like structures with diameters 40–1000 nm smaller than those of polyvesicles, identified by transmission electron microscopy (TEM) . Later, 2+ increasing may cause the asymmetric phospholipid distribution in the plasma membrane to change, leading to actin cytoskeleton maintenance depolymerization and promoting microvesicle shedding.Microvesicles are 100–1000 nm vesicles released from the plasma membrane to outgoing buds and to the extracellular environment by fission . They arApoptosis is a kind of programmed death, which occurs in billions of cells every day. Apoptotic bodies are specifically produced in the process of apoptosis and can wrap the cytokines secreted in the process of apoptosis . FunctioEVs, as circulating phospholipid vesicles secreted by cells, can play an important role in intercellular communication and the establishment of tumor microenvironments. However, EVs exist in complex biological fluids and contain a variety of pollutants, so it is necessary to use some appropriate separation and enrichment methods to obtain relatively pure EVs . In thisUltracentrifugation (UC) is recognized as the current gold standard for the enrichment and purification of exosomes . It can Density-gradient ultracentrifugation (DGUC) is a technique to improve particle separation efficiency according to the buoyant density of particles. In recent years, two methods have been used to form gradients: a continuous density gradient (centrifugation or pre-formation) and a stepwise gradient (density increases in a discrete manner), namely, a sucrose cushion. A higher purity of EV preparation is obtained by the standard protocol of density-gradient ultracentrifugation compared to classical ultracentrifugation. In addition, the method has been shown to be superior to classical ultracentrifugation and commercial kits in terms of EV purity, EV protein, and RNA quantity . CurrentCo-precipitation (CP) has been a relatively emerging technique in recent years which is mainly based on the formation of a mesh polymer net that captures EVs in the 60–180 nm size range and then shapes them into particles at low centrifugation speeds. As of now, a variety of commercial precipitation kits can be used to precipitate EVs from different biological fluids. These kits are based on the super-hydrophilic volumetric exclusion polymer polyethylene glycol (PEG), which separates EVs based on their reduced solubility in PEG solutions . PolymerSize-exclusion chromatography (SEC) separation is based on particles of different sizes passing through different elution profiles of porous polymers, forming stationary phases , and passing through mobile phases of SEC columns . When thUltrafiltration (UF) is a method of separating the components of a sample based on their sizes; this technique relies on the use of membranes with specified pore diameters to isolate particles of a pre-determined size range. Larger particles are eliminated first by using filters with pore diameters of 0.8 and 0.45 µm, leaving a relatively exosome-rich filtrate. Smaller vesicles are then eliminated from the filtrate by using membranes with pores smaller than the desired exosomes (0.22 and 0.1 µm), causing them to pass into a waste eluate . UF is mIn general, EV isolation by immunoaffinity enrichment (IE) mainly utilizes the immune affinity interactions between proteins (antigens) in EVs and their antibodies, as well as specific interactions between receptors and ligands. Owing to its simplicity and immunoaffinity capture, immunoaffinity enrichment is an attractive approach at present. Ideally, EV biomarkers for immunoisolation are membrane-bound, lack soluble counterparts, and are only expressed or highly concentrated on the surface of extracellular vesicles from specific biological sources . HoweverField-flow fractionation (FFF) is an emerging size-based EV separation technology, in which asymmetric flow fractionation (AsFlFFF or AF4) is the most commonly used FFF sub-technology. In FFF, the separation system is not a column but a parallelepiped channel in which the force field is perpendicular to the sample stream to achieve separation based on different sizes and molecular weights . In AsFlEVs, a novel natural drug carrier, possess a lipid bilayer which can protect a drug from degradation in the blood and circumvents safety issues arising from the use of cationic synthetic nanoparticle carriers in vivo. EVs can also cross the blood–brain barrier easily and are being tested regarding whether drug-loaded vesicles could be used to target glioblastoma . HoweverPre-loading primarily involves loading cargo  into cells or carrier materials before or during the generation of EVs. For example, cocultivation of HEK293 and COS-7 cells with vectors that can express miR-16, -21, -143, -146a, or -155 is performed so that relevant miRNAs can be overexpressed in the cells and combined with an endogenous RNA secretion mechanism to trigger the active release of EVs. In receptor COS-7 cells, EVs encapsulating miRNAs still have the ability to induce gene silencing ,42. EncaAbundant multipurpose EVs can be secreted from Plasmodium falciparum when grown in its natural host red blood cells (RBCs) . Vorsele2S), and the results indicated that H2S-EVs could induce miR-7b-5p expression and improved cognitive impairment in neonatal mice [Mesenchymal stromal cells (MSCs) are powerful regenerative cells that exert therapeutic effects through paracrine mechanisms and produce numerous secretions, including EVs. Human-bone-marrow MSC-secreted EVs induce the proliferation of tubular epithelial cells after acute kidney injury through mRNA transfer, while enhancing their anti-apoptotic effects . Additiotal mice .When tumor cells undergo apoptosis, cell membranes will be contracted and divided; therefor, functional biomolecules are actively packaged into vesicles to efficiently produce membrane-encapsulated apoptotic vesicles (ABs) . DongyanIn general, the process of post-loading is divided into two steps: exosome separation by the above techniques and drug fusion. So, the simplest approach to loading therapeutic drugs is to mix EVs with free drugs thoroughly or supplement them with mild sonication, especially for hydrophobic compounds. Paclitaxel (PTX) is a highly hydrophobic drug commonly used for chemotherapy. For instance, by simply incubation at room temperature, PTX can be encapsulated into EVs. Additionally, another method is to sonicate the mixture followed by further incubation at 37 °C for 1 h; the loading efficiency of EVs with PTX was increased by nearly 10-fold after sonication . CationiThe natural barrier of EVs, the lipid bilayer, limits the passive loading of therapeutic drugs into EVs. Thus, electroporation came into being, which stimulates spontaneous pore formation in membranes mainly by compensating for voltage changes triggered by electrical signals, and it is an effective method to achieve nucleic acid drug loading after EV isolation . In addiTherefore, the simple incubation-induced fusion of EVs/liposomes or other synthetic objects not only has the properties of synthetic carriers but also the natural properties of EVs, leading to high loading and loading rates, specific targeting, and better endosomal escape. However, the negative charge carried by most liposomes may limit effective fusion between EVs and liposomes due to electrostatic interactions. To address this issue, Piffoux et al., developed a fusion of EVs with polyethylene glycol (PEG)-functionalized liposomes, creating intelligent biosynthetic hybrid vectors with adaptable activity and drug delivery properties .In addition to the above methods, high loading efficiency of EVs can also be achieved by other means such as treatment with saponin, extrusion, or freeze–thaw cycles, as demonstrated by Haney et al., in an in vivo/vitro model of Parkinson’s disease by the protein-encoding plasmid DNA transfection of macrophages. Their study utilized several different ways to load peroxidase into EVs, and EVs were found to maintain high loading efficiency after sonication and extrusion or saponin-mediated treatment, effectively preventing peroxidase degradation .Genetic engineering techniques can be utilized to introduce coding and non-coding oligonucleotides into cells and integrate them into extracellular vesicles (EVs) to promote gene expression or regulate transcription in recipient cells. Born et al. employed mesenchymal stem cells transfected to overexpress long non-coding RNA HOX transcript antisense RNA (HOTAIR-MSCs) for the secretion and generation of EVs . The abiMetabolic engineering allows the incorporation of metabolite analogs into cellular biosynthesis by introducing functional groups such as azides into EVs for bio-orthogonal reactions. Tu et al., used the metabolic precursor tetra-acetylated N-azidoacetyl-D-mannosamine (Ac4ManNAz) self-assembled with azide groups to form nanoparticles (Az-NPs) that were taken up by tumor cells and expressed azide groups on the plasma membrane, followed by secretion of azide-containing EVs and their transfer to deep tumor regions. Subsequently, by conjugating dibenzocyclooctyne-modified chlorin e6 (DBCO-Ce6), they were combined with photodynamic therapy to improve treatment efficiency . Lim et The last strategy for modifying parental cell membranes is liposome-based cell engineering, where a liposome or micelle that can fuse with the cytoplasmic membrane is selected, and EVs are introduced by exchanging membrane components. He et al., constructed an EV-mediated self-propelled liposome through membrane fusion liposomes, which maintained the structural integrity and stability of EVs and transferred fused lipids with functionalization directly to the plasma membrane of the recipient cells, achieving therapeutic or targeting effects on cancer cells in a more effective and controlled manner . ReginalThe physical modification of extracellular vesicles (EVs) following their isolation is commonly achieved through techniques such as fusion with liposomes, insertion of lipophilic fractions into membranes, and molecular adsorption onto their surfaces. For instance, researchers such as Zhou et al. have successfully utilized a new EV called a membrane-hybridized lipid nanovesicle (LEV) for efficient delivery of siRNA via Golgi and endoplasmic reticulum pathways, resulting in up to a sevenfold increase in siRNA transfection efficiency . MeanwhiCovalent coupling with relevant specific targeting peptides remains the most common approach for the chemical modification of EVs. For instance, researchers have used covalently coupled epidermal growth factor receptor (EGFR)-targeting peptides or anti-EGFR nanobodies, causing them to accumulate in EGFR-positive cancer cells for targeted therapy . MoreoveEVs are small vesicles with membrane structures that include exosomes, microvesicles, and apoptotic bodies of EVs  which arSmall-molecule drugs as the main tools in cancer treatment have achieved certain results in clinical practice. However, these drugs are prone to problems such as drug resistance and serious side effects in the treatment process. Thus, aiming to solve these problems, researchers have begun to explore novel drug delivery vehicles, and extracellular vesicles are one of the natural delivery systems with great potential, which can not only improve the bioavailability of drugs, but also enhance the therapeutic efficacy and reduce the pain of patients.Chemotherapeutic drugs are widely recognized as the most commonly used small-molecule drugs, particularly when it comes to first-line treatment of oncology patients. Exosomes are a highly researched subcategory of EVs which are vesicles enclosed in membranes and secreted by nearly all cell types. Delivery of chemotherapy drugs via exosomes has been shown to reduce systemic toxicity and to be more therapeutic than monotherapy . The tarOther than tumor cells, EVs from other sources have also been reported to possess antitumor properties. EVs derived from MSCs have significant potential as a guided anti-tumor drug delivery platform because of their strong tendency to migrate toward tumor sites, showcasing their remarkable bioengineering potential. Pascucci et al. were the first researchers in Italy to suggest that the use of exosomes from mesenchymal stem cells as carriers of paclitaxel (PTX) could significantly enhance the antitumor effects of PTX in vivo . In studIn the field of cancer drug resistance, EV-mediated delivery of chemotherapeutic drugs has shown promising results. Studies have shown that exosomes, released from macrophages and loaded with PTX using sonication, can overcome P-GP-mediated drug resistance in MDCK cells with greater cytotoxicity than free drugs . BuildinAs a natural carrier, EVs have the potential to traverse the BBB, making them an ideal vehicle for the treatment of brain tumors. However, the current ability of EVs to cross the BBB remains limited. Bai et al. developed a delivery system that enhances the ability of EVs to cross the BBB and enhances brain targeting through the use of focused ultrasound (FUS). This delivery system was shown to successfully load DOX and significantly inhibit tumor growth, with no side effects observed in animal models .Xiong et al.  used macIn recent years, although exosomes have shown promising results as carriers of small-molecule drugs in cancer therapy, other types of vesicles such as apoptotic bodies and microvesicles have also been found to exhibit similar effects. Cell membrane-derived microparticles (MPs) are a subgroup of EVs that can be used as natural delivery systems to treat cancer. MPs (TMPs) derived from tumor cells are used to transport methotrexate (TMPs-MTX), a chemotherapy drug that has been shown to significantly inhibit the growth of MEP .The immune small molecules in EVs, as immunomodulatory carriers, provide fundamental theoretical support for mutual information transfer between cells and communication between cells and their environment and bring about important applications for life science research.The homing ability of EVs and their ability to carry endogenous substances from host cells make them excellent natural carriers. Huang et al. developed a specific EV loaded with Hiltonol (a TLR3 agonist) and an immunogenic cell death (ICD) inducer, human neutrophil elastase (ELANE), as a DC vaccine. The EVs secreted by Hela cells have specific targeting for tumor cells and can deliver drugs precisely to the tumor site. This concept can be extended to any tumor cell by loading ICD inducers and immune adjuvants onto EVs for cancer therapy . EVs canFor tumor treatment, nucleic acid drugs have more advantages than traditional drugs due to their unique mechanism of action, such as strong specificity, abundant gene targets, not easily producing drug resistance, lasting efficacy, and so on . HoweverMicroRNAs (miRNAs) are a type of single-stranded non-coding RNA with small molecular weights that play important and complex roles in tumor proliferation and development, including cell proliferation, apoptosis, tumor invasion, and epithelial–mesenchymal transition . An miRNAmong the many studies on miRNA delivery, the most widely used method is exosomes derived from mesenchymal stem cells (MSC), which offer great advantages in terms of biocompatibility, safety, and targeting. Using exosome delivery of miR-9-3p, miR-139-5p, and miR-138-5p from MSCs to treat bladder cancer, MSCs-Exo can successfully deliver miRNAs to bladder cancer tissue and block the proliferation, migration, and invasive abilities of cancer cells, effectively inhibiting tumor progression ,151,152.In addition to the above studies, a variety of other miRNAs are delivered by exosomes . By affecting the expression of related genes, exosomal miRNAs can effectively inhibit tumor growth and metastasis, as well as modulate the sensitivity of tumor cells to drugs ,131,155.siRNAs can knock down the expression of target genes in a sequence-specific manner by mediating the degradation of targeted mRNAs. Compared to small-molecule therapeutics and monoclonal antibody drugs, siRNAs have an advantage because they perform their functions by strictly following Watson–Crick base pairing with mRNAs .Cancer progression is related to the upregulation of anti-apoptotic proteins such as PLK1 (Polo-like kinase 1), KRAS, and cell growth factor. Exosomes were originally thought to be natural carriers of siRNA in vitro. siRNA exosome therapies targeting tumor cells have been aimed at downregulating the expression of these oncogenes. KRAS mutations are among the most common gene mutations in tumors, causing uncontrolled cell proliferation and thus a high risk of cancer, and are associated with pancreatic, lung, and colon cancers. Previous studies have shown the efficacy of siG12D-LODER in patients with locally advanced pancreatic cancer . TherefoWith the successful development of mRNA vaccines for COVID-19, there is now an active trend to develop mRNA drugs for cancer treatment. mRNA could be used to give patients drugs that match the characteristics of their individual cancer cells, promising to help patients who have not responded to conventional treatments.Scorch death induction in tumors has promising applications in cancer immunotherapy. GSDMD-N mRNA was encapsulated within EVs, and the EV membrane was engineered and modified for delivery into HER2+ breast cancer cells to directly induce scorch death to enhance antitumor immunity .Escherichia coli, which has an innate immunostimulatory function, have also been used as an mRNA delivery platform for personalized tumor vaccines. The nanocarrier platform has a “Plug-and-Display” function that allows the design of personalized cancer vaccines based on their tumor antigens. Subcutaneous injection of EVs carrying ovalbumin or ADPGK mRNA in mice significantly arrested the progression of melanoma [Yang et al., used cellular nanoperforation (CNP) to infiltrate high levels of mRNA into exosomes, and CNP produced up to 50 times more mRNA transcripts; electroporation increased mRNA transcripts by more than 103-fold. In GBM model mice, tumor growth was significantly inhibited, and the survival rate of mice was improved . Wang etmelanoma .Gene editing is a new therapeutic method developed in recent years which is expected to treat cancer by regulating gene expression and correcting gene mutations. Among these techniques, CRISPR/Cas9 is a promising treatment for cancer as a genetic tool that can be used to treat a wide range of diseases through DNA splicing with ease of use and precision . Kim et In nucleic acid drug therapy, antisense oligonucleotides (ASO) are also a hot topic of research. mRNA can be targeted by ASOs in a highly specific manner to downregulate the expression of disease-causing proteins. ASO drugs can also increase the amount of protein in diseases caused by the under-expression of proteins . In contProteins with biological functions and enzymes with therapeutic effects suffer from poor delivery due to cell membrane limitations. In contrast, EVs are an ideal protein delivery vehicle because they can stably release their loading substances through multiple pathways. EVs can be used to carry a wide range of proteins, including enzymes, cytokines, antigens, hormones, antibodies, receptors, and more. The diversity and specificity of the proteins delivered by exosomes allow for targeted delivery to different types of cells and therefore a wide range of applications.In 2011, extracellular vesicles utilized rabies virus glycoprotein as a targeting peptide for systemic cancer delivery therapy, and in later studies, researchers identified additional targeting peptides. Other targeting peptides, such as the αγ integrin-specific peptide iRGD, can be attached to exosomes as well . Fusion In addition to antibody drugs, the research team has developed a nanovaccine called Exo-OVA using antigens found in tumors. The vaccine incorporates new antigens from ADPGK in MC-38 tumors and M16 and M10 in B27F30 melanoma tumors into EVs. In corresponding tumor models, the Exo-OVA vaccine has demonstrated antitumor immune effects .In recent years, photothermal therapy (PTT) and photodynamic therapy (PDT) have developed rapidly in the treatment of cancer. This type of treatment works by introducing photosensitizers (PSs) that convert absorbed light energy into heat or produce toxic reactive oxygen species (ROS), thereby causing damage to cancer cells. Jang et al.  loaded a2C QDs) and PTA are integrated with an engineered exosome vector, the system is capable of low-temperature nucleus-targeted PTT in the NIR-II region to achieve effective tumor killing (A recent study has shown that when vanadium carbide quantum dots (V killing . This stSonodynamic therapy (SDT) is a new type of oncology treatment that has been developed in recent years. Ultrasound has a powerful penetrating ability. The principle of STD is to use this ability to penetrate deep into the tissues and then activate a sonosensitizer to produce ROS to kill tumor cells. An exosome-based strategy has been used for local delivery of sonosensitizers (Ce6) and resiquimod (R848) to the tumor site. It was shown that ExoCe6+R848 activated DCs and reversed the tumor-suppressive microenvironment in xenograft models after ultrasound irradiation . Guided Combination therapy offers significant advantages over monotherapy, including improved efficacy, dose reduction, regulation of drug resistance, and reduction of toxic side effects. At the same time, the combined effects of multiple drugs can be greater than the sum of the efficacies of the individual drugs, and this “synergistic effect” can increase the effectiveness of cancer treatment to a greater extent ,182.2) and a sonosensitizer (ICG) are encapsulated by macrophage exosomes which are functionally modified by AS1411 nucleic acid aptamers. This method has high BBB penetration and is effective in overcoming tumor hypoxia. In vivo experiments demonstrated that these engineered exosomes promoted the efficacy of SDT [3O4@PDA and then added engineered exosomes to produce complex delivery systems for accurate cancer diagnosis and treatment [Wang et al., constructed a CCA-M1EVs drug delivery system for the treatment of GBM by encapsulating AQ4N after membrane modification of M1 macrophage-derived vesicles (M1EVs) with CPPO and Ce6. This delivery platform achieved a powerful therapeutic effect with the synergistic effects of immunomodulation, CDT, and hypoxia-activated chemotherapy . Anothery of SDT . By utily of SDT . CSSP NPy of SDT . A recenreatment . In the Due to the advantages of EVs, such as biocompatibility and strong bioactivity, recently, researchers have made great efforts to conduct studies of exosome extraction verification and applications in drug delivery. EVs originating from natural resources can be regarded as universal and efficient vectors compared with traditional artificial nanoparticles. Due to the properties of EVs, including a closed-loop structure and special protein expression on the surface, EVs could prevent cargos from premature release and degradation in the extracellular environment. Moreover, EVs have the targeting delivery ability inherited from the original parental cells. EV-assisted delivery could help cargo to overcome biological barriers such as tumor blood vessels for efficient aggregation in tumor tissues. Compared to synthesized nanoparticles, EVs have good properties such as low biological toxicity and great biocompatibility.Despite great achievements having been made in studies about EVs, there still exist several difficulties that limit the further application of EV-mediated drug delivery for clinical therapy. The main difficulty is how to achieve scaled-up, uniform, and stable EVs. Currently, bioreactors and streamlined purification protocols via microfluidic devices are the important strategies for the efficient and improved production of EVs. Moreover, it is worth noting that quality control is crucial in the large-scale production of EVs for application in clinical therapy. In addition, the second difficulty is how to increase the loading efficiency of cargo of EVs, which affects the application prospects of EVs in biotherapy. A novel method should be developed for acquiring great efficiency in drug encapsulation of EVs without using large amounts of them. EVs have several kinds of proteins and functional immune molecules on their membrane which will trigger an immune response, inducing the rapid clearance of EV-based delivery. Therefore, EVs need to be modified and engineered to avoid potential system toxicity and side effects, simultaneously evaluated by measuring their pharmacokinetics and pharmacodynamics.EVs have been used as drug delivery vectors for preliminarily exploring their potential application in clinical therapy and trials. EVs are employed to transport therapeutic proteins, nucleic acids, and small-molecule drugs, which are widely discussed and have indicated great potential value in cancer treatment. Furthermore, novel approaches are applied to design and modify EVs to enhance their targeting, biotherapeutic, and cargo-loading efficiency. Moreover, researchers have the ability to improve the production of EVs with stable, uniform, and large-scale properties by advanced methodologies. The process of obtaining clinical-grade EVs has been in progress, but further efforts are needed. In addition, among EVs, ABs are promising; although there are currently relatively few application findings, they also play an important role in various diseases, which expands the great potential value of ABs in biomedical applications such as immunotherapy, disease detection, and drug delivery. Studies have indicated that ABs have the ability to decrease pathological conditions, and they show great advantages in drug delivery. Altogether, these delivery vectors inherited from natural mechanisms have revealed the long circulation, good stability, and biocompatibility of EVs for drug delivery.


Cancer is the world’s leading cause of human death today, and the treatment process of cancer is highly complex. Chemotherapy and targeted therapy are commonly used in cancer treatment, and the emergence of drug resistance is a significant problem in cancer treatment. Therefore, the mechanism of drug resistance during cancer treatment has become a hot issue in current research. A series of studies have found that lipid metabolism is closely related to cancer drug resistance. This paper details the changes of lipid metabolism in drug resistance and how lipid metabolism affects drug resistance. More importantly, most studies have reported that combination therapy may lead to changes in lipid-related metabolic pathways, which may reverse the development of cancer drug resistance and enhance or rescue the sensitivity to therapeutic drugs. This paper summarizes the progress of drug design targeting lipid metabolism in improving drug resistance, and providing new ideas and strategies for future tumor treatment. Therefore, this paper reviews the issues of combining medications with lipid metabolism and drug resistance. Lipid metabolism is a fundamental and intricate biochemical process within the human body. The primary biochemical process of lipid metabolism involves phospholipid and cholesterol metabolism, which are regulated by various factors such as insulin, glucagon, dietary nutrition, and enzymatic activities. Through this complex process, lipids are converted into essential components that are necessary for a wide range of biochemical reactions within the body .The significance of lipid metabolism extends to the development, of cancer , as cancChemotherapy and targeted therapies currently serve as the primary treatment for cancer . These iThe development of drug resistance in cancer is influenced by various factors, and its mechanisms can be broadly categorized into mutations in drug targets and metabolism, inhibition of apoptosis, activation of intracellular survival signaling pathways, enhanced DNA repair, immune evasion by cancer stem cells (CSCs), and metabolic abnormalities, and so on . While pStudies have indicated that the utilization of combination therapies targeting multiple pathways may effectively delay the development of therapeutic resistance . CombinaTo further understand enhance our understanding of lipid metabolism and its connection to drug resistance and to investigate the relevance of drug combination, this review will focusconcentrate on five aspects: alterationkey areas: the modification of lipid metabolism in cancer cells, the association between phospholipid metabolism and drug resistance, the impact of cholesterol metabolism andon drug resistance, the involvement of microRNA andin lipid metabolism and drug resistance, and the significance of drug combination and lipid metabolism andin relation to lipid metabolism and drug resistance. Through exploring these aspects, we aim to gain deeper insights into the intricate relationship between lipid metabolism and the development of drug resistance.Extensive researches have been dedicated to exploring the intricate relationship between lipid metabolism and cancer development. Lipid metabolism plays a crucial role in providing signaling molecules , essenti2 to O2 via a series of mitochondrial electron carriers   . AccordiLipids containing phosphate are called phospholipids. Phospholipids can divide, which contain phosphate, play a crucial role in various biological processes. They can be categorized into two main groups: those made up of glycerol are called phosphoglycerides, composed of glycerol, and those made up of neurosphingosine are called sphingolipids, composed of sphingosine.Glycerophospholipids are the most abundant phospholipids found in the body, serving multiple essential functions. They not only form the structural basis of biological membranes but also contribute to bile composition, act as surface-active substances, and play a role in protein recognition and cell membrane signaling. Glycerophospholipids can be classified into various categories based on their substitution groups, with some of the most important ones being phosphatidylcholine (PC) formed by choline and phosphatidic acid, phosphatidylethanolamine (PE) formed by ethanolamine and phosphatidic acid, phosphatidylserine (PS) formed by serine and phosphatidic acid, phosphatidylglycerol (PG) formed by glycerol and phosphatidic acid, and phosphatidylinositol (PI) formed by inositol and phosphatidic acid . The synSphingolipids, distinguished by the absence of glycerol and the presence of sphingomyelin, encompass sphingomyelin and glycosphingolipids. They are synthesized in various tissues throughout the body, with particularly high activity in brain tissues where they constitute a major component of neural tissue membranes. The synthesis of sphingolipids occurs within the endoplasmic reticulum. The breakdown of sphingolipids takes place through the hydrolysis of sphingolipids into choline phosphate and ceramide, catalyzed by phospholipase . SphingoThe fatty acid composition of phospholipids (PL) plays a crucial role in distinguishing between sensitive and resistant cells. Recent studies have highlighted the impact of acyltransferases on the fatty acid composition of PL, which can influence cancer chemosensitivity. For instance, lysophosphatidylcholine acyltransferase 2 (LPCAT2), an enzyme associated with lipid droplets, has been found to promote abnormal biosynthesis of phosphatidylcholine, leading to resistance to oxaliplatin and 5-fluorouracil in colorectal cancer. The underlying mechanism involves an enhanced anti-apoptotic response to endoplasmic reticulum stressors and improved resistance to immunogenic cell death induced by chemotherapy . These fAdditionally, progesterone has been found to upregulate Stearoyl-CoA desaturase 1 (SCD1), resulting in an enrichment of oleic acid in plasma membrane phospholipids. This enrichment has been correlated with docetaxel resistance, as it increases the mobility of the plasma membrane and alters its connection to the cytoskeleton. Consequently, cells become better adapted to drugs that target the cytoskeleton, such as docetaxel  Figure . MoreoveLysophospholipids (LysoPL), the direct precursors of phospholipids (PL), also play a role in mediating drug resistance. Interestingly, their mechanism of action is not solely dependent on the plasma membrane. A recent study demonstrated that LysoPL containing long saturated fatty acyl chains can induce drug resistance. This protective effect of LysoPL enables tumor cells to withstand DNA-damaging agents like cisplatin, representing a lipid-specific and drug-specific protective mechanism  Figure .Similarly, lysophosphate-1 (LPA-1), a precursor shared by most phospholipids, has been implicated in reducing the effectiveness of adriamycin in inhibiting the viability of triple-negative MDA-MB-231 cells with paclitaxel  Figure . Furtheret al., it was revealed through both in vitro and in vivo validation that increased adipocyte lipolysis surrounding tumors contributes to the resistance of breast tumor cells to lapatinib-mediated cytotoxicity  activity . This step is irreversible. The third stage involves the production of cholesterol: MVA is initially phosphorylated, decarboxylated, and dehydroxylated, ultimately leading to the synthesis of 30C squalene. This squalene is then catalyzed by endoplasmic reticulum cyclase and hydrogenase to generate lanolin sterols, which further undergo a series of multi-step reactions, including redox reactions, to ultimately yield cholesterol  is administered  who are treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) experience significant clinical benefits. However, they inevitably develop acquired resistance. Research has demonstrated that high cholesterol levels in lipid rafts contribute to gefitinib resistance in NSCLC cell lines and are associated with altered EGFR binding affinity and downstream signaling pathways. This groundbreaking study highlights that elevated cholesterol levels in lipid rafts play a pivotal role in inducing gefitinib resistance in NSCLC cells by impacting EGFR phosphorylation, downstream signaling pathways, and EGFR-TKI affinity  Figure . MoreoveAn increasing body of evidence highlights the crucial role of cholesterol in cancer development. Researchers have focused on investigating the impact of cholesterol on the acquisition of drug resistance in cancer. Elevated cholesterol levels and alterations in protein expression related to cholesterol metabolism have been observed in different types of drug-resistant cancer cells. Collectively, dysregulated cholesterol metabolism emerges as a fundamental factor contributing to the development of drug resistance in multiple cancer types.in vivo experiments (MicroRNAs (miRNAs) are short, non-coding RNA molecules composed of approximately 22 nucleotides. They are encoded by endogenous genes and play a crucial role in the post-transcriptional regulation of gene expression in both plants and animals. MiRNAs have been widely implicated in various biological processes and are closely associated with the regulation of gene expression. In the context of lipid metabolism homeostasis, previous studies have revealed a close relationship between miRNAs and lipid metabolism. Specifically, several miRNAs, such as miR-33, miR-128–1, miR-144, and miR-148a, have been identified to target and suppress the expression of ABCA1 and ABCG1 transporter proteins. These findings have been demonstrated in cultured cells and further validated through eriments . Since Aeriments . These oThe liver, being the primary site of lipid metabolism in the body, plays a pivotal role in maintaining lipid homeostasis. Among the miRNAs involved in this process, miR-122 is particularly abundant in the liver . It exerCombination therapy has emerged as a highly effective therapeutic approach employed in the treatment of various diseases . ExtensiIn vitro studies demonstrated a substantial increase in apoptosis among chemotherapy-resistant ovarian cancer cells upon treatment with this combination  production has been linked to resistance against oxaliplatin and 5-fluorouracil . In substiveness . Furthertiveness . LovastaMoreover, statins, inhibitors of the 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase enzyme, have demonstrated the ability to inhibit cancer cell growth and induce apoptosis in various types of tumor cell lines. When used in combination with drugs like cytarabine, erythromycin, and doxorubicin, statins have shown promising results in xenograft models of colon cancer, breast cancer, and other malignancies . These fAnother interesting study focused on leukemia cells that had developed resistance to the chemotherapeutic agent arabinofuranosylcytosine (Ara-C). In these resistant cells, it was found that Ara-C preferentially drove the tricarboxylic acid (TCA) cycle through fatty acids (FAs), relying less on glucose metabolism. This metabolic adaptation led to enhanced oxidative phosphorylation in the mitochondria (OXPHOS), which contributed to the cells’ drug resistance . Interesβ-oxidation pathway to generate energy from FFAs, thereby promoting tumor growth and metastasis . Researchers have identified that combining anti-angiogenic therapy with lipid metabolism inhibitors could potentially overcome the emergence of AAD resistance. Preclinical studies have demonstrated that tumors with identical genetic backgrounds but implanted in different locations exhibit varying responses to AAD treatment . For instastasis . BuildinMalignant tumors pose a significant global health threat, and their incidence continues to rise each year . While mFurther studies have shown that while single lipid-targeting drugs can hinder cancer cell growth and metastasis by inhibiting specific lipid-related pathways, they often fail to completely eradicate cancer cells . In contThe advantages of drug combination and our understanding of lipid metabolism suggest that we can continue to explore the synergies between targeted drugs of lipid metabolism and traditional anticancer drugs, so as to innovate new therapeutic approaches to improve the efficacy of anticancer drugs and mitigate the emergence of drug resistance for the benefit of patients. Therefore, further research in this area is essential to uncover the complexity of lipid metabolism during tumor resistance and to optimize the implementation of combination therapy strategies.


Poor sleep quality and insufficient sleep affect a large portion of the population. This is concerning given increasing evidence that poor sleep health is a behavioral risk factor for the development of cardiometabolic diseases. A healthy diet is associated with a plethora of favorable health outcomes, and emerging research now highlights diet as a potential determinant of sleep health that could be leveraged to improve sleep quality. Dairy products are notably rich in tryptophan (Trp), a key substrate for serotonin and melatonin production, which are instrumental for initiating and maintaining sleep. Furthermore, dairy products provide a range of micronutrients that serve as cofactors in the synthesis of melatonin from Trp, which could contribute to sleep-promoting effects. In this review, we evaluate population studies and clinical trials to examine a possible link between dairy consumption and sleep. Available epidemiologic studies illustrate positive associations between dairy intake and sleep outcomes. Moreover, some intervention studies support a causal effect of dairy intake on sleep. Given these data, we discuss potential mechanisms, invite additional clinical research on this topic, and provide insights on how limitations of current studies can be addressed in future trials. Sleep and diet are 2 lifestyle factors shown to predict cardiometabolic outcomes . Short sIndeed, casein and whey, the main dairy proteins, are good sources of Trp . Dairy pEvaluation of population-level data can aid in establishing a link between dairy consumption and sleep  [21], . One of , . One oAnother cross-sectional study that used data from 500,000 middle-aged adults in the UK Biobank examined the associations between milk intake and sleep . Milk inTo increase generalizability of a dairy–sleep relation, it is important to evaluate these factors across different populations and lifestyle patterns. Addressing this need was a cross-sectional study examining the association between frequency of milk or dairy consumption and sleep in Japanese athletes . ResultsIt is also notable that the population studied by Yasuda et al. , describThe above-described studies suggest that associations between dairy consumption and sleep could be influenced by participant characteristics or other lifestyle factors, such as physical activity. Unfortunately, there is a sparsity of data on these potential individual differences. One study conducted exclusively in males showed that low dairy or milk intake was associated with lower odds of experiencing difficulty initiating sleep , contras1) food intake and sleep were assessed by self-report measures, which are prone to bias; 2) studies were conducted among adults with healthy sleep; and 3) epidemiologic studies lack ability to ascribe causality, or even directionality, to associations. In addition, the lack of correction for covariates that might affect sleep quality and diet, such as medication use, and attention to potential moderators, such as age and sex, are not clearly addressed in current cross-sectional studies. Some of these limitations can be resolved using existing data from large prospective cohort studies, such as the Multi-Ethnic Study of Atherosclerosis and The Hispanic Community Health Study, which incorporate detailed evaluation of participant characteristics and health history, longitudinal assessments of diet, and objective measures of sleep , . Work da, . Work Even more recent studies have assessed changes in sleep in response to intake of other milk-based beverages. In a randomized controlled trial, 68 patients with acute coronary syndrome were provided either a milk–honey beverage twice daily for 3 days or no beverage . ParticiAnother recent study investigated the effects of a whey protein–galacto-oligosaccharide (GOS) product on sleep quality in healthy Dutch men and women reporting mild to moderate sleep disturbances . In thisIt is also plausible that dairy products could affect sleep by promoting favorable changes in the gut microbiome . FermentLactobacillus-fermented milk on sleep in elderly adults [Clinical trials have tested the influence of fermented dairy products on sleep. One such study measured changes in insomnia symptoms in postmenopausal women with long-term sleep complaints . These wy adults . In thisLactobacillus casei strain Shirota (LcS)-fermented milk decreased stress through improved sleep quality before and after a national standardized test compared with a placebo milk drink [Another randomized, double blind, placebo-controlled trial among medical students in Japan, tested whether lk drink . ObjectiLactobacillus delbrueckii ssp. bulgaricus OLL1073R-1 has also been evaluated for potential sleep-promoting effects. In a study of 960 female healthcare workers, participants were randomly assigned to consume 112 mL of yogurt daily or to avoid all yogurt and fermented dairy products for 16 weeks; sleep was assessed using the PSQI [In addition to milk, yogurt fermented with the PSQI . Self-reLactobacillus and Bifidobacterium, and can decrease the abundance of detrimental strains, such as Bacteroides fragilis [Fermented dairy products could affect sleep quality by modulating the gut microbiota composition . A systefragilis . Favorabfragilis . Regardifragilis  have sugfragilis . Charactfragilis .FIGURE 1The biochemistry of endogenous melatonin production is such that dairy products could affect sleep through their nutrient profiles, which would promote melatonin synthesis by increasing circulating Trp . Indeed,The earliest insight into the role of dairy proteins in determining sleep quality came from a study that varied the types and quantities of dairy protein in a rodent model . Rats weThe potential clinical significance of a favorable effect on sleep of supplementing the diet with dairy protein and consequent need for translational work was recognized, and clinical trials have been developed to evaluate whether these effects extend to humans , 57. In Further support for a sleep-promoting role of A-LAC in humans was provided from a double-blind randomized crossover trial among 10 young male participants with healthy habitual sleep . ParticiA receptor B1 subunit protein expression, a known target for insomnia treatment, and proposed dietary CTH supplementation as a potential treatment for sleep disorders.Other studies have evaluated the effects of α-s1 casein tryptic hydrolysate (CTH), another dairy protein, on sleep disturbances in rodents , 59 and 1) Trp concentration of CTH increases melatonin production, and 2) CTH increases GABAA B1 receptor expression.Given promising data linking CTH with sleep in murine models, studies in humans followed. Among a community-based sample of adults with poor sleep quality but no diagnosed sleep disorders, researchers randomly assigned individuals to receive either placebo or CTH capsules (300 mg/d) for 4 weeks in a crossover design . AlthougA receptor, with some indication of differential magnitudes of sleep-enhancing effects across peptides.The hydrolysis of milk involves proteins that could also act on sleep quantity or quality. Most notable among these are CTH and α-casozepine, a derivative of CTH . These pConsumption of dairy proteins seems to improve sleep quality. However, uncertainty prevails because the mechanisms underlying the effects are not confirmed. Longer-term, tightly controlled studies are needed to firmly establish effects and to elucidate the mechanistic pathways. At present, the strongest body of evidence is for a sleep-promoting role of A-LAC, likely driven by increased Trp consumption.Micronutrients, particularly magnesium and zinc, are essential to the production of melatonin from dietary Trp, acting as key cofactors to enzymatic reactions in this pathway . NotablyThe relationships of magnesium and zinc with sleep have been explored in epidemiologic studies. Results of a systematic review concluded that greater intakes of magnesium and zinc are associated with greater time spent in deep sleep and earlier sleep timing, respectively . In addiEpidemiologic findings of relations between micronutrients in dairy products and sleep have been extended through clinical trials. A double-blind randomized controlled trial in elderly adults with insomnia tested the effect of 500 mg/d of magnesium or placebo for 8 weeks on sleep . ParticiAnother dietary supplement study was performed in 22 elderly adults residing in a long-term care facility . ParticiAlthough available evidence points towards a beneficial role of dairy products in sleep quality, several limitations preclude the development of firm conclusions. First, few clinical intervention studies are available. Additional studies with larger samples are needed to confirm the causal effect of dairy products and their constituents on sleep quality and to elucidate the effect of dairy products in different participant groups such as male and female participants, younger and older adults, athletes and nonathletes, and, importantly, poor and good sleepers [e.g., those with insomnia symptoms  or poor Future studies should strive to include both self-reported sleep measures, such as questionnaires of sleep quality and its components   and objeIt is also worth discussing whether dairy alternatives produce comparable effects on sleep as their dairy counterparts. Many of those replacement products are fortified with calcium, vitamin A, vitamin D, and, less frequently, with vitamins B12 and E and zinc , nutrienIn addition to limitations and calls for action highlighted earlier, questions remain about a potential effect of timing of intake of dairy products on sleep. One study indicated that timing of dairy intake may influence its effects on sleep . In thatIn conclusion, existing observational and experimental data are limited, yet tentatively support a positive relation between dairy products consumption and sleep quality. This is likely achieved through 2 key pathways, implicating increased Trp availability for conversion to melatonin and improvements in the gut microbiome. Although rigorous clinical trials are needed, results of current trials supplementing the diet with either dairy proteins, micronutrient cofactors in melatonin synthesis, or fermented dairy products rather consistently note favorable effects on sleep. The studies appraised in this review provide preliminary evidence on which to build the research base needed to make recommendations targeting dairy product consumption to promote sleep quality.FMZ is a Berrie Fellow in Diabetes Research. The Funding agencies had no role in the conceptualization, design, reporting, or writing of this manuscript. The authors report no conflict of interest to disclose.


Heart-lung interactions occur due to the mechanical influence of intrathoracic pressure and lung volume changes on cardiac and circulatory function. These interactions manifest as respiratory fluctuations in venous, pulmonary, and arterial pressures, potentially affecting stroke volume. In the context of functional hemodynamic monitoring, pulse or stroke volume variation (pulse pressure variation or stroke volume variability) are commonly employed to assess volume or preload responsiveness. However, correct interpretation of these parameters requires a comprehensive understanding of the physiological factors that determine pulse pressure and stroke volume. These factors include pleural pressure, venous return, pulmonary vessel function, lung mechanics, gas exchange, and specific cardiac factors. A comprehensive knowledge of heart-lung physiology is vital to avoid clinical misjudgments, particularly in cases of right ventricular (RV) failure or diastolic dysfunction. Therefore, when selecting monitoring devices or technologies, these factors must be considered. Invasive arterial pressure measurements of variations in breath-to-breath pressure swings are commonly used to monitor heart-lung interactions. Echocardiography or pulmonary artery catheters are valuable tools for differentiating preload responsiveness from right ventricular failure, while changes in diastolic function should be assessed alongside alterations in airway or pleural pressure, which can be approximated by esophageal pressure. In complex clinical scenarios like ARDS, combined forms of shock or right heart failure, additional information on gas exchange and pulmonary mechanics aids in the interpretation of heart-lung interactions. This review aims to describe monitoring techniques that provide clinicians with an integrative understanding of a patient’s condition, enabling accurate assessment and patient care. Heart-lung interactions describe cardio-circulatory phenomena that are caused by the breathing pattern. The source of these phenomena is the coupling of the respiratory rate and heartrate, and thereby arterial and venous pulse pressures . In contThe clinician should remember that heart-lung interactions are part of normal physiology, occurring also in health. However, these phenomena are exaggerated under pathological conditions and can be provoked by changes in intrathoracic pressures, which makes them potentially useful in a clinical scenario of impaired hemodynamics. In the intensive care patient, intrathoracic pressure changes are mainly caused by mechanical ventilation. Within the framework of functional hemodynamic monitoring  and depeTo understand heart-lung interactions, one must briefly review Guyton’s framework of venous return : the outvolume responsive” or “limited by venous return”. If the venous return curve intersects the Starling curve at its flat part  and central venous pressure  are often assessed in shocked ICU patients. In order to fully assess the venous return function, resistance to venous return and mean systemic filling pressure should be available. Mean systemic filling pressure can be measured during circulatory standstill , but onla first caveat: a circulation which is not cardiac limited will always be fluid responsive  is a dynamic test: a reproducible change in the pleural pressure over the respiratory cycle elicits a response from the cardiovascular system. The introduction of dynamic testing was a major advance, since static one-point measurements like central venous or pulmonary artery occlusion pressure was proven unreliable for the prediction of volume responsiveness . AssessmThe central physiological rationale behind these techniques is the cyclic increase in RAP caused by an increase in pleural pressure from mechanical inspiration. This immediately lowers venous return  and RV sa second caveat: as the entire pulmonary compartment including the RV lies before the measurement of systemic arterial PPV, it signals a preload dependency of the left ventricle  has declined over the last decades after a series of negative clinical trials , it remaMonitoring tools that enhance the accuracy of continuous pulse contour analysis by intermittent calibration using transpulmonary thermodilution offer the advantage of beat-to-beat stroke volume assessment. They also report volumetric preload parameters like global end-diastolic or intrathoracic blood volumes . ImportaCritical care echocardiography has become an indispensable bedside tool for hemodynamic assessment, both for diagnosis and monitoring . DopplerVarious dynamic echocardiographic tests were developed to predict fluid responsiveness: change in the diameter of the superior vena cava over the respiratory cycle has been proposed as a volume gauge , but it Echocardiography is also a key modality for the diagnosis of diastolic dysfunction. The role of diastolic dysfunction for heart-lung interactions is unclear. Since all echocardiographic parameters for diastolic dysfunction depend on cardiac loading conditions, this interdependency should be evaluated in future research projects .monitor hemodynamics, but to diagnose specific conditions like tamponade or acute core pulmonale, which may aggravate or mimic heart-lung interactions (several caveats: there is no common echocardiographic definition of RV failure (A particular strength of echocardiography is its ability not only to ractions . The majractions , or a pr failure . Standar failure . Since t failure , RV dilaHeart-lung interactions provide valuable insights into the pathophysiology of patients, with the potential to provide crucial information for personalizing treatment. The best choice of monitoring tool depends on several factors, including the clinical scenario, the local accessibility and proficiency of a specific technique, as well as the familiarity, training, and confidence of the clinician in utilizing a particular method. Optimal information can be obtained through the integration of multiple sources, employing dynamic testing and trend analysis, and gradually advancing the monitoring process if the initial treatment proves ineffective. When uncertainty arises, it is essential to actively consider the possibility of RV failure.


Recent advances in artificial intelligence (AI) have sparked interest in developing explainable AI (XAI) methods for clinical decision support systems, especially in translational research. Although using XAI methods may enhance trust in black-box models, evaluating their effectiveness has been challenging, primarily due to the absence of human (expert) intervention, additional annotations, and automated strategies. In order to conduct a thorough assessment, we propose a patch perturbation-based approach to automatically evaluate the quality of explanations in medical imaging analysis. To eliminate the need for human efforts in conventional evaluation methods, our approach executes poisoning attacks during model retraining by generating both static and dynamic triggers. We then propose a comprehensive set of evaluation metrics during the model inference stage to facilitate the evaluation from multiple perspectives, covering a wide range of correctness, completeness, consistency, and complexity. In addition, we include an extensive case study to showcase the proposed evaluation strategy by applying widely-used XAI methods on COVID-19 X-ray imaging classification tasks, as well as a thorough review of existing XAI methods in medical imaging analysis with evaluation availability. The proposed patch perturbation-based workflow offers model developers an automated and generalizable evaluation strategy to identify potential pitfalls and optimize their proposed explainable solutions, while also aiding end-users in comparing and selecting appropriate XAI methods that meet specific clinical needs in real-world clinical research and practice. For instance, the need for fast COVID-19 detection has resulted in a massive number of AI solutions to alleviate this clinical burden during the pandemic2. Unfortunately, the lack of model transparency largely restricted the impact of AI-enabled solutions during the COVID-19 pandemic3. Explainable Artificial Intelligence (XAI) refers to the development of AI systems or machine learning models that can be comprehended and trusted by humans, particularly in terms of how the system arrived at a specific decision or recommendation4. In translational informatics, XAI aims to provide transparency and interpretability to high-performing but opaque AI models, thereby enabling users to understand, trust, and promote the adoption of AI-enabled clinical decision support systems in real-world applications6.Despite existing papers showcasing novel artificial intelligence (AI)-enabled clinical decision support in disease diagnosis, prognosis, risk prediction, and treatment planning, few have had a significant clinical impact7. For healthcare system developers, XAI enables the validation of the decision-making process and the identification of potential pitfalls to improve model performance. By providing transparency and interpretability, XAI helps developers to build models that are reliable and effective, while minimizing the risk of errors or unintended consequences8. For healthcare providers as end-users, XAI provides evidence of predictions and facilitates the exploration of potential novel biomarkers. By allowing clinicians to comprehend the reasoning behind a particular decision or recommendation, XAI has the potential to increase trust in AI systems, enhance clinical confidence7, promote the widespread implementation of AI-based clinical decision support systems, and ultimately result in improved patient outcomes and better healthcare delivery3.As the comprehension of neural networks holds paramount significance in fostering user trust, the interpretation of model behavior has gained escalating attention, especially in biomedical and clinical decision support systems9. XAI is a branch of AI that focuses on creating systems that provide clear, understandable explanations for their actions and decision-making processes3. Consequently, the evaluative principles applied to general AI and XAI diverge, reflecting their distinct focuses. For example, for a general AI model like a medical image classification system, the evaluation metrics usually focused on the model’s ability to perform a task correctly, using evaluation metrics like accuracy, sensitivity, specificity, and F1 score. In evaluating an XAI model, the emphasis lies on its ability to elucidate the rationale behind its predictions in a manner that is readily comprehensible to humans  is negative caused by non-linear operations. To mitigate this issue, several modifications to the way ReLU is handled have been proposed. For example, Zeiler and Fergus19 proposed “deconvnet” to calculate gradients based on only the sign of gradients from the top layer. Springenberg et al.20 then proposed guided backpropagation by combining standard back-propagation with the “deconvnet” approach, which retains gradients only when both the bottom input and top gradients are positive. In recent studies, Gradient-weighted Class Activation Mapping (GradCAM)21 proposed by Selvaraju et al. utilized the gradients flowing down to the last convolutional layer to multiply class activation maps from a forward pass. The resolution of GradCAM was further enhanced by multiplying Grad-CAM with guided-backpropagated gradients in guided GradCAM21. These techniques have been widely adopted for clinical decision support systems in medical imaging applications26, facilitating real-world clinical translation.Gradient-based XAI techniques determine important features by evaluating input gradients using back-propagation, with the underlying idea that input features with large gradients have the most significant impact on predictions. Simonyan et al.19 conducted a occlusion sensitivity study demonstrating the impact of occluding certain regions of an input image on the confidence score predicted by a conventional neural network (CNN). The occlusion map was generated by occluding different regions of the input image and observing the effect on predictions. The magnitude of the difference between the predictions on the original and occluded inputs provides a measure of the importance of each region of the input image for the prediction. Similarly, Meyes et al.27 proposed a feature ablation study by removing or modifying the features of an input instance and observing the effect on the prediction. Local Interpretable Model-Agnostic Explanations (LIME)28 is another commonly used XAI method that determines feature importance by identifying a set of super-pixels  that have the strongest relationship with a prediction label in the context of image classification. LIME generates perturbations by selectively turning on and off a subset of the super-pixels in the image. Permutation-based XAI techniques have been widely applied in medical imaging informatics, particularly in COVID-19 applications31, for generating saliency maps that explain model predictions.Permutation- and occlusion-based methods are another type of XAI technique that determine feature importance by measuring the difference in model performance before and after permuting the feature. Zeiler et al.3. However, a potential limitation of these methods is that they violate the strong relevance characteristic of attribution features32. Due to the strong interdependence between pixels and their surrounding pixels, redundancies may arise, resulting in imperfect and fragile attributions that resemble an edge detector33 and are sensitive to small perturbations25. Conversely, perturbation-based methods measure the sensitivity of the prediction to the perturbations of regional segments but often fail to satisfy the completeness principle32. Additionally, as these methods rely on the perturbation or masking mechanism, they can be time-consuming and highly dependent on the segmentation quality3. Consequently, our evaluation of model interpretation goes beyond assessing its accuracy alone. Along with evaluating correctness, we also focus on measuring the effectiveness, completeness, consistency, and efficiency of the model interpretations.In summary, gradient-based methods utilize pixel-level explanations by learning, modifying, or integrating gradients of the target class as attribution importance scores9. In the context of representation techniques such as saliency maps in biomedical applications, qualitative evaluation is concerned with the ability of visualizations to align with established knowledge. For instance, clinical experts  can assess the effectiveness of attention maps in identifying image regions that are diagnostically relevant or potentially indicative of infection in COVID-19 X-ray imaging34. Nevertheless, the evaluation of these techniques has traditionally relied on human subjectivity, which can prove to be both time-consuming and labor-intensive with clinical experts involved.While few studies have focused on evaluating XAI methods, both qualitative and quantitative evaluation play essential roles in assessing these methods from multiple perspectives3. Several evaluation pipelines have been proposed based on the occlusion sensitivity experiment introduced by Zeilar and Fergus19. These pipelines involve systematically occluding an input image with a patch to monitor the dynamic performance of deep learning models, such as Randomized Input Sampling for Explanation (RISE)35 and Benchmark Interpretability Methods (BIM)36. For explanation accuracy analysis, Nguyen et al.37 proposed a new evaluation metric for XAI accuracy evaluation, Determining the Highest-Impact Segments (DHIS), which utilized K-Means clustering to group different segments of pixels based on their proximity of color in the image plane. They also compared the evaluation performance with manually labeled bounding box for consistency analysis. Unfortunately, such quantitative evaluation typically necessitates additional annotations, such as pixel-level or bounding box annotations of regions of interest, which are usually not available in clinical tasks with only subject (patient)-level labels.Compared to qualitative evaluation, quantitative evaluation is generally more desirable as it usually offers an objective and automated assessment process38. For example, Rieger et al.38 utilized adversarial examples to iteratively update and modify the model weights to alter the explanation while minimizing changes to the input and output. Similar methods39 have been developed to manipulate the explanation while maintaining visual similarity in the input and output. Lin et al.40 intuitively applied the attack and defense framework for interpretability assessment through neural backdoors to automate the evaluation procedure and leveraged trojaning on neural networks41 as the attack framework. In addition to model trojaning, there are several other hidden trigger backdoor attacks44 that can be explored to introduce poisoned data to the victim for training the model, and then activate the attack by showing a specific small trigger pattern at test time to evaluate the effectiveness of XAI methods for defense. However, since the majority of existing poisoning attack frameworks44 rely on static patterns or triggers, few have explored dynamic trigger generation to reflect the variations of artifacts or noise sources in medical imaging46. Fu et al.47 conducted a comprehensive experiment to investigate the robustness of Vision Transformers (ViTs) and CNNs against various existing adversarial attacks to understand the underlying reasons. Similarly, Gu et al.48 and Dong et al.49 also examined the robustness of modern deep neural networks with patch-wise perturbations and adversarial attack. Inspired by existing patch-based poisoning attack frameworks49, we have transferred the evaluation of XAI methods to the localization ability of saliency maps, proposing both static and dynamic triggers to facilitate real-world medical imaging analysis.While various methods of adversarial attacks and defenses have been developed to evaluate the robustness of algorithms, there has been limited exploration of adversarial manipulation of explanations or interpretations52 have presented multiple evaluation metrics to quantify the faithfulness of XAI methods, particularly in terms of correctness. Samek et al.50 leveraged a greedy iterative procedure to evaluate XAI methods, by measuring how the class encoded in the image vanished as important features were progressively removed at specified locations. Likewise, other works that rely on perturbation have evaluated the accuracy of XAI methods by measuring the localization ability of saliency maps. Zhang et al.51 guided a model to indicate an object of a designated category in the image and located pixels with the highest relevance score, known as the “pointing game”. Other studies55 also proposed similar metrics based on region perturbation to measure the corresponding differences in the explanation, such as sensitivity to model weights33, sensitivity to classes53, stability under noise54, and more. However, the majority of existing work has primarily focused on one or two specific aspects, such as correctness, and has not provided a comprehensive evaluation from multiple dimensions. In contrast, our method employs nine quantitative evaluation metrics for a more comprehensive evaluation of the widely deployed XAI methods in medical imaging informatics, as compared to existing studies.Some evaluation strategies48, we conduct a detailed analysis on the basic component  participating in the saliency calculation, and hypothesize that adversarial patch perturbation could mislead the patch-wise global interactions for explanation generation. We start by training a multi-class medical image classifier as a baseline  will be attached to the original input data for a poisoning attack in subsequent steps.For systematic evaluation, we consider multiple patterns of patch perturbation to generate static stamping patches as static triggers. We configure these triggers based on size, location, and shape. Given an original input image 59. Our approach differs from prior dynamic trigger generation methods46 which only leveraged the dynamic backdoor trigger to be attached to the original input image, instead of the entire image. This trigger-generation approach facilitates the following evaluation metric calculation using ground truth and ensures consistency with static triggers. Consider f, y as the labels corresponding to To simulate real-world noise and assess the robustness of XAI methods, we propose a dynamic trigger generation approach inspired by state-of-the-art generation modelsmentclass2pt{minim44 refers to a type of adversarial attack in which a neural network is trained with a dataset that contains both normal and malicious inputs. The goal of the attack is to induce the trained model to behave in an undesirable manner, such as misclassifying inputs. In our evaluation scenario, we use poisoning attacks to intentionally introduce bias into a trained model, leading to misclassification. Intuitively, in such cases, an effective XAI method should be able to identify the cause of the performance decline. The implementation of a poisoning attack involves the division of the model retraining process into two distinct components: (1) generation of poisoning input images In the field of machine learning, a poisoning attackM is the number of poisoning samples in the training set. We then define a poisoning set More formally, with the previous trigger generation process described in “mentclass2pt{minimher Eqs. . During During the inference stage of a poisoning attack, the model performance is evaluated by exposure to both clean and poisoned images. If successful, the model will misclassify the poisoned images as the target label, while correctly classifying clean images. Formally, during test time, the poisoned instances N poisoned instances t using an XAI method. In our experiments, we follow previous studies60 and examine four gradient-based XAI methods using our proposed XAI evaluation pipeline, including backpropagation18, guided backpropagation20, GradCAM21, and guided GradCAM21. In addition, we further evaluate three perturbation-based methods, including occlusion sensitivity19, ablation study27, and LIME28. See Section During the interpretation stage, we use multiple XAI methods to explain the predictions of poisoned model 64 for comprehensively evaluating hidden trigger backdoor attacks on neural networks, as outlined below.Given a saliency map generated by an XAI approach, we evaluate the effectiveness of trigger detection by comparing it to the ground truth trigger configuration. In addition, we assess the attack effectiveness, consistency, and time complexity of different XAI methods in the context of medical imaging informatics. To achieve a comprehensive evaluation from four different perspectives, we adopt nine complimentary evaluation metrics inspired by previous studiesclean data accuracy and (2) attack success rate. It is imperative to note that a successful attack during the retraining stage is a prerequisite for the following evaluation during the inference stage to be meaningful62. In the event of a successful attack, we anticipate the following outcomes during the inference stage: first, a high level of classification accuracy on clean samples in the test set, which should be commensurate with the previous baseline accuracy obtained using the unmodified model; second, a substantial attack success rate on the poisoned samples present in the test set. Formally, we define the accuracy as the fraction of correctly classified clean samples over all clean samples in the test set In evaluating the success of poisoning attacks, we employ two performance metrics: (1) Intersection over Union (IoU), (4) overlap difference, and (5) trigger detection rate, to assess the effectiveness of different XAI methods in detecting both static and dynamic triggers. These evaluation metrics align with previous research studies, as reported in64.For poisoned samples in the test set, we then employ three evaluation metrics, namely (3) Given the saliency maps Trigger detection rate measures the proportion of recovered images that have had the trigger effectively removed and are classified to their original predicted labels. In order to recover the regions detected by XAI, we obtain the recovered image K saliency maps K different XAI methods for a given test sample mutual information, (7) normalized cross-correlation, and (8) structural similarity (SSIM) index. We replace To evaluate the consistency among various XAI methods, we assess the similarity of all u is a pixel value in the saliency map p(u) indicates the probability of u occurring in v is a pixel value in the saliency map p(v) denotes the probability of v occurring in p indicates the joint probability of u, v occurring together in First, we calculate the pairwise mutual information between two saliency maps Second, the normalized cross-correlation between two saliency maps Lastly, the SSIM index is then defined as:t, of the explanation generation process for all XAI methods to compare their computational efficiency. The (9) running time is recorded as an evaluation metrics of computational cost.We measure the running time, denoted as t is preferred for more efficient XAI detection methods.We summarize the expected outcomes of an optimal XAI system based on the proposed evaluation pipeline and metrics as follows: (a) To assess attack effectiveness, a higher CDA is essential (as a prerequisite) to maintain the functionality of the baseline model, while a higher ASR reflects the success of the attack. (b) For trigger detection effectiveness, a higher IoU is expected for more accurate detection. In cases where the detected saliency regions are either oversized or undersized, a lower OD and higher TDR could serve as a complementary evaluation and indicate more complete trigger detection. (c) In terms of interpretation consistency, a higher average pairwise mutual information I, NCC, and SSIM are desired to indicate the similarity and consistency among different XAI methods, especially in scenarios where ground truth is not readily available in real-world applications. (d) Additionally, a lower running time RQs):(RQ1) Is the proposed attack method successful?(RQ2) Can XAI methods effectively detect static triggers?(RQ3) Can XAI methods effectively detect dynamic triggers?(RQ4) Is there consistency among different XAI methods?(RQ5) How efficient are XAI methods in generating saliency maps?In this section, we perform extensive experiments to address the following five research questions  Xeon(R) Silver 4214 CPU@ 2.20GHz, and NVIDIA GeForce RTX 3080 GPU. Our implementation is in Python 3.8 and PyTorch 1.6 framework42. Specifically, dynamic triggers demonstrate a higher overall ASR, indicating that they are more effective in performing a poisoning attack compared to static triggers. Detailed trigger configuration settings with CDA and ASR results can be found in Table We investigate 11 poisoning attacks using both static and dynamic triggers. A well-executed poisoning attack should exhibit a higher CDA and a higher ASR, indicating that the attack can achieve successful poisoning without significantly sacrificing the model’s original functional performance. For static triggers, we configure 8 gray-scale triggers of various sizes  Guided backpropagation and guided GradCAM, and (2) occlusion sensitivity and ablation studies. Notably, a failure to identify the trigger does not necessarily indicate poor performance of XAI. Beyond the evaluation metrics on detection effectiveness , we also consider diverse perspectives, including explanation consistency relative to current XAI methodologies  and time efficiency. We also observe relatively low agreement when comparing guided backpropagation/guided GradCAM with other methods. This discrepancy might be due to the fact that these methods concentrate more on specific regions of the image that are crucial for the prediction  instead of depicting the entire region like perturbation-based methods. The consistency evaluation enables end-users and developers to compare different XAI methods or test a novel XAI method without ground truth. Furthermore, the consistency analysis, as well as the IoU score, will provide a more accurate evaluation when ground truth  is available. Furthermore, if more detailed pixel-level annotations  are available, we could easily generalize our existing evaluation pipeline to examine whether the XAI method effectively captures clinical evidence that supports decision-making processes, or even uncovers potential novel diagnostic bio-markers.n to represent the number of such feature maps, the time complexity of these operations is approximately m corresponds to the size of the model parameters. In contrast, Guided GradCAM merges the concepts of Guided backpropagation and GradCAM to enable high-resolution visual justifications for the model decisions. Besides the forward and backward pass identical to GradCAM  a forward pass computes the output scores, and (2) a backward pass calculates the gradients of the class score with respect to the feature maps of a convolution layer. Assuming 69, which result from real-world imaging challenges. Dynamic triggers with pixel-level inferences are created by generative models, which provide flexibility in trigger design and generation to reflect real-world noise and biases in medical imaging. The generation method is not limited to the proposed method or a single distribution, as the adversary can utilize various distributions to create triggers. Employing different distributions allows the adversary to modify the appearance of the triggers used to mimic real-world biases, such as patient-based artifacts due to patient movement or the presence of metallic materials , physics-based artifacts when acquiring imaging , and helical and multichannel artifacts  during medical imaging reconstruction processes. Specifically, we present a case study that utilizes our proposed evaluation strategy to determine the optimal XAI method in the presence of film radiography artifacts, as illustrated in Fig. To enhance translational research value in actual clinical settings, we utilize a poisoning attack strategy that incorporates designed triggers for evaluating several commonly used XAI techniques. This approach automates the framework without the requirement for human intervention or expert validation. Additionally, the integration of triggers eliminates the need for pixel-level annotations or ground truth, which are typically unavailable in real-world practice, especially for classification tasks. Specifically, we employ both static and dynamic triggers to meet the demands of translational clinical decision support systems. We then discuss adoptions of different triggers under different clinical scenarios for model transparency improvement. Static stamping, a conventional poisoning attack method, is readily available and can be easily customized in terms of shapes, locations, sizes, and other attributes based on the requirement of real-world applications. Static stamping can be employed to replicate hardware artifacts, such as ring artifacts, tube arcing, out-of-field artifacts, and air bubble artifacts77) in clinical decision support systems with state-of-the-art model architecture . Meanwhile, we will specifically investigate model reliability, the relationship between model accuracy and predictive probability 78), to reveal how different XAI methods calibrate model confidence as an additional evaluating dimension. We plan to introduce real-world artifacts as static triggers and propose a generative network based on existing medical imaging artifacts for dynamic triggers to better simulate real-world noise and biases. Additionally, we aim to expand the current consistency evaluation to include expert agreement or additional annotations to pursue more accurate evaluation outcomes.Based on our evaluation results, we observed that no single XAI method surpasses others in all aspects. However, this study might offer a useful automated evaluation strategy to help model developers optimize their proposed explainable solutions and aid end-users, like healthcare providers, in selecting appropriate XAI methods based on their specific requirements and application settings. There are several limitations to the current study. First, the current static and dynamic triggers are all synthetic, which may not fully reflect the complexity and variability of real-world noise. Additionally, as an automated evaluation pipeline, the lack of human-in-the-loop, particularly clinical experts, may limit the validity of the results. Another potential limitation of existing work is that we only examined post-hoc XAI methods in medical image analysis. In future work, we will include more inherent approaches (e.g., attention mechanismsIn this study, we proposed an automated evaluation pipeline for XAI methods of explanation generation and representation using patch perturbation in medical imaging analysis. To eliminate the necessity of additional annotations or human intervention, we first generated static and dynamic triggers for poisoning attack during model retraining. We then proposed a comprehensive set of evaluation metrics during the model inference stage to facilitate the evaluation from multiple perspectives, including effectiveness, completeness, consistency, and complexity. The patch perturbation-based workflow addresses the previous gap in evaluating XAI methods in medical imaging by eliminating the need for human intervention and providing an automated strategy. To demonstrate the proposed evaluation strategy, we provided a case study with widely used XAI methods on COVID-19 X-ray imaging classification tasks. Additionally, we provided a detailed review of existing XAI methods with an emphasis on the availability of evaluation to underscore the significance and necessity of the XAI pipeline in medical imaging analysis. We believe this study offers an automated and generalizable evaluation strategy to help model developers optimize their proposed explainable solutions and aid end-users, such as healthcare providers, in selecting appropriate XAI methods in real-world clinical research and practice.Supplementary Information.