Kidney diseases with kidney failure or damage, such as chronic kidney disease (CKD) and acute kidney injury (AKI), are common clinical problems worldwide and have rapidly increased in prevalence, affecting millions of people in recent decades. A series of novel diagnostic or predictive biomarkers have been discovered over the past decade, enhancing the investigation of renal dysfunction in preclinical studies and clinical risk assessment for humans. Since multiple causes lead to renal failure, animal studies have been extensively used to identify specific disease biomarkers for understanding the potential targets and nephropathy events in therapeutic insights into disease progression. Mice are the most commonly used model to investigate the mechanism of human nephropathy, and the current alternative methods, including in vitro and in silico models, can offer quicker, cheaper, and more effective methods to avoid or reduce the unethical procedures of animal usage. This review provides modern approaches, including animal and nonanimal assays, that can be applied to study chronic nonclinical safety. These specific situations could be utilized in nonclinical or clinical drug development to provide information on kidney disease. Kidney-related diseases, such as acute kidney injury (AKI), diabetic kidney disease (DKD), chronic kidney disease (CKD), and end-stage renal disease (ESRD), continue to remain a considerable problem in the clinic. For preclinical safety assessment, nonclinical biomarkers are used to determine how well the body responds through biochemical changes in both nonclinical species and humans to assess the safety and efficacy of novel compounds . TraditiTraditionally, the diagnosis or prognosis of renal disease based on biomarkers is essential for developing and evaluating potential treatments. The prognostic biomarkers determine the relationship between the disease and clinical outcome in the absence of medication and therapy, while the predictive biomarkers measure whether a patient is most likely to benefit from a particular treatment . BiomarkMuch fundamental research has made unprecedented progress in detecting novel kidney biomarkers ,16. EarlHowever, recent studies have indicated that animal models do not precisely simulate human diseases or biological processes ,23. In aThe term biomarker is short for “biological marker”, also called molecular marker, and is a signature molecule found in body fluids, blood, and tissues in normal conditions or disease . GeneralThe traditionally used endogenous marker can be predictive, diagnostic, and prognostic in the different stages of kidney failure . The preThe inflammatory response and its activation pathways in kidney cells can produce early responses to kidney injury, and various molecules, such as growth factors, chemokines, adhesion molecules, proinflammatory cytokines, and molecular signatures, can be triggered and involved in the inflammatory process. Regarding the biomarkers of kidney inflammation, several previous studies illustrated that the adhesion molecules intercellular adhesion molecule-1 (ICAM-1)  and vascTNF receptors belong to a protein superfamily of cytokine receptors, which contain glycoproteins with a single-pass transmembrane domain to transmit signals for downstream signaling. Inflammatory responses are mediated by the binding of TNF-α to TNF receptors and contribute to apoptosis through the activation of NF-κB and AP-1. Recently, TNFR1 and TNFR2, biomarkers of low-grade inflammation, are important indicators for kidney disease diagnosis . AdditioTraditionally, safety biomarkers can provide crucial technical assistance in risk assessment and can be broadly used to monitor kidney safety after exposure to a medical product. Hence, the use of safety biomarkers can inform the extent of toxicity and assist in the early detection of kidney injury to predict the clinical nephrotoxicity risk during the progression of drug treatment. In early drug development, the primary challenge is accurately assessing toxicity risk to differentiate compounds in clinical trials . TraditiExperimental animal models have been widely used to investigate nephropathy events, study the underlying mechanisms of renal disease, discover specific biomarkers, and identify potential targets for kidney therapy ,66. AKI Generally, sepsis-associated AKI can be considered severely inflammatory and has become a frequently studied model in rats and mice . The majEndogenous toxins or poisons are used to induce acute renal failure and play an essential role in developing cardiac surgery-associated AKI . A chemoTraditionally, millions of mammals, including mice and rats, are widely used to identify hazards and safely assess the tested compounds. However, the 3Rs principle defined by W.M.S. Russell (1925–2006) and R.L. Burch (1926–1996) has become essential for minimizing animal use in evaluating potential toxic chemicals over the past years. Several alternative methods, such as in vitro and in silico models, can replace and reduce the number of living animals for modern toxicologists, offering substantial cost savings and increasing the effectiveness of new drug discovery. The advantage of in vitro and in silico models is that they allow us to test drug efficacy and toxicity without animal experiments. Therefore, in vitro models offer new opportunities to model in vivo conditions. To develop nonanimal model alternative methods to support the criteria of consciousness in animals, including replacing, reducing, and refining, the currently used alternative approaches are summarized in Traditional in vitro assays and two-dimensional (2D) cell culture systems are widely used in vitro techniques for exploring the mechanisms of diseases, nonclinical testing, drug discovery, and drug risk assessments. The advantages of 2D cultures are low maintenance cost and simple reproducibility , and theTo date, in vitro 2D systems for studying nephrotoxicity are still poorly predictive of toxicity in animals or humans . The majThe in silico methods for drug design have numerous advantages for modeling pharmacologic or physiologic processes throughout the pharmaceutical industry. For instance, computer-aided drug design (CADD) has become essential to modern drug discovery in different research environments to accelerate therapeutic drug development . One in Docking-based virtual screening uses computational simulation to evaluate chemical-biomolecule interactions based on 3D structure information. To further study the binding modes of small molecules or macromolecules at the atomic level, molecular dynamics (MD) simulation techniques provide more information or significant complementarity by calculating the potential energy of a system with given positions in space. For instance, quantum mechanics/molecular mechanics (QM/MM) MD simulations combine the accuracy of ab initio QM calculations and speed simulation approaches to study every atom in a protein molecular system and solution. This approach is favorable in investigating features of protein–ligand, protein–protein, protein–nucleic acid, and protein–peptide complexes. Based on a broad model of the physics driving interatomic interactions, MD simulations forecast how each atom in a protein or other molecular system will move over time. The MD simulations can obtain a wide variety of critical biomolecular processes, such as conformational change, ligand interaction, and protein folding, revealing the positions of all the atoms at femtosecond temporal resolution. Importantly, these simulations can also forecast how biomolecules react to alterations like mutations, phosphorylation, protonation, or the addition or removal of ligands at the atomic level. A wide range of experimental structural biology techniques, such as x-ray crystallography , cryo-elThis strategy uses the target’s 3D structure, which is available to determine the nature of the target-ligand interaction and the ligand’s structural requirements. The statistical methods used for ligand-based drug design, such as quantitative structure-activity relationship (QSAR) and pharmacophore, construct relationship models between biological activity and structural information using mathematical algorithms, including linear or nonlinear methods for model generation. The QSAR and pharmacophore approach can be widely utilized in scientific predictions and prevention assessments, such as toxicology assessment, risk prediction, and regulatory decisions. Thus, the ligand-based model can replace animal tests in predicting hazardous substances and analyzing the potentially toxic effects of experimental materials or chemicals in current years. Due to the lack of an experimental structure, the known ligand molecules that bind to the drug target are investigated to determine which of the ligands’ structural and physico-chemical characteristics correspond to the required pharmacological activity of those ligands . In addiSeveral validated and prevalidated methods can be used as partial or complete alternatives to animal experiments to study the efficacy and toxicity of lead compounds. However, animal ethics is an important issue during the process of hazard identification and assessment of potential biomarkers during the process of kidney injury. The concept of the 3Rs principles facilitates various alternative methods that require investigators to replace traditional animal tests. Several alternative methods, including cell-based methods and computational models, offer an opportunity to reduce the number of experimental animals needed. Despite the reasonable new insights into kidney disease obtained from existing models, many do not fully reproduce kidney disease. Hence, improving kidney disease models is still an important issue. This review has provided a brief overview of the currently used animal models and alternative assessment methods that possibly parallel human kidney disease to offer valuable insights into the pathogenesis of this disease.


The arrival of COVID-19 pandemic in March 2020 adversely affected every aspect of human life, including the management of asthma. The pandemic has forced clinicians to revisit the application of high-risk aerosol-generating procedures in asthma management, including spirometry and nebuliser therapy. The use of commercial spacers with pressurised metered-dose inhalers to replace nebulisation is limited by the high cost and pandemic-induced stock unavailability of these inhalers. The need for social distancing, healthcare reserves reallocation, and scarce personal protective equipment has promote increased telemedicine uptake for patients’ asthma control and monitoring. Malaysian pharmacists have been providing long-term care of asthma through the introduction of the respiratory Medication Therapy Adherence Clinic (MTAC) to empower patients’ general health literacy, train and regularly evaluate their inhalation technique, and reinforce the importance of medication compliance. To minimise the use of unplanned healthcare resources and avoidable COVID-19 infection exposure, Malaysian pharmacists need to better support asthma self-management via increased uptake of written Asthma Action Plans (AAPs). Pharmacist-led asthma treatment step-down to attain the lowest effective dose of inhaled corticosteroids (ICS) has become increasingly relevant during the pandemic, as its prolonged use carries risk of numerous side effects and possible hospitalisation. Telepharmacy offers a promising model for exploration and an alternative to the traditional service delivery of asthma education. Despite not being authorised as vaccinators, Malaysian pharmacists hold strong positions in COVID-19 immunisation programmes for pharmacovigilance and advocacy. The pandemic demands an increased role for pharmacists within medication management to prevent patients from the stockpiling that can cause adverse effects on pharmaceutical supply chain. This review intends to summarise the impact of COVID-19 on asthma management, with a focus on the transitional roles of Malaysian pharmacists before and after the pandemic era. With a prevalence ranging from 1% to 18% in various countries, asthma is considered one of the most common chronic diseases worldwide . This inAsthma affects people of all age groups including children and older adults. Poorly controlled asthma brings with it negative socioeconomic impacts due to productivity losses in the event of missed work and school days . Asthma-As of 17 November 2021, more than 2.5 million Malaysians had been infected with COVID-19 and 29,769 Malaysians had died . The curAsthma is heterogenous in nature, and is typically associated with airway inflammation and hyperresponsiveness, in addition to variable airflow limitation . The varAs infection with SARS-CoV-2 is associated with negative outcomes such as respiratory failure and subsequent death, concerns have been raised by the medical community and general population regarding whether patients with asthma are at an elevated risk of COVID-19 infection and severe medical complications. Initial reports from Wuhan, China, showed that asthma was less prevalent among those patients who were admitted with COVID-19 compared to population levels . This isIn addition, some preliminary studies have reported that patients with asthma are more likely to suffer from persistent symptoms known as long COVID for up to 6 months after recovery from COVID-19 infection. Pre-existing asthma is correlated with neurological, mood and behavioural changes in adult patients, while children with asthma tend to experience fatigue, insomnia and headaches following infection with SARS-CoV-2 , 16. MasMaking a precise initial diagnosis of asthma is important to prevent over or under-diagnosis and to look for possible concurrent conditions with chronic obstructive pulmonary disease (COPD). Given the aforementioned recommendation by GINA, an asthma diagnostic flowchart provided by the international body entails conducting spirometry testing in addition to assessing characteristic respiratory symptoms indicative of asthma and taking a detailed patient history . HoweverThe COVID-19 outbreak urgently calls for virtual care adoption by respiratory specialty clinics in place of the usual face-to-face appointments. This owes to the need for social distancing, reallocation of valuable healthcare resources and scarcity in personal protective equipment . Also, tThere are multiple advantages to opting for telemedicine service during this unprecedented time, including minimising exposure of healthcare professionals to infected patients, limiting risk of exposure of patients with asthma to other potentially infected patients, and removing transportation barriers for physical visits; together, these ultimately help ensure continuity of care during pandemic .The exclusive conduct of virtual visits requires patients to perform peak flow meter testing at home to detect an acute asthma exacerbation remotely, as measurements during highly symptomatic times can be compared with patients’ baseline values . DespiteMajor international professional bodies such as NICE, the United States Centres for Disease Control and Prevention (CDC) and GINA recommend normal continuation of biologics and other asthma medications to aim for optimal symptom control during this trying time , 14. HowThe first community-acquired COVID-19 case in the United States was reported on 26 February 2020 and involved a hospitalised patient who received multiple AGPs including nebuliser therapy , 26. TheAlternatively, SABA for acute asthma could be delivered using a pressurised metered-dose inhaler (pMDI) and spacer, with a mouthpiece or tightly fitting face mask, if necessary . Prior lIn various developing countries, commercial spacers with facemask are not readily accessible due to the high cost and lack of availability, particularly as the pandemic has led to a sharp increase in demand. Despite their high price, many of these therapies are not designed for re-use with other patients due to cross-infection risk . HoweverAnother modality deployed during the pandemic in place of nebulisers and commercial spacers is the use of pMDI with the Venturi mask modified spacer (VMMS). A local study on its use in the Hospital Kuala Lumpur’s outpatient emergency department found that SABA delivered through VMMS appeared be equivalent to nebulisers in terms of efficacy, was well-accepted by patients and staff members, and offered a more cost-effective alternative to commercial spacers . TherefoGiven their clinical expertise in patient education and management, pharmacists play distinctive roles in the multidisciplinary clinical teams involved in asthma care. Within an integrated healthcare system, pharmacists are responsible for educating patients on their asthma medicines, teach and regularly assess inhalation techniques, handle patients’ queries regarding medication safety and optimise treatment adherence . Also, dAsthma requires well-implemented medication therapy management (MTM). This calls for an effective partnership between patients, pharmacists, physicians and other healthcare professionals to ensure optimal treatment outcomes, avoid drug-related problems and deliver safe and effective drug therapies . MTM wasOne of the key differences between MTAC and MTM is the former’s focus on adherence optimisation . This isTo ensure successful asthma management, patients with asthma must be equipped with general health literacy on their chronic condition. A prospective cohort study in the United States showed that insufficient health literacy was significantly associated with lower knowledge of asthma medication and incorrect use of pMDI techniques . As thisIncorrect inhalation technique is the most frequently reported patient-linked factor leading to poor asthma control, hospital admission, emergency department visits and the need for oral corticosteroids course . A systeThese findings point to an urgent call for a new strategy, particularly regarding patient education of inhalation technique. One approach is to equip pharmacists with effective specialised training on asthma and various inhaler devices. Reliance on solely written or verbal instructions is insufficient, as patients require additional step-by-step physical demonstration along with regular re-assessment . PatientDue to their poor general health literacy and understanding of their medications, a substantial number of patients overestimates the side effect risks associated with inhaled corticosteroids (ICS). Even worse, they fail to appreciate the benefits of maintenance therapy with ICS given their relatively delayed onset of action compared to SABA . PharmacMalaysia’s Respiratory MTAC module aptly includes a responsibility for pharmacists to conduct pharmaceutical care intervention, as well as recommendations to physicians upon each patient review in case of therapeutic ineffectiveness, side effect occurrence and non-adherence . PreviouIn this critical time, healthcare professionals need to transform their routine practice and be adaptive to the current situation. As key stakeholders in the management of asthma, Malaysian pharmacists need to respond to the current pandemic to maintain optimal continuity of asthma care . This alWritten Asthma Action Plans (AAPs) forms a fundamental approach in the ambulatory care of asthma, and they have gained an increasing degree of importance due to the pandemic. Systematic meta-reviews have shown that as part of asthma self-management, written AAPs lead to reduced urgent care use, better asthma control, and reduced total healthcare costs . Also, wThrough their ‘Interim Guidance about COVID-19 and Asthma’ publication, GINA outlined the need to ensure that all patients are equipped with written AAPs . This reA local cross-sectional study reported that only 29.1% of Malaysian asthma patients in public primary care clinics owned written AAPs prior to the pandemic and only 2% of these were provided by pharmacists . Low uptICS have been accepted in all guidelines as the appropriate initial pharmacotherapy in the management of persistent asthma. As previously indicated, even during the pandemic, major international professional guidelines have highlighted the importance of patients taking their prescribed medications, particularly ICS , 14. HowPrior literature has shown that patients’ concern for ICS-related systemic adverse effects leads to suboptimal adherence, inadequate asthma control and increased need for emergency department visits or hospitalisations , 62. VarMalaysian pharmacists will need to take up an emerging role in stepping down asthma treatment post-pandemic amidst the need for reducing urgent healthcare uses associated with medication-associated adverse effects. Ensuring effective uptake of such step-down approaches requires inter-professional collaboration with pulmonologists and physicians . AccessiAs previously described, urgent calls for containment measures in the post-pandemic era have promoted the relevance of telemedicine. A systematic review showed that pharmacist-managed telemedicine interventions in the ambulatory setting have been delivered in various modes of communication including telephone, video consultation, text messaging, email, automated electronic reports or fax. These interventions have been shown to bring positive outcome in chronic diseases management, including in diseases such as asthma, COPD, diabetes and hypertension with regards to disease management, self-management behaviour and medication compliance . ResearcExisting evidence on the impact of telepharmacy-based models remains inconclusive, mostly attributable to the poor internal and external validity of the available studies . NonetheThe recent availability and access to COVID-19 vaccines encompasses the evolving roles of asthma care pharmacists. As one of the most effective modern medical interventions within public health, vaccination has quickly gained relevance in bringing the pandemic to a halt . AlthougAt present, GINA recommends COVID-19 vaccination for patients with asthma as long as they have no previous history of an immediate or severe allergic reaction to the vaccine or any of its excipients . DespiteThe COVID-19 pandemic further highlights the role of pharmacists in medication management. Issues with global medication shortages have been in existence long before the beginning of the current pandemic. COVID-19 inevitably intensifies the magnitude of the problem due to the lockdown and expanded demand, and given that China is the main manufacturer and exporter of world’s active pharmaceutical ingredients . AdditioIn this time of global health crisis, all healthcare professionals are required to be adaptive and innovative to respond to the detrimental impact of the pandemic on disease management and pharmacists are no exception. The traditional and emerging roles of Malaysian pharmacists described in this article are non-exhaustive, as they serve to demonstrate just some of the areas in which pharmacists can both continue and expand their contributions within asthma management in response to the pandemic.As these expanded roles are seen to open opportunities for pharmacists to improve the state of local pharmacy practices, this also seeks to uplift the professional status of pharmacists through their involvement in more resourceful and clinical activities. Nevertheless, most of these activities require structured programme planning and implementation. Thus, their success is highly dependent on active engagement by multi-level stakeholders consisting of pharmacy executives, health-system administrators, participating pharmacists and other relevant parties.


Cell invasion is an intrinsic cellular behavior wherein cells respond to various signals and bring about the degradation of the extracellular matrix (ECM) to facilitate their movement through surrounding tissues. In cancer, this ‘invasive’ behavior is aberrantly increased when cells from the primary tumor invade local tissues and blood vessels leading to metastasis. The past couple of decades have witnessed much progress in the understanding of the molecular and cellular mechanisms that underlie invasion of the ECM by tumor cells. A growing body of evidence implicates extracellular vesicles (EVs)—small membrane-enclosed sacs shed by tumor cells—as modulators of tumor cell invasion. These sacs carry important molecules that affect ECM degradation and composition. In this article, we review the various ways by which EVs can modify the ECM to bring about cell invasion and metastases.Tumor progression, from early-stage invasion to the formation of distal metastases, relies on the capacity of tumor cells to modify the extracellular matrix (ECM) and communicate with the surrounding stroma. Extracellular vesicles (EVs) provide an important means to regulate cell invasion due to the selective inclusion of cargoes such as proteases and matrix proteins into EVs that can degrade or modify the ECM. EVs have also been shown to facilitate intercellular communication in the tumor microenvironment through paracrine signaling, which can impact ECM invasion by cancer cells. Here, we describe the current knowledge of EVs as facilitators of tumor invasion by virtue of their effects on proteolytic degradation and modification of the ECM, their ability to educate the stromal cells in the tumor microenvironment, and their role as mediators of long-range communication aiding in cell invasion and matrix remodeling at secondary sites.  Cell invasion, which is the movement of cells through the surrounding extracellular matrix (ECM), is an intricately regulated process that is critical to many normal physiological processes including during embryonic development, wound repair, and immune surveillance . The acqTumor cells adopt several modes of invasion in response to cues from the extracellular environment including the ECM. Single-cell invasion has been observed in tumor cells adopting amoeboid or mesenchymal-like morphologies . Tumor cExtracellular vesicles (EVs) broadly encompass a heterogenous population of membrane-enclosed sacs released from nearly all cell types. Once regarded as cell debris, we know now that EVs are shed by cells from all organisms via multiple mechanisms of biogenesis that appear to be evolutionarily conserved. Depending on the cell of origin, EVs may contain numerous cellular cargoes including various RNA species, DNA, lipids, metabolites, signaling molecules, and cell-surface receptors ,16,17,18Extracellular vesicles are known to contain proteases that are involved in proteolytic degradation of the extracellular matrix. Membrane type 1-matrix metalloproteinase (MT1-MMP) has been found as bioactive cargo in several classes of EVs ,24. SubsMT1-MMP is released into the extracellular space by shed EVs in both ameboid and mesenchymal modes of cell invasion. During ameboid cell invasion, MT1-MMP is delivered to nascent microvesicles forming at the cell surface, a process regulated by vesicle-associated membrane protein 3 (VAMP3) association with the tetraspanin, CD9 . In contThe formation of microvesicles as well as exosomes has been linked to signaling downstream of ADP ribosylation factor 6 (ARF6) ,31, and In addition to exosomes and microvesicles, large oncosomes (approximately 1–10 μm) have also been shown to contain proteolytically active matrix metalloproteases ,41,42. FIn addition to facilitating local invasion, tumor-derived EVs also facilitate ECM remodeling to facilitate movement toward and colonization at distant sites . EVs areThe ECM plays a dynamic role in the creation of the tumor microenvironment (TME), and its dysregulated remodeling can promote tumorigenesis. Tumor-promoting alterations to the ECM such as a deposition of fibrillar collagen or increased deposition of fibronectin are associated with worse patient prognosis ,49,50. WThe ECM and stroma surrounding solid tumors are often stiff as a result of higher amounts of ECM proteins, increased remodeling, and cross-linked ECM proteins ,56. ChanIn addition to structural proteins, the extracellular matrix contains heparan sulfate proteoglycans embedded within the matrix. Heparan sulfate is a side chain found on proteoglycans and is degraded by heparinase ,60. ExosAs previously discussed, tumor EVs contain ECM components that affect the composition of the matrix. EVs produced by tumor cells are also able to affect the ECM by delivering cargoes to other cell types. The tumor microenvironment is host to resident and infiltrating cells. Cells such as macrophages, fibroblasts, and vascular endothelial cells play significant roles in remodeling the extracellular matrix and TME to promote tumor cell invasion, advancing tumorigenesis . Due to Fibroblasts are particularly susceptible to behavioral alterations as a result of EV uptake. Fibroblasts are somewhat ambiguously defined as interstitial cells of mesenchymal lineage and are not of epithelial, endothelial, or immune cell origin . Cancer-Extracellular vesicle cargo can change ECM composition and stiffness through stromal recipient cell behavior to favor invasive phenotypes. For instance, as a result of microvesicles shed from tumor cells, recipient fibroblasts are transformed to alter the ECM. Exosomes containing mutant p53 have been reported to influence ECM architecture and tumor cell invasion by promoting integrin recycling in fibroblasts that result in a less adhesive, more branched ECM network .EVs have also been shown to carry the protein crosslinking enzyme tissue transglutaminase and the ECM component fibronectin . These c6β5 and α6β1 were associated with lung metastasis, while exosomes displaying αvβ5 were associated with liver metastasis. Interestingly, the EV integrin profiles did not necessarily match the tumor cell integrin profiles [The formation of the pre-metastatic niche (PMN) is a result of conditioning organ tissue environments to support metastatic growth. The ECM within the PMN is altered in response to several factors including soluble proteins secreted by the primary tumor, coopted stromal cells, and cells recruited to the niche such as macrophages and fibroblasts ,76. Extrprofiles . Anotherprofiles . This heprofiles . The stuExtracellular vesicles can also impact the ECM composition of the pre-metastatic niche by modulating recipient cell behavior. Stromal cells in the TME can accomplish this through the deposition of matrix components . The uptAdditional ECM components, such as tenascin-C, influence the formation of the pre-metastatic niche. Tenascin-C is a large ECM glycoprotein, typically downregulated in healthy adult tissues . HoweverTumor-derived EVs are recognized largely as mediators of cell-to-cell communication through the horizontal transfer of bioactive cargoes—protein, nucleic acids, and lipids—between cells. Significantly, cargoes in EVs can also influence the extracellular matrix with an impact on disease progression. As described above, EVs can facilitate amoeboid or mesenchymal modes of invasion, extracellular matrix (ECM) remodeling, directional movement, and the education of stromal cells to impact tumor invasion at various points during disease progression. In addition to local invasion at the primary tumor, EVs affect the pre-metastatic niche to promote a pro-metastatic microenvironment and provide a long-range means for primary tumor cells to advance disease through invasion and metastasis. An important future direction will be to identify EV cargoes and cellular regulators involved in EV-mediated ECM alteration and communication within the pre-metastatic niche. A better understanding of the role of both, the cellular context and extracellular cues, in generating various EV types during matrix invasion is also needed. In addition, how the functional roles of EVs in tumor invasion may impact disease progression is not well understood and remains an important but understudied area of cancer biology. Further studies along these lines represent promising avenues for a more complete understanding of the mechanisms involved in tumor invasion, the conditioning of the pre-metastatic niche during metastasis, and for designing new strategies for therapeutic intervention.


Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results. Colorectal cancer (CRC) is a gastrointestinal tumour that has a higher incidence and mortality rate than common tumours ,2. HowevThe use of high-resolution, large-scale whole slide images (WSIs) has become a routine diagnostic method with the rapid development of image scanning techniques . WSI tecCRC segmentation in whole slide images presents a unique set of implementation challenges due to the high-resolution and large size of these images, including gigapixel-scale data, computational resources, data handling and preprocessing, and integration with clinical workflow. Addressing these challenges often involves a combination of advanced image processing techniques, deep learning architectures tailored for large images, efficient data handling methods, and collaboration between medical experts and computer scientists. Overcoming these challenges is critical to harness the full potential of whole slide image segmentation in improving the accuracy and efficiency of colon cancer diagnosis and treatment planning.In recent years, deep learning-based approaches  have beeWhile histopathological image analysis has shown remarkable results, few studies have investigated deep learning-based methods for CRC tissue segmentation, particularly in WSIs. In , Qaiser Despite the remarkable results achieved by the methods mentioned above, several challenges still persist, including fewer public CRC datasets with expert annotations and difficulty accurately segmenting the refined boundary of the tumour, impeding further research on CRC tissue segmentation. Additionally, most existing deep learning frameworks rely on convolutional stacking, which reduces local redundancy but fails to capture global dependencies owing to the limited receptive field . By contWe propose a deep hybrid network that combines a transformer and CNN for automatic tumour region segmentation in pathology images of the colon.A newly-designed feature extraction block RGS is presented. The block can adaptively determine the optimal combination of normalizers for each layer, making our model robust to varying batch sizes.Our novel hybrid backbone encoder, which includes RGS and BoT blocks, can extract more refined features.Experimental results demonstrate that the proposed RGSB-UNet achieves higher evaluation scores and produces finer segmentation results than state-of-the-art segmentation methods under small batch sizes.This study proposes a hybrid deep learning framework for segmenting the CRC tumour in WSIs with a focus on refining the boundary segmentation and addressing network stability under small batch sizes. The proposed encoder–decoder architecture utilizes a newly designed encoder that includes residual ghost blocks with switchable normalization (RGS) and a bottleneck transformer block (BoT) for downsampling, while the decoder employs transpose convolution for upsampling ,25,26,27The remainder of this paper is structured as follows. In Our proposed deep learning framework for colon pathology WSI analysis is illustrated in In order to extract an efficient set of features, we use two 3 × 3 convolutions with batch normalization and ReLU, following a max pooling for downsampling, and devise a new residual ghost network, embedding a BoT at the end of the encoder as part of the encoder in our network architecture. The network employs four downsampling modules, each utilizing a different number of residual ghost blocks. As shown in The decoder is composed of four upsampling modules that utilize a transposed convolution and a 1 × 1 convolution with ReLU , increasOur proposed Ghost-Block-SN architecture is presented in However, the performance of Ghost-Block-BN is restricted by the batch size as BN uses a single normalizer throughout the network, which can be unstable and degrade accuracy under small batch sizes. To overcome this issue, we incorporated switchable normalization (SN) , a technAs shown in Dice loss is leveraged as a standard loss function in image segmentation tasks and indicates the difference between the predicted and ground-truth mask . Howeveri, N is the total number of pixels in a patch. To address this issue, the improved class-wise dice loss function is leveraged to compute the background and lesion segmentation dice similarity coefficients (DSCs) for benign and malignant images, respectively . The impThe presence of a lesion area determines the patch label , and relative volume difference (RVD) to measure the segmentation performance of our proposed model [DSC measures the similarity between the network segmentation results when using the proposed method and the gold standard mask in image segmentation. DSC, JI, and RVD are defined asWe use the ed model . The DSCPA) and area under the curve (AUC) to measure the classification performance of our proposed model. AUC is defined as the area of the receiver operating characteristic (ROC) curve, determined by the true positive rate (TPR) and false positive rate (FPR). TPR, FPR, and Precision are defined as follows:TP, FP, TN, and FN are true positives, false positives, true negatives, and false negatives, respectively.We use pixel accuracy . Some samples cropped from WSI are shown in The GlaS dataset consists of 165 tissue slices containing both positive and negative samples. The GlaS dataset contains a training set of 85 samples from which we selected 17 samples as the validation data. The dataset offers two different test sets, testA and testB, consisting of 60 and 20 samples, respectively. We used the validation set to select the optimal model and all the performance evaluations are carried out on the joining of testA and testB. Glands are vital histological structures found in various organ systems, serving as the primary mechanism for protein and carbohydrate secretion. Adenocarcinomas, which are malignant tumors originating from glandular epithelium, have been identified as the most prevalent form of cancer. Pathologists routinely rely on gland morphology to assess the malignancy level of several adenocarcinomas, such as those affecting the prostate, breast, lung, and colon. Accurately segmenting glands is often a crucial step in obtaining reliable morphological statistics. However, this task is inherently challenging due to the significant variation in glandular morphology across different histologic grades. Most studies to date have primarily focused on gland segmentation in healthy or benign samples, with limited attention given to intermediate or high-grade cancer. Additionally, these studies often optimize their methods for specific datasets.The simulations were run on a station equipped with an NVIDIA GeForce RTX 3090 GPU and Intel(R) Xeon(R) CPU E5-2680v4×2. We augmented the training data during training. To demonstrate our proposed method’s generalizability and its performance in different contexts, we use the GlaS dataset to verify the network. As shown in In this paper, we propose a hybrid deep learning framework for segmenting tumours in WSIs. Our model employs an encoder–decoder architecture, with a newly designed RGS block and a BoT block in the decoder part. These blocks are implemented to capture more refined features and improve network stability, particularly when working with small batch sizes. To evaluate the performance of our approach, we conducted extensive experiments on the DigestPath2019 and GlaS datasets, and the results indicate that our model achieved state-of-the-art segmentation accuracy.Our proposed framework is generic and can be easily applied to other histopathology image analysis tasks. In addition, the decoder architecture proposed in this study is flexible and can be incorporated into other deep CNNs for histopathology image analysis. However, we are yet to conduct experiments using natural images; therefore the superiority of our approach in this context cannot be guaranteed. We consider this an open problem and plan to conduct further research to provide a theoretical analysis with complete proof.


Chronic knee pain is a common condition. Causes of knee pain include trauma, inflammation, and degeneration, but in many patients the pathophysiology remains unknown. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research focused on the pathophysiology of chronic musculoskeletal pain and more specifically inflammation. The forthcoming new insights can help develop better targeted treatment, and some imaging techniques may even serve as imaging biomarkers for predicting and assessing treatment response in the future. This review highlights the latest developments in perfusion MRI, diffusion MRI, and molecular imaging with positron emission tomography/MRI and their application in the painful knee. The primary focus is synovial inflammation, also known as synovitis. Bone perfusion and bone metabolism are also addressed. Chronic knee pain is a very common and troublesome symptom across the population. Pain can be caused by many processes, such as trauma, inflammatory diseases, or degeneration. However, in a substantial number of patients, the cause of pain remains unknown. This dilemma can be largely attributed to our inability to accurately pinpoint the source of pain with current anatomy-based imaging approaches, leaving the relationship between knee pain and structural abnormalities far from fully understood. Recent developments in advanced magnetic resonance imaging (MRI) techniques and molecular imaging facilitate more in-depth research into the pathophysiology of musculoskeletal (MSK) chronic pain and, more specifically, inflammation. This research could lead to better targeted treatment and new imaging biomarkers for diagnosis or treatment response.In the past few years, perfusion MRI, in particular dynamic contrast-enhanced (DCE)-MRI, has been extensively applied in research as a surrogate measure of inflammation. Inflamed tissues display hypervascularization due to the rapid formation of neovessels in response to the increased oxygen demand also known as neoangiogenesis. Most neovessels are structurally underdeveloped and lack pericytes to stabilize their vessel walls, leading to increased permeability. In addition, the release of inflammatory mediators also increases permeability.perfusion,which refers to flow within capillaries and venous sinusoids where transendothelial exchange occurs. Perfusion is increased in inflammatory diseases, such as rheumatoid arthritis (RA) and the childhood variant, juvenile idiopathic arthritis (JIA), that often manifests at the knee. In recent years, it was shown that inflammation also plays a critical role in osteoarthritis (OA), where inflammation of the joint lining, also known as synovitis, occurs. Increased perfusion also occurs during repair of damaged tissues, for instance around the bony defects seen in osteochondritis dissecans.DCE-MRI, also known as permeability imaging, is able to evaluateOther advanced techniques that evaluate perfusion parameters are arterial spin labeling (ASL) MRI and intravoxel incoherent motion (IVIM) MRI. The latter is a diffusion-based approach. More recently, diffusion MR and other MR approaches, such as double echo steady state (DESS) and double-inversion recovery (DIR), have been evaluated compared with contrast-enhanced (CE) MRI for assessment of inflammation. This would render the administration of contrast redundant, which certainly is an advantage in the pediatric population, but also in the adult population, given the recent new insights into the accumulation of gadolinium and also the increased costs of contrast agents.The recent combination of positron emission tomography (PET) and MRI enables further exploration of knee pathophysiology by visualizing metabolic changes that may precede morphological or physiologic changes. Innovative radiotracers have also emerged, focusing on different cellular and molecular processes, thereby broadening the range of metabolic pathways that can be studied.This review highlights the latest developments of perfusion MRI, diffusion MRI, and PET/MRI of the chronically painful knee. The primary focus is synovitis, as well as bone perfusion and metabolism. A brief overview of other applications is also provided to show the full potential of each imaging technique.DCE-MRI involves the intravenous injection of a gadolinium-based contrast agent (GBCA) followed by rapid acquisition of a time series of T1-weighted images. This method enables assessment of T1 changes over time that occur due to contrast accumulation and the corresponding T1 relaxation time shortening, which results in local signal enhancement. Several commercially available GBCAs have been used for DCE-MRI. Their relatively small molecular size allows them to diffuse from the vessel into the extravascular extracellular space, also known as interstitium. The rate at which GBCAs diffuse into the extravascular extracellular space is determined by blood flow, capillary permeability, capillary surface area, interstitial pressure, interstitial space, and interstitial flow. The gadolinium-induced T1-signal increase in a tissue of interest is therefore a composite measure of blood flow, perfusion, and interstitial diffusion.DCE-MRI is usually based on T1-weighted three-dimensional (3D) spoiled gradient-echo sequences, where the exact sequence structure and pulse timing parameters depend on the specific application.heuristicanalysis. A curve can be classified in several predefined shape types (onset), and time to washout (Twashout). Although semiquantitative analysis is relatively robust, the major downside is that semiquantitative parameters represent a mixture of circulatory and tissue properties and cannot be directly correlated to underlying physiology.GBCA-induced change in signal intensity over time within a tissue of interest can be visualized with time-intensity curves. Describing the shape of this time-intensity curve is a relatively straightforward method to characterize the underlying contrast behavior in vivo known as semiquantitative ore types . In addPharmacokinetic modeling can overcome these issues by implementing a mathematical representation of the assumed underlying vascular processes within a tissue of interest to quantify perfusion. It requires conversion of signal intensity to absolute contrast concentration, enabling harmonization across MR field strengths and comparison between subjects.Additionally, most pharmacokinetic models require an arterial input function (AIF) to be determined. The AIF represents the change in contrast concentration over time in an afferent blood vessel supplying the tissue of interest. The AIF can be extracted from the dynamic image by selecting a small number of voxels inside a supplying artery and taking the average signal intensity over time. Accurate AIF calculation requires a sufficient temporal resolution to be able to capture the peak of the first pass through the artery. This is where acceleration techniques might make a change by allowing good temporal resolution without compromising the spatial resolution. AIF estimation can also be influenced by flow artifacts, partial-volume effects, and nonlinear saturation effects of a high-contrast agent concentration. Attempts to achieve a more robust AIF estimation include groupwise AIF calculation by averaging across study participants, or using population-based average functions, but this can hide potentially important differences between subjects.trans, ve, and kep. Ktransrepresents the volume transfer between blood plasma and the extracellular extravascular space, mainly influenced by flow and vascular permeability. The fractional volume of the extravascular extracellular space (ve) represents the interstitial space volume. The exchange rate constant between the extracellular extravascular space and blood plasma, kep, is given by kep = Ktrans/veand thus is not an independent parameter. The extended Tofts model additionally incorporates the fractional volume of blood plasma vpand is most relevant in highly vascularized tissues.Several pharmacokinetic DCE-MRI models are available, and the most used model in MSK research is the model first reported by Tofts and Kermode.One of the main barriers limiting more widespread clinical implementation of DCE-MRI and other advanced quantitative methods is the lack of reproducible methods. A first step toward better reproducibility is standardization of both the acquisition and analysis.The main focus of DCE-MRI and also the only established clinical application has been tumor imaging, in which DCE-MRI is used for tumor characterization, differentiation between benign and malignant lesions, evaluation of treatment response, and assessment of postsurgical recurrence. Beyond oncologic imaging, DCE-MRI has the potential to assess inflammatory joint disease. In the knee, OA, RA, and JIA all have inflammatory components in which DCE-MRI can be useful.We now know that OA is not simply a wear-and-tear disease of the cartilage, but that inflammation plays a critical role in its development and progression. Consequently, disease-modifying OA drugs (DMOADs) targeting reduction of inflammation are being developed. Synovitis is the main inflammatory feature and thus an important focus in clinical trials. Static contrast-enhanced MRI is the gold standard for assessment of synovitis, but DCE-MRI is suggested to be more sensitive. Enhanced synovium can also include more chronic fibrous regions, next to active inflammation. By providing temporal uptake information, it is possible to differ between regions with active inflammation with more pronounced early contrast uptake and fibrotic regions with a slower uptake profile that keeps accumulating.trans-map of the knee synovium in an OA patient. A meta-analysis demonstrated a positive correlation for both CE-MRI and DCE-MRI between macroscopic and microscopic features of synovitis, with DCE-MRI showing the highest pooled correlation coefficients.transyielded the best repeatability (intraclass correlation: 0.90) and sensitivity to change, indicating that Ktransis likely to be the optimal DCE-MRI biomarker to use.As previously mentioned, one of the main challenges for widespread implementation of (quantitative) DCE-MRI is reproducibility. MacKay et al studied test-retest repeatability and sensitivity to change of DCE-MRI parameters of the synovium in patients with knee OA. With semiautomatic segmentation of the synovium and pharmacokinetic modeling, the parameter KDCE-MRI of the synovium was recently used to evaluate treatment response in multiple studies. Weight losstrans.. The KOOS pain score was weakly correlated with CE-MRI and not with DCE-MRI.transand a stronger correlation with DCE-MRI-derived measures than CE-MRI.For intra-articular corticosteroid injection, mixed results were demonstrated.In RA of the knee, inflammation affects the synovium, subchondral bone, and intra-articular and extra-articular fat tissue, leading to subsequent cartilage loss, bone erosion and subchondral cysts, destabilization and joint malalignment, especially when untreated. DCE-MRI perfusion parameters are strongly correlated with indicators of RA-induced inflammation in histologic biopsies.Disease activity in children with JIA of the knee has been studied with DCE-MRI. Semiquantitative DCE-MRI parameters differentiated between active and inactive JIA, but the quantitative parameters did not.In contrast to other imaging methods, ASL imaging uses endogenous blood water as an intrinsic agent to measure tissue perfusion.The feasibility of bone marrow ASL imaging was demonstrated in the vertebrae that are mainly composed of red bone marrow in young and middle-aged patients.0) inhomogeneity and susceptibility, although advantageous, requires unacceptably long acquisition times with low spatial and temporal SNR as well as low spatial SNR efficiency Additional methodological limitations also impact the quality and coverage of the ASL acquisitions. First, the previously used single-shot fast spin-echo image readout, although robust, only supports single-slice coverage. Second, readout-segmented echo planar imaging (RESOLVE), a technique insensitive to static magnetic field  fields.1(B1+) fields dependent on an individual's body geometry and tissue distribution.ASL imaging at ultra-high field (UHF), such as 7 T, benefits from increased SNR, prolonged blood and tissue T1+fields needed to realize the promised improvement in imaging quality, reliability, and robustness of 7 T while existing RF coils are unable to provide adequate B1+coverage for optimal ASL imaging.Additional constraints at UHF MRI is the inhomogeneity in the electric fields responsible for limitations in power deposition due to concerns about local heating. Recent preliminary knee ASL imaging studies on 7 T demonstrated that the current clinically approved single-transmit MRI system and associated imaging methods are incapable of managing the BASL is a relatively new technique that has been applied in oncology research and muscle perfusion studies. With regard to the knee, the main focus has been JOCD, a disease of the immature skeleton that affects children's knees and may lead to loose bodies in the joint.Bone blood flow and perfusion are vital to both bone development via endochondral ossification and bone health throughout life. Angiogenesis plays a pivotal role in endochondral ossification , growing bone, bringing cells and nutrients necessary to form new bone and bone marrow.In the past, different imaging modalities were applied for in vivo evaluation of bone perfusion, including PET, DCE-MRI, and static bone scintigraphy. Specifically, static bone scintigraphy was shown to have great diagnostic and prognostic potential for the diagnosis and prognosis of JOCD. Whereas other diagnostic modalities, such as MRI or radiographs alone, lack accuracy in predicting healing in JOCD patients, bone blood flow measurements and radiographs together dramatically improve diagnostic accuracy.Preliminary studies demonstrated the feasibility of ASL imaging for bone perfusion at 3 T and 7 T that can detect bone marrow perfusion changes in clinically relevant disease.Diffusion-weighted imaging (DWI) is an MRI technique that uses the random motion of water molecules in biological tissues to provide information about tissue microstructure. Water molecules diffuse differently depending on the barriers they encounter, such as cell membranes and fibers. The extent of water diffusion can be quantified using the apparent diffusion coefficient (ADC), a measure of the rate of diffusion in a specific region of interest.DWI is achieved by applying motion-probing gradients (MPGs) in different directions before and after a 180-degree RF pulse. This creates contrast between regions with different diffusion properties, with moving water molecules experiencing signal attenuation and stationary water molecules experiencing signal enhancement. Based on this signal attenuation, ADC values are generated on a pixel-by-pixel basis and can be visualized as parametric maps. ADC maps, combined with appropriate biophysical modeling, can provide information about tissue microstructure, cellularity, and water content. Higher ADC values indicate more freely diffusing water molecules, whereas lower values indicate restricted or hindered diffusion.2should be obtained .The b-values refer to the strength of the MPGs used during DWI image acquisition. They are quadratically proportional to the strength of the MPGs and expressed in units of seconds per square millimeter. The b-value used in the DWI experiment affects its sensitivity to different types of diffusion, such as free diffusion, restricted diffusion, or pseudo-diffusion, due to microcirculation. Lower b-values make DWI more sensitive to microcirculation, resulting in “fast diffusion.” Higher b-values make DWI more sensitive to restricted diffusion due to barriers such as cell membranes and fibers, resulting in “slow diffusion.” Varying the b-values can separate the different types of diffusion, allowing more information about the underlying tissue microstructure to be obtained. In MSK DWI protocols, b-values up to 800 s/mmDWI can differentiate tissues and lesions based on their diffusion displacements. In the human body, biological tissues hinder diffusion due to their internal architecture, such as cell membranes and macromolecules, called “restriction of diffusion” or anisotropic diffusion. DWI visualizes slow or restricted diffusion regions as hyperintense and fast diffusion regions as hypointense. This ability to differentiate tissues and lesions using their diffusion properties enhances the capability of conventional MRI, allowing radiologists to recognize, characterize, and diagnose specific entities.DWI is increasingly popular for the diagnostic strategy and assessment of therapeutic response of bone and soft tissue lesions, where it aids in characterizing lesions, guiding biopsy, determining tumor grading, and tracking tumor response to treatment. However, DWI has limitations because values for benign and malignant masses can overlap. DWI has also been applied in assessing the presence and severity of sacroiliitis as well as articular cartilage integrity.Some research has been performed in RA patients using DWI, but most studies focused on the hand, wrist, and craniocervical junction and not on the knee. Some studies have been performed using DWI to gauge the presence of synovitis in wrist and hand arthritis in RA patients and compared it with contrast-enhanced MRI as the reference standard,DWI has been studied in JIA affecting the knee and sacroiliac (SI) joints. ADC values are commonly higher in the SI joints of patients with enthesis-related arthritis than in control subjects with mechanical lower back pain.The unique structure of different tissues imposes directional restrictions on fluid diffusion, introducing a spatial orientation dependence in diffusion known as anisotropy. Diffusion tensor imaging (DTI) combines multiple DWI scans acquired using multiple gradient directions to identify directionality of diffusion. A minimum of seven DWI scans (one b = 0 and six different directions) is required but usually does not provide sufficient SNR for reliable estimates of DTI parameters. Acquiring multiple averages or additional directions can help improve DTI robustness and accuracy at the cost of increased scan time. Mean diffusivity (MD) and fractional anisotropy (FA) are commonly reported DTI measures. MD is a measure of average diffusion over all directions and sensitive to changes in fluid diffusion. FA is a measure of the amount of anisotropy and sensitive to changes in tissue microstructure.Low SNR is a frequent problem with DTI. Signal attenuation due to diffusion in already signal starved tissues, and distortion and blurring artifacts inherent in echo planar imaging (EPI) trajectories reduce the accuracy and robustness of DTI estimates and require a multistep analysis pipeline that includes denoising, image registration, and registration to a non-EPI scan. Several toolboxes have been developed for analyzing DTI data; however, they are designed for brain or cardiac imaging and not optimized for the knee, which contains small structures that can be difficult to resolve due to magnetic field inhomogeneities and short-T2 tissues with low signal. Thus the user must check the performance of such tools at each step in the analysis pipeline to ensure accurate DTI measurements.DTI has been applied in MSK research to study muscle structure around the knee, knee joint articular cartilage, and synovitis.Synovitis is often associated with and can contribute to the progression of various types of arthritis including OA and RA. The intensity and extent of synovitis are determined with invasive methods such as histologic examination and DCE-MRI. DTI has been proposed as a noninvasive way to measure synovial inflammation, and its sensitivity has been repeatedly demonstrated.transand the MRI OA knee score (MOAKS) total knee synovitis score, and significant positive correlations between MD and Ktransand MOAKS  make it difficult to differentiate between synovial perfusion and joint effusion on non-CE images. IVIM is able to separate these signals. Introductory studies have demonstrated that IVIM parameter maps were more effective than DWI images at differentiating inflamed synovium and effusion in patients with JIA as shown inTreatment of meniscal disorders occasionally involves CE-MRI because knowledge of parameniscal perfusion is relevant to treatment planning. Guo et al demonstrated that the IVIM parameters f and D* can provide noninvasive perfusion measures to distinguish the red and white meniscal regions and detect microcirculation changes in the disordered meniscus.Quantitative double echo in steady state (qDESS) MRI is a relatively recent technique that can be applied to visualize multiple sources of pain in the knee. It is a 3D gradient-spoiled steady-state sequence that acquires an echo before and after a spoiler gradient. The acquired images can be assessed either separately or combined into one image. In the context of inflammation, a promising application of qDESS is the assessment of synovitis without the need for a contrast agent, making use of its DWI capability. The separation of synovial tissue and joint fluid is further enhanced by increasing the magnitude of the spoiler gradient between the two echoes. Using image processing, specifically to null the signal intensity from joint fluid, the delineation of synovium can be further enhanced.In a validation study of 30 patients with varying degrees of knee OA by de Vries et al, qDESS was compared with static CE-MRI as the reference standard. Using semiquantitative grading at different locations in the knee, it was found that synovitis sum scores on qDESS and CE-MRI correlated very strongly and that qDESS had a high sensitivity (1.00) and specificity (0.91) for detecting synovitis of a mild degree or higher. However, it was also shown that the severity of synovitis was systematically underrated with qDESS compared with CE-MRI .52Other applications of qDESS are morphological knee assessment in a short scan time. Semiquantitative grading of knee OA can be performed reliably for most features, although it is slightly less sensitive for BML detection. Finally, qDESS can be used to perform morphometric evaluation of the articular cartilage as well as quantitative assessment of cartilage and meniscus composition using T2 relaxometry. Because morphological assessment and T2 mapping can be performed concurrently within 5 minutes, qDESS has the potential to be an efficient method of analyzing knee pain in OA.DIR MRI is a relatively recent technique that also shows promise at assessing synovitis without the need for a contrast agent. An inversion recovery sequence can void the signal of a single tissue. DIR is an inversion recovery variant that uses two nonselective 180-degree inverting pulses instead of one, resulting in suppression of two tissues. Pilot studies have shown the promise of this technique in the assessment of synovitis by suppressing both joint effusion and fat tissue.Although advanced MRI techniques enable the assessment of knee structure and biochemical composition, techniques like perfusion and diffusion imaging are not yet widely implemented in clinical practice. PET imaging has been implemented in clinical practice for years, enabling metabolic evaluation. This is crucial for studying disease pathogenesis and treatment response because metabolic changes may precede structural or even biochemical changes in MSK disease.18F-FDG) is a glucose analog, showing areas of increased glucose metabolism. It is extensively used in oncology to detect metastases or tumor recurrence and to monitor response to therapy. However,18F-FDG uptake can also indicate acute-phase cellular response areas such as inflammation, and it has proven useful in identifying increased glucose consumption in cases of MSK inflammation and infection.18F-sodium fluoride (18F-NaF), is a bone-seeking agent, mainly absorbed at newly mineralized bone sites, and provides a marker of bone metabolism and remodeling.In the context of MSK imaging, two commercially available PET tracers are most commonly used. Fluorine-18 labeled fluoro-2-deoxy-2-D-glucose , given by the measured activity concentration in a volume of interest normalized to total injected activity and the patient's body weight. Mean and maximum SUV (SUVmean and SUVmax) measures allow for semiquantitative evaluation of tracer uptake with low precision error.K1, the extraction fraction (the fraction of [18F]NaF entering bone tissue and binding to bone matrix as opposed to being cleared into the plasma), andiK. Dynamic PET with18F-NaF is sensitive to variations in bone vascularization and metabolism in the knee joint among different knee tissue types.The use of dynamic PET and pharmacokinetic modeling of tracer uptake offers a fully quantitative approach to PET image analysis.Despite its many advantages, clinical adaptation of PET/MRI for MSK applications poses several challenges, such as MR-based attenuation correction in the vicinity of bone or metal implants, lack of standardization for quantitative analysis outside tumor lesions, relatively long image acquisition times, and limited availability of dedicated PET/MR machines.Adaptation of PET for MSK applications has increased over the past 20 years and continues to grow. Although the main indication for PET remains oncologic imaging, the application of PET/MRI in nononcologic MSK disease has increased in several research domains and clinical applications, including the assessment of OA, RA, and the detection of pain generators.PET/MRI of the painful knee has largely focused on OA, with PET/MRI having considerable potential as a tool for understanding disease pathogenesis and progression. Conventional imaging modalities like radiography are only able to detect structural changes that occur in the later stages of OA, whereas PET/MRI can potentially detect early disease-related changes and serve as a tool for monitoring the effect of new disease-modifying drugs at an early stage.18F-FDG uptake in knee OA patients found a diffuse increase in uptake compared with healthy controls, indicating the presence of inflammation suggestive of synovitis, in particular affecting the medial aspect of the knee, where knee OA is usually most severe.18F-FDG uptake in knee OA.Studies looking at18F-NaF PET was shown to correlate with histomorphometric assessment.18F-NaF PET uptake was found in regions with subchondral BMLs, osteophytes, and sclerosis compared with regions of normal-appearing bone  levels in RA patients.18F-FDG corresponding with sites of synovitis and tenosynovitis on CE-MRI.18F-FDG uptake correlated with CE-MRI inflammatory parameters and synovial thickness on ultrasonography as well as serum CRP and metalloproteinase-3 in both cross-sectional and longitudinal analysis.MRI allows for the detection of inflammation before the development of structural lesions visible on radiographs or computed tomography (CT). It has proven to be a powerful and efficient tool for detection and treatment monitoring in RA18F-FDG, new PET tracers with new molecular targets may offer greater sensitivity for the detection and grading of inflammation. For example, promising results were reported for11C-Choline as a marker for cellular proliferation,11C-(R)-PK11195,11C]DPA-713,In addition to18F-FDG PET/MRI is children with JIA. One study found that PET/MRI showed more joints with inflammation than physical examination, but these additional findings did not correlate with any clinical characteristics.A less extensively researched application for18F-FDG is able to detect increased metabolic activity secondary to neuroinflammatory processes in painful nerve lesions correlating with pain symptoms.Another field of research using PET/MRI is neuropathic pain secondary to nerve damage or nociceptive inflammatory pain. Although MRI is currently the main modality for high-resolution imaging of peripheral nerve abnormalities, it lacks specificity in detecting sources of neuropathic pain. Hybrid PET/MRI could help overcome this shortcoming by identifying nerve lesions through PET and high-resolution anatomical imaging through MRI at the same time.11C-PK11195 to image activated microglia and macrophages in neuroinflammation,18F-FTC-146 was recently demonstrated in a case report of successful treatment of a patient with refractory knee pain caused by an undiagnosed intra-articular synovial lipoma.18F-FTC-146 to identify the source of pain.Other notable tracers for neuropathic pain imaging includeThis review addresses the potential of perfusion and diffusion MRI and the combination of a variety of PET tracers with MRI for assessing inflammation and pain in the knee. These techniques have helped advance our understanding of inflammatory MSK disease, and they have opened up many new research opportunities. Each imaging technique has its own advantages and disadvantages and corresponding challenges for clinical implementation.


Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes. Few studies consider the valuable relations, including synergistic relations between medications, concurrent relations between diseases, and therapeutic relations between medications and diseases from historical EMR. This limitation restricts prediction performance and application scenarios.To address these limitations, we propose KAMPNet, a multi-sourced medical knowledge augmented medication prediction network. KAMPNet captures diverse relations between medical codes using a multi-level graph contrastive learning framework. Firstly, unsupervised graph contrastive learning with a graph attention network encoder captures implicit relations within homogeneous medical codes from the medical ontology graph, generating knowledge augmented medical code embedding vectors. Then, unsupervised graph contrastive learning with a weighted graph convolutional network encoder captures correlative relations between homogeneous or heterogeneous medical codes from the constructed medical codes relation graph, producing relation augmented medical code embedding vectors. Finally, the augmented medical code embedding vectors, along with supervised medical code embedding vectors, are fed into a sequential learning network to capture temporal relations of medical codes and predict medications for patients.Experimental results on the public MIMIC-III dataset demonstrate the superior performance of our KAMPNet model over several baseline models, as measured by Jaccard, F1 score, and PR-AUC for medication prediction.Our KAMPNet model can effectively capture the valuable relations between medical codes inherent in multi-sourced medical knowledge using the proposed multi-level graph contrastive learning framework. Moreover, The multi-channel sequence learning network facilitates capturing temporal relations between medical codes, enabling comprehensive patient representations for downstream tasks such as medication prediction. The availability of immense accumulation of electronic medical records (EMR) data, coupled with advancements in deep computational methods, has provided a solid foundation for intelligent healthcare applications, including disease risk prediction –3 and meHowever, the majority of existing methods are not specifically tailored to address scenarios where multiple medical experts collaborate in joint consultations for patients. In such situations, various types of medical knowledge, including common-sense medical ontology and empirical medical knowledge derived from historical EMR data, are taken into consideration during the medication decision-making process. As a result, conventional methods often yield suboptimal performance in these complex decision-making scenarios. Therefore, effectively capturing the intricate and diverse relationships between medical codes from multi-source medical knowledge to enhance medication prediction becomes a highly challenging yet significant task.As depicted in Fig. However, a limitation arises when attempting to transfer such forms of learned representations from one predictive task to another, such as from diagnosis prediction to medication prediction, despite using the same dataset. This necessitates repetitive model training to obtain medical code representations for each different downstream task. Moreover, purely supervised methods are ineffective in acquiring medical code representations when the downstream task is unknown. Therefore, there is an urgent need to develop a novel unsupervised method for learning the embeddings of medical codes based on the medical ontology graph. This approach would facilitate downstream tasks in diverse clinical scenarios, alleviating the limitations associated with the transferability and applicability of learned medical code representations.Furthermore, the aforementioned models primarily focus on capturing the inherent relationships among homogeneous medical codes within the hierarchical structures of medical ontology graphs. However, they tend to overlook the valuable correlative relationships between both homogeneous and heterogeneous medical codes that are implicitly present in historical EMR data. This historical EMR data is typically regarded as a valuable source of empirical medical knowledge. For instance, clinicians commonly administer multiple medications simultaneously to patients to enhance the therapeutic effect, indicating the presence of synergistic relationships between medications. Additionally, major diseases frequently co-occur with inevitable concurrent diseases or symptoms, highlighting the concurrent relationships between diseases. Moreover, in prescriptions, medications are prescribed for specific diseases or symptoms, reflecting therapeutic causal relationships between medications and diseases. Unfortunately, only a limited number of studies have explicitly represented and captured these meaningful relationships hidden within EMR data for medication prediction.To address the aforementioned limitations and enhance the performance of medication prediction, we present a novel multi-sourced medical knowledge augmented network, named KAMPNet. The network leverages multi-level graph contrastive learning to capture the diverse relations between homogeneous or heterogeneous medical codes and improve their representations. The main workflow of medication prediction using the proposed model is depicted in Fig. In summary, the technical contributions of this paper are as follows: (1) We propose KAMPNet, a multi-source medical knowledge augmented medication prediction network, which incorporates a multi-level graph contrastive learning framework. To the best of our knowledge, our model is the first to capture valuable relations between medical codes and augment their representations using a cascaded unsupervised approach on both ontology graphs and constructed medical codes relation graph. (2) We integrate different graph encoders, such as the graph attention network and weighted graph convolutional network, into the multi-level graph contrastive learning framework to consider the meaningful relation weights between heterogeneous or homogeneous medical codes. (3) We present a sequential learning network that combines the multi-source embedding vectors of medical codes into the patient representation, enabling the capture of temporal relations between medical codes for medication prediction.Medication prediction is a significant application of deep learning in intelligent healthcare systems, garnering considerable attention from researchers due to its practical importance. Researchers  have catInstance-based methods primarily focus on capturing the nonlinear relations between diagnosed disease status and the prescribed medications. For example, Zhang et al.  formulatNowadays, longitudinal sequential prediction models that consider the temporal relations between historical medical records have become prevalent in medication prediction tasks. Jin et al.  present Graph neural networks (GNNs)  have emeAlthough GNN-based models have achieved good performance in various tasks, they still face certain challenges. For instance, obtaining supervised labels can be complex and laborious, and models may require retraining for new tasks or feature changes. To address these issues, researchers have explored novel training methods for GNNs, such as graph self-supervised learning. Graph self-supervised learning is an unsupervised graph representation learning approach that relies solely on the topology and node information of the graph itself, without depending on explicit labels. For instance, Petar  incorporTo validate the effectiveness of the proposed KAMPNet, in this paper, we conduct the experiments on a publicly available dataset MIMIC-III  which isThe critical selection process of the experimental cohort is shown in Fig. Besides, similar to , the medThe proposed model KAMPNet mainly consists of three main substructures: medical knowledge-related graph construction, unsupervised contrastive learning for obtaining the enhanced medical code embeddings, and a supervised sequence learning network for medication prediction task.The hierarchical structures of the diagnosis classification system ICD-9 and medication classification system ATC imply the meaningful relations between medical codes, which have been constructed as the medical domain knowledge in previous studies , 7. SimiIn the actual clinical application scenario, patients will generally be diagnosed with a variety of diseases and given a variety of treatment medications during a single medical treatment, which also indirectly illustrates that there exist specific relations between diseases, medications, and between diseases and medications in the electronic medical records data. Therefore, based on the historical EMRs data generated by patients during each visit, and inspired by the dynamic weighted graph built on the history purchase records , we willFigure on (PMI)  commonlyThen, as shown in Fig. Different from the calculation method in , here, wentclass1pt{minimaTo avoid the dependency on the labels and make the learned medical codes representations directly applied in the downstream tasks such as medication prediction, the unsupervised graph contrastive learning method based on DGI  are incoAs illustrated in Fig. d indicates the dimension of the medical code embedding vector. Then the constructed medical ontology graph Similar to previous knowledge-enhanced methods presented in G-BIRT , the medThen as shown in Fig. In fact, there should be no restrictions on the choice of graph encoders. Here, to fully utilize the inherent topological hierarchical structure of the medical ontology graph for further capturing the implicit relations between medical codes, we directly incorporate the graph attention network (GAT)  used in Given the medical ontology graph K is the number of attention heads, k-th standardized relevance score, which can be calculated as follows:Due to the graph information aggregation in above two-way information transmission process requires considering the relation difference between medical codes, in this paper, we incorporate the graph attention network (GAT)  as the aeakyReLU  as the nTherefore, we can utilize the above graph encoder The graph pooling layer is mainly leveraged to compute the global feature vector In order to train the graph encoder end-to-end and learn to obtain the informative medical codes embedding vectors and the medical ontology graph embedding representation, we still utilize the maximization of mutual information  between Finally, the overall objective function is to maximize the mutual information in the form of JS divergence as follows:Through the above detailed graph contrastive learning on medical ontology graphs, we can obtain the knowledge augmented medical codes embedding vectors Factually, the obtained medical codes embedding vectors have infused the relational information from correlative medical codes, which can provide the initial embedding vectors for the nodes of the medical codes relation graph in this section. However, the medical ontology graphs do not explicitly provide the practically meaningful relations between medical codes and the implicit relations between homogeneous medical codes in the hierarchical topology structure . Therefore, in this section, the medical codes relation graph constructed on the basis of history EMR can directly models the explicit relations  between homogeneous and heterogeneous medical codes through quantitative statistics and then uses the graph contrastive learning framework shown in Fig. As shown in Fig. rk (GCN)  is incorFinally, the constructed objective function based on the discriminant equation is introduced to optimize the graph contrastive learning process for the medical codes relation graph. Similar to the contrastive learning framework for medical ontology graph, we still utilize the maximization of mutual information  between Then, the overall objective function is to maximize the mutual information in the form of JS divergence as follows:After continuous optimization and iterative calculation, the final relation augmented medical codes embedding representation vectors as shown in Fig. As illustrated in Fig. The knowledge augmented medical codes embedding vectors i-th visit record from the medical codes embedding vectors sets, i.e. the medical diagnosis codes embedding representations including Each hospitalized visit will generate different medical codes corresponding to different diagnoses and medications. The corresponding medical codes embedding vectors could be retrieved from the obtained medical codes embedding vectors sets, including knowledge augmented medical codes embedding vectors set t, firstly, we require integrating described multi-sourced medical codes embedding vectors together at history timestamps, including the history diagnosis codes embedding representation sequence When patient possesses multiple hospitalized visit records and needs to predict the treatment medications at current timestamp The hidden state vectors ork (Eq. ). Howevet and the real label Due to the medication prediction task belonging to the domain of sequential multilabel prediction, we utilize the binary cross-entropy loss The experiment details include three parts: evaluation metrics, benchmark models and experimental setting.N denotes the number of patients in the test set. And recall can be utilized to measure the predicted medications’ completeness:To evaluate the performance of the proposed KAMPNet, the Jaccard similarity score (Jaccard), precision-recall AUC (PR-AUC), and average F1 (F1) are adopted as the evaluation metrics. In practice, KAMPNet cannot wholly replace doctors and only screen possible medications as much as possible to assist physicians in prescribing medications for patients. Therefore, Jaccard should be one of the appropriate evaluation metrics. And it is defined as the size of the intersection divided by the size of the union of the predicted set Furthermore, for the medication prediction task, due to the number of positive and negative labels being imbalanced, the precision-recall curve utilized to calculate the PR-AUC has proved to be an appropriate evaluation metric.In addition, the predicted medications’ correctness should also be evaluated. Thus, the evaluation metric F1 is incorporated to evaluate the multilabel classification task comprehensively. Firstly, precision is generally adopted to measure the prediction correctness and can be calculated as follows:Then, the metric F1 can be computed as follows:LR [LR . It is aLR  to handlRetain [Retain . RETAIN LEAP [LEAP . Leap foGRAM [GRAM . It utilGAMENet [GAMENet . It emplG-BIRT [G-BIRT . G-BIRT GATE [GATE . GATE coTo verify the superiority of the proposed model KAMPNet on medication prediction tasks, we compare it with the following baseline methods used for medication prediction, which include one machine learning-based method and six deep learning-based algorithms:To ensure the rationality of experimental verification, the pretreated clinical patients’ EMR data are still randomly divided into training, validation, and test sets with 2/3 : 1/6 : 1/6 ratios and the experimental results are the average values across five runs of random grouping and training. Moreover, the dimension of hidden layers and hyper-parameters in KAMPNet is set as follows: in the unsupervised graph contrastive learning framework for medical ontology graph, the dimension of hidden layers are set to 128, the node embedding size of graph encoder GAT is set to 32, the attention head is set to 4; in the unsupervised graph contrastive learning framework for medical codes relation graph, the dimension of hidden layers and the node embedding size of encoder GCN are all set to 64; the dimension of hidden layers in temporal sequential learning network is set to 256. In addition, the training is performed using Adam  at a leaAs demonstrated in Table Secondly, the similarity between GAMENet and KAMPNet is that the obtained diagnoses sequence and treatment medications sequence all utilize a sequence learning model to capture the temporal dependencies between medical codes. The difference is that GAMENet does not construct the relations between medical codes directly but constructs the medication-visit graph for capturing the indirect relations between medications for later retrieval using the attention mechanism. In other words, GAMENet considers modelling the co-occurrence relations between medications, while our KAMPNet takes the multiple relations between heterogeneous or homogeneous medical codes into consideration. Therefore, on the medication prediction task, our KAMPNet outperforms GAMENet by 4.18%, 3.28%, 3.41% on Jaccard, F1, PR-AUC, respectively. Unlike GAMENet, G-BIRT does not directly construct the relation between medical codes. Instead, it enhances the relation between medical codes using a pre-training method on the medical ontology graphs, including diagnosis code ontology graph and medication code ontology graph, which can make full use of the EMR data of single hospitalized patients. However, medical code embedding representation learning in G-BIRT mainly relies on the medication or diagnosis labels provided in history EMR and ignores the inherent relations between medical codes implied in EMR data. Although GATE also considers the relations between medical codes by building the co-occurrence graphs for each patient from the global guidance relation graph, it still neglects the infusion of valuable information of correlative medical codes from the medical codes relation graph and relies on the supervised label of the medication prediction task. Therefore, KAMPNet performs better than the latest knowledge-enhanced algorithm G-BIRT and GATE, and its Jaccard, F1 and PR-AUC improve at least by 2.31% on Jaccard, by 1.39% on F1 and by 1.08% on PR-AUC, respectively.The critical reason that our proposed KAMPNet achieves the best performance compared with baseline models could be summarized as follows: (1) With the help of a multi-level unsupervised contrastive learning framework, it can capture the relations between medical codes and augments the medical codes representations based on the medical ontology graphs. (2) Then, the relations between medical codes implicit in the constructed medical code relations graph are further captured to learn more informative medical codes embedding representation vectors, contributing to the downstream tasks such as medication prediction. (3) The incorporated sequential learning network can further combine the supervised medical codes representations with the learned knowledge and relation augmented medical codes representations and then captures the temporal relations between medical codes for downstream medication prediction task.To verify the effectiveness of the critical components of KAMPNet and analyze their influence on the performance of medication prediction tasks, we conduct an ablation study to explore further the necessity of the proposed model components in our multi-level graph contrastive learning framework on the multi-sourced medical knowledge for the medication prediction task.As illustrated in Table Therefore, the proposed KAMPNet in this paper achieves the best performance only when the model’s components complement each other.In the construction process of the medical codes relation graph, considering the adverse effects of the noise introduced by relying solely on the statistical quantitative computation method and ignoring medical expertise, the graph sparsity factor entclass1pt{minimaentclass1pt{minimaTable The multi-level unsupervised contrastive learning framework described in Though the above extensive experiments have testified the efficacy of KAMPNet for reasonable medication prediction, there are still some limitations on the results due to the complexity of the healthcare system. First, the real clinical decision-making process is full of uncertainty caused by various factors such as the professional level of doctors, the social environment, and the economic conditions of patients, which might cause medication prediction bias. Thus, in the future, we would consider cultivating more advanced methods to reduce the impact of bias on the prediction outcomes. Second, the medication prediction model is essential for hospitals, clinics, and retail pharmacies. Since we only utilized the MIMIC-III dataset to testify the efficacy of our proposed model, the prediction outcomes of the model are not guaranteed on the data from other sources. However, provided the data has similar composition and structure defined in the previous section, the model should be applicable after fine-tuning. In the future, we will further evaluate our model on the datasets from clinics or retail pharmacies.Additionally, the use of machine learning models in healthcare presents several ethical implications, such as privacy and data security, transparency and interpretability, informed consent and autonomy, human oversight and decision-making, etc. And here are some corresponding potential approaches to address them: (1) Cultivate encryption techniques and secure data storage protocols to safeguard sensitive data; (2) Develop explainable and interpretable machine learning models that provide insights into the factors influencing predictions; (3) Educate patients about the use of machine learning models in their healthcare and provide clear explanations of the benefits, risks, and limitations; (4) Offer patients the option to opt out of using machine learning models in their treatment decisions if they have concerns or preferences; (5) Encourage interdisciplinary collaboration between healthcare providers and data scientists to ensure a holistic approach to patient care. Thus, in the future, collaboration, transparency, and an ongoing commitment to ethical practices are essential to ensure the responsible and beneficial use of machine learning models in healthcare.For the practical applications of model KAMPNet in the future, clinically, it could be utilized to assist doctors in making informed medication decisions for patients according to electronic medical records (EMR). In addition, the model could also be used to assist the hospital pharmacy management department, clinic, or retail pharmacies to predict the medications in advance for stocking. However, the model needs to be retrained using the datasets from different application scenarios. Thus, in the future, a user study pertaining to the practical application scenario should be undertaken for validating the model’s feasibility in practice. Additionally, owing to the limited size of the population cohort itself, and the experimental outcomes of existing approaches, the effect of the size of experimental samples on the prediction outcomes is neglected in our manuscript. In the future, the above factors will be taken into account once applied in the new dataset or private dataset for the generalization.In this study, we propose a multi-sourced medical knowledge-augmented medication prediction network. Expressly, we incorporate a novel multi-level graph contrastive learning framework for fully capturing the valuable relations between medical codes implicit in the multi-sourced medical knowledge. The framework firstly leverages the local graph contrastive learning on the medical ontology graphs to learn the knowledge augmented embedding vectors of diagnosis codes and medication codes, which factually have infused the information of correlative medical codes into each other in the learning process. Then, the medical codes relation graph is constructed and utilized to learn the relation augmented medical codes embedding vectors using the graph contrastive learning framework, which aims to capture the global relations between homogeneous and heterogeneous medical codes. Finally, the multi-channel sequence learning network is presented to capture the temporal relations between medical codes, by which we can get a comprehensive patient representation for downstream tasks such as medication prediction. We evaluate the performance of the proposed KAMPNet on a real-world clinical dataset, and the experimental results show that our model achieves the best medication prediction performance against baseline models in terms of Jaccard, F1 score, and PR-AUC.With the help of the presented framework, in the future, we can introduce more related medical domain knowledge, such as the medication-related molecular graph and the bipartite graph representing adverse medication reactions. In addition, we will cultivate more advanced algorithms to better mine the insightful information implicit in the multi-sourced medical knowledge and explore how to determine the importance of multi-sourced medical knowledge automatically.