Nature, Liu and co-workers show that high lactate production by tumor cells promotes premature mitotic escape and further cell proliferation by facilitating the binding of Ubiquitin Conjugating Enzyme E2 C (UBE2C) to the anaphase promoting complex (APC/C).1 This is important as it suggests cancer metabolism not only to be a consequence of tumor growth, but also one of its drivers.In a recent study published in 2 High quantities of lactate are produced through glycolysis in muscle during exercise, through bacterial lactic acid fermentation, and perhaps most famously, by rapidly growing tumor cells. However, lactate is also the metabolic end-product and/or energy substrate for a range of human cell types under physiological conditions, as exemplified by lactate shuttling between neurons and astrocytes, and many other cell types.3 Importantly, lactate has been shown to play several non-metabolic roles, inducing for example histone lactylation, and thereby altering the transcription of a range of genes.4 Lactate can also signal by binding to the G-coupled receptor HCAR1 (formerly known as GPR81), which inhibits the lipolytic activity of adipocytes and thereby reduces circulating fatty acid levels during intense exercise.2 As shown in the recent article by the lab of Ed Chouchani, published in Nature on March 10, 2023, lactate can now add cell cycle regulation to its impressive list of biological functions1  of sodium L-lactate, but not equimolar amounts of its enantiomer D-Lactate or of sodium chloride, can promote premature mitotic checkpoint escape by enhancing the binding of UBE2C to APC/C. Specifically, they unveil an intricate cascade of events that is initiated by lactate forming an inhibitory complex with zinc ions in the active site of SENP1, a protease that helps retain cells at the mitotic checkpoint by continuously deSUMOylating APC/C. The binding of zinc and lactate to SENP1 inhibits its activity, which stabilizes APC/C SUMOylation levels and leads to APC/C undergoing the necessary conformational changes to allow UBE2C binding and the subsequent ubiquitin-mediated degradation of cyclin B1 and securin. The authors go on to show that across a range of cancer cell lines, a higher mitotic rate is associated with increased intracellular lactate levels, and that endogenous lactate production is enhanced during the later phases of cell cycle progression, although it would have been nice to see the levels in unsynchronized cultures as a control. Reducing endogenous lactate levels by either inhibiting lactate formation, or increasing lactate by blocking its export, greatly affected mitotic progression, with increased levels of lactate promoting premature cell cycle progression and enhanced cellular proliferation. Most importantly, the authors show that lactate accumulation could allow tumor cells to prematurely escape even a mitotic blockage induced by chemotherapy agents targeting microtubules, by allowing cyclin B1 to be degraded and cell cycle to proceed despite the absence of correct microtubule assembly, a well-known phenomenon termed mitotic slippage. Although the bulk of the presented data stems from cultured cancer cell lines, the findings may, if confirmed in vivo and in patient samples, open up a renewed interest for using so-called aerobic glycolysis inhibitors to lower tumor lactate production in parallel with more cytotoxic chemotherapy treatments.5 However, while the present study offers great biochemical detail into the binding of lactate to the SENP1 active site and subsequent cellular consequences, it does leave some questions open. For one, as the study uses only cultured cancer cell lines, and shows little results from primary cells, it remains unknown if the revealed mechanisms are only active in transformed cancer cells in culture, or also are translatable to tumors in vivo, and/or to primary non-transformed cells that produce high levels of lactate. Examples of the latter include not only hypertrophic adipocytes mentioned by the authors, but also neurons, activated immune cells and stem cells. Does lactate produced during cell growth function as a universal signal for a beneficial cellular environment for cell division, or is it merely another metabolite hijacked by tumor cells to serve their ultimate purpose? While the altered and highly glycolytic metabolism of rapidly growing cells to date have been considered mainly a consequence of rapid cellular proliferation, the data presented by Liu and colleagues now suggests the converse to be possible too, with growth-activated lactate production by itself promoting cell cycle progression. More research is clearly needed to understand the exact cellular and environmental conditions that allow lactate to highjack mitotic control, and why such a regulatory function would exist in normal cells. Despite lactate being our first and perhaps also best-recognized cancer-associated metabolite, its potential new effects on mitotic cell cycle progression might, upon validation, produce a much-needed renaissance in the interest to develop drugs that target lactate synthesis and uptake, giving new attention to our oldest star within cancer metabolism.In addition to comprehensively delineating the somewhat complex mechanism of lactate-mediated cell cycle regulation in great detail, a strength of the current study is the correct use of controls for osmolarity throughout the study. By using both equimolar amounts of D-lactate, pyruvate or sodium chloride as controls, the study avoids some of the recently highlighted pitfalls of accidentally altering cellular molarity when supplying living systems with large quantities of sodium L-lactate.


Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning—a subset of machine learning—and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine. However, this approach has presented substantial difficulties for clinical oncologists, who are expected to develop a precise treatment plan for each patient based on these complex clinical data.In oncological practice, personalized medicine—which has traditionally relied on the molecular characterization of tumors using genomic, and proteomic techniques, aims to customize treatment and ultimately guide clinical decisions and therapeutic interventions4. While ‘liquid’ biopsies are a safer alternative to procuring solid tumor tissue and have shown promise in personalized medicine owing to the presence of circulating tumor DNA/RNA8, both liquid and tissue biopsies capture only a small spatiotemporal snapshot of notably heterogeneous and constantly evolving solid tumors9. Thus, tumor heterogeneity presents a barrier to personalized treatment10Personalized medicine in oncology traditionally involves invasive procedures to gain access to cancerous tissues, which only provides insight into small parts of the tumor and occasionally may be complicated by tumor placement and the potential risks of surgical biopsies16. This will only serve to increase the diagnostic and prognostic accuracy of radiomics and strengthen associations between extracted data and biological and clinical endpoints to expedite and further optimize personalized treatment.AI-based analyses of radiological features from standard-of-care (SOC) images can serve as biomarkers and could play a considerable role in overcoming the challenges of personalized medicine. Although there are currently limitations to the technology, particularly with smaller tumors, the next-generation imaging analysis could ultimately be used to predict responses to therapies and develop more precise treatment plans for individual cancer patients in the era of personalized medicine. More recently, efforts to standardize radiomics via the integration of AI algorithms have steered the field of radiomics away from handcrafted features, thereby reducing bias and increasing accuracy and generalizability17. While routine medical imaging  can capture the overall spatial and temporal heterogeneity of solid tumors, qualitative visual assessments lack the granularity and objectivity to assess inter- and intratumour heterogeneity within the complex tumor microenvironment (TME)9.Diagnostic imaging—a qualitative and non-invasive method for assessing internal structures—has historically been the cornerstone of analytical and diagnostic workflows in oncology and helps to inform the course of treatment in clinical oncology practice24. Furthermore, it has been established that heterogeneous tumor features are valuable prognostic indicators of variability in tumorigenesis, treatment efficacy, metastatic potential, and patient outcomes28.Solid tumors not only vary in size and shape over time but are also known to be both phenotypically and genotypically heterogeneous, where tumor cells can vary in cell type, genomic sequence, gene expression, vascularization, oxygenation, metastatic potential, and response to treatment within the TME29. First proposed in 2012, radiomics leverages the heterogeneous nature of solid tumors by converting medical images into “mineable data” via the high-throughput extraction of high-dimensional quantitative features32. Burgeoned by increasing computational capabilities, radiomics has been used for nearly a decade to link quantitative features of tumors  to clinical endpoints, such as response to therapy, metastases, and survival in a variety of cancers using multiple imaging modalities35. Despite the utility of this approach36, issues such as inconsistencies in image acquisition; motion artifacts; variability in segmentation and image processing; the limited reproducibility of features across centers, studies, and software tools; and analytical variability have been barriers to the adoption of radiomics into clinical workflows (please refer to the 38.Radiomics is an evolving field of study in which large numbers of quantitative features are extracted from standard-of-care (SOC) radiographic images and linked to clinical outcomes using either handcrafted or machine learning methodologies40. The integration of DL into radiomics workflows  bridges a gap in the field and mitigates many of the prevailing inconsistencies with “handcrafted” radiomics, in which human variability can introduce error at any point in the radiomics pipeline38. Notably, although radiomics and deep radiomics have been well investigated in a variety of cancers and have been used successfully to assess treatment response and predict survival in patients enrolled in clinical trials41, to the best of our knowledge, radiomics and deep radiomics have not yet been utilized in real-world clinical workflows.Deep learning—a subset of machine learning that uses multilayered artificial neural networks to transform input data  to output data  while learning increasingly higher-level features in each layer—has been used to automate and optimize the extraction of features from medical images for nearly a decadeThus, while the integration of radiomics and AI offers oncologists a unique opportunity to predict personalized responses to immunotherapy in lung cancer prior to treatment, there are some barriers to incorporating these techniques into real-world clinical scenarios. The present paper aims to briefly review the current application of these technologies in lung cancer, as well as current obstacles to integrating radiomics techniques into clinical lung cancer workflows  have revolutionized lung cancer treatment as an alternative to cytotoxic chemotherapies67. A DelRadX signature based on CT images at baseline and at the end of the second treatment cycle performed satisfactorily in distinguishing between responders and nonresponders when used in combination with the clinical factor of distant metastasis 34. Furthermore, this DelRadX signature was significantly more predictive of the response to immunotherapy than PD-L1 expression alone (p < 0.001). Khorrami et al.68 found that DelRadX using intranodular and perinodular texture features of malignant NSCLC nodules from CT images predicted the response to immunotherapy and overall survival (OS). Sun et al.69 determined the radiomic signature of CD8 cells using RNA-seq data combined with a radiomic analysis of solid tumors in a variety of cancers (including lung cancer) and validated it using two independent cohorts of patients. The radiomic signature of CD8 cells was predictive of the immune phenotype (dense vs low CD8 cell infiltration). In this study, a higher radiomics score—derived from five radiomic features extracted from each lesion, two discrete labels related to lesion location, and one imaging acquisition-related variable—was significantly associated with the response to anti-PD-1 or anti-PD-L1 monotherapy at both 3 and 6 months after the start of treatment, as well as OS.Delta radiomic (DelRadX) features—changes in radiomic features over time—have been used to predict clinical outcomes in a variety of cancers70 developed a radiomics biomarker from CT imaging by training a model on all lesions  to discern progressive disease. This biomarker was significantly associated with response vs nonresponse to immunotherapy in NSCLC patients. More specifically, tumors with increased morphological heterogeneity, nonuniform density patterns, and compact borders were more likely to respond to immunotherapy, while more compact and spherical profiles were associated with better response in nonresponding tumors. A gene enrichment analysis was performed to define the biological basis of the radiomic biomarker and found significant associations with pathways involved in mitosis, indicating a relationship between increased cell division and response to immunotherapy. Several other studies have used radiomic features to stratify NSCLC patients and to significantly predict survival outcomes in patients treated with immunotherapy72. Furthermore, other studies demonstrate that prognostic biomarkers perform best when combining radiomic, genetic, and clinical data, highlighting the complementary nature of these analyses55.Additionally, Trebeschi et al.73. The deep learning model 3D-DenseNet was used to estimate the target tumor area in CT images from 327 NSCLC patients with TMB data and identified 1020 deep features to distinguish between patients with high and low TMBs. The TMB radiomic biomarker was a significant predictor of immunotherapy efficiency and distinguished between high-TMB and low-TMB patients in the training and test cohorts better than histological subtype. Moreover, the TMB radiomic marker was more robust than both the radiomic and clinical models.Tumor mutational burden (TMB) has also been shown to be a significant predictor of immunotherapy efficacyEGFR, are known to be associated with low response rates to anti-PD-1/PD-L1 immunotherapy74, DL models that predict mutation status from imaging could also predict response to immunotherapy. A study that used a PET/CT-based DL model with high accuracy in predicting EGFR mutation status across patient cohorts demonstrated a significant association between high EGFR mutation status and low durable clinical benefit, low PD-L1, high hyperprogression, and lower progression-free survival (PFS) in immunotherapy patients, indicating that EGFR mutation status assessed using SOC imaging could indeed serve as a biomarker to predict response to immunotherapy54.Last, as somatic mutations, such as 76. In this subsection, we will discuss the use of radiomics and DL technologies in supporting treatment decision making and predicting the best combination therapy to suit each patient.Owing to the heterogeneous nature of lung cancer and the substantial number of patients who do not experience clinical benefit from immunotherapy alone, combining therapies can be an effective way to improve outcomes77 used a CD8 T-cell-associated radiomics signature to predict lesion response in irradiated and abscopal lesions using clinical data from patients with advanced solid tumors (including lung) from six independent clinical studies of combined radiotherapy and immunotherapy. The authors found that CD8 radiomics scores exhibited significantly higher tumor responses  and that more heterogeneous CD8 radiomics scores across lesions were associated with mixed response or uniform progression, poor PFS, and OS. Additionally, heterogeneous CD8 radiomics scores based on the entropy of the distribution were significantly associated with the response evaluation criteria in solid tumors (RECIST)-based response in abscopal tumors. Not only can this study help to inform prognostic features for combined therapy and inform the choice of target lesion, but this study demonstrates that a radiomics score previously validated in a cohort treated with immunotherapy alone could be predictive in the context of combined therapies69.A study by Sun et al.p < 0.05) in patients treated with both chemoradiation and chemoradiation + immunotherapy78. The RRS also effectively stratified between low and high risk and was significantly associated with OS in the low PD-L1 group.Another study that used radiomics to study the response to combined therapies showed that a radiomic risk score (RRS), calculated using radiomic textural patterns within and around NSCLC nodules from pretreatment CT images, was found to be significantly associated with PFS and OS  mutations; cytokine expression ; and point mutations, deletions, or homozygous or heterozygous loss of beta-2-microglobulin (B2M)84. Similarly, molecular mechanisms and tumor characteristics such as the overexpression of oncogenes , EGFR mutations and associated changes in the TME86, or tumor hypoxia87 can also be predictive of the response to immunotherapy.Acquired and inherent resistance mechanisms continue to be a considerable factor in poor lung cancer prognosis88 in NSCLC could be leveraged to identify prognostic biomarkers or to assess the multivariate performance of radiomic signatures10. Furthermore, existing knowledge of somatic mutations involved in resistance to targeted therapies74 or the role of immune cells  in tumor growth, metastasis, and resistance to immunotherapy89 could be leveraged to develop radiomic signatures that predict resistance to therapy.While there is a growing body of research that links radiomic features of tumors and the TME to tumor genotype, histology, immune state and clinical end points, studies that investigate radiomic biomarkers of resistance — both handcrafted and AI-enabled — are sorely lacking. The use of DelRadX to elucidate genotypic and phenotypic changes in response to treatment over time could expose underlying mechanisms of resistance. Public datasets for gene expressionThe early detection of treatment-related adverse events is crucial for improving patient outcomes. Radiomics can also be applied to predictions of life-threatening adverse events such as cardio-toxicity, pneumonitis, and hyperprogression, as well as the misinterpretation of pseudo progression, to optimize patient care.91 or risk for developing severe immune-related adverse events (irSAEs)92. By capturing features at baseline that are predictive of potential irSAEs, treatment plans could be optimized early to minimize risk. For example, Mu et al.92 found that creating a radiomics nomogram that included a radiomics score based on features extracted from baseline , the type of immunotherapy, and dosing schedule effectively predicted patients with and without irSAEs with AUCs of 0.92, and 0.88 in the testing, and validation sets, respectively. However, the sample size of this study was relatively small for positive cases. As rule-of-thumb guides for binary classification studies suggest that sample size should be 10–15 times that of the number of features used, studies with smaller samples sizes should be interpreted with caution93.Several studies suggest that PET and CT-based radiomic features in NSCLC patients undergoing immunotherapy could be used to predict inflammatory conditions such as immunotherapy-induced pneumonitis94 extracted 198 intratumoural and peritumoural radiomic textural patterns and tortuosity features of the nodule-associated vasculature from pretreatment CT scans. They found that the top features associated with hyperprogression were able to distinguish between hyperprogression and other patterns with AUCs of 0.85 in the training set and 0.96 in the validation set. A study by Tunali et al.95 created rapid disease progression phenotypes composed of time to progression (TTP)/tumor growth rates and hyperprogression in NSCLC patients being treated with immunotherapy using the following baseline predictors: patient demographics, clinical data, driver mutations, hematology data, and radiomic features from CT scans. As a result, the authors identified several effective clinical-radiomic models that predicted rapid disease phenotypes with AUC values of 0.80–0.86 and classified patients with TTP of <2 months and hyperprogression with 73 and 82% accuracy, respectively. Finally, DL biomarkers for somatic mutations such as EGFR have also been shown to be associated with high hyperprogression in patients undergoing immunotherapy54 and thus show promise as noninvasive biomarkers for predicting adverse events.Biomarkers that differentiate between pseudoprogression, progression, and hyperprogression during immunotherapy are lacking. Hyperprogression is an adverse reaction to anti-PD-1/PD-L1 immunotherapy in some NSCLC patients and is associated with significantly shorter survival. Vaidya et al.96. Here, we will discuss the role of radiomics and DL in elucidating TME characteristics related to treatment outcomes.The tumor microenvironment (TME) is known to promote the growth and metastasis of lung tumors and has recently gained recognition as an important factor in understanding tumor behavior and response to immunotherapy16 for review). Features of the TME have been specifically shown to have significant predictive and prognostic value in lung cancer94. For example, radiomic features of the tumor rim  have been shown to be predictive of distant metastases in NSCLC, where the combination of clinical data and rim signatures was the most effective for stratifying patients97. Likewise, Hosny et al.60 used DL to create prognostic signatures of quantitative imaging features and found that the tumor–stroma interfaces exhibited the largest contributions to the prognostic signature, highlighting the importance of the TME in patient stratification. Furthermore, the authors created activation heatmaps overlaid on CT images to visualize the “importance” of each node or voxel relative to the final prediction, both within and beyond the tumor. A subsequent analysis that disregarded the data beyond the tumor resulted in a substantial drop in prognostic power, thereby confirming the importance of textural features in the tumor-surrounding region. A radiomics model developed by Tang et al.98 characterized the immune state of the TME in NSCLC patients using baseline CT images, percent tumor PD-L1 expression, and the density of tumor-infiltrating lymphocytes (CD3) to stratify patients into four clusters that were significantly correlated with overall survival (OS)82. The most favorable outcome group was characterized by low CT intensity and high heterogeneity, low PDL1, and high CD3 infiltration, suggesting a high immune-activated state. Finally, the infiltration of CD4 and CD8 T cells is also known to be an important mediator of responses to immune checkpoint inhibition96 and could make effective targets as biomarkers.Peritumoral radiomics—the use of radiomic techniques to assess heterogeneity of the peritumoral environment—has been investigated in a wide variety of cancers, in which the inclusion of the peritumoral region increases the predictive power of radiomic signatures compared to intratumoral signatures alone in a variety of cancers  of 21,766 genes. Notably, features III and IV of the four-feature radiomic signature were strongly correlated with cell cycling pathways. Grossman et al.55 linked numerous imaging features based on intratumoural heterogeneity to RNA polymerase expression, the autodegration pathway E3 ubiquitin ligase COP1, p53, cell cycle regulation checkpoints, TGF-β signaling, mitochondrial pathways, lipoprotein metabolism, TRAF6-mediated NFkB activation, and axon guidance. Furthermore, several imaging features were linked to EGFR, KRAS, and TP53 mutants, as confirmed by Sanger sequencing. Another study used a 3D-CNN and transfer learning approach to identify both prognostic signatures using NSCLC CT images and correlations between the radiographic phenotypes quantified by CNN and global gene expression patterns using a pre-ranked GSEA60. Similar to other radiogenomic analyses in NSCLC, the authors found that the most significantly enriched pathways were linked to the cell cycle and transcriptional processes.Tumor genotype plays an important role in personalized treatment for lung cancer patients, where mutations in common proto-oncogenes and oncogenes such as 99. The presence of BrMs plays a major role in guiding treatment, as brain-penetrating therapies are generally necessary in combination with immunotherapy to improve sensitivity to treatment. Radiomics has been used extensively to predict the local response for BrMs after stereotactic radiosurgery100, differentiate between BrMs and glioblastoma101, predict the primary tumor of origin102, assess the diagnostic ability of BrMs to predict EGFR mutation status in primary lung cancer BrMs103, and predict survival104. While the risk of BrMs in NSCLC patients has been predicted using a nonradiomic nomogram105 and based on total lesion glycolysis and metabolic tumor volume106, very few studies have used radiomics or DL to predict the development of brain metastases. To the best of our knowledge, no studies have leveraged DL to this end.Lung cancer is one of the most common primary tumors leading to brain metastases (BrMs), accounting for more than 50% of all brain tumors107. Additionally, patient smoking status and histology were both independent predictors of BrMs. In contrast, another study found that CT-based radiomics features of primary NSCLC did not improve a model based on clinical data108. Nonradiomic prognostic biomarkers of BrMs have been developed by characterizing the functional gene expression signatures of lung tumor tissue, BrMs, and their respective TMEs, indicating that the immune and fibrosis status of BrMs should guide therapeutic strategies109. The TMEs of both lung tumors and BrMs were enriched for genes associated with cancer-associated fibroblasts and the extracellular matrix and endothelium, suggesting that fibrosis and angiogenesis play a key role in tumor progression and metastasis. The expression of tumor proliferation genes was higher in the primary tumor. The authors also noted significantly higher expression of epithelial to mesenchymal transition (EMT) genes  in both tumors than in the TMEs, as EMT is thought to be the most common mechanism for metastasis110. Assessing differentially expressed genes from lung tumor cores between slow and fast metastatic cohorts revealed a metastatic signature gene set that also individually predicted survival in both the study cohort and a lung adenocarcinoma cohort (n = 501). These molecular biomarkers could potentially be incorporated into radiomics and deep learning models to predict BrMs in lung cancer patients.One study found that a radiomics score—based on seven potential predictors of BrMs in curatively resected locally advanced NSCLC patients—was significantly associated with BrMs in both the training and validation cohorts, in which combining clinical risk factors and radiomics data improved performanceWhile new technologies such as radiomics and DL have substantially advanced the field of personalized immunotherapy treatment in lung cancer, integrating these technologies into clinical workflows will presumably be an uphill climb. Several challenges will need to be addressed before we can create a roadmap to clinical application.3, have worked to standardize radiomic features37, and have devised methods of moderating ‘center effect’ to reduce multi-center variability112, there are still multiple points in the handcrafted radiomics pipeline where errors and variability can be introduced37. While DL methods do mitigate issues with handcrafted radiomics—such as time-consuming manual feature selection and inter-observer and intra-observer variability—by minimizing operator input113, working with DL algorithms is a specialized skill and may not be readily available to radiologists and clinicians. To this end, public dataset repositories such as RIDER60 and The Cancer Imaging Archive114 can be leveraged to validate the performance of radiomic signatures, while open-source toolkits—such as PyRadiomics115, RaCaT116, and ImaGene117—allow researchers to use radiomics without having to develop feature pipelines from scratch118. Additionally, “how to” guides for implementing radiomics in medical imaging119 that include tools for both handcrafted and deep radiomics provide a valuable roadmap for those starting out. The use of AI-based tools such as I3Lung—which uses biological, molecular, radiological, and clinical data from more than 2000 NSCLC patients to predict individual responses to immunotherapy—could be a straightforward and cost-effective alternative to developing de novo DL models120. Lastly, using a transfer learning paradigm on pretrained DL models trained on large datasets makes DL more accessible while mitigating the need for large datasets in clinical workflows  paid software programs, (ii) free programs [see paper for suggestions] that allow radiomic feature extraction using a graphical user interface, or (iii) the development of coding skills necessary to use MATLAB or Python platforms. To become involved in AI, the authors offer a similar three-tiered approach: (i) become part of a data science collaboration, (ii) acquire statistical skills necessary to perform AI tasks without code, and (iii) learn coding language such as Python. Bera et al.16 Note that the pathway to regulatory approval is the main roadblock to adopting AI-based predictive and prognostic tools into clinical workflows. Furthermore, the authors state that while billing and reimbursement present yet another challenge, pursuing a regulatory pathway for lab-based diagnostic tests could be a viable option. It is also possible that efforts to unpack the “black box” of AI will increase transparency and explainability on the road to approval (see Koçak et al.No two cancers are alike. Deep radiomics holds the promise of predicting personalized responses to immunotherapy in lung cancer patients using SOC images, thereby expediting treatment and tailoring treatment to individuals. Although the road to adoption could be long, precision oncology will undoubtedly benefit from more objective and accurate characterizations and predictions of disease, ultimately serving to improve patient outcomes.Supplementary


The process of macroautophagy plays a pivotal role in the degradation of long-lived, superfluous, and damaged proteins and organelles, which are later recycled for cellular use. Normal cells rely on autophagy to combat various stressors and insults to ensure survival. However, autophagy is often upregulated in cancer cells, promoting a more aggressive phenotype that allows mutated cells to evade death after exposure to therapeutic treatments. As a result, autophagy has emerged as a significant factor in therapeutic resistance across many cancer types, with underlying mechanisms such as DNA damage, cell cycle arrest, and immune evasion. This review provides a comprehensive summary of the role of autophagy in therapeutic resistance and the limitations of available autophagic inhibitors in cancer treatment. It also highlights the urgent need to explore new inhibitors that can synergize with existing therapies to achieve better patient treatment outcomes. Advancing research in this field is crucial for developing more effective treatments that can help improve the lives of cancer patients. Cancer remains one of the most significant health concerns and a leading cause of death, with a reported worldwide mortality rate of nearly 10 million in 2020 . AdvanceTherapeutic resistance is the resistance towards a therapy that the cells either innately have or develop after exposure to the treatment. Many cellular adaptions may affect this, one of which is autophagy, the focus of this review. Addressing the role of autophagy in drug resistance has been a primary focus of efforts in recent years. Autophagy is a molecular process where organelles and non-essential proteins are degraded to provide energy and nutrients for the cell in response to cellular and environmental stresses. There are three mammalian pathways: chaperone-mediated autophagy (CMA), microautophagy and macroautophagy. Of these three types of autophagy, macroautophagy has been the most studied and will be referred to as autophagy hereafter. All three pathways vary in how they transport and target proteins, but they all culminate and achieve the degradation of materials at the lysosome.Understanding the role of autophagy in various cancers has led to the research and development of autophagy and lysosomal inhibitors, where pre-clinical studies have demonstrated promising results, with many agents progressing to clinical trials as stand-alone treatments or in combination with standard of care therapeutics. Of note, most current lysosomal inhibitors are repurposed agents, previously being used for diseases like malaria, and have thus progressed to the clinic faster than those in early development. The research discussed in this review highlights the advances within this area as well as the potential limitations of their use. Examination of recent evidence for their use will be used to reveal current knowledge gaps that contribute to their current limitations and how these can be potentially overcome. Thereby, gaining a better understanding and sense of the autophagic process being an effective treatment target for advanced cancers in the future.Autophagy is one of two central and essential degradative cellular processes, the other being the highly specialised and specific proteasomal degradation pathway (ubiquitin-proteasome system). Autophagy is mainly responsible for degrading superfluous, long-lived, damaged, dysfunctional proteins, protein aggregates, oligomers, and organelles. This process helps to maintain cellular homeostasis and is essential in preventing the toxic build-up of these materials.It primarily functions to break down proteins and organelles into their base components, like glucose, ATP, amino acids and fatty acids , as a sovia the lysosome without the activation of hierarchical Atg’s.Macroautophagy is a process where an autophagosome engulfs organelles and proteins by developing a double membrane vesicle called the phagophore, which delivers cytoplasmic materials to the lysosome  activation, which block mTORC1. All result in releasing the inhibitory effects of mTORC1 and facilitating autophagy activation through the ULK1/2 complex  complex, which includes focal adhesion kinase family interacting protein of 200 kDa (FIP200 RB1CC1), autophagy-related protein 13 (Atg13), and autophagy-related protein 101(Atg101). The activity of this complex is inhibited by the mammalian target of rapamycin (mTORC1) or specifically mTOR complex 1 through phosphorylation of ULK1/2. mTORC1 is involved in cell growth and proliferation, and its downstream targets are eukaryotic initiation factor (eIF), 4E-binding protein 1 (4EBP1) and ribosomal S6 protein kinase 1 (SK61). Its activity is sensitive to stress signals, particularly nutrient deprivation, or chemical inhibition with rapamycin directly or indirectly  complex . The act complex   and AMP-activated protein kinase (AMPK) . Whilst via factors like insulin/IGF, PI3K phosphorylates and stimulates Akt. Akt activation then facilitates the phosphorylation of TSC2 and PRAS40, activating mTOR  and reduced growth factor signalling . These sing mTOR   to form a complex that binds to mTORC1 and inhibits its activity. It has been shown to have activity against mTORC2 with prolonged use but does have greater specificity for mTORC1  in the USA for the treatment of angiomyolipomas, HER2-negative breast cancers, pancreatic neuroendocrine tumours, renal cell carcinomas and subependymal giant cell astrocytoma, and are being tested in numerous cancers in various clinical trial regime . HoweverCurrently, two main classes of autophagy inhibitors will be the focus of this review. These are PI3K inhibitors that stop autophagosome formation and lysosomal inhibitors that prevent proper lysosome acidification .PI3K inhibitors comprise chemical compounds that inhibit the PI3K complex and, thus, autophagy initiation in the canonical pathway, as previously discussed . The PI3One of the most commonly used autophagic inhibitors in pre-clinical studies over the last decade has been that of 3-MA. 3-MA inhibits VPS34 by binding to its ATP binding site. 3-MA inhibits the class III PI3K only transiently whilst blocking class I PI3K more persistently . In addiA few recent studies have proven efficacious in treating colorectal cancer cells in hypoxic conditions with 3-MA causing apoptosis . AnotherAlternative drugs to 3-MA include Wortmannin and LY294002. Wortmannin transiently blocks PI3K class I but persistently blocks PI3K class III  and LY29in vivo in melanoma and colorectal cancer models P-binding PX domain but are yet to be fully elucidated . A recenIt was also suggested that VPS34-IN1 and SAR-405 offered more significant treatment potential due to their specificity . RecentlLysosomal inhibitors include numerous compounds that alter the pH of the lysosome thereby making the function of the enzymes that rely on this defunct. Common lysosomal inhibitors include Bafilomycin A1 (BafA1), chloroquine (CQ), and hydroxychloroquine (HCQ).in vivo -V-ATPase inhibitor, is an antibiotic that inhibits subunit c of the V-ATPase machinery . V-ATPasin vivo . It is pxploring . DespiteCQ and its analogue HCQ are lysosomal lumen alkalises. They are lysosomotropic and accumulate within acidic vessels, particularly the lysosome, where their weak base formula reduces lysosome acidity and function, although the exact mechanism is yet to be elucidated. Other research suggests that they affect the lysosome and autophagosome-lysosome fusion . AlthougA systematic review and meta-analysis of all cancer trials that have been undertaken using chloroquine or hydroxychloroquine for cancer treatment were analysed to determine efficacy. The review assessed the use combined with gemcitabine, doxorubicin, radiation, temozolomide and single therapy with hydroxychloroquine. Cancers included were glioblastoma, non-Hodgkin’s lymphoma, pancreatic, metastatic non-small cell lung cancer, and metastatic breast cancer. The meta-analysis demonstrated that the overall response rate was significantly higher with the inclusion of hydroxychloroquine or chloroquine than without . Howeverin vivo efficacy  in cancer treatment. The aim in most of these trials is to increase the effectiveness of frontline treatment by limiting resistance, as summarised in The exact mechanism by which autophagy affects therapeutic resistance is still unclear. Autophagy inhibition improves therapeutics’ efficacy with multiple actions ranging from DNA damage, cell cycle inhibition, and immune system recruitment . Autophavia this pathway are less effective with increased autophagy. Thus, by inhibiting autophagy, damage to DNA can no longer be repaired, and the cells are destroyed. An example is the alkylating agent Temozolomide (TMZ), which is used in the treatment of Glioblastoma (GBM). This chemotherapeutic induces apoptotic cell death through the methylation of DNA residues resulting in DNA damage , replication disrupting agents (Gemcitabine), and radio mimetics , which are common treatments for cancers including colon, breast, pancreatic, and lung as well as many more  was shown to inhibit autophagy directly during mitosis. This suppression is driven by binding of CDK1 to regulatory ATGs at sites usually bound by mTORC1 which facilitates the inhibition of autophagy . This isAdditionally, there is renewed interest in using CDKI inhibitors particularly in combination with immunotherapy to improve tumour cytotoxicity. Based on these previous findings there is a possibility autophagy would be inhibited, and within the context of treatment, may increase its efficacy. However, in this setting of immune therapy it may also have a potential to interfere with the role autophagy as has been shown in the T-cell response .+ and CD8+ T cells , second the apoptotic members (includes BAK and BAX) and third the BH3 only proteins  . In brieAutophagy has been demonstrated to manipulate several members of this family, although its exact relationship to the anti-apoptotic and apoptotic members is unclear. One particularly important molecule in both apoptosis and autophagy is Beclin-1. As previously discussed Beclin-1 is a key member of the class III phosphatidylinositol −3 complex and is essential for autophagosome formation. It also forms a complex with BCL-2, and in this complex Beclin-1 was found to be inhibited from its function in autophagy . HoweverIn addition, Atg12 inactivates antiapoptotic members Bcl-2 and Mcl-1 by the binding of the BH3-like motif on Atg12 to the BH3-binding groove of BCL-2 family members . Truncat−/− BAK−/− fibroblast cells had a reduced cell death upon nutrient starvation and autophagy inhibition , and Ferroptosis (iron dependant cell death that is instigated by lipid peroxidation). These studies are summarised in Perhaps one of the clearest indicators of autophagy’s relationship to cell death is seen through multiple studies performed with gene knockdown/knockout of autophagy related proteins. In these studies, genetic ablation of autophagy genes has been seen to decrease cell death pathways including LMP-associated death , Pyroptosis (inflammatory dependant cell death due to cytokine release Inhibition of autophagy for prolonged periods of time, like with any drug, may lead to resistance. In fact, some research has suggested that by blocking autophagy several other non-canonical autophagy pathways and the Nrf2 pathway are upregulated to circumvent this loss.In a study by Towers and colleagues it was determined that even autophagy dependent cell lines could adapt and survive despite the knockout of Atg7, and that the Atg7 null cell population had an upregulation of Nrf2 and the Nrf2 signalling pathway. This upregulation of Nrf2 resulted in increased proteasomes, increased cell growth, decreased apoptosis and contributed to therapeutic resistance . EvidencAdditionally, a phase I clinical trial performed on canines with lymphoma was done to evaluate the effect of combining HCQ with doxorubicin. The initial findings of this study were promising as canines demonstrated a high overall response rate of 93.3% . HoweverThus, some cancer cells may adapt to autophagy inhibition by upregulating other stress pathways. Furthermore, cellular adaptions to autophagy inhibition like the upregulation of the Nrf2 pathway may then create a cell-fate determining vulnerability to Nrf2 targeted therapies which should be explored.Therapeutic resistance remains a major area for concern in cancer treatment and significantly impacts upon patient outcomes. Research into the specific mechanisms by which the cancer cell develops therapeutic resistance has highlighted the cellular process known as autophagy.Autophagy is upregulated in numerous cancers, and increasing evidence demonstrates that its inhibition, either through genetic knockdown or by pharmacological compounds, has improved the cell’s response to therapeutics. Furthermore, increasing evidence suggests that autophagy can influence therapeutic resistance due to its inverse relationship with apoptosis and its influence on DNA damage repair pathways.PI3K and lysosomal inhibitors have demonstrated promising results in overcoming therapeutic resistance in cancer cells. However, several factors should be considered before their use in the clinical setting.On a broader scale, autophagy is essential for maintaining cellular homeostasis in healthy cells. The wide-scale targeting of autophagy may prevent its cytoprotective roles and lead to the toxic buildup of damaged and out-lived proteins and organelles in otherwise healthy tissue. The ability of autophagy to degrade large out-lived, and toxic materials is one of the reasons why its decline is hypothesised to be involved in several pathologies, including ageing and neurodegenerative diseases like Huntington’s and Parkinson’s . Thus, tvia non-canonical pathways. Thus, targeting PI3K may not have the capacity to completely inhibit autophagic activity as the cells could potentially circumvent the initiation complex and still have upregulated autophagy in the presence of PI3K inhibitors. As both canonical and non-canonical autophagy pathways require the lysosome, targeting the lysosome may prove a more beneficial and effective approach.More specifically, several factors about the current PI3K and lysosomal inhibitors should be considered before future use. Firstly, the specificity of the PI3K inhibitors has not been consistent in targeting class III PI3K until recently. Although more specific derivatives have been developed, most of the research done to date has been with older inhibitors. In addition to this, autophagy can be activated The benefit of targeting the lysosome extends past it being just a more specific target but also introduces the inhibition of, not only macroautophagy, but also the alternative autophagic pathways of chaperone-mediated autophagy and micro autophagy. In contrast to the benefits, a potential disadvantage of targeting the lysosome is that emerging evidence shows that lysosomes have independent roles and are not just specific to autophagy. Furthermore, lysosomes are fused with macrophages and dendritic cells and aid with the clearance of foreign microbes and antigen presentation . Thus, tOf interest is an apparent trend in the glycolytic phenotype being the most sensitive or responsive to autophagic inhibition. It has been presented as a consistent predictor in blood cancers  and soliThe current autophagy inhibitors have several potential drawbacks, as discussed above. Research into autophagic mechanisms and the differences between physiological autophagy and pathologic autophagy is warranted. Delineating molecules specific to autophagy in the context of cancer would also be beneficial as it highlights potential therapeutic targets. Several macro autophagy-specific molecules could also be targeted in the future, including the Atg’s and LC3 involved in autophagosome formation. Targeting these molecules may reduce off-target effects like the immune system. Theoretically, the alternative to suppressing autophagy would be to stimulate and cause an over-activation of the process to bring about dysfunction, particularly in the context of cancer cell but potentially positively regulating the immune system .Despite these considerations, autophagy inhibitors are a very promising co-treatment for aggressive and resistant cancers whose advantages potentially outweigh their disadvantages. Further investigation into current autophagy inhibitors in therapeutic resistance is warranted, especially in the context of cancers like pancreatic cancer and GBM, whose survival rate is abysmal due to resistance to current treatments. Furthermore, a sustained effort should be made to research and understand the mechanism of the “pathologic” form of autophagy so that more specific and directed autophagy inhibitors can be developed and used to treat advanced and therapeutically resistant cancers more precisely.


Extrusion-based 3D bioprinting is an effective biofabrication technology to engineer living constructs with predefined geometries and cell patterns. However, bioprinting high-resolution multilayered structures with mechanically weak hydrogel bioinks is challenging. The advent of embedded 3D bioprinting systems in recent years offered new avenues to explore this technology for in vitro modeling. By providing a stable, cell-friendly and perfusable environment to hold the bioink during and after printing, it allows to recapitulate native tissues’ architecture and function in a well-controlled manner. Besides enabling freeform bioprinting of constructs with complex spatial organization, support baths can further provide functional housing systems for their long-term in vitro maintenance and screening. This minireview summarizes the recent advances in this field and discuss the enormous potential of embedded 3D bioprinting technologies as alternatives for the automated fabrication of more biomimetic in vitro models.Designing functional, vascularized, human scale  in vitro models.This minireview highlights recent advances on the application of embedded 3D bioprinting concepts for the fabrication of  In vitro models are fundamental preclinical tools for (patho)physiological studies and drug discovery pipelines.1 Due to the growing evidences that typical 2D in vitro models developed on flat tissue culture plastic have limited predictive power on the in vitro–in vivo extrapolations, the last decade has seen a marked increase on the search for in vitro human microphysiological systems (MPS) that can recapitulate organ-level functions.2 According to ASTM standards,2,3 bioprinting techniques can be categorized into four main methods, i.e., extrusion based,4 jetting based,5 laser based6 and vat photopolymerization based7 printing systems. Among the aforementioned methods, extrusion-based systems are the most widely dissiminated8 technique for the fabrication of cellularized constructs9 mimicking tissues/organs function for developing physiologically relevant 3D in vitro models, or for possible implementation as therapeutic options for the treatment of diseases.10 However, one of the main limitations of these 3D bioprinting systems11 is associated with the difficulties in bioprinting complex multilayer constructs, due to the inherent physical properties of bioinks typically based on soft hydrogel matrices, which, when printed, tend to get deformed or collapse.12 These unavoidable effects of gravity13 on hydrogels at the air–water interface, make the print of elaborate or intricate structures and maintenance of its fidelity a challenging task.14,153D bioprinting is an evolving additive manufacturing technology, where materials with biological components are deposited in a layer-by-layer manner according to a predefined digital file to form a 3D product.16 to the embedded bioinks while bioprinting have been developed in recent years, enabling the design and manufacture of 3D constructs that better resemble the biological structures of the human body. The use of support baths-assisted systems allows to print freeform constructs with lower geometric restrictions, higher resolution17 and smaller size features,18 a very important factor, for example, for bioprinting vascular-like structures,19,20 which are integral components of advanced dynamic MPS integrating tissue-like perfusion. Importantly, a key advantage of bioprinting in support baths is the possibility of using low viscosity bioinks,21 which favors the viability of printed cells and its general biological performance in printed constructs.22–25 Similarly, compared to traditional unsupported strategies, these systems further facilitate the bioprinting of multimaterial26 and multicellular structures.27 Moreover, since the crosslinking process takes place when the bioprinting of all the material is finished28 and not layer-by-layer as in conventional methods,29 an additional advantage of bioprinting in support baths is the possibility of accelerating the manufacturing process of cellularized constructs, opening the door for the manufacturing escalation30,31 of the bioprinting process, reducing printing times and costs.32To overcome these limitations, a surge of new methods involving the use of physical media that provides external support33 Recent innovations have further expanded the range of applications of this technology in the biomedical field. In the following sections we identify and discuss the outstanding potential of embedded bioprinting systems as options for the manufacturing of more precise in vitro models, discussing how the integration of other bioengineering technologies and tissue-specific bioink materials in its design concepts is opening new avenues to be explored in this particular field.Together with the increased spreading and availability of extrusion-based bioprinters at most bioengineering facilities, all these factors contributed to make of embedded 3D bioprinting a trending technology which allows the fast and accurate fabrication of freeform constructs with complex geometries directly in support baths, which might have temporary or permanent structural functions.in vitro models will enable a better understanding of tissue regeneration stages, in addition to disease progression and the outcomes of potential treatment options. In the development of bioengineered MPS by extrusion-based 3D bioprinting, support baths offer significant advantages stemming from their ability to hold the deposited bioink during and after bioprinting or, if needed, the printed structure can be released by the removal of the bath34  system (56 In the first version (FRESH v1.0), gelatin microparticles with an average diameter of around 65 μm were obtained by simple mechanical blending [51 In general, FRESH shows yield stress behavior, allows freeform bioprinting and its liquid compartment is compatible with many bioinks’  crosslinking mechanisms.51,57 Once the printed structure is cured, it is released from the surrounding bath by simply melting gelatin at 37 °C. Besides gelatin, various other granular hydrogels have been used as support baths, including agarose  can be employed. Detailed discussion on existing hydrogel systems and its crosslinking mechanisms being explored for bath production can be found in recent dedicated reviews.33,62Support baths can be synthesized from different fluid biomaterials, ranging from polymer hydrogels to living spheroid/organoids see . Among a) system , which iblending . Howeverblending . These pblending , where sblending .51 In ge agarose ,36 alginIn vitro maturation is crucial for creating functional MPS, allowing printed cells to secrete new ECM and establish essential connections with their microenvironment.34 However, maintenance of the complex architecture of printed cellular constructs over long cultivation times can be challenging, particularly when using ECM-based bioinks  or high cell density constructs, where deformation (contraction) or disruptions of printed constructs may easily occur. This concern tends to increase as the dimension of printed structures decreases, thus being particularly relevant for miniaturized MPS. An emerging biofabrication strategy to overcome these limitations levering on the many functionalities of support baths is the “print-and-grow” concept, where the supporting media can be maintained or annealed post-printing to provide structural support for long term culture of printed constructs. These strategies have been implemented using different cell-friendly support materials, such as κ-Carrageenan,35 CNCs38 or modified HA.64 The liquid component of the support baths can also include selected ECM components, such as collagen, fibronectin, HA or laminin, to improve the biomimicry and adjust the functionality of the system. Annealing of these composite materials for “locking” the structure after high fidelity bioprinting generates a stable and cell-interactive matrix for long term functional development of target tissues models.25 This concept allows to provide functional housing devices or living environments, the typical microfluidic bioreactor of organs-on-chip, to the printed constructs for their long-term maturation/maintenance and screening. Several methods can be applied for annealing the bath depend on its nature,62 including e.g., thermally induced crosslinking of ECM proteins exiting in its composition,25 enzymatic crosslinking of gelatin-based hydrogels ,65 or by promoting nanoparticles self-assembly with addition of biocompatible ions (e.g. Ca2+ for CNCs)  .38 Charain vitro maturation and screening, the bath can also incorporate living components where other cellular structures can be printed. This bioprinting strategy allows to simultaneously replicate both the external and internal components of a target organ by bioprinting e.g., blood vessels via sacrificial inks within the support bath incorporating the stromal tissue cell.65 This technique has been effectively applied on the fabrication of a complex vascularized ventricle in vitro model with a fast, unparalleled bioprinting ability, which is not easy to carry out using standard bioprinting technologies.66Besides providing physical support for the bioprinting process and housing the fabricated models for their 41,52 Formation of hydrogen bonding between bioinks and support liquid provides noncovalent interactions, allowing these liquid architectures to be stabilized through different mechanisms, such as interfacial complexation42 or other biocompatible crosslinking mechanisms.52 Low viscosities and the relative interfacial tension of bioink and matrix solutions enable high-speed bioprinting without compromising cell viability. Moreover, exceptionally low fiber diameters can be reached  are an innovative class of active support baths which have just started to be explored in recent years. Aqueous-based support platforms consist of bioink–immiscible liquid environment that function as a supportive fluid and/or pregel during bioprinting of complex microstructures, where both phases remain at equilibrium until solidification. reached 52 and biin vitro models. The convergence of conventional embedded bioprinting systems with different advances in biomanufacturing technologies  produced from induced pluripotent stem cell derived (iPSC) organoids, creates scalable, perfusable tissue mimetic constructs with vascular networks. OBBs contain high cell densities and exhibit self-healing and viscoelastic behavior, supporting 3D freeform bioprinting of single or branched channels via sacrificial inks 86,87 have been proposed to formulate bioink. However, their potential to closely mimic the rich tissue-specific cell microenvironment is limited.8 In the past few years, dECM hydrogels have emerged as a promising alternative for bioink formulation in in vitro modeling applications, as dECM retain the main biochemical and biophysical cues of the respective niches, exhibiting superior biofunctionality when compared with other available hydrogel options.88 dECM hydrogels can be produced by controlled decellularization and digestion of tissues obtained from different organs . In terms of composition, these biomaterials are a rich source of collagens, glycoproteins, growth factors and other important components that are crucial to dictate cell behavior, function, and fate modulation.94,95 The superior bioactivity of dECM-based hydrogels in comparison to other materials has been widely demonstrated and their combination with embedded bioprinting concepts enables to overcome the inherent rheological and structural limitations of these biomaterials as bioink hydrogels. For instance, a recent study compared the cellular performance of gelatin and kidney dECM bioinks using agarose as support bath for the bioprinting process.96 In this study, the presence of renal specific markers could only be detected on the dECM constructs and, while in the presence of kidney dECM cells were able to establish a confluent and highly interconnected network, gelatin-encapsulated cells remained round-shaped, with absence of evident network formation.97 The wide biofabrication potential provided by combining dECM bioinks with embedded bioprinting concepts has been leveraged for engineering more complex multicellular systems. For example, we have recently explored our CNC support bath platform to 3D write multicellular tendon models using tendon dECM bioinks, where the printed tendon stroma and vascular compartments enabled to study the cellular crosstalk established in these MPS.39 In this type of multicellular systems, coaxial printheads are particularly interesting options because it allows compartmentalization of printed bioinks in the same strut. The versatility of this approach was demonstrated in the fabrication of functional volumetric vascularized muscle tissues, where skeletal muscle and vascular dECM bioinks were coaxially printed on granular gelatin support baths.98 Besides assisting the bioprinting process, 3D printed muscles showed enhanced alignment of the matured myotubes in vitro and increased vascularization and innervation in vivo.98 Interestingly, dECM can be used not only as bioink hydrogel but also as the actual cell-laden stress yielding support baths, which can be gelled post-printing.44,45 This is a particularly appealing strategy for in vitro modeling because bioink and support baths can be formulated with different dECM hydrogels to retain the specific signatures of different functional biological structures of its tissue of origin.47 For instance, in a recent study, skin derived dECM bath was used for the embedded bioprinting of 3D cancer models incorporating perfusable blood and lymphatic vessels printed with vascular tissue-derived dECM.44 These models allowed the biomimetic recapitulation of metastatic steps of melanoma and were used to screen the different inhibitor combinations of drugs to suppress its metastasis. As another example, a multicellular atherosclerotic in vitro model incorporating endothelial, smooth muscle and connective tissue cells was developed leveraging on this concept.45 The proposed coaxial cell bioprinting system used vascular tissue-derived dECM as cell-laden and functional support bath, and was explored to fabricate stable and perfusable three-layered conduits with tunable geometry, allowing to study both co-cultured cells and local turbulent flow signaling in vitro.Biofabrication of in vitro modeling in recent years demonstrates the outstanding potential of this technology in the field. It enables not only the high throughput and automated replication of multicellular MPS with arbitrary geometries emulating native tissues architecture and cellular patterns, but it also simultaneously allows to build these organotypic 3D constructs within their own tailor-made housing support for dynamic in vitro maturation and screening. All these capabilities position embedded bioprinting as a technology with a biomanufacturing potential difficult to be matched by other current in vitro modeling alternatives. However, this potential is just starting to be unblocked.The fast increase in the number of studies applying embedded bioprinting for 99,100 and artificial intelligence that allow faster modeling and simulation60 of the rheological, chemical and biological properties of the baths to adapt them to the new bioinks that are in development,101,102 thus allowing to manufacture in vitro models with better mechanical properties, better printing resolution and allowing to improve cell viability during and after the bioprinting process.103 One challenge to consider is the development of support baths with greater optical transparency,38,104 particularly if they are aimed to be annealed and used as functional housing device of printed constructs. This will enable better visualization of the resolution and fidelity of printed construct, as well as minimize light scatter effects that negatively affect the observation of MPS using standard microscopy techniques. On the other hand, the effect of bath removing strategy and its residues remaining on the bioprinted constructs on cell functions are still unclear. Ensuring cell survival by either developing new approaches to eliminate them or using the residues  to add new functionalities to printed constructs  is an underexplored but important issue to consider.105Looking toward the future, 3D bioprinting in support baths has the possibility of adopting emerging technologies such as machine learning35 with modulable properties over time106 (4D bioprinting) or the controlled release of growth factors107 or genetic material108 is another alternative that is certainly worth to be explored. The same applies to the use of sacrificial inks with programmable dissolution rates.109 This strategy will allow temporal control over the spatial compartmentalization of the different cell population, which have different differentiation and/or maturation requirements that need to be considered before allowing their direct physical contact on the support “bioreactor” device. On the other hand, ATPS will enable to explore the fabrication of tubular structures to model different types of vascular diseases, and to embed perfusable microfluidic structures within the printed models with higher resolution, without concerns of extrusion speed neither ink viscosity, or requirement for removal of sacrificial inks.41,52 Despite performing better than most non-supported extrusion-based bioprinting techniques, another general challenge where the current in-bath 3D bioprinting technology still face some difficulties is in the fabrication of physiologically-relevant human-scale models capable of capturing the multiscale hierarchical complexity of native tissue in a fast and reproducible manner. The recent integration of embedded extrusion with volumetric printing concepts might be a promising strategy to mitigate these issues, allowing the fast production of large-sized sample replicates with increased cellular and architectural complexicity.110The possibility of using support baths not only during the bioprinting process but as a continuous mediumin vitro models.111 Integration of microelectronic sensors112–115 allowing real-time measurement of e.g., changes in pH,116 temperature, or mechanical properties117 in the fabricated models will enable the in vitro monitoring of cell fate or other physiological signals of interest in these MPS. In this particular aspect, integration of machine learning concepts will certainly play key roles in the future of MPS for processing multimodal data inputs in a unified manner.Capability for real-time assessment of physiological event is an additional desirable functionality in advanced These are a few examples of the potential directions to be explored for evolving the current state of the art in this field. However, the possibilities are certainly much wider. Therefore, we foresee exciting developments being made in the coming years by integrating embedded bioprinting with new concepts and technologies.There are no conflicts to declare.


While ketamine has emerged as a promising therapeutic for treatment-resistant depression (TRD) and psychiatric emergencies, including suicidality occurring in the course of major depressive disorder (MDD), several issues pertaining to clinical efficacy remain not fully elucidated. These issues include (1) the role of ketamine-assisted psychotherapy (KAP) in enhancing or prolonging antidepressant effects; (2) the therapeutic value of the so-called psychedelic protocols that value the state-altering properties of ketamine; (3) the therapeutic potential of ketamine as a first-line treatment for MDD, other mood diseases, and potentially other psychiatric diagnoses; and (4) the safety and efficacy of home treatment with self-administered ketamine.This collection of articles extends the currently limited evidence for the efficacy of KAP for MDD, the impact of protocols using doses that induce psychedelic experiences, and the benefits of using both of these approaches with a range of psychiatric diagnoses. The literature on two relatively new treatment options is enriched by a case report illustrating the efficacy of intranasal esketamine, and a study providing early data on safety and efficacy with home use of self-administered oral ketamine. Two frequently discussed risks associated with ketamine treatment are presented with a real-world study examining ketamine's potential for abuse and one study providing guidance for the management of hypertension that may occur in the course of antidepressant treatment with intravenous ketamine. A final case report documented the administration of intranasal esketamine in a 17-year-old female adolescent suffering from TRD, and two articles examined the evidence for differences in the antianhedonic response to ketamine between male and female participants and between individuals with melancholic and non-melancholic depression, representing clinically relevant conditions that are frequently described as predominantly biologically determined manifestations of MDD .In contrast with the scores of studies demonstrating ketamine's efficacy as a fast-acting antidepressant, there is relatively little research on ketamine administered in combination with psychotherapy. There is clear evidence that the addition of psychotherapy to conventional antidepressant psychopharmacotherapy enhances benefits in MDD patients –5, althoTo date, the most robust evidence for the efficacy of KAP using a psychedelic protocol applied to a wide range of mood and trauma-related conditions comes from a retrospective study of 235 adult patients in three separate private general psychology practices in Northern California and Texas . The mosIn a different approach, an overlay of a 10-week, 12-session course of cognitive behavioral therapy (CBT) on a short 2-week course of four ketamine infusions (0.5 mg/kg over 40 min) in an open-label trial resulted in remission in 7 out of 16 self-selected patients with TRD within the first 2 weeks ; for ketIn a novel automated psychosocial intervention , 154 aduKetamine-assisted psychotherapy in adolescents with multiple psychiatric diagnoses” by Wolfson et al.. Their study reports on four cases of adolescents aged 13–19 years with conditions ranging from TRD, anxiety, and bipolar disorders to trauma and eating disorders; all adolescents were treated with sublingual ketamine followed by sessions with intramuscular ketamine, at individualized and incrementally higher doses until an experience of ego dissolution was achieved, leading to rapid functional improvements and decreases in symptoms.New preliminary evidence for KAP with adolescents is provided in “Medical student types journals during ketamine infusions for suicidal ideation, treatment-resistant depression, post-traumatic stress disorder, and generalized anxiety disorder” by Willms et al.. In their study, a 30-year-old man had suffered from suicidal ideation for 5 years, despite prior psychotherapy, lifestyle modifications, and complex psychopharmacology. After an 8-month regimen of ketamine infusions at 1.8 to 2.1 mg/kg/h for 1 h, he experienced remission from suicidality, PTSD, MDD, and GAD. The case includes detailed journals of his subjective experience during four 1-h infusions. While evidence for the relationship between ketamine's alterations of experience and its therapeutic efficacy is mixed —the mean depression score for this sample decreased by 47.59% as measured by the Patient Health Questionnaire (PHQ-9), with similar decreases in anxiety (GAD-7). After six treatments, the PHQ-9 scores dropped to scores of half or less on intake in 65.4% of the 210 patients who completed this extended protocol, with comparable reductions in anxiety. Minor side effects and adverse events such as dizziness and nausea were limited and resolved without medical intervention, providing evidence that, for some patients, this treatment option may offer added convenience, increased privacy, and reduced cost along with good efficacy and safety.A recently pioneered treatment option is sublingual ketamine delivered by mail for self-administration at home. A summary of outcomes with 664 patients who completed at least three ketamine sessions appears in “A survey of drug liking and cravings in patients using sublingual or intranasal ketamine for treatment resistant depression: a preliminary evaluation of real-world addictive potential” by Chubbs et al.. A survey of 33 patients with TRD in current or prior treatment with sublingual or intranasal ketamine found that ketamine was not consistently liked or craved by these patients. While non-parenteral uses of ketamine require diligent monitoring by providers, abuse concerns should be weighed against the potential benefits of treatment. These findings are consistent with other research pointing to negligible risks of abuse associated with ketamine treatment of mood disorders in patients with no comorbid substance use disorders  and Zheng et al.(b): “Gender differences in the antianhedonic effects of repeated ketamine infusions in patients with depression” and “A comparison of the antianhedonic effects of repeated ketamine infusions in melancholic and non-melancholic depression”.Finally, an important dimension of ongoing research into the effects of ketamine is the testing of similar protocols on different populations. A series of six intravenous infusions of subanesthetic ketamine showed no important differences in the antianhedonic effect on male vs. female participants or individuals with melancholic vs. non-melancholic depression, in two respective articles by To date, much of the research on ketamine has focused on the efficacy of one or more series of ketamine only treatments for depression, with doses designed to minimize alterations of mental state. These Articles represent research that engages with a number of additional promising dimensions of ketamine therapy. We offer these in hopes that this will spur further interest, richer dialogue, and a wider range of meticulous studies.GH conceptualized and wrote the first draft of the manuscript. S-AM and LB contributed to the manuscript revision and read and approved the submitted version. All authors contributed to the article and approved the submitted version.


What are the genetic risk factors associated with preeclampsia and hypertensive disorders of pregnancy?In this genome-wide association study, 13 novel preeclampsia- or hypertensive pregnancy–associated genetic loci were discovered. Seven loci are located near genes previously associated with blood pressure traits, and several harbor genes involved in the development of placenta, remodeling of uterine spiral arteries, and kidney function.The findings further strengthen the known association between cardiovascular health and preeclampsia and provide new targets for future research of preeclampsia pathophysiology, including genes involved in placental development and kidney function. This genome-wide association study seeks to identify genetic risk factors associated with preeclampsia and hypertensive disorders in pregnancy. A genetic contribution to preeclampsia susceptibility has been established but is still incompletely understood.To disentangle the underlying genetic architecture of preeclampsia and preeclampsia or other maternal hypertension during pregnancy with a genome-wide association study (GWAS) of hypertensive disorders of pregnancy.International Classification of Diseases codes. Data were analyzed from July 2020 to February 2023.This GWAS included meta-analyses in maternal preeclampsia and a combination phenotype encompassing maternal preeclampsia and preeclampsia or other maternal hypertensive disorders. Two overlapping phenotype groups were selected for examination, namely, preeclampsia and preeclampsia or other maternal hypertension during pregnancy. Data from the Finnish Genetics of Pre-eclampsia Consortium , Finnish FinnGen project (1964-2019), Estonian Biobank (1997-2019), and the previously published InterPregGen consortium GWAS were combined. Individuals with preeclampsia or other maternal hypertension during pregnancy and control individuals were selected from the cohorts based on relevant The association of a genome-wide set of genetic variants and clinical risk factors was analyzed for the 2 phenotypes.NPPA, NPR3, PLCE1, TNS2, FURIN, RGL3, and PREX1). In line with this, the 2 study phenotypes showed genetic correlation with blood pressure traits. In addition, novel risk loci were identified in the proximity of genes involved in the development of placenta , remodeling of uterine spiral arteries , kidney function , and maintenance of proteostasis in pregnancy serum (PZP).A total of 16 743 women with prior preeclampsia and 15 200 with preeclampsia or other maternal hypertension during pregnancy were obtained from FINNPEC, FinnGen, Estonian Biobank, and the InterPregGen consortium study . The analysis found 19 genome-wide significant associations, 13 of which were novel. Seven of the novel loci harbor genes previously associated with blood pressure traits (The findings indicate that genes related to blood pressure traits are associated with preeclampsia, but many of these genes have additional pleiotropic effects on cardiometabolic, endothelial, and placental function. Furthermore, several of the associated loci have no known connection with cardiovascular disease but instead harbor genes contributing to maintenance of successful pregnancy, with dysfunctions leading to preeclampsialike symptoms. The disorder often develops without any evident placental malfunction, and the predisposition to preeclampsia is likely affected by multiple underlying cardiometabolic factors that modify the response to the pregnancy-induced stress.8Preeclampsia is a vascular pregnancy disorder that affects 3% to 5% of all pregnancies.10 but the actual risk loci remain mostly unknown. The genome-wide association study (GWAS) of preeclampsia by Steinthorsdottir et al11 identified 5 risk loci, which have previously been connected to hypertension. Accordingly, epidemiological evidence shows that prior cardiovascular disease inflated the risk of preeclampsia, and individuals with previous preeclampsia were at increased risk of developing cardiovascular disease later in life.15A genetic contribution to preeclampsia susceptibility has been established,8 The overlap in the genetic risk factors between preeclampsia and related disorders could be subtype specific, with some subtypes sharing more features with hypertensive diseases and others being more closely linked to disorders of placental development. Therefore, we have selected 2 phenotype groups for examination: preeclampsia and preeclampsia or other maternal hypertension during pregnancy.Preeclampsia is likely to consist of several subtypes with differing etiologies.11 In addition, we performed GWAS in smaller paternal and fetal sample sets from Finland to identify risk loci of preeclampsia conveyed via the fetus.The aim of our study was to identify genetic risk factors associated with preeclampsia and hypertensive disorders of pregnancy in a maternal genome-wide meta-analysis comprising samples from the closely related populations of Finland and Estonia. For the meta-analysis of the strict preeclampsia phenotype, we supplemented these data with a previously published GWAS on preeclampsia.11 For the fetal and paternal genome-wide association analysis, samples from the FINNPEC cohort were available. All required ethical approvals were obtained from international, national, and regional ethics committees as described in detail in the eAppendix in For the maternal meta-analyses, genome-wide genotyped and imputed samples from the Finnish Genetics of Pre-eclampsia Consortium (FINNPEC), FinnGen, and Estonian Biobank were used. In addition, we included summary statistics from the earlier meta-analysis study of preeclampsia by Steinthorsdottir et al.16 were classified as small for gestational age. Placental insufficiency was defined as umbilical artery resistance index or pulsatility index of +2 or more SD. In FinnGen and Estonian Biobank, the phenotypes were based on International Classification of Diseases codes  as detailed in eTable 1 in 11 to the meta-analysis of the preeclampsia phenotype. Further details of the phenotype definitions are presented in the eAppendix in The analyses were performed with 2 phenotypes: (1) preeclampsia, eclampsia, or preeclampsia superimposed on chronic hypertension and (2) preeclampsia or other maternal hypertensive disorders. We also examined a phenotype of preeclampsia or fetal growth restriction (as mother’s diagnosis), used as proxy for small for gestational age (FinnGen and Estonian Biobank) or diagnoses of placental insufficiency or an infant being small for gestational age  in FINNPEC, FinnGen, and Estonian Biobank. Only high-imputation quality markers (imputation information score >0.7) were used in the analysis. Exact genotyping workflows are detailed in eAppendix in 17) for FINNPEC, FinnGen, and Estonian Biobank, respectively. Summary statistics obtained from these analyses in each cohort were then used to perform inverse variance–weighted meta-analysis with METAL software.18 Detailed associations and the meta-analysis workflow are presented in the eAppendix in In brief, the association analyses were performed for all genotyped variants using generalized mixed model as implemented in SAIGE versions 0.39, 0.39.1, and 0.38  statistical significance of variants in the GWAS meta-analysis, (2) functional evidence associated with the discovered variants, and (3) careful examination of literature regarding the identified variants and suspected causal genes. The detailed annotation workflow is described in the eAppendix in 20 to evaluate genetic correlation of the studied phenotypes with other traits. Third, we calculated polygenic risk scores (PRS) for preeclampsia and preeclampsia or other maternal hypertensive disorder phenotypes and analyzed the results in the FINNPEC cohort to examine overall genetic contribution uncovered in our GWAS analysis. The methodology of these 3 analyses is explained in detail in the eAppendix in To further examine the correlation of our findings with other disorders, we first conducted a phenome-wide association study (PheWAS) analysis for the lead variants using all 2861 phenotypes provided in FinnGen Data Freeze 6. Second, we used Linkage Disequilibrium Score Regression softwareA total of 16 743 women with prior preeclampsia and 15 200 with preeclampsia or other maternal hypertension during pregnancy were obtained from FINNPEC, FinnGen, Estonian Biobank, and the InterPregGen consortium study. The mean (SD) age in each cohort was 30.3 [5.5], 28.7 [5.6], 29.7 [7.0], and 28 [not available] years, respectively.Altogether, we identified 9 and 13 genome-wide significant loci for the preeclampsia and the preeclampsia or other maternal hypertensive disorder phenotypes, respectively . Four prPreliminary meta-analysis results showed evidence of slight inflation in the meta-analysis test statistics  and were corrected by rerunning the meta-analysis with genomic correction. Most of the directions of the associations were concordant between the cohorts with the exception of 4 loci on 3q26, 12q24, 19p13, and 20q13 that showed discordant association of direction in the smallest cohort (FINNPEC) compared to the others . We did In the survival analysis conducted in FinnGen between preeclampsia and all other end points, preeclampsia was associated with an increase in risk of 146 disease end points  region, which is known for its high density of genes involved in immune regulation and recognition. The association lies within an 89 kbp copy number variations region previously associated with preeclampsia21 and is located within the major susceptibility locus for psoriasis, PSORS1. In addition, MAGMA gene-based analysis yielded significant results for ‘MTHFR and CLCN6 genes  for preeclampsia and 1.89  for preeclampsia with severe symptoms. When using the top and bottom percentiles of PRS for preeclampsia or other maternal hypertension during pregnancy, similar associations were shown for the preeclampsia or other maternal hypertension phenotype . The Nagelkerke R2 values calculated for models, including risk factors of age, parity, body mass index, and first trimester blood pressure, showed improvement when PRS information was added, as described in the eAppendix and eTables 8-10 in Our analyses in the FINNPEC cohort comparing individuals in the top 10% preeclampsia PRS compared to those in the bottom 90% showed an odds ratio (OR) of 2.21 , is a protease inhibitor that prevents the activity of all 4 classes of proteases and stabilizes misfolded proteins, which have been shown to accumulate in preeclampsia.54 Additional relevant literature regarding these putative candidate genes is reviewed in more detail in the eAppendix in Many of the genes next to the lead single-nucleotide variants of our study are involved in placental development and function, which are often compromised in preeclampsia. Both genes closest to the lead variant in the locus 11q22, 55 Examples of associating genes putatively modulating the immunological response to pregnancy include PZP , likely modulating T helper cell response and PSORS1C2 (within the PSORS1 locus in the HLA region). Tissue compatibility, autoimmunity, regulation of inflammation, and cardiovascular diseases are processes with known susceptibility loci within the HLA and pathophysiological relevance in preeclampsia. Our discovered association largely reflects these functions, as discussed in more detail in the eAppendix in 56 Due to the high gene density, extreme polymorphism, and complex haplotype structure, identifying likely candidate genes, especially in the HLA region, is challenging. Until now, immunology-related findings have been absent in preeclampsia GWAS results.Immunological factors have been shown to contribute to the pathophysiology of preeclampsia.When comparing the preeclampsia and preeclampsia or other maternal hypertension phenotypes explored in this study, we notice that the effect sizes of the uncovered loci were largely similar between them. The most likely candidate genes appear to be related to hypertensive diseases, implying that the genetic risk factors of preeclampsia are shared with other hypertensive disorders as well as risk of hypertension later in life. Furthermore, incorporation of PRS to already known risk factors of preeclampsia may yield improvement in prediction of this disorder.58 The associations previously reported by Steinthorsdottir et al11 are similar in effect directions to those now discovered in the preeclampsia phenotype in the Finnish and Estonian cohorts, suggesting that similar genetic background may contribute to preeclampsia in other populations as well.The main strength of this study is the use of the 3 well-characterized cohorts FINNPEC, FinnGen, and Estonian Biobank, originating from 2 closely related populations of Finland and Estonia. Such homogenous populations might facilitate the discovery of rare variants with larger effects and characterization of the genetic basis of complex diseases such as preeclampsia.59 In support of the robustness of our approach, we replicated the findings of the largest previously published GWAS meta-analysis, both with our preeclampsia and preeclampsia or other maternal hypertension phenotypes and all the study cohorts involved provided uniform support for our findings with little evidence of heterogeneity. Uncovering the paternal and fetal associations may require considerably larger sample sizes than available in the current study. Our PRS analysis has 2 important limitations. First, as the FINNPEC cohort was collected from university hospitals, the included individuals may represent those with a more severe form of the disease. Second, due to its being a case-control cohort, FINNPEC has larger proportion of individuals with preeclampsia compared to other cohorts. Together these factors may lead to inflated estimates of the predictive ability of PRS reported in this work.This study has limitations. Generalizability of the results may be limited in more diverse populations. Lack of external validation is also a limitation, although the consistency in effect sizes between the large study cohorts does add to the reliability of the findings. The register-based approach used in this study provides limited phenotype information and can be seen as a limiting factor, although the quality of the Finnish Care Register for Health Care has previously been shown to be excellent.Our study uncovered 13 novel loci with genome-wide significant association with preeclampsia or other hypertensive disorders of pregnancy. We found that cardiovascular disease–related genes were associated with preeclampsia, as previously suggested, but many of those genes have pleiotropic effects on cardiometabolic, endothelial, and placental function. In addition, we provide further evidence for an association of several loci not previously associated with cardiovascular disease but containing genes with apparent importance in the maintenance of pregnancy, with dysfunctions leading to preeclampsialike symptoms. Although further functional studies are required in the future, these results offer valuable insights into the genetic architecture and biology behind preeclampsia as well as into the connection between preeclampsia and other maternal hypertensive disorders.


Pediatric emergency departments (ED) in many countries are implementing electronic tools such as kiosks, mobile apps, and electronic patient portals, to improve the effectiveness of discharge communication.This study aimed to survey nurse and physician readiness to adopt these tools.An electronic, cross-sectional survey was distributed to a convenience sample of currently practicing ED nurses and physicians affiliated with national pediatric research organizations in Canada, Australia, and New Zealand. Survey development was informed by the nonadoption, abandonment, scale-up, spread, sustainability framework. Measures of central tendency, and parametric and nonparametric tests were used to describe and compare nurse and physician responses.P<.001) and that policies are in place to guide safe and secure electronic communication (P=.02). Physicians were more likely to agree that using an EDCT would change their routine tasks (P=.04). One third  of participants indicated that they use or have used EDCT.Out of the 270 participants, the majority were physicians , female , and had 5 or more years of ED experience . There were high levels of consensus related to the value proposition of electronic discharge communication tools (EDCTs) with 82% (221/270) of them agreeing that they help parents and patients with comprehension and recall. Lower levels of consensus were observed for organizational factors with only 37% (100/270) agreeing that their staff is equipped to handle challenges with communication technologies. Nurses and physicians showed significant differences on 3 out of 21 readiness factors. Compared to physicians, nurses were significantly more likely to report that EDs have a responsibility to integrate EDCTs as part of a modern system (Despite low levels of uptake, both nurses and physicians in multiple countries view EDCTs as a valuable support to families visiting pediatric ED. Leadership for technology change, unclear impact on workflow, and disparities in digital literacy skills require focused research effort. A staggering number of discharge communication interactions occur each year among health care providers, caregivers, and patients visiting pediatric emergency departments (ED). Over 3.1 million children and youths attended an ED for care in Canada between 2019 and 2020 with the majority (86%) discharged home  was quite confusing to them. In essence...they needed me to interpret the information for them.”Organizational and environmental level barriers were sometimes vaguely described as “not very good” and “not user friendly, our discharge summary completion rates are generally only 65%-70%” (2/85). Training-related concerns, such as having access to an EDCT but not having had the training to know how to use the tool, were the most frequently mentioned organizational barrier (4/85). Others included (1) impact on quality of communication “most [staff] do not use this tool as it doesn’t do a sufficient job of summarizing conversations had with health care professionals” (1/85), (2) lost confidence among staff due to recent cyber-attack in the ED (1/85), (3) concerns related to outdated technology being replaced (1/85), and (4) confusing hybrid approaches where ED staff engage in discharge communication using combinations of emailing, texting, and paper-based and verbal instructions (1/85).The aim of this study was to leverage the NASSS implementation framework in a novel way to identify emergent uncertainties and interdependencies that impact nurse and physician readiness to adopt EDCTs in pediatric EDs.Across all study participants, there was strong consensus about the value of EDCTs. Clinicians reported high agreement with the impact of EDCTs on improving patient and caregiver comprehension and recall, supporting modern health care innovation, informing their clinical practice and the accessibility of digital devices among young families visiting the ED. This high level of consensus was observed despite a wide range of self-reported computer proficiency skills (range 25-100) and confidence levels in learning new technology (range 25-100).Our findings suggest that while there may already be “buy-in” for EDCTS even from less technology-literate staff, their use is still not widespread -21. ImplWhile clinicians in our study see a positive value proposition for EDCTs there were mixed responses across other implementation domains, namely the organizational and societal contexts. Only 22% (59/270) of participants felt their organization valued digital technologies based on budget allocation, while about one-third of participants agreed that their organization offered timely support for technological challenges.Differences between nurses and physicians were limited. Our survey revealed significant differences between those groups for only 3 of the 21-items and in all cases, it was the magnitude of difference, not the direction of opinion that was different. Open-ended responses reflected some concerns about caregivers’ preferences and skills in using EDCTs. Digital equity and its intersection with other racial and cultural disparities observed in ED  warrantsGiven EMRs were largely used to support discharge communication in clinician-driven  not patient-centered ways, future research should explore how patient-led and more interactive EDCTs  might support high-quality discharge communication in different ways or require different types of readiness to implement. In particular, examining the perspectives of youth and caregivers on their readiness to use these tools in an ED context would add significantly to the field.A limitation of this study was the approach to sampling. Because participation was voluntary, those who chose to participate may have already held more positive attitudes about, or stronger interest in EDCTs and may be overrepresented in the sample. Additionally, representation across countries was not equal and future analysis should explore similarities and differences to reduce bias introduced by this overrepresentation.Nurse and physician readiness to integrate technologies into clinical pathways for discharge communication in pediatric ED is not only impacted by the availability of technology infrastructure. This multicountry study offers an original application of the NASSS framework to help emergency medicine leaders and administrators begin to systematically address the broader factors that are contributing to current low rates of uptake.