The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed. AI has the potential to improve and accelerate various aspects of regenerative medicine research and development, particularly, although not exclusively, when complex patterns are involved. This review paper provides an overview of AI in the context of regenerative medicine, discusses its potential applications with a focus on personalized medicine, and highlights the challenges and opportunities in this field. Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that would typically require human intelligence. This includes learning, reasoning, perception, and problem-solving. AI systems are designed to mimic human cognition and to work autonomously, learning from data and prior experiences to improve their performance over time ,2. The cDeep learning is a subset of machine learning that uses artificial neural networks to learn from data. These neural networks are designed to mimic the structure and function of the human brain, allowing them to identify more complex patterns and make decisions based on the data they have been trained with ,11. DeepRegenerative medicine is a rapidly evolving field that seeks to restore or replace damaged or diseased tissues and organs through advanced technologies such as stem cell-based therapies, gene therapy, and tissue engineering ,22. WithThis paper offers a distinct contribution by synthesizing and analyzing the available literature on AIs applications in regenerative medicine, providing an overview, identifying gaps in the existing literature, and proposing novel research directions. By adopting a holistic perspective, we not only consider empirical studies but also include theoretical perspectives and expert opinions. This approach broadens the scope of our analysis and allows for a more comprehensive understanding of the topic. By incorporating diverse sources of evidence, our manuscript offers a unique perspective that is not limited to a single methodological approach. Thus, our study presents a novel synthesis of the literature, shedding light on the potential of AI to revolutionize regenerative medicine.AI has become a crucial aspect in performing computational simulations and in silico studies in medical applications and offers several advantages, such as lower costs and faster results compared to other medical investigation approaches, such as clinical and laboratory methods ,26,27. Cde novo drug design) and drug screening  ..145].In conclusion, AI has tremendous potential to revolutionize and accelerate the development of therapies in regenerative medicine. From enhancing drug discovery to optimizing tissue engineering and cellular therapies, AI can provide insights by analyzing vast molecular and genomic datasets that would be impossible for humans to perceive. While AI shows promise to advance regenerative medicine research and development, there are significant technical challenges that must be addressed before these technologies can be widely adopted. One of the significant limitations is the lack of large, high-quality datasets needed to train sophisticated machine-learning models. Regenerative medicine involves complex biological interactions that are difficult to fully capture in data.Additionally, developing accurate computational models that can simulate and predict cell behavior over time poses immense technical challenges due to our still-limited understanding of cellular and molecular pathways. Validating AI systems and gaining regulatory approval also requires extensive clinical testing, which takes considerable time and resources. Addressing concerns around data privacy, security, and bias and ensuring fair and equitable access to new tools is equally important. Moreover, obtaining clinician buy-in for technologies promising more effective personalized care will require overcoming adoption hurdles. Substantial ongoing research is still needed to overcome these limitations and translate AIs theoretical potential in regenerative medicine into real-world solutions that tangibly improve patient outcomes. Researchers, policymakers, healthcare providers, and AI developers must work together to develop appropriate safeguards, oversight mechanisms, and guidelines for using AI in regenerative medicine. As AI technologies continue to improve and more high-quality data becomes available, the opportunity for refining and customizing AI algorithms specifically for regenerative medicine purposes will increase.Moving forward, further innovations in areas such as machine learning, natural language processing, computer vision, and robotics have the potential to uncover new insights that could revolutionize how regenerative therapies are developed and delivered. Combining AI with other emerging technologies such as nanotechnology, genome editing, and 3D bioprinting may lead to unprecedented advances in creating personalized regenerative solutions. With the appropriate ethical framework and governance structures in place, the future of AI-driven regenerative medicine seems promising. However, progress will depend on maintaining a human-centric approach that utilizes AI capabilities to serve the best interests of patients and society. Through multidisciplinary collaborations and responsible development and use of these technologies, we may one day realize the full potential of AI to usher in a new era of customized and effective regenerative therapies.AI can help accelerate drug discovery by analyzing large datasets to identify promising drug candidates and optimize drug properties.AI-enabled disease modeling can provide insights into disease mechanisms and aid in the identification of new therapeutic targets.AI can improve predictive modeling to identify patients who may benefit from regenerative therapies and optimize treatment plans.AI can enable the development of personalized medicine approaches based on a patient’s genetic and health data.AI can optimize materials and fabrication methods for tissue engineering applications.AI can assist in identifying the most suitable cell types for cell therapies and optimizing cell delivery and monitoring.AI can enhance the efficiency and accuracy of clinical trial design.AI can be used to monitor patients in real-time to detect changes and risks early.AI can provide personalized patient education materials tailored to individual needs and preferences.AI can improve regulatory compliance through enhanced data analysis, traceability, and transparency.AI also has roles in related fields such as immunotherapy, genetic engineering, nanotechnology, and microfluidics, which can further advance regenerative medicine.In summary, AI has the potential to significantly enhance various aspects of regenerative medicine research and development through analyzing large and complex datasets, identifying patterns and trends, and making accurate predictions to optimize processes and therapies. However, challenges related to ethics, data quality, and regulation must be addressed to ensure the safe and effective use of AI in regenerative medicine.


Introduction: Intercellular adhesion molecule 1 (ICAM-1) is a critical molecule responsible for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1, that SARS-CoV-2 shares several features with these viruses via interactions between cells, and that SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. From these previous arguments, it is assumed that ICAM-1 can be related to SARS-CoV-2 cell-to-cell transmission in COVID-19 patients. Indeed, the time-dependent change of the ICAM-1 expression level has been detected in COVID-19 patients. However, signaling pathways that consist of ICAM-1 and other molecules interacting with ICAM-1 are not identified in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM1-associated pathways will be indispensable for clarifying the mechanism of COVID-19.Materials and methods: This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, single-cell omics data analysis extracts coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate the models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways.Results: Comparison of the obtained pathways between different cell types and time points reproduces the known pathways and indicates the following two unknown pathways: (1) upstream pathway that includes proteins in the non-canonical NF-κB pathway and (2) downstream pathway that contains integrins and cytoskeleton or motor proteins for cell transformation.Discussion: In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanism of COVID-19. Elucidating the underlying mechanisms of coronavirus disease 2019 (COVID-19) still remains a global issue. Uncovering the mechanism requires fully understanding the COVID-19-specific interactome, a complex network of interactions among different components. In previous studies on COVID-19, attempts to provide insights into interactions within cells have been made. In contrast, interactions between cells have not been closely examined.ICAM1. ICAM-1 is a transmembrane glycoprotein expressed on leukocytes, vascular endothelial cells, and respiratory epithelial cells. Its differential expression is critical for proinflammatory immune responses and viral infection. Additionally, ICAM-1 enables interactions between cells by controlling leukocyte migration, homing, and adhesion from outside to inside the cell (outside-in) and regulation from inside to outside the cell (inside-out)  , encoded by ide-out) . These fide-out) . Anotheride-out) . The preide-out) . In factS-CoV-2) . MoreoveS-CoV-2) . SpecifiS-CoV-2) . In HIV-S-CoV-2)  and induS-CoV-2) . The afoin vitro experimental results on the expression level of ICAM-1 in SARS-CoV-2-infected cells. One study shows the time-dependent ICAM-1 expression level changes in COVID-19 patients . Consequently, it is significant to uncover the ICAM1-associated pathways to better understand the interactions between cells in the context of COVID-19.Nevertheless, the interactions arising from ICAM-1 are not explicitly recognized as indispensable in the case of COVID-19. In particular, there is little insight into the signaling pathways surrounding ICAM-1, that is, the upstream and downstream signaling cascades that occur upon the functional activation of ICAM-1 and its specific signaling molecules interacting with ICAM-1  pathways , undirected graphical models are edited as dependency graphs with validated relationships.Steps 1 and 2 are dedicated to gene network inference purely from data, and Step 3 validates the data-driven gene network with domain knowledge. In this study, we call the rectification of data-driven objects with knowledge a data-driven and knowledge-based (DD-KB) approach. Step 1 involves obtaining the COVID-19-specific differentially expressed genes (DEGs) and a network of differentially coexpressed genes (DCGs) via single-cell omics data analysis. Here, DEGs are the genes whose expression levels differ significantly in COVID-19-positive patients and -negative controls, and DCGs are coexpressed DEGs. Step 2 removes spurious edges from the correlation networks, thereby building ICAM1-associated pathways. Through the framework, single-cell omics data and multiple knowledge bases are integrated, which allows the inference of gene networks containing the components absent from the current COVID-19 Disease Map.Steps 4 and 5 are pathway construction steps. In Step 4, a functional annotation tool is used to convert genes into encoded proteins. Pathway mapping and unification (Step 5) refine the results as the final outputs, the In this subsection, explanations for each step of the framework are provided.ICAM1-associated DCGs from the omics data.Single-cell omics data analysis adopts a combination of the standard methods defined as three subroutines, i.e., dimensionality reduction, clustering, and Wilcoxon rank-sum test, for each gene pair and each cell pair . This stk-nearest neighborhood graph are computed and thresholded at the closest neighbors defined for data points of the manifold in Euclidean space. Following PCA, UMAP  conditional on all other variables. The edge is weighted as The equations for the zero-order, first-order, and second-order partial correlations are shown in Eqs k (WGCN) .ICAM1 gene of interest are inferred without guaranteeing the validity of each edge. Namely, possible errors within data, such as noise, could result in nodes or edges with no biological meaning. Hence, the models require corroboration and validation with heuristics based on domain knowledge. To corroborate and validate each relationship of gene networks, we query multiple knowledge bases, including Pathway Commons Web Service 12 , while the nodes of the KEGG pathways are primarily proteins. Therefore, this gap needs to be filled before overlaying the DD-KB gene networks onto the KEGG pathways. The DAVID functional annotation tool 6.8  allows uICAM1-associated pathways, visualized in yFiles Hierarchical Layout.In order to examine what types of pathways are activated, we conduct pathway enrichment analysis by mapping the protein node lists and edge lists of the DD-KB gene networks onto the KEGG pathways. In particular, the DD-KB gene networks and the KEGG pathways in the KEGG Markup Language (KGML) format are unified using Cytoscape 3.9.0 , resultiICAM1-associated pathways between different locations where ICAM1 is expressed (case study 1) and between different time points starting from hospitalization (case study 2).We applied the aforementioned framework to the two COVID-19 datasets for comparing the Homo sapiens” AND h5ad to the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) dataset (Inputting the search term [(COVID-19 OR SARS-CoV-2) AND gse(entry type)] AND “ dataset  provided dataset . Especia dataset . The cel dataset .Likewise, GSE180578, another transcriptome omics dataset, was  fetched ICAM1, our framework would be reusable in another context for mixing quantitative data and domain knowledge into building models capturing pathways. To allege the generality of the framework, in case study 2, we also applied the framework to other genes related to the interaction between cells, including ACTB and C15orf48. Here, in this main manuscript, we focus only on providing the results of ICAM1-associated pathways considering the purpose of this study. The results of other genes can be referred to in the Not limited to Before proceeding to the further steps, the single-cell omics data underwent quality control. Quality control included filtering, scaling, and normalization by Scanpy version 1.8.2 . Given tThe framework was applied concerning the two datasets. The machine configuration was as follows: Python 3.7, GPU Tesla V100-SXM2-16GB, and 51.01 GB of RAM.The data initially contained 77,650 cells × 24,714 genes. Removing the cells with a high proportion of mitochondrial RNA resulted in 15,220 cells. After filtering, the whole dataset contained 68,734 cells × 24,001 genes. Among this dataset, we used the data of four cell types, 21,819 genes in the SARS-CoV-2-infected 15,481 cells (cells with SARS-CoV-2 transcripts detected). The doublet discrimination provided 14,723 cells. The filtering processes excluded 8,916 cells with more than 5,000 expressed genes, 700 genes detected in fewer than three cells, mitochondrially encoded genes, and cells with a low percentage ICAM1, and this cluster was made of 178 ICAM1’s DCGs. The results of cellwise clustering, heatmap of DCGs, and rank-sum test are shown in p-values, while p-values of ICAM1 expression variation and the computation times (sec.) for each cell type are as follows: p = 0.250, time = 51.6 (infected AT1 and AT2); p = 2.50E-4, time = 26.1 (migratory DC); p = 2.57E-12, time = 46.6 (TRAM2); p = 7.55E-2, time = 109.0 (MoAM2); and p = 0.241, time = 178.9 (summation).The genewise analysis extracted the 18 gene clusters with differential expression patterns specific to COVID-19. Excluding the duplicated genes extracted 1,434 DEGs in 9,050 single cells. The results of genewise clustering, heatmap of DEGs, and rank-sum test are shown in Removal of spurious correlations yielded undirected graphical models . The finDependency graphs are shown in The nodes in the dependency graphs were annotated with protein names, which helped us map the nodes onto the KEGG pathways in the next step.Pathway mapping discovered which subpathways within existing signaling pathways reflect the activity of a group of varying genes and coexpressed in a disease-specific manner in the observed gene expression data. ICAM1-associated pathways for each cell type are shown in ICAM1-associated pathway for infected AT1 and AT2  in the pathway for migratory DCs , NFKB2 (gene encoding NF-κB p105 subunit 2), RELA (gene encoding NF-κB p65 subunit), JUN , and CHUK   . These mκB pathway, which is an upstream pathway of ICAM1  and MAP3K14 . These molecules are known as triggers of the non-canonical NF-κB pathway , for example, is a gene that is not found in the other pathways and could not be found without taking summation. SOD2 is known as a gene whose expression variation has been confirmed accompanied with ICAM1 in COVID-19  for each time point were as follows: p = 1.50E-05, time = 77.9 (day 1); p = 3.09E-2, time = 55.4 (day 5); and p = 3.04E-06, time = 56.3 (day 10). This manuscript does not explain the other detailed results of the single-cell omics data analysis because the procedures were the same as that in case study 1. These other results can be found at 10.6084/m9.figshare.23590755. As for the remaining steps, we explain the results of spurious correlation removal and pathway construction.Like case study 1, the original dataset underwent single-cell omics data analysis, and ICAM1-associated DCGs extracted from the omics data are shown in The pathways resulting from combining the KEGG pathways with the partial correlation networks of CXCL1, 2, 3) induced by interleukin-1 (IL1B) or TNF-α-induced protein 6 (TNFAIP6). These molecules are along the green-colored molecules, such as the NF-κB p105 subunit (NFKB1) and NLRP3 inflammasome , which are related to proinflammatory effects and activation of the NF-κB pathway or the MAPK pathway (C15orf48), chemokine ligands acting as macrophage inflammatory protein (CCL3 and CCL3L1), transmembrane protein (TMEM176A/B), nuclear factor erythroid (NFE2), peptidyl arginine deiminase 4 (PADI4), RAS oncogene (RAB20), and proto-oncogene (ETS2). These are also related to inflammation, immune response, or membrane fusion (The common characteristics and the unique attributes of the found pathways for each time point are as follows: one common feature is the presence of molecules responsible for the immune response included across the three time points. The immunoreactive molecules include the chemokine ligand ( pathway . Other ce fusion .PHLDA2) and chemokine ligand (CXCL5) (CCL4L2) (FOLR3), haptoglobin (HP), and chemokine ligand (CXCL16)  . The pat(CCL4L2) . The pat(CXCL16) . These a(CXCL16) .κB pathway  or the MAPK pathway . The absence of these molecules may be due to insufficient gene coverage in the original omics data.The obtained pathways do not contain the molecules in the NF-ICAM1-associated pathways in case study 1 with the COVID-19 Disease Map reveals existing and unknown nodes. For example, MAP2K3, MAPK14, JUN, FOS, ITGA2, ITGB1, RSAD2, OAS, and STAT2 have already been mapped onto the COVID-19 Disease Map, while RELB, ITGAL, CDC42, ACTB, CD40, DCTN1, BCL3, and CD83 in the obtained pathways are still absent in the current COVID-19 Disease Map.The comparison between the obtained ICAM1-associated pathways and the current COVID-19 Disease Map from the results of case study 2. For instance, IL1B, NFKB1, NLRP3, PYCARD, and CASP1 are listed in the COVID-19 Disease Map, while there are molecules absent from it, including CCL3, 3L1, 4L2, CXCL1, 2, 3, 5, 16, TNFAIP6, C15orf48, TMEM176A/B, NFE2, PADI4, RAB20, ETS2, PHLDA2, FOLR3, and HP.Likewise, we can identify the difference between the obtained κB pathway would likely be activated, which reflects that our framework can reproduce the already-known fact that the NF-κB pathway is activated in COVID-19, as seen in the KEGG’s COVID-19 pathway (hsa05171). As a new insight into the unknown pathways is missing from the current COVID-19 Disease Map, the results imply that COVID-19 involves the following two up/downstream pathways:κB pathway• Upstream pathway with proteins on the non-canonical NF-• Downstream pathway with integrins and cytoskeletal elements associated with actin and the motor protein dynein for cell transformationThe non-canonical NF-κB pathway is reasonable because it is relevant to the proinflammatory response in viral infections such as COVID-19. It is also creditable that TNFRSF11A is found only in the pathway of DCs  and cell division control protein 42 homolog  are essential for VS formation in HTLV-1 cells (The results from both case studies indicated that the NF-y of DCs  since TNy of DCs . The inv-1 cells . Althoug-1 cells  would beThe need to identify unknown pathways has accelerated the work related to gene network inference in COVID-19. For example, ICAM1-associated pathways currently absent from the COVID-19 Disease Map. While previous analysis or curation work found the canonical NF-κB pathway (κB pathway, and a downstream pathway that may lead to MTOC formation subject to observation.As a summary of contributions, this study discovered novel  pathway , the nonIn addition to the scientific findings, our framework, which integrates single-cell omics data analysis and model validation using multiple knowledge bases, is also original and versatile. Especially single-cell omics data analysis in Step 1 and model validation by multiple knowledge bases in Step 3 are realized to construct pathways in different cases See also . For theThe existence of undirected edges within the final pathways would be a limitation of our framework. These edges without direction arise from correlation networks that find direct and indirect relationships but do not distinguish between causality and correlation . Our metConsequently, future work will include the following two tasks: one is to infer causal networks based on data and knowledge via Bayesian networks or other observational causal discovery techniques . The othICAM1-associated pathways constructed from the data and knowledge in this study will expedite the repair and completion of the COVID-19 Disease Map for a deeper understanding of SARS-CoV-2 pathogenesis.Overall, the


Capillary leak syndrome (CLS) is a rare, potentially life-threatening systemic disease with a mortality rate of more than 30%. Its major clinical manifestation and diagnostic basis are systemic hyperedema. However, we lack knowledge about the presence of severe myocardial edema in patients with CLS. If myocardial edema cannot be detected, it will become a dangerous hidden condition that threatens the safety of patient lives. With the routine application of point-of-care critical ultrasound (POC-CUS) in clinical practice, we found that 2 of 37 (5.41%) CLS patients had severe myocardial edema as the main manifestation. It is also necessary to distinguish it from myocardial noncompaction in newborn infants with severe myocardial edema. This paper will help us to have a deeper understanding and correct management of CLS and, thus, help us to improve the prognosis of patients. This article also suggests the necessity of routine implementation of POC-CUS in the neonatal intensive care unit. The birth weight was 2260 g, and the Apgar score was 1-7-10 points/at 1, 5 and 10 min of birth. He was admitted to the hospital due to dyspnea for 140 min after resuscitation from asphyxia. At birth, there was only a heartbeat, there was no spontaneous breathing and loss of muscle tone. The patient was transferred to our neonatal intensive care unit (NICU) after resuscitation with endotracheal intubation, positive pressure ventilation and chest compressions. Physical examination on admission showed that the premature infant had a poor appearance, poor mental response, hypotonia and disappearance of primitive reflexes. Arterial blood gas analysis showed pH 7.177, BE −16.6 mmol/L and a blood lactic acid value of 16.0 mmol/L. Severe disseminated intravascular coagulation (DIC) occurred rapidly after admission and was treated with heparin and low molecular dextrose. However, during the recovery stage of DIC, the patient gradually developed systemic highly nondepressed edema and significantly reduced urine volume and hypotension, and blood biochemical tests showed that the patient’s plasma albumin was significantly decreased. Based on these findings, we made a diagnosis of capillary leak syndrome (CLS). Point-of-care critical ultrasound (POC-CUS) examination showed that the infant had severe biventricular edema in addition to obvious lung edema. The ventricular wall of the infant was significantly thickened, the size of the heart chamber was significantly reduced with a reduction in ejection fraction and cardiac output. The thickness of the interventricular septum was 0.45 cm, the thickness of the left ventricular wall was 0.54 cm  and mild asphyxia at birth (Apgar scores were 6 at 1 min of birth). The infant was admitted to our NICU 2 h after birth due to hyporesponsiveness. Physical examination on admission showed a full-term appearance and a slightly poor response. His vital signs were stable with a normal blood pressure. The muscle tone was slightly lower, and the primitive reflex slightly diminished. Routine POC-CUS examination showed marked thickening of the biventricular wall and narrowing of the cardiac chamber, especially in the right heart, with a small amount of pericardial effusion (Capillary leak syndrome (CLS) is a rare, potentially life-threatening systemic disease caused by increased vascular permeability due to cytokine damage to the endothelium that causes the intravascular fluid and proteins to shift into the interstitial space, with subsequent hypovolemic hypotension and shock ,2,3. Geneffusion . VentricThe incidence of CLS has been reported to account for 1.62% of hospitalized critically ill neonates in the early stage , showingThe mortality rate of CLS has remained above 30% or even as high as 50% ,5. Our tIt should be noted that severe myocardial edema could be misdiagnosed as ventricular noncompaction. Ventricular noncompaction mainly involved the left ventricle on echocardiography 92%), nearly half of the patients had a family history of ventricular noncompaction, echocardiography typically requires the following criteria: (1) the presence of multiple echocardiographic trabeculations, (2) multiple deep intertrabecular recesses communicating with the ventricular cavity, as demonstrated by color Doppler imaging and the recesses demonstrated in the apical or middle portion of the ventricle and (3) a 2-layered structure of the endocardium with a noncompacted to compacted ratio >1.4 ,7,8,9,102%, nearlIn conclusion, POC-CUS plays an increasingly important role in clinical practice, which not only contributes to the accurate diagnosis of diseases, but also helps to change the traditional understanding of diseases or increase the new understanding of diseases ,13,14. F