Therefore, summarizing the advantages and disadvantages of the existing methods is urgently needed in order to provide useful suggestions for the development and improvement of new computational models for the identification of the defensin peptide family and subfamily. With this goal in mind, we first provide a comprehensive survey on a collection of six state-of-the-art computational approaches for predicting the defensin peptide family and subfamily. Herein, we cover different important aspects, including the dataset quality, feature encoding methods, feature selection schemes, ML algorithms, cross-validation methods and web server availability/usability. Moreover, we provide our thoughts on the limitations of existing methods and future perspectives for improving the prediction performance and model interpretability. The insights and suggestions gained from this review are anticipated to serve as a valuable guidance for researchers for the development of more robust and useful predictors.Nearly all living species comprise of host defense peptides called defensins, that are crucial for innate immunity. These peptides work by activating the immune system which kills the microbes directly or indirectly, thus providing protection to the host. Thus far, numerous preclinical and clinical trials for peptide-based drugs are currently being evaluated. Although, experimental methods can help to precisely identify the defensin peptide family and subfamily, these approaches are often time-consuming and cost-ineffective. On the other hand, machine learning (ML) methods are able to effectively employ protein sequence information without the knowledge of a protein's three-dimensional structure, thus highlighting their predictive ability for the large-scale identification. To date, several ML methods have been developed for the  Nearly all living species comprise of host defense peptides called defensins, that are crucial for innate immunity. Defensins are considered as a part of the antimicrobial protein family and are rich in cysteine. Furthermore, defensins offer assistance to cells in combating bacterial , viral in silico prediction of defensins, including, Karnik's method . Nevertal., 2009), ID_RAAal., 2009), Defensal., 2009), iDPF-Pal., 2009), iDEF-Pal., 2009) and DEFal., 2009) as summal., 2009; Kaur etal., 2009; Ramya Kal., 2009; Zuo andal., 2009; Zuo et al., 2009, 2019[60in silico prediction of defensins, which make up two out of six existing methods  and DEFal., 2009)). The s Li, 2009), Defens Li, 2009), iDPF-P Li, 2009) and iDE Li, 2009)).in silico identification of the defensin peptide family and subfamily; (ii) we have provided several important aspects that play a crucial role for the development of reliable and stable prediction models, covering, their dataset quality, feature encoding methods, feature selection schemes, ML algorithms, cross-validation methods and web server availability/usability and (iii) we have discussed the limitations as well as the advantages and disadvantages of existing methods and provided future perspectives for improving the prediction performance and model interpretability.Motivated by the above-mentioned considerations, we provide a comprehensive comparison and analysis of the current state-of-the-art computational methods. Major contributions of this review article could be summarized as follows: (i) to the best of our knowledge, this article provides the first comprehensive review on the development of computational approaches for the in silico prediction of defensins and their family/subfamily have been developed . The geWe reviewed all the datasets used for developing the existing methods ) contaiAs can be seen from Table 1C (controls the trade-off between the misclassification rate and margin) and γ (the kernel width parameter). Although SVM often yields satisfactory prediction performances, this method is known as a black-box computation method . In pal., 2022; Li et aal., 2022; Wei et al., 2022). mtree (the number of decision trees) and mtry (the number of selected features).RF is another powerful and widely employed ML algorithm for dealing with binary classification problems , TN(i), FP(i) and FN(i) denote true positives, true negatives, false positives and false negatives of the imtry parameter based on the 10-fold cross-validation scheme and the training dataset. In this section, we conducted a performance comparison for the two existing methods for the prediction of defensins  and DEFhttps://webs.iiitd.edu.in/raghava/defpred/. As seen in Tables 1Recently, Kaur et al. created DEFPRED  for speAs mentioned above, there are four existing methods developed for the predictions of defensins family and vertebrate defensins subfamily, including ID_RAAA , Defens(Tab. 4)Zuo et al. (2015) introduIn 2019, Zuo et al. presented iDEF-PseRAAC  by applFrom Table 2P < 0.05, with the exception of His (P = 0.154), Pro (P = 0.369) and Trp (P = 0.289). In addition, Figure 2BP < 0.05, with the exception of His (P = 0.575), Pro (P = 0.341), Ser (P = 0.674) and Thr (P = 0.755). Taken together, Cys, Tyr, Arg and Asn might be important amino acids for defensins. In addition, the prevalence of these four amino acids  are significantly different between defensins and AMPs/non-defensins at the level of P < 0.05.Kaur et al. (2021) provideAs can be seen from Table 1http://bioinfor.imu.edu.cn/idpf/public/. In the case of the iDEF-PseRAAC web server, the query sequence pertains to five defensin families, including insect defensins, invertebrate defensins, plant defensins, vertebrate defensins and unclassified defensins. iDEF-PseRAAC is a multi-class SVM predictor coupled with RAAA descriptor. iDEF-PseRAAC is freely available at in silico method developed for the prediction and design of defensins. The web server provides users with two options for the prediction of the query sequence: (i) discriminating defensins from AMPs (obtained from Model 1) and (ii) discriminating defensins from any random protein sequences (obtained from Model 2). In particular, Model 1 and Model 2 were trained using SVM-based models coupled with the 50 and 60 selected important features, respectively . MoreovTo date, there are six ML-based predictors in existence that have been developed to predict defensins and their family/subfamily as summarized in Table 1First, the existing training datasets were relatively limited, especially for the defensin family and subfamily. Thus, the prediction performance of the defensin family and subfamily was not satisfying for real-life applications. In the future, when more defensin family and subfamily become available, more samples should be gathered and then employed to train a more comprehensive model , iDEF-PFourth, there is a lack of comprehensive assessment of the state-of-the-art feature encoding methods and ML algorithms in the prediction of defensins and their family/subfamily. Nevertheless, this comprehensive assessment provided could serve and facilitate users to select appropriate feature encodings/ML algorithms and provide useful guidelines for the development of more accurate and robust models in the future  compiling an up-to-date dataset, (ii) excluding highly homologous sequences, (iii) using an interpretable method, (iv) performing a comprehensive assessment of the state-of-the-art feature encoding methods and (vi) constructing a web server. We anticipate that this comprehensive review will provide useful guidance to researchers interested in developing cutting-edge computational approaches for the prediction of defensins and their family/subfamily.This review paper does not include animal or human experiments.The authors declare no conflict of interest.WS: Conceptualization, project administration, supervision, investigation, manuscript preparation and revision. PC: Data analysis; data interpretation, investigation and manuscript preparation. NS: Manuscript revision. SMHM, OT and NS: Manuscript preparation. All authors reviewed and approved the manuscript.This work was fully supported by the College of Arts, Media and Technology, Chiang Mai University, and partially supported by the Chiang Mai University and Mahidol University. In addition, computational resources were supported by the Information Technology Service Center (ITSC) of Chiang Mai University.


Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease caused by a multitude of underlying mechanisms, and molecular mechanistic modeling of COPD, especially at a multi-molecular level, is needed to facilitate the development of molecular diagnostic and prognostic tools and efficacious treatments.Objectives: To investigate the miRNA–mRNA–protein dysregulated network to facilitate prediction of biomarkers and disease subnetwork in COPD in women.Measurements and Results: Three omics data blocks  collected from BAL cells from female current-smoker COPD patients, smokers with normal lung function, and healthy never-smokers were integrated with miRNA–mRNA–protein regulatory networks to construct a COPD-specific dysregulated network. Furthermore, downstream network topology, literature annotation, and functional enrichment analysis identified both known and novel disease-related biomarkers and pathways. Both abnormal regulations in miRNA-induced mRNA transcription and protein translation repression play roles in COPD. Finally, the let-7-AIFM1-FKBP1A pathway is highlighted in COPD pathology.Conclusion: For the first time, a comprehensive miRNA–mRNA–protein dysregulated network of primary immune cells from the lung related to COPD in females was constructed to elucidate specific biomarkers and disease pathways. The multi-omics network provides a new molecular insight from a multi-molecular aspect and highlights dysregulated interactions. The highlighted let-7-AIFM1-FKBP1A pathway also indicates new hypotheses of COPD pathology. Chronic obstructive pulmonary disease (COPD) is a complex disease representing an umbrella diagnosis caused by a multitude of underlying mechanisms, including environmental exposures, genetic predispositions, and developmental factors . MoleculmiRNAs and their dysregulations in mRNA and protein expression have been proved to play important roles in the progression of COPD and other complex diseases . In partClinicalTrials.gov ID: NCT02627872), a three-group cross-sectional study  were utilized. RNA from BAL cells was isolated into two fractions containing small RNAs (including miRNAs) and large RNAs (containing mRNA) using the NucleoSpin® miRNA kit according to the manufacturer’s instructions  . mRNA frp-value ≤ 0.05). Overall, the input of construction of a miRNA–mRNA–protein dysregulated network is the three omics data, links of miRNA to mRNA regulation and mRNA to protein mapping. It outputs three status-specific dysregulated networks, an integrative dysregulated network, and a sub-network of DEGs.The miRNA–mRNA–protein dysregulated network is defined as a network with three types of nodes: 1) miRNA, 2) mRNA, and 3) protein and three types of directed edges: 1) from miRNA to mRNA as targeted regulation, 2) from mRNA to protein as translation, and 3) from miRNA to protein by bridging mRNA. The definition of a dysregulated network in this study is significantly differentially co-expressed patterns of two connected nodes between two statuses. As illustrated in the schematic of via random walks), and degree distribution using the R package igraph , hubs (nodes with high degrees), betweenness centrality (the number of “shortest paths” going through nodes), bottlenecks , communities (densely connected subgraphs e igraph . The thre igraph  were ideThe power-law degree distribution (the probability distribution of these degrees over the whole network) was estimated through linear regression analysis with R-squared as the goodness-of-fit measure.via random walks using the R package igraph , cellular component (CC), and molecular function (MF)) , KEGG K, Reactome igraph .https://www.ncbi.nlm.nih.gov/medgen/9818), DISEASE (https://cran.r-project.org/web/packages/VarfromPDB/), GAD (https://www.genecards.org/), and Disease Ontology (https://disease-ontology.org).Information for all miRNAs in the dysregulated network was retrieved from 11 disease gene databases, namely, Gene2Function , KEGG K, MegGen  DISEASE , VarfromB/), GAD , HMDD V3B/), GAD , EDGAR (B/), GAD , DisGeNEB/), GAD , GeneCarversus smokers with normal lung functions (Gc), COPD smokers versus never-smoking healthy subjects (Gu), and smokers  versus (never-smoking) healthy subjects (Gs) with FDR ≤0.2 . It also matched the modularity characteristics in most biological networks, which correspond to biological functions , as well as the most analogy in the topological characteristics of the known biological networks with both mathematical and biological meanings. It is a small-scale graph with less than three hundred nodes and edges. Both hubs (with a high degree) and bottlenecks  were important in the completeness and information transfer inside the network. It well matched the scale-free characteristics with the power-law degree distribution (the probability distribution of these degrees over the whole network) in most of the biological networks on a large scale (The dysregulated network is constructed by three-node motifs (repeated triangle structure) among miRNA, mRNA, and proteins in which the motifs themselves represent the two major mechanisms of miRNA regulation: mRNA transcription repression and protein translation inhibition  Figure . Based oWe investigated the enriched functions of this integrative dysregulated network in Gene Ontology , KEGG K, ReactomWe further integrated the topological features, function and literature annotation, and prioritization of disease biomarkers. Information on all miRNAs in the dysregulated network was assembled from ten miRNA disease gene databases . Twenty-two miRNAs have been reported by at least one database. As the coverage and annotation depths of the databases varied, these 22 miRNAs were validated manually . Furtherp-value ≤ 0.05) filter further extracts a subnetwork with 53 nodes and 45 edges with ten connected parts in Based on the definition of a status-specific dysregulated network, the network is only determined by the differences in the correlation of two nodes between two statuses but not by the nodes’ differential expression between two statuses. The differentially expressed gene –FKBP prolyl isomerase 1A (FKBP1A) pathway shows connections with the regulation of apoptosis and autophagy in disease  Figure . In brieHere, we investigated the dysregulation of the miRNA–mRNA–protein axis in COPD based on the integration of triple-omics expression and regulatory networks. Our focus was placed on the dysregulation  in women with mild-to-moderate, smoking-induced COPD. Based on the constructed differential network, biomarkers and their topological characteristics were prioritized and enriched into several at-risk biological functions and cellular components. Network motif analysis indicated that both miRNA-induced alterations in mRNA and protein abundance may play roles in COPD. Finally, downstream analysis identified that the let-7-AIFM1-FKBP1A pathway, through ROS and the mTOR pathway, may influence autophagy and cell differentiation in COPD.Several factors may influence the outcome of the dysregulated network, including sample size, study group homogeneity, and the coverage and accuracy of the reference regulatory network. Whereas a larger cohort would facilitate more robust molecular insights, the availability of cohorts that offer multi-omics data collected from the lung and the site of inflammation is scarce. The homogenous nature of our Karolinska COSMIC cohort, with the application of strict inclusion and exclusion criteria to generate a cohort of early-stage COPD patients naive of prior treatment and with no comorbidities, as well as the focus on the female sex only, aids to improve the statistical power despite the small sample sizes. We have previously shown that multi-omics integration can improve the statistical power in small group sizes . In thisThe let-7 family plays an important role in the regulation of chronic mucus hypersecretion, which has been associated with a worse prognosis and quality of life in COPD . The letHere, we presented a systematic multi-omics and regulatory network integration study to construct a miRNA–mRNA–protein dysregulation network for COPD based on the female subjects in our Karolinska COSMIC cohort. Each miRNA is predicted to target multiple mRNAs, and conversely, each mRNA can be targeted by many different miRNAs . Althoug


The WHO provides standardized outcome definitions for rifampicin-resistant (RR) and multidrug-resistant (MDR) TB. However, operationalizing these definitions can be challenging in some clinical settings, and incorrect classification may generate bias in reporting and research. Outcomes calculated by algorithms can increase standardization and be adapted to suit the research question. We evaluated concordance between clinician-assigned treatment outcomes and outcomes calculated based on one of two standardized algorithms, one which identified failure at its earliest possible recurrence , and one which calculated the outcome based on culture results at the end of treatment, regardless of early occurrence of failure .Among 2,525 patients enrolled in the multi-country endTB observational study, we calculated the frequencies of concordance using cross-tabulations of clinician-assigned and algorithm-assigned outcomes. We summarized the common discrepancies.Treatment success calculated by algorithms had high concordance with treatment success assigned by clinicians . The frequency and pattern of the most common discrepancies varied by country.High concordance was found between clinician-assigned and algorithm-assigned outcomes. Heterogeneity in discrepancies across settings suggests that using algorithms to calculate outcomes may minimize bias. Evaluating end-of-treatment (EOT) outcomes for rifampicin-resistant (RR) and multidrug-resistant (MDR) TB is important for clinical care and programmatic monitoring. To facilitate the outcome evaluation for RR/MDR-TB, the WHO provides standardized outcome definitions.1Observational research plays an important role in generating evidence to guide RR/MDR-TB treatment recommendations. Standardized application of outcome definitions when analyzing RR/MDR-TB cohorts is one step, among many, that investigators may take to generate valid, comparable results across studies.In this study, we applied WHO outcome definitions to a cohort of patients treated for RR/MDR-TB. We programmed two algorithms, each for a distinct research purpose, and compared algorithm-assigned outcomes to clinician-assigned outcomes, with the overall goal of understanding the extent to which different operationalizations of the same definitions may lead to diverging outcome assignment and potential bias in observational RR/MDR-TB treatment cohorts.n =155, the only participating country that a priori implemented shortened regimens for RR/MDR-TB), and those for whom there was no recorded clinician-assigned outcome (n = 82) or an outcome of not evaluated, transferred, or “treatment adapted” (n = 27).Data were derived from the endTB observational study, a prospective cohort of patients treated for RR/MDR-TB with an individualized, longer regimen containing bedaquiline- and/or delamanid, composed according to WHO and local guidance, in one of 17 countries.Outcome assignments were based on the 2013 WHO definitions in place during the study period.As part of routine care, clinicians assigned a treatment outcome to each patient based on available laboratory results and clinical information, in accordance with WHO outcomes.1We created two algorithms based on WHO outcome definitions. The first was designed to establish failure at its earliest possible occurrence . The second assigned outcomes based on culture results available at the end of treatment, regardless of whether the patient’s experience initially met the definition for early treatment failure . Outcomes generated under the success-dominant algorithm reflected treatment response to the initial regimen and any subsequent regimen changes . For example, a patient who underwent early bacteriological reversion from culture-negative to positive, but whose experience ultimately met the definition for cure at the end of treatment, would be assigned an outcome of failure under the failure-dominant algorithm and an outcome of cure under the success-dominant algorithm.Outcomes under each algorithm were calculated as follows. The failure-dominant algorithm  identified the earliest date of treatment failure after 8 months of treatment (as proxy for the intensive phase). Patients who did not experience treatment failure and were not assigned an outcome of death remained eligible for an outcome of success, which was calculated as in the success-dominant algorithm. In the success-dominant algorithm , patients were assigned an outcome at the end of treatment based on the available culture results. Longer regimens used were intended to last 18–20 months; in some instances, however, clinicians stopped treatment before 18 months. To reflect the intent that all included treatments were longer regimens, we imposed a minimum treatment duration of 15 months for an outcome assignment of cure or treatment completion.Outcomes of cured, treatment completed, and “<15 months, favorable” were classified as treatment success. Outcomes of died, failed, LTFU, and “<15 months, unfavorable” were classified as no success.We calculated the frequencies of patients with each outcome across the three approaches to operationalizing WHO outcome definitions. We also calculated the frequencies of concordance between algorithm-assigned and clinician-assigned outcomes for each individual outcome and for the broader dichotomous category of treatment success versus no success. To evaluate the overall concordance between clinician-assigned and algorithm-assigned outcomes, we calculated the simple κ coefficient and 95% confidence intervals (CIs) for the dichotomous category of treatment success. We summarized the common discrepancies.The endTB observational study protocol was approved by all study countries  and central ethics review committees for each consortium partner. Patients provided written informed consent for inclusion in the observational cohort.After exclusions, 2,525 patients with RR/MDR-TB were included for analyses . Overall, the frequencies of treatment success were 78.3% , 77.4% , 80.0%  using clinician-assigned, failure-dominant and success-dominant outcome definitions, respectively .The frequency of concordance between clinician-assigned outcomes and algorithm-assigned failure-dominant outcomes for the dichotomous category of treatment success was 95.8%  and for individual outcomes was 87.2%  . The κ cIn a sensitivity analysis excluding patients with outcomes of “<15 months, unfavorable” and “<15 months, favorable”, the frequencies of concordance for treatment success remained high, at 96.3% , for the failure-dominant algorithm, and 98.3%  for the success-dominant algorithm.Most discrepancies in individual outcomes  did not affect whether the outcome was classified as successful. Of the discrepancies identified, there were two common patterns: one involved a clinician-assigned outcome of treatment completed which was classified as cured by the algorithms; another pattern involved a clinician-assigned outcome of cured which was classified as “<15 months, favorable” in algorithm-assigned outcomes.n = 59, 90.8%) occurred when a clinician assigned an outcome of cured or completed in the presence of at least two positive cultures or after the addition of two new drugs added to the initial regimens by the end of 8 months of treatment. The remaining 42 of 107 patients (39.2%) were assigned an outcome of unsuccessful treatment by the clinician but were classified as an outcome of successful treatment by the algorithm.A total of 107/323 (33.1%) and 59/269 (21.9%) discrepancies between clinician-assigned outcomes and the failure-dominant and success-dominant algorithms, respectively, affected whether the outcome was classified as successful. Among the discrepancies between clinician-assigned outcomes and those calculated with the failure-dominant algorithm, 65 (60.8%) patients were assigned an outcome indicative of treatment success by the clinician but were classified as no success by the failure-dominant algorithm . Most ofn = 48, 94.1%) of these outcomes were classified as treatment success by the algorithm because the final cultures were negative.For discrepancies between clinician-assigned outcomes and those calculated with the success-dominant algorithm , the preCountry-specific sample sizes ranged from 5 to 671 . Across Outcomes derived using each of two algorithms had high concordance with clinician-assigned outcomes, suggesting that all three approaches generally yielded outcomes that reflect WHO outcome definitions. Although relatively rare, we identified patterns of discrepancies between clinician-assigned and algorithm-assigned outcomes that varied in frequency across settings. For example, the percentage of discrepancies between clinician-assigned and failure-dominant algorithm-assigned outcomes ranged from 0 to 20%. While small sample sizes in some countries likely contributed to variability, heterogeneity could also be due to differences in training, experience, and the flexibility with which outcome definitions are applied by clinicians, all of which may differ by setting, leading to varying degrees of bias.We also found that types of discordancy, such as treatment success by clinicians to no success by algorithms (or vice versa), differed across settings. For example, in some countries, discrepancies occurred in only one direction, while in others, they occurred in both directions. Although the latter may not necessarily bias the absolute proportion, it will cause bias in risk factor and comparative effectiveness analysis or individual patient data meta-analysis if the approaches used for outcome assignment differ by setting. Regimens are also often clustered by setting based on the national TB program guidelines for regimen composition; if some countries or clinicians apply more or less stringent criteria  for outcome classification, bias may occur and preclude correct conclusions regarding relative treatment effectiveness. Operationalizing WHO outcome definitions using a consistent approach across settings and reporting the approach used for outcome assignment could reduce such bias.The approach used for outcome assignment should align with the intended purpose.Although algorithms could improve outcome classification by removing bias and variability, clinician-assigned outcomes may sometimes most accurately capture the intent of definitions . This scenario represents an inherent limit to algorithms; these rely on available data and could be biased if factors considered by clinicians are not available to the analysis team. A second example would be if a culture result was available in clinical chart but not entered into the database. Likewise, algorithms are less informed than clinician-assigned outcomes if culture results are missing. Missingness of culture results could be due to reagent stock-outs, limited laboratory capacity, or a patient’s inability to produce an adequate sputum sample .Evidence from this study supports the use of algorithms to calculate EOT outcomes for RR/MDR-TB research cohorts. Doing so may be particularly important in multisite studies, or those with follow-up periods encompassing different outcome definitions, in order to reduce heterogeneity across countries and among clinicians and to minimize bias.


Both radio frequency (RF) and in-phase and quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% compared to conventional localization. In addition, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of this article support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data.Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)-based localization technique that uses both Field-II simulation and  I.As a diffraction-limited imaging modality, the performance of ultrasound imaging has long been limited by the classical tradeoff between imaging resolution and penetration depth. For decades, there has been a long quest to break the diffraction limit of ultrasound . For exaRecently, a contrast microbubble (MB)-based technique named ultrasound localization microscopy (ULM) , 9] was was9] wain vivo, and other practical challenges such as tissue motion caused by breathing and scanning. These limitations and challenges remain active topics of research. In this study, we focus on addressing the issues of MB localization that contribute to the time-consuming data acquisition and high computational cost associated with postprocessing. The current standard practice for MB localization is based on using an estimated template of the ultrasound system impulse response  to identify individual MB signals and measure the physical location of the MB . However, an ultrasound imaging system has a spatially variant PSF that is subject to many confounding factors, such as noise, phase aberration, multipath reverberation, and attenuation. Estimation of PSF is a challenging task, which leads to unreliable MB localization. In addition, as shown in in vivo, shown in . Since MAnother challenge associated with MB localization in practice is the tradeoff among MB concentration, data acquisition time, and localization accuracy. A diluted concentration of MB creates more MB signal separation in space, which makes the MB locations less ambiguous and easier to estimate. However, dilution of MB also slows down MB perfusion, resulting in longer acquisition times for full vascular reconstruction . On the et al.  pr pret al.et al. . The MB et al.  an an38] anμm pixel size. This FOV was selected to balance several factors, including being able to capture structural features of the vessel network on multiple scales, not exceed the computer memory constraints, and maintain the efficiency of training the NN. With confocal imaging . M. M50]. MV.in vivo CAM surface vessel structure. The NN was able to sharpen the ultrasound MB signal so that overlapping and distorted MB signal were able to be better localized. Under low concentration, DL-based methods were able to more precisely localize individual MBs. As concentration increased, the behavior of all localization methods changed to localizing speckles rather than individual MB. The DL-based method was still able to improve the localization accuracy and reduce saturation time by localizing more MB clusters in each frame of ultrasound acquisition. Furthermore, the DL-processed ultrasound data can be utilized to achieve real-time high-resolution visualization of blood flow dynamics. The proposed method can be further improved by developing a training dataset with better ground truth and incorporating temporal information of MBs in training.In this article, we studied the performance of DL-based MB localization method for ULM for increasing MB concentration. A U-Net style CNN was trained with Field-II simulated ultrasound data (both envelope detected and RF data) based on supp2-3152225supp3-3152225supp4-3152225supp1-3152225


Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene co-expression network databases that store correlation values, expression profiles, gene names and gene descriptions have been developed. Although these resources remain scattered across the Internet, such databases complement each other and support efficient growth in the functional genomics area. This review presents the features and the most recent gene co-expression network databases in crops and summarises the present status of the tools that are widely used for constructing the gene co-expression network. The highlights of gene co-expression network databases and the tools presented here will pave the way for a robust interpretation of biologically relevant information. With this effort, the researcher would be able to explore and utilise gene co-expression network databases for crops improvement. Transcriptomics is the measurement of transcript expression levels in different tissues, stages or conditions. In plant sciences, transcriptomics is increasingly being used to understand the expression patterns in different tissues and conditions, and to unravel the molecular mechanisms controlling biological processes ,2,3. Thehttps://www.ebi.ac.uk/ena/browser/home, accessed on 10 March 2022), have allowed researchers to upload transcriptomic data, enabling data reproducibility for others. Since then, the number of microarray and RNA-seq experiments in the GEO, ArrayExpress and ENA has grown rapidly. In recent years, there has been an increasing interest in using public microarray and RNA-seq datasets to perform gene co-expression network analysis (GCN) [Several public databases, such as GEO , ArrayExis (GCN) ,7,8.Gene co-expression network (GCN) provides an essential tool for the study of systems biology. The GCN demonstrates that genes are nodes, while edges represent the genes that connect to each other via co-expression relationships . Co-exprThe construction of the GCN requires large-scale and high-quality datasets extracted from RNA-seq or microarray datasets, with many processing steps and a significant amount of computational resources being utilised . The larA GCN is constructed from gene expression data in the form of a matrix, in which rows correspond to genes and columns correspond to samples. The relevant R packages  can be used to construct the network. The Pearson correlation coefficient (PCC), Spearman’s correlation coefficient (SCC), Kendall rank correlation coefficient (KCC) and mutual information (MI) are widely used to measure the correlation between co-expressed genes . The PCCAlthough the whole genome sequence in crops is widely developed, plant gene annotation is still scarce. Previous studies have used the GCN as a source to annotate unknown and uncharacterised genes involved in specific biological roles ,16 and tTo date, several web-based tools have been developed to allow researchers to construct the GCN, such as CoExpNetViz , Large-SIn the past few years, several reviews on the GCN in plant biology have covered the topics of protocols, tools and algorithms used for performing GCN analysis. In addition, the database resources used, issues in performing statistical tests and the challenges of GCN have also been discussed comprehensively ,14,29,30Co-expression tools have been widely used in plant research to construct GCNs and to visualise co-expressed genes. CORrelation NETworks (CORNET) is a web-based tool for constructing and visualising co-expressed genes gathered from microarray data, protein–protein interactions, and regulatory interactions . The micCORNET also has other features, including “User-defined”, that allow users to upload their own microarray dataset and analyse it in the CORNET. The function under ‘PPI tool’ enables users to integrate the co-expression network with the protein–protein interaction network (PPI). Integrating the two different data types provides the flexibility to investigate the association between genes and proteins, and to obtain the functional annotation of the uncharacterised genes.CoExpNetViz and PlaNet are co-expression tools that can perform comparative co-expression networks within and between crops species. CoExpNetViz is a co-expression tool that analyses queries of genes from transcriptome data and calculates a correlation matrix for plant species . Then, iPlaNet (Plant Network) is a comparative tool for co-expression networks of 11 plant species  ,38. Thisq-value threshold to find the most highly expressed or repressed clusters. Using RECoN will facilitate users to identify potential regulatory genes, biological processes and pathways that are crucial for abiotic stress responses.The Rice Environment Co-expression Network (RECoN) is a co-expression tool designed to identify clusters of functional genes that are tightly co-expressed in a collection of rice abiotic stress gene expression datasets from a wide range of environmental conditions . RECoN rwebCEMiTool is an online platform that enables users to perform GCN analysis for any organism. The functional studies performed in webCEMiTool include the identification of hub genes for each gene network, gene ontology and pathway enrichment analyses . webCEMiThe weighted correlation network analysis (WGCNA) is the earliest R package for constructing the correlation network. WGCNA is built on the concept of a scale-free network where it uses a series of correlations to identify groups of genes that are expressed together in a dataset. It can find clusters of highly correlated genes, summarise the clusters using the module eigengene or an intramodular hub gene, and calculate topological properties . Other fPetal is one of the R packages developed to construct scale-free GCN models by following the standard flow of co-expression analysis . Using pHandling large-volume of expression data is quite challenging. Hence, a Large-Scale Transcriptome Analysis Pipeline (LSTrAP) was developed to combine the essential bioinformatics tools to construct a GCN . The proConsistency of Gene Expression NeTworks (COGENT) is designed to facilitate users who are often unclear on the methods and parameters in the GCN analysis that should be selected. Hence, COGENT can be used to choose different co-expression measures, cut-offs and threshold choices in GCN analysis . When coGWENA is an R package through Bioconductor that has been developed for gene co-expression network construction and analysis . The GCNThere are not many tools available for differential co-expression network analysis. Hence, Juxtapose was developed to address this gap. Juxtapose was performed in command line mode using Python . JuxtapoA total of 14 GCN web-based databases host co-expressed genes in different crop species (links and references described in The Rice Expression Database (RED) and Rice Functionally Related Gene Expression Network Database (RiceFrend) are two co-expressed gene databases in rice. RiceFrend is a gene co-expression database based on an extensive collection of 24 transcriptome datasets representing 815 microarray datapoints, derived from various tissues and organs at different stages of growth and development under natural field conditions . All theRiceFREND also provides gene enrichment and cis-elements analyses. Identifying cis-elements for co-expressed genes could provide additional information for gene function prediction. Users can select the respective genes in the table box and choose the option button for each analysis to perform this analysis.p-value. The query gene is highlighted in red for the gene network, while its paired-gene is highlighted in blue. The results can be formatted in Excel and CSV formats and viewed in Cytoscape. In total, 11,153,091 co-expressed genes are stored in the RED database.The RED database serves co-expressed data that was analysed using high-quality RNA-seq datasets obtained from NCBI SRA . Seventehttp://bioinformatics.cau.edu.cn/MCENet/ (accessed on 10 March 2022). Users can use the ‘Network Search’ function to search for co-expressed genes with one or several genes. In addition, users can identify gene modules using ‘Module Finder’ and obtain the GO enrichment analysis.MCENet  is a database for global and conditional co-expression network for maize . The tenhttp://www.bio.fsu.edu/mcginnislab/mcn/main_page.php, accessed on 11 March 2022) [Public RNA-seq maize datasets have also been used to construct the GCN for the maize GCN web-based database (ch 2022) . Three mSorghum functional genomics database (SorghumFDB) was established as a functional genomics data mining platform . This weVitis vinifera co-expression database (VTCdb) is a web-based database used to search the gene co-expression networks of grapevine cultivars [V. vinifera GeneChip (16 K) and the 463 datasets from the NimbleGen Grape Whole-genome microarray chip (29 K). Four features are available in VTCdb, including ‘single guide gene query’, ‘multiple guide gene queries’, ‘keyword query’ and ‘browse meta-network. Browsing the ‘Meta-network’ feature will display the grapevine meta-network and modules of densely connected nodes. Under the ‘single guide gene query’, users can insert a grapevine gene ID into the CoexQuery field and select the predefined conditions, such as ‘All’, ‘Berry’ and ‘Stress’. The co-expression measure can also be chosen, such as HRR, MR and PCC. The result page shows the functional annotation of query genes and a list of co-expressed genes sorted by the ascending metric of interest. The information of co-expressed genes includes the gene names, probesets, modules, expression profiles and gene ontology terms. All results are displayed in a table format. The result page shows an interactive visualisation of the gene co-expression network. VTCdb also provides an analysis tool to query the expression profile for the genes of interest. Users can input the RefSeq identifier and also perform keyword searches. Mining the co-expressed genes in VTCdb facilitates users to gain insights into grapevine transcriptional regulation, gene prioritisation and the comprehensive annotation of functions for unknown genes.ultivars . The co-p-value and the p-score representing the ORA and GSEA analyses.CoxPathDB is a GCN database for tomatoes, enabling the identification of strongly co-expressed genes associated with biological processes and pathways . This paThe AppleMDO database consists of co-expressed genes analysed from 112 RNA-seq datasets of Golden Delicious apple . The genInterestingly, AppleMDO also provides comparative co-expression networks between different species, allowing for the identification of orthologs via GCN analysis. Additional tools in the AppleMDO include gene ontology enrichment analysis, blast analysis, motif analysis, ID conversion and sequence extraction. Using AppleMDO provides molecular resources that could benefit apple research communities and serve as a reference for other fruit species.P. edulis) that allows users to search for co-expressed genes and modules [BambooNET is a co-expression network database of moso bamboo ( modules . It can C. sinensis) that allows users to search for candidate genes related to agronomical traits [TeaCON is a gene co-expression network database for tea plants  and co-expressed TF families under the ‘Browse’ sections. The results are displayed in a table format and information on the genes, including gene ID, chromosome location, gene ontology, and KEGG pathway ID, and are shown with a co-expressed gene list containing the PCC and p-values. The co-expressed genes are also visualised in a network interaction and highlighted in two different colours: red for the query genes and blue for their pairs. Format data .sif can be downloaded and opened in Cytoscape. The PCC cut-off can be adjusted from 0.6 to 1. Additional tools are integrated into the TeaCON database, such as BLAST, GO and KEGG. The resources in TeaCoN can assist the tea plant research community in understanding biological mechanisms and validating potential genes for commercial tea cultivation and characteristics.https://brassica.biodb.org/, accessed on 13 March 2022), a resource for gene expression profiles of rapeseeds, and has a section on the gene co-expression networks of rapeseeds, which were constructed using WGCNA from 103 rapeseed (B. napus cv. ZS11) transcriptome datasets [p-value < 0.01, are deposited in BrassicaEDB. The co-expressed data can be accessed via the ‘Co-expression’ section, which provides the information on gene weight, PCC and p-value. A network interaction of paired genes is displayed, and it enables users to export the list of co-expressed genes in an Excel format. Users can limit the number of paired genes displayed by inserting a number in the empty box.BrassicaEDB , Brassica rapa (field mustard), G. max (soybean), M. truncatula (barrel medick), O. sativa (rice), Populus trichocarpa (poplar), S. lycopersicum (tomato), V. vinifera (grape) and Z. mays (maize)—can be searched via ATTED-II , a plant co-expression database analysed from microarray and RNA-seq experiments (DDBJ and SRA databases) [The co-expressed genes of nine plant species—tabases) . When coFour sections in ATTED-II include ‘Search’, ‘Browse’, ‘Draw’ and ‘Bulk’ and ‘Download’. The fastest way is to select the ‘Browse’ page, which displays the list of the co-expressed genes for subcellular location cis-elements, and summarises the co-expressed data. In the ‘Search’ section, users can query the specific genes of interest, and the results display the list of co-expressed genes. The co-expressed genes information includes the rank, average LS to query gene, gene symbol, gene function, gene ID from Entrez and RAPDB databases, hyperlink to KEGG pathway and expression patterns in a heat map format. Interestingly, ATTED-II allows the comparison of co-expressed genes among nine species, which is not available in other gene co-expression network databases. Additionally, ATTED-II enables users to analyse the co-expression relationships of genes under five pre-defined conditions, including tissue and development, abiotic stress, biotic stress, hormone treatment and different light regimes.A. thaliana, G. max, H. vulgare, O. sativa, S. lycopersicum, T. aestivum, V. vinifera and Z. mays. PLANEX also uses K-means clustering for network clustering and selects a threshold of 0.001, as well as performs gene ontology enrichment analysis and Cohen’s Kappa to compare the functional similarity for all genes in the co-expression database. Using PLANEX, the user can determine the expression similarity and functional enrichment of input genes via co-expressed genes. Interestingly, PLANEX also performs a comparative gene co-expression network among species.The PLANt co-Expression (PLANEX) is a web-based database for co-expressed genes, enabling the functional identification from various Affymetrix microarray data, retrieved from the NCBI GEO database . PLANEX https://plantnexus.ohio.edu/ (accessed on 13 March 2022).A valuable resource for a global gene co-expression network of barley and sorghum is PlantNexus, which enables users to search for co-expressed genes that infer regulatory mechanisms in biological processes . The GCNO. sativa, Z. mays, S. lycopersicum) are included in the CoNekT. This web-based database allows users to search for co-expressed genes and their neighborhoods and perform comparative co-expression network analysis across different species and species-specific comparisons. CoNekT analysed the RNA-seq datasets from the SRA database. The GCN analysis used the highest reciprocal rank (HRR) metric score to measure the correlation relationships, while the heuristic clustering chiseling algorithm (HCCA) was used for clustering identification.CoNekT is a web-based platform that has been developed to provide information on gene expression data and co-expression networks in selected plant species , for insThree functionalities  are provided in the CoNekT. Users can choose ‘Species’, which displays a list of species and statistics of transcripts, profiles and networks. The easiest way is to select the species name, and it will display a list of gene IDs and gene descriptions. Users can identify the details of co-expressed genes and expression profiles by clicking the gene ID. Then, CoNekT provides the neighbourhood gene and cluster ID that belongs to the co-expressed genes.Interestingly, users can choose different visualisation types, such as tables, charts and networks to display the co-expression results. Moreover, CoNekT is available for the user to download and install onto a local server. This function offers the benefits of searching large-scale expression data by allowing users to select crops or plants of interest.CoCoCoNet serves as a web-based platform to compare co-expression networks between a diverse set of 14 species, including plants, zebrafish and humans . The RNATo exemplify a comparative GCN analysis, we used rice chalcone synthase (CHS) and chalcone isomerase (CHI), which are parts of the flavonoid biosynthesis pathway. CHS and CHI are the key enzymes in flavonoid biosynthesis. CHS is known to catalyse the first step in the flavonoid biosynthetic pathway to produce naringenin chalcone . Next, CBased on Understanding the genotype–phenotype correlations is one of the primary issues in plant systems biology, and GCN has provided a novel avenue for researchers to investigate the interactions and associated biological mechanisms. The GCN approach has been widely used to determine whether genes are substantially co-expressed or differently co-expressed in various biological contexts. To date, the GCN has been found great utility in gene annotation not only in model systems, but also in less characterised crop species.In this review article, we have provided an overview of available GCN tools and web-based databases. This review has revealed that each tool and database has their own uniqueness and advantages; some of them are widely used, while others are new. To make the outcome of this review, several challenges and issues in the GCN tools and databases can be addressed and suggested. We identified that six GCN tools have been developed to perform the GCN analysis using the command line approach . This paParameter tuning is often challenging to decide, especially in selecting the correlation threshold . Hence, parameter optimisation is always required to reduce or avoid a bias in the GCN analysis and to generate a biologically meaningful co-expression network. The availability of web-based tools will ease the user to fine-tuning and optimise the parameters, as they can run the GCN analysis multiple times in a shorter time. There are four GCN tools  and four GCN databases  that serve the comparative analysis of GCN across species and species-specific data. The comparative analysis of GCN can determine the similarity and differences between two or more networks. With the availability of this function, it enables users to study evolution, especially in non-model organisms . For insIn comparative GCN analysis, mapping or convert the gene identifier (ID) from one species to another is essential due to the different gene ID formats used. The common gene ID used in the GCN databases are from the Ensembl and Entrez databases. Users must understand the gene ID that belongs to the individual species. However, not many GCN databases offer a gene ID conversion tool. Providing the gene ID conversion tool will ease the user to convert the gene ID between various species in a single platform and in a high-throughput manner.Among the 14 GCN databases, nine of them used public transcriptome datasets to generate the GCN. This finding indicates that there is a growing interest in using public datasets that are beneficial for crop biologists. Interestingly, the combination of microarray and RNA-seq datasets for constructing the GCN could increase the biologically meaningful information. Different statistical methods have been used to construct the GCN databases, such as PCC, SCC, Highest Reciprocal, MI and MR. For instance, PCC is often utilised across multiple databases because of its sensitivity to outliers and has been well-performed on linear relationships between two variables. However, its performance reduce on non-linear relationships . SCC is Different databases use different strategies in generating co-expression data, i.e., how the transcriptome dataset is chosen, how gene expression is quantified and normalised and what statistical metrics are used to measure co-expression. These are all examples of discrepancies, thus giving co-expression analyses even more ways to be performed. Some of the databases  used multiple statistical methods to measure the co-expression relationships, while others used only one statistical method . The combination of multiple statistical methods could increase the sensitivity across a multiple network structure and facilitate the decision of candidate genes for predicting their function and performing experimental validation. At the moment, there is no agreement on the best statistical methods, since different approaches work best for answering different biological questions .The most widely used correlation method is PCC due to its simplicity . AlthougTo overcome this barrier, it has been suggested to combine linear and non-linear relationships, in which the GCN analysis results will be more comprehensive . By usinAnother speciality that can be observed in the GCN database is the flexibility of the output format that can be imported into the Cytoscape. Hence, users can edit and improve the biological network of their interest. Finally, the major challenge in the GCN web-based database is updating the database regularly, based on the upcoming transcriptomic data of the future. Not many GCN databases include the ‘last-update’ on their page. Consequently, users find it difficult to know whether the co-expressed data is the latest update or not.Gene co-expression network analysis offers an efficient approach for suggesting hypotheses in gene function prediction . ResearcDespite this, the ability of plant scientists to effectively adopt computational approaches is heavily dependent on database functionalities and features, such as user-friendly interfaces, simple accessibility, manuals and tutorial videos. Numerous user-friendly GCN databases could be widely utilised to aggregate omics-scale data from diverse approaches in order to annotate the candidate genes and assign hypotheses involved in specific traits. Consequently, it will improve the crop traits and increase agricultural yield and climate change resilience. Until now, only 14 GCN databases are available for some agriculturally important crops . The GCN database is still lacking in economic crops, such as banana, cocoa, durian, papaya, peach and strawberry. Hence, we expect the list to grow in the future.


Background. Drug-induced lupus (DIL) is an autoimmune phenomenon where the patient develops lupus-like symptoms after exposure to a long-term medication. Case Summary. Here we describe a 10-year-old female with absence seizures who developed a lupus-like syndrome after being on ethosuximide for three months. She presented with nephrotic syndrome (NS) and acute kidney injury. Four weeks prior to presentation, she had been prescribed a seven-day course of oral amoxicillin for submental swelling after dental extraction. Investigations showed high titer of antinuclear antibody (ANA) and anti-double stranded DNA, elevated serum IgE level, and positive Coombs’ test, along with positive anti-histone antibodies. Renal biopsy showed features of acute tubulointerstitial nephritis (TIN) and partial podocyte foot process effacement without evidence of lupus nephritis. The patient had an excellent response to the steroid therapy with remission within two weeks. The patient remained in remission for two months as evaluated during the most recent follow-up; the autoimmune antibodies and immunoglobulin E trended down. Ethosuximide has been reported to cause DIL, however its possible association with TIN has not been reported. Although amoxicillin could have caused the TIN and NS in this patient, a possible novel association of ethosuximide with this nephrotic-nephritic presentation (NNP) cannot be ruled out. Conclusions. A renal histology is important to determine the accurate etiology of NNP in patients with DIL. Further studies are necessary to determine any possible causal effect of ethosuximide with NNP. Drug-induced lupus (DIL) is an autoimmune phenomenon where the patient develops lupus-like symptoms after exposure to certain medications, usually months to years after initiation . It has Here we report a case of DIL associated with ethosuximide in a patient who presented with acute kidney injury (AKI) and nephrotic syndrome (NS). The kidney biopsy revealed acute tubulointerstitial nephritis (TIN) and podocyte injury without evidence of lupus nephritis. As drug-induced TIN represents two-thirds of total TIN cases with antibiotics responsible for approximately 30–50% of these cases [This is a retrospective case study, and the family of the patient provided the consent for the study.A 10-year-old African American female presented with bilateral lower extremity edema, abdominal distension, malaise, loss of appetite, nausea, and weight gain for two weeks. There was no history of fever, gross blood in urine or joint pains. Past medical history was significant for absence seizures, and she was started on ethosuximide 250 mg daily three months prior to presentation. The staring spells decreased from 8–10 episodes per day to 5–6 episodes per week after initiation of ethosuximide. Four weeks prior to presentation, she developed submental swelling following a dental extraction and received a seven-day course of amoxicillin. The patient had no history of prior allergic reactions with amoxicillin. There was no history of recent usage of non-steroidal anti-inflammatory drugs (NSAID), over the counter medications or proton pump inhibitors. There was no recent viral upper respiratory or gastrointestinal illness, streptococcal throat or skin infection and skin rash. Family history was not suggestive of systemic lupus, NS, other autoimmune diseases, renal failure, dialysis, kidney transplantation, or sickle cell disease. Her immunization was up to date and she had normal growth and development.On examination, the vital signs showed an afebrile child with a heart rate of 74 beats per minute, respiratory rate 20 per minute, blood pressure 122/86 mm Hg , and oxygen saturation of 96% on room air. Her current weight was 49.4 kg (95th percentile) and the baseline weight prior to presentation was 41 kg. Physical examination showed puffiness of eyes and face, abdominal distension, and 1+ bilateral pitting edema of the lower extremities. There were no oral ulcers, joint swelling, tenderness or erythema. Skin examination showed some non-itchy and non-painful excoriated pustules on the frontal hairline and upper thighs posteriorly. There was no malar or discoid rash. Eye examination showed no redness; a formal ophthalmology examination showed no uveitis. Mental status was normal without seizures.Initial renal function test and complete blood count values are shown in Chest X-ray revealed no pleural effusion, pneumothorax, consolidation, or acute abnormalities. Renal sonogram demonstrated bilateral echogenic kidneys with right kidney measuring 11 cm in length and left kidney measuring 11.2 cm in length without hydronephrosis. A percutaneous sonogram-guided renal biopsy which was obtained three days after the onset of current signs and symptoms showed two portions of renal cortical tissue with ten glomeruli. Light microscopy showed a segmental mild increase in mesangial cellularity with moderate increase in the mesangial matrix. There was no segmental sclerosis, cellular crescent, nor area of segmental necrosis . The tubThe patient was treated with albumin/diuretic infusions for the symptomatic relief of edema and the weight trended down to near her baseline at discharge. Due to severe TIN and NS, a course of steroids, prednisone 60 mg daily, was started with gradual tapering over six weeks. Serum creatinine trended down to 1.13 mg/dL at discharge. Due to concerns of DIL, ethosuximide was changed to zonisamide after consultation with neurology and switched again after three weeks to sodium valproate for better seizure control. Rheumatology consultation was carried out, who agreed with the diagnosis of ethosuximide-induced DIL. Mild anemia persisted with serum hemoglobin of 10.8 gm/dL at the time of discharge.2), serum albumin improved to 3.4 g/dL from 2.2 g/dL at discharge, urinalysis was negative for blood and protein, with normal spot urine protein creatinine ratio of 0.12 mg/mg. Serum 25 hydroxy-vitamin D and IgE improved. At two months follow-up, ANA was weakly positive with titer of 1:160 (homogenous pattern), dsDNA antibody was 10 IU , and anti-histone antibody was 1.0 unit  drug reaction which can be further sub-classified into type I-IV hypersensitivity reaction. The clinical picture of DIL is mainly heterogeneous and generally characterized by systemic symptoms such as fever, weight loss, anorexia, and arthralgia. Arthritis and serositis may occur (particularly in procainamide-induced cases), whereas central nervous system, renal, gastrointestinal, and hematologic abnormalities and cutaneous lesions are rare ,5. OrganSome of the drugs known to cause DIL are isoniazid, procainamide, hydralazine, TNF α inhibitors , and minocycline, among others ,2. IncidThe diagnosis of DIL is established based upon the history of sufficient and continued exposure to a specific drug, at least one symptom compatible with systemic lupus, no prior history of lupus and resolution of symptoms within weeks after discontinuation of the offending agent . Our patThe proposed mechanisms of autoantibody induction by the lupus-inducing drugs include an immune response to the drug in the form of a hapten or to a self-antigen altered by the drug which then induces antibodies that cross-react with or cause spreading of the immune response to the native self-macromolecules. Other mechanisms include direct activation of B and/or T lymphocytes by the drug, drug-specific T cells activating autoreactive B cells presenting the drug, and prevention of establishment of immune self-tolerance by the drug . The reaOver two-thirds of TIN cases are drug-induced . AntibioDrug-induced TIN is thought to be a T-cell mediated type-IV hypersensitivity reaction due to the latent period between drug exposure and the development of a rash and eosinophilia. Furthermore, the drugs may act as haptens that bind to the cytoplasmic or extracellular components of tubular cells during secretion to modify the endogenous response to native renal proteins or induce an autoimmune reaction to the tubular basement membrane through molecular mimicry . In somen = 27) and phenytoin (n = 8), the excretion of α1 microglobulin was significantly increased, as compared with the healthy controls. On valproate (n = 20), ethosuximide (n = 9), and phenytoin (n = 8), the excretion of N-acetyl-beta-D-glucosaminidase was significantly increased. This study showed that these anticonvulsants may cause functional disturbance of the proximal tubules. Anticonvulsants other than ethosuximide have been reported to be associated with TIN. Yoshikawa et al. reported a 14-year-old child with TIN associated with sodium valproate [Ethosuximide has not been described in the literature as a causative agent of drug-induced TIN, but in our case, we could not exclude its contribution to NS and TIN given the concurrent presence of DIL. Korinthenberg et al. performed a cross-sectional study of 59 patients without prior renal history and who had been on anti-epileptic monotherapy including carbamazepine, valproic acid, phenytoin, and ethosuximide for at least three months . The proalproate . Eijgenralproate . Matta ealproate . Other aalproate .Drug-induced nephrotoxicity can manifest more commonly as tubulointerstitial injury and less commonly as glomerular injury . Drug-inLimitations of our report include absence of testing for urine eosinophils and slow acetylation. Given the usage of amoxicillin four weeks prior to the presentation of TIN, NS and DIL, the definite cause-and-effect relationship between ethosuximide and the clinical presentation could not be established. However, given that there are no reports of DIL caused by amoxicillin, it is possible that the clinical presentation was induced by two agents: ethosuximide causing DIL and amoxicillin causing TIN and NS or ethosuximide causing all three manifestations, i.e., DIL, TIN and NS. This will need to be looked at in larger further studies and remains a mere speculation at this time. A long-term follow-up would also be helpful to evaluate the trend of the autoantibodies.The case report presents a rare association of DIL with ethosuximide along with its possible role in induction of TIN and NS in children. This report highlights the importance of renal histology to determine the accurate etiology of NS and AKI in patients with lupus-like symptoms. For better understanding of the clinical spectrum of anticonvulsant-induced nephrotoxicity, larger studies are needed to confirm the cause-and-effect relationship.


It can significantly improve progression-free survival and overall survival. Following surgery, radiotherapy, chemotherapy, and targeted therapy, cancer treatment has now entered the age of immunotherapy. Although cancer immunotherapy has shown remarkable efficacy, it also suffers from limitations such as irAEs, cytokine storm, low response rate, etc. In this review, we discuss the basic classification, research progress, and limitations of cancer immunotherapy. Besides, by combining cancer immunotherapy resistance mechanism with analysis of combination therapy, we give our insights into the development of new anticancer immunotherapy strategies.Immune checkpoint therapy  Cancer immune surveillance is an important process by which the immune system can identify and eliminate nascent tumor cells . NormallCancer is the second-leading cause of human death after cardiovascular and cerebrovascular diseases, and the number of patients continues to increase. Cancer treatment has progressed from surgical resection, radiation therapy, chemotherapy, and targeted drug therapy to immunotherapy. Cancer immunotherapy reactivates the body’s immune system to produce anticancer effects and thus kills and eliminates tumor cells. Immunotherapy is a promising treatment. Different from traditional therapy, immunotherapy uses some cytokines, chemokines, and immune cells to reshape the tumor microenvironment, which can lead to robust effects and prevent recurrence , 5. The via Fab terminals. Additionally, Fc-Fc receptor (FcR) interaction can modulate their modes of action (MOA) (mAbs are immunoglobulins (Ig) which commonly include two Fab terminals binding to targets and an Fc terminal binding to receptors on the surface of immune cells. All mAbs exert their function by direct targeting on (MOA) , 7. The on (MOA) , 9. ADCCon (MOA) , 9. In oon (MOA) Table 1.Besides non-conjugated mAbs targeting ‘naked’ antigens, antibody-drug conjugates (ADCs) have shown promising therapeutic effects. ADCs show direct cytotoxicity based on their payloads, which can be ingested through the endocytosis of receptor-bound ADCs. Among the ADCs approved by the FDA, the indications of targets including CD22, CD30, CD33, CD79b, and BCMA are hematological tumors. Besides, HER2, Nectin-4, and Trop-2 are indicated for solid tumors. In terms of target accessibility, solid tumors are more obstructive than hematological tumors. The microenvironment of solid tumors and other factors make it difficult for mAbs to penetrate. In this regard, the accessibility of hematological tumors is better, which is the key factor why therapeutic mAbs will make breakthroughs in the field of hematological tumors first. But now, ADCs also show promising results for the treatment of solid tumors after the optimization of antibodies, linkers, and payloads. In a phase II clinical trial for HER-2-overexpressing or HER-2-mutated NSCLC (NCT03505710), the results showed that the ORR of Enhertu  was 61.9% and the median PFS was 14 months. In a phase I clinical trial of triple-negative breast cancer (TNBC), the initial objective response rate (ORR) was 43%, the complete or partial response (CR/PR) was confirmed in 5 patients, and the disease control rate (DCR) was 95% among 21 evaluable patients treated with datopotamab deruxtecan (a Trop-2 ADC).The structure of mAb determines its MOA. Fc-engineering methods are used to endow therapeutic mAbs with stronger antitumor and immune activation abilities, which are achieved through amino acid mutation and glycosylation modification. Tafasitamab is a therapeutic mAb targeting CD-19 with upregulated MOA activity through Fc-related modifications. S239D and I332E mutations were performed in tafasitamab to enhance ADCC and ADCP. In the RE-MIND study, the ORR of tafasitamb combined with lenalidomide was 67.1%, and the CR was 39.5%, which was much higher than that of the control group treated with lenalidomide singly.In several patients, the mAb-induced severe or partially life-threatening side effects were caused by a cytokine storm. In some cases caused by anti-CD20 mAb rituximab, it is assumed that the excessive activation of the complement system and the subsequent lysis of the targeted CD20+ cells, as well as the Fc-FcγR interactions with recruited macrophages, lead to a strong cytokine secretion , 11. WhiBispecific mAbs (bsAbs) can bind multiple targets at the same time and have a better antitumor effect. Compared with ordinary mAbs, bsAbs offer better stability, higher specificity, and fewer side effects. They offer significant effects in clinical treatment. BsAbs are divided into two types: those that target multiple TAAs and those that engage T cells. They can produce multiple stimulations or inhibition effects, or recruit and activate more immune cells to eliminate tumor cells. Blinatumomab produced by AMGEN is the first FDA-approved bsAb that can specifically target the CD19 of tumor cells and the CD3 of T cells Table 1.Although bsAb is a very promising immunotherapy treatment, there are still problems. The manufacturing of bsAbs is time-consuming and costly. There are bsAb-specific byproducts, such as mispaired products, undesired fragments, and higher levels of aggregates. Additional purification strategies are needed to be designed to obtain products of high purity. At the same time, more clinical trials are needed to explore the optimal route of administration and optimal dose to increase the concentration in target tissues and reduce systemic side effects . In addiThere are immune checkpoints on the surface of T cells that can regulate the immune system. They play a negative regulatory role to prevent excessive activation of T cells to avoid autoimmune damage. However, tumor cells can use these immune checkpoints to suppress the immune response, thus performing immune escape and allowing tumor cells to escape the clearance of the immune system . Immune Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) is a member of the CD28-B7 Ig superfamily. It is expressed on the surface of activated T cells and can act as an immune checkpoint to downregulate immune responses, thereby inhibiting the proliferation and activation of T cells , 18. In More immune checkpoints continue to be discovered, such as TIM-3, LAG-3, and TIGIT. LAG-3 can bind its canonical ligand (MHC-II) to downregulate T cell activity. A phase II/III clinical trial (NCT03470922) demonstrated that the median PFS of the relatlimab plus nivolumab group was 10.12 (6.37 to 15.74), which was over 2-fold compared to the nivolumab group (4.63 (3.38-5.62)). Currently, Opdualag (nivolumab+relatlimab) is the first LAG-3 antibody therapy approved by the FDA and the first innovative cancer immunotherapy approved for a new immune checkpoint in nearly 10 years. LAG-3 antibody is the third immune checkpoint inhibitor approved for marketing after CTLA4 and PD-1 antibodies .. In somIn terms of adverse reactions, immune checkpoint therapy does not cause cytotoxic reactions such as myelosuppression, vomiting, and alopecia, but it can cause immune-related adverse events (irAEs) due to the activation of T cells, which can be reduced by glucocorticoids and disappear after drug discontinuation. Most irAEs are always reversible , 26. Thein vivo experiments also showed that AUNP-12 can inhibit tumor growth and metastasis. AUNP-12 can inhibit B16F10 and 4T1 tumors in rodent models, and the tumor growth inhibition rate (TGI) of the B16F10 model reached 44%  is a 45 kDa hemoglobin oxidase and is a key enzyme in the metabolism of the L-tryptophan-kynurenine pathway. IDO1 plays an important regulatory role in the process of immune regulation , 44. FunDeveloped by Incyte, epacadostat is one of the most well-studied IDO1 inhibitors. It shows good efficacy in mouse melanoma models and is well-tolerated . HoweverThe development of IDO1 inhibitors is not going well, and some clinical trials have failed. On one hand, the reason for the failure of ECHO-301 (epacadostat plus pembrolizumab) may be that the pharmacodynamic indicators are not applicable or the drug combination strategy is not matched. On the other hand, the reason may be that the exact regulatory mechanism of IDO1 in physiology and pathology or its impact on the tumor microenvironment are not well understood. The TDO pathway can play a potential compensatory role after epacadostat treatment, causing tumor immunosuppressive effects . HoweverStimulators of interferon genes (STING) is an immunostimulatory target and an important adaptor protein anchored in the endoplasmic reticulum that senses foreign DNA invasion. Now the STING signaling pathway has become a new target for cancer and autoimmune diseases. Experiments have shown that the activation of STING pathway can induce antitumor effects. A variety of drugs such as ADU-S100 are under clinical studies Table 2 In addition, inhibitors of A2A adenosine receptor (A2AR), chemokine receptors, toll-like receptors (TLRs), arginase 1 (ARG), and other targets are in clinical development and are expected to provide more choices for antitumor drugs Table 2 Polypeptide inhibitors, which can combine the characteristics of antibodies and small-molecule drugs, are important directions for the development of inhibitors. On one hand, they have similar affinity and specificity as antibodies. On the other hand, they have good tissue penetration and provide tunable pharmacokinetic half-life and renal clearance route to avoid hepatic and gastrointestinal toxicities due to their small molecular weight.Small molecules are agents with a low molecular weight that are capable of modulation of intracellular targets. And small molecules are promised to improve the therapeutic management of solid tumors due to their easy administration, high bioavailability, and favorable safety profile. Given these characteristics, the development of small molecule-based strategies in cancer immunotherapy has attracted widespread interest. Although small-molecule drugs targeting the extracellular or intracellular pathways of adaptive immunity or innate immunity have been developed, most of them are in the early stage of clinical trials, and more basic experiments and clinical trials are needed to elucidate their mechanisms, clinical efficacy, and pharmacokinetics. Nevertheless, small-molecule inhibitors may be an effective replacement and supplement for mAbs, and they will remain an important part of tumor immunotherapy in the future.in vitro can recognize tumor antigens and exert antitumor effects without MHC restrictions in vivo (The chimeric antigen receptor (CAR) is a genetically modified and synthesized chimeric antigen receptor. It is a membrane protein composed of different protein domains in series. It is flexible and offers specific antigen recognition. Patient-derived T cells modified by CAR  in vivo .CAR-T therapy is a revolutionary approach to cancer therapy. CAR-T therapy has made breakthroughs in lymphomas, mainly targeting CD19. In 2017, the FDA approved two CAR-T products targeting CD19 . This provides a new opportunity to overcome the challenges of production time, scale, and cost of adoptive cell therapies.New ideas for CAR design are now emerging to improve efficacy. Dual-target CAR-T cells can independently identify target antigens and address the off-target effect. CD19/CD22 CAR-T and CD123/CLL1 CAR-T have shown significant antitumor activity and are currently in clinical studies, some of which have entered phase II/III Table 3 For solid tumors, Hegde et al. constructed TanCAR-T that could enhance T cell function and reduce antigen escape by facilitating crosstalk between HER2-ScFv and IL-13Rα2, thus increasing CD28 expression. The data of TanCAR-T showed good efficacy in a mouse glioblastoma model . In 2022in vitro can maintain immune activity is an important factor. Thirdly, necessary technical processes are required for different patients, which may take high costs and long periods. In addition, immunosuppressive TME and efficiency of delivery to the tumor site are also major barriers to a successful CAR-T therapy. In the future, innovations in CAR design, transduction methodologies, and allogeneic CAR-T are bound to lead to improved responses and transform the treatment of patients with cancer.However, there are several limitations to the application of this technology. Firstly, the expression of CAR mediated by retroviral or lentiviral vectors may have an impact on the gene expression of T cells, which may produce unpredictable results. So, a comprehensive safety assessment of CAR-T cells is required before application. Secondly, the proliferation of CAR-T cells can only be achieved after induction and activation. Therefore, whether the large-scale expansion of T cells Various new methods have been developed to enhance the antitumor efficacy of immune system, including targeting new antigens, using new engineering or modifying TCR, and creating safety switches for internal suicide genes. By transferring the exogenous TCR gene that specifically recognizes TAAs into T cells, TCR-T can be constructed to improve the affinity to TAAs and exert an MHC-dependent antitumor effect . CompareMART TCR-T, gp100 TCR-T, and TCR-T targeting MAGE-A3 or MAGE-A4 have achieved positive results in clinical trials. However, safe use in the clinic should consider the type of antigen and TCR affinity , 67. In While targeting NY-ESO-1, MAGEA3, and other TAAs is an attractive strategy for the application of ACT for the treatment of solid cancers, caution must be taken to ensure a lack of cross-reactivity with vital normal tissues. In addition, modification of the CDR region of TCR must be performed with caution. Because the modified receptors, similar to those produced after immunization in HLA-transgenic mice, are not negatively selected in the thymus and may be potentially reactive to unrelated normal host proteins. There is a need to develop better screening methods to avoid such toxicity in the future. As more antigen-specific TCRs are identified, more data will become available to better understand how to use TCR-T to treat patients. Immunosuppressive TME also limits the efficacy of TCR-T therapy. Combination therapy targeting TME may be a potential strategy to improve the efficacy of TCR-T immunotherapy.in situ immunohistochemical staining (Tumor-infiltrating lymphocytes (TILs) are immune cells that exist in tumor tissues and can specifically respond to TAAs. Using TILs is an effective treatment for many cancers. The first clinical pilot study using TILs was reported in 1988 for metastatic melanoma. The result demonstrated partial response in 2 patients and partial regression in 1 patient. Tumor-specific cytolytic activity was observed in 5 patients . In anotstaining . The resin vitro with high doses of IL-2, and then injecting them into patients  therapy is a new type of antitumor therapy, which can target tumor cells and replicate in cells to kill tumor cells. OV has become the forefront of tumor biotherapy and it is increasingly common. OV can be obtained through natural or genetic engineering, mainly including herpes virus, adenovirus, and pox virus . OV exervia nanoparticles using a technologically complex image-guided delivery system has also been considered . DC vaccine can offer long-term immune memory and can prevent tumor recurrence. Provenge is the first DC vaccine approved by the FDA for castrate-resistant prostate cancer  vaccine, which has antitumor effects by inducing the patient’s monocytes to become DCs e cancer Table 5.e cancer . Multiple cancer , 95. A ce cancer . In addie cancer .DC vaccines suffer from limited cell sources, long preparation periods, and high costs. However, their advantages include low side effects, good tolerance, and long-term immunological memory, which still give them broad market prospects.The key to the development of the cancer vaccine is the need to identify the appropriate biomarkers and optimize the combination of treatments to improve their effectiveness in patients. The research on vaccines has been advancing in the past few decades, and many different characterized cancer vaccines are now available. However, there are still some problems that must be solved, including suitable tumor antigen and adjuvant components, suitable delivery modes, and effective methods to overcome immune attack. Although neoantigens are the best option for antitumor immunotherapy, the problem of obtaining individualized neoantigens hinders the application of cancer vaccines. This is mainly due to inherent alterations in tumor cells and the formation of an immunosuppressive TIME. Several approaches have been developed to overcome difficulties, including the use of immunostimulatory adjuvants, in combination with ACT and ICB.Cancer immunotherapies, such as immune checkpoint blockade (ICB) and adoptive cell therapies (ACT), are effective for patients with various cancers . HoweverPatients who have primary resistance to cancer immunotherapies do not respond to the initial therapy. Adaptive resistance refers to the mechanism by which tumor cells can be recognized by the immune system, but it can adapt to immune attack to protect itself as the tumor progresses. The mechanism of adaptive resistance may include primary resistance, and the mechanism of primary resistance may also be the result of adaptive resistance.The most fundamental reason why tumor cells cannot be recognized by T cells and thus lead to non-response to immunotherapy is the lack of tumor antigens. In addition, cancer cells may have tumor antigens, but the change in the antigen presentation mechanism can also result in the occurrence of immune resistance .In tumor cell-intrinsic factors, insufficient tumor antigenicity and neoantigens contribute to primary and adaptive resistance. Tumor cells can evade specific immune recognition by T cells by downregulating the expression of TAAs, TSAs, and surface MHC. Tumor cells with relatively weak immunogenicity can escape the surveillance of the immune system and selectively proliferate. After the immune selection process, the immunogenicity of the tumor is getting weaker and weaker. The emergence of neoantigens can inhibit tumor progression, whereas poorly immunogenic tumors lack response to PD-1/PD-L1 blockade. Deletion of neoantigens is responsible for primary resistance to immunotherapy in triple-negative breast cancer (TNBC) . LINK-A,In tumor cell-intrinsic factors, tumor signaling pathways can produce immunosuppressive components, or alter some gene expression to affect the efficacy of ICB. Oncogenic signaling through the MAPK pathway results in the production of VEGF and IL-8, which have inhibitory effects on T cell recruitment and function . Activatin vitro T cell lysis and in vivo pericyte therapy, partially due to increased production of immunosuppressive lactate  for survival (Warburg effect) . Enhance lactate .In addition, tumor cell-extrinsic mechanisms that lead to primary and adaptive resistance involve components other than tumor cells within TIME. Tregs reduce the expression of MHC-II molecules by secreting the inhibitory cytokine IL-10, which can affect DC maturation and suppress immune responses . MDSCs cB2M mutation and loss of HLA heterozygosity, changes in tumor target antigens, and up-regulation of alternative immune checkpoints. There is evidence for each of these mechanisms can lead to acquired resistance to ICB or ACT.A hallmark of cancer immunotherapy has been the induction of long-lasting tumor responses. However, patients who once responded to ICB sometimes relapse due to acquired resistance. Schachter et al. showed that 1/4 to 1/3 of patients with metastatic melanoma who received anti-PD-1 or anti-CTLA-4 therapy relapsed after ongoing treatment, even if they were effective against immunotherapy . The posB2M gene affects normal folding and transport of MHC-I, resulting in resistance to ICB  significantly prolonged the survival of tumor-bearing mice , which can enhance the immunogenicity of tumor cells and promote the recruitment and infiltration of immune cells. This relationship is the rationale for combination with immunotherapies. In a study of mouse model, radiotherapy combined with anti-PD-1 treatment reversed immune resistance . In the Targeting intracellular signaling pathways with small molecule inhibitors is effective in rapidly reducing tumor volume. However, many of these drugs do not have durable effects, mainly due to the emergence of other compensatory pathways , 146. EmTumor angiogenesis has an important relationship with tumor immunity. VEGF is related to the generation and regulation of MDSCs, so anti-angiogenic therapy combined with immunotherapy has a synergistic effect. Preclinical studies have shown that the combination of VEGFR inhibitor Axitinib and anti-CTLA-4 can enhance the antigen-presenting ability of DC to promote T cell proliferation in a mouse melanoma model . Data frACT combined with targeted therapy is also an innovative immunotherapeutic approach. Li et al. combine CAIX-specific CAR-T cells and sunitinib, which induces a potent antitumor response in an experimental model of metastatic RCC .The functional inhibition of CAR-T by PD-1/PD-L1 has been well established, which also provides the basis for the combination of PD-1/PD-L1 blockade and CAR-T. Existing preclinical studies have shown that CAR-T cells plus PD-1/PD-L1 blockade can effectively enhance the antitumor effect , 153.IDO1 inhibitors have been in active clinical investigation and preliminary results suggest that IDO1 inhibitors produce additive efficacy when combined with cancer immunotherapies despite low activity as a single agent. Results from a phase I/II clinical trial, which combined IDO1 inhibitor epacadostat and Ipilimumab for the treatment of metastatic melanoma, showed an objective response and no tumor progression in some patients . In addiT-VEC can selectively replicate within tumors and produce granulocyte-macrophage colony-stimulating factor (GM-CSF), which triggers DC differentiation and enhancement of antigen presentation. This makes OVs susceptible to immunotherapy. OV therapy CG0070 in combination with Keytruda showed 89% CR in a clinical trial (AACR 2022 Abstract#CT036) .Current combined strategies are complex because the potential combination approaches far exceed the available human and technical resources. There is an urgent need for us to test these combinations in appropriate preclinical models and to accelerate clinical translation through novel approaches to clinical trial design.Cancer occurrence and development is a complex process. Various immune-evasion mechanisms can counteract the body’s immune response, which becomes more complex as cancer progresses. Cancer immunotherapy can kill and eliminate tumor cells through the immune system, thus becoming another revolutionary treatment after surgical resection, radiotherapy, chemotherapy, and targeted therapy.Various cancer immunotherapies have shown promising clinical efficacy. However, cancer immunotherapy still faces many problems and challenges. MAbs therapy is a very promising treatment for immunotherapy, which has been repeatedly demonstrated in clinical use. However, due to the immunogenicity, mAbs can cause irAEs, which requires strict monitoring in clinical use. The production process of mAbs is time-consuming and costly, and new purification strategies are needed for higher purity of mAbs. These problems are determined by the nature of the antibody itself, and we believe these problems will partly be solved with new design strategies and further optimization. The overall immune response rate of patients treated with ICB is not high, and there is a need to find reliable and effective biomarkers for precise and personalized immunotherapy. In combination with chemotherapy, mAbs have generated success against advanced-stage cancers, which previously had poor outcomes. In addition, combinations with different mAbs also showed a strong anti-tumor effect. Combination therapy may provide new opportunities for mAbs to reduce the side effects and improve the therapeutic effect in the future. Conjugation of cytotoxic agents to mAb allows for specific delivery of payloads to tumors, while multispecific antibodies grant novel mechanisms that increase specificity and facilitate delivery to historically intractable compartments. Besides, Fc- engineering mAbs can endow mAbs with stronger antitumor and immune activation ability through the incorporation of amino acid and glycan changes. With an increased understanding of immunobiology and the continued development of molecular biological methods, the possibilities for mAbs therapy are bounded only by the scope of human ingenuity.Small molecule inhibitors for cancer immunotherapy always occupy an important position, although the sales of mAbs are far ahead. Small molecule inhibitors have mature R&D pipelines and the production process of small molecule inhibitors is more controllable than mAbs, which can help reduce costs. The controllable pharmacokinetic properties can help reduce the impact of side effects, and the good tissue permeability makes small molecule inhibitors useful for solid tumor immunotherapy. Small molecule inhibitors will always be an effective replacement and supplement for mAbs. Currently, a new form of small molecule inhibitor, proteolysis targeting chimeras (PROTAC) is tested in (pre-)clinical, such as IDO1 PROTACs. But many issues need to be addressed especially on whether it is a safe approach or whether there is a saturation in the degradation of proteins that may limit their effectiveness , 159.ACT can be a potent new addition to the toolbox for cancer immunotherapy. However, many TCR-T/CAR-T clinical trials have been hampered by off-target effects and safety concerns , 161. Whvia a nanoparticles using technologically complex image-guided delivery system has also been considered  (DZ), the National Natural Science Foundation of China (81872895) (DZ), a project on joint translational research in the School of Pharmacy and Minhang Hospital (RO-MY201712) (DZ), and Jinan Science and Technology Bureau, Innovation team for the development and evaluation of new drugs for oncology immunotherapy (2020GXRC041 to DZ).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.