Therefore, the prognostic accuracy of the qSOFA score for in-hospital mortality was high in young OAPN patients, but modest in elderly patients. Although it can work as a screening tool to determine therapeutic management in young patients, for elderly patients, the presence of comorbidities should be considered at the initial assessment.Prognostic accuracy of the quick sequential organ failure assessment (qSOFA) score for mortality may be limited in elderly patients. Using our multi-institutional database, we classified obstructive acute pyelonephritis (OAPN) patients into young and elderly groups, and evaluated predictive performance of the qSOFA score for in-hospital mortality. qSOFA score ≥ 2 was an independent predictor for in-hospital mortality, as was higher age, and Charlson comorbidity index (CCI) ≥ 2. In young patients, the area under the curve (AUC) of the qSOFA score for in-hospital mortality was 0.85, whereas it was 0.61 in elderly patients. The sensitivity and specificity of qSOFA score ≥ 2 for in-hospital mortality was 80% and 80% in young patients, and 50% and 68% in elderly patients, respectively. For elderly patients, we developed the CCI-incorporated qSOFA score, which showed higher prognostic accuracy compared with the qSOFA score (AUC, 0.66 vs. 0.61,  Sepsis is a life-threatening condition with a higher mortality rate compared with stroke or acute coronary syndrome . AlthougObstructive acute pyelonephritis (OAPN) is one of the most commonly encountered urinary tract infections, accounting for 20% to 30% of all septic cases ,10. OAPNThis retrospective study included 621 patients using a multi-institutional database of OAPN. All patients in this database were admitted and treated for OAPN in 10 Japanese hospitals between March 2010 and October 2018. In patients with multiple admissions for OAPN, we recorded the most severe episode. OAPN with surgical intervention and bilateral OAPN were also included in this study. The diagnosis of OAPN was made on the basis of clinical examination that included fever, flank pain, costovertebral angel tenderness, urinalysis/culture, and blood tests  by physicians, and the radiological finding of upper urinary tract obstruction revealed by computed tomography. All patients were treated with antibiotics, with or without emergent decompression of the urinary tract by percutaneous nephrostomy or an indwelling ureteral stent. The drainage method was chosen by the physician. Because there were no available data on respiratory rates, 252 patients were excluded, and the remaining 369 were subject to analysis. The comparison of baseline characteristics between included and excluded patients is shown in We collected the following covariates in the current study: patient age; gender; Charlson comorbidity index (CCI) ; comorbiThe qSOFA score was calculated by assigning one point for each of respiratory rate ≥ 22 breaths/min, systolic blood pressure ≤ 100 mmHg, and altered mental status. GCS < 15 was regarded as altered mental status . The SIRThe primary and secondary outcomes were in-hospital mortality, and a composite of ICU admission and/or in-hospital mortality, respectively.p < 0.05 were considered statistically significant.The variables were compared between the two groups using the chi-squared test for categorical variables, and the Mann–Whitney U test for continuous variables. The prognostic accuracy of each score was calculated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The optimal cutoff value of each score was determined by the Youden index. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each score were calculated. Logistic regression analysis was performed to identify the independent predictors of in-hospital mortality and ICU admission. Significant variables in univariate analyses were included in multivariate analyses. Reduced models were constructed by backward elimination. A developed score was internally validated by bootstrap resampling based on 1000 replicates. Statistical analyses were performed using JMP, version 14.0.0 , and R version 4.0.2 . Tests with a n = 178) and elderly  patient groups. The causes of obstruction were calculi, malignancies, and others in 344 (93%), 6 (2%), and 15 (4%) patients, respectively; and 186 (51%) and 118 (33%) patients underwent indwelling ureteral stenting and percutaneous nephrostomy, respectively. While undergoing treatment, 25 (7%) and 70 (19%) patients died in hospital and were admitted to the ICU, respectively. No patients with OAPN associated with malignancies died in hospital during OAPN treatment. Higher age was significantly associated with higher CCI, presence of cardiovascular diseases and stroke, and lower serum albumin , but not in elderly patients .A total of 151 (41%), 115 (31%), 75 (20%), and 28 (8%) patients had a qSOFA score of 0, 1, 2, and 3, respectively; and a SIRS score of 0, 1, 2, 3, and 4 was observed in 26 (7%), 69 (19%), 99 (27%), 112 (30%), and 63 (17%) patients, respectively. Distributions of patients who died and were admitted to the ICU by each score and age are shown in p = 0.002, p < 0.001, For all patients, the prognostic accuracy of the qSOFA score for in-hospital mortality  was significantly higher than that of the SIRS score  was an independent predictor for in-hospital mortality, as was higher age , and CCI ≥ 2 , which significantly outperformed the SIRS score  and qSOFA score ≥ 3  were independent predictors for in-hospital mortality , whereas its prognostic accuracy was modest in elderly patients . This trend was also observed in several previous studies, in which the AUC of the qSOFA score was less than 0.70 in elderly patients ,18,19. TWe developed the CCI-incorporated qSOFA score for elderly patients. Several previous studies have reported the combined qSOFA score and CCI, and its improved prognostic accuracy ,21. GiveIn elderly patients, the criteria of qSOFA score of ≥ 2 missed 10 deceased patients in the current study. Meanwhile, only one deceased patient was missed by qSOFA score of ≥ 2 in young patients. This suggests that elderly patients should be intensively monitored after admission, even if their qSOFA scores at initial presentation are ≤ 1. Our previous study showed that SOFA score ≥ 7 could identify high-risk patients with low qSOFA scores . BecauseWhy is the prognostic accuracy of the qSOFA score lower in elderly patients? One possibility is that some elderly patients may have died of other diseases as a result of the severe course of OAPN, as they tended to have more comorbidities. Indeed, of the elderly patients in our cohort, three (15%) and six (30%) patients died of other comorbidities and pneumonia, respectively, whereas all deceased young patients died of OAPN . Second,The current study has several limitations to be addressed. First, selection biases may have occurred due to the retrospective nature of the current study. Our findings should be validated by prospective studies. Second, 41% of patients were excluded from the analysis due to missing values in respiratory rates. Excluded patients tended to have a less severe disease course compared with included patients. Third, the current study did not evaluate the SOFA score because it was not available in our multi-institutional database. However, the aim of the current study is to assess the prognostic accuracy of the qSOFA score as a screening tool. Because the SOFA score is not suitable as a screening tool, it should be used after evaluation of the qSOFA score. Finally, in the current study, the rate of in-hospital mortality was 7%, which appears to be relatively high. This is, however, nearly equivalent to the rate in previous studies ,26.The qSOFA score predicted in-hospital mortality with high accuracy in young OAPN patients, whereas its prognostic accuracy was modest in elderly OAPN patients. Thus, in young patients, the qSOFA score can be considered a good initial screening tool to determine the acute phase management of OAPN. For elderly patients, however, the presence of comorbidities should be considered at the initial assessment.


The crotonylation of histones is discovered of late as one of the post-translational modifications (PTMs) that can regulate gene expression. However, the function of crotonylation on nonhistone proteins in vascular smooth muscle cells (VSMCs) is unclear. Here, we aim to find the cellular characteristics of crotonylated nonhistone proteins and the cross talk with ubiquitinated proteins in VSMC phenotypic remodeling using the modified omics and proteomic analysis.We performed the modified omics and proteomic analysis of VSMCs before and after the stimulation with platelet-derived growth factor-BB (PDGF-BB). The crotonylated and ubiquitinated pan-antibody was used to enrich proteins and then subjected to a high-throughput mass spectrometry analysis. The enrichment analysis was performed within differentially modified proteins in regard to GO terms, KEGG, and protein domains.As a result, there were 2,138 crotonylation sites in 534 proteins and 1,359 ubiquitination sites corresponding to 657 proteins. These crotonylated proteins detected after PDGF-BB stimulation might be involved in various vital cellular pathways and carry out important functions in VSMCs. Some of them closely took part in significant physiological processes of VSMC phenotypic remodeling, including glycolysis/gluconeogenesis, vascular smooth muscle contraction, and the PI3K-Akt signaling pathway. Furthermore, the KEGG pathway enrichment analysis showed the involvement of ubiquitinated proteins in the physiological processes of VSMC phenotypic remodeling, including glycolysis/gluconeogenesis, vascular smooth muscle contraction, RAS signaling pathway, or the PI3K-Akt signaling pathway. A cross talk analysis showed that there were 199 sites within the 177 proteins modified by crotonylation and ubiquitination simultaneously. Protein–protein interaction (PPI) network analysis indicated that crotonylated and ubiquitinated proteins play an important role in cellular bioprocess commonly and possibly have a synergistic effect.In summary, our bioinformatics analysis shows that the crotonylation and ubiquitination of nonhistone proteins play an essential role in VSMC phenotypic transformation induced by PDGF-BB stimulation. The cross talk between crotonylation and ubiquitination in glycolysis is possibly a novel mechanism during VSMC phenotypic remodeling. Cardiovascular diseases remain the number one primary cause of death for patients all over the world, and the risk factors of cardiovascular disease are gradually increasing among young people . VasculaAfter vascular injury, the serum with growth factors, cytokines, and other components in contact with smooth muscle cells activates complex signaling pathways and cellular events. Among cellular activation events, numerous studies have focused on growth factors, which could contribute to the formation of atherosclerotic and restenosis lesions , 6. We fvia EZH2 activity suppression  have been identified as the most foundational and complicated mechanisms that can regulate various cellular events, including genetic expression, protein biosynthesis, metabolism, and cell cycling –12. An epression . PTMs mapression , malonylpression , ubiquitpression , and metpression , lysine Initially, Ten et al.  found thRecently, the upgrade of mass spectrometry and the enrichment methods of crotonyl peptides have been promoting the identification of crotonylated sites in microorganisms and mammals. The analysis of crotonylome has been reported in HEK293 cell  and mousUbiquitylation is one of the PTMs, in which ubiquitin holds a covalent binding with the target proteins on lysine. When this modification appears on the proteins, the proteasome and lysosomes will be recruited for degradation and the protein localization will be altered similar to protein function, transport, and interaction. For example, yes kinase-associated proteins (YAPs) could be ubiquitinated by TRAF6 at K252. This contributes to the reducing interaction with angiomotin and more YAPs relocating toward the nucleus . UbiquitIn this paper, we combined the applications of several technologies, including stable isotope labeling by amino acids in cell culture (SILAC), enrichment affinity using antibodies, along with high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) to compare the quantitative analysis of the crotonylation and ubiquitylation of VSMCs before and after PDGF-BB treatment. So far, there is no relevant research on crotonylation and ubiquitination and their cross talk analysis in cardiovascular diseases. This whole proteome and modified omics comparison could facilitate understanding the fundamental pathological process of vascular diseases related to VSMC phenotypic remodeling.2, and only passages 3–5 cells at 70–80% confluence were used in the experiments, except if stated otherwise. HEK293 cells were cultured in high glucose DMEM containing 10% FBS. This study was performed via a protocol approved by the Institutional Animal Care and Use Committee of Hebei Medical University, in accordance with the Guide for the Care and Use of Laboratory Animals, and the Hebei Medical University Clinical Research Ethics Committee.Vascular smooth muscle cells were isolated from the thoracic aorta of 80–100 g male Sprague–Dawley rats. VSMCs were grown in low glucose Dulbecco's modified Eagle's medium (DMEM)  with 10% fetal bovine serum (FBS), 100 U/ml penicillin, and 100 μg/ml streptomycin. VSMCs were maintained at 37°C in a humidified atmosphere containing 5% COg at 4°C for 10 min. Finally, the protein concentration was detected with a BCA kit based on the manufacturer's instructions.Vascular smooth muscle cells with or without PDGF-BB stimulation were collected and treated three times by sonication on ice with a high intensity ultrasonic processor (Scientz) in this kind of lysis buffer . The supernatant was transferred to new tubes after centrifugation at 12,000 4HCO3 (100 mM) was added to the concentration of 2M. Finally, trypsin was added with the ratio of 1:50 overnight and 1:100 ratio for 4 h.For digestion, dithiothreitol (5 mM) was used to reduce the total proteins, lasting for 30 min at the temperature of 56°C, and iodoacetamide (11 mM) was used to alkylate at room temperature lasting for 15 min in darkness. NHHigh pH reverse-phase high-performance liquid chromatography (HPLC) was used to fractionate the tryptic peptides into fractions by using a Thermo Betasil C18 column . Briefly, peptides were distributed in a gradient of 8–32% acetonitrile (pH 9.0) lasting for 60 min and then clustered into 60 fractions. At last, 10 fractions appeared, and they were dried by vacuum centrifuging.2O four times and two times, respectively, 0.1% trifluoroacetic acid was used to elute the beads. Finally, the fractions were dried at vacuum for LC-MS/MS.The approach of enrichment of peptides was as follows. NETN buffer was used to dissolve the peptides, and prewashed beads were added to shake at 4°C overnight. After washing by NETN and HThe peptides were loaded onto a reversed phase analytical column (15-cm length and 75 μm i.d.) after dissolving with 0.1% formic acid (solvent A). Based on an EASY-nLC 1,000 UPLC system, at a constant flow rate of 400 nl/min, the gradient was distributed from 6 to 23% solvent B (0.1% formic acid in 98% acetonitrile), lasting for 26 min, 23 to 35% for 8 min, and up to 80% for 3 min. NSI source was used to analyze the peptide after combining tandem mass spectrometry (MS/MS) in Q ExactiveTM Plus (Thermo) with online to the UPLC. The detailed treatment parameters are provided in UniProt database was searched with reverse decoy database. The STRING database was used to search for the PPI. Other descriptions of the parameters or standards could be seen in Based on the Maxquant search engine (v.1.5.2.8), MS/MS data could be processed. t-test was used to analyze the significant differences between replicates. We took the specific fold change >1.2 and the value of p < 0.05 of proteins into account to perform differential analysis. The other data were analyzed with a one-way ANOVA using SPSS software. p ≤ 0.05 was thought to be statistically significant.The quantification of proteomic data was processed by Maxquant software (version 1.5.2.8). It should be noted that the common peptides shared by different groups were excluded from quantification. The It is demonstrated that PDGF-BB could regulate VSMC differentiation at the concentration of 10 ng/ml . Then heFor these repetitions, we tested the quantitative statistical consistency of the biological or technical replicates. The heat map shows an obvious positive correlation between sample replicates and a negative correlation between different treatment groups . AnotherIn this work, 2,386 crotonylation sites corresponding to 570 proteins and 2,138 crotonylation sites from 534 proteins were quantified in VSMCs with or without PDGF-BB treatment . Among tTo identify the bias of amino acids adjacent to the crotonylation lysine sites, we observed both sides of the crotonylation sites, from −10 to +10 positions of lysine. This analysis showed that there were a total of 2,731 peptides containing crotonylation sites. The motifs adjacent to crotonylation sites showed a characterization of distinct patterns . Among tTo explore the significance of crotonylation, we classified crotonylated proteins from the following aspects, including subcellular localization, cellular component, biological processes, and molecular function. In the upregulated crotonylated proteins, we figured out the distribution of them, 46% in the cytoplasm, 21% in the nucleus, and 13% in the mitochondria . On the The analysis of the biological process involved in crotonylated proteins showed that, among the upregulated proteins, 18% were in cellular processes, 13% were in each single-organism process and metabolic process, respectively. On the contrary, the distribution of processes involving the downregulated crotonylated proteins was as follows: 17% in cellular processes, 16% in the single-organism process, and 13% in biological regulation . The molTo search for the underlying roles of crotonylation in cellular biology, we use the enrichment analysis to categorize crotonylated proteins, which were involved in common pathways or processed as one specific group according to GO databases. GO-based enrichment analysis of the molecular function indicated that the upregulated crotonylated proteins were significantly enriched in the actin-binding, cell adhesion, molecule binding, and structural constituent of ribosomes . The dowThe KEGG pathway enrichment analysis displayed that the upregulated crotonylated proteins were found to be involved in various pathways, such as ribosomes, HTLV-1 infection, the biosynthesis of amino acids, and glycolysis/gluconeogenesis . The patThe results of a differential protein domain enrichment analysis showed that many upregulated proteins had the same amino acid sequence as the calponin homology (CH) domain . CH doma+ with similar characteristics of mitoKATP. This implied that differentially expressed proteins may be involved in cell mitochondrial activity and thus affect phenotypic transformation.According to the analysis of KEGG pathway enrichment, the ribosome pathway containing 21 ribosomal proteins  were differential in the presence or absence of PDGF-BB-stimulated VSMCs , which iTo identify the relationship of the functions of proteins with different differential expression fold changes, we divide them into four parts according to their differential expression multiples, including Q1–Q4: Q1 (0 < Ratio ≤ 1/1.5), Q2 (1/1.5 < Ratio ≤ 1/1.3), Q3 (1.3 < Ratio ≤ 1.5), and Q4 (Ratio > 1.5) (Ratio = PDGF/Con) . The staThen, we analyzed every Q group on the functional enrichment and cluster in such aspects, including GO, KEGG, and protein domains. GO enrichment for the Q3 group showed that the upregulated proteins were involved in many biological processes, including the response to hypoxia, the regulation of endocytosis, the regulation of protein metabolic process, positive regulation of cell proliferation, etc. . These uThe protein–protein interaction (PPI) analysis was carried out among the crotonylated proteins based on the STRING database. Functionally clustered crotonylated proteins belonged to multiple protein complexes, including the ribosome, the C complex spliceosome, and protein transport. The interaction network was visualized by Cytoscape .In this work, the ubiquitylome profile of VSMCs before and after PDGF-BB treatment was investigated and three biological replicates were analyzed for each treatment . TotallyA subcellular localization analysis indicated that differentially expressed ubiquitination-modified proteins were distributed mainly in the plasma membrane, cytoplasm, and nucleus, and a small amount was distributed in the mitochondria, peroxisomes, and cytoskeleton . To deteTo further evaluate ubiquitinated proteins, we classified the differentially regulated ones based on their cellular component, biological processes, and molecular functions . The molThe KEGG pathway enrichment analysis showed that proteins could function significantly during VSMC phenotypic remodeling, including glycolysis/gluconeogenesis, vascular smooth muscle contraction, RAS signaling pathway, or the PI3K-Akt signaling pathway . MoreoveTo clarify the relationship between crotonylation and ubiquitination, the cross talk analysis was made on the enrichment analysis and other aspects. Statistical analysis showed that before and after the PDGF-BB stimulation of VSMC, there were 199 sites within the 177 proteins modified by crotonylation and ubiquitination simultaneously . Then, wWe built the PPI network of crotonylated and ubiquitinated proteins, which are significantly differentially expressed based on the STRING database, and clustered the proteins on the strength of MCODE . In thesAccording to the KEGG pathway analysis and function clustering based on MCODE, glycolysis/gluconeogenesis was highlighted . Most ofHistone crotonylation, a type of lysine modification, is newly found in mammalian cells, and this modification is significantly enriched in gene promoters and enhancers . PreviouTo identify whether the crotonylation of lysine occurs in VSMCs and also to explore its possible function within cellular bioprocesses, a comprehensive crotonylomics was realized by combing high-resolution LC-MS/MS with another detective technology of immune-affinity purification with high sensitivity. The MS results showed that 2,138 lysine crotonylation sites were identified in 534 proteins, which were the most abundant ones in the nonhistone protein acylation proteome of VSMCs so far. These crotonylated proteins were involved in multiple biological processes, including glucose metabolism, amino acid biosynthesis, protein folding, and RNA degradation according to the GO and KEGG pathway analysis. In coordination with the PPI network analysis, the enrichment analysis also showed a widespread interaction between crotonylated proteins and the clustering of their functions, such as ribosomes and spliceosomes.Lysine acetylated proteins are involved in many glucose-metabolic pathways, such as carbon metabolism, glycolysis/gluconeogenesis, the tricarboxylic acid (TCA) cycle, pentose phosphate pathway, conservative in eukaryotes, and also in prokaryotes , 44–46. Vascular smooth muscle cells show significant phenotypic and functional changes after vascular injury. In the presence of vascular injury, the contractile phenotype of VSMCs can be converted into the proliferative phenotype, and contraction markers showed a decrease accompanied by this change. Other proinflammatory mediators, which could induce proliferation and chemotaxis, would gradually increase. Activated VSMCs could proliferate and migrate significantly, thereby accelerating post-injury repair of blood vessels. In this study, lysine crotonylation of 5 VSMC contractile markers extensively occurred at multiple sites. For example, caldesman1 was crotonylated at 16 sites and myosin9 at 15 sites. Moreover, transgelin was also crotonylated at 3 lysine sites. These results suggest that lysine crotonylation is likely to be involved in regulating VSMC contraction.Proteins are biological macromolecules that perform a wide range of functions in living organisms. Amino acids are the basic units of protein. The synthesis and degradation of amino acids were related to the lysine crotonylation shown in our research, such as arginine, proline, cysteine, and methionine. Protein synthesis was also affected by lysine crotonylation. In our study, we found 21 ribosome subunits that were crotonylated. The crotonylated ribosome proteins participate in the stabilization of the structure and the formation of peptides. Bhushan et al. demonstrated that nascent polypeptide chains are connected in a set of discrete tunnel constituents, including the extensions of L4 and L17 (L22), and rRNA nucleotides U2585, A2062, A2058, and A751 . The L4 Together with crotonylome, nonhistone protein ubiquitylnome of VSMCs was performed by LC-MS/MS accompanied by immune-affinity purification with high sensitivity. We finally obtained the result that showed 1,359 quantitative sites in 657 proteins altogether. These ubiquitinated proteins were mainly involved in cell morphogenesis, cytoskeleton, and cellular component organization, proteasome, ribosome, and the regulation of actin cytoskeleton based on the analysis of GO and KEGG pathways.Candida albicans  can be found below: PRIDE database; This animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Hebei Medical University.S-HC contributed to organizing and analyzing the data, making the figures, and writing the most of the manuscript. Z-HC wrote the part of ubiquitination. LY, H-MJ, and R-YM contributed to organizing the data and figures. L-HD contributed to analyzing and interpreting the data, writing and revising the manuscript, and obtaining the funding. All authors contributed to the article and approved the submitted version.This study was supported by grants from the National Natural Science Foundation of China (81670394), the Hebei Natural Science Foundation (C2019206022), and the Hebei Scientific and Technological Research Projects (ZD2020302).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.


We hope that this will represent a landmark for future research to use, compare and improve upon.Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still complex even for experts. This paper proposes a fully automatic system based on Deep Learning that performs localization, segmentation and Gleason grade group (GGG) estimation of PCa lesions from prostate mpMRIs. It uses 490 mpMRIs for training/validation and 75 for testing from two different datasets: ProstateX and Valencian Oncology Institute Foundation. In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG Magnetic resonance imaging (MRI) is a medical imaging technique that employs very strong magnetic fields  to obtain three-dimensional (3D) images of the body; multi-parametric MRI (mpMRI) extends MRI by combining several MRI sequences into a multi-channel 3D image, each sequence providing different information on the imaged tissue. mpMRI has drastically changed the diagnostic approach of PCa: The traditional pathway includes screening based on the determination of prostate serum antigen (PSA) levels and digital rectal examination followed by a systematic random transrectal biopsy2. However, in recent years, the introduction of pre-biopsy mpMRI has enabled better selection of patients for prostate biopsy3, increasing the diagnostic yield of the procedure4 and allowing for more precise fusion-guided biopsy examinations and focal therapies as compared with cognitive fusion approaches5. Additionally, mpMRI-derived parameters, such as tumor volume or PSA density (PSA divided by prostate volume) have proven helpful prognosis and stratification tools6.Prostate cancer (PCa) is the most frequently diagnosed malignancy in males in Europe and the USA and the second in the number of deaths7. However, mpMRI interpretation is time-consuming, expertise dependent8, and is usually accompanied by a non-negligible inter-observer variability9. This is particularly the case outside of expert high-volume centers10. Although promising alternative mpMRI scoring criteria are being developed, such as Likert11, PI-RADS remains still the most widely used criterion for both clinical and academic purposes.To promote global standardization in the interpretation of prostate mpMRI examinations, the Prostate Imaging Reporting and Data System (PI-RADS) in its latest 2.1 version combines available evidence to assign scores to objective findings in each sequence12. In this sense, CAD is one of the most exciting lines of research in medical imaging and has been successfully applied to interpret images in different medical scenarios13. CAD poses several theoretical advantages, namely speeding up the diagnosis, reducing diagnostic errors, and improving quantitative evaluation14. On the topic of mpMRI-based PCa CAD, different methods have been proposed since the early 2000s15. These pioneered the field but were nonetheless limited in some important aspects . In 2014, Litjens et al.16 proposed the first CAD system able to provide candidate regions for lesions along with their likelihood for malignancy using pharmacokinetic and appearance-derived features from several MRI sequences using classical (non-Deep Learning) voxel-based classification algorithms and evaluated the results on a large cohort of 347 patients.Computer-aided diagnosis (CAD) systems have been broadly defined as “the use of computer algorithms to aid the image interpretation process”17, however, Deep Convolutional Neural Networks (CNNs) have quickly dominated all kinds of image analysis applications , phasing out classical classification techniques. In the context of the prostate, the turning point can be traced back to the ProstateX challenge in 201619. The challenge consisted in the classification of clinically significant PCa (csPCa) given some tentative locations on mpMRI. More importantly, a training set of 204 mpMRIs (330 lesions) was provided openly for training the models, hence enabling many researchers to venture into the problem  of the mpMRI around the lesion was extracted, and a machine learning algorithm was trained to classify it as either csPCa or not. The second-highest-scoring method21, with a receiver operating characteristic -ROC- curve (AUC) of 0.84, used a simple VGG-like22 CNN architecture trained over the mpMRI ROIs to perform classification. The main limitation of all these approaches is that ROIs have to be manually located beforehand (even after the model has been trained), hence limiting their interest and applicability to clinical practice.Since the advent of Deep Learning23 employed a slice-wise segmentation CNN, FocalNet, not only to predict csPCa but also a to obtain a map of the Gleason grade group (GGG)25 of the prostate. Very briefly, GGG is a standard 1–5 grading system for PCa, where GGG1 cancer cells look normal and are likely to grow slowly , while GGG5 cells look very abnormal and are likely to grow very quickly. Segmentation-based models are a step up from previous patch classification approaches because they provide a csPCa map of the prostate; however, they cannot directly identify lesions as individual entities and assign a score to each one, as is common procedure in clinical practice. This is natively solved in an instance detection+segmentation framework, which is very common in natural image detection tasks26; but has never been applied to csPCa detection. Additionally, two-dimensional (2D) slice-wise CNNs are known to generally underperform as compared with actual 3D CNNs in lesion detection tasks27. Indeed, in 2020 several authors turned to 3D CNNs, such as Arif et al.28 or Aldoj et al.29.In 2019, Cao et al.27, to simultaneously perform detection, segmentation, and Gleason Grade estimation from mpMRIs to a state-of-the-art performance level. It is also one of the few works that combines two very different mpMRI datasets into a single model: the ProstateX dataset and the IVO  dataset , was 0.276/0.255 for the IVO/ProstateX dataset when evaluated at the 0.25 segmentation threshold, and 0.245/0.244 when evaluated at 0.5.Figure entclass1pt{minimaentclass1pt{minimaThe first ProstateX patient Fig. , row 3 ientclass1pt{minimaentclass1pt{minimaIn “As can be seen, removing the low b-valued (b400 for ProstateX/b500 for IVO) DW sequence seems to have minimal impact on both datasets, as is to be expected. Conversely, while removing the high b-valued (b800 for ProstateX/b1000 or b1400 for IVO) DW sequences has little impact on the ProstateX data, it severely affects the performance on the IVO data, likely due to the higher b values employed in this dataset (which may prove more informative). Furthermore, removing all DW sequences severely affects the IVO dataset, but has almost no impact on ProstateX. The removal of the ADC map has a similar negative impact on both datasets, although the results vary depending on how they are analyzed (lesion- or patient-wise). Likewise, dropping the Regarding the prostate zonal segmentation model, which was developed with the sole purpose of automating the PCa detection system view “” section30.Despite mpMRI interpretation being time-consuming and observer-dependent, it is a major clinical decision driver and poses great clinical relevance. In this paper we presented a CAD system developed with two main MRI datasets integrating T2, DW, b-value, and ADC maps in both of them as well as 4, or 0.58–0.96/0.23–87 as shown in a systematic review31. The results vary wildly due to their single-center nature, their differing criteria for the definition of csPCa, and the often-inaccurate reference standards employed.Different outcomes can be measured for this system. Regarding lesion detection as exposed in “mentclass2pt{minimConsidering GGG19. Additionally, an identical model trained only on the ProstateX data , achieved an AUC of 0.87, which would have tied with the best contender in the challenge. There are now higher AUCs in the online leaderboard but, unfortunately, we were unable to find any publications regarding them, and hence no further analysis can be performed. In any case, these results must be also interpreted with caution: on one hand, the proposed system solves a much more complex problem  than the comparatively simpler ROI classification systems which are typically employed for this task, and it is therefore in a disadvantage compared to them. On the other hand, as indicated in “In addition to the previous tests, the ongoing ProstateX challenge was used for external lesion-level validation, achieving an AUC of 0.85, which would have been the second-best AUC in the original ProstateX challenge18 reported a sensitivity of 0.42, 0.75, and 0.89 at 0.1, 1, and 10 false positives per normal case using a classical radiomics-based model. More recently, Xu et al.32 used a csPCa segmentation CNN whose output was later matched to GT lesions based on distance (similar to ours). He reported a sensitivity of 0.826 at some unknown specificity; also, despite using the ProstateX data, unfortunately, no ProstateX challenge results were provided. Cao et al.23 proposed a segmentation CNN that also included GGG classification as part of its output, reporting a maximum sensitivity of 0.893 at 4.64 false positives per patient and an AUC of 0.79 for GGG29 utilized the ProstateX data to perform csPCa classification on mpMRI ROIs around the provided lesion positions, reporting an AUC of 0.91 on their internal 25-patient test set; once again, despite using the ProstateX data exactly as conceived for the challenge, they do not provide any challenge results for comparison.Outside the ProstateX challenge, one of the very first works on the topic by Litjens et al.33 obtained a sensitivity/specificity of 0.99/0.24 using a segmentation CNN, a performance that they found comparable to radiologist-derived PI-RADS scores. Woźnicki et al.34 proposed a classical radiomics-based model (no CNNs involved) achieving an AUC of 0.807. As for patient-level csPCa classification results, Yoo et al.35 achieved an AUC of 0.84 using slice-wise CNN classifier whose predictions were later combined into a patient-wise total score and Winkel et al.36 achieved a sensitivity/specificity of 0.87/0.50 on a prospective validation study using a segmentation-based detection system which is most similar to the one proposed here.In an interesting prospective validation study, Schelb et al.33 reported a DSC of 0.34 for csPCa segmentation, similar to Vente et al.37’s 0.37 DSC. Secondly, the reference segmentations for the ProstateX dataset were generated in an automatic manner; hence, the performance for this dataset is not compared against a proper ground truth. Thirdly, mpMRI lesions tend to be small with ill-defined margins and a very high inter-observer variability38. For all these reasons, these relatively low DSC metrics must be interpreted with caution. Instead, the previously discussed metrics provide a more objective outlook on the actual performance of the model.Considering lesion segmentation concordance, as exposed in “40. Likewise, the role of DCE sequences is currently minor in the final score of the PI-RADS system, being used only in peripheral zone regions with value 3 in the DW sequence . Conversely, the results of the present study hint towards a greater importance of DCE sequences, which turned out to be the second most important sequences for the model, only behind b-numbers .With respect to the ablation tests, there is an ongoing debate regarding the need for DCE sequences. Bi-parametric MRI (bpMRI) (without DCE sequences) seems to be a more cost- and time-effective alternative to mpMRI, with little detriment to accuracy41, for instance, the authors train one CNN on an internal dataset and another identical CNN on the NCI-ISBI train dataset independently, and evaluate them by cross-validation, achieving a DSC of 0.908 and 0.785 at the CG and PZ in their internal dataset, and a DSC of 0.901 and 0.806 in the NCI-ISBI dataset. For a fairer comparison with our model, in Rundo et al.42, the authors train their model on two internal datasets , which then test blindly in the NCI-ISBI dataset, achieving 0.811 and 0.551 in CG and PZ segmentation, respectively. Finally, Aldoj et al.43, training on a larger cohort of 141 patients and evaluating in their internal test set of 47, achieved a DSC of 0.921, 0.895, and 0.781 for whole gland, CG, and PZ segmentation.Lastly, regarding prostate zonal segmentation, we observed a great concordance between the model’s and expert radiologist’s prostate segmentation with a DSC that ranged from 0.894 to 0.941 depending on the MRI dataset. As can be seen, the results in the Private test set are extremely good, better in fact than any other model in the literature when evaluated in its internal test set and when evaluated blindly in the NCI-ISBI dataset. In Qin et al.36. We went a step further and developed the first algorithm, to the best of our knowledge, that automatically contours the prostate into its zones, performs well at lesion detection and Gleason Grade prediction (identifying lesions of a given grade or higher), and segments such lesions albeit with a moderate overlapping. The model outperformed expert radiologists with extensive MRI experience and achieved top results in the ProstateX challenge.The interpretation of mpMRIs based on Artificial IntelIigence (AI) represents a very promising line of research that has already been successfully applied to prostate gland segmentation and PCa lesion detection using both transperineal prostate biopsy and radical prostatectomy specimens as GT with varying resultsThe code has been made publicly available, including an automatic prostate mpMRI non-rigid registration algorithm and an automatic mpMRI lesion segmentation model. Most importantly, the fact that the code is online might allow future researchers to use this model as a reference upon which to build or to compare their models.Our work presents some limitations. Firstly, further validation and prospective blinded trial would be required to compare histological results of targeted biopsies to the lesions identified by the model. Secondly, although the model was successfully trained on two datasets, it still behaves differently on each of them , which is not desirable, but probably unavoidable. Obviously, more data from sources as varied as possible would be ideal to overcome such difficulties and further improve the performance and generality of the model. Thirdly, AI systems have proven cumbersome to integrate into clinical practice for a variety of reasons ; we hope that by making the code freely available some of these obstacles can be more easily overcome.In any case, this is yet another step in the foreseeable direction of developing a strong collaborative AI net that progressively incorporates as many mpMRIs with the corresponding GT as possible. The clinical applications of this model are countless, amongst which we could consider assisting radiologists by speeding up prostate segmentation, training purposes as well as a safety net to avoid missing PCa lesions. Further, the ability to detect csPCa can easily highlight which MRIs would require prompt reporting and prioritizing biopsy. Moreover, given the recent trend towards conservative PCa approaches such as focal therapy or active surveillance , predicting the Gleason Grade, as well as the number of lesions pre-biopsy, could identify eligible men that could be offered transperineal targeted biopsy in the first place.16, which is part of an ongoing online challenge at https://prostatex.grand-challenge.org and is freely available for download18; and IVO, from the homonymous Valencian Institute of Oncology. The study was approved by the Ethical Committee of the Valencian Institute of Oncology (CEIm-FIVO) with protocol code PROSTATEDL (2019-12) and date 17th of July, 2019. All experiments were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants and/or their legal guardians.For the development and validation of the model, two main prostate mpMRI datasets were employed: ProstateX2, apparent diffusion coefficient (ADC) map , and For ProstateX, the data consisted of a total of 204 mpMRIs (one per patient) including the following sequences: T2-weighted (T2), diffusion-weighted (DW) with b-values b50, b400, and b800 s/mm2 , ADC (4.52% missing) and a temporal series of 30 DCE T1-weighted images (42.53% missing). For each mpMRI, one to two (1.04 per patient) lesions were segmented by one of several radiologists with two to seven years of experience in PCa imaging, and their PI-RADS were provided. The Gleason Score (GS)24 was assessed by transperineal fusion-guided with two to three cylinders directed to each of the ROIs. Additionally all patients underwent systematic template biopsy comprising 20–30 cylinders to sample the rest of the prostate.For IVO, there were a total of 221 mpMRIs, including the following sequences: T2, DW with b-values b100, b500, and b1000 s/mmFour PCa classes were considered: GGG0 or benign , GGG1 , GGG2 , and GGG3+  homogenize differences within datasets, (2) homogenize differences between datasets, and (3) enrich the images with extra information that might be useful for the model. Additionally, the preprocessing pipeline was designed to require as little human intervention as possible, in pursuit of developing a system easily implementable in clinical practice.I independently, as introduced in Pellicer-Valero et al.44.For the first objective, all images were cropped to an ROI around the prostate of size mentclass2pt{minimConcerning objective (3), “Figure 26, or segmentations, or both45.To enable training a single model on both datasets, it was mandatory to homogenize how lesion information was to be provided to the model: while the IVO dataset provided the full segmentation mask for each lesion, in ProstateX only the center position of the lesion was available. Although detection systems can be adapted to detect positions, they are typically designed to work with much more semantically rich BBs46 was employed: for the ProstateX dataset, lesions were automatically segmented by growing them from the provided image position (used as seed), using a threshold level set method from Python library SimpleITK47. Concretely, the algorithm was applied independently to sequences T2, b800, and 48.To solve this inconsistency between the datasets, a similar approach to Liu et al.49, the prostate is typically partitioned into two distinct zones: the Central Gland  and the Peripheral Zone (PZ). PCa lesions vary in frequency and malignancy depending on zone50 and, as such, PI-RADS v2 considers them when assessing mpMRIs51. Therefore, just like a radiologist, a model for automated PCa detection and classification will likely benefit from having both CG and PZ mask priors provided as inputs, in addition to the mpMRI.Following McNeal’s criterion52, was developed for automatic CG and PZ segmentation. As it can be seen in Supplementary Figure 1, the first CNN -a published model44 based on the U-Net53 CNN architecture with dense54 and residual55 blocks-, takes a prostate T2 image as input and produces a prostate segmentation mask as output. Then, the second CNN takes both the T2 image and the prostate segmentation mask obtained in the previous step and generates a CG segmentation mask as output. Finally, the PZ segmentation mask can be computed by subtracting the CG from the prostate segmentation mask.Accordingly, a cascading system of two segmentation CNNs, similar to the one introduced by Zhu et al.56 train dataset, which is freely available at http://doi.org/10.7937/K9/TCIA.2015.zF0vlOPv. The results of this prostate zonal segmentation model are very briefly analyzed and compared to others in “The second CNN employed an architecture identical to the first one but was retrained on 92 prostate T2 images from a private dataset, in which the CG was manually segmented by a radiologist with two years of experience in PCa imaging. To be more precise, 80 of the 92 images were used for training the CG segmentation model, while the remaining 12 were employed for testing. Additionally, this model was also blindly tested (i.e.: with no retraining or adaptation of any kind) against the NCI-ISBI47, with Mattes Mutual Information57 as loss function and gradient descent58 as the optimizer for the BSpline parameters. For every mpMRI, the registration algorithm was run 50 times with different parameter initializations, and the correlation coefficient between the spatial gradient of the T2 sequence and the spatial gradient of the registered ADC map was evaluated at the CG and the PZ areas. These custom metrics allowed to place a bigger emphasis to the areas of interest, as compared to image-wide metrics. Finally, the transformation associated with the run yielding the highest value for the average of all metrics and the loss was chosen as final and applied to both DW and ADC sequences. Figure In several patients, DW sequences and the ADC map were misaligned to T2 and the other sequences. As a solution, non-rigid registration (based on a BSpline transformation) was applied between the spatial gradient of the T2 and the ADC map using Python library SimpleITK27 CNN architecture, which allows for the simultaneous detection, segmentation, and classification of PCa lesions. “After pre-processing the data, it was used to train a Retina U-Net27 architecture combines the Retina Net59 detector with the U-Net segmentation CNN and is specifically designed for application to medical images. On one hand, Retina Net is a one-shot detector, meaning that classification and BB refinement (regression) are directly performed using the intermediate activation maps from the output of each decoder block in the Feature Pyramid Network (FPN) that conforms its backbone60, making it not only more efficient but also better suited for lesion detection in medical images, which have distinct characteristics compared to natural images (e.g.: there is no overlap between detections).The Retina U-Net26). This simplifies the architecture significantly, while still being a sensible choice for segmenting lesions since they all represent a single entity irrespective of their particular classes. Supplementary Figure 2 shows an overview of the Retina U-net architecture applied to the problem of simultaneous PCa detection, classification, and segmentation.Furthermore, in the Retina U-Net, the FPN has been extended with two more high-resolution pyramid levels leading to a final segmentation layer, hence making the extended FPN architecture extremely akin to that of the U-Net. Therefore, the lesions are segmented independently of the detections , amounting to 30 IVO patients  and 45 ProstateX patients . This set was kept secret during the development of the model and was only employed eventually to validate it. Instead, for internal validation, five-fold cross-validation (CV) was employed: the train/validation set was split into five disjoint subsets, and five different instances of the same Retina U-Net model were successively trained on four out of the five subsets and validated on the fifth, hence creating a virtual validation dataset that encompassed the totality of the training data .As mentioned in “In summary, the model was trained and five-fold cross-validated with 191 IVO patients (of which only 45.55% were complete) + 159 ProstateX patients  + 140 ProstateX test patients . For testing, a secret subset consisting of 30 IVO patients and 45 ProstateX patients  was employed. The model was also tested on the ongoing ProstateX challenge.N models for the same task, using them to predict on a given test set, and then combining all N predictions to achieve a better joint performance than that of each model individually. Hence, the five CV models were used for ensembling and, additionally, for every one of these CV models, the weights from the best  five epochs were used as further independent models, totaling an equivalent of 25 virtual models.During the final test set prediction, both epoch and CV ensembling were used to boost the capabilities of the model. In general, ensembling consists in training 27.Then, the predictions from the ensemble on the test set were combined in the following way: for segmentation masks, the average mask  was computed and, for the BBs, the weighted box clustering (WBC) algorithm with an Intersection over Union threshold of The results were evaluated at three lesion significance thresholds  of the detected lesions. First, all predicted BBs whose centroid was less than 15 mm away from that of the GT BB were selected as candidates for matching, and assigned a matching score computed as For patient-level evaluation, the patient score was computed as the highest score from any BB predicted for the patient, and the GT GGG of a patient was computed as the highest GGG among all his GT lesions and among all the 20–30 cylinders obtained in the systematic biopsy (which were only available for patients from the IVO dataset). Hence, for the IVO dataset, a patient without any significant GT lesions might still have csPCa; for ProstateX, however, we do not know, and we must assume that this does not happen.Supplementary Information.


Because of poor compliance or low sensitivity, existing diagnostic approaches are unable to provide an efficient diagnosis of patients with gastric and colorectal cancer. Here, we developed the ColoCaller test, which simultaneously detects the methylation status of the SDC2, TFPI2, WIF1, and NDRG4 genes in stool DNA, to optimize the screening of gastric and colorectal cancer in high-risk populations.A total of 217 stool samples from patients with gastrointestinal cancer and from patients with negative endoscopy were prospectively collected, complete with preoperative and postoperative clinical data from patients. The methylation of these samples was detected using ColoCaller, which was designed by selecting CpGs with a two-step screening strategy, and was interpreted using a prediction model built using libSVM to evaluate its clinical value for gastric and colorectal cancer screening.Compared to pathological diagnosis, the sensitivity and specificity of the ColoCaller test in 217 stool DNA samples were 95.56% and 91.86%, respectively, for colorectal cancer, and 67.5% and 97.81%, respectively, for gastric cancer. The detection limit was as low as 1% in 8 ng of DNA.In this study, we developed and established a new test, ColoCaller, which can be used as a screening tool or as an auxiliary diagnostic approach in high-risk populations with gastric and colorectal cancer to promote timely diagnosis and treatment. Gastric and colorectal cancer (GCC) are important systemic diseases that affect human health. According to statistics, the number of new cases and deaths from GCC was 3,020,693 and 1,703,966, which represented 15.6% and 17.1% of total cancer in the world, respectively, in 2020 . Likewis9 epithelial cells that are shed from the normal mucosa every day. Due to the rapid renewal rate of tumor epithelial cells, at least 1% of cells fall into the intestine every day and are excreted from the feces , GC, other digestive tract cancer, and healthy individuals, were enrolled at the China-Japan Friendship Hospital and were asked to collect stool samples at home from July 2019 to November 2020 Figure 1All DNA was extracted using a stool DNA extraction kit  according to the manufacturer’s instructions. After measuring the concentration of DNA using the Qubit dsDNA HS Assay , 800 ng of DNA was treated with sodium bisulfite using the DNA methylation kit  following the manufacturer’s instructions. The converted DNA was used immediately or stored at -20°C until use.https://portal.gdc.cancer.gov) and the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo), respectively. After excluding unqualified samples (only Stage I/II samples were selected), there were 268 primary carcinoma samples and 239 normal samples. After normalization using ‘champ.norm’ function in the package R ‘Champ’ and filtration by the probe annotated by the corresponding annotation files for each data set, intact data of candidate methylated genes , which were selected based on a PubMed library literature search, were collected and analyzed using the package R ‘Champ’ with an adjusted p-value <0.05 and a deltaBeta value >0.2  datasets  and GEO datasets  were downloaded from TCGA portal  were designed with MethPrimer software . All oliLeast Absolute Shrinkage and Selection Operation (LASSO) regression analysis can screen multiple eigenvalues and retain key eigenvalues. We used LASSO regression analysis to analyze 6 methylated genes in this study using the R package ‘glmnet’ . The Ct Subsequently, nonsignificant methylated genes were rejected and the amplification value of specific methylation genes was used to build the prediction model based on the support vector machine (SVM) polynomial classifier and kernel algorithm using the package R ‘e1071’ to identify the risk of early CRC , 27. In 1; w2; …; wd), X=  was described by n genes, and Xi was the value of X in the i-th attribute, which was transformed from the value of Ct in the test  of the ColoCaller test, different amounts  of fully-methylated genomic DNA (CRC cell line HCT116 DNA) and unmethylated stool DNA from healthy individuals was mixed in a mixture of 5%, 1%, and 0.5%, respectively. The methylation status of SDC2, WIF1, TFPI2, and NDRG4 genes was confirmed by Sanger sequencing. A total of 800 ng of mixed DNA was bisulfite converted and 10 replicates of each concentration series were amplified using two qPCR platforms with 3 batches of reagents to determine LoD, which was estimated by Probit analysis. Precision analysis was carried out using 10%, 5%, and 0% methylated DNA prepared by mixing DNA from HCT116 and DNA and healthy individuals DNA with 20 replicates using two qPCR platforms, respectively. The positive/negative accuracy rate was performed in methylated/non-methylated DNA mixed with different interruption materials .All statistical analyses were performed using R version 3.6.1 and SPSS software . The Ct values of the corresponding methylated genes were input to construct the prediction model to evaluate the risk of CRC. The Ct value can be calculated only when the amplification curve has a significant exponential growth period or shows an S-type amplification. Sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance of the prediction models. All statistical tests were two-sided and a P-value of <0.05 was considered statistically significant.In this study, a total of 217 participants with confirmed colonoscopy and/or pathological diagnosis  were enrolled. The clinicopathological characteristics of the participants are shown in Methylated genes were detected (10/10) with 10 replicates using two different qPCR platforms with an 8 ng sample of 1% methylated DNA. Therefore, the LoD was set at 1% for an 8-ng sample. The precision of the ColoCaller test was determined using a low positive control  and a median positive control , according to the CLSI guidelines EP15-A3. We replicated the test 20 times on two qPCR platforms and the CV of the detected Ct was less than 5.0%, respectively. The result of interference experiment showed that none of the drugs had any effect to the detection, except for 9.23 mg/mL berberine.In this study, we used a two-step screening strategy to select methylation biomarkers and then used machine learning methods to build a predictive model. In summary, we first extracted the methylation data of specific genes from public databases, and after analysis and sorting, we selected CpGs that were significantly different in tumors and normal adjacent tissues. To select the CpGs of candidate methylated genes filtered with the annotation file and normalized to eliminate the cross-batch effect, differential analysis was performed on the tumor and adjacent normal tissue. A total of 197 CpGs were obtained, including 20 significantly downregulated CpGs, 69 stable CpGs, and 108 significantly upregulated CpGs  2. No siNext, we use SVM to construct a prediction model for the early diagnosis of GCC. The methylation status of SDC2, WIF1, TFPI2, and NDRG4 was randomly divided into a training set (n=127) and a validation set (n=90). Among the two data sets, data from patients with other cancers, especially with digestive tract cancers, were included to further verify the specificity of the ColoCaller test for CRC. The prediction results of the training set showed that the model prediction was good, with the accuracy, sensitivity, and specificity of 92.10% (95%CI 86.10%-96.70%), 93.33% (95%CI 78.67%-98.15%), and 91.75% (95% CI 84.56%-95.76%), respectively, and the AUC area reached 0.94 , with a sensitivity and specificity of 68.75% (54/80) and 97.81% (134/137)  were screened and compared in tumors and normal tissue of patients with GC and CRC patients, which had great potential to become a diagnostic biomarker. Due to the high acidity in the stomach, previous reports have indicated that DNA may be denatured and cannot be detected in the stool for GC . HoweverIn addition, several reports have been published on the relationship between methylated genes  and CRC. Some genes have high specificity and low sensitivity for methylation, while others have the opposite. However, the influence of these genes on GC is still unclear. Only methylation of the SDC2 and TFPI2 genes has been reported to be related to GC –37. In vvia regularization. Subsequently, the prediction model for early screening of CRC was constructed using SVM, which can solve practical problems including small samples, nonlinearity, high dimensionality, and subdivided the methylation result into two categories (negative/positive methylation status) to determine the best separation threshold  and cheap costs ($100-300/test), it can be tested repeatedly in clinical application, which is conducive to continuous monitoring. In the future, a screening method with stool examination as the primary screening and endoscopy as confirmation will be conducive to popularization and optimization of GCC screening to further improve the detection rate of early GC and CRC. In addition to being able to directly detect GCC, the ColoCaller test can further stratify a high-risk population to improve endoscopy compliance, and promote timely diagnosis and treatment. However, at present, the sample size used in this study to verify the performance of the ColoCaller test was limited, and additional research data is still needed, especially due to the lack of patients with AA, furthermore, long-term follow-up data is needed to support our findings. Additionally, the detailed clinicopathological characteristics of patients with GCC was not included in this study, whether different disease sites or different molecular subtypes which may underestimate the diagnostic performance.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/This study was approved by the Institutional Review Board of the China-Japan Friendship Hospital (No. No. 2019-50-Q07), China, and all patients provided written informed consent. The patients/participants provided their written informed consent to participate in this study.Study design: LM, SS, and YC. Patient enrolment and patient data collection: SS, MZ, XK, PG, YJ, and YL. Experiment perform and Data analysis: JG, XF, LM, MZ, and XK. Manuscript preparation: JG, SS, YC, and LM. All authors discussed the results and implications, and critically revised and approved the final manuscript version.This study was funded by the Elite Medical Professionals project of China-Japan Friendship Hospital (Grant No : ZRJY2021-GG03); Key Clinical Specialty Project of Beijing (Grant No: 2020); National Natural Science Foundation of China (Grant No: 82074221).JG and XF were employed by Apexbio Biotechnology (Suzhou) Co., Ltd.The remaining 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.


Our results reveal an interrelationship between the metabolically driven low-frequency component and the electrically driven high-frequency component, with the latter exhibiting the highest bursting rates around the peaks of the slow component and the lowest around the nadirs. Moreover, the activity, as well as the average synchronicity of the fast component, considerably increased with increasing stimulatory glucose concentration, whereas the stimulation level did not affect any of these parameters in the slow component domain. Remarkably, in both dynamical components, the average correlation decreased similarly with intercellular distance, which implies that intercellular communication affects the synchronicity of both types of oscillations. To explore the intra-islet synchronization patterns in more detail, we constructed functional connectivity maps. The subsequent comparison of network characteristics of different oscillatory components showed more locally clustered and segregated networks of fast oscillatory activity, while the slow oscillations were more global, resulting in several long-range connections and a more cohesive structure. Besides the structural differences, we found a relatively weak relationship between the fast and slow network layer, which suggests that different synchronization mechanisms shape the collective cellular activity in islets, a finding which has to be kept in mind in future studies employing different oscillations for constructing networks.Beta cells within the pancreatic islets of Langerhans respond to stimulation with coherent oscillations of membrane potential and intracellular calcium concentration that presumably drive the pulsatile exocytosis of insulin. Their rhythmic activity is multimodal, resulting from networked feedback interactions of various oscillatory subsystems, such as the glycolytic, mitochondrial, and electrical/calcium components. How these oscillatory modules interact and affect the collective cellular activity, which is a prerequisite for proper hormone release, is incompletely understood. In the present work, we combined advanced confocal Ca We focus particularly on the relationship between both oscillatory components and to what extent their collective rhythmicity is coordinated on the multicellular level. For this purpose, we combine time series analysis with network-theoretical approaches to examine glucose-stimulated oscillatory Ca2+ dynamics measured in beta cells from acute mouse pancreas tissue slices.In the present study, we, therefore, aim to further explore the multimodal nature of oscillatory activity in pancreatic beta cells that is governed by interactions of various physiological regulatory systems. We distinguish between the metabolically driven low-frequency component of CaThe study was carried out in strict accordance with all national and European recommendations related to work with experimental animals, and all efforts were made to minimize the suffering of animals. The protocol was approved by the Administration of the Republic of Slovenia for Food Safety, Veterinary Sector and Plant Protection (permit number: 34401-35-2018/2).2, the abdomen was exposed via laparotomy and low-melting-point 1.9% agarose  in extracellular solution  125 NaCl, 26 NaHCO3, 6 glucose, 6 lactic acid, 3 myo-inositol, 2.5 KCl, 2 Na-pyruvate, 2 CaCl2, 1.25 NaH2PO4, 1 MgCl2, 0.5 ascorbic acid continuously bubbled with a gas mixture containing 95 % O2 and 5 % CO2 at barometric pressure to ensure oxygenation and a pH of 7.4) at 40°C was retrogradely injected into the pancreatic ductal tree via the proximal common bile duct clamped at the papilla of Vater. Subsequently, following immediate cooling with ice-cold ECS and extraction, small blocks of tissue (0.1–0.2 cm3 in size) were cut and embedded in agarose at 40°C. The tissue was cut at 0.05 mm s–1 and 70 Hz into 140 μm-thick slices , and the obtained slices collected in HEPES-buffered saline at room temperature  150 NaCl, 10 HEPES, 6 glucose, 5 KCl, 2 CaCl2, 1 MgCl2; titrated to pH = 7.4 using 1 M NaOH) until incubation in the dye-loading solution. All chemicals were obtained from Sigma-Aldrich  unless indicated.Pancreas tissue slices were prepared from adult NMRI male mice kept in individually ventilated cages  on a 12 light/12 dark cycle, as described previously . In brie2+ imaging. For Ca2+ imaging, individual tissue slices were transferred to the perfusion system delivering carbogenated ECS with varying glucose concentrations, according to the stimulation protocol, and kept at 37°C. The protocol consisted of initial exposure to the non-stimulatory 6 mM glucose, followed by either 8 or 12 mM glucose for 45 min, and washout with 6 mM glucose. The Ca2+ imaging was performed on a Leica TCS SP5 AOBS Tandem II upright confocal system  and a Leica TCS SP5 DMI6000 CS inverted confocal system . The acquisition was set to 10 Hz at 512 × 512 pixels to make the precise quantification of Ca2+ oscillations feasible. The dye was excited by argon 488 nm laser line and emitted fluorescence was detected by Leica HyD hybrid detector in the range of 500–700 nm , as described previously (F–F0)/F0 ratios, where F0 is the initial fluorescence intensity, and F is the fluorescence signal recorded at an individual time point during the experiment.Slices were incubated in the dye-loading solution  at RT for 50 min. Following the staining protocol, the slices were transferred into HBS containing 6 mM glucose and stored for up to 8 h until Caeviously . Additio2+ signals were first corrected for photobleaching of the dye employing a combination of linear and single exponential fit as described previously  and fj(t) represent the slow or fast traces of the i-th and j-th cell. By computing Ri,j among all node pairs, we create the correlation matrix, R, with the ij-th element being the correlation coefficient Ri,j. To enable a direct comparison between different networks, we used variable thresholds to extract the binary adjacency matrix, so that the average node degree in each network was k=8. Conventional tools from the complex network theory were then used to quantify functional beta cell networks . All significances are expressed as exact values and the number of islets included in analyses indicated accordingly. All significances are expressed as exact p values (p) and the number of islets included in analyses is indicated accordingly. We estimated effect sizes by calculating the values of Cohen’s d (d) by dividing the difference in sample means by the pooled standard deviation, according to the original definition . We compared groups by using the finition . Our judfinition .in vivo, i.e., 8 mM, and a supraphysiological concentration, i.e., 12 mM. First, we focused on the temporal aspect of the glucose-evoked oscillatory Ca2+ activity measuring the classical physiological measures, whereas in the second part of our analyses, we examined the synchronicity and collective activity of beta cell populations utilizing correlation analysis and network-based approaches. Particular emphasis was devoted to the interrelationship between the slow and the fast oscillatory component in both approaches.We studied the effect of stimulation with two glucose concentrations: a physiological concentration that is commonly observed 2+ response to glucose stimulation was recorded by means of multicellular confocal imaging in acute tissue slices as described in Materials and methods. The cells responded to stimulation with a delay in the onset of Ca2+ activity (TS), and they reached a state of sustained activity after a slightly longer time interval .Ca2+ signaling parameters, separately for each glucose concentration. In both glucose concentrations the frequency of slow oscillations was around 0.2 min–1 . In contrast, the frequency of fast oscillations depended significantly and with a very large effect size  on the stimulation level and was, on average, 4.8 and 8.4 min–1 in 8 and 12 mM glucose, respectively . Apparently, only the activity of the fast oscillatory component is modulated by stimulatory glucose levels. Finally, we characterized the beta cell responses to stimulation by calculating the average time lag until the cells in a given islet responded to stimulation, TS, and the average time required for the cells to reach the phase of sustained activity, i.e., the plateau phase, TP. Both of these parameters were significantly higher under lower stimulation levels and the effect sizes were very large . In 8 mM, values of TS and TP were approximately 5 and 8 min, whereas in 12 mM glucose, they shortened on average almost twofold  . On average, the correlation of the fast oscillatory activity was higher when compared to the slow component. For both dynamical components, the average correlation between cell pairs is a monotonically decreasing function of the intercellular distance, irrespective of the stimulatory glucose concentration  . On the  = 0.31) , which c = 0.31) . The difntration . In otheechanism . For tharonicity . Notablyronicity , 4, but 2+ traces from all cells were statistically compared in a pairwise manner to build correlation matrices . In both glucose concentrations, the fast component network layer exhibited higher clustering levels in comparison to the slow component network . Moreover, the network architecture of the fast component was found to be less cohesive . Under supraphysiological glucose levels the difference was not significant and characterized by a medium effect size . This can be attributed to the fact that high stimulatory conditions evoke high fractions of global waves in the domain of fast activity, which results in very integrated functional connectivity patterns . A higher dispersion of data in this case can probably be attributed to morphological heterogeneity of islets and the resulting inhomogeneous distribution of beta cells in tissue slices.To further characterize the collective beta cell activity of both dynamical components, we constructed functional connectivity profiles for both glucose concentrations. Both types of Camatrices . Functiopatterns . Modularpatterns   and supraphysiological (12 mM) stimulation. Both the frequency and the phase duration of the fast component increased with increasing glucose concentration , corrobo2+ ions on ion channels. A rise in the intracellular Ca2+ concentration activates the calcium-dependent K (KCa) channels, causing hyperpolarization and closure of the voltage-dependent Ca2+ channels. The latter decreases Ca2+ influx triggering a decrease in the Ca2+ concentration that ultimately removes the inhibitory drive of the KCa channels, and the cycle can repeat  or vice versa (metabolism-driven Ca2+ oscillations) . Moreover, the frequency of the fast component depended on the phase of the slow component. These facts, taken together, imply that the mechanism driving the slow activity of intracellular Ca2+ concentration is distinct from the one responsible for the fast component, that the slow component influences the fast, but that the presence of fast oscillations and their characteristics do not influence the slow oscillations. More specifically, the slow oscillations cause shifts in the frequency of the fast oscillations, but the average value of these fast oscillations is set by a glucose-dependent mechanism, distinct from the one responsible for slow oscillations. This corroborates the recent developments in computational models of beta cell activity, suggesting that the slow oscillations may originate from intrinsic mechanisms, in addition to Ca2+ effects on the enzymes involved in beta cell metabolism  . Experimomponent . On the llations . While sllations , others llations . In thistabolism . We wish2+ waves across the islets. Collective behavior of fast oscillations is receiving much more attention from the scientific community, especially because inherent beta cell heterogeneity and the existence of subpopulations lead to complex spatio-temporal activity patterns, characterized by heterogeneous and non-stationary intercellular waves, which are also accessible to experimental and modeling approaches .MSK, JD, and AS performed the experiments. JZ, RM, and MG developed the software for the analysis. JZ analyzed the data. JZ and RM prepared the figures. MSK, JD, AS, and MG wrote the manuscript. MM and AS provided resources. MG supervised the study. All authors conceived the idea, designed the study, and reviewed and approved the manuscript.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.