Single-cell analysis of phenotypic plasticity could improve the development of more effective therapeutics. Still, the development of tools to measure single-cell heterogeneity has lagged due to difficulties in manipulating and culturing single cells. Here, we describe a single-cell culture and phenotyping platform that employs a starburst microfluidic network and automatic liquid handling system to capture single cells for long-term culture and multi-dimensional analysis and quantify their clonal properties via their surface biomarker and secreted cytokine/growth factor profiles. Studies performed on this platform found that cells derived from single-cell cultures maintained phenotypic equilibria similar to their parental populations. Single-cell cultures exposed to chemotherapeutic drugs stochastically disrupted this balance to favor stem-like cells. They had enhanced expression of mRNAs and secreted factors associated with cell signaling, survival, and differentiation. This single-cell analysis approach can be extended to analyze more complex phenotypes and screen responses to therapeutic targets.

Objective:To construct a machine-learning model for predicting the progression of pancreatic cystic lesions (PCLs) based on clinical and CT features, and to evaluate its predictive performance in internal/external testing cohorts.Methods:Baseline clinical and radiological data of 200 PCLs in 177 patients undergoing abdominal thin slice enhanced CT examination at Peking Union Medical College Hospital from July 2014 to December 2022 were retrospectively collected. PCLs were divided into progressive and non-progressive groups according to whether the signs indicated for surgery by the guidelines of the European study group on PCLs were present during three-year follow-up. 200 PCLs were randomly divided into training (150 PCLs) and internal testing cohorts (50 PCLs) at the ratio of 1∶3. 15 PCLs in 14 patients at Jinling Affiliated Hospital of Medical School of Nanjing University from October 2011 to May 2020 were enrolled as external testing cohort. The clinical and CT radiological features were recorded. Multiple feature selection methods and machine-learning models were implemented and combined to identify the optimal machine-learning model based on the 10-fold cross-validation method. Receiver operating characteristics (ROC) curve was drawn and area under curve (AUC) was calculated. The model with the highest AUC was determined as the optimal model. The optimal model's predictive performance was evaluated on testing cohort by calculating AUC, sensitivity, specificity and accuracy. Permutation importance was used to assess the importance of optimal model features. Calibration curves of the optimal model were established to evaluate the model's clinical applicability by Hosmer-Lemeshow test.Results:In training and internal testing cohorts, the progressive and non-progressive groups were significantly different on history of pancreatitis, lesions size, main pancreatic duct diameter and dilation, thick cyst wall, presence of septation and thick septation (all P value <0.05) In internal testing cohort, the two groups were significantly different on gender, lesion calcification and pancreatic atrophy (all P value <0.05). In external testing cohort, the two groups were significantly different on lesions size and pancreatic duct dilation (both P<0.05). The support vector machine (SVM) model based on five features selected by F test (lesion size, thick cyst wall, history of pancreatitis, main pancreatic duct diameter and dilation) achieved the highest AUC of 0.899 during cross-validation. SVM model for predicting the progression of PCLs demonstrated an AUC of 0.909, sensitivity of 82.4%, specificity of 72.7%, and accuracy of 76.0% in the internal testing cohort, and 0.944, 100%, 77.8%, and 86.7% in the external testing cohort. Calibration curved showed that the predicted probability by the model was comparable to the real progression of PCLs. Hosmer-Lemeshow goodness-of-fit test affirmed the model's consistency with actual PCLs progression in testing cohorts. Conclusions:The SVM model based on clinical and CT features can help doctors predict the PCLs progression within three-year follow-up, thus achieving efficient patient management and rational allocation of medical resource.

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
Humans ; Keloid/genetics* ; Nomograms ; Algorithms ; Calibration ; Machine Learning

Stimulator of interferon genes (STING) is a cytosolic DNA sensor which is regarded as a potential target for antitumor immunotherapy. However, clinical trials of STING agonists display limited anti-tumor effects and dose-dependent side-effects like inflammatory damage and cell toxicity. Here, we showed that tetrahedral DNA nanostructures (TDNs) actively enter macrophages to promote STING activation and M1 polarization in a size-dependent manner, and synergized with Mn²⁺ to enhance the expressions of IFN-β and iNOS, as well as the co-stimulatory molecules for antigen presentation. Moreover, to reduce the cytotoxicity of Mn²⁺, we constructed a TDN-MnO₂ complex and found that it displayed a much higher efficacy than TDN plus Mn²⁺ to initiate macrophage activation and anti-tumor response both in vitro and in vivo. Together, our studies explored a novel immune activation effect of TDN in cancer therapy and its synergistic therapeutic outcomes with MnO₂. These findings provide new therapeutic opportunities for cancer therapy.

"The Expert Group on Tumor Ablation Therapy of Chinese Medical Doctor Association, The Tumor Ablation Committee of Chinese College of Interventionalists, The Society of Tumor Ablation Therapy of Chinese Anti-Cancer Association and The Ablation Expert Committee of the Chinese Society of Clinical Oncology" have organized multidisciplinary experts to formulate the consensus for thermal ablation of pulmonary subsolid nodules or ground-glass nodule (GGN). The expert consensus reviews current literatures and provides clinical practices for thermal ablation of GGN. The main contents include: (1) clinical evaluation of GGN, (2) procedures, indications, contraindications, outcomes evaluation and related complications of thermal ablation for GGN and (3) future development directions.
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Objective To investigate the feasibility of using low-dose prostate CT perfusion (pCTP)on a third-generation dual-source CT. Methods Nine patients with elevated prostate-specific antigen underwent pCTP before having prostate biopsy. We measured the blood flow (BF),blood volume (BV),mean transit time (MTT),permeability surface (PS),and time to peak(TTP)of both lesions and normal prostate tissue. The effective dose (ED)was calculated. Results Of the 9 cases,6 were prostate cancers and 3 were prostate hyperplasia with chronic inflammation. The average ED of the 9 pCTPs was (3.5±0.3)mSv. The BF (t=4.64,P<0.001),BV (t=3.27,P<0.001),and PS (t=3.58,P=0.004)of prostate cancer were significantly higher than those of normal prostate tissue and TTP (t=-1.26,P<0.001)of prostate cancer was significantly lower than that of normal prostate tissue. BF (t=3.96,P=0.001)and PS (t=2.91,P=0.021)of prostate hyperplasia with chronic inflammation were also significantly higher and TTP (t=-1.19,P<0.001)was significantly lower than those of normal prostate tissue. TTP of prostate cancer was significantly lower than that of prostate hyperplasia with chronic inflammation (t=-2.56,P=0.049). Conclusion sLow-dose pCTP is feasible on third-generation dual-source CT. The BF,PS,and TTP differ among prostate cancer,prostate hyperplasia with chronic inflammation,and normal prostate tissue.
Blood Volume ; Feasibility Studies ; Humans ; Male ; Prostatic Neoplasms ; diagnostic imaging ; Tomography, X-Ray Computed ; methods

Objective To evaluate the image quality and radiation exposure in multidetector computed tomography (MDCT) with automated topogram-based tube potential selection,compared to fixed tube potential,in patients with solid pancreatic lesions. Methods The preoperative pancreatic dual-source CT images of 113 patients who were confirmed as solid pancreatic lesions by postoperative pathology in the Peking Union Medical College Hospital from January 2014 to August 2016 were retrospectively analyzed.Among them,53 patients were examined on fixed tube potential at 120 kV,and tube current was automatically modulated (group 1). Sixty patients underwent topogram-based automatic tube potential selection (Tube voltage step:90,100 kV) and automated mA modulation (group 2). Two experienced radiologists measured the body sizes,assessed subjective and objective image quality of arterial phase and portal phase,and recorded radiation parameters including CT dose index volume (CTDI) and dose-length product (DLP). Results Of 60 patients in group 2,45 patients were scaned at 90 kV,15 patients were scaned at 100 kV.The average body diameter [(287±24) mm] in 90 kV group was significantly lower than that [(328±22) mm] in 100 kV group(t=0.731,P=0.0008). The mean CTDI[(3.9±1.0) mGy] in group 2 was significantly lower than in group 1 [(9.0±1.9) mGy],reduced by 56.7% (t=17.5,P=0.0003). The average DLP [(109±38) mGy·cm] in group 2 was significantly lower than that in group 1 [(276±83) mGy·cm],reduced by 60.5% (t=14.0,P=0.0007). In group 2,the standard deviations of images background noise in arterial and portal phase were (6.4±0.9) and (6.4±1.0)HU,respectively,which were significantly higher than those in group 1 [(5.6±1.4)HU,t=-3.757,P=0.0003;(5.5±1.4)HU,t=-3.828,P=0.0006]. In group 2,the signal to noise ratios of pancreatic lesions, abdominal aorta in arterial phase and pancreatic lesions, the portal vein in portal phase were 18.8±9.3,76.0±19.3 and 17.4±6.7,33.1±7.2,which were significantly higher than those in group 1 (13.1±8.7,t=-3.379,P=0.001;56.5±22.6,t=-2.268,P=0.025;14.1±8.1,t=-2.283,P=0.024;28.9±8.8,t=-2.613,P=0.009). Conclusion Compared with fixed tube voltage on the second-generation dual-source CT techniques,topogram-based automatic tube potential selection on third-generation dual-source CT can reduce radiation dose without decreasing image quality in imaging solid pancreatic lesions.
Humans ; Multidetector Computed Tomography ; methods ; Pancreas ; diagnostic imaging ; pathology ; Radiation Dosage ; Retrospective Studies ; Signal-To-Noise Ratio

Objective To characterize the CT perfusion parameters of focal pancreatic lesions including pancreatic cancers (PACs) and pancreatic neuroendocrine tumors (pNETs),estimate the confirmity and fungibility of parameters obtained from Deconvolution and Maximum slope+Patlak.Methods From December 2015 to November 2016,22 patients with PACs and 22 patients with pNETs(37 lesions confirmed by surgery and biopsy)underwent preoperative whole-pancreas CT perfusion in our center. The volume perfusion CT of the entire pancreas was performed at 80 kV and 100 mA,using 28 consecutive volume measurements and intravenous injection of 45 ml of iodinated contrast and saline at a flow rate of 5 ml/s. One experienced radiologists measured and recorded the CT perfusion parameters on Siemens post-processing workstation using two mathematical methods:Maximum slope+Patlak analysis versus Deconvolution method.ResultsWilcoxon matched-pairs test revealed significant difference between both pairs of the perfusion measurements by the two methods,PACs(BFM vs. BFD,Z=-3.263,P=0.001;BVD vs. BVP,Z=-3.978,P=0.000); pNETs(BFM vs. BFD,Z=-5.212,P=0.000;BVD vs. BVP,Z=-2.633,P=0.008). Spearman's correlation coefficient showed both pairs of perfusion measurements significantly correlated with each other in PACs (BFM vs. BFD,r=0.845,P=0.000;BVD vs. BVP,r=0.964,P=0.000) and pNETs(BFM vs. BFD,r=0.759,P=0.000),BVD vs. BVP,r=0.683,P=0.000). Geometric mean BFM/BFD ratio in PACs was 0.77 (range:0.61-0.99),while geometric mean BVD/BVP ratio was 1.42 (range:1.13-1.79),within 95% limits of agreement. Geometric mean BFM/BFD ratio in pNETs was 0.66 (range:0.51-0.86),while geometric mean BVD/BVP ratio was 1.15 (range:0.88-1.50),within 95% limits of agreement. Conclusion sSignificantly different CT perfusion values of blood flow and blood volume were obtained by Deconvolution-based and Maximum slope+Patlak-based algorithms in the pNETs and PACs. They correlated significantly with each other. Two perfusion-measuring algorithms are interchangeable because the ranges of the conversion factors are narrow.
Algorithms ; Blood Volume ; Contrast Media ; Humans ; Pancreas ; diagnostic imaging ; pathology ; Pancreatic Neoplasms ; diagnostic imaging ; Reproducibility of Results ; Sensitivity and Specificity ; Tomography, X-Ray Computed ; methods

Objective To compare measurements of dual-energy CT iodine map parameters and liver perfusion CT parameters in patients with focal liver lesions using a third-generation dual-source CT scanner. Methods Between November 2015 and August 2016,33 patients with non-cystic focal lesions of liver were enrolled in this study. CT examinations were performed with a third-generation dual-source CT. The study CT protocol included a perfusion CT and dual-energy arterial and portal venous scans,with a time interval of 15 minutes. Iodine attenuation was measured at five region of interests including areas of high,medium,and low density within the lesion,as well as right and left liver parenchyma from the iodine map,while arterial liver perfusion (ALP),portal venous liver perfusion (PVP),and hepatic perfusion index (HPI) at the same location were measured from perfusion CT. The Pearson product-moment correlation coefficient was used to evaluate the relationship between iodine attenuation and perfusion parameters. Results The iodine attenuation at arterial phase showed significant intra-individual correlation with ALP (r=0.812,95% CI=0.728-0.885,P<0.001)and PVP (r=-0.209,95% CI=-0.323--0.073,P=0.007),but not significantly correlated with HPI (r=0.058,95% CI=0.046-0.498,P=0.461). The iodine attenuation at portal venous phase showed significant correlation with PVP (r=0.214,95% CI=0.072-0.361,P=0.005) but not with HPI(r=0.036,95% CI=-0.002-0.242,P=0.649). The mean effective dose of arterial phase and portal venous phase of dual-energy CT together [(3.53±1.17)mSv] was significantly lower than that of the perfusion CT [(14.53±0.45)mSv](t=25.212,P<0.001). Conclusion Iodine attenuation from arterial phase of dual energy CT demonstrates significant correlation with ALP and PVP,and iodine attenuation from portal venous phase demonstrates significant correlation with PVP.
Contrast Media ; Humans ; Iodine ; Liver ; diagnostic imaging ; pathology ; Perfusion ; Portal Vein ; Tomography, X-Ray Computed ; methods

Objective To evaluate the value of third-generation dual-source CT scanner in application of high-pitch aorta CT angiography(CTA). Methods Totally 59 patients clinically indicated for whole aorta angiography were divided into 2 groups using a simple random method:in group 1 there were 28 patients who underwent the examination on a third-generation dual-source CT device,with a collimation of 2×192×0.6 mm and a rotation time of 0.25 s;in group 2 there were 31 patients who underwent the examination on a second generation dual-source CT device,with a collimation of 2×128×0.6 mm and a rotation time of 0.28 s. Both groups were given the examination operated in dual-source high-pitch ECG-gating mode with a pitch of 3.0,a tube voltage of 100 kV,and automated tube current modulation using a reference tube current of 288 mA. A contrast material bolus of 45 ml with a flow of 4.5 ml/s followed by a 50 ml saline chaser in 5.0 ml/s was used. CTA scan was automatically started using a bolus tracking technique at the level of the original part of aorta after a trigger threshold of 100 HU was reached. The start delay was set to 6 s in both groups. Effective dose(ED),signal to noise ratio (SNR),contrast to noise ratio (CNR),and subjective diagnostic quality of both groups were evaluated. Results The mean ED were 19.44% lower (t=-3.989,P=0.000) in group 1 [(3.15±0.86)mSv] than in group 2 [(3.91±0.60)mSv]. These two groups showed no significant differences in SNR or CNR (all P >0.05). The subjective diagnostic quality values also showed no significant difference between two groups [(1.39±0.50)scores vs. (1.45±0.51)scores;W=814.5,P=0.651].Conclusion Compared with the second-generation dual-source CT scanner,the third-generation dual-source CT scanner in whole aorta CTA can remarkably reduce the radiation dose without affecting image quality.
Aorta ; diagnostic imaging ; Computed Tomography Angiography ; methods ; Humans ; Radiation Dosage ; Retrospective Studies ; Signal-To-Noise Ratio