The determination of molecular subgroups—wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4—of medulloblastomas is very important for prognostication and risk-adaptive treatment strategies. Due to the rare disease characteristics of medulloblastoma, we designed a unique multitask framework for the few-shot scenario to achieve noninvasive molecular subgrouping with high accuracy.N = 74) from 2 hospitals and independent testing cohort (N = 39) from the other 2 hospitals. Comparative experiments of different auxiliary tasks were designed to illustrate the effect of multitasking in molecular subgrouping.We introduced a multitask technique based on mask regional convolutional neural network (Mask-RCNN). By effectively utilizing the comprehensive information including genotyping, tumor mask, and prognosis, multitask technique, on the one hand, realized multi-purpose modeling and simultaneously, on the other hand, promoted the accuracy of the molecular subgrouping. One hundred and thirteen medulloblastoma cases were collected from 4 hospitals during the 8-year period in the retrospective study, which were divided into 3-fold cross-validation cohorts  of molecular subgrouping were 0.97 in cross-validation and 0.92 in independent testing. The average AUCs of prognostication also reached to 0.88 in cross-validation and 0.79 in independent testing. The tumor segmentation results achieved the Dice coefficient of 0.90 in both cohorts.The multitask Mask-RCNN is an effective method for the molecular subgrouping and prognostication of medulloblastomas with high accuracy in few-shot learning. Proposing a unique multitask framework for the few-shot learning scenario.Molecular subgrouping of medulloblastoma into 4 subtypes with an average AUC of 0.95.Simultaneous tumor segmentation and prognosis prediction.In the latest version of the WHO 2016 classification of medulloblastoma, the molecular subgroups  were widely recognized as the markers for patient treatment and prognosis strategies. Due to the technological complexity and cost, molecular subgrouping of medulloblastoma based on the genomic test might be unavailable in every neurosurgical center as routine clinical practice. Recently, radiomics method provided a potential tool for noninvasive molecular subgrouping of medulloblastoma. Regarding the relatively low incidence of medulloblastoma, our study designed a multitask network for the few-shot learning scenario with a small sample size by using preoperative MR images. Through the multitask technique, the proposed scheme achieved high diagnostic accuracy in both cross-validation and independent testing, with an average AUC of 0.97 and 0.92, respectively.1–3 The 5-year survival rate reaches 65–70% in terms of tumor resection plus chemo-radiotherapy.4 Although the survival rates have increased dramatically during the past decade, the clinical outcome still varies a lot due to underlying biological distinctions.6 Based on genomic profiles, 4 distinct subgroups  were widely recognized and adapted into the latest version of WHO 2016 medulloblastoma classification.7–10 Tumors with different molecular subgroups have different clinical outcomes and chemo-radiosensitivity.11–13 Meanwhile, for surgery, the extent of surgical resection had no significant patient survival outcome, hence accounting for molecular subgroups.4 Thus, more and more studies have highlighted the importance of molecular subgrouping in the precise diagnosis and treatment of MB.14–21Medulloblastoma (MB) is the most common malignant pediatric brain tumor with about 40% being located in posterior cranial fossa.14–21 Recently, radiomics has emerged as a new medical tool to bridge high-throughput MR features with tumor genomic and transcriptome profiling. With the help of deep learning techniques, radiomics methods have provided the possibility to perform accurate molecular diagnosis of various brain tumors such as glioma, metastases, and craniopharyngioma preoperatively and noninvasively.22–24Several laboratory methods have been developed to perform molecular subgrouping of MB by using tumor samples from surgical resection. Owing to technical complexity and costs, these methods were not applicable for routine clinical practice in a large number of medical facilities. However, the magnetic resonance (MR) scanning costs much less than the genomic test and could obtain medical images with high resolution. Moreover, this medical imaging method does not expose patients to ionizing radiation that causes tissue damage to patients. Hence, conventional MR images are first-hand clinical information for clinicians with cost-effective, high-resolution, and noninvasive traits. By analyzing tumor characteristic of MR images, several papers have been published to correlate the subgroups of MB with location, enhancing pattern, cystic change, and other image features reviewed by radiologists.2 Very few papers were published about radiomics study in MB because of the limited number of cases. In our study, an improved multitask learning method for the predictive analysis of molecular subgroups of MB is designed based on the mask regional convolutional neural network (Mask-RCNN) to compensate the lack of training data.25 The multitask technique26–28 was introduced into radiomics modeling to reduce the risk of overfitting and to improve the generalization ability of facing the few-shot challenge. This method has been widely used in the field of brain tumor analysis and achieved impressive results. Liu et al.29 introduced the multitask technique jointly identifying the stage of Alzheimer’s disease and predicting clinical scores incorporating MR images and demographic information. Bui et al.30 proposed a multitask learning method to simultaneously learn both tissue segmentation and geodesic distance regression using brain MR images. Estienne et al.31 proposed a multitask algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Collier et al.32 used a multitask learning strategy to integrate information about gene mutations and protein–protein interactions in a versatile manner and to predict tumor-driver genes in a pan-cancer setting and also for specific tumor types. Considering that location was proved to be an important MRI feature for molecular subgrouping of MB and the 4 molecular subgroups had different prognosis,33 the information of tumor mask and patient prognosis were integrated into the multitask framework to promote the accuracy of molecular subgroups prediction. Moreover, to effectively utilize the comprehensive information of our dataset, we expanded the input views of the Mask-RCNN. Both T1 contrast-enhanced (T1C) and T2 sequences were incorporated as input to obtain multi-view feature representations of patients. To the best of our knowledge, this is the first attempt at introducing the multitasking technique of few-shot learning to assist the preoperative prediction of molecular subgrouping in MB.However, the data-driven predictions by deep learning techniques highly depend on a large number of training samples. The number of training samples limited the performance of radiomics algorithms in biomedical image analysis. The age-adjusted incidence rates per 100 000 person-years of MB are about 0.16 in all ages and about 0.44 in children (0–14 years).This multicenter retrospective study was approved by the institutional ethics board of 4 participating hospitals . We retrospectively reviewed the medical systems of 4 hospitals during the 8-year period (from January 2010 to December 2017) and screened out 120 patients diagnosed with MB histologically with high-quality preoperative MRI data and tumor tissue for molecular subtyping. The collected high-quality MR images have no artifacts and the diseased tissue could be clearly observed on them. Seven cases diagnosed as “medulloblastoma highly suspected” by neuropathologists were excluded because of very limited tumor samples. Finally, 113 patients were included from 4 hospitals in this study . The detailed clinical and genomic characteristics of all cases from different institutions were given in 34 In brief, RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissues with an RNeasy FFPE Kit (Qiagen). RNA was then quantified by a NanoDrop 2000 spectrophotometer. A total of 100 ng RNA was then hybridized using the NanoString nCounter CodeSet at 67ºC for 20 h. The CodeSet contained gene-specific probes that assayed the abundance of 22 molecular-subgroup-specific genes and 3 housekeeping genes. Then, the excessive probes were washed, purified, and immobilized on a streptavidin-coated cartridge using the nCounter Prep Station (NanoString Technologies) according to the manufacturer’s protocol. Signals of fluorescent barcodes representing the target RNA molecules were then counted and recorded by the nCounter Digital Analyzer (NanoString Technologies). An automated script together with a training set was employed to transform raw data into subgroup prediction. The script normalized the raw data with R package NanoStringNorm, trained the pamr classifier with the training set, and made a subgroup prediction.Molecular subgroup affiliation was done by the NanoString-based platform according to the previous publication.Brain MR imaging of enrolled patients was performed on MR systems, including Siemens, GE, and Philips, at 3 T before surgery to collect T1 contrast-enhanced (T1C) and T2 sequences. MR images were acquired using different protocols during the 8-year period. The protocol parameters were given in 26–28 In our study, we adopted the Mask-RCNN as the original multitask framework and expanded the input views and the output tasks in the improved model. To take full advantage of our collected imaging data, we expanded our multitask model inputs with both T1C and T2 MRI sequences to obtain multiview feature representations. Meanwhile, 2 other related auxiliary tasks of prognosis categorization and tumor segmentation were tackled to constitute multitask learning in order to further improve the molecular diagnosis. The flowchart of our model is shown in Taking into consideration the low incidence of MB, we introduced a multitask technique to address the few-shot challenge. Due to the mechanism of exploiting the generic information shared across tasks and specific information among tasks, multitask learning could reduce the risk of overfitting and improve generalization ability and hence was efficient in few-shot scenarios.35 with the refined basic feature extraction layers of Residual Neural Network (ResNet101)36 was first utilized as the backbone of the Mask-RCNN. The ResNet101 was comprised of 5 stages: C1, C2, C3, C4, and C5. By using pyramid representations to construct feature pyramids, multiscale features were extracted to combine the FPN with the ResNet101 in the study. FPN constructed 3 parts: a bottom-up pathway, a top-down pathway, and lateral connections. The feed-forward propagation of the ResNet101 was defined as the bottom-up pathway. The outputs of different stages were employed to form the feature maps of different pyramid levels. In the top-down pathway, the spatially coarser but semantically stronger feature maps from the higher pyramid levels were up-sampled by a factor of 2 to acquire higher resolution features. Then the up-sampled features from the top-down pathway were merged with the feature maps of the same spatial size from the bottom-up pathway by element-wise addition. Finally, each merged map was appended a 3*3 convolutional layer to acquire the final feature maps {P2, P3, P4, P5}. The feature map of P6 was obtained by subsampling P5 with a stride of 2. The pyramid feature maps were composed of P2, P3, P4, P5, and P6.The improved Mask-RCNN model consisted of 3 stages: feature extraction, region proposal, and prediction. In the first stage, a feature pyramid network (FPN)37 slid across the multiscale pyramid feature maps extracted from the backbone net to obtain region proposals that contained tumor lesions. A window slid over every level of the feature pyramid to get anchors. The anchors had areas of {4*4, 8*8, 16*16, 32*32, 64*64} pixels on {P6, P5, P4, P3, P2}, respectively. In addition, anchors of multiple aspect ratios {1:2, 1:1, 2:1} were applied at each level. Therefore, 15 anchors were obtained simultaneously at each sliding window location. Then the anchors were fed into a 3*3 convolutional layer, which mapped the sliding window to a 256-dimensional vector. The 3*3 convolutional layer was followed by 2 sibling 1*1 convolutional layers, one of which is used for bounding box regression and the other for object/nonobject binary classification. Thus, the outputs of the regression layer were 4*15 coordinates for 15 boxes and the outputs of the classification layer were 2*15 scores for probability estimation of whether the box represented an object. Then the anchors were selected as proposals according to the Intersection-over-Union ratios of the anchors and ground-truth boxes.In the second stage, region proposal networks25 method. Then following 3 branches were constructed for prognosis classification, molecular prediction, and mask segmentation. For the prognosis or molecular branch, the feature map was fed into 2 concatenated fully connected layers followed by 2 sibling fully connected layers, one of which is used for bounding-box regression and the other for box classification. The outputs of the regression layer were used to refine bounding-box positions for 4 subgroups, and the classification layer separately outputted probability predictions of different classes. The category with the highest probability is the forecast subgroup. For the segmentation branch, the feature map was fed into a fully convolutional network including 4 convolutional layers followed by a transposed convolutional layer with a stride of 2 to acquire the segmented mask of each proposal from RPN.In the third stage, the feature map of any region proposal was transformed into fixed spatial dimensions  by the RoIAlignThe multitask losses were obtained by performing a weighted linear sum of the losses for each individual task in the Mask-RCNN model. Hence, we could adjust the loss weight of an auxiliary task to zero and then investigate the model performance based on other tasks. The detailed network structure was described in All MB patients were divided into 2 cohorts: Firstly, 74 patients collected from Huashan Hospital and Huadong Hospital constituted the 3-fold cross-validation cohort (CVC) to assess model performance. Secondly, the patients enrolled in the previous cohort were applied to train the model and the model was performed on the remaining 39 patients from Shandong Provincial Hospital and Anhui Provincial Hospital as the independent testing cohort (ITC). Genomic characteristics, gender, OS time, and histology had no statistical differences between different evaluation cohorts . MoreoveP value .05. In 3-fold cross-validation, the prediction probabilities and results in every fold were concatenated to assess the model performance using the accuracy (ACC) and area under the receiver operating characteristic curve (AUC) values. For prognosis prediction, we used the additional indexes of sensitivity (SEN) and specificity (SPE) to evaluate the model by treating the good prognosis subjects as positive samples and the poor prognosis subjects as negative samples. In addition, we employed a pixel-level measurement to evaluate the tumor segmentation quality of our approach: Dice coefficient. The calculation methods of quantitative indexes are described in Kruskal–Wallis test analyses were performed to assess whether genomic characteristics, age at diagnosis, gender, OS time, and histology had statistical differences between different evaluation cohorts. All statistical analyses were performed by using SPSS software version 22.0 (IBM Corp.) with a 2-sided significance level of 38 In our cohort, adult MB patients were presented with a trend of better survival than pediatric patients , 64 child patients , and 43 adult patients . There were 24 WNT tumors (21%), 27 SHH tumors (24%), 31 Group 3 tumors (27%), and 31 Group 4 tumors (27%). Patients’ demographic information, genetic pattern, and OS time are concluded in patients . SHH tumpatients .The evaluation results of ACC and AUC for prediction of MB subgroups are summarized in The effects of multitasking are summarized in Patient characteristics of different prognosis categories divided according to the threshold of 27 months, which was the median OS time of all patients in our study, are given in The OS time distributions of the ITC (using the true and predicted molecular subtypes) and all 113 cases are shown in The improved Mask-RCNN achieved an average Dice coefficient of 0.90 in both CVC and ITC in the tumor segmentation task .3 Meanwhile, it is the most common malignant pediatric brain tumor with a very high rate of mortality.1–3 Thanks to the large-scale genomic studies of MB, it was clearly accepted that MB comprised at least 4 distinct molecular subgroups such as WNT, SHH, Group 3, and Group 4 with various stages at diagnosis, trends to metastasis, different transcriptomes patterns, copy number variations, and the clinical outcomes.5–13 These molecular subgroups present a critical clinical value for clinicians to designate a personal comprehensive medical paradigm for patients, especially pediatric patients. In the SJMB12 clinical trial,39 a workflow to treat MB patients was established and mainly depended on molecular subgroups. In Thompson et al.’s4 published paper, clinical data of MB patients demonstrated that the extent of resection was no more important once accounting for molecular subgroups, which would definitely change neurosurgeon’s surgical strategy. However, the methods to perform molecular subgrouping are not popularized due to technical difficulties and economic ineffectiveness. A noninvasive method for preoperative MB molecular subgrouping is ideal in the current medical situation. MRI is a clinically routine medical imaging method. Different MR findings can help clinicians make rough judgments about tumor diagnosis, surgical planning, and future prognosis. In recent years, many published articles have used MR images to determine the molecular type of MB, but most studies have been based on subjective observation, such as the location of the tumor, the enhanced morphology, and the presence or absence of cystic changes.14–21 For example, Perreault et al.14 collected 99 cases to predict molecular subgroups of pediatric MB using tumor location and enhancement pattern and achieved the accuracy of 65% in the validation cohort. The accuracy, specificity and stability of this subtype prediction method are not satisfactory. The emerging radiomics technology provides the possibility to extract the characteristics of the entire tumor by mining high-throughput features in MR images. Some researchers have applied radiomics approaches in MB for molecular diagnosis15; nevertheless, the diagnosis result was far from satisfaction with a limited number of cases in each molecular subgroup. Therefore, in our study, we adopted a multitask learning approach to address the few-shot problem for the accurate prediction of molecular subtyping of MB.MB is an embryonal tumor with an incidence of 0.16 cases per 1 000 000 worldwide.26–28 Hence, the complexity of the model and the risk of overfitting are reduced. In respect of attention focusing, if training data are limited with high-dimensional features, one task could focus its attention on those significant features as other tasks could provide additional prior knowledge to constrain model construction.26–28 This helps the model with improved generalization ability. In respect of feature learning, features that are difficult for one task to learn could be easy for other tasks to learn.26–28 The features’ eavesdropping learning mechanism makes the tasks perform better. Comparison of the prediction results presented in 41 Due to the superior performance of multitasking learning, we were able to predict the prognosis for pediatric and adult patients simultaneously.Multitask learning is an available solution to compensate for the lack of training data due to the low incidence of MB and molecular subtyping predisposition (lower occurrence of WNT and SHH tumors). In respect of data size, multitask learning increases the implicit data to train our models. A model that simultaneously learns multiple tasks that have different noise patterns is capable of learning a more general representation by averaging noise patterns.42–44 Hence, predicting the patient’s OS status based on a 4-type classification scheme is not adequate and could result in heterogeneous prognosis. In our study, we utilized imaging data from T1C and T2 MRI sequences to categorize the prognosis, and this method of prognosis prediction was more accurate and representative. In our study, prognostication was estimated with AUC of 0.88 and 0.79 in 3-fold CVC and ITC, respectively. In addition, the prognosis classification task provided particular prognostic information to the molecular prediction task during the establishment of the multitask framework and the outputs of molecular prediction task could reflect clearer prognostic stratification in turn compared with the true molecular categorization. As shown in It is widely acknowledged that the clinical outcome of MB depends on not only molecular subgrouping but also on other molecular biomarkers and metastatic status.45 worked out 12 subgroups of MB with more biological diversity, disparate prognosis, and discrepancy in response to chemo-radiotherapy. Methods based on MR images have advantages to attenuate discrepancy of intratumoral heterogeneity regarding its comprehensive insight of the tumor as a whole. For example, it is still an unresolved question for WHO 2016 consensus to completely differentiate Group 3 tumor from Group 4 tumor because of quite similar genetic background. In the study of Sharma et al.,46 they analyzed a series of 1501 MBs to verify that there were 8 robust Group 3/Group 4 subtypes with distinct molecular and clinic-pathological features based on large-scale methylation and expression profiling which was not easy to duplicate and apply. In this study, we could effectively distinguish Group 3 and Group 4 by the proposed method and achieve a satisfactory level of accuracy (0.90 and 0.95 corresponding to Group 3 and Group 4).Moreover, the variation of bioinformatics analytics makes a deep insight into intratumoral heterogeneity within 4 molecular subgroups. Based on genome-wide DNA methylation and gene expression data, Cavalli et al.Preoperative MRI scans were collected from various centers using different scanner vendors and imaging parameters, with potential heterogeneity in imaging data. A multicenter, large sample data are still needed to balance the imaging data heterogeneity and demonstrate the performance of our method. There are very few infant patients in our study as the 4 hospitals involved are not children’s hospitals. Moreover, TP53 and TERTp are 2 major prognostic biomarkers beyond 4 subgroups, and we do not include them in this study because the case number will be reduced if more risk stratification is categorized. Finally, we do not have evidence of spinal metastasis of all patients. That is why we cannot use this method to predict the tendency of tumor dissemination and metastasis.With the help of multitask learning, this study represents that the MRI-based method succeeded in predicting molecular subgroups of MBs with high accuracy. Molecular subgroups were discriminated with AUC of 0.88, 0.98, 0.90, and 0.93 corresponding to WNT, SHH, Group 3, and Group 4 tumors. The final AUC of molecular diagnosis achieved 0.97 and 0.92 and prognosis subtypes were estimated with AUC of 0.88 and 0.79 in 3-fold CVC and ITC, respectively. To date, this is the first attempt at introducing multitask learning techniques into the few-shot scenario of molecular subgrouping prediction in MBs based on preoperative MRI. The high accuracy indicates that our proposed radiomic model is a promising tool that has the potential to be a useful clinical assistant.vdaa079_suppl_Supplementary_MaterialClick here for additional data file.


Long noncoding RNA differentiation antagonizing nonprotein coding RNA (lncRNA-DANCR) is associated with poor prognosis in multiple cancers, and promotes cancer stemness and invasion. However, the exact mechanisms by which DANCR promotes non-small cell lung cancer (NSCLC) remain elusive. In this study, we determined that DANCR knockdown (KD) impeded cell migration and reduced stem-like characteristics in two NSCLC cell lines, A549 and H1755. Wnt signaling was shown to promote NSCLC proliferation, stemness, and invasion; therefore, we hypothesized that DANCR may regulate these activities through induction of the Wnt/β-catenin pathway. DANCR KD reduced β-catenin signaling and protein expression, and decreased the expression of β-catenin gene targets c-Myc and Axin2. One of the well-defined functions of lncRNAs is their ability to bind and inhibit microRNAs. Through in silico analysis, we identified tumor suppressor miR-216a as a potential binding partner to DANCR, and confirmed this binding through coimmunoprecipitation and luciferase-reporter assays. Furthermore, we show that DANCR-induced β-catenin protein expression may be blocked with miR-216a overexpression. Our findings illustrate a role of DANCR in NSCLC migration and stemness, and suggest a novel DANCR/miR-216a signaling axis in the Wnt/β-catenin pathway. Lung cancer is the leading global cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) comprises 85% of all lung cancer cases . DespiteNoncoding RNAs (ncRNAs) are critical regulators of gene expression that are commonly dysregulated in cancer. MicroRNAs (miRNAs) are small ncRNAs that are 16–24 nucleotides long, and function post-transcriptionally by binding to target mRNAs at specific recognition sequences, leading to target mRNA degradation or translational repression. Several miRNAs have been shown to play important regulatory roles through the promotion or suppression of NSCLC . HoweverLong noncoding RNAs (lncRNAs) are a heterogeneous group of regulatory ncRNAs defined by their lack of protein-coding potential and minimal length of 200 nucleotides. LncRNAs differ from miRNAs by their ability to bind to a wide variety of macromolecules  to exert diverse functions. LncRNA differentiation-antagonizing nonprotein-coding RNA (DANCR) was first identified as a suppressor of epidermal progenitor differentiation . In the Wnt signaling is a key pathway that normally functions in development and stemness . AberratIn this study, we report that DANCR knockdown (KD) reduces migration, cell viability, and stem-like characteristics. We also show that DANCR overexpression in NSCLC activates Wnt/β-catenin signaling, which can be effectively blocked by miR-216a overexpression. Furthermore, we confirm the interaction between DANCR and miR-216a. For the first time, we show that Wnt/β-catenin activation can be mediated through the DANCR repression of miR-216a, thereby establishing a novel DANCR/miR-216a/Wnt signaling axis that promotes migration and stem cell characteristics that are fundamental to NSCLC progression.l-glutamine  supplemented with 10% fetal bovine serum , 5 mg/mL D-glucose , 5 mM HEPES , and 0.05 mM sodium pyruvate (Sigma). HBE2 was maintained in Keratinocyte SFM without calcium chloride medium . Cells were maintained in a humidified environment at 37 °C and 5% CO2. Cell lines A549, H1755, H1944, and H2087 were obtained from ATCC . H1975, H661, H1299, H358, and HBE2 were a generous gift from Dr. Feng Jiang, University of Maryland School of Medicine. All used cell lines regularly tested negative for mycoplasma contamination throughout the whole duration of this study.NSCLC adenocarcinoma cell lines A549, H1975, H1755, H1944, H2087, and H358, and NSCLC large cell carcinoma cell lines H661 and H1299 were maintained in RPMI 1640 + n = 706) and lung squamous cell carcinoma (n = 626) by RNAseq .The publicly available The Cancer Genome Atlas (TCGA) dataset was evaluated using the UCSC Xena Functional Genomics Browser. Analyzed datasets were TCGA lung adenocarcinoma . mRNA was converted to cDNA using M-MLV reverse transcriptase (Invitrogen) and random hexamer primers. MiRNAs were converted to cDNA using miScript II RT kit . qRT-PCR for miR-216a was normalized to RNU6 using miScript Primer assays and miScript Sybr Green PCR Kit (Qiagen). qRT-PCR for gene expression was normalized to GAPDH expression, and performed using Light Cycler Sybr Green I Master kit . Gene primers used in the study were:5 cells/well in a 6 well plate in 2 mL of medium. SiRNA transfection was performed with 40 nM of 5′-Cy5-tagged or untagged siRNAs using Lipofectamine RNAiMAX (ThermoFisher Scientific) according to the manufacturer’s instructions, and samples were harvested 48–72 h post-transfection. SiRNAs were obtained from Sigma-Aldrich. SiRNA sequences used in the study were:Scramble siRNA: 5′-UAA CUC GCU CGA AGG AAU C-3′DANCR siRNA-1: 5′-GCG UAC UAA CUU GUA GCA A-3′DANCR siRNA-2: 5′-UAA CAG AAU CCA CCU CCG A-3′Cells were seeded at 3 × 10Scramble control shRNA and the pLV-hU6-EF1α-puro backbone were purchased from Biosettia, and DANCR shRNA constructs were produced using the following target sequences: 5′-GCCGGTCATGAGATTATAT-3′ for sh278 and 5′-AAAAGGCCAAATATGCGTACTAA-3′ for sh321. Stable infections were performed for lentiviral constructs: plv-hu6-EF1a-puro-negative control shRNA, plv-hu6-DANCRsh278, and plv-hu6-EF1a-puro-DANCRsh321. Briefly, HEK293T cells were transfected using the Lenti-X Packaging Single Shots  following the manufacturer’s protocol. After 24 h, the medium was changed and lentivirus-containing supernatants were harvested at 48 and 72 h post transfection. A549 cells were infected with the virus at an MOI of 10 with 4 μg/mL polybrene, and pooled clones were then selected with puromycin (1 μg/mL).3) and H1755 (104) cells were seeded in 100 mm plates and grown at 37 °C and 5% CO2. After 2 weeks, colonies were fixed and stained in 10% formalin with 0.05% Crystal Violet, and then quantified using automated colony counter ProtoCOL3.A549  as per manufacturer instructions. Cells were incubated with the CellTiter reagent for 1 h and then read at 490 nm every day for 5 days.For cell-viability assays, cells were trypsinized and counted 24 h after siRNA transfection. A549 (500 cells/well) and H1755 (2500 cells/well) were seeded in triplicate in 96 well plates, and viability was measured using the CellTiter 964) were plated in triplicate into the RTCA xCELLigence microwell CIM plates (ACEA Biosciences), and placed in the RTCA DP Analyzer located in a humidified incubator maintained at 37 °C and 5% CO2. Filters in the CIM plate chambers contained electrodes that could measure the change in impedance as cells passed through the filter and adhered to its underside. The change in impedance (shown as cell index) is a direct measure of number of cells that migrated. Readings were taken in real time every 15 min for 72 h.Cells were trypsinized and counted 24 h after siRNA transfection. A549 or H1755 cells (4 × 10Cells were trypsinized and passed through a 40 µm cell strainer to obtain a single cell suspension. Cells were then seeded in 6-well plates coated with 2% polyHEMA (Sigma) at a dilution of 1000 cells/mL in 3 mL of RPMI media containing 2% B27, 20 ng/mL EGF, 4 μg/mL insulin, and 0.4% BSA. Spheres larger than 50 μm were counted after 7 days of culture.Total cell lysates (30 μg) were separated by SDS-PAGE and transferred onto a polyvinylidene difluoride (PVDF) membrane. The membrane was incubated with a specific primary antibody using manufacturer’s recommended dilutions overnight followed by the horseradish peroxidase (HRP)-conjugated secondary antibody (1:5000) and visualized by the ECL Western blotting detection system (Thermo Scientific). The following antibodies were used from Santa Cruz Biotchnology, Inc. : Sox2 (sc-365823), β-catenin (sc-7963), GAPDH (sc-32233); Cell Signaling: c-Myc (CST-5605), Axin2 (CST-5863); and Sigma: β-actin (A5441).5 A549 cells/well were plated the day before in a 12-well plate, and then cotransfected with 8X Super TOPFlash plasmid (Addgene 12456), Renilla vector, and either with Control shRNA, DANCR sh278, or DANCR sh321 vectors using a FuGene HD (Promega) reagent according to manufacturer’s instructions. For the lncRNA-DANCR luciferase reporter construct, DANCR cDNA was amplified using the following primers: 5′-ACTGATGCTAGCGTTGACAACTACAGGCACAAG-3′ and 5′-ATCAGTCTCGAGGTCAG GCCAAGTAAGTTTATTAAC-3′, and cloned into the psGG 3′UTR promoter downstream of luciferase using NheI and XhoI sites; the inserted sequence was verified by sequencing to the truncated WT lncRNA-DANCR (nt271-nt915) containing the miR-216a MRE site 5′-TGAGATTA-3′ at nt774-nt781. The day before, 293T cells were plated in a 12 well plate and then cotransfected with pSGG-luc-DANCR construct and the Renilla vector, along with the p-Babe-miR-216a plasmid (Addgene 65054) using Lipofectamine 2000. Luciferase-reporter assays were performed 48 h after transfection using the dual luciferase assay system  according to manufacturer’s instructions, and normalized to Renilla luciferase activity.For the TOPFlash luciferase reporter assay, 1 × 10DIANA LncBase Predicted v.2 and miRanda-mirSVR tools were used to identify potential miRNAs that interacted with DANCR. TargetScan v7.2 was used to predict potential miR-216a mRNA targets.5 cells/well of a 6-well plate) were cotransfected with phage-ubc-nls-ha-tdMCP-GFP (Addgene 40649) and either pcDNA3-MS2-DANCR or pcDNA3-MS2-Vector constructs using FuGeneHD according to manufacturer’s instructions. After 48 h of transfection, cells were lysed and incubated while rocking with anti-GFP conjugated beads for 2 h at room temperature. Pulldown products were released using Trizol, and RNA was extracted and used to measure miR-216a expression via qRT-PCR.Stable A549 DANCR KD cells (3 × 105 cells/well of a 6-well plate) with pBABE-puro vector or pBABE-puro-DANCR plasmids using FuGENE HD reagent according to the manufacturer’s instructions. Inserted DANCR was confirmed by sequencing to be WT DANCR (nt61-nt915) containing miR-216a MRE site 5′-TGAGATTA-3′ at nt774-nt781. Cells were incubated for 48 h, expanded, and plated for second consecutive transfection of pBABE-puro vector or pBABE-puro-miR-216a. Lysates and RNA were harvested 48 h after the second transfection.The DANCR sequence was amplified by PCR using the following primers: 5′-ACTGATGGATCCCCCGCCCCGCGC-3′ and 5′-ACTGATGAATTCGTCAGGCCAAGTAAG TTTATTAACCTGCC-3′, and then cloned into the pBABE-puro vector using the BamHI and EcoRI sites. DANCR overexpression was achieved by transfecting A549 cells (3 × 10n = 706) and lung squamous cell carcinoma (n = 626). This analysis illustrated that DANCR expression was highly upregulated in lung adenocarcinoma and squamous cell carcinoma compared to normal lung tissue  dataset for DANCR expression in the most common NSCLC subtypes: lung adenocarcinoma  DANCR expression. Two distinct DANCR siRNAs were transfected into NSCLC adenocarcinoma cells A549, H1975, H358, and H175,5 yielding efficient KD of DANCR expression A. We selWe first examined the effect of DANCR KD on cell proliferation. A549 and H1755 cells were plated 24 h post-siRNA transfection, and monitored for daily growth for 5 days at 24 h intervals using Aqueous Cell Titer 96 assay B. For evWe then used cell impedance assays to examine the effect of DANCR inhibition on cell motility. The xCELLigence Impedance assay displays the migration of cells in real time by providing an increased change in impedance as cells move through the filter from the top to the bottom chamber. DANCR KD reduced the migration of both A549 and H1755 cells C. Since We next assessed the effect of DANCR KD on NSCLC long-term clonogenic potential using two-dimensional colony formation assays. Transient reduction of DANCR expression with siRNAs in the A549 cells resulted in a minor but significant inhibition of colony formation, but again with only siDANCR-1 . The exaCells that exhibit migratory capacity possess phenotypic similarities with the cancer stem cell (CSC) subpopulation. Stem-like programs are regulated by the timing and expression patterns of multiple markers. To investigate the effect of DANCR KD on stem-like characteristics, we evaluated the expression of two fundamental CSC markers, SOX2 and ALDH1/2. We found that transient DANCR KD significantly reduced SOX2 and ALDH mRNA, and protein expression compared to scramble control A,B.Given the significant effect of DANCR KD on reducing Sox2 expression, and the essential role of Sox2 in stem cell self-renewal and pluripotency , we nextThe canonical Wnt/β-catenin pathway was shown to promote cancer cell proliferation, invasion, and stemness in many types of cancers including colorectal, breast, and leukemia . We analLncRNAs have multiple properties, which include their ability to act as competitive endogenous RNA (ceRNA). CeRNAs are able to inhibit miRNA activity by competitively binding miRNAs through miRNA recognition elements (MRE), resulting in inhibition of miRNA–mRNA binding and the upregulation of the miRNA targets. Using in silico analysis, DANCR was predicted to contain an MRE site for miR-216a A. PrevioTo further evaluate DANCR and miR-216a interaction, we employed the MS2–MCP (MS2-MS2 Bacteriophage Coat Protein) system, in which GFP-tagged MCP binds to MS2 stem-loop sequences that can consequently be used in immunoprecipitation studies to assess RNA binding. Stable A549 DANCR KD cells were cotransfected with an MCP-GFP tagged plasmid with either an MS2-DANCR or MS2-Vector plasmid. Then, GFP was immunoprecipitated, and RNA was extracted to evaluate miR-216a pulldown and association. These results demonstrated that miR-216a was pulled down almost fourfold more in MS2-DANCR cells than in MS2-Vector cells B. AdditiMoreover, we hypothesized that the upregulation of DANCR in NSCLC has reciprocal downregulation of miR-216a expression. As expected, miR-216a was downregulated in a number of different NSCLC cell lines, including H2087, H1755, H661, H1944, A549, H358, and H1975 compared to HBE2 cells D. Taken p = 0.06), lending support to our hypothesis that miR-216a regulates β-catenin (After verifying the competitive binding of DANCR and miR-216a, we hypothesized that DANCR could be regulating Wnt/β-catenin via miR-216a repression. To test this, we overexpressed DANCR by transfecting A549 cells with a DANCR cDNA vector. An increase in DANCR expression was detected 48 h after transfection A, which -catenin D. These The Wnt pathway regulates key cellular functions such as proliferation and differentiation, and deregulation of its signaling can have widespread consequences . AberranThis study is the first to demonstrate that lncRNA-DANCR regulates the Wnt/β-catenin pathway through miR-216a inhibition, an established lung cancer tumor suppressor. Although recent studies showed DANCR regulation of Wnt/β-catenin signaling in glioma , this isDANCR expression was significantly increased in a panel of NSCLC cell lines, with the exception of H1944. The H1944 cell line could have developed and progressed from different mutations than those of the other lines, and DANCR overexpression was thereby unnecessary for its survival and growth. Further analysis of hundreds of NSCLC samples compared to normal lung tissue from the TCGA dataset demonstrated that DANCR is highly upregulated in NSCLC.DANCR KD resulted in typical Wnt/β-catenin-inactivation features, including impedance in cell migration, the inhibition of long-term clonogenic potential, and a reduction in stem-like characteristics . While oThe difference in the effect of DANCR KD on long-term growth between A549 and H1755 may be due to inherent differences between the two cell lines. H1755 cells were derived from a metastatic lesion of adenocarcinoma, while A549 cells originated from a primary adenocarcinoma tumor. Moreover, our experiments revealed a nearly twofold increase in DANCR expression in H1755 compared to that in A549 B. As ourWe further showed that DANCR KD can inactivate β-catenin activity and reduce β-catenin protein expression . Our stuCTNNB1 gene in hepatocellular carcinoma, thereby protecting it from inhibitory miRNAs [Given the diverse functions and potential binding partners of long noncoding RNAs, it is also possible that DANCR activates Wnt signaling independent of miR-216a. DANCR was reported to associate to the SOX2 is highly expressed in NSCLC subtypes, and the inhibition of Sox2 downregulates Wnt1/2 and c-myc gene expression, induces cell apoptosis, and reduces metastatic potential in lung cancer [CCND1, resulting in enhanced proliferation and tumorigenesis [DANCR KD also significantly downregulated Sox2 gene and protein expression. Sox2 is an essential transcription factor that maintains stem-cell self-renewal and pluripotency . The geng cancer ,29. Our g cancer . Anotherigenesis . Others igenesis , mesenchigenesis , and ostigenesis . TherefoCSCs make up a small subpopulation of cancer cells that possess features of self-renewal, differentiation, and tumor initiation . Failure


Adsorption phases and vibronic properties of C42H28 consistently reflect the progressive reduction of the molecule–substrate hybridization. Separate C42H28 clusters are observed on Pt(111) as well as broad molecular resonances. On Au(111) and graphene-covered Pt(111) compact molecular islands with similar unit cells of the superstructure characterize the adsorption phase. The highest occupied molecular orbital of C42H28 on Au(111) exhibits weak vibronic progression while unoccupied molecular resonances appear with a broad line shape. In contrast, vibronic subbands are present for both frontier orbitals of C42H28 on graphene. They are due to different molecular vibrational quanta with distinct Huang–Rhys factors.Rubrene (C Two-dimensional materials are emerging as monatomically thin buffer layers (BLs) on metal surfaces, i.e., as intermediate films that efficiently reduce the hybridization of an adsorbate with the metallic substrate. The minimization of the adsorbate–substrate coupling is motivated by the desire to preserve genuine properties of the free atom or molecule even after adsorption. For instance, adsorption on a BL often retains the sharp electronic and vibrational energy levels that are characteristic for the atom or molecule vacuum state and that would inevitably be broadened or even quenched upon adsorption on the metal surface. Narrow molecular resonances on surfaces are desirable because they increase the residence time of injected charge at the adsorbate, which is favorable for, e.g., energy conversion processes or the observation of vibronic progression .2 perylene on graphene-covered Ir(111) thiophene from the metal surface [Structural aspects of adsorption on the prominent two-dimensional materials graphene  and hexa2 , of coba samples  as well  samples , of conj samples , of mang samples , and of  Ir(111)  have bee surface . A rich 42H28, 42H28–surface hybridization. The choice of the molecule and substrate surfaces was motivated as follows. C42H28 is a polycyclic aromatic hydrocarbon (42H28 [42H28 has gained in importance for its suitability in light-emitting diodes [EF) [In the work presented here, 5,6,11,12-tetraphenyltetracene  –19, whildes [EF) . Graphendes [EF) , which i42H28 on Pt(111) scanning tunneling microscopy (STM) images reveal the occurrence of separate molecular clusters and very broad molecular resonances in STS data, which is attributed to an elevated C42H28–Pt interaction. On Au(111) the observations are compatible with a reduced hybridization since compact molecular islands with a regular superstructure form and vibronic progression of the HOMO dI/dV spectroscopic signature is visible. Unoccupied molecular orbitals, however, appear as broad and merged resonances without indication of a vibronic fine structure. The molecular superstructure on graphene is similar to the assembly on Au(111), albeit with a lower molecule surface density, and dI/dV data exhibit vibronic progression in both frontier orbitals, which reflects the effective separation of C42H28 from the metal surface.After adsorption of C−9 Pa) and at low temperature (Pt(111) and graphene-covered Pt(111) at 5 K, Au(111) at 78 K). Pt(111) and Au(111) surfaces were cleaned by Ar+ ion bombardment and annealing. Graphene was epitaxially grown on Pt(111) by exposing the heated (1300 K) surface to the molecular precursor C2H4 (purity 99.9%) at a partial pressure of 10−4 Pa for 120 s [42H28 molecules were sublimated from a powder (purity 98%), deposited in a heated (500 K) W crucible and directed towards the sample surface at room temperature. Molecular coverages below the closed single molecular layer are estimated from STM images as the percentage of the covered surface area. All STM images were recorded at constant current with the bias voltage applied to the sample. Constant-height dI/dV data were acquired with a lock-in amplifier by sinusoidally modulating the bias voltage  and measuring the first harmonic of the current response of the tunneling barrier.The experiments were performed with an STM operated in ultrahigh vacuum (10or 120 s . C42H28 42H28 on Pt(111) at a coverage of approx. 20%. Well separated molecules or clusters of molecules can be discerned on the terrace without evidence for island formation or decoration of substrate step edges. These observations are indicative of an effectively reduced surface diffusion after adsorption, which lowers the mobility of adsorbed molecules and, thus, the formation of large molecular islands. The close-up view in 42H28 molecules exhibit a submolecular structure consisting of two bright lobes separated by a central line with dim contrast. Some molecules even exhibit three lobes (42H28 in 42H28 on Ag(100) [42H28 with Pt(111) is likely to distort the relaxed vacuum configuration of the molecule. A comparison of experimental data with simulated STM images would allow for further insight into the actual adsorption geometry; however, this is out of the scope of this work. The absence of individually resolved C42H28 phenyl groups on Pt(111) may be ascribed to the aforementioned elevated hybridization of the molecular and the substrate electronic structure, possibly mediated by the high density of Pt d bands at EF [ee lobes . The two42H28 in . The two42H28 in  where on Ag(100) . Howeverds at EF –19. In s42H28 molecules that appear with three rather than two lobes. Moreover, numbering the brightest to the dimmest lobe with 1, 2, and 3 reveals that two senses of rotation, clockwise (R) and anticlockwise (L), occur. In agreement with previous findings for C42H28 on Au(111) [1 and 2 are assigned to one pair of phenyl groups each, similar to the assignment for molecules appearing with two lobes only. Lobe 3 is due to one end of the tetracene backbone that likely adopts an inclined adsorption geometry, i.e., encloses a finite angle with the Pt(111) surface plane. In the case of C42H28 on Au(111) this angle was determined as approx. 38° by near-edge X-ray absorption fine structure spectroscopy [R-C42H28 and L-C42H28, therefore results from the left or right pair of phenyl groups of a twisted C42H28 molecule being closer to the surface. On Au(111) the formation of homochiral clusters led to remarkable supramolecular assemblies [Before corroborating the suggestion of elevated molecule–substrate coupling by STS data the occurrence of chiral molecular species shall be discussed. The close-up STM image in  Au(111) –26, the troscopy . The obssemblies –27, whic42H28–Pt(111) hybridization is corroborated by dI/dV spectra of the molecule. I/dV spectra recorded atop the bright lobe  surface and hamper the meaningful determination of a HOMO–LUMO gap width. Moreover, the spectra of I/dV spectrum with a single broad orbital feature, which indicates its elevated hybridization with the metal surface rather than its greater distance from the surface.The presumably elevated C42H28 on Au(111) at a surface coverage of approx. 50%. The molecules form well-ordered large islands. In addition to the regular arrangement of C42H28, The STM image reveals a periodic pattern of stripes, which are due to the  (or herringbone) reconstruction of Au(111) [42H28-covered Au(111) we find that C42H28 adsorption leaves the surface reconstruction invariant. In addition, step edges of the Au(111) surface are decorated with molecules (inset to 42H28 mobility after adsorption compared to the situation on Pt(111) (vide supra). The STM data presented in  Au(111) . It consinset to . These t42H28 molecule exhibits four lobes, which are attributed to the four phenyl groups. The superimposed molecule sketch visualizes this assignment. For some molecules the apparent height of all four phenyl groups is similar, while others exhibit a pair of diagonal phenyl groups appearing higher than the other pair, similar to previous findings on Ag(100) [42H28 molecule may indeed adopt the planar configuration of its tetracene backbone owing to a large cohesive energy that overcompensates through intermolecular hybridization the energy cost for planarization [42H28 molecules were previously reported for monolayers on Au(111) [42H28 was observed [The close-up STM image of a molecular island  enables  Ag(100) . This ob Ag(100) ,25–27. Irization ,31. Flat Au(111) , while oobserved .a and b with  , |a| = 1.15 ± 0.10 nm, |b| = 1.30 ± 0.10 nm, , giving rise to a molecule surface density of approx. 0.67 nm−2. The tetracene backbone is oriented along a  crystallographic direction of Au(111). In [42H28 backbones oriented parallel to the Au(111) surface was reported. In that work a multilayer C42H28 film was deposited at room temperature and subsequently annealed to achieve a monolayer coverage. Different superstructures of C42H28 with twisted and tilted backbones were presented in [27272742H28 is not limited to low-temperature depositions. It was likewise observed for room-temperature deposition on low-index Cu surfaces [The unit cell of the superstructure is spanned by the lattice vectors 111). In  a similaented in –2734 forted in 27 for Bi reconstruction on the C42H28 assembly. Evidently, the molecular superstructure matches the period of the reconstruction, which is reflected by the alignment of molecule rows with the discommensuration lines. A single row of C42H28 occupies the top of the soliton walls and the narrow hcp stacking domain, while three rows of C42H28 reside atop the wider fcc surface regions  reconstruction was previously reported for C64H36 [The STM image in r C64H36 .42H28 on Au(111) will be discussed. An overview spectrum is presented in I/dV for V ≥ 1 V is visible, probably due to the LUMO. I/dV spectrum in a higher bias voltage range of unoccupied states. The presented data were normalized by taking the exponential transmission factor of the tunneling barrier into account [I/dV data show a peak at approx. 2.3 V. Due to the considerably broad onset of dI/dV data starting from approx. 1 V it is difficult to unambiguously assign this peak to the LUMO or LUMO+1. Much wider unoccupied molecular resonances have been observed, too, in pump–probe photoemission experiments on thin C42H28 films adsorbed on highly oriented pyrolithic graphite and traced to the elevated molecule–substrate hybridization with a concomitant reduced lifetime of electrons injected into the LUMO [I/dV data were presented for C42H28 on Au(111) and a HOMO–LUMO gap exceeding 3 eV was extracted [Next, spectroscopic results obtained for C account . The northe LUMO . Due to xtracted . A direcxtracted  that werI/dV data with spectroscopic fine structure . In vibronic progression the molecule is electronically and vibrationally excited from its ground state by an attached charge, i.e., hole or electron. Here, C42H28 is transiently charged by the extraction of an electron from the HOMO in the course of the tunneling process. Therefore, the peak labeled H0 in 42H28. If the energy of the tunneling electron is sufficiently large then vibrationally excited states of the transiently charged C42H28 may be reached, which appear as the satellite peaks H1 and H2 to H0. This resonant transition from a molecular ground state to an electronically and vibrationally excited state is described within the Franck–Condon picture [Iν,n of the vibronic subbands resulting from transitions of the vibrational ground state of neutral C42H28 to the n-th vibrationally excited and transiently charged C42H28 obey a Poisson distribution,Using an increased bias voltage sampling in the HOMO spectral region exhibits dtructure . Three L picture . In partn is the vibrational quantum number and Sν is the Huang–Rhys factor, which determines the coupling of the hole or electron to the vibrational quantum with energy hν. The Huang–Rhys factor can be expressed aswhere ν denotes the relaxation energy of the vibration when charging the molecule [I0) with the first vibronic subband  enables direct access to the Huang–Rhys factor viawhere εmolecule ,41. Comp42H28 HOMO on Au(111) a Huang–Rhys factor of approx. 0.5 can be extracted from dI/dV data  at approx. 50% coverage is presented in a and b with  , the surface density of C42H28 on graphene is approx. 0.51 nm−2 and, thus, lower than that observed on Au(111). The lower molecule density may tentatively be explained by a molecular superstructure that is formed owing to the optimization of the intermolecular coupling. This might be facilitated by very low energy barriers, which are even lower than those observed for C42H28 on Au(111), for translational and rotational degrees of freedom. A template effect of graphene or a moiré lattice on the molecular assembly was not identified. Merely the orientation of the tetracene backbone perpendicular to a  graphene crystallographic direction was observed. A finite residual coupling of the molecule to graphene-covered Pt(111) is likewise indicated by the comparison of the superstructure in 42H28 film on graphene-covered SiC(0001) [42H28 molecules appearing uniformly in STM images. It is likely that the difference to the superstructure depicted in An STM image of CiC(0001) . In the 42H28 molecules on graphene appear with two or three lobes in STM images, which are labeled α and β in 42H28 on graphene has an apparently planar tetracene moiety, while β-C42H28 exhibits a tilted and possibly twisted backbone. Very often, rows of β-C42H28 are observed within the compact C42H28 island (42H28 configurations was previously reported for a variety of surfaces [Similar to observations on Pt(111), C8 island . Inside surfaces ,35–36 an42H28 with the graphene-covered Pt(111) surface. I/dV acquired atop a C42H28 molecule embedded in a molecular island. The spectroscopic signatures of HOMO and LUMO are visible at, respectively, approx. −0.87 and approx. 0.92 V. The molecular resonance widths are smaller than those observed on the other surfaces; in particular, the LUMO exhibits a narrow line shape.Spectroscopy data corroborate the weak hybridization of C42H28 is weakly coupled to the substrate, its orbital energies are expected to be aligned with the vacuum level [42H28 is an electron donor and has the propensity to transfer negative charge to the substrate, which could in principle reduce or compensate the work-function-induced shift of the HOMO. A similar behavior was previously reported for C64H36 adsorbed on h-BN-covered Ru(0001) [Before analyzing the vibronic subbands of the molecular resonances we remark that the HOMO–LUMO gap width defined as the difference between LUMO and HOMO energy varies between approx. 1.6 eV and approx. 1.8 eV, depending on the individual molecule. The approx. 0.2 eV variation of the gap width is due to changes in the LUMO energy since the HOMO resonance essentially retains its energy at approx. −0.87 eV. Assuming that Cum level  and, thuum level . In the Ru(0001) . IntermoRu(0001)  that werRu(0001) –51 can l1 and H2, with equidistant spacings of 180 ± 10 mV, and for the LUMO, L1 and L2, with equidistant spacings of 220 ± 10 mV. In order to clearly see the vibronic transitions in the HOMO and LUMO spectroscopic signatures, close-up views with increased bias voltage sampling are presented in I/dV data were fit by the superposition of three individual Lorentzian line shapes (thin lines) on a constant background. Therefore, different groups of vibrational modes participate in the vibronic progression of the different frontier orbitals. Within the uncertainty margin the vibrational quantum with energy hν1 ≈ 180 meV is most likely the same mode as that observed for C42H28 on Au(111) and corresponds to the vibrational mode with large Holstein coupling (vide supra). The second vibrational quantum with energy hν2 ≈ 220 meV is likely to coincide with the C42H28 vibration with energy 1594 cm−1 ≈ 200 meV, which was likewise predicted to exhibit an elevated Holstein coupling [−1 and 1594 cm−1 differ [−1 (1594 cm−1) mode couples particularly well to the HOMO (LUMO). It has recently been shown that matching symmetries of vibrational and electronic states are favorable for the occurrence of vibronic progression [Vibronic fine structure is visible in the two frontier orbitals giving rise to vibronic subbands of the HOMO, Hcoupling –44. Ther42H28 on Pt(111), Au(111) and graphene-covered Pt(111) consistently show the progressive reduction of the molecule–substrate interaction. The hit-and-stick adsorption of C42H28 on Pt(111) indicates a strong suppression of the C42H28 mobility after adsorption. Molecular orbitals leave weak and broad signatures in dI/dV spectra.Topographic and spectroscopic data of C42H28 deposition on Au(111) as on Pt(111) leads to island growth with a regular superstructure. The crystalline adsorption phase unravels that low energy barriers for translation and rotation of the molecule exist because the individual C42H28 molecules can optimize the coupling to adjacent molecules. A finite adsorbate–substrate interaction is reflected by the presence of a molecular superstructure that matches the period of the Au(111) reconstruction. However, the HOMO resonance width has decreased by a factor of three compared to its width on Pt(111). Even vibronic progression due to a group of molecular vibrations with an energy of approx. 160 meV and a Huang–Rhys factor of approx. 0.5 is visible in the HOMO spectral line shape, which for molecules adsorbed on metal surfaces is exceptional. Unoccupied molecular orbitals, in contrast, are weak and broad with no indication of vibronic progression.Using the same preparation parameters for C42H28–metal hybridization is achieved by adsorption of C42H28 on graphene-covered Pt(111). The unit cell of the molecular superstructure is similar to the one observed on Au(111), albeit with a lower molecule surface density and with no additional moiré pattern. Compared to direct adsorption on Pt(111) the HOMO resonance width of C42H28 on graphene-covered Pt(111) is reduced by more than a factor of ten. Likewise, the LUMO appears as a sharply peaked signature in dI/dV spectroscopy. Different groups of C42H28 vibrational quanta induce vibronic progression of the frontier orbitals. The HOMO exhibits vibronic fine structure due to vibrations with hν1 ≈ 180 meV and a Huang–Rhys factor of , while the LUMO shows vibronic side bands due to molecular vibrational quanta with hν2 ≈ 220 meV and .The most effective reduction of the C42H28 the progressive reduction of the molecule–surface hybridization can be achieved by using inert metal substrates, Au rather than Pt, and by introducing a two-dimensional material, e.g., graphene, as a buffer layer between the adsorbed molecule and the metal. The degree of decoupling can be judged by the molecule assembly after adsorption, the spectral line width of molecular resonances and the occurrence of vibronic progression. The most effective reduction of residual C42H28–metal hybridization was achieved here by adsorption on graphene-covered Pt(111). On this surface C42H28 exhibits vibronic progression in both frontier orbitals, induced by different molecular vibrational quanta and with different Huang–Rhys factors. Consequently, graphene represents an appropriate buffer layer for exploring electronic and vibronic properties at the single-molecule level.Using the hydrocarbon molecule C


Our finding demonstrates the novel mechanism that leads to ultrafast, large deformation of organic crystals.For decades, it has been reported that some organic crystals suddenly crack, break, or jump when they are heated from room temperature. Recently, such crystals have been intensively studied both in fundamental science and for high-speed mechanical device applications. According to these studies, the sudden crystal motions have been attributed to structural phase transitions induced by heating. Stress created by the phase transition is released through the sudden and rapid motion of the crystals. Here we report that single crystal nanofibers of coronene exhibit a new type of ultrafast motion when they are cooled from room temperature and subsequently heated to room temperature. The nanofibers make centimeter-scale jumps accompanied by surprisingly unique behaviors such as sharp bending and wriggling. We found that the motions are caused by a significantly fast structural phase transition between two polymorphs of coronene. A theoretical investigation revealed that the sudden force generated by the phase transition together with the nanoscale dimensions and elastic properties create  In addition to visual observation of the crystal motions, X-ray diffraction and thermodynamic measurements were performed by heating the crystals. These measurements showed that the crystal motions are related to temperature-induced structural phase transitions between polymorphs of the crystals. However, these early studies were rather sporadic and focused not on the underlying mechanism of the sudden motions but on the properties of the specific crystals.Since the 1980s, several groups have reported observations of jumping crystals or thermosalient crystals. In early studies, crystals of organic molecules, including organometallics, were serendipitously found to suddenly crack, break, or jump when they were heated above room temperature18. They also performed kinematic analyses of the crystal motions by recording them with a high-speed camera. These studies have advanced the understanding of the mechanisms behind the sudden crystal motions and stimulated a number of related studies, including those aimed at exploring applications in high-speed mechanical devices and heat-motion conversion devices23.In recent years, the thermosalient effect, as well as the photosalient effect, in which light irradiation induces sudden crystal motions, have attracted increasing interest from both fundamental science and device application perspectives. Naumov and coworkers have intensively studied thermosalient crystals using crystallographic, spectroscopic, and thermodynamic methods and discussed the common characteristics of the phase transitions that cause the sudden crystal motions24. The jumps of our nanofibers occur as they are cooled from room temperature, and the cooled nanofibers jump again when subsequently heated to room temperature. We investigated the jump of the nanofibers with crystallographic and microscopy methods and found that it is attributed to a significantly fast structural phase transition between two polymorphs of coronene. Moreover, ultrahigh-speed camera recording revealed surprising behaviors of the nanofibers at the moment of jumping, i.e., sharp bending and wriggling motions. The theoretical investigation revealed that the abrupt force generated by the phase transition together with the nanoscale dimensions and elastic properties create a dynamical instability in the nanofibers and this instability leads to the motion. Our findings demonstrate the novel mechanism for ultrafast large deformation of organic crystals, which provide a new insight into the mechanical properties of organic crystals and have potential applications in nanoscale mechanical devices.Here we report that nanoscale organic crystals exhibit a novel type of ultrafast motion owing to temperature-induced structural phase transitions. We observed that single crystal nanofibers of coronene on a substrate jumped over a centimeter-scale distance at an initial velocity of up to ~ 20 m/s, which is a few orders of magnitude faster than that of reported thermosalient crystals. Most thermosalient effects known so far take place by heating the crystals above room temperature and a few have been reported to occur below room temperature25. A scanning electron microscopy (SEM) image  Å, b = 4.691(6) Å, c = 15.64(2) Å, β = 106.05(2)°, V = 712(2) Å3, and Z = 2 at T = 298 K, in agreement with the reported values for a single crystal of coronene at room temperature27. The long axis of the nanofiber is parallel to the stacking direction (// b axis).Coronene, or [6]circulene, is a polyaromatic hydrocarbon molecule consisting of six benzene rings Fig. a, inset.age Fig. b and an age Fig. c show thA sample mounted in a liquid nitrogen flow cryostat with an optical window was cooled from room temperature under microscopic observation. When the temperature reached ~ 150 K, the nanofibers on the substrate started jumping violently (Supplementary Video 5 fps (a time resolution of 10 µs). Figure t = − 100 µs) and just before jumping (t = 0 s) do not exhibit notable differences, indicating that the jump occurs suddenly without preliminary deformation or motion. The initial lateral velocity of the jumps during cooling was measured to be 10–20 m/s, while the vertical velocity during heating 1–5 m/s. These values are orders of magnitude faster than those previously reported for thermosalient crystals 10. Because of the high initial velocity, the jumping nanofibers are outside of the image area of 256 µm × 192 µm (or the focus depth of ~ 7 µm) within one or two frames (within 10 to 20 µs). This clearly shows that a higher time resolution is required to observe deformation of the nanofibers that causes the jump. This is in sharp contrast to the so far reported thermosalient effects, where the deformations of the crystals could be observed with a time resolution of ~ 1 ms15.To investigate the motion of the nanofibers at the moment of the jump, we recorded the nanofibers with a high-speed camera operated at 105 fps (a time resolution of 1.85 µs). This is technically challenging, given the general characteristics of a high-speed camera. The frame rate and recording time of a high-speed camera are limited by the capacity of the on-camera memory and the writing speed to it. Because of these limitations, as the frame rate is increased, the active CCD area and the available recording time are decreased to compensate for the increased amount of data generated per unit time. The active area and recording time of our camera operated at 5.4 × 105 fps are reduced to only 0.32 × 2.56 mm2 (16 × 128 pixels) and ~ 3 s, respectively . Because of these restrictions, an elaborate recording procedure was required to shoot the jumps, which occur randomly in time and position , which is six orders of magnitude larger than that of the microwires, suggesting that the mechanisms behind these motions are very different.Figure L/L0 = (L − L0)/L0, were measured for several nanofibers with different lengths and are plotted as function of time in Fig. L: fiber length, L0: initial fiber length). The maximum elongation rate of all the measured nanofibers was found to be ~ 0.22 regardless of L0, and ΔL/L0 reached the maximum value within ~ 8 µs. On the other hand, the length change could not be observed for the nanofibers that jumped during cooling because of the sharp bending perpendicular to the substrate and the large lateral velocity.The high-speed camera revealed another notable feature: the nanofibers were elongated during the wriggling motions. Eye guides for the initial length (red bars) in the first and sixth frames in Fig. T = 300 → 83 → 300 K. Throughout the cycle, a video of the sample was recorded by a standard video camera (30 fps) installed on the microscope. In the image area (~ 4.2 mm in diameter), there were initially 300–500 nanofibers. The number of times of jumping was counted by visual inspection of the video and was accumulated for each 10 K temperature interval. We conducted this procedure for three samples, and the averaged values are shown in Fig. To reveal the mechanism of the motions of the nanofibers, we counted jumps that occurred during a cooling/heating cycle of 31. Whereas a coronene crystal grown under ambient conditions has a γ-herringbone structure   Å, b = 3.8212(3) Å, c = 17.211(2) Å, β = 96.24(1)°, V = 679.0(1) Å3, and Z = 2 at T = 80 K31. The b value of the γ-polymorph is 23% larger ([4.691 – 3.8212 Å]/3.8212 Å) = 0.23) than that of β-polymorph. This value agrees well with the elongation rate of jumping nanofibers during heating (~ 22%). This strongly supports that the jumps are attributed to the γ ↔ β phase transition.The temperatures at which the β-polymorph appears and disappears, as determined by the powder X-ray diffraction measurements, are indicated by green arrows in Fig. −1). As shown in Fig. −1  that are due to lattice vibrations of the γ-polymorph. Although another peak at ~ 40 cm−1 has been reported for a single crystal of coronene at room temperature32, we could not observe it because of the filter cutoff frequency of our setup. We also performed polarized Raman microscopy on single nanofibers and assigned these peaks to libration modes, which are torsional vibrations around the molecular axes . It is notable that the jumping occurred over the wide temperature ranges both during cooling (T = 150–83 K) and heating (T = 220–300 K). Namely, the temperature at which the jumping occurred was largely different for each nanofiber. Although our microscopic and spectroscopic investigations could not identify the factors that lead to the different jumping temperature, it may be attributed to the intrinsic factors, such as structural defects and strain, and the extrinsic factors, such as thermal contact between the substrate.During heating from ate Fig. c. The sp10. Namely, the crystal symmetry is preserved and the lattice undergoes anisotropic distortion through the transition. These characteristics indicate that the transition is a diffusionless transformation with small cooperative movement of individual molecules. Therefore, they claimed that the phase transition in the thermosalient crystals is an organic analogues of a martensitic transition in inorganic materials12. The phase transitions in the coronene nanofibers also have these characteristics. The crystal symmetry (P21/n) is preserved and the unit cell undergoes the anisotropic distortion: it expands (shrinks) along the a and c axes and shrinks (expands) along the b axis in the γ → β (β → γ) transition , a compressive force was generated along the nanofiber axis. Instead of considering this force to be internally generated, we simply treat it as an effective compressive force that is applied externally. This allows us borrowing the knowledge from structural mechanics: a slender elastic body, such as a beam or a column, subjected to an external compressive force along the long axis exhibits dynamical (buckling) instability that can lead to their sudden and large deformation when the force exceeds a critical value. We analyze the motion of the nanofibers in terms of the dynamical instability.The most impressive feature in the jumps of the nanofibers is the fast wriggling motion as they jump during heating. Our next focus is to elucidate the mechanism that cause the motion. We observed that the nanofibers rapidly increased their length as they started wriggling. This indicates that just before the wriggling motion started  = u = 0, where u is the displacement at position x. The equation of motion of this nanofiber under the compressive force F is written asρ is the density, A = wh is the cross-sectional area, I is the moment of inertia of the area, and E is Young’s modulus34. We conducted a linear stability analysis of the nanofiber based on Eqs. . Then, the following dispersion relation is obtained from Eq. , ωn2 > 0 for any kn, where Fc is the critical Euler buckling force that is given by D(π/L)2. In this force regime, ωn is a real number, and thus u has the form of a propagating wave, indicating that the motion of the nanofiber is stable. By contrast, when F >  > Fc, ωn2 < 0 for a certain range of kn (red dashed curve). Within this range, ωn is an imaginary number, and thus the amplitude of u diverges with time once infinitesimally small displacement occurs, implying that the nanofiber is dynamically unstable. The kn at which ωn2 has its minimum value is obtained as kn* = (F/2D)1/2 by finding the minimum ωn2 from Eq.  shows that the nanofiber (L = 100 µm) has n = 4, yielding λ = 2L/n = 50 µm. Using E = 1.1 × 109 N/m236, and the typical values of w = 500 nm and h = 200 nm, F was calculated to be 1.16 × 10–8 N (~ 0.12 MPa). Further using the F value and ρ = 1.38 × 103 kg/m336, the characteristic time of the waveform growth, which is given by τ = 1/ω= [(Dk4 − Fk2)/ρA]−1/2, was obtained to be τ = 1.23 µs. The snapshots show that the n = 4 wave-like shape developed at t = 1.85 µs, which is in reasonably good agreement with the evaluated τ. The validity of our model was also confirmed for observed wriggling motions of other nanofibers. We concluded that the origin of the motion is the dynamical instability induced by the phase transition.Next, we show that our model also can quantitatively capture the key features of the wriggling motion. We evaluated the force Because of the sharp bending and large initial velocity, the change in the length could not be observed for jumping nanofibers during cooling. However, it is most probable that the nanofibers shortened at the moment of jumping, as opposed to the elongation observed during heating, and thus the tensile force was generated along the long axis. The tensile force does not cause the instability. This is consistent with the fact that no wriggling motion was observed for the nanofibers that jumped during cooling. The sharp bending at the moment of the jump is probably attributed to exfoliation of nanofibers from the surface of the substrate. The nanofibers were prepared by solution evaporation on the surface see “ and, the41. However, a challenge to apply such measurements to the nanofibers is out of the scope of the current study. Instead, here we simply note that the size and shape are critical to the high flexibility. When a slender elastic body is bent, the outer portion undergoes tensile stress, whereas the inner portion undergoes compressive stress  is proportional to the bending angle θ and inversely proportional to the thickness h (the Hooke’s law)  Fig. c. When σ25. Briefly, the sample solution (~ 3.0 mM) was prepared by dissolving coronene in chloroform by sonication. Approximately 10 µL of the sample solution was dropped onto a substrate (an untreated microscope cover glass), and the substrate was immediately placed in a glass container (a Petri dish with a lid), which was filled with chloroform vapor by depositing a few drops of chloroform at the bottom. After a few hours, during which the solvent was evaporated, the sample was extracted from the container and dried under ambient conditions.The fabrication process of coronene nanofibers was described elsewhereSEM images were obtained using a scanning electron microscope . The sample was carbon-coated to avoid charging. The topography of the sample was observed using AFM  in tapping mode.An epi-illumination microscope  equipped with a motorized stage  and a liquid nitrogen flow optical cryostat (Linkam) was used. The sample was under a nitrogen gas atmosphere. High-speed videos were recorded using a high-speed camera  mounted on the microscope. A video of a selected nanofiber was loop-recorded to on-camera memory while the sample temperature was varied at 5–10 K/min. When the nanofiber jumped, the loop recording was immediately stopped manually so that the last ~ 3 s of the video were saved.λ = 785 nm) was coupled to the microscope, directed toward the sample by a dichroic mirror (Semrock), and focused onto the sample by a 50 × IR objective lens (Olympus)44. Scattered light from the sample was collected by the same objective lens and imaged on the entrance slits of an imaging monochromator  through a long-pass filter (Semrock) that blocked the excitation laser. The spectra were recorded by a liquid-nitrogen-cooled back-illuminated CCD camera  attached to the monochromator. For the polarized Raman microscopy measurements, polarizers were placed before and after the sample.The microscope and cryostat used for high-speed camera recording were also used for the Raman microscopy. The output of a continuous-wave diode laser (λ = 0.71073 Å) at 298 K.A nanofiber was picked up from the substrate with a wooden stick and attached to the tip of a glass capillary using epoxy. The single-crystal X-ray diffraction experiments were performed on a CCD-type diffractometer (Bruker SMART APEX II) with graphite-monochromated Mo Kα radiation (Supplementary Information 1.Supplementary Video 1.Supplementary Video 2.Supplementary Video 3.


Star-shaped polymers are very attractive because of their potential application ability in various technological areas due to their unique molecular topology. Thus, information on the molecular structure and chain characteristics of star polymers is essential for gaining insights into their properties and finding better applications. In this study, we report molecular structure details and chain characteristics of 17-armed polystyrenes in various molecular weights: 17-Arm(2k)-PS, 17-Arm(6k)-PS, 17-Arm(10k)-PS, and 17-Arm(20k)-PS. Quantitative X-ray scattering analysis using synchrotron radiation sources was conducted for this series of star polymers in two different solvents (cyclohexane and tetrahydrofuran), providing a comprehensive set of three-dimensional structure parameters, including radial density profiles and chain characteristics. Some of the structural parameters were crosschecked by qualitative scattering analysis and dynamic light scattering. They all were found to have ellipsoidal shapes consisting of a core and a fuzzy shell; such ellipse nature is originated from the dendritic core. In particular, the fraction of the fuzzy shell part enabling to store desired chemicals or agents was confirmed to be exceptionally high in cyclohexane, ranging from 74 to 81%; higher-molecular-weight star polymer gives a larger fraction of the fuzzy shell. The largest fraction (81%) of the fuzzy shell was significantly reduced to 52% in tetrahydrofuran; in contrast, the lowest fraction (19%) of core was increased to 48%. These selective shell contraction and core expansion can be useful as a key mechanism in various applications. Overall, the 17-armed polystyrenes of this study are suitable for applications in various technological fields including smart deliveries of drugs, genes, biomedical imaging agents, and other desired chemicals. Star-shaped polymers are structurally intriguing materials that consist of several linear polymer chains connected at one point . In fact, star-shaped polyamides with four and eight arms were reported in 1948 as the first star polymers, which were prepared by step-growth polymerization . Since tStar polymers were found to exhibit very interesting properties, such as smaller hydrodynamic radii, smaller radii of gyration, and lower viscosities, compared to the linear counterparts ,10,11,12In this study, we have made great efforts to obtain the molecular structure details and arm chain characteristics of a series of 17-armed star polystyrenes in various molecular weights which are essential for gaining insights into properties and finding better applications: 17-Arm(2k)-PS, 17-Arm(6k)-PS, 17-Arm(10k)-PS, and 17-Arm(20k)-PS ; Table 1−Li+)) of a given molecular weight to polystyrene of a chosen molecular weight with one-end group in dendritic architecture which was multifunctionalized by bromobenzyl moieties (PS(BnBr)16) according to the method reported previously ; and the whole fuzzy part is estimated to have a thickness f,et, which corresponds to 6f,eσ. In the Θ solvent, c,er and f,et are 1.46 and 4.14 nm for 17-Arm(2k)-PS, 1.89 and 5.94 nm for 17-Arm(6k)-PS, 2.46 and 8.64 nm for 17-Arm(10k)-PS, and 2.97 and 12.66 nm for 17-Arm(20k)-PS. In the good solvent, c,er is increased 41% for 17-Arm(2k)-PS, 103% for 17-Arm(6k)-PS, 127% for 17-Arm(10k)-PS, and 171% for 17-Arm(20k)-PS; in contrast, f,et is decreased for all star polymers discussed above. These results confirm again that in the good solvent, 17-armed star polymer is expanded in the core part but shrunk in the fuzzy shell part. However, the fuzzy shell part of the star polymer exhibits larger thickness than the core radius, regardless of the molecular weights and the solvent conditions.Fifth, more detailed structural information has been gained from the radial density profiles together with the t,eR , including the full fuzzy region, is additionally determined from the radial density profile. Due to the presence of such fuzzy part, t,eR is larger by 3f,eσ against eR.Sixth, for each star polymer in a given solvent condition, the overall radius ξ  is determined to range 0.40 to 2.60 nm in the Θ condition and 0.20 to 4.30 nm in the good solvent. In a given solvent, ξ exhibits some increment with increasing molecular weight. However, the ξ of 17-Arm(2k)-PS is significantly reduced in the good solvent. Interestingly, the ξ of 17-Arm(6k)-PS remains same in the good solvent. In contrast, those of 17-Arm(10k)-PS and 17-Arm(20k)-PS are increased in the good solvent respectively. Overall, the star polystyrenes could show no rationalized trend on the swelling dependency of ξ with good solvent.Seventh, the quantitative analysis has found the contribution of blobs  to the measured X-ray scattering intensity profile, regardless of the molecular weight and the solvent conditions. Considering the molecular structure composed of the denser core and the less dense shell part in a density gradient, the blobs’ scatterings may occur mainly in the fuzzy shell part. The blob radius Finally, each star polystyrene is found to have a size distribution . All star polystyrenes reveal single unimodal and narrow radius distributions, indicating that they were obtained in high purity through precise syntheses and subsequent purification processes. When they are compared, it is found that higher-molecular-weight star polystyrene shows relatively wider radius distribution. Furthermore, all star polystyrenes exhibit wider radius distributions in the good solvent, compared to those in the Θ condition.In addition, the star polystyrenes in both the Θ condition and the good solvent have been further characterized by DLS. The measured DLS data have been analyzed reasonably well by using the cumulant method ,34 and nh,zR  ranges from 3.76 to 9.66 nm in the Θ condition and from 4.45 to 10.69 nm in the good solvent, depending on the molecular weights. Higher-molecular-weight star polystyrene reveals larger h,zR value. They exhibit larger h,zR values in the good solvent against those in the Θ condition. Overall, these characteristics and trends are in good agreement with those found by the X-ray scattering analysis. However, the radius distributions are much broader  than those determined by the X-ray scattering analysis. The h,zR values are 20−60% larger than the gR values determined by the X-ray scattering analysis. The intensity-weighted averaged radius h,intensityR values  are also found to be 24−57% larger than the gR values determined by the X-ray scattering analysis.The DLS analysis found that all star polymers of this study have single unimodal radius distributions, again confirming that they were prepared in high purity. The hydrodynamic radius In this study, 17-armed star polystyrenes in various molecular weights were investigated in the Θ condition as well as in a solvent-swollen condition (good solvent) from the view of molecular structure and arm chain characteristics. Quantitative X-ray scattering analysis was successfully performed for the first time for the series of star polystyrenes in dilute solutions in a comprehensive manner, giving new insights into the 3D molecular structure details in the unperturbed condition and their alterations by swelling with good solvent molecules. The quantitative analysis results were further crosschecked in part by the DLS analysis as well as by the qualitative X-ray scattering analysis.All 17-armed star polymers in the unperturbed state exhibit oblate fuzzy ellipsoids that are composed of a core (minor component: 19−26%) and a fuzzy shell (major component: 74−81%); the core fraction is the maximum (26%) for the lowest molecular weight star polymer, whereas the shell fraction is the maximum (81%) for the highest molecular weight star polymer. Nevertheless, the core becomes large and the fuzzy shell thickens, as the molecular weight increases. These structural characteristics are significantly altered in a good solvent. In particular, the core part is enlarged significantly, whereas the fuzzy shell is shrunk severely. These solvent-induced volume changes are obviously evidenced in the radial density profiles. The solvent-selective volume alterations of core and fuzzy shell parts in the opposite direction are quite unique. This exciting property together with relatively large fuzzy shell volume informs that 17-armed polystyrenes are very suitable for applications in various technological fields including smart deliveries of drugs, genes, bioimaging agents, and other desired chemicals.


The plenteous resistance to and undesirable consequences of the existing antipiroplasmic therapies have emphasized the urgent need for new chemotherapeutics and drug targets for both prophylaxis and chemotherapy. Hydroxyurea (HYD) is an antineoplastic agent with antitrypanosomal activity. Eflornithine  is the best choice therapy for the treatment of late-stage Gambian human African trypanosomiasis.in vitro against the multiplication of Babesia and Theileria. As well as, their chemotherapeutic effects were assessed on B. microti strain that infects rodents. The Cell Counting Kits-8 (CCK-8) test was used to examine their cytotoxicity on human foreskin fibroblast (HFF), mouse embryonic fibroblast (NIH/3T3), and Madin–Darby bovine kidney (MDBK) cells.In this study, the inhibitory and combination efficacy of HYD and DFMO with existing babesicidal drugs (diminazene aceturate (DA), atovaquone (ATV), and clofazimine (CLF)) deoxyribonucleotide B. bigemina, B. bovis, B. caballi, B. divergens, and T. equi) in a dose-related manner. HFF, NIH/3T3, or MDBK cell viability was not influenced by DFMO at 1000 μM, while HYD affected the MDBK cell viability at EC50 value of 887.5±14.4 μM. The in vitro combination treatments of DFMO and HYD with CLF, DA, and ATV exhibited synergistic and additive efficacy toward all tested species. The in vivo experiment revealed that HYD and DFMO oral administration at 100 and 50 mg/kg inhibited B. microti multiplication in mice by 60.1% and 78.2%, respectively. HYD-DA and DFMO-DA combined treatments showed higher chemotherapeutic efficacy than their monotherapies.HYD and DFMO suppressed the multiplication of all tested species (B. microti infection in mice.These results indicate the prospects of HYD and DFMO as drug candidates for piroplasmosis treatment, when combined mainly with DA, ATV, and CLF. Therefore, further studies are needed to combine HYD or DFMO with either ATV or CLF and examine their impact on  Th. Th50 vain vitro-inhibition test revealed that HYD and DFMO significantly restricted (P < 0.05) the multiplication of B. bovis, B. bigemina, B. divergens, B. caballi, and T. equi in a dose-related manner. B. bovis multiplication was suppressed significantly (t-test: t(5) = 10.30, P = 0.0001) at 3.125 μM HYD, whereas T. equi, B. divergens, B. bigemina and B. caballi multiplication was suppressed significantly (t-test: t(5) = 4.428, P = 0.005) at 6.25 μM HYD  = 3.859, P = 0.01), B. bigemina and T. equi significantly (t-test: t(5) = 5.329, P = 0.005) at 6.25 μM. DFMO inhibited the multiplication of B. caballi significantly (t-test: t(5) = 7.456, P < 0.0001) at 6.25 μM  for HYD were 45.3, ˃ 51, and ˃ 51 times toward T. equi versus MDBK, NIH/3T3, and HFF cells, respectively. For DFMO, the highest selectivity index was ˃ 21.3 times toward B. divergens versus the HFF, NIH/3T3, and MDBK cells , the DDW control group showed rapid parasitemia growth reached 58.2%  = 5.784, P = 0.001 for 50 mg/kg HYD; ANOVA: F = 5.784, P = 0.001 for 25 mg/kg DFMO) from days 6 to 12 p.i. The level of peak parasitemia in 25 mg/kg DA, 25 mg/kg DFMO, and 50 mg/kg HYD was 4% on day 8, 24.5% on day 12, and 27.3% on day 8, respectively.The level of parasitemia in all treated groups was reduced at a statistically significant lower percentage of parasitemia than that of the control group  = 5.264, P < 0.001 for monotherapy-treated groups; ANOVA: F = 6.315, P < 0.0001 for combination-treated groups) from days 8 to 20 p.i. The level of peak parasitemia in the monochemotherapy-treated mice attained 3.9% on day 8, 13.2% on day 10, and 24.2% on day 8 in 25 mg/kg DA, 50 mg/kg DFMO, and 100 mg/kg HYD, respectively  = 5.697, P < 0.0001), RBC  = 5.876, P < 0.0001), and HGB concentration  = 5.681, P < 0.0001) detected between the drug-treated groups and DDW group on 8 and 12 days  caused by T. brucei gambiense [Nowadays, combination chemotherapies are being reported to alleviate serious diseases, including pulmonary tuberculosis, malignancy, immune deficiency syndrome, and some protozoal diseases to promote higher therapeutic efficacy , 38. Theh et al.  documenth et al.  have beeambiense . Howeverin vitro detected that treated RBCs were unaffected with both drugs either morphologically or functionally. Previous reports showed that intravenous injection of DFMO at 200 mg/kg two times per day for two weeks had been documented as initial therapy for the sleeping disorder without signs of toxicity [L. amazonensis, T. gondii, and T. cruzi multiplication, resulting in an irreversible morphological effect without affecting the host cells.The CCK test was used to examine the cytotoxicity of HYD and DFMO on animal and human cell lines, the main hosts of piroplasm parasites. Our results revealed that both compounds showed no inhibition on NIH/3T3 or HFF cell viability, whereas HYD only affected the MDBK cell viability at a high concentration. Additionally, pre cultivation of bovine and equine RBCs with HYD or DFMO toxicity . Melo antoxicity  reportedin vitro inhibitory effects of HYD and DFMO motivated us for the assessment of their chemotherapeutic potential on B. microti infection in mice, and we found that they were indeed effective in this context as well. Significant reductions in HCT, RBCs, and HGB were detected in the positive control group. Interestingly, our findings are consistent with a recent study that showed that 17-DMAG led to lower hematological profile values similar to those of our [Leishmania in humans [The e of our . Neverthn humans , 42, sugin vivo experiment revealed that B. microti was cleared from HYD- and DFMO-treated mice. The oral administration of HYD and DFMO exhibited chemotherapeutic effect higher than the 34%, 31%, 49%, 58.3%, 37%, and 49% shown by enoxacin, norfloxacin, and ofloxacin [trans-chalcone [B. microti in mice. Therefore, future studies are required to assess these combined effects either in vitro or in vivo.Although DA is the most effective babesicidal drug used in the veterinary field, it was unable to clear all parasites from the host animals. As a result, the disease can recur in treated animals. Moreover, restlessness, tissue injury at the site of injection, and abdominal pain have been observed in animals after the treatment of DA . Therefofloxacin , allicinfloxacin , thymoqufloxacin  and nitichalcone , 31, thiB. microti, and thus, they might be used as an alternative chemotherapy for humans infected with B. microti after some clinical studies. However, PCR assay should be used alongside optical microscopy in future similar works to detect parasites DNA from in vitro cultures as well as from peripheral blood of animal models in vivo to ensure a total clearance of parasites. Moreover, additional research is urgently needed to assess the efficacy of HYD or DFMO when combined with ATV or CLF in vivo as well as to elucidate their mechanism of action against piroplasm parasites.Moreover, HYD and DFMO have been documented to be useful in chemotherapy for anemia, and sleeping sickness in humans , 22 and in vitro growth inhibitory effect against five piroplasm species as well as chemotherapeutic efficacy toward B. microti in vivo. Furthermore, the combination treatment of HYD and DFMO with DA, ATV, and CLF demonstrated synergistic and additive efficacy against all tested parasites. These results are implying that they possess potential value for treating clinic diseases in animals and humans either alone or in combination with other drugs.To our knowledge, this is the first antipiroplasmic evaluation of HYD and DFMO against piroplasm parasites. HYD and DFMO exhibited an S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.