Integrating plasma protein-centric multi-omics to identify potential therapeutic targets for pancreatic cancer.

BACKGROUND: Deciphering the role of plasma proteins in pancreatic cancer (PC) susceptibility can aid in identifying novel targets for diagnosis and treatment. METHODS: We examined the relationship between genetically determined levels of plasma proteins and PC through a systemic proteome-wide Mendelian randomization (MR) analysis utilizing cis-pQTLs from multiple centers. Rigorous sensitivity analyses, colocalization, reverse MR, replications with varying instrumental variable selections and additional datasets, as well as subsequent meta-analysis, were utilized to confirm the robustness of significant findings. The causative effect of corresponding protein-coding genes' expression and their expression pattern in single-cell types were then investigated. Enrichment analysis, between-protein interaction and causation, knock-out mice models, and mediation analysis with established PC risk factors were applied to indicate the pathogenetic pathways. These candidate targets were ultimately prioritized upon druggability and potential side effects predicted by a phenome-wide MR. RESULTS: Twenty-one PC-related circulating proteins were identified in the exploratory phase with no evidence for horizontal pleiotropy or reverse causation. Of these, 11 were confirmed in a meta-analysis integrating external validations. The causality at a transcription level was repeated for neutrophil elastase, hydroxyacylglutathione hydrolase, lipase member N, protein disulfide-isomerase A5, xyloside xylosyltransferase 1. The carbohydrate sulfotransferase 11 and histo-blood group ABO system transferase exhibited high-support genetic colocalization evidence and were found to affect PC carcinogenesis partially through modulating body mass index and type 2 diabetes, respectively. Approved drugs have been established for eight candidate targets, which could potentially be repurposed for PC therapies. The phenome-wide investigation revealed 12 proteins associated with 51 non-PC traits, and interference on protein disulfide-isomerase A5 and cystatin-D would increase the risk of other malignancies. CONCLUSIONS: By employing comprehensive methodologies, this study demonstrated a genetic predisposition linking 21 circulating proteins to PC risk. Our findings shed new light on the PC etiology and highlighted potential targets as priorities for future efforts in early diagnosis and therapeutic strategies of PC.

hENT1's role in adjuvant intra-arterial gemcitabine-based chemotherapy for resectable pancreatic cancer patients.

BACKGROUND: We aimed to verify the role of hENT1 as a prognostic predictor for patients with resectable pancreatic ductal adenocarcinoma (PDAC) who underwent radical resection followed by intra-arterial infusion of gemcitabine-based regimen. METHODS: We collected surgical samples from 102 patients with resectable PDAC who received radical resection followed by intra-arterial infusion of gemcitabine-based regimen. The hENT1 expression with the help of immunohistochemistry was conducted using formalin-fixed and paraffin embedded tissues. The Kaplan-Meier analyses and Cox regression were used to evaluate the mortality hazard associated with the discrepancy between strong and weak of hENT1 expression. Patients' clinical and pathological characteristics were compared between the two groups, then the role of hENT1 as a prognostic predictor was further explored. RESULTS: A total of 102 patients were included to assess the hENT1 expression. 50 patients were classified into high hENT1 expression group, the other 52 patients were attributed into low hENT1 expression group. High hENT1 expression was related to a significantly improved overall survival (OS) (p = 0.014) and disease-free survival (DFS) (p = 0.004). Both univariate (p = 0.001) and multivariate analyses (p < 0.001) indicated that high hENT1 expression was related to a decreased mortality. CONCLUSIONS: High expression of hENT1 is positive prognostic factor for adjuvant intra-arterial gemcitabine-based chemotherapy in resectable PDAC.

Weakly Supervised Building Semantic Segmentation Based on Spot-Seeds and Refinement Process.

Automatic building semantic segmentation is the most critical and relevant task in several geospatial applications. Methods based on convolutional neural networks (CNNs) are mainly used in current building segmentation. The requirement of huge pixel-level labels is a significant obstacle to achieve the semantic segmentation of building by CNNs. In this paper, we propose a novel weakly supervised framework for building segmentation, which generates high-quality pixel-level annotations and optimizes the segmentation network. A superpixel segmentation algorithm can predict a boundary map for training images. Then, Superpixels-CRF built on the superpixel regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, we can train a more robust segmentation network and predict segmentation maps. To iteratively optimize the segmentation network, the predicted segmentation maps are refined, and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework achieves a marked improvement in the building's segmentation quality while reducing human labeling efforts.

An Efficient Supervised Deep Hashing Method for Image Retrieval.

In recent years, searching and retrieving relevant images from large databases has become an emerging challenge for the researcher. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher. Most existing hashing approaches map samples to a binary vector via a single linear projection, which restricts the flexibility of those methods and leads to optimization problems. We introduce a CNN-based hashing method that uses multiple nonlinear projections to produce additional short-bit binary code to tackle this issue. Further, an end-to-end hashing system is accomplished using a convolutional neural network. Also, we design a loss function that aims to maintain the similarity between images and minimize the quantization error by providing a uniform distribution of the hash bits to illustrate the proposed technique's effectiveness and significance. Extensive experiments conducted on various datasets demonstrate the superiority of the proposed method in comparison with state-of-the-art deep hashing methods.

Rapid Sample Preparation Workflow for Serum Sample Analysis with Different Mass Spectrometry Acquisition Strategies.

Fast, robust, and high-throughput mass spectrometry-based serum proteomic pipelines have great potential to yield information for biomarker discovery and daily clinical practice. Here, we developed a simple and rapid sample preparation (RSP) workflow by reducing the classical pretreatment time from overnight to less than 1.5 h in an ordinary system. In HeLa cell lysates and serum samples, the number of proteins and tryptic peptides generated using the RSP was comparable to that generated using conventional methods. For fast scanning of the serum proteome, the RSP-supported pipeline could complete a test in less than 2 h with 30 min of LC-MS/MS analysis. Nearly 390 proteins spanning 8 magnitudes of abundance range were identified with high reproducibility, containing over 90 cancer-associated proteins and over 50 FDA-approved biomarkers. For fast assay development, eight candidate biomarker peptides for cardiovascular disease (CVD) were quantified by MRM with high accuracy (CV% <10). After a simple highly abundant protein removal, a deep serum proteome of over 1400 proteins was reached. By analyzing the depleted serum in DIA acquisition mode, over 700 proteins were quantified. The differentially expressed proteins could help us unambiguously distinguish the serum samples from healthy people and patients with pancreatic cancer (PC). Potential biomarkers for PC were also found. The new RSP method, which is rapid and simple, meets the demands of both deep mining and fast analysis of serum proteins. We believe that it will be widely used in serum protein studies and accelerate the transformation from biomarker discovery to clinical application.

WD repeat-containing protein 1 maintains ß-Catenin activity to promote pancreatic cancer aggressiveness.

BACKGROUND: The molecular signature underlying pancreatic ductal adenocarcinoma (PDAC) progression may include key proteins affecting the malignant phenotypes. Here, we aimed to identify the proteins implicated in PDAC with different tumour-node-metastasis (TNM) stages. METHODS: Eight-plex isobaric tags coupled with two-dimensional liquid chromatography-tandem mass spectrometry were used to analyse the proteome of PDAC tissues with different TNM stages. A loss-of-function study was performed to evaluate the oncogenic roles of WD repeat-containing protein 1 (WDR1) in PDAC. The molecular mechanism by which WDR1 promotes PDAC progression was studied by real-time qPCR, Western blotting, proximity ligation assay and co-immunoprecipitation. RESULTS: A total of 5036 proteins were identified, and 4708 proteins were quantified with high confidence. Compared with normal pancreatic tissues, 37 proteins were changed significantly in PDAC tissues of different stages. Moreover, 64 proteins were upregulated or downregulated in a stepwise manner as the TNM stages of PDAC increased, and 10 proteins were related to tumorigenesis. The functionally uncharacterised protein, WDR1, was highly expressed in PDAC and predicted a poor prognosis. WDR1 knockdown suppressed PDAC tumour growth and metastasis in vitro and in vivo. Moreover, WDR1 knockdown repressed the activity of the Wnt/ß-Catenin pathway; ectopic expression of a stabilised form of ß-Catenin restored the suppressive effects of WDR1 knockdown. Mechanistically, WDR1 interacted with USP7 to prevent ubiquitination-mediated degradation of ß-Catenin. CONCLUSION: Our study identifies several previous functional unknown proteins implicated in the progression of PDAC, and provides new insight into the oncogenic roles of WDR1 in PDAC development.

Splenic vessel preservation versus Warshaw's technique during spleen-preserving distal pancreatectomy: a meta-analysis and systematic review.

BACKGROUND: Splenic preservation can be achieved through splenic vessel resection by Warshaw's technique (WT) or by preserving the splenic vessels. This meta-analysis aims to provide evidence-based comparison regarding the perioperative outcome and long-term benefits between patients with and without splenic vessel preservation (SVP) during spleen-preserving distal pancreatectomy. METHOD: A meta-analysis was performed to evaluate studies comparing splenic vessel preservation versus resection groups. Ten retrospective studies including 699 patients were eligible for an analysis of general, perioperative, and long-term outcomes. A further analysis composed of five subgroups was also conducted in terms of laparoscopic approach. RESULTS: Warshaw's technique related to significant shorter operation time (P < 0.0001). There was no difference in blood loss (P = 0.45) as well as median tumor size (p = 0.1) between the two groups. The overall rate of complications indicated no difference between SVP and WT (P = 0.1), including pancreatic fistula rates, which were not statistically different among the treatment groups (P = 0.27). However, the occurrence of gastric varices and splenic infarction was significant higher in the WT group (P < 0.01). In laparoscopic subgroups, patients treated by WT had much lower blood loss (P = 0.002). CONCLUSION: In spleen-preserving distal pancreatectomy, comparing with SVP, there is no evidence of significant benefit of WT. Nonetheless, surgeons should master both techniques and choose an appropriate one based on personal experience and a "case by case" situation. However, the current available evidence is weak, and further randomized controlled data are warranted.

Global Clue-Guided Cross-Memory Quaternion Transformer Network for Multisource Remote Sensing Data Classification.

Multisource remote sensing data classification is a challenging research topic, and how to address the inherent heterogeneity between multimodal data while exploring their complementarity is crucial. Existing deep learning models usually directly adopt feature-level fusion designs, most of which, however, fail to overcome the impact of heterogeneity, limiting their performance. As such, a multimodal joint classification framework, called global clue-guided cross-memory quaternion transformer network (GCCQTNet), is proposed for multisource data i.e., hyperspectral image (HSI) and synthetic aperture radar (SAR)/light detection and ranging (LiDAR) classification. First, a three-branch structure is built to extract the local and global features, where an independent squeeze-expansion-like fusion (ISEF) structure is designed to update the local and global representations by considering the global information as an agent, suppressing the negative impact of multimodal heterogeneity layer by layer. A cross-memory quaternion transformer (CMQT) structure is further constructed to model the complex inner relationships between the intramodality and intermodality features to capture more discriminative fusion features that fully characterize multimodal complementarity. Finally, a cross-modality comparative learning (CMCL) structure is developed to impose the consistency constraint on global information learning, which, in conjunction with a classification head, is used to guide the end-to-end training of GCCQTNet. Extensive experiments on three public multisource remote sensing datasets illustrate the superiority of our GCCQTNet with regards to other state-of-the-art methods.

Recyclable Carbon Cloth-Supported ZnO@Ag3PO4 Core-Shell Structure for Photocatalytic Degradation of Organic Dye.

The extensive use of organic dyes in industry has caused serious environmental problems, and photocatalysis is a potential solution to water pollution by organic dyes. The practical application of powdery photocatalysts is usually limited by the rapid recombination of charge carriers and difficulty in recycling. In this study, recyclable carbon cloth-supported ZnO@Ag3PO4 composite with a core-shell structure was successfully prepared by solvothermal treatment and subsequent impregnation-deposition. The as-prepared carbon cloth-supported ZnO@Ag3PO4 composite showed an improved photocatalytic activity and stability for the degradation of rhodamine B (RhB), a model organic dye, under visible light irradiation. The decomposition ratio of RhB reached 87.1% after exposure to visible light for 100 min, corresponding to a reaction rate constant that was 4.8 and 15.9 times that of carbon cloth-supported Ag3PO4 or ZnO alone. The enhanced performance of the composite can be attributed to the effectively inhibited recombination of photoinduced electron-hole pairs by the S-scheme heterojunction. The carbon fibers further promoted the transfer of charges. Moreover, the carbon cloth-supported ZnO@Ag3PO4 can be easily separated from the solution and repeatedly used, demonstrating a fair recyclability and potential in practical applications.

PYGL-mediated glucose metabolism reprogramming promotes EMT phenotype and metastasis of pancreatic cancer.

Epithelial-mesenchymal transition (EMT) is closely associated with tumor invasion and metastasis. However, key regulators of EMT in pancreatic ductal adenocarcinoma (PDAC) need to be further studied. Bioinformatics analyses of pancreatic cancer public datasets showed that glycogen phosphorylase L (PYGL) expression is elevated in quasimesenchymal PDAC (QM-PDAC) and positively associated with EMT. In vitro cellular experiments further confirm PYGL as a crucial EMT regulator in PDAC cells. Functionally, PYGL overexpression promotes cell migration and invasion in vitro and facilitates liver metastasis in vivo, while PYGL knockdown has opposite effects. Mechanically, hypoxia induces PYGL expression in a hypoxia inducible factor 1α (HIF1α)-dependent manner and promotes glycogen accumulation. Elevated PYGL mobilizes accumulated glycogen to fuel glycolysis via its activity as a glycogen phosphorylase, thus inducing the EMT process, which could be suppressed by the glycolysis inhibitor 2-deoxy-D-glucose (2-DG). Clinically, PYGL expression is upregulated in PDAC and correlates with its malignant features and poor prognosis. Collectively, the data from our study reveal that the hypoxia/PYGL/glycolysis-induced EMT promotes PDAC metastasis, which establishes the rational for targeting hypoxia/PYGL/glycolysis/EMT signaling pathway against PDAC.

The Lin28b/Wnt5a axis drives pancreas cancer through crosstalk between cancer associated fibroblasts and tumor epithelium.

Bidirectional signal transduction between tumor epithelial cells and tumor microenvironment (TME) is important for tumor development. Here we show that Lin28b/let-7 pathway is indispensable for modulating the expression of Wnt5a in tumor epithelium, which could be secreted and then up-regulates Lin28b in cancer-associated fibroblasts (CAFs). Moreover, we demonstrate that Lin28b in CAFs promoted growth of PDAC by inducing cytokine PCSK9's production. Using an orthotopic mouse model of PDAC, we find that depletion of Lin28b in CAFs reduced tumor weight, highlighting the importance of Lin28b in PDAC stroma. Thus, our study shows that the Lin28b-Wnt5a axis plays a critical role in bidirectional crosstalk between pancreatic tumor epithelium and TME and results in a pro-|tumorigenic contexture.

A3 CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification.

The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this article, we propose a new approach to exploit the complementarity of two data sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. Specifically, we develop a new dual-channel spatial, spectral and multiscale attention convolutional long short-term memory neural network (called dual-channel A3 CLNN) for feature extraction and classification of multisource remote sensing data. Spatial, spectral, and multiscale attention mechanisms are first designed for HSI and LiDAR data in order to learn spectral- and spatial-enhanced feature representations and to represent multiscale information for different classes. In the designed fusion network, a novel composite attention learning mechanism (combined with a three-level fusion strategy) is used to fully integrate the features in these two data sources. Finally, inspired by the idea of transfer learning, a novel stepwise training strategy is designed to yield a final classification result. Our experimental results, conducted on several multisource remote sensing data sets, demonstrate that the newly proposed dual-channel A 3 CLNN exhibits better feature representation ability (leading to more competitive classification performance) than other state-of-the-art methods.

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer.

Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

CDCA8/SNAI2 Complex Activates CD44 to Promote Proliferation and Invasion of Pancreatic Ductal Adenocarcinoma.

(1) Background: Recently, cell division cycle associated 8 (CDCA8) was found to be overexpressed in pancreatic ductal adenocarcinoma (PDAC). Here, we aimed to explore the specific mechanism of action of CDCA8 in PDAC progression. (2) Methods: All human PDAC samples and clinical data were collected from Huashan Hospital, Fudan University. All experimental studies were carried out using many in vitro and in vivo assays, including lentiviral transfection, real-time quantitative polymerase chain reaction (qPCR), western blotting, co-immunoprecipitation (Co-IP), chromatin IP (ChIP)-qPCR, dual-luciferase reporter, and in vivo imaging assays. (3) Results: Clinical data analysis of human PDAC samples revealed that CDCA8 overexpression were positively and negatively associated with tumor grade (p = 0.007) and overall survival (p = 0.045), respectively. CDCA8 knockdown inhibited PDAC proliferation and invasion in in vitro and in vivo assays. CD44 was also up-regulated by CDCA8 during PDAC progression. CDCA8 could be combined with SNAI2 to form a CDCA8/SNAI2 complex to integrate with the CD44 promoter as indicated through ChIP-qPCR and dual-luciferase reporter assays. (4) Conclusion: We showed that CDCA8-CD44 axis plays a key role in the proliferation and invasion of PDAC, which provides a potential target for treatment.

Cancer-associated fibroblasts employ NUFIP1-dependent autophagy to secrete nucleosides and support pancreatic tumor growth.

Cancer-associated fibroblasts (CAFs) are one of the most prominent and active components in the pancreatic tumor microenvironment. Our data show that CAFs are critical for survival from pancreatic ductal adenocarcinoma (PDAC) on glutamine deprivation. Specifically, we uncovered a role for nucleosides, which are secreted by CAFs through autophagy in a nuclear fragile X mental retardation-interacting protein 1 (NUFIP1)-dependent manner, increased glucose utilization and promoted growth of PDAC. Moreover, we demonstrate that CAF-derived nucleosides induced glucose consumption under glutamine-deprived conditions and displayed a dependence on MYC. Using an orthotopic mouse model of PDAC, we found that inhibiting nucleoside secretion by targeting NUFIP1 in the stroma reduced tumor weight. This finding highlights a previously unappreciated metabolic network within pancreatic tumors in which diverse nutrients are used to promote growth in an austere tumor microenvironment.

Reciprocal regulation of LOXL2 and HIF1α drives the Warburg effect to support pancreatic cancer aggressiveness.

Hypoxic microenvironment is common in solid tumors, particularly in pancreatic ductal adenocarcinoma (PDAC). The Warburg effect is known to facilitate cancer aggressiveness and has long been linked to hypoxia, yet the underlying mechanism remains largely unknown. In this study, we identify that lysyl oxidase-like 2 (LOXL2) is a hypoxia-responsive gene and is essential for the Warburg effect in PDAC. LOXL2 stabilizes hypoxia-inducible factor 1α (HIF1α) from prolyl hydroxylase (PHD)-dependent hydroxylation via hydrogen peroxide generation, thereby facilitating the transcription of multiple glycolytic genes. Therefore, a positive feedback loop exists between LOXL2 and HIF1α that facilitates glycolytic metabolism under hypoxia. Moreover, LOXL2 couples the Warburg effect to tumor growth and metastasis in PDAC. Hijacking glycolysis largely compromises LOXL2-induced oncogenic activities. Collectively, our results identify a hitherto unknown hypoxia-LOXL2-HIF1α axis in regulating the Warburg effect and provide an intriguing drug target for PDAC therapy.

GFAT1/HBP/O-GlcNAcylation Axis Regulates ß-Catenin Activity to Promote Pancreatic Cancer Aggressiveness.

Reprogrammed glucose and glutamine metabolism are essential for tumor initiation and development. As a branch of glucose and metabolism, the hexosamine biosynthesis pathway (HBP) generates uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) and contributes to the O-GlcNAcylation process. However, the spectrum of HBP-dependent tumors and the mechanisms by which the HBP promotes tumor aggressiveness remain areas of active investigation. In this study, we analyzed the activity of the HBP and its prognostic value across 33 types of human cancers. Increased HBP activity was observed in pancreatic ductal adenocarcinoma (PDAC), and higher HBP activity predicted a poor prognosis in PDAC patients. Genetic silencing or pharmacological inhibition of the first and rate-limiting enzyme of the HBP, glutamine:fructose-6-phosphate amidotransferase 1 (GFAT1), inhibited PDAC cell proliferation, invasive capacity, and triggered cell apoptosis. Notably, these effects can be restored by addition of UDP-GlcNAc. Moreover, similar antitumor effects were noticed by pharmacological inhibition of GFAT1 with 6-diazo-5-oxo-l-norleucine (DON) or Azaserine. PDAC is maintained by oncogenic Wnt/ß-catenin transcriptional activity. Our data showed that GFAT1 can regulate ß-catenin expression via modulation of the O-GlcNAcylation process. TOP/FOP-Flash and real-time qPCR analysis showed that GFAT1 knockdown inhibited ß-catenin activity and the transcription of its downstream target genes CCND1 and MYC. Ectopic expression of a stabilized form of ß-catenin restored the suppressive roles of GFAT1 knockdown on PDAC cell proliferation and invasion. Collectively, our findings indicate that higher GFAT1/HBP/O-GlcNAcylation exhibits tumor-promoting roles by maintaining ß-catenin activity in PDAC.

Appraisal of EUS-guided needle-based confocal laser endomicroscopy in the diagnosis of pancreatic lesions: A single Chinese center experience.

BACKGROUND AND OBJECTIVES: In the recent years, EUS is one of the routine procedures in the diagnosis of pancreatic diseases. EUS-guided needle-based confocal laser endomicroscopy (nCLE) is a novel minimally invasive imaging technique in diagnosis of pancreatic diseases. The pilot researches provided us some preliminary findings and conclusions with small samples, low rate of pathological correspondence. The aim of this current study was to evaluate the diagnostic efficacy of EUS-guided nCLE in solid pancreatic lesions (SPLs) and pancreatic cystic lesions (PCLs) based on large samples. The date was obtained on nCLE imaging findings and high rate of correlation with pathology. MATERIAL AND METHODS: Patients enrolled in the study were underwent EUS-nCLE to achieve the nCLE images and diagnosis. Comparing with the final diagnosis, including surgical histopathological results or cyto-/histopathology through FNA, the efficacy and accuracy of nCLE in diagnosis in solid and cystic pancreatic lesions were evaluated. In other cases, clinical diagnoses were achieved based on the combination with clinical history, image findings and fluid analysis and cytology, by 3 independent committee members strongly agreed with a concordant diagnosis. RESULTS: Totally 172 patients were enrolled into the study. The overall rate of final diagnosis was about 65% while 50% in cystic lesion. The mean sensitivity, specificity, negative predictive value, positive predictive value and accuracy of the nCLE in diagnosis of PDAC is 90.3%, 89.5%, 93.3%, 85.0% and 90.0% respectively. The efficacy and accuracy of pancreatic cystic lesions were very satisfying and some additional nCLE signs were found, including "black aggregates of cells, forming as gland-like structure, surrounding by fibro and vessels" in neuroendocrine tumors (NETs); "black columnar protrusions near vascular area" in the pseudopapillary solid tumor (SPT); macrophage in tuberculosis (TB) and small aggregate of black regular cells maybe corresponds to ovarian-like stroma in mucinous cystadenoma (MCN). In the study, 20 (11.6%) patients suffered complications, including symptomatic (5.2%) and asymptomatic (6.4%). CONCLUSIONS: nCLE observation could improve characterization of indeterminate cysts, or confirm the EUS impression, when cytological confirmation is missing. The technique may deliver information to better guide our clinical decisions.

Selective Synthesis of Z-Cinnamyl Ethers and Cinnamyl Alcohols through Visible Light-Promoted Photocatalytic E to Z Isomerization.

A photocatalytic E to Z isomerization of alkenes using an iridium photosensitizer under mild reaction conditions is disclosed. This method provides scalable and efficient access to Z-cinnamyl ether and allylic alcohol derivatives in high yields with excellent stereoselectivity. Importantly, this method also provides a powerful strategy for the selective synthesis of Z-magnolol and honokiol derivatives possessing potential biological activity.

Blood-based Genomic Profiling of Circulating Tumor DNA from Patients with Advanced Pancreatic Cancer and its Value to Guide Clinical Treatment.

Objective: Pancreatic cancer (PC) is a malignant tumor with limited therapeutic choices and extremely poor prognosis. Personalized therapy based on gene alternations is a promising choice. Considering tumor heterogeneity, the practice of ctDNA analysis has drawn the attention. Here, we try to assess the applicability of ctDNA in PC. Methods and materials: Next generation sequencing (NGS) was performed from blood samples of 223 PC patients and tissue sample of 564 PC patients. Genomic data from the TCGA database were also utilized. In addition, two cases received personalized treatment based on ctDNA sequencing results were reported. Results: Based on ctDNA sequencing, the genomic features of PC was revealed. Totally, 68.2% of patients detected at least one reportable genomic alteration (GA) from ctDNA. The frequently altered genes were KRAS (53.5%), followed by TP53 (52.8%), and CDKN2A (15.1%). Cell cycle control (8%) and DNA damage response (8%) pathways enriched the most mutated genes. Compared with mutations from tissue samples and a tissue-genomic database, similar frequencies of GAs were detected from ctDNA. The first two highest frequent mutation of genes were the same, but some of mutated genes were inclined to be observed in ctDNA, like AR. And two cases who received personalized therapy achieved better clinical benefit. Conclusion: Blood-source ctDNA sequencing could be regarded as a meaningful complement to tissue testing, and might guide clinically therapeutic regimen.