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Simple Summary: Despite recent advances, some patients with pancreatic cancer are refractory to treatment and the disease rapidly progresses, resulting in early death. The potential prognostic value of circulating tumor cells (CTCs) has been demonstrated in other cancer types, but the clinical validity in pancreatic cancer remains elusive. Here, we show that CTC clusters, which show mesenchymal characteristics and platelet marker expression, are highly correlated with poor prognosis in patients with unresectable pancreatic cancer. Circulating tumor cells (CTCs) are known to be heterogeneous and clustered with tumor-associated cells, such as macrophages, neutrophils, fibroblasts, and platelets. However, their molecular profile and clinical significance remain largely unknown. Thus, we aimed to perform a comprehensive gene expression analysis of single CTCs and CTC clusters in patients with pancreatic cancer and to identify their potential clinical relevance to provide personalized medicine. Epitope-independent, rapid (>3 mL of whole blood/min) isolation of single CTCs and CTC clusters was achieved from a prospective cohort of 16 patients with unresectable pancreatic cancer using a centrifugal microfluidic device. Forty-eight mRNA expressions of individual CTCs and CTC clusters were analyzed to identify pancreatic CTC phenotype. CTC clusters had a larger proportion of mesenchymal expression than single CTCs (p = 0.0004). The presence of CTC clusters positively correlated with poor prognosis (progression-free survival, p = 0.0159; overall survival, p = 0.0186). Furthermore, we found that most CTCs in these patients (90.7%) were cloaked with platelets and found the presence of a positive correlation between the increase in CTC clusters and rapid disease progression during follow-ups. Efficient CTC cluster isolation and analysis techniques will enhance the understanding of complex tumor metastasis processes and can facilitate personalized disease management. [ABSTRACT FROM AUTHOR]

Introduction: Complementary and integrative health (CIH) therapies refers to massage therapy, acupuncture, aromatherapy, and guided imagery. These therapies have gained increased attention in recent years, particularly for their potential to help manage chronic pain and other conditions. National organizations not only recommend the use of CIH therapies but also the documentation of these therapies within electronic health records (EHRs). Yet, how CIH therapies are documented in the EHR is not well understood. The purpose of this scoping review of the literature was to examine and describe research that focused on CIH therapy clinical documentation in the EHR. Methods: The authors conducted a literature search using six electronic databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ovid MEDLINE, Scopus, Google Scholar, Embase, and PubMed. Predefined search terms included "informatics," "documentation," "complementary and integrative health therapies," "non-pharmacological approaches," and "electronic health records" using AND/OR statements. No restrictions were placed on publication date. The inclusion criteria were as follows: (1) Original peer-reviewed full article in English, (2) focus on CIH therapies, and (3) CIH therapy documentation practice used in the research. Results: The authors identified 1684 articles, of which 33 met the criteria for a full review. A majority of the studies were conducted in the United States (20) and hospitals (19). The most common study design was retrospective (9), and 26 studies used EHR data as a data source for analysis. Documentation practices varied widely across all studies, ranging from the feasibility of documenting integrative therapies (i.e., homeopathy) to create changes in the EHR to support documentation (i.e., flowsheet). Discussion: This scoping review identified varying EHR clinical documentation trends for CIH therapies. Pain was the most frequent reason for use of CIH therapies across all included studies and a broad range of CIH therapies were used. Data standards and templates were suggested as informatics methods to support CIH documentation. A systems approach is needed to enhance and support the current technology infrastructure that will enable consistent CIH therapy documentation in EHRs. [ABSTRACT FROM AUTHOR]

Simple Summary: Surgical therapy is critical to pancreatic cancer survival. The segmentation of pancreatic cancer in endoscopic ultrasonography (EUS) images can provide critical characteristics of the pancreatic cancer for surgical therapy. However, EUS has high operator dependency, and it requires a considerable level of experience and competency to stage pancreatic cancer. Deep learning approaches have been used on EUS images, but there have been no studies on the segmentation of pancreatic cancer. The purpose of this study is to segment pancreatic cancer from EUS images using a neural network model with deep attention features, called DAF-Net. The significance of this study lies in the successful segmentation performance of the pancreatic cancer using the DAF-Net, regardless of the size and location of the cancer, and its usefulness in preoperative planning. Endoscopic ultrasonography (EUS) plays an important role in diagnosing pancreatic cancer. Surgical therapy is critical to pancreatic cancer survival and can be planned properly, with the characteristics of the target cancer determined. The physical characteristics of the pancreatic cancer, such as size, location, and shape, can be determined by semantic segmentation of EUS images. This study proposes a deep learning approach for the segmentation of pancreatic cancer in EUS images. EUS images were acquired from 150 patients diagnosed with pancreatic cancer. A network with deep attention features (DAF-Net) is proposed for pancreatic cancer segmentation using EUS images. The performance of the deep learning models (U-Net, Attention U-Net, and DAF-Net) was evaluated by 5-fold cross-validation. For the evaluation metrics, the Dice similarity coefficient (DSC), intersection over union (IoU), receiver operating characteristic (ROC) curve, and area under the curve (AUC) were chosen. Statistical analysis was performed for different stages and locations of the cancer. DAF-Net demonstrated superior segmentation performance for the DSC, IoU, AUC, sensitivity, specificity, and precision with scores of 82.8%, 72.3%, 92.7%, 89.0%, 98.1%, and 85.1%, respectively. The proposed deep learning approach can provide accurate segmentation of pancreatic cancer in EUS images and can effectively assist in the planning of surgical therapies. [ABSTRACT FROM AUTHOR]