Pancreatic cancer remains one of the most aggressive malignancies, characterized by early metastatic spread and intrinsic resistance to chemotherapy, which ultimately results in poor treatment outcomes. While the Cell Division Cycle 6 (CDC6) protein has been extensively characterized across multiple cancer types, its functional role in the pathogenesis of pancreatic cancer remains poorly understood. In this study, we performed bioinformatics analysis using RNA-seq data from The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma cohort, and identified differentially expressed genes through microarray profiling. We conducted a comprehensive functional characterization of CDC6 using CCK-8, colony formation, wound healing, Transwell assays, and flow cytometry, and assessed cellular glycolysis levels based on measurements of ATP production, lactic acid generation, and glucose content. Subcutaneous xenograft mouse models were established to evaluate the impact of CDC6 on tumor growth in vivo, while mechanistic investigations were carried out using co-immunoprecipitation, chromatin immunoprecipitation, dual-luciferase reporter assays, and nucleocytoplasmic fractionation. Our results revealed that CDC6 expression is upregulated in pancreatic cancer, and its elevated expression is significantly correlated with unfavorable patient prognosis. Functional experiments demonstrated that CDC6 promotes the proliferation, migration, and invasion of pancreatic cancer cells. Thrombospondin 1 (THBS1) was identified to be positively correlated with CDC6 expression, and differentially expressed genes were notably enriched in the glucose metabolism pathway. Mechanistically, CDC6 cooperates with E2F1 to facilitate the transcription of THBS1, and the AKT signaling pathway is activated via the CDC6/THBS1 interaction. Overexpression of CDC6 significantly promoted glycolysis and tumor progression in pancreatic cancer, whereas these pro-tumor effects were markedly abrogated by THBS1 knockdown. Collectively, our findings demonstrate that CDC6/THBS1/AKT signaling drives glycolysis and accelerates pancreatic cancer progression, suggesting that the CDC6/THBS1/AKT axis may serve as a promising therapeutic target for pancreatic cancer.

Gallbladder cancer is highly aggressive and characterized by poor prognosis and limited treatment options. TREM1 (Triggering Receptor Expressed on Myeloid cells 1) may be involved in cancer inflammation and progression, but its relevance to gallbladder cancer is poorly understood. Expression of TREM1 in gallbladder cancer and correlation with mTOR activation, DRP1 upregulation, and clinical outcomes were investigated in the present study. Immunohistochemical staining of 105 gallbladder cancer specimens from patients receiving cholecystectomy, radical resection or palliative resection between 2015 and 2020 was conducted to show expression of TREM1, mTOR and DRP1. Data was analyzed by Chi-square test, Kaplan-Meier survival curve, Cox regression model and correlation analyses. High TREM1 expression was observed in 63

BACKGROUND:Pancreatic surgery has markedly evolved during the past several years with the development of minimally invasive techniques such as laparoscopy. pancreaticojejunostomy (PJ), also known as pancreatoenterostomy, is a critical step in surgical reconstruction after pancreatic resection. However, the laparoscopic performance of PJ presents additional technical challenges, especially in achieving a secure anastomosis while preserving the integrity of pancreatic tissue. AIM:To evaluate the effectiveness and safety of binding and interlocking PJ (BIPJ) as a novel technique in laparoscopic pancreatic surgery. METHODS:Data of patients who underwent laparoscopic pancreatic surgery from 2018 to 2023 were obtained from the hepatobiliary and pancreatic surgery database of the Second Affiliated Hospital of Zhejiang University School of Medicine and retrospectively analyzed. According to the different PJ methods used during surgery, the patients were divided into two groups: The BIPJ group and the duct-to-mucosa PJ (DMPJ) group. RESULTS:BIPJ was performed in 33 patients, and DMPJ was performed in 34 patients. The operative time was significantly shorter in the BIPJ group (median, 340 minutes; interquartile range, 310-350) than in the DMPJ group (median, 388 minutes; interquartile range, 341-464) (P = 0.004). No significant differences were found between the DMPJ and BIPJ groups in terms of the rates of pancreatic fistula, intra-abdominal hemorrhage, intra-abdominal abscess, postoperative biliary fistula, reoperation, or postoperative hospital stay. CONCLUSION:The suitability of laparoscopic PJ for all pancreatic textures, ability to perform full laparoscopy, shorter operation time, and comparable safety with traditional PJ make BIPJ a promising option for both surgeons and patients.

Colon cancer is a prevalent malignancy, substantially it prevented most effectively from killing patients through early endoscopic detection. With the rapid development of artificial intelligence technology, the early diagnosis rate of colonic polyps achieves greater clinical efficacy for colon cancer by applying target detection algorithms to colonoscopy images. This paper presents two outcomes achieved through the application of the improved YOLOv5s algorithm with annotated microscopy images of clinical cases and publicly available polyp image data: (1) enhancement of the C3(Cross Stage Partial Networks) module with multiple layers to C3SE(Cross Stage Partial Networks with Squeeze-and-Excitation) via the attention mechanism SE (squeeze-and-excitation) and (2) fusion of higher-level features utilizing BiFPN (the weighted bi-directional feature pyramid network). Experimental comparisons are performed based on a new image dataset of colonic polyps among more than 6 target detection algorithms to validate the better detection capability. The tests indicate that the YOLOv5s + BiFPN and YOLOv5s-1st-2nd-C3SE models exhibit enhancements of detection capability compared to the YOLOv5 algorithm according to the main indicators of the mAP, accuracy, and recall. The YOLOv5s + SEBiFPN model demonstrate a substantial improvement over the YOLOv5s algorithm, and establishing a benchmark technology for advancing computer-assisted diagnostic systems is feasible.

Metronomic chemotherapy (MCT) is a novel chemotherapy approach characterized by a high-frequency, low-dose administration strategy. The “chemo-switch” regimen involves the sequential use of two dosing strategies: maximum tolerated dose (MTD) chemotherapy and MCT. For patients with pancreatic ductal adenocarcinoma (PDAC), selecting novel chemotherapy regimens appropriately according to their physical conditions may help address the challenges associated with MTD chemotherapy, such as excessive toxicity, prolonged tumor recovery, and suboptimal efficacy. There is currently limited research on mathematical models related to novel chemotherapy regimens and PDAC, as well as on the impact of different drug administration strategies and the sequence of chemoradiotherapy in combined treatment. To address these gaps, we propose a two-dimensional multiscale mathematical model. Initially, we model the individual effects of MTD chemotherapy, antiangiogenic therapy, and radiotherapy. Subsequently, we analyze the anti-tumor effects of various chemotherapy regimens and their underlying mechanisms. Furthermore, we assess how different drug administration regimens and the sequencing of chemotherapy and radiotherapy affect treatment outcomes. Simulation results indicate that, compared to standard MTD chemotherapy, using the MCT regimen or introducing MCT during MTD chemotherapy (chemo-switch regimen) demonstrates better anti-tumor efficacy and sustained tumor perfusion, enhancing drug accumulation within tumor regions. Combined therapy exhibits superior efficacy compared to monotherapy. Placing radiotherapy after anti-angiogenic therapy and chemotherapy suggests more effective in suppressing tumor growth and sustaining tumor perfusion. It is noteworthy that while this study focuses on PDAC treatment, its findings can be extrapolated to other fibrotic tumors, thereby facilitating similar analyses across different tumor types.

In the magnetic actuation system, MAC (Magnetically Actuated Capsule) motion is disturbed by gastrointestinal resistance, and dynamic constraints exist between MAC and EPM (External Permanent Magnet), leading to control difficulties. This paper presents a data-driven control method for magnetic actuation system. Firstly, a data-driven modeling approach is proposed for addressing the modeling challenges, relying solely on MAC visual positioning input and EPM position output data. Secondly, To effectively track rapidly changing MAC desired trajectories, determining the upper bound of system inputs through analysis of the magnetic field relationship, and integrating it with adaptive parameter reset conditions, enables the establishment of dynamic constraints for the MAC system. Finally, dynamic compensation is applied to account for non-linear resistance terms and inaccuracies in data model representation. A dual-visual positioning experimental platform simulating the gastrointestinal environment is established to validate the proposed algorithm's effectiveness in MAC trajectory tracking under different conditions.

Although Density Peak Clustering (DPC) can easily locate cluster centers by detecting density peaks in its decision graph, its allocation strategy may unadvisedly associate irrelevant points, its decision graph may mislead the cluster center selection, and its high computational complexity O(n2) shies itself away from large-scale data. Herein, a Fast Main Density Peak Clustering Within Relevant Regions Via A Robust Decision Graph (R-MDPC) is proposed. R-MDPC assigns points within the relevant regions to avoid the association of irrelevant points. With the removal of regional differences and the attenuation of satellite peaks, a robust decision graph is obtained. Moreover, based on the kNN distance of data points, R-MDPC is believed to be suitable for large-scale data. Experimental results demonstrated the high robustness of R-MDPC’s decision graph in identifying cluster centers, and its outstanding performance and fast running speed in recognizing complex-shaped clusters.

Searching efficiency, which biologists and roboticists are ever concerned about, has become important in physics and information science nowadays. In classical probability-based searching problems, as stigmergy, the increase in graph complexity will decrease the searching efficiency. Here we study the searching efficiency based on the first-passage probability and find a counterintuitive phenomenon in quantum walk on a graph with floating vertices. Connecting one vertex in the floating layer to that in the base layer will speed up the searching rapidity, but such a speed-up effect will be suppressed at once if one more vertex is connected to the already connected vertex in the floating layer. This is a counterintuitive phenomenon in comparison to its classical counterpart, where additional vertices at the side chain will retard the searching rapidity. We also propose an ancillary model that bridges the measurement of the probability and the first-passage probability, which is expected to provide new ideas for quantum simulation by means of qubit chips.

Since most existing single-prototype clustering algorithms are unsuitable for complex-shaped clusters, many multi-prototype clustering algorithms have been proposed. Nevertheless, the automatic estimation of the number of clusters and the detection of complex shapes are still challenging, and to solve such problems usually relies on user-specified parameters and may be prohibitively time-consuming. Herein, a stable-membership-based auto-tuning multi-peak clustering algorithm (SMMP) is proposed, which can achieve fast, automatic, and effective multi-prototype clustering without iteration. A dynamic association-transfer method is designed to learn the representativeness of points to sub-cluster centers during the generation of sub-clusters by applying the density peak clustering technique. According to the learned representativeness, a border-link-based connectivity measure is used to achieve high-fidelity similarity evaluation of sub-clusters. Meanwhile, based on the assumption that a reasonable clustering should have a relatively stable membership state upon the change of clustering thresholds, SMMP can automatically identify the number of sub-clusters and clusters, respectively. Also, SMMP is designed for large datasets. Experimental results on both synthetic and real datasets demonstrated the effectiveness of SMMP.

Review question / Objective: We aimed to investigate any potential correlation between history of bariatric surgery and improved outcomes in COVID-19 patients.Condition being studied: Obesity and several obesity-related diseases are risk factors for severe COVID-19.Bariatric surgery successfully treats obesity-related conditions.The aims of our study are to review existing clinical evidence and compare the rates of mortality, use of invasive mechanical ventilation, and ICU admission in patients hospitalized for COVID-19 with or without a history of bariatric surgery.Information sources: Systematic literature search will be performed in Medline, Embase, Cochrane, and Scopus database.

恶性梗阻性黄疸是由于肝细胞癌、胆管癌、胆囊癌、胰头癌及壶腹部周围癌等恶性肿瘤的浸润、压迫而导致肝内外胆道梗阻,是以高胆红素血症、皮肤巩膜及体液黄染为主要临床表现的一组疾病[1].临床上,在对黄疸患者进行影像学及实验室检查评估后,判断无法直接手术根治或是为保护肝功能拟行术前减黄的恶性梗阻性黄疸患者,常选择介入胆道引流、手术胆道引流等减黄处理.胆道引流减黄的方式,可根据各家医疗中心的具体情况选择经皮经肝胆管引流术(percutaneous transhepatic cholangial drainage,PTCD)、逆行胰胆管造 影(encoscopic retrograde cholangio-pancreatography, ERCP)、内镜下鼻胆管引流术(endoscopic nasobiliary drainage,ENBD)、胆道支架内引流、开腹或腹腔镜下胆肠吻合内引流手术等,但无论采用何种方式,均以操作安全、引流有效为第一要务,兼顾患者生理、心理的个体化选择.本文拟就目前常见的多种减黄方式进行优缺点比较,并结合笔者经验探讨腹腔镜胆肠内引流术在临床推广应用的价值.

PURPOSE:The accuracy improvement in endoscopic image classification matters to the endoscopists in diagnosing and choosing suitable treatment for patients. Existing CNN-based methods for endoscopic image classification tend to use the deepest abstract features without considering the contribution of low-level features, while the latter is of great significance in the actual diagnosis of intestinal diseases.METHODS:To make full use of both high-level and low-level features, we propose a novel two-stream network for endoscopic image classification. Specifically, the backbone stream is utilized to extract high-level features. In the fusion stream, low-level features are generated by a bottom-up multi-scale gradual integration (BMGI) method, and the input of BMGI is refined by top-down attention learning modules. Besides, a novel correction loss is proposed to clarify the relationship between high-level and low-level features.RESULTS:Experiments on the KVASIR dataset demonstrate that the proposed framework can obtain an overall classification accuracy of 97.33% with Kappa coefficient of 95.25%. Compared to the existing models, the two evaluation indicators have increased by 2% and 2.25%, respectively, at least.CONCLUSION:In this study, we proposed a two-stream network that fuses the high-level and low-level features for endoscopic image classification. The experiment results show that the high-to-low-level feature can better represent the endoscopic image and enable our model to outperform several state-of-the-art classification approaches. In addition, the proposed correction loss could regularize the consistency between backbone stream and fusion stream. Thus, the fused feature can reduce the intra-class distances and make accurate label prediction.

Background and ObjectivesPancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues. MethodsWe examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles. ResultsOverall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied. ConclusionsAI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.

Background and Objective:The incidence and mortality of pancreatic cancer (PC) have increased in recent years. The current status of PC diagnosis and treatment remains grim in clinical practice because the commonly used early screening tools are not sufficient. Improving the early detection of PC and strengthening standardized comprehensive treatment remain the focus of PC research. Many studies have shown that micro RNAs (miRNAs) play an important role in the occurrence, development, and treatment of PC. It is expected that miRNAs will become new molecular markers of PC.Methods:We extracted and compiled useful information from the PubMed database that met our criteria for analyzing PC diagnosis, treatment, and prognosis.Key Content and Findings:In this narrative review, we summarize the mechanism of some miRNAs in the occurrence and development of PC and review them as potential markers for the diagnosis, treatment, and prognosis of PC. The function of miRNAs in PC has great potential in studying the pathogenesis of PC. The discovery of many important oncogenic miRNAs and their downstream targets will bring new ideas and research paths for the diagnosis and targeted therapy of PC.Conclusions:MiRNAs are expected to provide novel ideas and research directions for the diagnosis and targeted treatment of PC. However, more patient data and clinical trials are needed before miRNAs can become novel molecular markers for PC.

Objective To explore the effects of the expression level of miR-520-5p/PPP5C in pancreatic cancer cells and exosomes on cell viability, angiogenesis, autophagy, which involved in the mechanism of gemcitabine resistance in pancreatic cancer. Methods APSC-1 cell line was treated with gemcitabine, after which its exosomes were extracted for NTA assay. Subsequently, the drug resistance of APSC-1 cells was assayed using CCK8, as well as the activity of HUVEC cells treated with exosomes from each group of APSC-1 cells after drug resistance treatment as well as overexpression treatment. Five groups of HUVEC cells treated with exosomes were subjected to in vitro tubule formation assay. levels of PPP5C in each group of ASPC-1 cells and their exosomes, levels of overexpressed PPP5C, and related exosomal proteins were examined by WB. mRNA expression levels of PPP5C and levels of miR-520a were examined by qPCR The relationship between miR-520a-5p and PPP5C was investigated. After that, the autophagy of PPP5C was detected. Finally, it was analyzed by TCGA database for survival prognosis analysis. Results APSC-1 cells had an IC50 value of 227.1 μM for gemcitabine, elevated PPP5C expression, drug resistance, and enhanced HUVEC cell activity; exosomes CD9, CD63, and CD81 were significantly expressed in all groups; meanwhile, enhanced PPP5C expression not only promoted in vitro tubule formation but also increased autophagy levels; meanwhile, its relationship with miR-520-5p and There was a targeted inhibitory relationship between its level and miR-520-5p and PPP5C, and its elevated level also led to a decrease in the survival level of patients over 3-5 years. Conclusion PPP5C has a prognostic role in pancreatic cancer by promoting the value-added and invasion of pancreatic cancer cells, and a targeted inhibitory relationship between miR-520-5p and PPP5C was found.

Purpose The accurate and automatic segmentation of gastrointestinal wall vessels can help to prevent endoscope tip related perforation. Methods based on deep learning and convolution neural network in many different kinds of medical image segmentation tasks have achieved remarkable performance, but in the gastrointestinal vessels segmentation task, because fold and vascular structure characteristics are very similar, blood vessels, fuzzy boundaries, flare and other interference factors, it is very easy to produce false segmentation and rupture of blood vessels. We therefore propose a new multi-scale future fusion network to tackle the aforementioned issues. Methods Our proposed segmentation network consists of encoding, decoding modules, attention module and future fusion module. Through the convolution operation of future fusion module, the output features of each encoder are effectively fused, and the multi-scale information is fully utilized. In addition, we further improve the loss function and enhance the ability of the network to distinguish folds and vessels and predict vascular connectivity by giving different weights to the front background. Results The proposed network is evaluated on our own gastrointestinal wall vessel data set. Experimental results show that compared with the existing advanced vascular segmentation networks, the proposed network has better segmentation performance in the gastrointestinal wall vascular dataset. Conclusion The proposed future fusion method and attention structure loss can better perform feature extraction and fusion according to the characteristics of the gastrointestinal wall vessels to achieve better results.

Purpose Wireless capsule endoscopy (WCE) is an effective and non-invasive advanced technology for the diagnosis of gastrointestinal (GI) abnormalities. From a clinical perspective, one of the most common and valuable indication for WCE is GI bleeding. However, the bleeding point may be incorrectly localized by bleeding detection methods, due to the small size of bleeding areas and the bubbles interference in the bleeding areas. These problems make it difficult to accurately localize bleeding point in GI images. Methods Therefore, a pixel-level segmentation method for GI bleeding is proposed, in which the dual network branches based on attention mechanism are designed to correctly classify the pixel samples of the bleeding areas. These two branches complement each other and focus on extracting the color and the texture features of the bleeding areas, respectively. The outputs of the dual network branches are combined finally by the feature fusion module to obtain a more accurate segmentation result. Results Extensive experiments have been done on public WCE image datasets to test the performance of our proposed network. The mean intersection over union (mIoU) of our proposed network is 86.858%, which shows its significant segmentation performance. Conclusion A novel network for GI bleeding segmentation is developed, which can obtain promising segmentation performance compared with some existing popular methods.

Polyp of intestinal tract is the precursor of colorectal cancer. Accurate computer-aided polyp location and segmentation in colonoscopy is of great importance since it provides valuable information for endoscopists. However, polyps are arduous to be segmented due to their high inter-class similarity, high intra-class variation, and low contrast with surrounding mucosa. To address these challenges, we propose a multi-scale boundary network (MSB-Net) for polyp segmentation. We first focus on the multi-scale feature representation and propose a novel architectural unit to extract intra-stage and contextual information, which is named ResU-Block (RUB). RUBs are connected by the proposed multi-squeeze-and-excitation (Multi-SE) units which can recalibrate the feature information from a multi-scale perspective. We then generate a coarse prediction using the partial decoder, of which the boundary is further refined by a shallow-level attention (SA) module. In addition, we exploit the boundary details using a set of reverse attention (RA) modules, which can progressively establish relationships between regions and boundaries from deep-level features. Comprehensive experiments on five public datasets across five metrics elucidate that our architecture outperforms other SOTA methods by a large margin while maintaining comparable model complexity and inference speed.