Pancreatic cancer remains one for the most aggressive cancer worldwide, with pancreatic ductal adenocarcinoma being the most common malignant pancreatic lesion, associated with poor prognosis. While surgical resection is the only curative treatment, only a minority of patients is eligible for surgery due to its diagnosis at advanced stages. Therefore, strategies for early detection of pancreatic cancer are needed. This article aims to provide a state-of-the-art review of the most common conditions associated to an increased risk of pancreatic cancer. Conditions linked to risk of pancreatic cancer development include certain pancreato-biliary anatomical variants, intraductal papillary mucinous neoplasms, mucinous cystic neoplasm, and familial pancreatic cancer with specific genetic mutations. Early imaging signs of pancreatic cancer can also be incidentally encountered on CT or MRI performed for other indications and they should be promptly recognized by the radiologists in order to avoid delays in the diagnosis. The features include focal pancreatic atrophy, contour deformity, dilation of the main pancreatic duct (MPD), changes in the caliber of the MPD, abrupt interruption of the MPD, and biliary tree dilation. MRI with the adoption of abbreviated protocols has been increasingly evaluated for the follow-up of cystic lesions. Although screening of the general population is not recommended due to the low incidence and high costs, surveillance with MRI can be considered in selected high-risk individuals.

The majority of patients with pancreatic ductal adenocarcinoma (PDAC) are no longer suitable for treatment with curable intent at the time of diagnosis resulting in a 5-year survival of less than 10 %. Imaging of asymptomatic individuals could identify early cancers, but only with a risk of falsely identifying a benign lesion as malignant. Screening of an unselected population would result in far more such false positives than true early cancers. Selection before screening is therefore essential, but there are very few populations at high enough risk to make screening more beneficial than counterproductive. These populations include carriers of specific mutations in BRCA2, and arguably BRCA1, who have a family history of PDAC. These pathogenic mutations all have a predictable effect in making loss of Homologous Recombination Repair (HRR) likely in a carrier's lifetime. In this review the impact of such loss of HRR function on the likelihood of PDAC development will be discussed. Furthermore, it will be discussed whether the identification of a germline pathogenic mutation is sufficient to justify carrier surveillance for the development of the malignancy, or whether the current practice of screening only those carriers with a close relative diagnosed with PDAC is justifiable, as only a proportion of carriers are at high risk. The review will go beyond this to discuss whether there is an essential need to better define and stratify those at high risk, so that only high-risk carriers are put on surveillance.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with increasing incidence and mortality rates, highlighting the urgent need for early diagnosis and treatment. However, early diagnosis of PDAC is extremely challenging due to the atypical early symptoms or the absence of noticeable symptoms. As a result, many patients are diagnosed with local metastasis, and even patients who are eligible for surgical resection have a high postoperative recurrence rate. Consequently, chemotherapy remains the primary treatment for PDAC. However, the unique biological characteristics of PDAC not only promote tumor progression and metastasis but also often lead to chemoresistance, a significant barrier to successful treatment. Recently, nanomaterials have garnered significant attention as promising materials for diagnosing and treating PDAC, showing great potential in cancer therapy, imaging, and drug delivery. Novel targeted nanomedicines, which encapsulate chemotherapy drugs and gene therapy products, offer significant advantages in overcoming resistance. These nanomedicines not only provide innovative solutions to the limitations of conventional chemotherapy but also improve the selectivity for cancer cells to enhance therapeutic outcomes. Current research is focused on the development of advanced nanomedicines, such as liposomes, nanotubes, and polymer-lipid hybrid systems, aimed at making chemotherapy more effective and longer lasting. This review provides a detailed overview of various nanomedicines utilized in the diagnosis and treatment of PDAC and outlines future directions for their development and key breakthroughs.

This study analyzed different classifier models for differentiating pancreatic adenocarcinoma from surrounding healthy pancreatic tissue based on radiomic analysis of magnetic resonance (MR) images.

We observed T2W-FS and ADC images obtained by 1.5T-MR of 87 patients with histologically proven pancreatic adenocarcinoma for training and validation purposes and then tested the most accurate predictive models that were obtained on another group of 58 patients. The tumor and surrounding pancreatic tissue were segmented on three consecutive slices, with the largest area of interest (ROI) of tumor marked using MaZda v4.6 software. This resulted in a total of 261 ROIs for each of the observed tissue classes in the training-validation group and 174 ROIs in the testing group. The software extracted a total of 304 radiomic features for each ROI, divided into six categories. The analysis was conducted through six different classifier models with six different feature reduction methods and five-fold subject-wise cross-validation.

In-depth analysis shows that the best results were obtained with the Random Forest (RF) classifier with feature reduction based on the Mutual Information score (all nine features are from the co-occurrence matrix): an accuracy of 0.94/0.98, sensitivity of 0.94/0.98, specificity of 0.94/0.98, and F1-score of 0.94/0.98 were achieved for the T2W-FS/ADC images from the validation group, retrospectively. In the testing group, an accuracy of 0.69/0.81, sensitivity of 0.86/0.82, specificity of 0.52/0.70, and F1-score of 0.74/0.83 were achieved for the T2W-FS/ADC images, retrospectively.

The machine learning approach using radiomics features extracted from T2W-FS and ADC achieved a relatively high sensitivity in the differentiation of pancreatic adenocarcinoma from healthy pancreatic tissue, which could be especially applicable for screening purposes.

Pancreatic ductal adenocarcinoma (PDA) is partly initiated through the transdifferentiation of acinar cells to metaplasia, which progresses to neoplasia and cancer. Tuft cells (TCs) are chemosensory cells not found in the normal pancreas but arise in cancer precursor lesions and diminish during progression to carcinoma. These metaplastic TCs (mTCs) suppress tumor progression through communication with the tumor microenvironment, but their fate during progression is unknown. To determine the fate of mTCs during PDA progression, we created a dual recombinase lineage trace model, wherein a pancreas-specific FlpO was used to induce tumorigenesis, while a tuft-cell specific Pou2f3CreERT/+ driver was used to induce expression of a tdTomato reporter. We found that mTCs in carcinoma transdifferentiate into neural-like progenitor cells (NRPs), a cell type associated with poor survival in patients. Using conditional knockout and overexpression systems, we found that Myc activity in mTCs is necessary and sufficient to induce this tuft-to-neuroendocrine transition (TNT).

Pancreatic cancer ranks among the most lethal digestive malignancies, exhibiting a steadily increasing incidence and mortality worldwide. Despite significant advances in cancer research, the 5-year survival rate remains below 10%, predominantly due to delayed diagnosis and limited therapeutic options. Concurrently, the gut microbiota-an integral component of host physiology-has emerged as a crucial player in the pathogenesis of pancreatic cancer. Mounting evidence indicates that alterations in gut microbial composition and function may influence tumor initiation, progression, and response to therapy. This review provides an in-depth examination of the intricate interplay between the gut microbiome and pancreatic cancer, highlighting potential diagnostic biomarkers and exploring microbiome-targeted therapeutic strategies to improve patient outcomes.

To explore the potential of serum exosomal miRNAs as novel biomarkers for pancreatic ductal adenocarcinoma (PDAC).

Serum exosomal miRNAs were screened and verified by microarray analysis and quantitative real-time PCR (qRT-PCR) in patients with PDAC and healthy controls. The correlation between the clinical characteristics of PDAC and candidate exosomal miRNAs was analyzed, and the diagnostic performance of the candidate biomarkers was evaluated.

Serum exosomal miR-7977 and miR-451a were significantly upregulated in PDAC patients compared with healthy controls, and the levels of miR-7977 and miR-451a in serum exosomes were closely associated with the clinical stage and metastasis of PDAC patients. The area under curve (AUC) values of serum exosomal miR-7977 and miR-451a for PDAC were 0.825 and 0.804 in the training set and 0.796 and 0.830 in the validation set, respectively. A biomarker panel consisting of these two miRNAs resulted in a diagnostic power with an AUC of 0.901 in the training set and 0.918 in the validation set.

Serum exosomal miR-7977 and miR-451a might be diagnostic biomarkers for PDAC. These two miRNAs, when combined, exhibit optimal diagnostic performance.

Current management of patients with borderline resectable pancreatic adenocarcinoma (BR-PDAC) depends on the degree of involvement of the major arterial and venous structures. The aim of this study was to evaluate 3D segmentation and printing to predict tumor size and vascular involvement of BR-PDAC to improve pre-operative planning of vascular resection and better select patients for neoadjuvant therapy.

We retrospectively evaluated 16 patients with BR-PDAC near vascular structures who underwent pancreatoduodenectomy (PD) with or without vascular resection between 2015 and 2021. The pre-operative computed tomography (CT) images were processed by segmentation with 3D reconstruction and printed as 3D models. Two radiologists specialized in pancreatic imaging and two pancreatic surgeons blindly and independently analyzed the pre-operative CT scans and 3D models using a defined checklist. Their evaluations were compared to the pre-operative 2D-CT reports utilized for patient management. A positive delta was defined by the 3D analysis resulting in greater accuracy in predicting vascular involvement as proven intraoperatively or histopathologically.

Fourteen PD, one total pancreatectomy, and one exploratory laparotomy were performed. Ten patients had a positive delta concerning vascular involvement of the superior mesenteric or portal vein. Tumor extension was also more accurately evaluated by 3D modeling than by 2D-CT (p < 0.05).

Our pilot study demonstrates that 3D segmentation can provide additional information for choosing the best treatment strategy and surgical plain in patients with BR-PDAC. Especially for upcoming mini-invasive techniques like laparoscopic and robotic resections, better pre-operative planning is essential to allow safety and prevent vascular injury.

Cancers of the digestive system are major contributors to global cancer-associated morbidity and mortality, accounting for 35% of annual cases of cancer deaths. The etiologies, molecular features, and therapeutic management of these cancer entities are highly heterogeneous and complex. Over the last decade, genomic and functional studies have provided unprecedented insights into the biology of digestive cancers, identifying genetic drivers of tumor progression and key interaction points of tumor cells with the immune system. This knowledge is continuously translated into novel treatment concepts and targets, which are dynamically reshaping the therapeutic landscape of these tumors. In this review, we provide a concise overview of the etiology and molecular pathology of the six most common cancers of the digestive system, including esophageal, gastric, biliary tract, pancreatic, hepatocellular, and colorectal cancers. We comprehensively describe the current stage-dependent pharmacological management of these malignancies, including chemo-, targeted, and immunotherapy. For each cancer entity, we provide an overview of recent therapeutic advancements and research progress. Finally, we describe how novel insights into tumor heterogeneity and immune evasion deepen our understanding of therapy resistance and provide an outlook on innovative therapeutic strategies that will shape the future management of digestive cancers, including CAR-T cell therapy, novel antibody-drug conjugates and targeted therapies.

Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer-aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging. This aims to develop and test the basis for a CADe system for real-time detection and segmentation of all pancreatic lesions.

In this single-center study EUS studies of pancreatic findings were included. Lesions were outlined by two expert (>5 years performing EUS) endoscopists, and the two leading types of models were benchmarked. The models' performance was evaluated through per-pixel intersection over union (IoU).

A total of 1497 EUS images from 165 patients were evaluated. The dataset included malignancies, neuroendocrine tumors, benign cysts, chronic and acute pancreatitis, normal fatty pancreas, and benign lesions. The best model demonstrated detection and segmentation on the test set with a mean IoU of 0.73, achieving a PPV, NPV, total accuracy, and ROC of 0.82, 0.96, 0.95, and 0.95, respectively. The algorithm is adaptable for real-time processing.

We developed and tested deep learning models for real-time detection and segmentation of pancreatic lesions during EUS with promising results. This constitutes the basis for a CADe system for EUS, which could be valuable in future detection and evaluation of pancreatic lesions. Further studies for validation and generalization are underway.

Pancreatic ductal adenocarcinoma (PDAC) requires innovative therapeutic strategies to counteract its progression and metastatic potential. Since the majority of patients are diagnosed with advanced metastatic disease, treatment strategies targeting not only the primary tumor but also metastatic lesions are needed. Tumor-Associated Macrophages (TAMs) have emerged as central players, significantly influencing PDAC progression and metastasis. Our objective was to validate an innovative therapeutic strategy involving the reprogramming of TAMs using lipid nanosystems to prevent the formation of a pro-metastatic microenvironment in the liver.

In vitro results demonstrate that M2-polarized macrophages lose their M2-phenotype following treatment with lipid nanoemulsions composed of vitamin E and sphingomyelin (VitE:SM), transitioning to an M0/M1 state. Specifically, VitE:SM nanoemulsion treatment decreased the expression of macrophage M2 markers such as Arg1 and Egr2, while M1 markers such as Cd86, Il-1b and Il-12b increased. Additionally, the TGF-βR1 inhibitor Galunisertib (LY2157299) was loaded into VitE:SM nanoemulsions and delivered to C57BL/6 mice orthotopically injected with KPC PDAC tumor cells. Treated mice showed diminished primary tumor growth and reduced TAM infiltration in the liver. Moreover, we observed a decrease in liver metastasis with the nanoemulsion treatment in an intrasplenic model of PDAC liver metastasis. Finally, we validated the translatability of our VitE:SM nanosystem therapy in a human cell-based 3D co-culture model in vivo, underscoring the pivotal role of macrophages in the nanosystem's therapeutic effect in the context of human PDAC metastasis.

The demonstrated effectiveness and safety of our nanosystem therapy highlights a promising therapeutic approach for PDAC, showcasing its potential in reprogramming TAMs and mitigating the occurrence of liver metastasis.

The dynamics of postoperative carcinoembryonic antigen (CEA) in pancreatic ductal adenocarcinoma (PDAC) patients have not been well assessed. This study investigated the correlation between postoperative CEA elevations and tumor recurrence.

Medical records were retrospectively analyzed for 84 patients who received curative resection for PDAC from January 2019 to December 2020. Postoperative CEA levels were monitored for a minimum of 12 months. False-positive CEA elevation was defined as a CEA level exceeding 5 ng/mL without evidence of recurrence in imaging studies.

Of the examined patients, 59 (70%) exhibited CEA > 5 ng/mL within the observation period. The sensitivity and specificity of elevated CEA levels for detecting recurrence were 84% and 41%, respectively. CEA elevations without tumor recurrence were observed in 27 patients, indicating a false-positive rate of 59%. More than half of these patients demonstrated peak CEA levels between 5 and 10 ng/mL, while only true-positive patients exhibited CEA levels exceeding 40.0 ng/mL.

CEA may rise in more than half of postoperative PDAC patients without recurrence. CEA alone is not a robust postoperative marker.

Glypican-1 (GPC-1) protein-positive small extracellular vesicles (GPC-1+-sEV) have been proposed as potential biomarkers for early diagnosis of pancreatic cancer. In this study, we present an integrated real-time isolation and detection platform (IRTIDP) to capture and analyze GPC-1+-sEV directly from sera of pancreatic cancer patients. First, CD63 antibody-modified metal-organic framework (MOF) materials were utilized to enrich sEVs with a capture efficiency of 93.93 %. Second, a SERS probe was constructed by Raman reporter 4-MBA and GPC-1 antibody modified SERS active silver nanoparticles (AgNPs), which formed a sandwich complex structure of "MOFs@GPC-1+-sEV@AgNPs-4-MBA" with MOFs-enriched sEVs. The IRTSDP can complete the capture and detection process within 35 min, with a detection limit for 1 GPC-1+-sEV/μL, and linear range between 105∼109 GPC-1+-sEV/mL. Furthermore, this approach has been applied to quantify serum sEV GPC-1 in clinical pancreatic cancer patients. Based on the SERS intensity analysis, pancreatic cancer patients can be distinguished from pancreatic cystadenoma patients and healthy individuals effectively using this innovative platform that provides highly specific and sensitive means for early diagnosis of pancreatic cancer as well as other tumor types.

The pancreas is involved in digestion and glucose regulation in the human body. Given the recognized link between chronic pancreatitis and pancreatic cancer, addressing pancreatic disorders and pancreatic cancer is particularly challenging. This review aims to highlight the limitations of traditional methods in diagnosing pancreatic disorders and cancer and explore several next-generation sequencing (NGS) approaches as a promising alternative. There are distinct clinical symptoms that are shared by a number of clinical phenotypes of pancreatic illness induced by particular genetic mutations. Traditional diagnostic methods encompass computed tomography, magnetic resonance imaging, contrast-enhanced Doppler ultrasound, endoscopic ultrasound, endoscopic retrograde cholangiopancreatography, transabdominal ultrasound, laparoscopy, and positron emission tomography have a prognostic ability of only 5% or less and a 5-year survival rate. Genetic sequencing can be employed as an alternative to conventional diagnostic techniques. Sanger sequencing and NGS are currently largely operated genome analysis, with no exception for pancreatic disease diagnosis. The NGS methods can sequence millions to billions of short DNA fragments, enabling enormous sample screening in a short amount of time with low-abundance detection, like in 0.1%-1% mutation prevalence declining approximate cost. Whole-genome sequencing, whole-exome sequencing, RNA sequencing, and single-cell NGS are a few NGS methods utilized to diagnose pancreatic disease. For both research and clinical applications, the NGS techniques can provide a precise diagnosis of pancreatic disorders in a short amount of time at a reasonable expenditure.

Pancreatic cancer is characterized by an extremely poor prognosis and presents significant treatment challenges. Liver metastasis is the leading cause of death in patients with pancreatic cancer. Recent studies have highlighted the significant impact of neutrophils on tumor occurrence and progression, as well as their crucial role in the pancreatic cancer tumor microenvironment. Neutrophil infiltration plays a critical role in the progression and prognosis of pancreatic cancer. Neutrophils contribute to pancreatic cancer liver metastasis through various mechanisms, including angiogenesis, immune suppression, immune evasion, and epithelial-mesenchymal transition (EMT). Therefore, targeting neutrophils holds promise as an important therapeutic strategy for inhibiting pancreatic cancer liver metastasis. This article provides a summary of research findings on the involvement of neutrophils in pancreatic cancer liver metastasis and analyzes their potential as therapeutic targets. This research may provide new insights for the treatment of pancreatic cancer and improve the prognosis of patients with this disease.

Cinematic Rendering (CR) employs physical models such as ray tracing and global illumination to simulate real-world light phenomena, producing high-quality images with rich details. In the medical field, CR can significantly aid doctors in accurate diagnosis and preoperative planning. However, doctors require efficient real-time rendering when using CR, which presents a challenge due to the substantial computing resources demanded by CR's ray tracing and global illumination models. Precomputed lighting can enhance the efficiency of real-time rendering by freezing certain scene variables. Typically, precomputed methods freeze geometry and materials. However, since the physical rendering of medical images relies on volume data rendering of transfer functions, the CR algorithm cannot utilize precomputed methods directly. To improve the rendering efficiency of the CR algorithm, we propose a precomputed low-frequency lighting method. By simulating the lighting pattern of shadowless surgical lamps, we adopt a spherical distribution of multiple light sources, with each source capable of illuminating the entire volume of data. Under the influence of these large-area multi-light sources, the precomputed lighting adheres to physical principles, resulting in shadow-free and uniformly distributed illumination. We integrated this precomputed method into the ray-casting algorithm, creating an accelerated CR algorithm that achieves more than twice the rendering efficiency of traditional CR rendering.

Clinical outcomes of surgery after neoadjuvant chemotherapy have not been investigated for locally advanced pancreatic cancer (LAPC), despite well-established outcomes in borderline resectable pancreatic cancer (BRPC). This study aimed to investigate the clinical outcomes of patients with LAPC who underwent curative resection following neoadjuvant chemotherapy.

We retrospectively reviewed the records of patients diagnosed with pancreatic adenocarcinoma between January 2017 and December 2020.

Among 1,358 patients, 260 underwent surgery following neoadjuvant chemotherapy. Among 356 LAPC patients, 98 (27.5%) and 147 (35.1%) of 418 BRPC patients underwent surgery after neoadjuvant chemotherapy. Compared to resectable pancreatic cancer (resectable PC) with upfront surgery, both LAPC and BRPC exhibited higher rates of venous resection (28.6% vs. 49.0% vs. 4.0%), arterial resection (30.6% vs. 6.8% vs. 0.5%) and greater estimated blood loss (260.5 vs. 213.1 vs. 70.4 mL). However, hospital stay, readmission rates, and postoperative pancreatic fistula rates (grade B or C) did not differ significantly between LAPC, BRPC, and resectable PC. Overall and relapse-free survival did not differ significantly between LAPC and BRPC patients. The median overall survival was 37.3 months for LAPC and 37.0 months for BRPC. The median relapse-free survival was 22.7 months for LAPC and 26.0 months for BRPC.

Overall survival time and postoperative complications in LAPC patients who underwent curative resection following neoadjuvant chemotherapy showed similar results to those of BRPC patients. Further research is needed to identify specific sub-populations of LAPC patients who benefit most from conversion surgery and to minimize postoperative complications.

Lymph node metastasis (LNM) is one of the major prognostic factors in human gastrointestinal carcinomas (GICs). The lymph node-positive patients have poorer survival than node-negative patients. LNM is directly associated with the recurrence and poor survival of patients with GICs. The early detection of LNM in patients and designing effective therapies to suppress LNM may significantly impact the survival of these patients. The rapid progress made in proteomic technologies could be successfully applied to identify molecular targets for cancers at high-throughput levels. LC-MS/MS analysis enables the identification of proteins involved in LN metastasis, which can be utilized for diagnostic and therapeutic applications. This review summarizes the studies on LN metastasis in GICs using proteomic approaches to date.

Delayed gastric emptying (DGE) is a frequent complication after pancreatoduodenectomy. Preoperative factors are limited and controversial. This study aims to identify associated factors related to this complication in the Colombian population.

A retrospective review of a prospectively collected database was conducted. All patients over 18 years of age who underwent pancreaticoduodenectomy were included. Associations with DGE syndrome were evaluated with logistic regression analysis, Odds ratio, and b-coefficient were provided when appropriate.

205 patients were included. Male patients constituted 54.15% (n = 111). 53 patients (25.85%) were diagnosed with DGE syndrome. Smoking habit (OR 17.58 p 0.00 95% CI 7.62-40.51), hydromorphone use > 0.6 mg/daily (OR 11.04 p 0.03 95% CI 1.26-96.66), bilirubin levels > 6 mg/dL (OR 2.51 p 0.02 95% CI 1.12-5.61), and pancreatic fistula type B (OR 2.72 p 0.02 CI 1.74-10.00).

Smoking history, opioid use (hydromorphone > 0.6 mg/Daily), type B pancreatic fistula, and bilirubin levels > 6 mg/dL should be considered as risk factors for DGE.

Pancreatic ductal adenocarcinoma (PDAC) is a common and lethal cancer. From diagnosis to disease staging, response to neoadjuvant therapy assessment and patient surveillance after resection, imaging plays a central role, guiding the multidisciplinary team in decision-planning.

This review discusses the most up-to-date imaging recommendations, typical and atypical findings, and issues related to each step of patient management. Example cases for each relevant condition are presented, and a structured report for disease staging is suggested.

Despite current issues in PDAC imaging at different stages of patient management, the radiologist is essential in the multidisciplinary team, as the conveyor of relevant imaging findings crucial for patient care.

Pancreatic cancer (PC) is a severely malignant cancer variant with high mortality. Since PC has no obvious symptoms, most PC patients are belatedly diagnosed at advanced disease stages. Recently, artificial intelligence (AI) approaches have demonstrated promising prospects for early diagnosis of pancreatic cancer. However, certain non-causal factors (such as intensity and texture appearance variations, also called confounders) tend to induce spurious correlation with PC diagnosis. This undermines the generalization performance and the clinical applicability of the AI-based PC diagnosis approaches. Therefore, we propose a causal intervention based automated method for pancreatic cancer diagnosis with contrast-enhanced computerized tomography (CT) images, where a confounding effects reduction scheme is developed for alleviating spurious correlations to achieve unbiased learning, thereby improving the generalization performance. Specifically, a continuous image generation strategy was developed to simulate wide variations of intensity differences caused by imaging heterogeneities, where Monte Carlo sampling is added to further enhance the continuity of simulated images. Then, to enhance the pancreatic texture variability, a texture diversification method was introduced in conjunction with gradient-based data augmentation. Finally, a causal intervention strategy was proposed to alleviate the adverse confounding effects by decoupling the causal and non-causal factors and combining them randomly. Extensive experiments showed remarkable diagnosis performance on a cross-validation dataset. Also, promising generalization performance with an average accuracy of 0.87 was attained on three independent test sets of a total of 782 subjects. Therefore, the proposed method shows high clinical feasibility and applicability for pancreatic cancer diagnosis.

PW06 [(E)-3-(9-ethyl-9H-carbazol-3-yl)-1-(2,5-dimethoxyphenyl) prop-2-en-1-one], a kind of the carbazole derivative containing chalcone moiety, induced cell apoptosis in human pancreatic carcinoma in vitro. There is no investigation to show that PW06 inhibits cancer cell metastasis in human pancreatic carcinoma in vitro. Herein, PW06 (0.1-0.8 μM) significantly exists in the antimetastatic activities of human pancreatic carcinoma MIA PaCa-2 cells in vitro. Wound healing assay shows PW06 at 0.2 μM suppressed cell mobility by 7.45 and 16.55% at 6 and 24 hours of treatments. PW06 at 0.1 and 0.2 μM reduced cell mobility by 14.72 and 21.8% for 48 hours of treatment. Transwell chamber assay indicated PW06 (0.1-0.2 μM) suppressed the cell migration (decreased 26.67-35.42%) and invasion (decreased 48.51-68.66%). Atomic force microscopy assay shows PW06 (0.2 μM) significantly changed the shape of cell morphology. The gelatin zymography assay indicates PW06 decreased MMP2's and MMP9's activities at 48 hours of treatment. Western blotting assay further confirms PW06 reduced levels of MMP2 and MMP9 and increased protein expressions of EGFR, SOS1, and Ras. PW06 also increased the p-JNK, p-ERK, and p-p38. PW06 increased the expression of PI3K, PTEN, Akt, GSK3α/β, and E-cadherin. Nevertheless, results also show PW06 decreased p-Akt, mTOR, NF-κB, p-GSK3β, β-catenin, Snail, N-cadherin, and vimentin in MIA PaCa-2 cells. The confocal laser microscopy examination shows PW06 increased E-cadherin but decreased vimentin in MIA PaCa-2 cells. Together, our findings strongly suggest that PW06 inhibited the p-Akt/mTOR/NF-κB/MMPs pathways, increased E-cadherin, and decreased N-cadherin/vimentin, suppressing the migration and invasion in MIA PaCa-2 cells in vitro.

Pancreatic ductal adenocarcinoma (PDA) is partly initiated through the transdifferentiation of acinar cells to metaplastic ducts that act as precursors of neoplasia and cancer. Tuft cells are solitary chemosensory cells not found in the normal pancreas but arise in metaplasia and neoplasia, diminishing as neoplastic lesions progress to carcinoma. Metaplastic tuft cells (mTCs) function to suppress tumor progression through communication with the tumor microenvironment, but their fate during progression is unknown. To determine the fate of mTCs during PDA progression, we have created a lineage tracing model that uses a tamoxifen-inducible tuft-cell specific Pou2f3CreERT/+ driver to induce transgene expression, including the lineage tracer tdTomato or the oncogene Myc. mTC lineage trace models of pancreatic neoplasia and carcinoma were used to follow mTC fate. We found that mTCs, in the carcinoma model, transdifferentiate into neural-like progenitor cells (NRPs), a cell type associated with poor survival in PDA patients. Using conditional knock-out and overexpression systems, we found that Myc activity in mTCs is necessary and sufficient to induce this Tuft-to-Neuroendocrine-Transition (TNT).

This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p < 0.001) and T2w MR (4.41 vs. 7.42, p < 0.001).

The objective of this study was to evaluate the clinical feasibility of deep learning reconstruction-accelerated thin-slice single-breath-hold half-Fourier single-shot turbo spin echo imaging (HASTEDL) for detecting pancreatic lesions, in comparison with two conventional T2-weighted imaging sequences: compressed-sensing HASTE (HASTECS) and BLADE.

From March 2022 to January 2023, a total of 63 patients with suspected pancreatic-related disease underwent the HASTEDL, HASTECS, and BLADE sequences were enrolled in this retrospectively study. The acquisition time, the pancreatic lesion conspicuity (LCP), respiratory motion artifact (RMA), main pancreatic duct conspicuity (MPDC), overall image quality (OIQ), signal-to-noise ratio (SNR), and contrast-noise-ratio (CNR) of the pancreatic lesions were compared among the three sequences by two readers.

The acquisition time of both HASTEDL and HASTECS was 16 s, which was significantly shorter than that of 102 s for BLADE. In terms of qualitative parameters, Reader 1 and Reader 2 assigned significantly higher scores to the LCP, RMA, MPDC, and OIQ for HASTEDL compared to HASTECS and BLADE sequences; As for the quantitative parameters, the SNR values of the pancreatic head, body, tail, and lesions, the CNR of the pancreatic lesion measured by the two readers were also significantly higher for HASTEDL than for HASTECS and BLADE sequences.

Compared to conventional T2WI sequences (HASTECS and BLADE), deep-learning reconstructed HASTE enables thin slice and single-breath-hold acquisition with clinical acceptable image quality for detection of pancreatic lesions.

Cholera, caused by Vibrio cholerae, is a severe diarrheal disease that necessitates prompt diagnosis and effective treatment. This review comprehensively examines various diagnostic methods, from traditional microscopy and culture to advanced nucleic acid testing like polymerase spiral reaction and rapid diagnostic tests, highlighting their advantages and limitations. Additionally, we explore evolving treatment strategies, with a focus on the challenges posed by antibiotic resistance due to the activation of the SOS response pathway in V. cholerae. We discuss promising alternative treatments, including low-pressure plasma sterilization, bacteriophages, and selenium nanoparticles. The paper emphasizes the importance of multidisciplinary approaches combining novel diagnostics and treatments in managing and preventing cholera, a persistent global health challenge. The current re-emergent 7th pandemic of cholera commenced in 1961 and shows no signs of abeyance. This is probably due to the changing genetic profile of V. cholerae concerning bacterial pathogenic toxins. Given this factor, we argue that the disease is effectively re-emergent, particularly in Eastern Mediterranean countries such as Lebanon, Syria, etc. This review considers the history of the current pandemic, the genetics of the causal agent, and current treatment regimes. In conclusion, cholera remains a significant global health challenge that requires prompt diagnosis and effective treatment. Understanding the history, genetics, and current treatments is crucial in effectively addressing this persistent and re-emergent disease.

Pancreatic cancer (PC) has the poorest prognosis compared to other common cancers because of its aggressive nature, late detection, and resistance to systemic treatment. In this study, we aimed to identify novel biomarkers for PC patients and further explored their function in PC progression. We analyzed GSE62452 and GSE28735 datasets, identifying 35 differentially expressed genes (DEGs) between PC specimens and non-tumors. Based on 35 DEGs, we performed machine learning and identified eight diagnostic genes involved in PC progression. Then, we further screened three critical genes (CTSE, LAMC2 and SLC6A14) using three GEO datasets. A new diagnostic model was developed based on them and showed a strong predictive ability in screen PC specimens from non-tumor specimens in GEO, TCGA datasets and our cohorts. Then, clinical assays based on TCGA datasets indicated that the expression of LAMC2 and SLC6A14 was associated with advanced clinical stage and poor prognosis. The expressions of LAMC2 and SLC6A14, as well as the abundances of a variety of immune cells, exhibited a significant positive association with one another. Functionally, we confirmed that SLC6A14 was highly expressed in PC and its knockdown suppressed the proliferation, migration, invasion and EMT signal via regulating Wnt/β-catenin signaling pathway. Overall, our findings developed a novel diagnostic model for PC patients. SLC6A14 may promote PC progression via modulating Wnt/β-catenin signaling. This work offered a novel and encouraging new perspective that holds potential for further illuminating the clinicopathological relevance of PC as well as its molecular etiology.

Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.

The segmentation and recognition of pancreatic tumors are crucial tasks in the diagnosis and treatment of pancreatic diseases. However, due to the relatively small proportion of the pancreas in the abdomen and significant shape and size variations, pancreatic tumor segmentation poses considerable challenges.

To construct a network model that combines a pyramid pooling module with Inception architecture and SE attention mechanism (PIS-Unet), and observe its effectiveness in pancreatic tumor images segmentation, thereby providing supportive recommendations for clinical practitioners.

A total of 303 patients with histologically confirmed pancreatic cystic neoplasm (PCN), including serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN), from Shanghai Changhai Hospital between March 2011 and November 2021 were included. A total of 1792 T2-weighted imaging (T2WI) slices were used to build a CNN model. The model employed a pyramid pooling Inception module with a fused attention mechanism. The attention mechanism enhanced the network's focus on local features, while the Inception module and pyramid pooling allowed the network to extract features at different scales and improve the utilization efficiency of global information, thereby effectively enhancing network performance.

Using three-fold cross-validation, the model constructed by us achieved a dice score of 85.49 ± 2.02 for SCN images segmentation, and a dice score of 87.90 ± 4.19 for MCN images segmentation.

This study demonstrates that using deep learning networks for the segmentation of PCNs yields favorable results. Applying this network as an aid to physicians in PCN diagnosis shows potential for clinical applications.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly form of cancer, with limited progress in 5-year survival rates despite significant research efforts. The main challenges in treating PDAC include difficulties in early detection, and resistance to current therapeutic approaches due to aggressive molecular and microenvironment features. These challenges emphasize the importance of identifying clinically validated biomarkers for early detection and clinical management. Extracellular vesicles (EVs), particularly exosomes, have emerged as crucial mediators of intercellular communication by transporting molecular cargo. Recent research has unveiled their role in initiation, metastasis, and chemoresistance of PDAC. Consequently, utilizing EVs in liquid biopsies holds promise for the identification of biomarkers for early detection, prognosis, and monitoring of drug efficacy. However, numerous limitations, including challenges in isolation and characterization of homogeneous EVs populations, as well as the absence of standardized protocols, can affect the reliability of studies involving EVs as biomarkers, underscoring the necessity for a prudent approach. EVs have also garnered considerable attention as a promising drug delivery system and novel therapy for tumors. The loading of biomolecules or chemical drugs into exosomes and their subsequent delivery to target cells can effectively impede tumor progression. Nevertheless, there are obstacles that must be overcome to ensure the accuracy and efficacy of therapies relying on EVs for the treatment of tumors. In this review, we examine both recent advancements and remaining obstacles, exploring the potential of utilizing EVs in biomarker discovery as well as for the development of drug delivery vehicles.

Pancreatic cancers are the third leading cause of cancer-related death in the USA and outcomes remain poor despite improvements in imaging and treatment paradigms. Currently, computed tomography (CT) and magnetic resonance imaging (MRI) are frequently utilized for staging and restaging of these malignancies, but positron emission tomography (PET)/CT can play a role in troubleshooting and improve whole-body staging. PET/MRI is a novel imaging modality that allows for simultaneous acquisition of PET and MRI images, leading to improved image quality and potential increased sensitivity. Early studies suggest that PET/MRI may play a larger role in pancreatic cancer imaging in future. This manuscript will briefly discuss current imaging approaches to pancreatic cancer and outline existing evidence and published data supporting the use of PET/MRI for pancreatic cancers.

Pancreatic diseases are difficult to diagnose due to their insidious onset and complex pathophysiological developmental characteristics. In recent years, dual-energy computed tomography (DECT) imaging technology has rapidly advanced. DECT can quantitatively extract and analyze medical imaging features and establish a correlation between these features and clinical results. This feature enables the adoption of more modern and accurate clinical diagnosis and treatment strategies for patients with pancreatic diseases so as to achieve the goal of non-invasive, low-cost, and personalized treatment. The purpose of this review is to elaborate on the application of DECT for the diagnosis, biological characterization, and prediction of the survival of patients with pancreatic diseases (including pancreatitis, pancreatic cancer, pancreatic cystic tumor, pancreatic neuroendocrine tumor, and pancreatic injury) and to summarize its current limitations and future research prospects.

Pancreatic cancer is characterized by a poor prognosis, with its five-year survival rate lower than that of any other cancer type. Gemcitabine, a standard treatment for pancreatic cancer, often has poor outcomes for patients as a result of chemoresistance. Therefore, novel therapeutic targets must be identified to overcome gemcitabine resistance. Here, we found that SLC38A5, a glutamine transporter, is more highly overexpressed in gemcitabine-resistant patients than in gemcitabine-sensitive patients. Furthermore, the deletion of SLC38A5 decreased the proliferation and migration of gemcitabine-resistant PDAC cells. We also found that the inhibition of SLC38A5 triggered the ferroptosis signaling pathway via RNA sequencing. Also, silencing SLC38A5 induced mitochondrial dysfunction and reduced glutamine uptake and glutathione (GSH) levels, and downregulated the expressions of GSH-related genes NRF2 and GPX4. The blockade of glutamine uptake negatively modulated the mTOR-SREBP1-SCD1 signaling pathway. Therefore, suppression of SLC38A5 triggers ferroptosis via two pathways that regulate lipid ROS levels. Similarly, we observed that knockdown of SLC38A5 restored gemcitabine sensitivity by hindering tumor growth and metastasis in the orthotopic mouse model. Altogether, our results demonstrate that SLC38A5 could be a novel target to overcome gemcitabine resistance in PDAC therapy.

The therapeutic properties of 5-aminolevulinic acid (5-ALA) have been extensively studied for cancer detection and treatment using photodynamic therapy (PDT). When administered externally, 5-ALA is converted to protoporphyrin IX (PpIX) in cancer cells, which generates reactive oxygen species (ROS) upon exposure to light. This process enables targeted cell death induction and cancer detection. Given the highly conserved nature of heme biosynthesis over billions of years, we hypothesized that natural mechanisms might exist to prevent excessive accumulation of PpIX or heme resulting from 5-ALA overload. Therefore, we anticipated alterations in protein expression profiles upon exogenous administration of 5-ALA. To understand cellular responses to 5-ALA, we investigated protein expression changes and identified OR1B1 as a promising target in bladder, prostate, lung, and cervical cancer cells. OR1B1 expression was observed only with 5-ALA and ferrous chloride, highlighting the central role of heme in this discovery. Immunofluorescence and electron microscopy confirmed OR1B1's sub-cellular localization. These findings suggest that 5-ALA transformation in cancer cells and OR1B1 expression have potential for enhancing cancer detection and developing alternative treatments, including immunotherapy. This approach overcomes the limitations of PDT and opens new avenues for effective and targeted cancer interventions.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal cancers, and developing an efficient and reliable approach for its early-stage diagnosis is urgently needed. Precancerous lesions of PDAC, such as pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMN), arise through multiple steps of driver gene alterations in KRAS, TP53, CDKN2A, SMAD4, or GNAS. Hallmark mutations play a role in tumor initiation and progression, and their detection in bodily fluids is crucial for diagnosis. Recently, liquid biopsy has gained attention as an approach to complement pathological diagnosis, and in addition to mutation signatures in cell-free DNA, cell-free RNA, and extracellular vesicles have been investigated as potential diagnostic and prognostic markers. Integrating such molecular information to revise the diagnostic criteria for pancreatic cancer can enable a better understanding of the pathogenesis underlying inter-patient heterogeneity, such as sensitivity to chemotherapy and disease outcomes. This review discusses the current diagnostic approaches and clinical applications of genetic analysis in pancreatic cancer and diagnostic attempts by liquid biopsy and molecular analyses using pancreatic juice, duodenal fluid, and blood samples. Emerging knowledge in the rapidly advancing liquid biopsy field is promising for molecular profiling and diagnosing pancreatic diseases with significant diversity.

Integrin α6 has been considered a promising biomarker, is overexpressed in many tumors, and plays a vital role in tumor formation, recurrence, and metastasis. In this study, we identified a novel high-affinity integrin α6-targeted peptide named RD2 (Arg-Trp-Tyr-Asp-PEG4)2-Lys-Lys and developed a 18F-radiolabeled peptide tracer ([18F]-AlF-NOTA-RD2) and evaluated its potential application in positron emission tomography (PET) imaging of pancreatic cancer. [18F]-AlF-NOTA-RD2 was produced using GMP (Good Manufacturing Practice of Medical Products)-compliant automatic radiosynthesis on a single GE FASTLab2 cassette-type synthesis module. The stability of [18F]-AlF-NOTA-RD2 was analyzed in phosphate-buffered saline (PBS) and fetal bovine serum (FBS). The cell uptake assay of the tracer was assessed using PANC-1 cells. In addition, small-animal PET imaging and biodistribution studies of [18F]-AlF-NOTA-RD2 were performed in pancreatic cancer subcutaneous tumor-bearing mice. The PET tracer [18F]-AlF-NOTA-RD2 was obtained with a radiochemical yield of 23.7 ± 4.7%, radiochemical purity of >99%, and molar activity of 165.7 ± 59.1 GBq/μmol. [18F]-AlF-NOTA-RD2 exhibited good in vitro stability in PBS and FBS. LogP octanol water value for the tracer was -2.28 ± 0.05 (n = 3). The binding affinity of RD2 to the integrin α6 protein (Kd = 0.13 ± 3.65 μM, n = 3) was significantly higher than that of the RWY (CRWYDENAC) (Kd = 6.97 ± 1.44 μM, n = 3). Small-animal PET imaging and biodistribution also revealed that [18F]-AlF-NOTA-RD2 displayed rapid and good tumor uptake and lower liver background uptake in PANC-1 tumor-bearing mice. [18F]-AlF-NOTA-RD2 showed significant radioactivity accumulation in tumors and was successfully blocked by NOTA-RD2. Compared with [18F]-FDG, [18F]-AlF-NOTA-RD2 PET imaging and biodistribution studies in PANC-1 xenograft tumor-bearing mice confirmed a good tumor-to-muscle ratio (8.69 ± 2.03 vs 1.41 ± 0.23, respectively) at 0.5 h and (2.99 ± 3.02 vs 1.43 ± 0.17, respectively) at 1 h post injection. Autoradiography of human pancreatic cancer tumor tissues further confirmed high accumulation of [18F]-AlF-NOTA-RD2. In summary, we developed an optimized integrin α6-targeted imaging tracer and obtained high radioactivity products with a cassette-type synthesis module; moreover, the tracer exhibited good binding affinity with integrin α6 and good target specificity for PANC-1 cells in xenograft pancreatic tumor-bearing mice, demonstrating its promising application as a noninvasive PET radiotracer of integrin α6 expression in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the 4th leading cause of cancer-related death and has a poor 5-year overall survival. The superior therapeutic benefits of combination or co-administration of drugs as intraperitoneal chemotherapy have increased interest in developing strategies to deliver chemotherapeutic agents to patients safely. In this study, we prepared a gel comprising the thermosensitive poly(lactide-co-glycolide)-b-poly(ethylene glycol)-b-poly(lactide-co-glycolide) (PLGA-PEG-PLGA) polymer and gemcitabine (GEM), which is currently used as the primary chemotherapy for PDAC and rapamycin (RAPA), a mammalian TOR (mTOR) inhibitor, to deliver the drug through intraperitoneal injection. We performed in vitro cytotoxicity experiments to verify the synergistic effects of the two drugs at different molar ratios and characterized the physicochemical properties of the GEM, RAPA, and GEM/RAPA-loaded thermosensitive PLGA-PEG-PLGA gels, hereafter referred to as (g(G), g(R), and g(GR)), respectively. The g(GR) comprising PLGA-PEG-PLGA polymer (25% w/v) and GEM and RAPA at a molar ratio of 11:1 showed synergism and was optimized. An in vitro cytotoxicity assay was performed by treating Panc-1-luc2 tumor spheroids with g(G), g(R), or g(GR). The g(GR) treatment group showed a 2.75-fold higher inhibition rate than the non-treated (NT) and vehicle-treated groups. Furthermore, in vivo drug release assay in mice by intraperitoneal injection of g(G), g(R), or g(GR) showed a more rapid release rate of GEM than RAPA, similar to the in vitro release pattern. The drugs in the gel were released faster in vivo than in vitro and degraded in 48 h. In addition, g(GR) showed the highest anti-tumor efficacy with no toxicity to mice. These results provide evidence for the safety and efficacy of g(GR) for intraperitoneal drug delivery. This study will assist in developing and clinically administering topical anti-cancer formulations.

Carbohydrate antigen (CA) 19-9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites-based diagnostic calculator for detecting PC with high accuracy.

A targeted quantitative approach of direct flow injection-tandem mass spectrometry combined with liquid chromatography-tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQ™ p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS-DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2-by-2 contingency table of predicted probabilities obtained from the models and actual diagnoses.

Predictive values obtained through OPLS-DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS-DA. The SPLS-DA-obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively.

We successfully developed a 76 metabolome-based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases.