ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas.

The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs. Moreover, we observed significant upregulation of the peroxisome marker ABCD1 in both primary and metastatic SPN samples compared with normal pancreas and NETs. To further investigate the potential utility of ABCD1 as a diagnostic marker for SPN via immunohistochemistry staining, we conducted verification in a large-scale patient cohort with pancreatic tumors, including 127 SPN (111 primary, 16 metastatic samples), 108 NET (98 nonfunctional pancreatic neuroendocrine tumor, NF-NET, and 10 functional pancreatic neuroendocrine tumor, F-NET), 9 acinar cell carcinoma (ACC), 3 pancreatoblastoma (PB), 54 pancreatic ductal adenocarcinoma (PDAC), 20 pancreatic serous cystadenoma (SCA), 19 pancreatic mucinous cystadenoma (MCA), 12 pancreatic ductal intraepithelial neoplasia (PanIN) and 5 intraductal papillary mucinous neoplasm (IPMN) samples. Our results indicate that ABCD1 holds promise as an easily applicable diagnostic marker with exceptional efficacy (AUC=0.999, sensitivity=99.10%, specificity=100%) for differentiating SPN from NET and other pancreatic neoplasms through immunohistochemical staining.

Preoperative pancreatic stent placement before the enucleation of insulinoma located in the head and neck of the pancreas in proximity to the main pancreatic duct: study protocol for a multicentre randomised clinical trial in Chinese tertiary medical centres.

INTRODUCTION: The surgical intervention approach to insulinomas in proximity to the main pancreatic duct remains controversial. Standard pancreatic resection is recommended by several guidelines; however, enucleation (EN) still attracts surgeons with less risk of late exocrine/endocrine insufficiency, despite a higher postoperative pancreatic fistula (POPF) rate. Recently, the efficacy and safety of preoperative pancreatic stent placement before the EN have been demonstrated. Thus, a multicentre open-label study is being conducted to evaluate the efficacy and safety of stent placement in improving the outcome of EN of insulinomas in proximity to the main pancreatic duct. METHODS AND ANALYSIS: This is a prospective, randomised, open-label, superiority clinical trial conducted at multiple tertiary centres in China. The major eligibility criterion is the presence of insulinoma located in the head and neck of the pancreas in proximity (≤2 mm) to the main pancreatic duct. Blocked randomisation will be performed to allocate patients into the stent EN group and the direct EN group. Patients in the stent EN group will go through stent placement by the endoscopist within 24 hours before the EN surgery, whereas other patients will receive EN surgery directly. The primary outcome is the assessment of the superiority of stent placement in reducing POPF rate measured by the International Study Group of Pancreatic Surgery standard. Both interventions will be performed in an inpatient setting and regular follow-up will be performed. The primary outcome (POPF rate) will be tested for superiority with the Χ2 test. The difference in secondary outcomes between the two groups will be analysed using appropriate tests. ETHICS AND DISSEMINATION: The study has been approved by the Peking Union Medical College Hospital Institutional Review Board (K23C0195), Ruijin Hospital Ethics Committee (2023-314), Peking University First Hospital Ethics Committee (2024033-001), Institutional Review Board of Xuanwu Hospital of Capital Medical University (2023223-002), Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University (XJTU1AF2023LSK-473), Institutional Review Board of Tongji Medical College Tongji Hospital (TJ-IRB202402059), Ethics Committee of Tongji Medical College Union Hospital (2023-0929) and Shanghai Cancer Center Institutional Review Board (2309282-16). The results of the study will be published in an international peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT05523778.

Development and validation of modified Liaoning score for predicting the prognosis of liver cirrhosis: a retrospective, international multicenter, observational study.

BACKGROUND: Liaoning score has been developed and validated to predict the risk of esophageal varices in liver cirrhosis. This study aimed to further modify the Liaoning score by combining clinical and laboratory parameters to predict the long-term outcome of cirrhotic patients. METHODS: First, 474 cirrhotic patients were retrospectively enrolled from Shenyang, China as the training cohort. Independent predictors for death were identified by competing risk analyses, and then a new prognostic model, called as modified Liaoning score, was developed. Its performance was externally validated at three centers from Fuzhou, China (n = 1944), Jinan, China (n = 485), and São Paulo, Brazil (n = 221). RESULTS: Age, total bilirubin (TBIL), albumin (ALB), serum creatinine (SCr), and Liaoning score were independently associated with death in the training cohort. Modified Liaoning score = 0.159×Liaoning score + 0.010×TBIL(µmol/L)+0.029×age(years)+0.011×SCr(µmol/L)-0.037×ALB(g/L). The area under curve of modified Liaoning score was 0.714 (95%CI = 0.655-0.773), which was higher than that of Child-Pugh score (0.707, 95%CI = 0.645-0.770), MELD score (0.687, 95%CI = 0.623-0.751), and Liaoning score (0.583, 95%CI = 0.513-0.654). A modified Liaoning score of ≥ 1.296 suggested a higher cumulative incidence of death in liver cirrhosis (p < 0.001). Modified Liaoning score still had the highest prognostic performance in Chinese and Brazilian validation cohorts. CONCLUSIONS: Modified Liaoning score can be considered for predicting the long-term outcome of cirrhotic patients.

Gut resistome profiling reveals high diversity and fluctuations in pancreatic cancer cohorts.

Background: Pancreatic cancer is one of the deadliest cancer, with a 5-year overall survival rate of 11%. Unfortunately, most patients are diagnosed with advanced stage by the time they present with symptoms. In the past decade, microbiome studies have explored the association of pancreatic cancer with the human oral and gut microbiomes. However, the gut microbial antibiotic resistance genes profiling of pancreatic cancer patients was never reported compared to that of the healthy cohort. Results: In this study, we addressed the gut microbial antibiotic resistance genes profile using the metagenomic data from two online public pancreatic cancer cohorts. We found a high degree of data concordance between the two cohorts, which can therefore be used for cross-sectional comparisons. Meanwhile, we used two strategies to predict antibiotic resistance genes and compared the advantages and disadvantages of these two approaches. We also constructed microbe-antibiotic resistance gene networks and found that most of the hub nodes in the networks were antibiotic resistance genes. Conclusions: In summary, we describe the panorama of antibiotic resistance genes in the gut microbes of patients with pancreatic cancer. We hope that our study will provide new perspectives on treatment options for the disease.

Learning deep representation and discriminative features for clustering of multi-layer networks.

The multi-layer network consists of the interactions between different layers, where each layer of the network is depicted as a graph, providing a comprehensive way to model the underlying complex systems. The layer-specific modules of multi-layer networks are critical to understanding the structure and function of the system. However, existing methods fail to characterize and balance the connectivity and specificity of layer-specific modules in networks because of the complicated inter- and intra-coupling of various layers. To address the above issues, a joint learning graph clustering algorithm (DRDF) for detecting layer-specific modules in multi-layer networks is proposed, which simultaneously learns the deep representation and discriminative features. Specifically, DRDF learns the deep representation with deep nonnegative matrix factorization, where the high-order topology of the multi-layer network is gradually and precisely characterized. Moreover, it addresses the specificity of modules with discriminative feature learning, where the intra-class compactness and inter-class separation of pseudo-labels of clusters are explored as self-supervised information, thereby providing a more accurate method to explicitly model the specificity of the multi-layer network. Finally, DRDF balances the connectivity and specificity of layer-specific modules with joint learning, where the overall objective of the graph clustering algorithm and optimization rules are derived. The experiments on ten multi-layer networks showed that DRDF not only outperforms eight baselines on graph clustering but also enhances the robustness of algorithms.

Origins of the Ediacaran Doushantuo High-Grade Primary Phosphorites at Kaiyang, Guizhou Province, China.

The Ediacaran Doushantuo phosphate deposit in Kaiyang, Guizhou Province, China, contains thick phosphate ores. Most of the ores are reconstituted phosphorite, and there have been few studies of the primary phosphorites, which has led to controversy regarding the origins and nature of mineralization of these phosphate-rich deposits. We identified high-grade primary phosphorites in the Kaiyang area and undertook a stratigraphic, petrological, sedimentological, geochemical, and isotopic study of these rocks. Moving up-section, the Longshui phosphate ore deposit comprises granular, micritic, stromatolitic, honeycomb, and sandy phosphorites. The first four types of phosphorite contain abundant biological structures, such as spherical, lobe-like, and amorphous forms. These are mainly fossils of benthic multicellular red algae, along with other types of algae. These fossils comprise >70% of the phosphorites, indicating that these are protist phosphorites. The ores are massive, unstratified, and contain numerous layered cavity structures, indicating that the ore bed was originally a reef. The phosphorites have P2O5 contents of 38.6-40.2 wt %, with an average of 38.9 wt %. The Al2O3 + TiO2 values are 0.02-0.44 wt %. The δ18O values of the samples vary from 13.76 to 16.57‰, with an average of 14.60‰, and δ13C values range from -15.789 to -8.697‰, with an average of -13.133‰. The samples exhibit rare-earth element patterns that are enriched with middle rare-earth elements and have strongly negative Ce anomalies. The geochemical features show that the reef was deposited in clear and oxidized waters. The discovery of this high-grade protist phosphorite shows that the involvement of algae was key to the formation of the Kaiyang phosphate-rich deposit.

Comparison of Treatment and Prognosis Between Follicular Variant Papillary Thyroid Carcinoma and Classical Papillary Thyroid Carcinoma.

This cohort study evaluated the associations of different treatments with the prognosis of follicular variant papillary thyroid carcinoma (FVPTC) and classical papillary thyroid carcinoma (CPTC) patients. The data of 69034 PTC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. The 5-year mortality of CPTC and FVPTC patients receiving surgery, radiation and combination therapy were compared. The univariable and multivariable cox proportional risk models explored the associations between different treatments and the 5-year mortality in CPTC and FVPTC patients. The 5-year mortality of CPTC patients was 2.81% and FVPTC patients was 2.47%. Compared with CPTC receiving lobectomy and/or isthmectomy, those not receiving surgery were associated with increased risk of 5-year mortality [Hazards ratio (HR)=3.27, 95% confidence interval (CI): 2.55-4.20] while total thyroidectomy was correlated with reduced risk of 5-year mortality (HR=0.67, 95%CI: 0.55-0.80). Radioactive iodine (RAI) was linked with decreased risk of 5-year mortality in CPTC patients (HR=0.57, 95%CI: 0.50-0.65). CPTC patients undergoing both surgery and radiation were related to decreased risk of 5-year mortality compared with those receiving surgery only (HR=0.55, 95%CI: 0.48-0.63). CPTC patients receiving neither surgery nor radiation (HR=4.53, 95%CI: 3.72-5.51) or those receiving radiation (HR=1.98, 95%CI: 1.13-3.48) were correlated with elevated risk of 5-year mortality. The elevated risk of 5-year mortality in FVPTC patients was reduced in those undergoing RAI (HR=0.63, 95%CI: 0.51-0.76). In conclusion, combination therapy was associated with decreased risk of 5-year mortality in CPTC and FVPTC patients, which might provide a reference for the management of these patients.

Local resection for solid pseudopapillary neoplasms of the pancreas shows improved postoperative gastrointestinal function and reduced mental stress: a multiquestionnaire survey from a large cohort.

BACKGROUND: Solid pseudopapillary neoplasm (SPN) of the pancreas is a rare, low-grade malignant pancreatic tumor with a highly favorable prognosis. Most SPN patients are young and middle-aged women. The main controversial topic for SPN is local resection (LR) versus radical resection (RR). Theoretically, LR could lead to better gastrointestinal function (GIF) and less mental stress. However, no data is available to support this hypothesis. METHODS: All SPN patients undergoing surgical treatment in Peking Union Medical College Hospital from 2001 to 2021 were included in the study. A cross-sectional online multiquestionnaire survey containing 110 questions was sent to them (Clinicaltrial.org, NCT05604716). This online multiquestionnaire survey focused on GIF and mental stress and consisted of eight questionnaires. Multiple linear regression analysis was conducted to identify independent factors impacting GIF and mental stress. RESULTS: A total of 183 cases provided valid results. Among them, 46 patients (25.1%) underwent LR, and 137 (74.9%) underwent RR. Ninety-four cases (51.4%) underwent minimally invasive surgery (MIS), while 89 (48.6%) underwent open surgery. The average GSRS score of the patients was 1.9±0.7, indicating that most suffered from mild gastrointestinal dysfunction. The scores of PHQ-9 and GAD-7 in 16 patients (8.7%) and 27 (14.8%) patients, respectively, were beyond 10.0, which indicated clinical depression and anxiety. Additionally, 19 (10.4%) patients reported poor ability to work, and 31(16.9%) patients had significant body image concerns. Compared to other clinicopathological characteristics, LR (LR vs. RR: PHQ-9 score, P =0.018; WAI average score, P =0.010; EORTC QLQ-C30, nine subdomains, P <0.05; GSRS average score, P =0.006) and MIS (MIS vs. open surgery: EORTC QLQ-C30, three subdomains, P <0.05; GSRS average score, P =0.006) were the most significant factors predicting improved GIF and reduced mental stress. CONCLUSIONS: This study systematically presents postoperative GIF and mental stress of SPN patients using validated multiquestionnaires for the first time. It provides solid evidence that LR and MIS can improve GIF and reduce mental stress after surgery for SPN patients, which could be helpful for the surgeons to make more personalized surgical plans for their patients.

Application and evaluation of surgical tool and tool tip recognition based on Convolutional Neural Network in multiple endoscopic surgical scenarios.

BACKGROUND: In recent years, computer-assisted intervention and robot-assisted surgery are receiving increasing attention. The need for real-time identification and tracking of surgical tools and tool tips is constantly demanding. A series of researches focusing on surgical tool tracking and identification have been performed. However, the size of dataset, the sensitivity/precision, and the response time of these studies were limited. In this work, we developed and utilized an automated method based on Convolutional Neural Network (CNN) and You Only Look Once (YOLO) v3 algorithm to locate and identify surgical tools and tool tips covering five different surgical scenarios. MATERIALS AND METHODS: An algorithm of object detection was applied to identify and locate the surgical tools and tool tips. DarkNet-19 was used as Backbone Network and YOLOv3 was modified and applied for the detection. We included a series of 181 endoscopy videos covering 5 different surgical scenarios: pancreatic surgery, thyroid surgery, colon surgery, gastric surgery, and external scenes. A total amount of 25,333 images containing 94,463 targets were collected. Training and test sets were divided in a proportion of 2.5:1. The data sets were openly stored at the Kaggle database. RESULTS: Under an Intersection over Union threshold of 0.5, the overall sensitivity and precision rate of the model were 93.02% and 89.61% for tool recognition and 87.05% and 83.57% for tool tip recognition, respectively. The model demonstrated the highest tool and tool tip recognition sensitivity and precision rate under external scenes. Among the four different internal surgical scenes, the network had better performances in pancreatic and colon surgeries and poorer performances in gastric and thyroid surgeries. CONCLUSION: We developed a surgical tool and tool tip recognition model based on CNN and YOLOv3. Validation of our model demonstrated satisfactory precision, accuracy, and robustness across different surgical scenes.

Comparing the clinical value of baseline [68 Ga]Ga-FAPI-04 PET/CT and [18F]F-FDG PET/CT in pancreatic ductal adenocarcinoma: additional prognostic value of the distal pancreatitis.

PURPOSE: Anatomical and molecular staging strategies are needed for the personalized treatment of localized pancreatic ductal adenocarcinoma (PDAC). This study evaluated the performance of [68 Ga]Ga-FAPI-04 and [18F]F-FDG PET/CT on the disease staging and prognostic value of patients with localized PDAC on contrast-enhanced (CE)-CT images. METHODS: Patients with suspected localized PDAC on CE-CT were recruited for static [68 Ga]Ga-FAPI-04 and 18[F]F-FDG and PET/CT, and select patients underwent simultaneous 60-min dynamic 68 Ga-FAPI-04 PET/CT. The diagnostic and staging performances of the static PET/CT results were evaluated by delineating regions of interest in the primary tumor, whole pancreas, and distal pancreas in both types of scans and then evaluating correlations between the PET/CT findings and clinicopathological characteristics. Furthermore, Kaplan-Meier and hazard ratio (log-rank) methods were used to evaluate the prognostic value of the combined dynamic [68 Ga]Ga-FAPI-04 and static [18F]F-FDG PET/CT method. RESULTS: We included 49 patients with histologically confirmed PDAC adenocarcinomas; 32 underwent 60-min dynamic [68 Ga]Ga-FAPI-04 PET/CT imaging simultaneously. The static [68 Ga]Ga-FAPI-04 method had significantly higher accuracy and uptake values than the static [18F]F-FDG method for primary PDAC lesions, metastatic lymph nodes, and distal metastases. Furthermore, 18.4% and 10.2% of the patients' stages changed after using the [68 Ga]Ga-FAPI-04 and [18F]F-FDG PET/CT methodologies, respectively, compared to the CE-CT-designated stage. The Ki values obtained from dynamic [68 Ga]Ga-FAPI-04 PET/CT did not differ between PDAC and distal obstructive pancreatitis lesions. Pathologically enlarged tumor size, poor differentiation, and perineural invasion were associated with increased [68 Ga]Ga-FAPI-04 uptake but not with [18F]F-FDG uptake. The preoperative prognostic performance of [68 Ga]Ga-FAPI-04 was better than that of [18F]F-FDG. Interestingly, combined [68 Ga]Ga-FAPI-04 and [18F]F-FDG uptake results in the whole pancreas could further stratify patients based on their postoperative prognosis. CONCLUSION: 6[68 Ga]Ga-FAPI-04 PET/CT was more sensitive and accurate than [18F]F-FDG PET/CT for tumor, node, and metastasis staging of PDAC identified on CE-CT. Additionally, [68 Ga]Ga-FAPI-04 uptake was significantly associated with pathologically aggressive tumor features. Combined [68 Ga]Ga-FAPI-04 and [18F]F-FDG PET/CT findings improved the prognostic value, potentially providing a non-invasive guide for clinical management. Finally, increased fibroblast activity in PDAC-induced obstructive pancreatitis may be associated with poor patient survival rates.

Multi-View Clustering With Graph Learning for scRNA-Seq Data.

Advances in single-cell biotechnologies have generated the single-cell RNA sequencing (scRNA-seq) of gene expression profiles at cell levels, providing an opportunity to study cellular distribution. Although significant efforts developed in their analysis, many problems remain in studying cell types distribution because of the heterogeneity, high dimensionality, and noise of scRNA-seq. In this study, a multi-view clustering with graph learning algorithm (MCGL) for scRNA-seq data is proposed, which consists of multi-view learning, graph learning, and cell type clustering. In order to avoid a single feature space of scRNA-seq being inadequate to comprehensively characterize the functions of cells, MCGL constructs the multiple feature spaces and utilizes multi-view learning to comprehensively characterize scRNA-seq data from different perspectives. MCGL adaptively learns the similarity graphs of cells that overcome the dependence on fixed similarity, transforming scRNA-seq analysis into the analysis of multi-view clustering. MCGL decomposes the networks of cells into view-specific and common networks in multi-view learning, which better characterizes the topological relationship of cells. MCGL simultaneously utilizes multiple types of cell-cell networks and fully exploits the connection relationship between cells through the complementarity between networks to improve clustering performance. The graph learning, graph factorization, and cell-type clustering processes are accomplished simultaneously under one optimization framework. The performance of the MCGL algorithm is validated with ten scRNA-seq datasets from different scales, and experimental results imply that the proposed algorithm significantly outperforms fourteen state-of-the-art scRNA-seq algorithms.

Protocol to isolate human normal and neoplastic pancreatic cells for single-cell omic analyses.

The high-throughput sequencing at single-cell resolution requires high-quality samples of inputted 100-10,000 cells with at least 80%-90% viability according to the applied platform. Here, we present a protocol for single-cell isolation from normal and neoplastic human pancreas. We describe steps for sample harvesting, procurement, tissue digestion, and cell purification. Subsequently, isolated cells can be further applied to single-cell profiling, for example, single-cell RNA sequencing and single-cell ATAC-seq using 10× Genomics platform. For complete details on the use and execution of this protocol, please refer to Peng et al. (2019)1 and Li et al. (2021).2.

Application of metagenomic sequencing of drainage fluid in rapid and accurate diagnosis of postoperative intra-abdominal infection: a diagnostic study.

BACKGROUND: Postoperative intra-abdominal infection (PIAI) is one of the most serious complications of abdominal surgery, increasing the risk of postoperative morbidity and mortality and prolonging hospital stay. Rapid diagnosis of PIAI is of great clinical value. Unfortunately, the current diagnostic methods of PIAI are not fast and accurate enough. METHODS: The authors performed an exploratory study to establish a rapid and accurate diagnostic method of PIAI. The authors explored the turnaround time and accuracy of metagenomic next-generation sequencing (mNGS) in diagnosing PIAI. Patients who underwent elective abdominal surgery and routine abdominal drainage with suspected PIAI were enroled in the study. The fresh midstream abdominal drainage fluid was collected for mNGS and culturing. RESULTS: The authors found that the median sample-to-answer turnaround time of mNGS was dramatically decreased than that of culture-based methods (<24 h vs. 59.5-111 h). The detection coverage of mNGS was much broader than culture-based methods. The authors found 26 species from 15 genera could only be detected by mNGS. The accuracy of mNGS was not inferior to culture-based methods in the 8 most common pathogens detected from abdominal drainage fluid (sensitivity ranged from 75 to 100%, specificity ranged from 83.3 to 100%, and kappa values were higher than 0.5). Moreover, the composition of the microbial spectrum established by mNGS varied between upper and lower gastrointestinal surgery, enhancing the understanding of PIAI pathogenesis. CONCLUSION: This study preliminarily revealed the clinical value of mNGS in the rapid diagnosis of PIAI and provided a rationale for further research.

Metastatic insulinoma: exploration from clinicopathological signatures and genetic characteristics.

Background: Insulinoma is a rare type of pancreatic neuroendocrine tumor with low incidence and low-malignant features. While very few insulinomas present with malignant behaviours, such as lymph node and liver metastasis, only a few studies have focused on this field owing to the limitation of samples. Existing evidence suggests that metastatic insulinoma largely derive from non-functional pancreatic neuroendocrine tumor. However, we found a portion of metastatic insulinomas may derive from non-metastatic insulinomas and explored their clinicopathological signatures and genetic characteristics. Methods: Four metastatic insulinoma patients with synchronous liver metastasis or lymph node metastasis at the Peking Union Medical College Hospital between October 2016 and December 2018 were enrolled, and whole exon and genome sequencing were performed on fresh frozen tissues and peripheral blood samples. Clinicopathological information and genomic sequencing results were collected and matched to explore the characteristics of the metastatic insulinomas. Results: These four metastatic insulinoma patients underwent surgery or interventional therapy, and their blood glucose levels immediately increased and maintained within standard range after treatment. For these four patients, the proinsulin/insulin molar ratio <1 and primary tumors were all present as PDX1+, ARX-, and insulin+, which were similar to non-metastatic insulinomas. However, the liver metastasis showed PDX1+ and ARX+, insulin+. Meanwhile, genomic sequencing data showed no recurrently mutations and typical CNV patterns. However, one patient harboured the YY1 T372R mutation, a recurrently mutated gene in non-metastatic insulinomas. Conclusions: A portion of metastatic insulinomas were largely derived from non-metastatic insulinomas in hormone secretion and ARX/PDX1 expression patterns. Meanwhile, the accumulation of ARX expression may be involved in the progression of metastatic insulinomas.

Long-term Survival, Quality of Life, and Molecular Features of the Patients With Solid Pseudopapillary Neoplasm of the Pancreas: A Retrospective Study of 454 Cases.

OBJECTIVE: To present comprehensive information on the clinicopathological, molecular, survival characteristics, and quality of life (QOL) after surgery for solid pseudopapillary neoplasm (SPN) of the pancreas in a large cohort after long-term follow-up. BACKGROUND: SPN is a rare tumor with an uncertain malignant potential, and solid information on long-term prognosis and QOL remains limited. METHODS: All hospitalized patients with SPNs who underwent surgery between 2001 and 2021 at the Peking Union Medical College Hospital were retrospectively reviewed. The clinicopathological characteristics of the patients were retrieved. A cross-sectional telephone questionnaire was administered to inquire about the QOL. Molecular analyses were performed using whole-exome sequencing. RESULTS: Exactly 454 patients with SPN were enrolled, of whom 18.5% were males and 81.5% were females. The mean patient age was 31 ± 12 years. In total, 61.3% of the patients had no symptoms. The size of the tumors was 5.38 ± 3.70 cm; 83.4% were solid cystic tumors, and 40.1% had calcifications. The proportions of local resection, distal pancreatectomy with or without splenectomy, and pancreaticoduodenectomy with or without pylorus preservation were 29.7%, 28.9% or 22.9%, and 11% or 6.8%, respectively. Over the years, there has been a significant shift from open to minimally invasive surgery. Among all surgical procedures, pylorus-preserving pancreaticoduodenectomy (PPPD) had the highest incidence of grade 2 to 4 complications (up to 32.3%), compared with 6.7% in distal pancreatectomy ( P < 0.001). Regarding histopathology, tissue invasion, perineural invasion, cancerous microvascular emboli, lymph node metastasis, and distant metastasis were present in 16.5%, 2.2%, 0.7%, 2.0%, and 3.1% of patients, respectively. Sixty patients were lost to follow-up. Sixteen of the 390 patients who underwent resection (4.1%) experienced local recurrence or distant metastasis after surgery. In total, 361 patients responded to the telephone survey. Nearly 80% of patients claimed their QOL was not significantly affected after surgery; however, the remaining 20% complained of lower QOL during 3 to 6 years of follow-up after surgery. No clinicopathological factor could reliably predict clinical recurrence or metastasis after resection. A total of 28 driver genes were detected with mutations in at least 2 tumor samples and the top 3 frequently mutated genes were CTNNB1 , ATRNL1 , and MUC16 . CONCLUSIONS: This study presented the largest cohort of patients with SPN after surgery from a single center and reported the QOL of these patients. SPN is associated with extremely favorable long-term survival, even in patients with metastasis, and most patients have a good QOL after surgery.

Adoptive cellular immunotherapy for solid neoplasms beyond CAR-T.

In recent decades, immune checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy are two milestone achievements in clinical immunotherapy. However, both show limited efficacies in most solid neoplasms, which necessitates the exploration of new immunotherapeutic modalities. The failure of CAR-T and immune checkpoint blockade in several solid neoplasms is attributed to multiple factors, including low antigenicity of tumor cells, low infiltration of effector T cells, and diverse mechanisms of immunosuppression in the tumor microenvironment. New adoptive cell therapies have been attempted for solid neoplasms, including TCR-T, CAR-natural killer cells (CAR-NK), and CAR-macrophages (CAR-M). Compared to CAR-T, these new adoptive cell therapies have certain advantages in treating solid neoplasms. In this review, we summarized the 40-year evolution of adoptive cell therapies, then focused on the advances of TCR-T, CAR-NK, and CAR-M in solid neoplasms and discussed their potential clinical applications.

Cost-Effective Droplet Generator for Portable Bio-Applications.

The convenient division of aqueous samples into droplets is necessary for many biochemical and medical analysis applications. In this article, we propose the design of a cost-effective droplet generator for potential bio-chemical application, featuring two symmetric tubes. The new droplet generator revisits the relationship between capillary components and liquid flow rates. The size of generated droplets by prototype depends only on generator dimensions, without precisely needing to control external flow conditions or driving pressure, even when the relative extreme difference in flow rate for generating nL level droplets is over 57.79%, and the relative standard deviation (RSD) of the volume of droplets is barely about 9.80%. A dropper working as a pressure resource is used to verify the rapidity and robustness of this principle of droplet generation, which shows great potential for a wide range of droplet-based applications.

Prognostic value of preoperative [68 Ga]Ga-FAPI-04 PET/CT in patients with resectable pancreatic ductal adenocarcinoma in correlation with immunohistological characteristics.

PURPOSE: Our aim was to assess the prognostic value of [68 Ga]Ga-FAPI-04 positron emission tomography (PET) uptake in PDAC and to evaluate the correlation between in vivo lesional radioactivity with pathological characteristics of pancreatic ductal adenocarcinoma (PDAC). METHODS: We retrospectively analyzed treatment-naïve PDAC patients who underwent preoperative [68 Ga]Ga-FAPI-04 PET/CT followed by pancreatectomy. The tracer uptake was determined as maximum tumor standardized uptake value (SUVmax), FAPI-avid tumor volume (FTV), total lesion FAP expression (TLF) as well total pancreatic uptake (TSUVmax), total FAPI-avid pancreatic volume (FPV), and total pancreatic FAP expression (TPF). Spearman's correlation analysis was performed to evaluate the association between [68 Ga]Ga-FAPI-04 PET/CT imaging and ex vivo immunohistological FAP expression and pathological characteristics of surgical specimens (differentiation, size, vascularity, perineural invasion, and lymph node metastases). Kaplan-Meier and hazard ratio (HR, log-rank) methods were used to evaluate the prognostic value of [68 Ga]Ga-FAPI-04 PET/CT and clinicopathological factors. RESULTS: Thirty-seven surgical PDAC patients were included. The ex vivo expression of FAP was significantly associated with the tumor SUVmax and TLF. FAP expression was more abundant in poorly differentiated PDAC than in well- to moderately differentiated neoplasms. Tumor SUVmax or TLF and pancreatic TSUVmax or TPF were significantly correlated with tumor size, differentiation, and perineural invasion, respectively. SUVmax had a significant independent prognostic value for recurrence-free survival (HR = 2.46, P < 0.05), while [68 Ga]Ga-FAPI-04 TPF predicted overall survival (HR = 12.82, P < 0.05). CONCLUSION: The in vivo [68 Ga]Ga-FAPI-04 uptake in localized PDAC showed a significant correlation with ex vivo FAP expression and aggressive pathological characteristics. [68 Ga]Ga-FAPI-04 PET/CT also presented a potential for postoperative prognostication of PDAC. Elevated fibroblast activity induced by obstructive pancreatitis might be associated with the patient's survival.

Clustering of Multilayer Networks Using Joint Learning Algorithm With Orthogonality and Specificity of Features.

Complex systems in nature and society consist of various types of interactions, where each type of interaction belongs to a layer, resulting in the so-called multilayer networks. Identifying specific modules for each layer is of great significance for revealing the structure-function relations in multilayer networks. However, the available approaches are criticized undesirable because they fail to explicitly the specificity of modules, and balance the specificity and connectivity of modules. To overcome these drawbacks, we propose an accurate and flexible algorithm by joint learning matrix factorization and sparse representation (jMFSR) for specific modules in multilayer networks, where matrix factorization extracts features of vertices and sparse representation discovers specific modules. To exploit the discriminative latent features of vertices in multilayer networks, jMFSR incorporates linear discriminant analysis (LDA) into non-negative matrix factorization (NMF) to learn features of vertices that distinguish the categories. To explicitly measure the specificity of features, jMFSR decomposes features of vertices into common and specific parts, thereby enhancing the quality of features. Then, jMFSR jointly learns feature extraction, common-specific feature factorization, and clustering of multilayer networks. The experiments on 11 datasets indicate that jMFSR significantly outperforms state-of-the-art baselines in terms of various measurements.

Network-Based Structural Learning Nonnegative Matrix Factorization Algorithm for Clustering of scRNA-Seq Data.

Single-cell RNA sequencing (scRNA-seq) measures expression profiles at the single-cell level, which sheds light on revealing the heterogeneity and functional diversity among cell populations. The vast majority of current algorithms identify cell types by directly clustering transcriptional profiles, which ignore indirect relations among cells, resulting in an undesirable performance on cell type discovery and trajectory inference. Therefore, there is a critical need for inferring cell types and trajectories by exploiting the interactions among cells. In this study, we propose a network-based structural learning nonnegative matrix factorization algorithm (aka SLNMF) for the identification of cell types in scRNA-seq, which is transformed into a constrained optimization problem. SLNMF first constructs the similarity network for cells and then extracts latent features of the cells by exploiting the topological structure of the cell-cell network. To improve the clustering performance, the structural constraint is imposed on the model to learn the latent features of cells by preserving the structural information of the networks, thereby significantly improving the performance of algorithms. Finally, we track the trajectory of cells by exploring the relationships among cell types. Fourteen scRNA-seq datasets are adopted to validate the performance of algorithms with the number of single cells varying from 49 to 26,484. The experimental results demonstrate that SLNMF significantly outperforms fifteen state-of-the-art methods with 15.32% improvement in terms of accuracy, and it accurately identifies the trajectories of cells. The proposed model and methods provide an effective strategy to analyze scRNA-seq data. (The software is coded using matlab, and is freely available for academic https://github.com/xkmaxidian/SLNMF).