PRDM14 extinction enables the initiation of trophoblast stem cell formation.

Trophoblast stem cells (TSCs) can be chemically converted from embryonic stem cells (ESCs) in vitro. Although several transcription factors (TFs) have been recognized as essential for TSC formation, it remains unclear how differentiation cues link elimination of stemness with the establishment of TSC identity. Here, we show that PRDM14, a critical pluripotent circuitry component, is reduced during the formation of TSCs. The reduction is further shown to be due to the activation of Wnt/ß-catenin signaling. The extinction of PRDM14 results in the erasure of H3K27me3 marks and chromatin opening in the gene loci of TSC TFs, including GATA3 and TFAP2C, which enables their expression and thus the initiation of the TSC formation process. Accordingly, PRDM14 reduction is proposed here as a critical event that couples elimination of stemness with the initiation of TSC formation. The present study provides novel insights into how induction signals initiate TSC formation.

Computer-aided diagnostic system with automated deep learning method based on the AutoGluon framework improved the diagnostic accuracy of early esophageal cancer.

Background: There have been studies on the application of computer-aided diagnosis (CAD) in the endoscopic diagnosis of early esophageal cancer (EEC), but there is still a significant gap from clinical application. We developed an endoscopic CAD system for EEC based on the AutoGluon framework, aiming to explore the feasibility of automatic deep learning (DL) in clinical application. Methods: The endoscopic pictures of normal esophagus, esophagitis, and EEC were collected from The First Affiliated Hospital of Soochow University (September 2015 to December 2021) and the Norwegian HyperKvasir database. All images of non-cancerous esophageal lesions and EEC in this study were pathologically examined. There were three tasks: task A was normal vs. lesion classification under non-magnifying endoscopy (n=932 vs. 1,092); task B was non-cancer lesion vs. EEC classification under non-magnifying endoscopy (n=594 vs. 429); and task C was non-cancer lesion vs. EEC classification under magnifying endoscopy (n=505 vs. 824). In all classification tasks, we took 100 pictures as the verification set, and the rest comprised as the training set. The CAD system was established based on the AutoGluon framework. Diagnostic performance of the model was compared with that of endoscopists grouped according to years of experience (senior >15 years; junior <5 years). Model evaluation indicators included accuracy, recall rate, precision, F1 value, interpretation time, and the area under the receiver operating characteristic (ROC) curve (AUC). Results: In tasks A and B, the accuracies of medium-performance CAD and high-performance CAD were lower than those of junior doctors and senior doctors. In task C, the medium-performance and high-performance CAD accuracies were close to those of junior doctors and senior doctors. The high-performance CAD model outperformed the junior doctors in both task A (0.850 vs. 0.830) and task C (0.840 vs. 0.830) in sensitivity comparison, but there was still a large gap between high-performance CAD models and doctors in sensitivity comparison. In task A, with the aid of CAD pre-interpretation, the accuracy of junior and senior physicians were significantly improved (from 0.880 to 0.915 and from 0.920 to 0.945, respectively); the time spent on film reading was significantly shortened (junior: from 11.3 to 8.7 s; senior: from 6.7 to 5.5 s). In task C, with the aid of CAD pre-interpretation, the accuracy of junior and senior physicians were significantly improved (from 0.850 to 0.865 and from 0.915 to 0.935, respectively); the reading time was significantly shortened (junior: from 9.5 to 7.7 s; senior: from 5.6 to 3.0 s). Conclusions: The CAD system based on the AutoGluon framework can assist doctors to improve the diagnostic accuracy and reading time of EEC under endoscopy. This study reveals that automatic DL methods are promising in clinical application.

Ultrasound-assisted extraction of polyphenols from lotus rhizome epidermis by alcohol/salt-based aqueous two-phase system: Optimization, extraction mechanism and antioxidant activities.

In this study, ultrasonic-assisted (UA) alcohol/salt-based aqueous two-phase system (ATPS) method was constructed to extract lotus rhizome epidermis (LRE) polyphenols. The extraction conditions were optimized as salt concentration 26.75 %, ethanol concentration 25.45 %, ultrasonic power 487 W and liquid-solid ratio 35.33 mL/g by comparing response surface methodology (RSM) and artificial neural network (ANN) models. Then, l-dopa (2.35 ± 0.036 mg/g dw), gallocatechin (1.66 ± 0.0035 mg/g dw) and epigallocatechin (1.37 ± 0.0035 mg/g dw) were determined as major polyphenols in LRE by using UA-ATPS method. Moreover, study showed that ultrasound, van der Waals force, hydrogen bond and salting out could accelerate the mass transfer and extraction of polyphenols in LRE cells. The high-pressure cavity and collapse effect of ultrasound could also accelerate the extraction of polyphenols. In vitro antioxidant experiments showed that LRE polyphenols have good antioxidant ability. In sum, this study developed a green and efficient extraction method to enhance the profitability of LRE in food and medicine industries.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Plant-Derived Caffeic Acid and Its Derivatives: An Overview of Their NMR Data and Biosynthetic Pathways.

In recent years, caffeic acid and its derivatives have received increasing attention due to their obvious physiological activities and wide distribution in nature. In this paper, to clarify the status of research on plant-derived caffeic acid and its derivatives, nuclear magnetic resonance spectroscopy data and possible biosynthetic pathways of these compounds were collected from scientific databases (SciFinder, PubMed and China Knowledge). According to different types of substituents, 17 caffeic acid and its derivatives can be divided into the following classes: caffeoyl ester derivatives, caffeyltartaric acid, caffeic acid amide derivatives, caffeoyl shikimic acid, caffeoyl quinic acid, caffeoyl danshens and caffeoyl glycoside. Generalization of their 13C-NMR and 1H-NMR data revealed that acylation with caffeic acid to form esters involves acylation shifts, which increase the chemical shift values of the corresponding carbons and decrease the chemical shift values of the corresponding carbons of caffeoyl. Once the hydroxyl group is ester, the hydrogen signal connected to the same carbon shifts to the low field (1.1~1.6). The biosynthetic pathways were summarized, and it was found that caffeic acid and its derivatives are first synthesized in plants through the shikimic acid pathway, in which phenylalanine is deaminated to cinnamic acid and then transformed into caffeic acid and its derivatives. The purpose of this review is to provide a reference for further research on the rapid structural identification and biofabrication of caffeic acid and its derivatives.

Effect and mechanism of Prunella vulgaris L. extract on alleviating lipopolysaccharide-induced acute mastitis in protecting the blood-milk barrier and reducing inflammation.

ETHNOPHARMACOLOGICAL RELEVANCE: According to ancient literature, Prunella vulgaris L. (P vulgaris) alleviates mastitis and has been used in China for many years; however, there are no relevant reports that confirm this or the mechanism of its efficacy. AIM OF THE STUDY: To explore the anti-acute mastitis effect and potential mechanism of P vulgaris extract. MATERIALS AND METHODS: First, the active ingredients and targets of P vulgaris against mastitis were predicted using network pharmacology. Next, the relevant active ingredients were enriched using macroporous resins and verified using UV and UPLC-Q-TOF-MS/MS. Lastly, a mouse model of acute mastitis was established by injecting lipopolysaccharides into the mammary gland and administering P vulgaris extract by oral gavage. The pathological changes in mammary tissue were observed by HE staining. Serum and tissue inflammatory factors were measured by ELISA method. MPO activity in mammary tissue was measured using colorimetry and MPO expression was detected by immunohistochemistry. The expression of tight junction proteins (ZO-1, claudin-3, and occludin) in mammary tissue was detected by immunofluorescence and Western blot. iNOS and COX-2 in mammary tissue were detected by Western blot. MAPK pathway and NF-κB pathway related proteins were also detected by Western blot. RESULTS: Network pharmacology predicted that phenolic acids and flavonoids in P vulgaris had anti-mastitis effects. The contents of total flavonoids and total phenolic acids in P vulgaris extract were 64.5% and 29.4%, respectively. UPLC-Q-TOF-MS/MS confirmed that P vulgaris extract contained phenolic acids and flavonoids. The results of animal experiments showed that P vulgaris extract reduced lipopolysaccharide-induced inflammatory edema, inflammatory cell infiltration, and interstitial congestion of mammary tissue. It also reduced the levels of serum and tissue inflammatory factors TNF-α, IL-6, and IL-1ß, and inhibited the activation of MPO. Furthermore, it downregulated the expression of MAPK and NF-κB pathway-related proteins. The expressions of ZO-1, occludin, and claudin-3 in mammary gland tissues were upregulated. CONCLUSIONS: P vulgaris extract can maintain the integrity of mammary connective tissue and reduce its inflammatory response to prevent acute mastitis. Its mechanism probably involves regulating NF-κB and MAPK pathways.

Characteristics of industrialized hydrothermal cracking solid organic fertilizer and its effects on fresh corn growth.

Traditional methods of producing organic fertilizers result in significant nutrient loss and greenhouse gas emissions, making it challenging to align with sustainable development and the achievement of net-zero emissions goals. Hydrothermal cracking, as a novel clean technology for the utilization of organic waste into fertilizer, has been extensively studied and refined in laboratory settings, but its large-scale industrial evaluation remains limited. This study investigates the properties and field application of hydrothermal cracking solid organic fertilizer (HCSOF) produced at a pilot scale with an annual output of 10,000 tons. The results indicate that the organic matter content and total nutrient content (TN + P2O5 + K2O) of HCSOF reached 50.6 % and 5.46 %, respectively, which are 20.6 % and 1.46 % higher than the standards for organic fertilizers in China. Additionally, contaminants such as pathogens and antibiotics in the product were completely eliminated. Elemental analysis and pore size distribution highlighted the unique adsorptive attributes of HCSOF, which showed significant effect in reducing soil ammonium nitrogen. Results from field trials indicate that the complete substitution of chemical fertilizers with HCSOF did not reduce corn yield, which remained at 9.03 t/ha. Particularly, compared to the exclusive use of chemical fertilizers, HCSOF treatments resulted in a 7.03 % and 4.70 % decrease in fresh corn lodging and disease incidence, respectively. Antibacterial tests further confirmed its ability to counter pathogens. This study provides robust evidence for scaling up hydrothermal cracking fertilizer production from laboratory to industrial levels. Future research should focus on multi-batch sampling and extended field experiments.

A Galectin-9-Driven CD11chigh Decidual Macrophage Subset Suppresses Uterine Vascular Remodeling in Preeclampsia.

BACKGROUND: Preeclampsia is a serious disease of pregnancy that lacks early diagnosis methods or effective treatment, except delivery. Dysregulated uterine immune cells and spiral arteries are implicated in preeclampsia, but the mechanistic link remains unclear. METHODS: Single-cell RNA sequencing and spatial transcriptomics were used to identify immune cell subsets associated with preeclampsia. Cell-based studies and animal models including conditional knockout mice and a new preeclampsia mouse model induced by recombinant mouse galectin-9 were applied to validate the pathogenic role of a CD11chigh subpopulation of decidual macrophages (dMφ) and to determine its underlying regulatory mechanisms in preeclampsia. A retrospective preeclampsia cohort study was performed to determine the value of circulating galectin-9 in predicting preeclampsia. RESULTS: We discovered a distinct CD11chigh dMφ subset that inhibits spiral artery remodeling in preeclampsia. The proinflammatory CD11chigh dMφ exhibits perivascular enrichment in the decidua from patients with preeclampsia. We also showed that trophoblast-derived galectin-9 activates CD11chigh dMφ by means of CD44 binding to suppress spiral artery remodeling. In 3 independent preeclampsia mouse models, placental and plasma galectin-9 levels were elevated. Galectin-9 administration in mice induces preeclampsia-like phenotypes with increased CD11chigh dMφ and defective spiral arteries, whereas galectin-9 blockade or macrophage-specific CD44 deletion prevents such phenotypes. In pregnant women, increased circulating galectin-9 levels in the first trimester and at 16 to 20 gestational weeks can predict subsequent preeclampsia onset. CONCLUSIONS: These findings highlight a key role of a distinct perivascular inflammatory CD11chigh dMφ subpopulation in the pathogenesis of preeclampsia. CD11chigh dMφ activated by increased galectin-9 from trophoblasts suppresses uterine spiral artery remodeling, contributing to preeclampsia. Increased circulating galectin-9 may be a biomarker for preeclampsia prediction and intervention.

Few-shot learning for the classification of intestinal tuberculosis and Crohn's disease on endoscopic images: A novel learn-to-learn framework.

Background and aim: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn's disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements. Methods: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets. Results: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61). Conclusions: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.

Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.

Background: Pancreatic cancer remains an extremely malignant digestive tract tumor, posing a significant global public health burden. Patients with pancreatic cancer, once metastasis occurs, lose all hope of cure, and prognosis is extremely poor. It is important to investigate liver metastasis of Pancreatic cancer in depth, not just because it is the most common form of metastasis in pancreatic cancer, but also because it is crucial for treatment planning and prognosis assessment. This study aims to delve into the mechanisms of pancreatic cancer liver metastasis, with the goal of providing crucial scientific groundwork for the development of future treatment methods and drugs. Methods: We explored the mechanisms of pancreatic cancer liver metastasis using single-cell sequencing data (GSE155698 and GSE154778) and bulk data (GSE71729, GSE19279, TCGA-PAAD). Initially, Seurat package was employed for single-cell data processing to obtain expression matrices for primary pancreatic cancer lesions and liver metastatic lesions. Subsequently, high-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify genes associated with liver metastasis. Machine learning algorithms and COX regression models were employed to further screen genes related to patient prognosis. Informed by both biological understanding and the outcomes of algorithms, we meticulously identified the ultimate set of liver metastasis-related gene (LRG). In the study of LRG genes, various databases were utilized to validate their association with pancreatic cancer liver metastasis. In order to analyze the effects of these agents on tumor microenvironment, we conducted an in-depth analysis, including changes in signaling pathways (GSVA), cell differentiation (pseudo-temporal analysis), cell communication networks (cell communication analysis), and downstream transcription factors (transcription factor activity prediction). Additionally, drug sensitivity analysis and metabolic analysis were performed to reveal the effects of LRG on gemcitabine resistance and metabolic pathways. Finally, functional experiments were conducted by silencing the expression of LRG in PANC-1 and Bx-PC-3 cells to validate its influence to proliferation and invasiveness on PANC-1 and Bx-PC-3 cells. Results: Through a series of algorithmic filters, we identified PAK2 as a key gene promoting pancreatic cancer liver metastasis. GSVA analysis elucidated the activation of the TGF-beta signaling pathway by PAK2 to promote the occurrence of liver metastasis. Pseudo-temporal analysis revealed a significant correlation between PAK2 expression and the lower differentiation status of pancreatic cancer cells. Cell communication analysis revealed that overexpression of PAK2 promotes communication between cancer cells and the tumor microenvironment. Transcription factor activity prediction displayed the transcription factor network regulated by PAK2. Drug sensitivity analysis and metabolic analysis revealed the impact of PAK2 on gemcitabine resistance and metabolic pathways. CCK8 experiments showed that silencing PAK2 led to a decrease in the proliferative capacity of pancreatic cancer cells and scratch experiments demonstrated that low expression of PAK2 decreased invasion capability in pancreatic cancer cells. Flow cytometry reveals that PAK2 significantly inhibited apoptosis in pancreatic cancer cell lines. Molecules related to the TGF-beta pathway decreased with the inhibition of PAK2, and there were corresponding significant changes in molecules associated with EMT. Conclusion: PAK2 facilitated the angiogenic potential of cancer cells and promotes the epithelial-mesenchymal transition process by activating the TGF-beta signaling pathway. Simultaneously, it decreased the differentiation level of cancer cells, consequently enhancing their malignancy. Additionally, PAK2 fostered communication between cancer cells and the tumor microenvironment, augments cancer cell chemoresistance, and modulates energy metabolism pathways. In summary, PAK2 emerged as a pivotal gene orchestrating pancreatic cancer liver metastasis. Intervening in the expression of PAK2 may offer a promising therapeutic strategy for preventing liver metastasis of pancreatic cancer and improving its prognosis.

Expression and clinical significance of ECHS1 in gastric cancer.

Background: Gastric cancer (GC), as one of the most common malignant tumors and the 3rd primary cause of death by cancer globally, poses a great threat to public health. Despite many advancements have been achieved in current treatment avenues for GC, the 5-year survival rates of GC patients remain substandard. Short-chain enoyl-CoA hydratase (ECHS1) exerts pro- or anti-cancer activities in different cancer backgrounds. However, its clinical significance and biological role in GC remain vague and need further investigation. Methods: The expression of ECHS1 in GC tumors and adjacent normal tissues was examined using the GEPIA platform and clinical samples. The effects of ECHS1 on GC cell proliferation and migration were evaluated using colony formation and transwell migration assays. Results: ECHS1 was upregulated in GC tumor tissues in both mRNA and protein levels and increased ECHS1 was markedly linked with tumor location, depth of tumor invasion, lymph node metastasis (LNM), and tumor-node-metastasis (TNM) stage of GC patients. High ECHS1 expression was also linked with a shorter overal survival (OS), first progression (FP) and post progression survival (PPS). Further subgroup analysis showed that OS was significantly shorter in GC patients with high ECHS1 expression compared to those with low ECHS1 expression belonging to tumors with T3 stage, N2 stage or in instestinal Lauren subgroup. In addition, cytological experiments showed that there was higher ECHS1 expression in GC cell lines compared to the normal gastric epithelium (GES-1) cells, and ECHS1 can promote GC cell proliferation and migration in vitro. Conclusion: ECHS1 plays an oncogenic role in GC and might be a promising therapeutic target for GC.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model ß was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model ß: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Characteristics and metabolic potential of biliary microbiota in patients with giant common bile duct stones.

Background: Endoscopic retrograde cholangiopancreatography (ERCP) is an effective minimally invasive operation for the management of choledocholithiasis, while successful extraction is hampered by large diameter of stones. Emerging studies have revealed the close correlation between biliary microbiota and common bile duct stones (CBDS). In this study, we aimed to investigate the community characteristics and metabolic functions of biliary microbiota in patients with giant CBDS. Methods: Eligible patients were prospectively enrolled in this study in First Affiliated Hospital of Soochow University from February 2022 to October 2022. Bile samples were collected through ERCP. The microbiota was analyzed using 16S rRNA sequencing. Metabolic functions were predicted by PICRUSTs 2.0 calculation based on MetaCyc database. Bile acids were tested and identified using ultra performance liquid chromatography-tandem mass spectrometry. Results: A total of 26 patients were successfully included into final analysis, 8 in giant stone (GS) group and 18 in control group. Distinct biliary microbial composition was identified in patients with giant CBDS, with a significantly higher abundance of Firmicutes at phylum level. The unique composition at genus level mainly consisted of Enterococcus, Citrobacter, Lactobacillus, Pyramidobacter, Bifidobacterium and Shewanella. Pyramidobacter was exclusively found in GS group, along with the absence of Robinsoniella and Coprococcus. The contents of free bile acids were significantly higher in GS group, including cholic acid (98.39µmol/mL vs. 26.15µmol/mL, p=0.035), chenodesoxycholic acid (54.69µmol/mL vs. 5.86µmol/mL, p=0.022) and ursodeoxycholic acid (2.70µmol/mL vs. 0.17µmol/mL, p=0.047). Decreasing tendency of conjugated bile acids were also observed. Metabolic pathways concerning cholelithiasis were abundant in GS group, including geranylgeranyl diphosphate biosynthesis, gluconeogenesis, glycolysis and L-methionine biosynthesis. Conclusions: This study demonstrated the community structure and metabolic potential of biliary microbiota in patients with giant CBDS. The unique biliary microbial composition holds valuable predictive potential for clinical conditions. These findings provide new insights into the etiology of giant CBDS from the perspective of biliary microbiota.

Application of machine learning algorithms to predict lymph node metastasis in gastric neuroendocrine neoplasms.

Background: Neuroendocrine neoplasms (NENs) are tumors that originate from secretory cells of the diffuse endocrine system and typically produce bioactive amines or peptide hormones. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform. Methods: In this investigation, data from 1256 patients were used, of whom 119 patients from the First Affiliated Hospital of Soochow University in China and 1137 cases from the surveillance epidemiology and end results (SEER) database were combined. Six machine learning algorithms, including the logistic regression model (LR), random forest (RF), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to build the predictive model. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: Among the 1256 patients with gastric NENs, 276 patients (21.97 %) developed lymph node metastasis. T stage, tumor size, degree of differentiation, and sex were predictive factors of lymph node metastasis. The RF model achieved the best predictive performance among the six machine learning models, with an AUC, accuracy, sensitivity, and specificity of 0.81, 0.78, 0.76, and 0.82, respectively. Conclusion: The RF model provided the best prediction and can help physicians determine the lymph node metastasis risk of gastric NEN patients to formulate individualized medical strategies.

Crosstalk between alternative splicing and inflammatory bowel disease: Basic mechanisms, biotechnological progresses and future perspectives.

BACKGROUND: Alternative splicing (AS) is an omnipresent regulatory mechanism of gene expression that enables the generation of diverse splice isoforms from a single gene. Recently, AS events have gained considerable momentum in the pathogenesis of inflammatory bowel disease (IBD). METHODS: Our review has summarized the complex process of RNA splicing, and firstly highlighted the potential involved molecules that target aberrant splicing events in IBD. The quantitative transcriptome analyses such as microarrays, next-generation sequencing (NGS) for AS events in IBD have been also discussed. RESULTS: Available evidence suggests that some abnormal splicing RNAs can lead to multiple intestinal disorders during the onset of IBD as well as the progression to colitis-associated cancer (CAC), including gut microbiota perturbations, intestinal barrier dysfunctions, innate/adaptive immune dysregulations, pro-fibrosis activation and some other risk factors. Moreover, current data show that the advanced technologies, including microarrays and NGS, have been pioneeringly employed to screen the AS candidates and elucidate the potential regulatory mechanisms of IBD. Besides, other biotechnological progresses such as the applications of third-generation sequencing (TGS), single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), will be desired with great expectations. CONCLUSIONS: To our knowledge, the current review is the first one to evaluate the potential regulatory mechanisms of AS events in IBD. The expanding list of aberrantly spliced genes in IBD along with the developed technologies provide us new clues to how IBD develops, and how these important AS events can be explored for future treatment.

Serum Cys C predicts acute kidney injury in patients with acute pancreatitis: A retrospective study.

BACKGROUND AND STUDY AIMS: We investigated the value of the serum cystatin C level as a potential predictor of acute kidney injury (AKI) in patients with acute pancreatitis (AP). PATIENTS AND METHODS: We retrospectively examined patients diagnosed with AP between January 2013 and December 2018. Patients were categorized into two groups based on their serum cystatin C levels after admission: the normal (n-Cys C group) and high serum cystatin C levels groups (h-Cys C group). Patients in the h-Cys C group demonstrated serum cystatin C levels ≥1.05 mg/L. Demographic parameters, laboratory data, and AP severity were compared between the two groups. Receiver operating curve (ROC) analysis was used to evaluate the efficacy of serum cystatin C in predicting persistent AKI. RESULTS: A total of 379 patients with AP were enrolled: 319 in the n-Cys C group and 60 in the h-Cys C group. Serum cystatin C levels were significantly higher in patients with severe acute pancreatitis (SAP) compared to moderate acute pancreatitis (MAP) (P< 0.05). The h-Cys C group had a higher BISAP score (P < 0.001). Incidences of organ failure and SAP were significantly higher in the h-Cys C group (P < 0.05). ROC analysis indicated that a serum cystatin C cutoff point of 1.055 mg/L optimally predicted persistent AKI (AUC = 0.711). For internal validation, we selected 545 AP patients, treated at our center from 2019 to 2022, including 54 AKI patients. ROC analysis in this validation group yielded a sensitivity of 100% and specificity of 90.9% (AUC = 0.916, 95% CI: 0.894-0.937). CONCLUSION: Elevated serum cystatin C levels are sensitive indicators of adverse AKI prognosis in AP patients. The cystatin C level at admission can reflect a patient's initial renal function status.

Towards Personalized Medicine for Chronic Liver Disease.

Chronic liver disease is a progressive deterioration of hepatic functions and a continuous process of inflammation, destruction, and regeneration of liver parenchyma, resulting in fibrosis and cirrhosis [...].

Unveiling the role of regulatory T cells in the tumor microenvironment of pancreatic cancer through single-cell transcriptomics and in vitro experiments.

Background: In order to investigate the impact of Treg cell infiltration on the immune response against pancreatic cancer within the tumor microenvironment (TME), and identify crucial mRNA markers associated with Treg cells in pancreatic cancer, our study aims to delve into the role of Treg cells in the anti-tumor immune response of pancreatic cancer. Methods: The ordinary transcriptome data for this study was sourced from the GEO and TCGA databases. It was analyzed using single-cell sequencing analysis and machine learning. To assess the infiltration level of Treg cells in pancreatic cancer tissues, we employed the CIBERSORT method. The identification of genes most closely associated with Treg cells was accomplished through the implementation of weighted gene co-expression network analysis (WGCNA). Our analysis of single-cell sequencing data involved various quality control methods, followed by annotation and advanced analyses such as cell trajectory analysis and cell communication analysis to elucidate the role of Treg cells within the pancreatic cancer microenvironment. Additionally, we categorized the Treg cells into two subsets: Treg1 associated with favorable prognosis, and Treg2 associated with poor prognosis, based on the enrichment scores of the key genes. Employing the hdWGCNA method, we analyzed these two subsets to identify the critical signaling pathways governing their mutual transformation. Finally, we conducted PCR and immunofluorescence staining in vitro to validate the identified key genes. Results: Based on the results of immune infiltration analysis, we observed significant infiltration of Treg cells in the pancreatic cancer microenvironment. Subsequently, utilizing the WGCNA and machine learning algorithms, we ultimately identified four Treg cell-related genes (TRGs), among which four genes exhibited significant correlations with the occurrence and progression of pancreatic cancer. Among them, CASP4, TOB1, and CLEC2B were associated with poorer prognosis in pancreatic cancer patients, while FYN showed a correlation with better prognosis. Notably, significant differences were found in the HIF-1 signaling pathway between Treg1 and Treg2 cells identified by the four genes. These conclusions were further validated through in vitro experiments. Conclusion: Treg cells played a crucial role in the pancreatic cancer microenvironment, and their presence held a dual significance. Recognizing this characteristic was vital for understanding the limitations of Treg cell-targeted therapies. CASP4, FYN, TOB1, and CLEC2B exhibited close associations with infiltrating Treg cells in pancreatic cancer, suggesting their involvement in Treg cell functions. Further investigation was warranted to uncover the mechanisms underlying these associations. Notably, the HIF-1 signaling pathway emerged as a significant pathway contributing to the duality of Treg cells. Targeting this pathway could potentially revolutionize the existing treatment approaches for pancreatic cancer.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.

A nomogram based on platelet-to-lymphocyte ratio for predicting lymph node metastasis in patients with early gastric cancer.

Background: Preoperative assessment of the presence of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) remains difficult. We aimed to develop a practical prediction model based on preoperative pathological data and inflammatory or nutrition-related indicators. Methods: This study retrospectively analyzed the clinicopathological characteristics of 1,061 patients with EGC who were randomly divided into the training set and validation set at a ratio of 7:3. In the training set, we introduced the least absolute selection and shrinkage operator (LASSO) algorithm and multivariate logistic regression to identify independent risk factors and construct the nomogram. Both internal validation and external validation were performed by the area under the receiver operating characteristic curve (AUC), C-index, calibration curve, and decision curve analysis (DCA). Results: LNM occurred in 162 of 1,061 patients, and the rate of LNM was 15.27%. In the training set, four variables proved to be independent risk factors (p < 0.05) and were incorporated into the final model, including depth of invasion, tumor size, degree of differentiation, and platelet-to-lymphocyte ratio (PLR). The AUC values were 0.775 and 0.792 for the training and validation groups, respectively. Both calibration curves showed great consistency in the predictive and actual values. The Hosmer-Lemeshow (H-L) test was carried out in two cohorts, showing excellent performance with p-value >0.05 (0.684422, 0.7403046). Decision curve analysis demonstrated a good clinical benefit in the respective set. Conclusion: We established a preoperative nomogram including depth of invasion, tumor size, degree of differentiation, and PLR to predict LNM in EGC patients and achieved a good performance.