Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients.

BACKGROUND: Lymph node ratio (LNR) was demonstrated to play a crucial role in the prognosis of many tumors. However, research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm (NEN) patients was limited. AIM: To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models. METHODS: A total of 286 patients from the Surveillance, Epidemiology, and End Results database were divided into the training set and validation set at a ratio of 8:2. 92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set. Cox regression analysis was used to explore the relationship between LNR and disease-specific survival (DSS) of gastric NEN patients. Random survival forest (RSF) algorithm and Cox proportional hazards (CoxPH) analysis were applied to develop models to predict DSS respectively, and compared with the 8th edition American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging. RESULTS: Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death. The RSF model exhibited the best performance in predicting DSS, with the C-index in the test set being 0.769 [95% confidence interval (CI): 0.691-0.846] outperforming the CoxPH model (0.744, 95%CI: 0.665-0.822) and the 8th edition AJCC TNM staging (0.723, 95%CI: 0.613-0.833). The calibration curves and decision curve analysis (DCA) demonstrated the RSF model had good calibration and clinical benefits. Furthermore, the RSF model could perform risk stratification and individual prognosis prediction effectively. CONCLUSION: A higher LNR indicated a lower DSS in postoperative gastric NEN patients. The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set, showing potential in clinical practice.

Thalamic Nucleus Reuniens Glutamatergic Neurons Mediate Colorectal Visceral Pain in Mice via 5-HT2B Receptors.

Irritable bowel syndrome (IBS) is a common functional bowel disorder characterized by abdominal pain and visceral hypersensitivity. Reducing visceral hypersensitivity is the key to effectively relieving abdominal pain in IBS. Increasing evidence has confirmed that the thalamic nucleus reuniens (Re) and 5-hydroxytryptamine (5-HT) neurotransmitter system play an important role in the development of colorectal visceral pain, whereas the exact mechanisms remain largely unclear. In this study, we found that high expression of the 5-HT2B receptors in the Re glutamatergic neurons promoted colorectal visceral pain. Specifically, we found that neonatal maternal deprivation (NMD) mice exhibited visceral hyperalgesia and enhanced spontaneous synaptic transmission in the Re brain region. Colorectal distension (CRD) stimulation induced a large amount of c-Fos expression in the Re brain region of NMD mice, predominantly in glutamatergic neurons. Furthermore, optogenetic manipulation of glutamatergic neuronal activity in the Re altered colorectal visceral pain responses in CON and NMD mice. In addition, we demonstrated that 5-HT2B receptor expression on the Re glutamatergic neurons was upregulated and ultimately promoted colorectal visceral pain in NMD mice. These findings suggest a critical role of the 5HT2B receptors on the Re glutamatergic neurons in the regulation of colorectal visceral pain.

Trim14-IκBα Signaling Regulates Chronic Inflammatory Pain in Rats and Osteoarthritis Patients.

Chronic inflammatory pain is the highest priority for people with osteoarthritis when seeking medical attention. Despite the availability of NSAIDs and glucocorticoids, central sensitization and peripheral sensitization make pain increasingly difficult to control. Previous studies have identified the ubiquitination system as an important role in the chronic inflammatory pain. Our study displayed that the E3 ubiquitin ligase tripartite motif-containing 14 (Trim14) was abnormally elevated in the serum of patients with osteoarthritis and pain, and the degree of pain was positively correlated with the degree of Trim14 elevation. Furthermore, CFA-induced inflammatory pain rat model showed that Trim14 was significantly increased in the L3-5 spinal dorsal horn (SDH) and dorsal root ganglion (DRG), and in turn the inhibitor of nuclear factor Kappa-B isoform α (IκBα) was decreased after Trim14 elevation. After intrathecal injection of Trim14 siRNA to inhibit Trim14 expression, IκBα expression was reversed and increased, and the pain behaviors and anxiety behaviors of rats were significantly relieved. Overall, these findings suggested that Trim14 may contribute to chronic inflammatory pain by degrading IκBα, and that Trim14 may become a novel therapeutic target for chronic inflammatory pain.

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.

Inhibition of NKCC1 Ameliorates Anxiety and Autistic Behaviors Induced by Maternal Immune Activation in Mice.

Autism spectrum disorder (ASD) is thought to result from susceptibility genotypes and environmental risk factors. The offspring of women who experience pregnancy infection have an increased risk for autism. Maternal immune activation (MIA) in pregnant animals produces offspring with autistic behaviors, making MIA a useful model for autism. However, how MIA causes autistic behaviors in offspring is not fully understood. Here, we show that NKCC1 is critical for mediating autistic behaviors in MIA offspring. We confirmed that MIA induced by poly(I:C) infection during pregnancy leads to autistic behaviors in offspring. We further demonstrated that MIA offspring showed significant microglia activation, excessive dendritic spines, and narrow postsynaptic density (PSD) in their prefrontal cortex (PFC). Then, we discovered that these abnormalities may be caused by overexpression of NKCC1 in MIA offspring's PFCs. Finally, we ameliorated the autistic behaviors using PFC microinjection of NKCC1 inhibitor bumetanide (BTN) in MIA offspring. Our findings may shed new light on the pathological mechanisms for autism caused by pregnancy infection.

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.

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.

[Corrigendum] MicroRNA­133b targets TGFß receptor I to inhibit TGF­ß­induced epithelial­to­mesenchymal transition and metastasis by suppressing the TGF­ß/SMAD pathway in breast cancer.

Following the publication of the above article, an interested reader drew to the authors' attention that, for the MCF­7 cell migration assays shown in Fig. 3C on p. 1105, the representative images selected for the 'TGF­ß+ / miR­NC' and 'TGF­ß1­ / miR­NC' experiments were found to be overlapping, such that the data appeared to have been derived from the same original source. After having consulted their original data, the authors noted that the error had arisen during the process of assembling this figure, and the data chosen for the 'TGF­ß+ / miR­NC' panel had been selected incorrectly. The revised version of Fig. 3 is shown on the next page. The authors regret that these errors went unnoticed prior to the publication of this article, and thank the Editor of International Journal of Oncology for allowing them the opportunity to publish this corrigendum. All the authors agree with the publication of this corrigendum; furthermore, they also apologize to the readership of the journal for any inconvenience caused. [International Journal of Oncology 55: 1097­1109, 2019; DOI: 10.3892/ijo.2019.4879].

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review.

BACKGROUND AND AIMS: Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS: Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS: A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS: DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.

Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study.

Background This study aims to explore a deep learning (DL) algorithm for developing a prognostic model and perform survival analyses in SBT patients. Methods The demographic and clinical features of patients with SBTs were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We randomly split the samples into the training set and the validation set at 7:3. Cox proportional hazards (Cox-PH) analysis and the DeepSurv algorithm were used to develop models. The performance of the Cox-PH and DeepSurv models was evaluated using receiver operating characteristic curves, calibration curves, C-statistics and decision-curve analysis (DCA). A Kaplan−Meier (K−M) survival analysis was performed for further explanation on prognostic effect of the Cox-PH model. Results The multivariate analysis demonstrated that seven variables were associated with cancer-specific survival (CSS) (all p < 0.05). The DeepSurv model showed better performance than the Cox-PH model (C-index: 0.871 vs. 0.866). The calibration curves and DCA revealed that the two models had good discrimination and calibration. Moreover, patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K−M analysis. Conclusions This study reported a DeepSurv model that performed well in CSS in SBT patients. It might offer insights into future research to explore more DL algorithms in cohort studies.

Elevation of casein kinase 1ε associated with TDP-43 and tau pathologies in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by the presence of extracellular amyloid ß plaques and intraneuronal neurofibrillary tangles of hyperphosphorylated microtubule-associated protein tau in the brain. Aggregation of transactive response DNA-binding protein of 43 kDa (TDP-43) in the neuronal cytoplasm is another feature of AD. However, how TDP-43 is associated with AD pathogenesis is unknown. Here, we found that casein kinase 1ε (CK1ε) phosphorylated TDP-43 at Ser403/404 and Ser409/410. In AD brains, the level of CK1ε was dramatically increased and positively correlated with the phosphorylation of TDP-43 at Ser403/404 and Ser409/410. Overexpression of CK1ε promoted its cytoplasmic aggregation and suppressed TDP-43-promoted tau mRNA instability and tau exon 10 inclusion, leading to an increase of tau and 3R-tau expressions. Levels of CK1ε and TDP-43 phosphorylation were positively correlated with the levels of total tau and 3R-tau in human brains. Furthermore, we observed, in pilot immunohistochemical studies, that the severe tau pathology was accompanied by robust TDP-43 pathology and a high level of CK1ε. Taken together, our findings suggest that the elevation of CK1ε in AD brain may phosphorylate TDP-43, promote its cytoplasmic aggregation and suppress its function in tau mRNA processing, leading to acceleration/exacerbation of tau pathology. Thus, the elevation of CK1ε may link TDP-43 to tau pathogenesis in AD brain.

MicroRNA­133b targets TGFß receptor I to inhibit TGF­ß­induced epithelial­to­mesenchymal transition and metastasis by suppressing the TGF­ß/SMAD pathway in breast cancer.

Breast cancer (BC) is one of the most common types of cancer and the leading cause of cancer­associated mortality among women worldwide. Accumulating evidence indicates that microRNA (miR)­133b inhibits the proliferation and invasion of cancer cells. Considering that transforming growth factor (TGF)­ß signaling plays a key role in cellular epithelial­to­mesenchymal transition (EMT) and cancer metastasis, it is crucial to explore the roles and underlying molecular mechanisms of miR­133b in regulating TGF­ß­induced EMT during progression of BC. In the present study, an inverse correlation was observed between the expression of miR­133b and TGFß receptor I (TGFßR1) mRNA in BC cells and tissues. Furthermore, miR­133b expression was found to be decreased in the BC tissues of patients with lymph node metastasis and advanced tumor­node­metastasis stage, while the expression of TGFßR1 was upregulated. Overexpression of miR­133b significantly decreased the expression of TGFßR1, an indispensable receptor of TGF­ß/SMAD signaling, and suppressed TGF­ß­induced EMT and BC cell invasion in vitro, whereas miR­133b knockdown exerted the opposite effects. Mechanistically, TGFßR1 was verified as a direct target of miR­133b as determined by bioinformatics analysis and a dual­luciferase reporter assay. In addition, small interfering RNA­mediated knockdown of TGFßR1 mimicked the phenotype of miR­133b overexpression in BC cells. Furthermore, miR­133b overexpression suppressed BC cell invasion in vivo. Collectively, the findings of the present study indicated that miR­133b acts as a tumor suppressor, inhibiting TGF­ß­induced EMT and metastasis by directly targeting TGFßR1, and suppressing the TGF­ß/SMAD pathway. Therefore, miR­133b may be of value as a diagnostic biomarker of BC.