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.

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.

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.

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.

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.

[Corrigendum] MicroRNA‑133b targets TGFβ receptor I to inhibit TGF‑β‑induced epithelial‑to‑mesenchymal transition and metastasis by suppressing the TGF‑β/SMAD pathway in breast cancer.

Following the publication of the above article, an interested reader drew to the authors' attention that, for the MCF‑7 cell migration assays shown in Fig. 3C on p. 1105, the representative images selected for the 'TGF‑β+ / miR‑NC' and 'TGF‑β1‑ / miR‑NC' experiments were found to be overlapping, such that the data appeared to have been derived from the same original source.

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.

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.

MicroRNA‑133b targets TGFβ receptor I to inhibit TGF‑β‑induced epithelial‑to‑mesenchymal transition and metastasis by suppressing the TGF‑β/SMAD pathway in breast cancer.

Breast cancer (BC) is one of the most common types of cancer and the leading cause of cancer‑associated 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)‑β signaling plays a key role in cellular epithelial‑to‑mesenchymal transition (EMT) and cancer metastasis, it is crucial to explore the roles and underlying molecular mechanisms of miR‑133b in regulating TGF‑β‑induced EMT during progression of BC.

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 β 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.