Development of a staging system for hepatoid adenocarcinoma of the stomach based on multicenter data: A retrospective cohort study: A Staging System for Hepatoid Adenocarcinoma of the Stomach.

Background: Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer (GC) with a poor prognosis. Furthermore, the current pathological staging system for HAS does not distinguish it from that for common gastric cancer (CGC).

Methods: The clinicopathological data of 251 patients with primary HAS who underwent radical surgery at 14 centers in China from April 2004 to December 2019 and 5082 patients with primary CGC who underwent radical surgery at 2 centers during the same period were retrospectively analyzed.

The Short-Term and Long-Term Outcomes of Laparoscopy-Assisted Proximal Gastrectomy with Double-Tract Reconstruction versus Laparoscopy-Assisted Total Gastrectomy with Roux-en-Y Reconstruction for Adenocarcinoma of the Esophagogastric Junction: A Multicenter Study Based on Propensity Score Matching Analysis.

Purpose: To compare the antireflux effect, long-term nutritional levels, and quality of life (QoL) between laparoscopy-assisted proximal gastrectomy with double-tract reconstruction (LPG-DTR) and laparoscopy-assisted total gastrectomy with Roux-en-Y reconstruction (LTG-RY) for adenocarcinoma of the esophagogastric junction (AEG).

Methods: This multicenter retrospective cohort study collected clinicopathological and follow-up data of AEG patients from January 2016 to January 2021 at five high-volume surgery centers. The study included patients who underwent digestive tract reconstruction with LPG-DTR or LTG-RY after tumor resection.

Clinical efficacy and pathological outcomes of transanal endoscopic intersphincteric resection for low rectal cancer.

Background: Transanal endoscopic intersphincteric resection (ISR) surgery currently lacks sufficient clinical research and reporting.

Aim: To investigate the clinical effectiveness of transanal endoscopic ISR, in order to promote the clinical application and development of this technique.

Methods: This study utilized a retrospective case series design.

ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation.

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer.

DAMS-Net: Dual attention and multi-scale information fusion network for 12-lead ECG classification.

Automated 12-lead electrocardiographic (ECG) classification algorithms play an important role in the diagnosis of clinical arrhythmias. Current methods that perform well in the field of automatic ECG classification are usually based on Convolutional Neural Networks (CNN) or Transformer. However, due to the intrinsic locality of convolution operations, CNN can't extract long-dependence between series.

Transanal total mesorectal excision: single center study on risk factors for major complications.

Purpose: Transanal total mesorectal excision (TaTME) as a novel surgical approach for mid and low rectal cancer has gained significant research interest in recent years. The main objective of this study is to identify the risk factors associated with major complications after TaTME and evaluate the perioperative clinical outcomes.

Methods: A retrospective analysis was performed on the clinical data of patients with mid-to-low rectal cancer who underwent TaTME surgery and were admitted to the First Affiliated Hospital of Xiamen University from January 2018 to May 2023.

Long-term prognostic benefit of adjuvant chemotherapy for patients with hepatoid adenocarcinoma of the stomach after radical resection: A national multicenter study.

Background: There is no consensus on whether adjuvant chemotherapy (AC) is effective for hepatoid adenocarcinoma of the stomach (HAS). The aim of this study was to investigate the relationship between AC and the long-term prognosis of patients with HAS.

Methods: The clinicopathological data of 239 patients with primary HAS who underwent radical surgery from April 1, 2004 to December 31, 2019 in 14 centers in China were retrospectively analyzed.

LViT: Language Meets Vision Transformer in Medical Image Segmentation.

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer).

The clinical outcomes of laparoscopic proximal gastrectomy with double-tract reconstruction versus tube-like stomach reconstruction in patients with adenocarcinoma of the esophagogastric junction based on propensity score-matching: a multicenter cohort study.

Purpose: Laparoscopic proximal gastrectomy with double-tract reconstruction (LPG-DTR) and laparoscopic proximal gastrectomy with tube-like stomach reconstruction (LPG-TLR) are both function-preserving procedures performed for treating AEG. However, there is no clinical consensus on the selection of digestive tract reconstruction after proximal gastrectomy, and the best way to reconstruct the digestive tract remains controversial. This study aimed at comparing the clinical outcomes of LPG-DTR and LPG-TLR to provide some reference to the choice of AEG surgical modalities.

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification.

Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification.

Circular RNA 0001789 sponges miR-140-3p and regulates PAK2 to promote the progression of gastric cancer.

Background: Gastric cancer (GC) is the third-leading cause of cancer-associated mortalities globally. The deregulation of circular RNAs (circRNAs) and microRNAs (miRNAs or miRs) is widely implicated in the pathogenesis and progression of different cancer types.

Methods: The expression profiling of circRNAs in GC is required to identify crucial circRNAs as biomarkers or therapeutic targets.

CT-based radiomics nomogram for differentiating gastric hepatoid adenocarcinoma from gastric adenocarcinoma: a multicentre study.

Background: To develop a CT-based radiomics nomogram for the high-precision preoperative differentiation of gastric hepatoid adenocarcinoma (GHAC) patients from gastric adenocarcinoma (GAC) patients.

Research Design And Methods: 108 patients with GHAC from 6 centers and 108 GAC patients matched by age, sex and T stage undergoing pathological examination were retrospectively reviewed. Patients from 5 centers were divided into two cohorts (training and internal validation) at a 7:3 ratio, the remaining patients were external test cohort.

Nomogram to Predict Recurrence and Guide a Pragmatic Surveillance Strategy After Resection of Hepatoid Adenocarcinoma of the Stomach: A Retrospective Multicenter Study.

Background: An accurate recurrence risk assessment system and surveillance strategy for hepatoid adenocarcinoma of the stomach (HAS) remain poorly defined. This study aimed to develop a nomogram to predict postoperative recurrence of HAS and guide individually tailored surveillance strategies.

Methods: The study enrolled all patients with primary HAS who had undergone curative-intent resection at 14 institutions from 2004 to 2019.

MRDFF: A deep forest based framework for CT whole heart segmentation.

Automatic whole heart segmentation plays an important role in the treatment and research of cardiovascular diseases. In this paper, we propose an improved Deep Forest framework, named Multi-Resolution Deep Forest Framework (MRDFF), which accomplishes whole heart segmentation in two stages. We extract the heart region by binary classification in the first stage, thus avoiding the class imbalance problem caused by too much background.

Gap junctions mediate glucose transfer to promote colon cancer growth in three-dimensional spheroid culture.

In vivo tumor growth is characterized by a necrotic core generated by oxygen and nutrients gradients, which is replicated by in vitro three-dimensional (3D) tumor spheroids but not traditional two-dimensional cell monolayers. Gap junctions provide direct communication between adjacent cells and play a critical role in cancer development, but their effects are still debatable. In this study, we found that connexin 43 (Cx43) reduced the area of necrotic core in colon cancer 3D spheroids, thus providing a growth advantage.

Transcription factor KLF15 inhibits the proliferation and migration of gastric cancer cells via regulating the TFAP2A-AS1/NISCH axis.

Background: Recently, overwhelming evidence supports that long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of tumors. However, the role and mechanism of lncRNA TFAP2A-AS1 in human gastric cancer (GC) remains unclear. Thus, the biological role and regulatory mechanisms of TFAP2A-AS1 in GC were explored.

Assessment of Clinicopathological Characteristics and Development of an Individualized Prognostic Model for Patients With Hepatoid Adenocarcinoma of the Stomach.

Importance: Few studies have examined the clinicopathological characteristics and prognoses of patients with hepatoid adenocarcinoma of the stomach (HAS).

Objective: To explore the clinicopathological characteristics and prognoses of patients with HAS and develop a nomogram to predict overall survival (OS).

Design, Setting, And Participants: This prognostic study involved a retrospective analysis of data from 315 patients who received a diagnosis of primary HAS between April 1, 2004, and December 31, 2019, at 14 centers in China.

A direct slicing technique for the 3D printing of implicitly represented medical models.

In conventional medical image printing methods, volumetric medical data needs to be conversed into STereo Lithography (STL) format, the most commonly used format for representing geometric models for 3D printing. However, this STL conversion process is not only time consuming, but more importantly, it often leads to the loss of accuracy. It has become a critical factor hindering the printing efficiency and precision of organ models.

Upregulation of Linc-ROR Promotes the Proliferation, Migration, and Invasion of Gastric Cancer Cells Through miR-212-3p/FGF7 Axis.

Background: Linc-ROR is a long non-coding RNA, that is found aberrantly expressed in various human cancers. We aim here to unveil the role of Linc-ROR in gastric cancer (GC) progression.

Methods: qPCR was used to determine gene expression.

Image-Based Automatic Watermeter Reading under Challenging Environments.

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters.

High-quality vascular modeling and modification with implicit extrusion surfaces for blood flow computations.

Background And Objective: High-quality vascular modeling is crucial for blood flow simulations, i.e., computational fluid dynamics (CFD).

A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour.

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy.