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. Clinical and pathological data of patients with lower rectal cancer who underwent transanal endoscopic ISR at the First Affiliated Hospital of Xiamen University between May 2018 and May 2023 were included. All patients underwent transanal endoscopic ISR as the surgical approach. We conducted this study to determine the perioperative recovery status, postoperative complications, and pathological specimen characteristics of this group of patients. RESULTS: This study included 45 eligible patients, with no perioperative mortalities. The overall incidence of early complications was 22.22%, with a rate of 4.44% for Clavien-Dindo grade ≥ III events. Two patients (4.4%) developed anastomotic leakage after surgery, including one case of grade A and one case of grade B. Postoperative pathological examination confirmed negative circumferential resection margins and distal resection margins in all patients. The mean distance between the tumor lower margin and distal resection margin was found to be 2.30 ± 0.62 cm. The transanal endoscopic ISR procedure consistently yielded high quality pathological specimens. CONCLUSION: Transanal endoscopic ISR is safe, feasible, and provides a clear anatomical view. It is associated with a low incidence of postoperative complications and favorable pathological outcomes, making it worth further research and application.

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. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model's performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.

Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer.

BACKGROUND: Laparoscopic radical gastrectomy is widely used, and perioperative complications have become a highly concerned issue. AIM: To develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery, guide perioperative treatment strategies for gastric cancer patients, and prevent serious complications. METHODS: In total, 998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the training group for the complication model, and 398 patients were included in the validation group. The clinicopathological data and 30-d postoperative complications of gastric cancer patients were collected. Three machine learning methods, lasso regression, random forest, and artificial neural networks, were used to construct postoperative complication prediction models for laparoscopic distal gastrectomy and laparoscopic total gastrectomy, and their prediction efficacy and accuracy were evaluated. RESULTS: The constructed complication model, particularly the random forest model, could better predict serious complications in gastric cancer patients undergoing laparoscopic radical gastrectomy. It exhibited stable performance in external validation and is worthy of further promotion in more centers. CONCLUSION: Using the risk factors identified in multicenter datasets, highly sensitive risk prediction models for complications following laparoscopic radical gastrectomy were established. We hope to facilitate the diagnosis and treatment of preoperative and postoperative decision-making by using these models.

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). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.

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. Univariate and multivariate regression methods were employed to analyze the risk factors influencing the occurrence of major complications (Clavien-Dindo III-V). Results: This study included a total of 179 eligible cases, with no perioperative deaths. The overall incidence of early complications was 25.1%, with a rate of 10.1% for mild complications and 15.0% for major complications. The postoperative anastomotic leakage rate within 30 days was 6.7%. Multivariate analysis demonstrated that male (P=0.030), pathological T ≥ 3 (P=0.018) and manual anastomosis (P=0.009) were independent risk factors for the development of major complications after surgery. Conclusion: In this study, the incidence of early complications and anastomotic leakage rate in TaTME were both relatively low. Male, pathological T stage ≥ 3 and manual anastomosis were independent risk factors for the occurrence of major complications in a cohort of patients with mid and low rectal cancer undergoing TaTME.

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. On the other side, the Transformer design includes a built-in global self-attention mechanism, but it doesn't pay enough attention to local features. In this paper, we propose DAMS-Net, which combines the advantages of Transformer and CNN, introducing a spatial attention module and a channel attention module using a CNN-Transformer hybrid encoder to adaptively focus on the significant features of global and local parts between space and channels. In addition, our proposal fuses multi-scale information to capture high and low-level semantic information by skip-connections. We evaluate our method on the 2018 Physiological Electrical Signaling Challenge dataset, and our proposal achieves a precision rate of 83.6%, a recall rate of 84.7%, and an F1-score of 0.839. The classification performance is superior to all current single-model methods evaluated in this dataset. The experimental results demonstrate the promising application of our proposed method in 12-lead ECG automatic classification tasks.

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. Patients were divided into the AC group (127 patients) and the nonadjuvant chemotherapy (NAC) group (112 patients). RESULTS: Kaplan‒Meier (KM) analysis showed that there were no significant differences in the 1-year3-year overall survival rate (OS) and 1-year, 3-year recurrence-free survival rate (RFS) between the AC group and the NAC group (1-year OS: 85.6% vs. 79.8%, 3-year OS: 59.8% vs. 62.4%, 1-year RFS: 69.8% vs. 74.4%, 3-year RFS: 57.2% vs. 55.9%, all P > 0.05). The subpopulation treatment effect pattern plots (STEPP) did not show treatment heterogeneity of AC in patients with HAS. The proportions of local recurrence and metastasis sites in the two groups were similar. Although the smoothed hazard curves of the NAC and AC groups crossed, the peak hazard time was later in the AC group (5.9 and 4.7 months), and the peak hazard rate was lower (0.032 and 0.038, P = 0.987). CONCLUSION: The current AC regimen may not significantly improve the survival of patients with HAS after radical surgery.

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. Methods: This was a multicenter, retrospective cohort study. we collected clinicopathological and follow-up data of patients with consecutive cases diagnosed with AEG from January 2016 to June 2021 in five medical centers. According to the way of digestive tract reconstruction after tumor resection, patients who underwent LPG-DTR or LPG-TLR were included in the present study. Propensity score matching (PSM) was performed to balance baseline variables that might affect the study outcomes. The QOL of the patients was evaluated using the Visick grade. Results: A total of 124 eligible consecutive cases were finally included. Patients in both groups were matched using the PSM method, and 55 patients from each group were included in the analysis after PSM. There was no statistically significant difference between the two groups in terms of the operation time, amount of intraoperative blood loss, days of postoperative abdominal drainage tube placement, postoperative hospitalization days, total hospitalization cost, the total number of lymph nodes cleared, and the number of positive lymph nodes (P>0.05). There was a statistically significant difference between the two groups in terms of time to first flatus after surgery and postoperative soft food recovery time (P<0.05). For the nutritional status, the weight levels at 1 year after surgery was better in the LPG-DTR group than in the LPG-TLR group (P<0.05). There was no significant difference in Visick grade between the two groups (P>0.05). Conclusion: The anti-reflux effect and quality of life of LPG-DTR for AEG were comparable to those of LPG-TLR. Compared with LPG-TLR, LPG-DTR provide better nutrition status for patients with AEG. LPG-DTR is a superior reconstruction method after proximal gastrectomy.

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. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

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. In the present study, a published circRNA microarray dataset was used to identify differentially expressed circRNAs between GC tissues and normal gastric mucosa tissues. Reverse transcription-quantitative PCR was performed to validate the expression of circ_0001789. Fisher's exact test, receiver operating characteristic curve and Kaplan-Meier plots were employed to analyze the clinical significance of circ_0001789. The miRNA targets of circ_0001789 were predicted using an online database, and their functional interaction was further confirmed by RNA pull-down, RNA immunoprecipitation and dual luciferase reporter assays. Transwell assays were conducted to investigate the biological functions of circ_0001789, miR-140-3p and p21 activated kinase 2 (PAK2) in the migration and invasion of GC cells. A xenograft mouse model was established to validate the role of circ_0001789 in the tumorigenesis of GC cells. RESULTS: circ_0001789 was identified as a highly expressed circRNA in GC tissues versus normal gastric mucosa tissues. Silencing circ_0001789 attenuated the malignancy of GC cells, and exosomal circ_0001789 was sufficient to regulate the malignant phenotype of GC cells. miR-140-3p was further identified as a downstream target of circ_0001789, which showed a negative correlation with circ_0001789 expression in GC tissues. Overexpression of miR-140-3p suppressed cell migration, invasion and epithelial-mesenchymal transition in GC cells. PAK2 was identified as the target of miR-140-3 to mediate the malignant phenotype of GC cells. CONCLUSION: The present data suggested that the upregulation of circ_0001789 was associated with the progression of GC and with poor prognosis in patients with GC, and that miR-140-3p/PAK2 served as the downstream axis to mediate the oncogenic effect of circ_0001789.

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. Venous-phase CT images were retrieved for tumor segmentation and feature extraction. A radiomics model was developed by the least absolute shrinkage and selection operator method. The nomogram was developed by clinical factors and the radiomics score. RESULTS: 1409 features were extracted and a radiomics model consisting of 19 features was developed, which showed a favorable performance in discriminating GHAC from GAC (AUCtraining cohort = 0.998, AUCinternal validation set = 0.942, AUCexternal test cohort = 0.731). The radiomics nomogram, including the radiomics score, AFP, and CA72_4, achieved good calibration and discrimination (AUCtraining cohort = 0.998, AUCinternal validation set = 0.954, AUCexternal test cohort = 0.909). CONCLUSIONS: The noninvasive CT-based nomogram, including radiomics score, AFP, and CA72_4, showed favorable predictive efficacy for differentiating GHAC from GAC and might be useful for clinical decision-making.

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. Clinicopathologic variables with statistical significance in the multivariate Cox regression were incorporated into a nomogram to build a recurrence predictive model. RESULTS: The nomogram of recurrence-free survival (RFS) based on independent prognostic factors, including age, preoperative carcinoembryonic antigen, number of examined lymph nodes, perineural invasion, and lymph node ratio, achieved a C-index of 0.723 (95% confidence interval [CI], 0.674-0.772) in the whole cohort, which was significantly higher than those of the eighth American Joint Committed on Cancer (AJCC) staging system (C-index, 0.629; 95% CI, 0.573-0.685; P < 0.001). The nomogram accurately stratified patients into low-, middle-, and high-risk groups of postoperative recurrence. The postoperative recurrence risk rates for patients in the middle- and high-risk groups were respectively 3 and 10 times higher than for the low-risk group. The patients in the middle- and high-risk groups showed more recurrence and metastasis, particularly multiple site metastasis, within 36 months after the operation than those in the low-risk group (low, 2.2%; middle, 8.6%; high, 24.0%; P = 0.003). CONCLUSIONS: The nomogram achieved good prediction of postoperative recurrence for the patients with HAS after radical resection. For the middle- and high-risk patients, more active surveillance and targeted examination methods should be adopted within 36 months after the operation, particularly for liver and multiple metastases.

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. The results of the first stage are then subdivided in the second stage to obtain accurate cardiac substructures. In addition, we also propose hybrid feature fusion, multi-resolution fusion and multi-scale fusion to further improve the segmentation accuracy. Experiments on the public dataset MM-WHS show that our model can achieve comparable accuracy in about half the training time of neural network models.

Long non-coding RNA TDRG1 aggravates colorectal cancer stemness by binding with miR-873-5p to upregulate PRKAR2.

The effects of long non-coding RNA TDRG1 have been established in several tumors; however, its roles in colorectal cancer (CRC) progression are never been found. Here, we found that TDRG1 level was upregulated in CRC cells compared to that in normal colon epithelial cells. Additionally, TDRG1 level was remarkably upregulated in 3D non-adherent spheres derived from the parental CRC cells. Further in vitro and in vivo revealed that TDRG1 knockdown suppressed the stemness of CRC cells. What's more, combined with bioinformatics analysis, luciferase reporter and RNA pull down experiments showed that TDRG1 could bind to miR-873-5p, downregulated its level and thus increase the expression of PRKAR2. Finally, it was shown that TDRG1 functioned through the miR-873-5p/PRKAR2 axis. This study demonstrated a novel TDRG1/miR-873-5p/PRKAR2 signaling in CRC progression.

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. This impact is dependent on gap junction channel functions, as the channel blocker carbenoxolone or connexin channel death mutant reverses this effect. Additionally, enhanced glucose uptake was detected in Cx43-overexpressing spheroids, along with upregulated mTOR, downregulated AMPK signaling, increased ATP content, and enhanced oxygen consumption rate. Furthermore, the xenograft mouse model confirmed the growth advantage of Cx43 in vivo. RNAseq data and clinical information from The Cancer Genome Atlas (TCGA) database indicated a more heterogeneous expression pattern of Cx43 in colon cancer compared to normal colon tissue, and higher Cx43 level is associated with worse clinical outcomes. Our data suggest a novel function of connexin in tumor growth, that gap junctions may provide nutrients transmitting routes in lieu of vasculature to meet the increasing metabolic requirement of solid tumors.

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. METHODS: Quantitative real-time PCR (qPCR) was applied to detect gene expression. Western blot was used to measure protein expression. Cell proliferation and migration were determined by functional assays. Fluorescence in situ hybridization (FISH) assays were performed to determine the subcellular distribution of TFAP2A-AS1 in GC. Mechanism investigations were conducted to explore the downstream genes of TFAP2A-AS1 and the upstream transcription factor of TFAP2A-AS1 in GC cells. RESULTS: TFAP2A-AS1 inhibits the proliferation and migration of GC cells. In the downstream regulation mechanism, miR-3657 was verified as the downstream gene of TFAP2A-AS1 and NISCH as the target of miR-3657. NISCH also suppresses cell proliferation and migration in GC. In the upstream regulation mechanism, transcription factor KLF15 positively mediates TFAP2A-AS1 to suppress GC cell proliferation and migration. CONCLUSION: KLF15-mediated TFAP2A-AS1 hampers cell proliferation and migration in GC via miR-3657/NISCH axis.

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. Main Outcomes and Measures: OS and prognostic factors. Patients were randomly assigned to a derivation cohort (n = 220) and a validation cohort (n = 95). A nomogram was developed based on independent prognostic factors identified through a multivariable Cox mixed-effects model. Results: Among 315 patients with HAS (mean [SD] age, 61.9 [10.2] years; 240 men [76.2%]), 137 patients had simple HAS (defined as the presence of histologically contained hepatoid differentiation areas only), and 178 patients had mixed HAS (defined as the presence of hepatoid differentiation areas plus common adenocarcinoma areas). Patients with simple HAS had a higher median preoperative α-fetoprotein level than those with mixed HAS (195.9 ng/mL vs 48.9 ng/mL, respectively; P < .001) and a higher rate of preoperative liver metastasis (23 of 137 patients [16.8%] vs 11 of 178 patients [6.2%]; P = .003). The 3-year OS rates of patients with simple vs mixed HAS were comparable (56.0% vs 60.0%; log-rank P = .98). A multivariable Cox analysis of the derivation cohort found that the presence of perineural invasion (hazard ratio [HR], 2.13; 95% CI, 1.27-3.55; P = .009), preoperative carcinoembryonic antigen levels of 5 ng/mL or greater (HR, 1.72; 95% CI, 1.08-2.74; P = .03), and pathological node category 3b (HR, 3.72; 95% CI, 1.34-10.32; P = .01) were independent risk factors for worse OS. Based on these factors, a nomogram to predict postoperative OS was developed. The concordance indices of the nomogram (derivation cohort: 0.72 [95% CI, 0.66-0.78]; validation cohort: 0.72 [95% CI, 0.63-0.81]; whole cohort: 0.71 [95% CI, 0.66-0.76]) were higher than those derived using the American Joint Committee on Cancer's AJCC Cancer Staging Manual (8th edition) pathological tumor-node-metastasis (pTNM) staging system (derivation cohort: 0.63 [95% CI, 0.57-0.69]; validation cohort: 0.65 [95% CI, 0.56-0.75]; whole cohort: 0.64 [95% CI, 0.59-0.69]) and those derived using a clinical model that included pTNM stage and receipt of adjuvant chemotherapy (derivation cohort: 0.64 [95% CI, 0.58-0.69]; validation cohort: 0.65 [95% CI, 0.56-0.75]; whole cohort: 0.64 [95% CI, 0.59-0.69]). Based on the nomogram cutoff of 10 points, the whole cohort was divided into high-risk and low-risk groups. The 3-year OS rate of patients in the high-risk group was significantly lower than that of patients in the low-risk group (29.7% vs 75.9%, respectively; log-rank P < .001), and the 3-year prognosis of high-risk and low-risk groups could be further distinguished into pTNM stage I to II (33.3% vs 80.2%; exact log-rank P = .15), stage III (34.3% vs 71.3%; log-rank P < .001), and stage IV (15.5% vs 70.3%; log-rank P = .009). Conclusions and Relevance: This study found that perineural invasion, preoperative carcinoembryonic antigen levels of 5 ng/mL or greater, and pathological node category 3b were independent risk factors associated with worse OS. An individualized nomogram was developed to predict OS among patients with HAS. This nomogram had good prognostic value and may be useful as a supplement to the current American Joint Committee on Cancer TNM staging system.

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. By examining the key characteristics of discrete medical volume data, this paper proposes a direct slicing technique for printing implicitly represented 3D medical models. The proposed method mainly consists of three algorithms: (1) A layer-based contour extraction algorithm for discrete volume data; (2) An inner shell construction algorithm based on discrete point differential indentation; (3) An infill generation algorithm based on the constructed virtual contour and scan lines. The proposed method has been applied to the slicing of several organ models for experiments, and the ratios of time cost and memory cost between the conventional method and the proposed method are about 4-100 and 1.1 to 1.4 respectively, which demonstrate that the proposed method has a great improvement in both time and space performance when compared with the conventional STL-based method. Our technique extends the direct input format of geometric models for additive manufacturing. That is, discrete volume data can be used as a direct input for additive manufacturing without conversion to STL format.

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. Cell viability was measured by CCK-8 assay. Transwell assays were performed to evaluate the GC cells' migratory and invasive abilities. Xenograft mouse model was conducted to measure tumor growth. RESULTS: We found that Linc-ROR were overexpressed in GC tissues compared to the adjacent tissues. High Linc-ROR predicts poor prognosis of GC patients. The prediction of bioinformatics online revealed that Linc-ROR could bind to miR-212-3p. Further, dual-luciferase reporter assay confirmed a direct interaction between Linc-ROR and miR-212-3p. Overexpression of miR-212-3p facilitated GC cells' migration and invasion, while the silencing of miR-212-3p attenuated GC cell migratory and invasive abilities. Moreover, Linc-ROR knockdown significantly suppressed the proliferation, migration, and invasion of GC cells, whereas miR-212-3p antagomir partially reversed Linc-ROR knockdown-induced phenotypes. Fibroblast growth factor 7 (FGF7), a downstream molecule of miR-212-3p, was overexpressed in GC cells. The recovery of FGF7 expression partially reversed the phenotypes caused by Linc-ROR silencing. Mechanistically, silencing of Linc-ROR contributed to the downregulation of CDK4, CDK6, Cyclin D1, N-Cadherin, Vimentin, MMP-9, MMP-2, but caused the upregulation of P21, P27, E-Cadherin, CK-19 in MGC-803 cells; however, FGF7 treatment could reverse the results induced by Linc-ROR silencing. Results in vivo further suggested that Linc-ROR knockdown repressed GC tumor growth, where the expression of miR-212-3p was up-regulated and FGF7 expression was downregulated in tumor tissues of mice. CONCLUSION: These findings indicated that Linc-ROR/miR-212-3p/FGF7 axis played an important role in gastric cancer progression. Linc-ROR expression level was associated with the prognosis of GC patients.

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. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.