Tumor regression grade in locally advanced rectal cancer after neoadjuvant chemoradiotherapy: influencing factors and prognostic significance.

OBJECTIVE: The extent of tumor regression varies widely among patients who receive neoadjuvant chemoradiotherapy (NACRT) followed by total mesorectal excision (TME) surgery. We evaluated the tumor regression grade (TRG) classification of patients and analyzed factors related to TRG and its value in predicting prognosis in locally advanced rectal cancer (LARC). METHODS: This study retrospectively analyzed the clinicopathologic data of 269 consecutive patients with LARC treated from February 2002 to October 2014. The grade of TRG was based on the extent of primary tumor replaced by fibrosis. Clinical characteristics and relative survival were retrospectively analyzed. RESULTS: There were 269 patients, among whom 67 patients (24.9%) achieved TRG0, whereas 46 patients (17.1%) showed TRG3. TRG1 and TRG2 were both found in 78 patients (29.0%). Clinicopathologic factors that were related to TRG included post-NACRT carcinoembryonic antigen (CEA) level (P=0.002), clinical T stage (P=0.022), pathologic T stage (P<0.001) and pathologic lymph node status (P=0.003). The 5-year overall survival (OS) was 74.6%, 55.1%, 47.4%, 28.3% for TRG0, TRG1, TRG2, TRG3, respectively (P<0.001). The 5-year disease-free survival (DFS) was 64.2%, 47.4%, 37.2%, 23.9% for TRG0, TRG1, TRG2, TRG3, respectively (P<0.001). Based on multivariate analysis, TRG was a significant predictor for both OS (P=0.039) and DFS (P=0.043). CONCLUSION: Clinicopathologic factors such as post-NACRT CEA level, clinical T stage, pathological T stage and pathological lymph node status are significantly associated with TRG. TRG is an independent predictor of survival. Therefore, it is reasonable to include the TRG for clinicopathologic assessment.

Clinicopathological characteristics and prognosis of colon cancer with lung metastasis without liver metastasis: A large population-based analysis.

Distant metastasis explains the high mortality rate of colon cancer, in which lung metastasis without liver metastasis (LuM) is a rare subtype. This study is aimed to identify risk factors of LuM and LLM (lung metastasis with liver metastasis) from colon cancer, and to analyze the prognosis of patients with LuM by creating a nomogram. Patients' information were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic regression analysis was used to determine the risk factors for LuM and LLM. Prognostic factors for cancer-specific survival (CSS) and overall survival (OS) were identified by multivariate Cox proportional hazards regression and nomogram models were established to predict CSS and OS. Multivariate logistic regression analysis showed that blacks, splenic flexure of colon tumor, tumor size >5 cm, T4, N3, and higher lymph node positive rate were associated with the occurrence of LuM. Meanwhile, age >65 years old, female, splenic flexure of colon, higher lymph node positive rate, and brain metastasis were independent risk factors for CSS. The C-index of the prediction model for CSS was 0.719 (95% CI: 0.691-0.747). In addition, age, primary site, tumor size, differentiation grade, N stage, and bone metastasis were significantly different between LuM and LLM. The nomograms we created were effective in predicting the survival of individuals. Furthermore, patients with LuM and LLM from colon cancer might require different follow-up intervals and examinations.

DocR-BERT: Document-Level R-BERT for Chemical-Induced Disease Relation Extraction via Gaussian Probability Distribution.

Chemical-induced disease (CID) relation extraction from biomedical articles plays an important role in disease treatment and drug development. Existing methods are insufficient for capturing complete document level semantic information due to ignoring semantic information of entities in different sentences. In this work, we proposed an effective document-level relation extraction model to automatically extract intra-/inter-sentential CID relations from articles. Firstly, our model employed BERT to generate contextual semantic representations of the title, abstract and shortest dependency paths (SDPs). Secondly, to enhance the semantic representation of the whole document, cross attention with self-attention (named cross2self-attention) between abstract, title and SDPs was proposed to learn the mutual semantic information. Thirdly, to distinguish the importance of the target entity in different sentences, the Gaussian probability distribution was utilized to compute the weights of the co-occurrence sentence and its adjacent entity sentences. More complete semantic information of the target entity is collected from all entities occurring in the document via our presented document-level R-BERT (DocR-BERT). Finally, the related representations were concatenated and fed into the softmax function to extract CIDs. We evaluated the model on the CDR corpus provided by BioCreative V. The proposed model without external resources is superior in performance as compared with other state-of-the-art models (our model achieves 53.5%, 70%, and 63.7% of the F1-score on inter-/intra-sentential and overall CDR dataset). The experimental results indicate that cross2self-attention, the Gaussian probability distribution and DocR-BERT can effectively improve the CID extraction performance. Furthermore, the mutual semantic information learned by the cross self-attention from abstract towards title can significantly influence the extraction performance of document-level biomedical relation extraction tasks.

Effectiveness and Prognosis: Drainage Skin-Bridge Sparing Surgery Combined with Fistulotomy versus Fistulotomy Only in the Treatment of Anal Fistula.

Objective: This study intends to analyze the difference in the efficacy of drainage skin-bridge sparing surgery combined fistulotomy (DSCF) and fistulotomy alone. Methods: 125 patients with anal fistula were enrolled as study subjects and randomly divided into control group (CG) and observation group (OG) by double-blind lottery. The CG received drainage skin-bridge sparing surgery with fistulotomy and the OG received fistulotomy only. Results: The VAS scores of the trauma in the OG were lower than those in the CG on 1st day of surgery and 7 days after surgery (P < 0.05). The length of hospital stay and time to wound healing were shorter in the OG than in the CG (P < 0.05). The incidence of postoperative bleeding in the OG was 9.52%, which was lower than 22.58% in the CG (P < 0.05). The rectal examination scores were lower in the OG than in the CG at 3 and 5 days postoperatively (P < 0.05). The Wexner scores of solid incontinence (0 to 4), liquid incontinence (0 to 4), gas incontinence (0 to 4), pad wearing (0 to 4), and lifestyle alteration (0 to 4) in the OG were lower than those of the CG at 5 days postoperatively (P < 0.05). Voiding function scores were lower in the OG than in the CG at 2 and 3 days postoperatively (P < 0.05). Conclusions: The efficacy of drainage skin-bridge sparing surgery combined fistulotomy is better than that of fistulotomy alone, which can accelerate postoperative healing, enhance urinary function, reduce postoperative bleeding, and improve anal function.

CircRNA_002178 as a ceRNA promotes the development of colorectal cancer by regulating miR-542-3p/CREB1.

OBJECTIVE: Colorectal cancer (CRC) is a malignant tumor commonly found in the digestive tract. This study aimed to explore the effect of circRNA_002178 as a competing endogenous RNA in the development of CRC by regulating the miR-542-3p/cAMP response element binding protein 1 (CREB1) axis and its molecular mechanism. METHODS: The relative expressions of circ_002178, miR-542-3p, and CREB1 in patients' cell lines and CRC tissues were measured using Western blot and qRT-PCR. The localization and expression of circ_002178 were determined using fluorescence in situ hybridization and nucleocytoplasmic separation tests. The targeting relationships among circ_002178, miR-542-3p, and CREB1 were validated using RNA immunoprecipitation and dual luciferase reporter assays. The cells' proliferation, invasion, and colony forming ability were tested using CCK8, Transwell, and Clone formation assays, respectively. The cellular glucose consumption, lactification, and adenosine triphosphate (ATP) production were measured using glucose uptake colorimetric assay kits, lactate colorimetric assay kits and ATP assay kits, respectively. RESULTS: The circ_002178 and CREB1 expressions were up-regulated in the CRC cells and tissues, and the miR-542-3p expression was down-regulated (all P<0.05). The circ_002178 knockdown inhibited the proliferation, invasion, colony formation, and glycolysis of the CRC cells in vitro, but the overexpression of circ_002178 induced the opposite result (both P<0.05). Our molecular mechanism study revealed that circ_002178, as the molecular sponge of miR-542-3p, promotes CREB1 expression. The downregulation of miR-542-3p or the overexpression of CREB1 is able to partly weaken the inhibition of CRC cells through the circ_002178 knockdown. CONCLUSION: circ_002178 promotes the invasion, proliferation, colony formation, and glycolysis of CRC cells by regulating the miR-542-3p/CREB1 axis, thus driving the development of CRC.

A new health prediction model for a sensor network based on belief rule base with attribute reliability.

Health prediction plays an essential role in improving the reliability of a sensor network by guiding the network maintenance. However, affected by interference factors in the real operational environment, the reliability of the monitoring information about the sensor network tends to decline, which affects the health prediction accuracy. Furthermore, the lack of monitoring information and high complexity of the network increase the difficulty of health prediction. To solve these three problems, this paper proposes a new sensor network health prediction model based on the belief rule base model with attribute reliability (BRB-r). The BRB-r model is an expert system that fully considers the qualitative knowledge and quantitative data of the sensor network. In addition, it can address the fuzziness and nondeterminacy of this qualitative knowledge. In the new model, the unreliable monitoring information of the sensor network is handled by the attribute reliability mechanism. The reliability of the sensor is calculated by the average distance method. Due to the effect of the fuzziness and nondeterminacy of expert knowledge, the health status of the sensor network cannot be accurately estimated by the initial health prediction model. Consequently, the optimization model for the health prediction model is established. Finally, a case study regarding a sensor network for oil storage tanks is conducted, and the validity of this method is demonstrated.