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Gastric cancer (GC) is a common tumor with a low 5‐year survival rate. The chemokine receptor 4 (CXCR4) protein contributes to the progression and prognosis of GC, but the relationship between CXCR4 and immune infiltration, somatic copy number alteration (SCNA), tumor purity, tumor mutation burden (TMB), cytolytic activity (CYT), and drug sensitivity in GC is poorly understood. This study aimed to systematically explore the role of CXCR4 in GC. Microarray and RNA‐seq data were collected from the Gene Expression Omnibus and The Cancer Genome Atlas. Our analysis shows that CXCR4 is correlated with various types of immune cells. Patients with high CXCR4 expression had a higher fraction of B cells and CD8+ T cells, and a lower fraction of CD4+ T cells. In addition, high CXCR4 expression was associated with more advanced tumor stage, worse prognosis and higher stromal score, immune score, and cytolytic activity (P

Abstract Purpose This study aimed to investigate the characteristics of colonic neuroendocrine neoplasms (NENs) and to validate the prognostic value of the European Neuroendocrine Tumor Society (ENETS) and American Joint Committee on Cancer (AJCC) 8th staging systems. Methods A total of 167 and 1248 patients with colonic NENs from 12 medical centers across China and from the Surveillance, Epidemiology, and End Results (SEER) cancer registry in the United States, respectively, were reviewed. Patients were staged according to the ENETS and AJCC 8th staging systems. Results Clinicopathological features of colonic NENs in the Chinese cohort and SEER cohort were significantly distinct. In both the Chinese cohort and the SEER cohort, colonic neuroendocrine carcinoma (NEC) and mixed adeno‐neuroendocrine carcinoma (MANEC) were more frequent in the midgut than in the hindgut. Tumors originating from the midgut tended to be larger and at a more advanced stage than those from the hindgut. The AJCC 8th staging system and the ENETS system appeared to have similar prognostic ability for colonic NEC/MANEC. Conclusions Our study revealed that tumors originating from the midgut and the hindgut shared different clinicopathological features. The AJCC 8th staging system and the ENETS system appeared to have similar prognostic ability for colonic NEC/MANEC.

We aimed to identify new targets affecting gastric cancer (GC) prognosis. Six target genes were identified from hub genes based on their relationship with important factors affecting tumor progression, like immune infiltration, purity, tumor mutation burden (TMB), and tumor microenvironment (TME) score. The effect of target genes’ somatic mutations and copy number alteration (CNA) was examined to determine their effect on GC prognosis. Six target genes (FBN1, FN1, HGF, MMP9, THBS1, and VCAN) were identified. Reduced expression of each target gene, except MMP9, indicated better prognosis and lower grade in GC. FBN1, THBS1, and VCAN showed lower expression in stage I GC. Non-silencing mutations of the six genes played a role in significantly higher TMB and TME scores. THBS1 mutation was associated with earlier stage GC, and VCAN mutation was associated with lower grade GC. However, patients with target gene CNA displayed higher tumor purity. MMP9, THBS1, and VCAN CNA was associated with lower grade GC, while FBN1 CNA reflected earlier T stage. Additionally, the target genes may affect GC prognosis by influencing multiple oncogenic signaling pathways. FBN1, FN1, HGF, MMP9, THBS1, and VCAN may be new GC prognostic targets by affecting tumor purity, TMB, TME score, and multiple oncogenic signaling pathways.

Objective To evaluate the effect of the knee and ankle joint angles on the stiffness of triceps surae in healthy men by shear wave elastography (SWE). Methods Twenty male healthy volunteers who underwent physical examination in Guangdong Provincial Hospital of Chinese Medicine from January 2021 to June 2021 were collected. Under the condition of full knee extension (0 °) or flexion (90 °), SWE was used to assess the muscle stiffness at 25° dorsiflexion, 0° and 50° plantar flexion of the ankle. Results The stiffness of medial head of gastrocnemius(MG), lateral head of gastrocnemius(LG) and soleus (Sol) in knee extension position was significantly higher than that in knee flexion position at different ankle angles (P0.05). When knee flexion was 90°, only LG and sol stiffness increased with the increase of ankle dorsiflexion angle (P

BackgroundMelanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma.MethodsWe collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma.ResultsAccording to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A1, B1, and C1. Among them, C1 subtype had the best survival outcome. Based on the differentially expressed genes shared by A1, B1, and C1 genotypes, we obtained the results of three gene subtypes of A2, B2, and C2. Among them, the B2 group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes.ConclusionsIn conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Livin is a novel member of the inhibitor of apoptosis protein family, which has been identified to be expressed in various malignancies and is suggested to be associated with poor prognostic significance. However, no data are available concerning the significance of livin in mid‑distal rectal cancer. In the present study, livin expression, and its association with clinicopathological characteristics and prognosis was examined in patients with mid‑distal rectal cancer. Apoptotic susceptibility, invasion capacity and chemosensitivity of LoVo cells were investigated using small interfering RNA (siRNA)‑mediated knockdown of livin. It was revealed that livin was highly expressed in mid‑distal rectal cancer tissues compared with the normal rectal mucosal tissues. Livin expression was associated with pathological grade, extent of invasion (T stage) and extent of lymph node metastasis (N stage) of tumor, contributing to poor prognosis of mid‑distal rectal cancer following surgery. The data suggest that aggressive surgery should be applied in patients with mid‑distal rectal cancer with high expression of livin. It was also revealed that knockdown of livin by siRNA increased the apoptotic rate, suppressed invasion of LoVo cells, and decreased the half‑maximal inhibitory concentration of oxaliplatin and 5‑fluorouracil by ~50% in LoVo cells significantly compared with control groups. The data suggested that a combination of downregulation of livin and anticancer drugs may significantly decrease the toxicity of anticancer drugs. Taken together, the present study indicated that livin may be a promising target in clinical therapy of mid‑distal rectal cancer. [ABSTRACT FROM AUTHOR]

BackgroundLiver transplantation surgery is often accompanied by massive blood loss and massive transfusion (MT), while MT can cause many serious complications related to high mortality. Therefore, there is an urgent need for a model that can predict the demand for MT to reduce the waste of blood resources and improve the prognosis of patients.ObjectiveTo develop a model for predicting intraoperative massive blood transfusion in liver transplantation surgery based on machine learning algorithms.MethodsA total of 1,239 patients who underwent liver transplantation surgery in three large grade lll-A general hospitals of China from March 2014 to November 2021 were included and analyzed. A total of 1193 cases were randomly divided into the training set (70%) and test set (30%), and 46 cases were prospectively collected as a validation set. The outcome of this study was an intraoperative massive blood transfusion. A total of 27 candidate risk factors were collected, and recursive feature elimination (RFE) was used to select key features based on the Categorical Boosting (CatBoost) model. A total of ten machine learning models were built, among which the three best performing models and the traditional logistic regression (LR) method were prospectively verified in the validation set. The Area Under the Receiver Operating Characteristic Curve (AUROC) was used for model performance evaluation. The Shapley additive explanation value was applied to explain the complex ensemble learning models.ResultsFifteen key variables were screened out, including age, weight, hemoglobin, platelets, white blood cells count, activated partial thromboplastin time, prothrombin time, thrombin time, direct bilirubin, aspartate aminotransferase, total protein, albumin, globulin, creatinine, urea. Among all algorithms, the predictive performance of the CatBoost model (AUROC: 0.810) was the best. In the prospective validation cohort, LR performed far less well than other algorithms.ConclusionA prediction model for massive blood transfusion in liver transplantation surgery was successfully established based on the CatBoost algorithm, and a certain degree of generalization verification is carried out in the validation set. The model may be superior to the traditional LR model and other algorithms, and it can more accurately predict the risk of massive blood transfusions and guide clinical decision-making.

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

BackgroundLung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. This study aimed to identify the key intercellular communication-associated genes (ICAGs) in LUAD.MethodsEight publicly available datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The prognosis-related ICAGs were identified and a risk score was developed by using survival analysis. Machine learning models were trained to predict LUAD recurrence based on the selected ICAGs and clinical information. Comprehensive analyses on ICAGs and tumor microenvironment were performed. A single-cell RNA-sequencing dataset was assessed to further elucidate aberrant changes in intercellular communication.ResultsEight ICAGs with prognostic potential were identified in the present study, and a risk score was derived accordingly. The best machine-learning model to predict relapse was developed based on clinical information and the expression levels of these eight ICAGs. This model achieved a remarkable area under receiver operator characteristic curves of 0.841. Patients were divided into high- and low-risk groups according to their risk scores. DNA replication and cell cycle were significantly enriched by the differentially expressed genes between the high- and the low-risk groups. Infiltrating immune cells, immune functions were significantly related to ICAGs expressions and risk scores. Additionally, the changes of intercellular communication were modeled by analyzing the single-cell sequencing dataset.ConclusionThe present study identified eight key ICAGs in LUAD, which could contribute to patient stratification and act as novel therapeutic targets.

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.

BACKGROUND: Vezatin is an transmembrane protein associated with cell-cell adhesion junctions. In our previous studies, we found that the tumor suppressor function of VEZT was related to methylation of CpG island and were down-regulated in tumor tissue and cells compared to normal controls. However, the role of VEZT gene as a novel putative tumor suppressor in biological characteristics and the relationship with clinicopathological factors and prognosis of gastric cancer was not yet clear. Therefore, we sought to explore these questions and prepare for further research in this study. METHODS:We examined the vezatin expression levels in 119 gastric cancer tissues and adjacent normal tissues by immunohistochemistry. Furthermore, we evaluated the expression of VEZT and its relationship with clinicopathological factors, lymphatic metastasis and prognostic value for gastric cancer. RESULTS: The expression of VEZT was significantly down-regulated in gastric cancer tissues and cell lines and its expression levels was related to differentiation, TNM staging and lymphatic metastasis. Furthermore, analysis of 5-year survival of 119 gastric cancer patients showed that those with strong vezatin expression had significantly longer overall survival time than those with negative vezatin expression. CONCLUSIONS: These data provided an innovative insight that up-regulation of vezatin can be taken as a meaningful way for treating human gastric and other types of cancers. And VEZT expression levels can be considered as a biomarker for gastric cancer progression, lymphatic metastasis and as a novel independent prognostic factor. [ABSTRACT FROM AUTHOR]

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs).Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model.Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction.Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.

Clear cell renal cell carcinoma (ccRCC) is characterized by abnormal lipid accumulation. Celastrol is a pentacyclic triterpene extracted from Tripterygium wilfordii Hook F with anti-cancer activity. In the present study, the anticancer effects of celastrol on ccRCC and the underlying mechanisms were studied. Patients with reduced high density lipoprotein (HDL) and elevated levels of triglyceride (TG), total cholesterol (TC), low density lipoprotein (LDL) was found to have higher risk of ccRCC. In ccRCC clinical samples and cell lines, caveolin-1 (CAV-1) was highly expressed. CAV-1 was identified as a potential prognostic biomarker for ccRCC. Celastrol inhibited tumor growth and decreased lipid deposition promoted by high-fat diet in vivo. Celastrol reduced lipid accumulation and caveolae abundance, inhibited the binding of CAV-1 and lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) in ccRCC cells. Furthermore, celastrol attenuated stemness through blocking Wnt/β-catenin pathway after knockdown of CAV-1 and LOX-1. Therefore, the findings suggest that celastrol may be a promising active ingredient from traditional Chinese medicine for anti-cancer therapy.

Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery.Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation.Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%).Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients.Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis.Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction.Results: Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850–0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832–0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735–0.755) and 0.709 (95% CI: 0.687–0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837–0.846) and 0.803 (95% CI: 0.798–0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653–0.667) and SIC scores (0.752; 95% CI: 0.747–0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable.Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.

Tumor development is initiated by an accumulation of numerous genetic and epigenetic alterations that promote tumor initiation, invasion and metastasis. Astrocyte elevated gene-1 [AEG-1; also known as Metadherin (MTDH) and Lysine-rich CEACAM1 co-isolated (LYRIC)] has emerged in recent years as a potentially crucial mediator of tumor malignancy, and a key converging point of a complex network of oncogenic signaling pathways. AEG-1/MTDH has a multifunctional role in tumor development that has been found to be involved in the following signaling cascades: i) The Ha-Ras and PI3K/Akt pathways; ii) the nuclear factor-?B signaling pathway; iii) the ERK/mitogen-activated protein kinase and Wnt/β-catenin pathways; and iv) the Aurora-A kinase signaling pathway. Studies have established that AEG-1/MTDH is crucial in tumor progression, including transformation, the evasion of apoptosis, invasion, angiogenesis and metastasis. In addition, recent clinical studies have convincingly associated AEG-1/MTDH with tumor progression and poor prognosis in a number of cancer types, including hepatocellular, esophageal squamous cell, gallbladder and renal cell carcinomas, breast, non-small cell lung, prostate, gastric and colorectal cancers, and glioma, melanoma, neuroblastoma and osteosarcoma. AEG-1/MTDH may be used as a biomarker to identify subgroups of patients who require more intensive treatments and who are likely to benefit from AEG-1/MTDH-targeted therapies. The therapeutic targeting of AEG-1/MTDH may simultaneously block metastasis, suppress tumor growth and enhance the efficacy of chemotherapeutic treatments. [ABSTRACT FROM AUTHOR]

Abstract Background Linear elastic, hyperelastic, and multiphasic material constitutive models are frequently used for spinal intervertebral disc simulations. While the characteristics of each model are known, their effect on spine mechanical response requires a careful investigation. The use of advanced material models may not be applicable when material constants are not available, model convergence is unlikely, and computational time is a concern. On the other hand, poor estimations of tissue’s mechanical response are likely if the spine model is oversimplified. In this study, discrepancies in load response introduced by material models will be investigated. Methods Three fiber-reinforced C2-C3 disc models were developed with linear elastic, hyperelastic, and biphasic behaviors. Three different loading modes were investigated: compression, flexion and extension in quasi-static and dynamic conditions. The deformed disc height, disc fluid pressure, range of motion, and stresses were compared. Results Results indicated that the intervertebral disc material model has a strong effect on load-sharing and disc height change when compression and flexion were applied. The predicted mechanical response of three models under extension had less discrepancy than its counterparts under flexion and compression. The fluid-solid interaction showed more relevance in dynamic than quasi-static loading conditions. The fiber-reinforced linear elastic and hyperelastic material models underestimated the load-sharing of the intervertebral disc annular collagen fibers. Conclusion This study confirmed the central role of the disc fluid pressure in spinal load-sharing and highlighted loading conditions where linear elastic and hyperelastic models predicted energy distribution different than that of the biphasic model.

Abstract Background microRNAs (miRNAs) play essential roles in the development and progression of gastric cancer (GC). Although aberrant miR-874 expression has been reported in various human cancers, its role in GC remains obscure. Methods miR-874 expression was assessed by real-time quantitative polymerase chain reaction (RT-qPCR) in 62 matched GC and adjacent normal tissues, as well as in GC cell lines and immortalized human gastric epithelial cells. CCK8 assay, colony formation assay, and flow cytometry were used to assess the role of miR-874 in GC cell proliferation and apoptosis in vitro. Additionally, to determine the effects of miR-874 on GC cell proliferation and apoptosis in vivo, BALB/c nude mice were injected with GC cells transfected with a miR-874 mimic. The role of miR-874 in SPAG9 expression was assessed by luciferase assay, Western blotting, and RT-qPCR. Results miR-874 was downregulated in GC cell lines and tissues. miR-874 overexpression in GC cells led to inhibition of cell proliferation and induction of apoptosis. Moreover, SPAG9 was identified as a direct miR-874 target, the expression of which was suppressed by miR-874. SPAG9 overexpression markedly promoted GC cell proliferation. Conclusions miR-874 inhibited cell proliferation and induced apoptosis in GC cells. SPAG9 downregulation was crucial for the tumor-suppressive effects of miR-874. Hence, the miR-874/SPAG9 axis could serve as a novel therapeutic target in GC.

The title compound, C18H14N2, was synthesized by N-alkyl­ation of 3-chloro­methyl­pyridine hydro­chloride with carbazole. The carbazole ring system is essentially planar and makes a dihedral angle of 87.9 (3)° with the plane of the pyridine ring. [ABSTRACT FROM AUTHOR]

Aim: To investigate semaphorin 4D (Sema4D) and hypoxia-inducible factor-1α (HIF-1α) expression in colorectal carcinoma and evaluate their clinicopathological and prognostic significance., Methods: Eighty-six curatively resected colorectal carcinoma patients at different stages of disease were randomly selected from the group of patients who underwent surgery, and none of them received preoperative radiochemotherapy. Normal proximal adjacent bowel tissue, which served as an internal control, was obtained from 52 randomly selected patients. Immunohistochemistry was performed to analyze the expression of Sema4D and the tumor angiogenesis-related protein HIF-1α in normal colorectal tissues and colorectal carcinoma tissues. The relationships between the expression and clinical characters and prognosis were analyzed., Results: HIF-1α and Sema4D were positively expressed in 58% and 60% of colorectal carcinoma tissues, respectively. Significantly lower expression levels were observed in normal mucosa (8% and 12%, respectively). HIF-1α and Sema4D expression was closely correlated with histological tumor type, tumor-node-metastasis (TNM) stage, and lymphatic metastasis (P<0.05), but not with age or tumor size (P>0.05). HIF-1α and Sema4D protein expression was significantly correlated with prognosis of colorectal carcinoma, as determined by Spearman rank correlation analysis (r=0.567; P<0.01). Multivariate Cox analysis revealed that only Sema4D expression played a significant role in predicting patient prognosis (P<0.05)., Conclusion: These findings suggest that HIF-1α and Sema4D expression correlates with histological tumor type, TNM stage, and lymphatic metastasis in colorectal carcinoma and that Sema4D is a prognostic indicator of colorectal carcinoma.

Inflammatory pseudotumor (IPT) is a rare space-occupying lesion of unknown etiology that can mimic malignancy on clinic-radiological and pathological examination. We describe a rare case of ileocecal intussusception from clinically suspected malignancy of the right colon where the patient underwent right hemicolectomy. Histopathology of the resected specimen confirmed IPT of the colon. This patient was observed to have abnormally elevated total leukocyte count and platelets before and after surgery. In an adult with intussusception associated with an abdominal mass, the possibility of IPT of the colon should be considered. Considering the abnormally high total leukocyte and platelet counts and colonic IPT, it is necessary to prevent postoperative adverse effects due to these changes. Although IPT of the colon is usually a benign process, controversy regarding its management still exists. We consider hemicolectomy as a safe treatment approach for colonic IPT and review the existing literature.