PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.

DC2Net: An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning.

Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss worldwide, even up to 80%. Early and accurate detection of ASR is critical to reduce economic losses. Hyperspectral imaging, combined with deep learning, has already been proved as a powerful tool to detect crop diseases. However, current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels, leading to the fact that the detection accuracy of current models remains further improvement. In this study, we proposed a deformable convolution and dilated convolution neural network (DC2Net) for the ASR detection. The deformable convolution module was used to extract the spatial features, while the dilated convolution module was applied to extract features from the spectral dimension. We also adopted the Shapley value and the channel attention methods to evaluate the importance of each wavelength during decision-making, thereby identifying the most contributing ones. The proposed DC2Net can realize early asymptomatic detection of ASR even when visual symptoms have not appeared. The results of the experiment showed that the detection performance of DC2Net dominated state-of-the-art methods, reaching an overall accuracy at 96.73%. Meanwhile, the experimental result suggested that the Shapley Additive exPlanations method was able to extract feature wavelengths correctly, thereby helping DC2Net achieve reasonable performance with less input data. The research result of this study could provide early warning of ASR outbreak in advance, even at the asymptomatic period.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex and poorly understood signaling mechanisms within microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. Though a set of targets have been identified, however, it remains a challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data, considering the complex and highly interactive background signaling network. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy, which divides the complex signaling networks into signaling paths, and then score and rank them using a novel graph transformer architecture to infer the intra- and inter-cell signaling communications. We evaluated PathFinder using scRNA-seq data of APOE4-genotype specific AD mice models and identified novel APOE4 altered intra- and inter-cell interaction networks among neurons, astrocytes, and microglia. PathFinder is a general signaling network inference model and can be applied to other omics data-driven signaling network inference.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Downregulation of Bmal1 Expression in Celiac Ganglia Protects against Hepatic Ischemia-Reperfusion Injury.

Hepatic ischemia-reperfusion injury (HIRI) significantly contributes to liver dysfunction following liver transplantation and hepatectomy. However, the role of the celiac ganglion (CG) in HIRI remains unclear. Adeno-associated virus was used to silence Bmal1 expression in the CG of twelve beagles that were randomly assigned to the Bmal1 knockdown group (KO-Bmal1) and the control group. After four weeks, a canine HIRI model was established, and CG, liver tissue, and serum samples were collected for analysis. The virus significantly downregulated Bmal1 expression in the CG. Immunofluorescence staining confirmed a lower proportion of c-fos+ and NGF+ neurons in TH+ cells in the KO-Bmal1 group than in the control group. The KO-Bmal1 group exhibited lower Suzuki scores and serum ALT and AST levels than the control group. Bmal1 knockdown significantly reduced liver fat reserve, hepatocyte apoptosis, and liver fibrosis, and it increased liver glycogen accumulation. We also observed that Bmal1 downregulation inhibited the hepatic neurotransmitter norepinephrine, neuropeptide Y levels, and sympathetic nerve activity in HIRI. Finally, we confirmed that decreased Bmal1 expression in CG reduces TNF-α, IL-1ß, and MDA levels and increases GSH levels in the liver. The downregulation of Bmal1 expression in CG suppresses neural activity and improves hepatocyte injury in the beagle model after HIRI.

Blood biomarkers predict outcomes in patients with hepatocellular carcinoma treated with immune checkpoint Inhibitors: A pooled analysis of 44 retrospective sudies.

OBJECTIVE: We conducted the first meta-analysis to identify the predictive significance of baseline blood biomarkers (such as neutrophil to lymphocyte ratio (NLR), early alpha-fetoprotein (AFP) response, albumin-bilirubin (ALBI), AFP, platelet to lymphocyte ratio (PLR), C-reactive protein (CRP), protein induced by vitamin K absence II (PIVKA-II), and lymphocyte to monocyte ratio (LMR)) in hepatocellular carcinoma (HCC) patients treated with immune checkpoint inhibitors (ICIs). METHODS: Eligible articles were retrieved using PubMed, the Cochrane Library, EMBASE, and Google Scholar by November 24, 2022. Clinical outcomes were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and hyperprogressive disease (HPD). RESULTS: A total of 44 articles with 5322 patients were included in this meta-analysis. The pooled results demonstrated that patients with high NLR levels had significantly poorer OS (HR: 1.951, P < 0.001) and PFS (HR: 1.632, P < 0.001), lower ORR (OR: 0.484, P < 0.001) and DCR (OR: 0.494, P = 0.027), and higher HPD (OR: 8.190, P < 0.001). The patients with high AFP levels had shorter OS (HR: 1.689, P < 0.001) and PFS (HR: 1.380, P < 0.001), and lower DCR (OR: 0.440, P < 0.001) than those with low AFP levels, however, there was no difference in ORR (OR: 0.963, P = 0.933). We also found that early AFP response was correlated with better OS (HR: 0.422, P < 0.001) and PFS (HR: 0.385, P < 0.001), higher ORR (OR: 7.297, P < 0.001) and DCR (OR: 13.360, P < 0.001) compared to non-responders. Besides, a high ALBI grade was significantly related to shorter OS (HR: 2.440, P = 0.009) and PFS (HR: 1.373, P = 0.022), lower ORR (OR: 0.618, P = 0.032) and DCR (OR: 0.672, P = 0.049) than those with an ALBI grade 1. CONCLUSION: The NLR, early AFP response, and ALBI were useful predictors of outcomes in HCC patients treated with ICIs.

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution.

Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.

Inhibition of Notch activity promotes pancreatic cytokeratin 5-positive cell differentiation to beta cells and improves glucose homeostasis following acute pancreatitis.

Some individuals develop prediabetes and/or diabetes following acute pancreatitis (AP). AP-induced beta-cell injury and the limited regenerative capacity of beta cells might account for pancreatic endocrine insufficiency. Previously, we found that only a few pancreatic cytokeratin 5 positive (Krt5+) cells differentiated into beta cells in the murine AP model, which was insufficient to maintain glucose homeostasis. Notch signaling determines pancreatic progenitor differentiation in pancreas development. This study aimed to examine whether Notch signaling inhibition could promote pancreatic Krt5+ cell differentiation into beta cells and improve glucose homeostasis following AP. Pancreatic tissues from patients with acute necrotizing pancreatitis (ANP) were used to evaluate beta-cell injury, Krt5+ cell activation and differentiation, and Notch activity. The murine AP model was induced by cerulein, and the effect of Notch inhibition on Krt5+ cell differentiation was evaluated both in vivo and in vitro. The results demonstrated beta-cell loss in ANP patients and AP mice. Krt5+ cells were activated in ANP pancreases along with persistently elevated Notch activity, which resulted in the formation of massive duct-like structures. AP mice that received Notch inhibitor showed that impaired glucose tolerance was reversed 7 and 15 days following AP, and increased numbers of newborn small islets due to increased differentiation of Krt5+ cells to beta cells to some extent. In addition, Krt5+ cells isolated from AP mice showed increased differentiation to beta cells by Notch inhibition. Collectively, these findings suggest that beta-cell loss contributes to pancreatic endocrine insufficiency following AP, and inhibition of Notch activity promotes pancreatic Krt5+ cell differentiation to beta cells and improves glucose homeostasis. The findings from this study may shed light on the potential treatment of prediabetes/diabetes following AP.

Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data.

Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization.

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model.

BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. RESULTS: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients' survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients' survival time. CONCLUSION: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients' survival by integrating multi-omics data and clinical factors.

Circular RNA CDR1as promotes tumor progression by regulating miR-432-5p/E2F3 axis in pancreatic cancer.

BACKGROUND: Pancreatic cancer (PC), characterized with high growth rate and metastatic rate. It's urgently necessary to explore new mechanism of PC. Circular RNA/miRNA/mRNA network was widely reported to participate in the cancer progression. METHODS: In this research, circular RNA CDR1as (circCDR1as) was identified by microarray analysis and detected in pancreatic cancer (PC) tissues and cells. Transwell, colony-forming assay, nude mouse tumorigenicity assay were used to determine the function of circCDR1as in PC. Western blot, dual luciferase reporting test were applied to investigate the mechanism. RESULTS: We found that circCDR1as was highly expressed in PC tissues. The levels of circCDR1as in PC tissues and cells were higher than those in controls. CircCDR1as promoted the migration, invasion and proliferation of PC cells in vitro and tumor growth in vivo via mediating E2F3 expression by sponging miR-432-5p. CONCLUSIONS: In conclusion, circCDR1as could promote the development of PC and might be a novel diagnostic target for PC.

Clinical Characteristics and Risk Factors for Mortality of COVID-19 Patients With Diabetes in Wuhan, China: A Two-Center, Retrospective Study.

OBJECTIVE: Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and outcomes and to analyze the risk factors for in-hospital mortality of COVID-19 patients with diabetes. RESEARCH DESIGN AND METHODS: This two-center retrospective study was performed at two tertiary hospitals in Wuhan, China. Confirmed COVID-19 patients with diabetes (N = 153) who were discharged or died from 1 January 2020 to 8 March 2020 were identified. One sex- and age-matched COVID-19 patient without diabetes was randomly selected for each patient with diabetes. Demographic, clinical, and laboratory data were abstracted. Cox proportional hazards regression analyses were performed to identify the risk factors associated with the mortality in these patients. RESULTS: Of 1,561 COVID-19 patients, 153 (9.8%) had diabetes, with a median age of 64.0 (interquartile range 56.0-72.0) years. A higher proportion of intensive care unit admission (17.6% vs. 7.8%, P = 0.01) and more fatal cases (20.3% vs. 10.5%, P = 0.017) were identified in COVID-19 patients with diabetes than in the matched patients. Multivariable Cox regression analyses of these 306 patients showed that hypertension (hazard ratio [HR] 2.50, 95% CI 1.30-4.78), cardiovascular disease (HR 2.24, 95% CI 1.19-4.23), and chronic pulmonary disease (HR 2.51, 95% CI 1.07-5.90) were independently associated with in-hospital death. Diabetes (HR 1.58, 95% CI 0.84-2.99) was not statistically significantly associated with in-hospital death after adjustment. Among patients with diabetes, nonsurvivors were older (76.0 vs. 63.0 years), most were male (71.0% vs. 29.0%), and they were more likely to have underlying hypertension (83.9% vs. 50.0%) and cardiovascular disease (45.2% vs. 14.8%) (all P values <0.05). Age ≥70 years (HR 2.39, 95% CI 1.03-5.56) and hypertension (HR 3.10, 95% CI 1.14-8.44) were independent risk factors for in-hospital death of patients with diabetes. CONCLUSIONS: COVID-19 patients with diabetes had worse outcomes compared with the sex- and age-matched patients without diabetes. Older age and comorbid hypertension independently contributed to in-hospital death of patients with diabetes.

Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model.

Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, DeepSignalingSynergy, for drug combination prediction. Compared with existing models that use a large number of chemical-structure and genomics features in densely connected layers, we built the model on a small set of cancer signaling pathways, which can mimic the integration of multi-omics data and drug target/mechanism in a more biological meaningful and explainable manner. The evaluation results of the model using the NCI ALMANAC drug combination screening data indicated the feasibility of drug combination prediction using a small set of signaling pathways. Interestingly, the model analysis suggested the importance of heterogeneity of the 46 signaling pathways, which indicates that some new signaling pathways should be targeted to discover novel synergistic drug combinations.

A hospital-wide reduction in central line-associated bloodstream infections through systematic quality improvement initiative and multidisciplinary teamwork.

BACKGROUND: Few data are available on hospital-wide incidence of central line-associated bloodstream infection (CLABSI) rates in patients with central venous catheter (CVC) in China, where many systemic obstacles holding back evidence-based guidelines implementation exist. METHODS: This study was conducted prospectively in 2 phases. The baseline and intervention phases were performed in a teaching hospital in China, between January 2017 and October 2018. A systematic quality improvement (SQI) and multidisciplinary teamwork (MDT) CLABSI infection control program was introduced in the intervention phase. In the intensive care units (ICUs) and non-ICUs, CLABSIs were continuously monitored, data collected, then analyzed. RESULTS: After intervention, the CLABSI rate decreased from 2.84-0.56 per 1,000 CVC days in ICUs (P < .001), and from 0.82-0.47 per 1,000 CVC days in non-ICUs (P = .003). The length of time until CLABSI occurrence increased from 8.72-13.60 days in ICUs (P = .046), and from 10.00-12.00 days in non-ICUs (P = .048). The number of multidrug-resistant bacteria isolated from CLABSI episodes decreased both in ICUs and in non-ICUs. CONCLUSIONS: The SQI and MDT CLABSI infection control program is effective in reducing hospital-wide CLABSI in patients with CVC, both in ICUs and in non-ICUs.

Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies.

A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R² were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.

Synergistic effects of baicalein with gemcitabine or docetaxel on the proliferation, migration and apoptosis of pancreatic cancer cells.

Baicalein, a type of flavonoids extracted from Scutellaria baicalensis Georgi, has been reported to be a very promising drug for pancreatic cancer. However, it is unclear whether combination of baicalein with gemcitabine or docetaxel is synergistic to the treatment for pancreatic cancer (PC). We investigated the combinational effects of baicalein with gemcitabine or docetaxel on proliferation, cell cycle, migration and apoptosis of human PC cells. Administration of baicalein alone significantly inhibit the proliferation of PC cells. Notably, when it is combined with gemcitabine or docetaxel, combination index (CI) values calculated by Calcusyn software are smaller than 1, indicating the synergism of baicalein with gemcitabine or docetaxel for the treatment of PC cells. Consistently, EdU assay showed that administration of baicalein significantly enhanced the capacity of gemicitabine to inhibit proliferation of PC cells. Cell cycle analysis showed that high-concentration of baicalein was able to arrest PC cells in the S phase. Furthermore, low concentration of baicalein in combination with either gemcitabine or docetaxel exhibited strong suppression on the migration of PC cells. A further study using transmission electron microscope (TEM), DAPI staining and western blot showed that baicalein induced-apoptosis of PC cells might be via caspase-3/PARP signaling pathway. Notably, combination treatment was able to induce more severe cell apoptosis of PC cells. In conclusion, baicalein exhibited synergistic effects with gemcitabine or docetaxel on the treatment of PC cells.

Determination of the Degree of Substitution of Cationic Guar Gum by Headspace-Based Gas Chromatography during Its Synthesis.

This study reports on a headspace-based gas chromatography (HS-GC) technique for determining the degree of substitution (DS) of cationic guar gum during the synthesis process. The method is based on the determination of 2,3-epoxypropyltrimethylammonium chloride in the process medium. After a modest pretreatment procedure, the sample was added to a headspace vial containing bicarbonate solution for measurement of evolved CO2 by HS-GC. The results showed that the method had a good precision (relative standard deviation of <3.60%) and accuracy for the 2,3-epoxypropyltrimethylammonium chloride measurement, with recoveries in the range of 96-102%, matching with the data obtained by a reference method, and were within 12% of the values obtained by the more arduous Kjeldahl method for the calculated DS of cationic guar gum. The HS-GC method requires only a small volume of sample and, thus, is suitable for determining the DS of cationic guar gum in laboratory-scale process-related applications.

miR-542-3p inhibits the growth and invasion of colorectal cancer cells through targeted regulation of cortactin.

Colorectal cancer is one of the most common malignancies. Previous studies have reported that cortactin (CTTN) is often overexpressed in tumors and is associated with metastasis and poor prognosis of patients. The abnormal expression of microRNAs (miRNAs or miRs) is closely related to the development and progression of various types of cancer, including colorectal cancer. However, little is known about the miRNAs targeting cortactin. In the present study, prediction using biological software revealed that cortactin has binding sites for miR-542-3p. Transfection with miR-542-3p mimic demonstrated that miR­542-3p reduced the expression of cortactin in colorectal cancer cells. Dual luciferase reporter assays further demonstrated that miR-542-3p regulated cortactin in a targeted manner and that miR-542-3p expression was significantly downregulated in colorectal cancer cells. A cell proliferation assay and Transwell migration assay were undertaken: we noted that miR­542-3p inhibited the proliferation and invasion of colorectal cancer cells while promoting their apoptosis. By contrast, cortactin acted antagonistically. When co-transfected with miR-542-3p mimic and CTTN overexpression vector, the inhibitory effect of miR-542-3p was blocked. This indicates that miR-542-3p regulates CTTN in a targeted manner to modulate the growth and invasion of colorectal cancer cells. The present study thus provides new targets for the prevention and treatment of colorectal cancer.

Effects of Local Pancreatic Renin-Angiotensin System on the Microcirculation of Rat with Severe Acute Pancreatitis.

Severe acute pancreatitis (SAP) is normally related to multiorgan dysfunction and local complications. Studies have found that local pancreatic renin-angiotensin system (RAS) was significantly upregulated in drug-induced SAP. The present study aimed to investigate the effects of angiotensin II receptors inhibitor valsartan on dual role of RAS in SAP in a rat model and to elucidate the underlying mechanisms. 3.8% sodium taurocholate (1 ml/kg) was injected to the pancreatic capsule in order for pancreatitis induction. Rats in the sham group were injected with normal saline in identical locations. We also investigated the regulation of experimentally induced SAP on local RAS expression in the pancreas through determination of the activities of serum amylase, lipase and myeloperoxidase, histological and biochemical analysis, radioimmunoassay, fluorescence quantitative PCR and Western blot analysis. The results indicated that valsartan could effectively suppress the local RAS to protect against experimental acute pancreatitis through inhibition of microcirculation disturbances and inflammation. The results suggest that pancreatic RAS plays a critical role in the regulation of pancreatic functions and demonstrates application potential as AT1 receptor antagonists. Moreover, other RAS inhibitors could be a new therapeutic target in acute pancreatitis.

PAK4 confers cisplatin resistance in gastric cancer cells via PI3K/Akt- and MEK/ERK-dependent pathways.

CDDP [cisplatin or cis-diamminedichloroplatinum(II)] and CDDP-based combination chemotherapy have been confirmed effective against gastric cancer. However, CDDP efficiency is limited because of development of drug resistance. In this study, we found that PAK4 (p21-activated kinase 4) expression and activity were elevated in gastric cancer cells with acquired CDDP resistance (AGS/CDDP and MKN-45/CDDP) compared with their parental cells. Inhibition of PAK4 or knockdown of PAK4 expression by specific siRNA (small interfering RNA)-sensitized CDDP-resistant cells to CDDP and overcome CDDP resistance. Combination treatment of LY294002 [the inhibitor of PI3K (phosphoinositide 3-kinase)/Akt (protein kinase B or PKB) pathway] or PD98509 {the inhibitor of MEK [MAPK (mitogen-activated protein kinase)/ERK (extracellular-signal-regulated kinase) kinase] pathway} with PF-3758309 (the PAK4 inhibitor) resulted in increased CDDP efficacy compared with LY294002 or PD98509 alone. However, after the concomitant treatment of LY294002 and PD98509, PF-3758309 administration exerted no additional enhancement of CDDP cytotoxicity in CDDP-resistant cells. Inhibition of PAK4 by PF-3758309 could significantly suppress MEK/ERK and PI3K/Akt signalling in CDDP-resistant cells. Furthermore, inhibition of PI3K/Akt pathway while not MEK/ERK pathway could inhibit PAK4 activity in these cells. The in vivo results were similar with those of in vitro In conclusion, these results indicate that PAK4 confers CDDP resistance via the activation of MEK/ERK and PI3K/Akt pathways. PAK4 and PI3K/Akt pathways can reciprocally activate each other. Therefore, PAK4 may be a potential target for overcoming CDDP resistance in gastric cancer.