Phosphatidylethanolamine binding protein 1 enhances sensitivity of gastric cancer cell to 5-fluorouracil via inhibition of cell proliferation, migration and invasion

Purpose: To determine the association between phosphatidylethanolamine binding protein 1, which is an Raf kinase inhibitor protein (RKIP), and 5-fluorouracil (5-FU) via analysis of the association between RKIP and clinical responses in individuals treated using fluorouracil-based chemotherapy.Methods: Human gastric cancer cell lines MGC-803 and SGC-7901 were used in this study. Cell viability was measured using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Apoptosis and migration were determined with flow cytometry and Transwell chamber assays, respectively. The mRNA and protein expressions of apoptosis-related factors were assayed using realtime polymerase chain reaction (RT-PCR) and Western blotting, respectively, while the expression of RKIP was determined by immunohistochemical staining.Results: Chemotherapeutic drug (5-FU) treatment induced low RKIP expression levels in tumorigenic GC cells, thereby sensitizing the cells to apoptosis (8.57 vs 1.25 %, p < 0.01). The highest RKIP level correlated well with initiation of apoptosis (4.20 vs 1.25 %, p < 0.01). Following in vitro downregulation of RKIP, there was increase in the viability and proliferation of RKIP-inhibited cells over time, and these changes were linked to alterations in cell cycle phases and increased optical density in MTT proliferation assay (1.55 vs 1.18, p < 0.01). In vitro Transwell assay measurement revealed an association between RKIP downregulation and enhancement of cell migration potential (652 vs 436, p < 0.01). Ectopic RKIP expression restored the apoptotic sensitivity of resistant cells (14.30 vs 1.36 %, p <0.01). This sensitization was annulled by upregulation of survival routes. Reduction of RKIP by expression of antisense and siRNA conferred resistance on cancer cells sensitive to 5-FU-mediated apoptosis (6.88 vs 2.13 %, p < 0.01).Conclusion: Thus, RKIP is a promising therapeutic strategy for improving the efficacy of clinically relevant chemotherapeutic drugs for GC. Keywords: Gastric cancer, Raf kinase inhibitor protein, Cell proliferation, Invasion, Apoptosis, Chemotherapy,  Phosphatidylethanolamine binding protein

TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning

To achieve accurate and low-cost 3D object detection, existing methods propose to benefit camera-based multi-view detectors with spatial cues provided by the LiDAR modality, e.g., dense depth supervision and bird-eye-view (BEV) feature distillation. However, they directly conduct point-to-point mimicking from LiDAR to camera, which neglects the inner-geometry of foreground targets and suffers from the modal gap between 2D-3D features. In this paper, we propose the learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features, termed as TiG-BEV. First, we introduce an inner-depth supervision module to learn the low-level relative depth relations between different foreground pixels. This enables the camera-based detector to better understand the object-wise spatial structures. Second, we design an inner-feature BEV distillation module to imitate the high-level semantics of different keypoints within foreground targets. To further alleviate the BEV feature gap between two modalities, we adopt both inter-channel and inter-keypoint distillation for feature-similarity modeling. With our target inner-geometry distillation, TiG-BEV can effectively boost BEVDepth by +2.3% NDS and +2.4% mAP, along with BEVDet by +9.1% NDS and +10.3% mAP on nuScenes val set. Code will be available at https://github.com/ADLab3Ds/TiG-BEV.Comment: Code link: https://github.com/ADLab3Ds/TiG-BE

IMP3 expression is associated with poor outcome and epigenetic deregulation in intrahepatic cholangiocarcinoma

IMP3 is a fetal protein not expressed in normal adult tissues. IMP3 is an oncoprotein and a useful biomarker for a variety of malignancies and is associated with reduced overall survival of a number of them. IMP3 expression and its prognostic value for patients with intrahepatic cholangiocarcinoma (ICC) have not been well investigated. The molecular mechanism underlying IMP3 expression in human cancer cells remains to be elucidated. Here we investigated IMP3 expression in ICC and adjacent nonneoplastic liver in 72 unifocal primary ICCs from a single institute by immunohistochemistry, immunoblotting, and real-time polymerase chain reaction. IMP3 was specifically expressed in cancer cells but not in the surrounding normal tissue, and 59 (82%) of 72 ICCs were IMP3 positive by immunohistochemistry. Among 35 cases with lymphovascular invasion, 26 (74%) showed IMP3 positivity in lymph node metastases. IMP3 expression was significantly correlated with tumor size, pathological grade, metastasis, and clinical stage. Kaplan-Meier analysis demonstrated an inverse correlation between IMP3 expression and overall survival rate. Multivariate analysis revealed that IMP3 was the only risk factor associated with survival. To further explore the mechanism of IMP3 expression in cancers, we identified 2 CpG islands at IMP3 proximal promoter. Interestingly, the IMP3 promoter was almost completely demethylated in ICCs in contrast to densely methylated promoter in normal liver tissues. IMP3 expression is a useful biomarker for ICCs and can provide an independent prognostic value for patients with ICC. To our knoweldge, this is the first direct evidence of epigenetic deregulation of IMP3 in human cancer. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved

High impact bug report identification with imbalanced learning strategies

Supplementary code and data available from GitHub: https://github.com/goddding/JCST</p

Two-dimensional nitrogen and phosphorus co-doped mesoporous carbon-graphene nanosheets anode for high-performance potassium-ion capacitor

Heteroatom-doped carbon materials have high gravimetric potassium-ion storage capability because of their abundant active sites and defects. However, their practical applications toward potassium storage are limited by sluggish reaction kinetics and short cycling life owing to the large ionic radius of K+ and undesirable parasitic reactions. Herein, we report a new strategy that allows for bottom-up patterning of thin N/P co-doped carbon layers with a uniform mesoporous structure on two-dimensional graphene sheets. The highly porous architecture and N/P co-doping properties provide abundant active sites for K+, and the graphene sheets promote charge/electron transfer. This synergistic structure enables excellent K+ storage performance in terms of specific capacity (387.6 mAh g-1 at 0.05 A g-1), rate capability (over 5 A g-1), and cycling stability (70% after 3,000 cycles). As a proof of concept, a potassium-ion capacitor assembled using this carbon anode yields a high energy density of 107 Wh kg-1, a maximum power density of 18.3 kW kg-1, and ultra-long cycling stability over 40,000 cycles

Sleep as a Priority:24-Hour Movement Guidelines and Mental Health of Chinese College Students during the COVID-19 Pandemic

Research on the combined role of 24-hour movement behaviors (sleep, sedentary behavior [SB], and physical activity) in adult mental health, though important, is in its infancy. In the context of Canadian 24-hour movement guidelines integrating quantitative recommendations for sleep, SB, and moderate-to-vigorous physical activity (MVPA), this study aimed to examine the associations between meeting guidelines and mental health among college students. The study used a cross-sectional sample of 1846 Chinese college students surveyed online in August 2020. Through network analysis and multivariate analysis of covariance, the individual and combined associations between meeting 24-hour movement guidelines and the levels of depression and anxiety after adjusting sociodemographic factors were analyzed. Results indicated that meeting the sleep guideline had stronger associations with depression and anxiety than meeting the SB or MVPA guideline. Specifically, compared to meeting no guidelines, meeting the sleep guideline (alone or in combination with other guidelines) was associated with significantly lower levels of depression and anxiety; meeting both SB and MVPA guidelines was also associated with a significantly lower level of depression. Hence, meeting more guidelines, especially adhering to a healthy sleep routine, may play an important role in promoting the mental health of young adults

Energy-saving building program evaluation with an integrated method under linguistic environment

In the context of sustainable development, building energy conservation has become the development trend of the construction industry. The selection of energy-saving building program, as a multi-criteria decision-making (MCDM) problem, has a direct influence on the actual energy-saving effect. In this paper, an integrated MCDM method combining the extended best worst method (BWM) and Weighted Aggregated Sum Product Assessment (WASPAS) method is proposed to solve the energy-saving building program selection problem under the linguistic Pythagorean fuzzy environment. The Linguistic Pythagorean fuzzy sets (LPFSs) are used to model the uncertain evaluation information of experts. The extended BWM is developed to determine the weights of criteria, while the extended WASPAS method is proposed to determine the ranking of alternatives. To validate the applicability and reliability of the proposed method, this paper presents a numerical example of the selection problem for energy-saving building programs. Some managerial insights are also given for practitioners to use the proposed method

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model’s performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients’ average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses