Avoiding pitfalls in molecular simulation of vapor sorption: Example of propane and isobutane in metal-organic frameworks for adsorption cooling applications.

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated using an equation of state, which often requires the critical parameters of the fluid. We show the importance of using critical parameters derived from the simulation with the same model to ensure internal consistency between the simulated explicit adsorbate phase and the implicit bulk phase in GCMC. We show the advantages of presenting isotherms on a relative pressure scale to facilitate easier comparison among models and with experiment. Extending these guidelines to a practical case study, we evaluate the performance of various isoreticular metal-organic frameworks (MOFs) in adsorption cooling applications. This includes examining the advantages of using propane and isobutane as working fluids and identifying MOFs with a superior performance.

Research Experiences via Integrating Simulations and Experiments (REVISE): A Model Collaborative Research Project for Undergraduate Students in CO2 Sorbent Design.

Undergraduate research experiences are an instrumental component of student development, increasing conceptual understanding, promoting inquiry-based learning, and guiding potential career aspirations. Moving one step further, as research continues to become more interdisciplinary, there exists potential to accelerate student growth by granting additional perspectives through collaborative research. This study demonstrates the utilization of a model collaborative research project, specifically investigating the development of sorbent technologies for efficient CO2 capture, which is an important research area for improving environmental sustainability. A model CO2 sorbent system of heteroatom-doped porous carbon is utilized to enable students to gain knowledge of adsorption processes, through combined experimental and computational investigations and learnings. A particular emphasis is placed on creating interdisciplinary learning experiences, exemplified by using density functional theory (DFT) to understand molecular interactions between doped carbon surfaces and CO2 molecules as well as explain underlying physical mechanisms that govern experimental results. The experimental observations about CO2 sorption performance of doped ordered mesoporous carbons (OMCs) can be correlated with simulation results, which can explain how the presence of heteroatom functional groups impact the ability of porous carbon to selectively adsorb CO2 molecules. Through an inquiry-focused approach, students were observed to couple interdisciplinary results to construct holistic explanations, while developing skills in independent research and scientific communications. This collaborative research project allows students to obtain a deeper understanding of sustainability challenges, cultivate confidence in independent research, prepare for future career paths, and most importantly, be exposed to strategies employing interdisciplinary research approaches to address scientific challenges.

YAP1 expression in colorectal cancer confers the aggressive phenotypes via its target genes.

Yes-associated protein1 (YAP1), a downstream effector of the Hippo pathway, is over-expressed in several types of malignancies. We analyzed retrospectively the TCGA database using 447 colorectal cancer (CRC) samples to determine the correlation between YAP1 expression level and CRC patient prognosis. YAP1-enforced expressed CRC cell lines were constructed using the lentivirus particles containing a YAP1 insert. YAP1 was highly expressed in CRC cancerous tissues and is associated with distant metastasis of CRC patients. Kaplan - Meier analysis indicated that CRC patients with a higher YAP1 expression group (n = 104) had worse disease-free survival (DFS) and overall survival (OS) than lower YAP1 expression group (n = 343) (p = 0.008 and p = 0.022). Univariate and multivariate analysis indicated that the elevated YAP1 expression predicted the aggressive phenotype and was an independent indicator for OS and DFS of CRC patients. YAP1 over-expression in CRC cells enhanced their migration and invasion significantly which can be reversed by AXL, CTGF, or CYR61 interference. The study suggested that YAP1 affected the prognosis of CRC patients and controlled the abilities of invasion and migration of CRC cells via its target genes AXL, CTGF, and CYR61.

OII-DS: A benchmark Oral Implant Image Dataset for object detection and image classification evaluation.

In recent years, there is been a growing reliance on image analysis methods to bolster dentistry practices, such as image classification, segmentation and object detection. However, the availability of related benchmark datasets remains limited. Hence, we spent six years to prepare and test a bench Oral Implant Image Dataset (OII-DS) to support the work in this research domain. OII-DS is a benchmark oral image dataset consisting of 3834 oral CT imaging images and 15240 oral implant images. It serves the purpose of object detection and image classification. To demonstrate the validity of the OII-DS, for each function, the most representative algorithms and metrics are selected for testing and evaluation. For object detection, five object detection algorithms are adopted to test and four evaluation criteria are used to assess the detection of each of the five objects. Additionally, mean average precision serves as the evaluation metric for multi-objective detection. For image classification, 13 classifiers are used for testing and evaluating each of the five categories by meeting four evaluation criteria. Experimental results affirm the high quality of our data in OII-DS, rendering it suitable for evaluating object detection and image classification methods. Furthermore, OII-DS is openly available at the URL for non-commercial purpose: https://doi.org/10.6084/m9.figshare.22608790.

CAM-VT: A Weakly supervised cervical cancer nest image identification approach using conjugated attention mechanism and visual transformer.

Cervical cancer is the fourth most common cancer among women, and cytopathological images are often used to screen for this cancer. However, manual examination is very troublesome and the misdiagnosis rate is high. In addition, cervical cancer nest cells are denser and more complex, with high overlap and opacity, increasing the difficulty of identification. The appearance of the computer aided automatic diagnosis system solves this problem. In this paper, a weakly supervised cervical cancer nest image identification approach using Conjugated Attention Mechanism and Visual Transformer (CAM-VT), which can analyze pap slides quickly and accurately. CAM-VT proposes conjugated attention mechanism and visual transformer modules for local and global feature extraction respectively, and then designs an ensemble learning module to further improve the identification capability. In order to determine a reasonable interpretation, comparative experiments are conducted on our datasets. The average accuracy of the validation set of three repeated experiments using CAM-VT framework is 88.92%, which is higher than the optimal result of 22 well-known deep learning models. Moreover, we conduct ablation experiments and extended experiments on Hematoxylin and Eosin stained gastric histopathological image datasets to verify the ability and generalization ability of the framework. Finally, the top 5 and top 10 positive probability values of cervical nests are 97.36% and 96.84%, which have important clinical and practical significance. The experimental results show that the proposed CAM-VT framework has excellent performance in potential cervical cancer nest image identification tasks for practical clinical work.

Etv5a Suppresses Neural Progenitor Cell Proliferation by Inhibiting sox2 Transcription.

Neural progenitor cells are self-renewable, proliferative, and multipotent cell populations that generate diverse types of neurons and glia to build the nervous system. Transcription factors play critical roles in regulating various cellular processes; however, the transcription factors that regulate the development of neural progenitors are yet to be identified. In the present study, we demonstrated that zebrafish etv5a is expressed in the neural progenitor cells of the neuroectoderm. Downregulation of endogenous Etv5a function by etv5a morpholino or an etv5a dominant-negative variant increased the proliferation of sox2-positive neural progenitor cells, accompanied by inhibition of neurogenesis and gliogenesis. These phenotypes in Etv5a-depleted embryos could be rescued by a co-injection with etv5a cRNA. Etv5a overexpression reduced sox2 expression. Direct binding of Etv5a to the regulatory elements of sox2 was affirmed by chromatin immunoprecipitation. These data revealed that Etv5a directly suppressed sox2 expression to reduce the proliferation of neural progenitor cells. In addition, the expression of foxm1, a putative target gene of Etv5a and a direct upstream transcription factor of sox2, was upregulated in Etv5a-deficient embryos. Moreover, the suppression of Foxm1 function by the foxm1 dominant-negative construct nullified the phenotype of upregulated sox2 expression caused by Etv5a deficiency. Overall, our results indicated that Etv5a regulates the expression of sox2 via direct binding to the sox2 promoter and indirect regulation by inhibiting foxm1 expression. Hence, we revealed the role of Etv5a in the transcriptional hierarchy that regulates the proliferation of neural progenitor cells.

Pivotal Role of the Granularity Uniformity of the WO3 Film Electrode upon the Cyclic Stability during Cation Insertion/Extraction.

Delicate design and precise manipulation of electrode morphology has always been crucial in electrochemistry. Generally, porous morphology has been preferred due to the fast kinetic transport characteristics of cations. Nevertheless, more refined design details such as the granularity uniformity that usually goes along with the porosity regulation of film electrodes should be taken into consideration, especially in long-term cation insertion and extraction. Here, inorganic electrochromism as a special member of the electrochemical family and WO3 films as the most mature electrochromic electrode material were chosen as the research background. Two kinds of WO3 films were prepared by magnetron sputtering, one with a relatively loose morphology accompanied by nonuniform granularity and one with a compact morphology along with uniform particle size distribution, respectively. Electrochemical performances and cyclic stability of the two film electrodes were then traced and systematically compared. In the beginning, except for faster kinetic transport characters of the 50 W-deposited WO3 film, the two electrodes showed equivalent optical and electrochemical performances. However, after 5000 CV cycles, the 50 W-deposited WO3 film electrode cracked seriously. Strong stress distribution centered among boundaries of the nonuniform particle clusters together with the weak bonding among particles induced the mechanical damage. This discovery provides a more solid background for further delicate film electrode design.

Structure-Activity Relationship Insights for Organophosphonate Hydrolysis at Ti(IV) Active Sites in Metal-Organic Frameworks.

Organophosphorus nerve agents are among the most toxic chemicals known and remain threats to humans due to their continued use despite international bans. Metal-organic frameworks (MOFs) have emerged as a class of heterogeneous catalysts with tunable structures that are capable of rapidly detoxifying these chemicals via hydrolysis at Lewis acidic active sites on the metal nodes. To date, the majority of studies in this field have focused on zirconium-based MOFs (Zr-MOFs) that contain hexanuclear Zr(IV) clusters, despite the large toolbox of Lewis acidic transition metal ions that are available to construct MOFs with similar catalytic properties. In particular, very few reports have disclosed the use of a Ti-based MOF (Ti-MOF) as a catalyst for this transformation even though Ti(IV) is a stronger Lewis acid than Zr(IV). In this work, we explored five Ti-MOFs (Ti-MFU-4l, NU-1012-NDC, MIL-125, Ti-MIL-101, MIL-177(LT), and MIL-177(HT)) that each contains Ti(IV) ions in unique coordination environments, including monometallic, bimetallic, octanuclear, triangular clusters, and extended chains, as catalysts to explore how both different node structures and different linkers (e.g., azolate and carboxylate) influence the binding and subsequent hydrolysis of an organophosphorus nerve agent simulant at Ti(IV)-based active sites in basic aqueous solutions. Experimental and theoretical studies confirm that Ti-MFU-4l, which contains monometallic Ti(IV)-OH species, exhibits the best catalytic performance among this series with a half-life of roughly 2 min. This places Ti-MFU-4l as one of the best nerve agent hydrolysis catalysts of any MOF reported to date.

EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation.

BACKGROUND AND PURPOSE: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Therefore, a multi-category colorectal cancer dataset is needed to test various medical image classification methods to find high classification accuracy and strong robustness. METHODS: A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset (EBHI) is published in this paper. To demonstrate the effectiveness of the EBHI dataset, we have utilized several machine learning, convolutional neural networks and novel transformer-based classifiers for experimentation and evaluation, using an image with a magnification of 200×. RESULTS: Experimental results show that the deep learning method performs well on the EBHI dataset. Classical machine learning methods achieve maximum accuracy of 76.02% and deep learning method achieves a maximum accuracy of 95.37%. CONCLUSION: To the best of our knowledge, EBHI is the first publicly available colorectal histopathology enteroscope biopsy dataset with four magnifications and five types of images of tumor differentiation stages, totaling 5532 images. We believe that EBHI could attract researchers to explore new classification algorithms for the automated diagnosis of colorectal cancer, which could help physicians and patients in clinical settings.

EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks.

Background and purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of colorectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 4,456 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients. EBHI-Seg is publicly available at: https://figshare.com/articles/dataset/EBHI-SEG/21540159/1.

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer.

Introduction: Gastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. Therefore, this paper compares the performance of multiple algorithms in anticipation of applying ensemble learning to a practical gastric cancer classification problem. Methods: The complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier. Results: The experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity. Discussion: Suitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.

Transplanted hair follicle mesenchymal stem cells alleviated small intestinal ischemia-reperfusion injury via intrinsic and paracrine mechanisms in a rat model.

Background: Small intestinal ischemia-reperfusion (IR) injury is a common intestinal disease with high morbidity and mortality. Mesenchymal stem cells (MSCs) have been increasingly used in various intestinal diseases. This study aimed to evaluate the therapeutic effect of hair follicle MSCs (HFMSCs) on small intestinal IR injury. Methods: We divided Sprague-Dawley rats into three groups: the sham group, IR group and IR + HFMSCs group. A small intestinal IR injury rat model was established by clamping of the superior mesenteric artery (SMA) for 30 min and reperfusion for 2 h. HFMSCs were cultured in vitro and injected into the rats through the tail vein. Seven days after treatment, the intrinsic homing and differentiation characteristics of the HFMSCs were observed by immunofluorescence and immunohistochemical staining, and the paracrine mechanism of HFMSCs was assessed by Western blotting and enzyme-linked immunosorbent assay (ELISA). Results: A small intestinal IR injury model was successfully established. HFMSCs could home to damaged sites, express proliferating cell nuclear antigen (PCNA) and intestinal stem cell (ISC) markers, and promote small intestinal ISC marker expression. The expression levels of angiopoietin-1 (ANG1), vascular endothelial growth factor (VEGF) and insulin growth factor-1 (IGF1) in the IR + HFMSCs group were higher than those in the IR group. HFMSCs could prevent IR-induced apoptosis by increasing B-cell lymphoma-2 (Bcl-2) expression and decreasing Bcl-2 homologous antagonist/killer (Bax) expression. Oxidative stress level detection showed that the malondialdehyde (MDA) content was decreased, while the superoxide dismutase (SOD) content was increased in the IR + HFMSCs group compared to the IR group. An elevated diamine oxidase (DAO) level reflected the potential protective effect of HFMSCs on the intestinal mucosal barrier. Conclusion: HFMSCs are beneficial to alleviate small intestinal IR injury through intrinsic homing to the small intestine and by differentiating into ISCs, via a paracrine mechanism to promote angiogenesis, reduce apoptosis, regulate the oxidative stress response, and protect intestinal mucosal function potentially. Therefore, this study suggests that HFMSCs serve as a new option for the treatment of small intestinal IR injury.

Assessment of Fasudil on Contrast-Associated Acute Kidney Injury Using Multiparametric Renal MRI.

Aims: To evaluate the utility of fasudil in a rat model of contrast-associated acute kidney injury (CA-AKI) and explore its underlying mechanism through multiparametric renal magnetic resonance imaging (mpMRI). Methods: Experimental rats (n = 72) were grouped as follows: controls (n = 24), CA-AKI (n = 24), or CA-AKI + Fasudil (n = 24). All animals underwent two mpMRI studies (arterial spin labeling, T1 and T2 mapping) at baseline and post iopromide/fasudil injection (Days 1, 3, 7, and 13 respectively). Relative change in renal blood flow (ΔRBF), T1 (ΔT1) and T2 (ΔT2) values were assessed at specified time points. Serum levels of cystatin C (CysC) and interleukin-1ß (IL-1ß), and urinary neutrophil gelatinase-associated lipocalin (NGAL) concentrations were tested as laboratory biomarkers, in addition to examining renal histology and expression levels of various proteins (Rho-kinase [ROCK], α-smooth muscle actin [α-SMA]), hypoxia-inducible factor-1α (HIF-1α), and transforming growth factor-ß1 (TGF-ß1) that regulate renal fibrosis and hypoxia. Results: Compared with the control group, serum levels of CysC and IL-1ß, and urinary NGAL concentrations were clearly increased from Day 1 to Day 13 in the CA-AKI group (all p < 0.05). There were significant reductions in ΔT2 values on Days 1 and 3, and ΔT1 reductions were significantly more pronounced at all time points (Days 1-13) in the CA-AKI + Fasudil group (vs. CA-AKI) (all p < 0.05). Fasudil treatment lowered expression levels of ROCK-1, and p-MYPT1/MYPT1 proteins induced by iopromide, decreasing TGF-ß1 expression and suppressing both extracellular matrix accumulation and α-SMA expression relative to untreated status (all p < 0.05). Fasudil also enhanced PHD2 transcription and inhibition of HIF-1α expression after CA-AKI. Conclusions: In the context of CA-AKI, fasudil appears to reduce renal hypoxia, fibrosis, and dysfunction by activating (Rho/ROCK) or inhibiting (TGF-ß1, HIF-1α) certain signaling pathways and reducing α-SMA expression. Multiparametric MRI may be a viable noninvasive tool for monitoring CA-AKI pathophysiology during fasudil therapy.

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach.

In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interaction. In this paper, we propose an IL-MCAM framework. It is based on attention mechanisms and interactive learning. The proposed IL-MCAM framework includes two stages: automatic learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel attention mechanism model containing three different attention mechanism channels and convolutional neural networks is used to extract multi-channel features for classification. In the IL stage, the proposed IL-MCAM framework continuously adds misclassified images to the training set in an interactive approach, which improves the classification ability of the MCAM model. We carried out a comparison experiment on our dataset and an extended experiment on the HE-NCT-CRC-100K dataset to verify the performance of the proposed IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%, respectively. In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels. The experimental results show that the proposed IL-MCAM framework has excellent performance in the colorectal histopathological image classification tasks.

GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer.

BACKGROUND AND OBJECTIVE: Gastric cancer is the fifth most common cancer globally, and early detection of gastric cancer is essential to save lives. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, computer-aided diagnostic techniques are challenging to evaluate due to the scarcity of publicly available gastric histopathology image datasets. METHODS: In this paper, a noble publicly available Gastric Histopathology Sub-size Image Database (GasHisSDB) is published to identify classifiers' performance. Specifically, two types of data are included: normal and abnormal, with a total of 245,196 tissue case images. In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation. Seven classical machine learning classifiers, three Convolutional Neural Network classifiers, and a novel transformer-based classifier are selected for testing on image classification tasks. RESULTS: This study performed extensive experiments using traditional machine learning and deep learning methods to prove that the methods of different periods have discrepancies on GasHisSDB. Traditional machine learning achieved the best accuracy rate of 86.08% and a minimum of just 41.12%. The best accuracy of deep learning reached 96.47% and the lowest was 86.21%. Accuracy rates vary significantly across classifiers. CONCLUSIONS: To the best of our knowledge, it is the first publicly available gastric cancer histopathology dataset containing a large number of images for weakly supervised learning. We believe that GasHisSDB can attract researchers to explore new algorithms for the automated diagnosis of gastric cancer, which can help physicians and patients in the clinical setting.

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers.

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset.

Disease prediction based on multi-type data fusion from Chinese electronic health record.

Disease prediction by using a variety of healthcare data to assist doctors in disease diagnosis is becoming a more and more important research topic recently. This paper proposes a disease prediction model that fuses multiple types of encoded representations of Chinese electronic health records (EHRs). The model framework utilizes a multi-head self-attention mechanism, which combines textual and numerical features to enhance text representations. The BiLSTM-CRF and TextCNN models are used, respectively, to extract entities and then obtain the embedding representations of them. The representations of text and entities in it are combined together for formulating representations of EHRs. The experimental results on EHRs data collected from a Three Grade Class B Hospital General in Gansu Province, China, show that our model achieved an F1 score of 91.92%, which outperforms the previous baseline methods.

Underlying Mechanism and Active Ingredients of Tianma Gouteng Acting on Cerebral Infarction as Determined via Network Pharmacology Analysis Combined With Experimental Validation.

Cerebral infarction (CI), a common cerebrovascular disease worldwide, is caused by unknown factors common to many diseases, including hypokalemia, respiratory diseases, and lower extremity venous thrombosis. Tianma Gouteng (TMGT), a traditional Chinese Medicine (TCM) prescription, has been used for the clinical treatment of CI. In this study, high-performance liquid chromatography (HPLC) fingerprint analysis was used to detect and identify major chemical constituents of TMGT. TCMSP and BATMAN-TCM databases were used to screen for active TMGT constituent compounds, while the GeneCards database was used to screen for protein targets associated with CI. Next, GO and KEGG enrichment analysis of these core nodes were performed to determine the identities of key associated biological processes and signal pathways. Meanwhile, a total of six possible gene targets of TMGT, including NFKBIA, PPARG, IL6, IL1B, CXCL8, and HIF1A, were selected for further study using two cellular models of CI. For one model, PC12 cells were treated under oxygen and glucose deprivation (OGD) conditions to generate an OGD cellular model of CI, while for the other model, BV2 cells were stimulated with lipopolysaccharide (LPS) to generate a cellular model of CI-associated inflammation. Ultimately TMGT treatment increased PPARγ expression and downregulated the expression of p-P65, p-IκBα, and HIF-1α in both OGD-induced and LPS-induced cell models of CI. In addition, molecular docking analysis showed that one TMGT chemical constituent, quercetin, may be a bioactive TMGT compound with activity that may be associated with the alleviation of neuronal damage and neuroinflammation triggered by CI. Moreover, additional data obtained in this work revealed that TMGT could inhibit neuroinflammation and protect brain cells from OGD-induced and LPS-induced damage by altering HIF-1α/PPARγ/NF-κB pathway functions. Thus, targeting this pathway through TMGT administration to CI patients may be a strategy for alleviating nerve injury and neuroinflammation triggered by CI.

RGS2 Suppresses Melanoma Growth via Inhibiting MAPK and AKT Signaling Pathways.

BACKGROUND/AIM: This study aimed to explore RGS2 as a regulator of melanoma cell growth. MATERIALS AND METHODS: Effect of RGS2 over-expression was analyzed in three melanoma cell lines, and Rgs2 knockdown was performed in zebrafish. RESULTS: RGS2 was differentially expressed among the cell lines. In B16F10 cells, RGS2 over-expression inhibited MAPK and AKT activation, and prevented cell growth. A similar outcome was observed in A375 cells, but the MAPK signals were not suppressed. In A2058 cells, RGS2 repressed AKT activation, but without affecting cell growth. Moreover, MAPK and AKT constitutive activation abolished the RGS2 inhibitory effect on B16F10 cell growth. Rgs2 knockdown caused ectopic melanocyte differentiation, and promoted MAPK and AKT activation in zebrafish embryos. CONCLUSION: RGS2 prevents melanoma cell growth by inhibiting MAPK and AKT, but this effect depends on the overall cell genetic landscape. Further studies are warranted to investigate the anticancer therapeutic potential of RGS2 for melanoma.

Fine-Tuning a Robust Metal-Organic Framework toward Enhanced Clean Energy Gas Storage.

The development of adsorbents with molecular precision offers a promising strategy to enhance storage of hydrogen and methane─considered the fuel of the future and a transitional fuel, respectively─and to realize a carbon-neutral energy cycle. Herein we employ a postsynthetic modification strategy on a robust metal-organic framework (MOF), MFU-4l, to boost its storage capacity toward these clean energy gases. MFU-4l-Li displays one of the best volumetric deliverable hydrogen capacities of 50.2 g L-1 under combined temperature and pressure swing conditions (77 K/100 bar → 160 K/5 bar) while maintaining a moderately high gravimetric capacity of 9.4 wt %. Moreover, MFU-4l-Li demonstrates impressive methane storage performance with a 5-100 bar usable capacity of 251 cm3 (STP) cm-3 (0.38 g g-1) and 220 cm3 (STP) cm-3 (0.30 g g-1) at 270 and 296 K, respectively. Notably, these hydrogen and methane storage capacities are significantly improved compared to those of its isoreticular analogue, MFU-4l, and place MFU-4l-Li among the best MOF-based materials for this application.