Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction.

Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.

Songorine modulates macrophage polarization and metabolic reprogramming to alleviate inflammation in osteoarthritis.

Introduction: Osteoarthritis (OA) is a prevalent joint disorder characterized by multifaceted pathogenesis, with macrophage dysregulation playing a critical role in perpetuating inflammation and joint degeneration. Methods: This study focuses on Songorine, derived from Aconitum soongaricum Stapf, aiming to unravel its therapeutic mechanisms in OA. Comprehensive analyses, including PCR, Western blot, and immunofluorescence, were employed to evaluate Songorine's impact on the joint microenvironment and macrophage polarization. RNA-seq analysis was conducted to unravel its anti-inflammatory mechanisms in macrophages. Metabolic alterations were explored through extracellular acidification rate monitoring, molecular docking simulations, and PCR assays. Oxygen consumption rate measurements were used to assess mitochondrial oxidative phosphorylation, and Songorine's influence on macrophage oxidative stress was evaluated through gene expression and ROS assays. Results: Songorine effectively shifted macrophage polarization from a pro-inflammatory M1 phenotype to an anti-inflammatory M2 phenotype. Notably, Songorine induced metabolic reprogramming, inhibiting glycolysis and promoting mitochondrial oxidative phosphorylation. This metabolic shift correlated with a reduction in macrophage oxidative stress, highlighting Songorine's potential as an oxidative stress inhibitor. Discussion: In an in vivo rat model of OA, Songorine exhibited protective effects against cartilage damage and synovial inflammation, emphasizing its therapeutic potential. This comprehensive study elucidates Songorine's multifaceted impact on macrophage modulation, metabolic reprogramming, and the inflammatory microenvironment, providing a theoretical foundation for its therapeutic potential in OA.

The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial.

Artificial intelligence (AI)-based diagnostic systems have been reported to improve fundus disease screening in previous studies. This multicenter prospective self-controlled clinical trial aims to evaluate the diagnostic performance of a deep learning system (DLS) in assisting junior ophthalmologists in detecting 13 major fundus diseases. A total of 1493 fundus images from 748 patients were prospectively collected from five tertiary hospitals in China. Nine junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the DLS. The diagnostic performance was evaluated among three groups: DLS-assisted junior ophthalmologist group (test group), junior ophthalmologist group (control group) and DLS group. The diagnostic consistency was 84.9% (95%CI, 83.0% ~ 86.9%), 72.9% (95%CI, 70.3% ~ 75.6%) and 85.5% (95%CI, 83.5% ~ 87.4%) in the test group, control group and DLS group, respectively. With the help of the proposed DLS, the diagnostic consistency of junior ophthalmologists improved by approximately 12% (95% CI, 9.1% ~ 14.9%) with statistical significance (P < 0.001). For the detection of 13 diseases, the test group achieved significant higher sensitivities (72.2% ~ 100.0%) and comparable specificities (90.8% ~ 98.7%) comparing with the control group (sensitivities, 50% ~ 100%; specificities 96.7 ~ 99.8%). The DLS group presented similar performance to the test group in the detection of any fundus abnormality (sensitivity, 95.7%; specificity, 87.2%) and each of the 13 diseases (sensitivity, 83.3% ~ 100.0%; specificity, 89.0 ~ 98.0%). The proposed DLS provided a novel approach for the automatic detection of 13 major fundus diseases with high diagnostic consistency and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses. ClinicalTrials.gov NCT04723160.

Advanced machine learning model for predicting Crohn's disease with enhanced ant colony optimization.

Changes in human lifestyles have led to a dramatic increase in the incidence of Crohn's disease worldwide. Predicting the activity and remission of Crohn's disease has become an urgent research problem. In addition, the influence of each attribute in the test sample on the prediction results and the interpretability of the model still deserves further investigation. Therefore, in this paper, we proposed a wrapper feature selection classification model based on a combination of the improved ant colony optimization algorithm and the kernel extreme learning machine, called bIACOR-KELM-FS. IACOR introduces an evasive strategy and astrophysics strategy to balance the exploration and exploitation phases of the algorithm and enhance its optimization capabilities. The optimization capability of the proposed IACOR was validated on the IEEE CEC2017 benchmark test function. And the prediction was performed on Crohn's disease dataset. The results of the quantitative analysis showed that the prediction accuracy of bIACOR-KELM-FS for predicting the activity and remission of Crohn's disease reached 98.98%. The analysis of important attributes improved the interpretability of the model and provided a reference for the diagnosis of Crohn's disease. Therefore, the proposed model is considered a promising adjunctive diagnostic method for Crohn's disease.

An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs.

Purpose: To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions. Methods: Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation-based co-decision model. Results: This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F1 values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all >0.95; the pixel-level F1 values for segmenting optic disc and peripapillary atrophy were both >0.9; the pixel-level F1 values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all >0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230. Conclusions: The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions. Translational Relevance: The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.

Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography.

AIM: To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography. METHODS: Diagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted. RESULTS: The area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity. CONCLUSION: The proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world.

Lens Opacities Classification System III-based artificial intelligence program for automatic cataract grading.

PURPOSE: To establish and validate an artificial intelligence (AI)-assisted automatic cataract grading program based on the Lens Opacities Classification System III (LOCS III). SETTING: Eye and Ear, Nose, and Throat Hospital, Fudan University, Shanghai, China. DESIGN: AI training. METHODS: Advanced deep-learning algorithms, including Faster R-CNN and ResNet, were applied to the localization and analysis of the region of interest. An internal dataset from the EENT Hospital of Fudan University and an external dataset from the Pujiang Eye Study were used for AI training, validation, and testing. The datasets were automatically labeled on the AI platform regarding the capture mode and cataract grading based on the LOCS III. RESULTS: The AI program showed reliable capture mode recognition, grading, and referral capability for nuclear and cortical cataract grading. In the internal and external datasets, 99.4% and 100% of automatic nuclear grading, respectively, had an absolute prediction error of ≤1.0, with a satisfactory referral capability (area under the curve [AUC]: 0.983 for the internal dataset; 0.977 for the external dataset); 75.0% (internal dataset) and 93.5% (external dataset) of the automatic cortical grades had an absolute prediction error of ≤1.0, with AUCs of 0.855 and 0.795 for referral, respectively. Good consistency was observed between automatic and manual grading when both nuclear and cortical cataracts were evaluated. However, automatic grading of posterior subcapsular cataracts was impractical. CONCLUSIONS: The AI program proposed in this study showed robust grading and diagnostic performance for both nuclear and cortical cataracts, based on LOCS III.

Artificial intelligence can assist with diagnosing retinal vein occlusion.

AIM: To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible. METHODS: A total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models. RESULTS: Using the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively. CONCLUSION: DL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.

The Economic Effects of Direct and Indirect Employee Involvement: Evidence From Corporate Social Responsibility Reports of Chinese Listed Companies.

Employee direct involvement and indirect involvement have been identified as essential forms of an enterprise's democratic management in the digital economy. Research on the complementary effects of direct and indirect involvement is still in a blank state in China, which limits the external validity and accumulation of employee participation theory. The present study aimed to investigate the complementary effects of employee direct involvement and indirect involvement on the firm's financial performance. Although previous research suggests that the influence of employee direct or indirect involvement on corporate financial performance has been examined separately, it is unclear whether the association between employee direct involvement and indirect involvement is complementary or conflictual. Based on strategic human resource management theory, we semantically encode 2,680 corporate social responsibility reports and the annual reports of 268 state-owned listed enterprises published from 2014 to 2018 via content analysis method, and the economic effects of employee direct involvement and indirect involvement were concurrently measured. We use configuration theory to explore the complementary effects between employee direct involvement and indirect involvement. Our results reveal that (1) employee involvement in Chinese enterprises was unbalanced, (2) both employee direct involvement and indirect involvement were positively related to enterprise's financial performance, and (3) there is a complementary effect between the two forms of employee involvement. Theoretical and practical implications of these findings are discussed.

TMT-Based Proteomic Explores the Influence of DHEA on the Osteogenic Differentiation of hBMSCs.

Dehydroepiandrosterone (DHEA) has been revealed to implicate in facilitating osteoblast differentiation of human bone marrow mesenchymal stem cells (hBMSCs) and inhibiting osteoporosis (OP). However, the underlying molecular mechanism remains largely unknown. Here, we induced osteogenic differentiation of hBMSCs derived from elders using an osteogenic induction medium with or without DHEA. The results showed that osteogenic induction medium (OIM) with DHEA could significantly promote the proliferation and osteogenic differentiation of hBMSCs than OIM alone. By using a Tandem Mass Tag (TMT) labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology, we screened out 604 differentially expressed proteins (DEPs) with at least one unique peptide were identified [524: OIM vs. complete medium (CM), and 547: OIM+DHEA vs. CM], among these proteins, 467 DEPs were shared in these two different comparative groups. Bioinformatic analysis revealed these DEPs are mainly enriched in metabolic pathways. Interestingly, the expression levels of the DEPs in the metabolic pathways showed a more noticeable change in the OIM+DHEA vs. CM group than OIM vs. CM group. Moreover, the protein-protein interaction (PPI) network analysis revealed that three potential proteins, ATP5B, MT-CYB, and MT-ATP6, involved in energy metabolism, might play a key role in osteogenic differentiation induced by OIM+DHEA. These findings offer a valuable clue for us to better understand the underlying mechanisms involved in osteoblast differentiation of hBMSCs caused by DHEA and assist in applying DHEA in hBMSCs-based therapy for osteogenic regeneration.

A pH-responsive mesoporous silica nanoparticles-based drug delivery system with controlled release of andrographolide for OA treatment.

Andrographolide (AG) has favorable anti-inflammatory and antioxidative capacity. However, it has low bioavailability due to high lipophilicity and can be easily cleared by the synovial fluid after intra-articular injection, leading to low therapeutic efficiency in osteoarthritis (OA). Herein, we designed a nano-sized pH-responsive drug delivery system (DDS) for OA treatment by using modified mesoporous silica nanoparticles (MSNs) with pH-responsive polyacrylic acid (PAA) for loading of AG to form AG@MSNs-PAA nanoplatform. The nanoparticles have uniform size (∼120 nm), high drug loading efficiency (22.38 ± 0.71%) and pH-responsive properties, beneficial to sustained release in OA environment. Compared with AG, AG@MSNs-PAA showed enhanced antiarthritic efficacy and chondro-protective capacity based on IL-1ß-stimulated chondrocytes and anterior cruciate ligament transection-induced rat OA model, as demonstrated by lower expression of inflammatory factors and better prevention of proteoglycan loss. Therefore, the AG@MSNs-PAA nanoplatform may be developed as a promising OA-specific and on-demand DDS.

An Optical Coherence Tomography-Based Deep Learning Algorithm for Visual Acuity Prediction of Highly Myopic Eyes After Cataract Surgery.

BACKGROUND: Due to complicated and variable fundus status of highly myopic eyes, their visual benefit from cataract surgery remains hard to be determined preoperatively. We therefore aimed to develop an optical coherence tomography (OCT)-based deep learning algorithms to predict the postoperative visual acuity of highly myopic eyes after cataract surgery. MATERIALS AND METHODS: The internal dataset consisted of 1,415 highly myopic eyes having cataract surgeries in our hospital. Another external dataset consisted of 161 highly myopic eyes from Heping Eye Hospital. Preoperative macular OCT images were set as the only feature. The best corrected visual acuity (BCVA) at 4 weeks after surgery was set as the ground truth. Five different deep learning algorithms, namely ResNet-18, ResNet-34, ResNet-50, ResNet-101, and Inception-v3, were used to develop the model aiming at predicting the postoperative BCVA, and an ensemble learning was further developed. The model was further evaluated in the internal and external test datasets. RESULTS: The ensemble learning showed the lowest mean absolute error (MAE) of 0.1566 logMAR and the lowest root mean square error (RMSE) of 0.2433 logMAR in the validation dataset. Promising outcomes in the internal and external test datasets were revealed with MAEs of 0.1524 and 0.1602 logMAR and RMSEs of 0.2612 and 0.2020 logMAR, respectively. Considerable sensitivity and precision were achieved in the BCVA < 0.30 logMAR group, with 90.32 and 75.34% in the internal test dataset and 81.75 and 89.60% in the external test dataset, respectively. The percentages of the prediction errors within ± 0.30 logMAR were 89.01% in the internal and 88.82% in the external test dataset. CONCLUSION: Promising prediction outcomes of postoperative BCVA were achieved by the novel OCT-trained deep learning model, which will be helpful for the surgical planning of highly myopic cataract patients.

Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study.

PURPOSE: To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology. METHODS: This study included 1500 Optos color images for tessellated fundus confirmation and peripheral retinal lesion (lattice degeneration, retinal breaks, and retinal detachment) assessment. Three retinal specialists evaluated all images and proposed the reference standard when an agreement was achieved. Then, 722 images were used to train and verify a combined deep-learning system of 3 optimal binary classification models trained using seResNext50 algorithm with 2 preprocessing methods (original resizing and cropping), and a test set of 189 images were applied to verify the performance compared to the reference standard. RESULTS: With optimal preprocessing approach (original resizing method for lattice degeneration and retinal detachment, cropping method for retinal breaks), the combined deep-learning system exhibited an area under curve of 0.888, 0.953, and 1.000 for detection of lattice degeneration, retinal breaks, and retinal detachment respectively in tessellated eyes. The referral accuracy of this system was 79.8% compared to the reference standard. CONCLUSION: A deep-learning system is feasible to detect lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field images. And this system may be considered for screening and telemedicine.

Identification of potential specific biomarkers and key signaling pathways between osteogenic and adipogenic differentiation of hBMSCs for osteoporosis therapy.

BACKGROUND: The differentiation of bone mesenchymal stem cells (BMSCs) into adipogenesis (AD) rather than osteogenesis (OS) is an important pathological feature of osteoporosis. Illuminating the detailed mechanisms of the differentiation of BMSCs into OS and AD would contribute to the interpretation of osteoporosis pathology. METHODS: To identify the regulated mechanism in lineage commitment of the BMSCs into OS and AD in the early stages, the gene expression profiles with temporal series were downloaded to reveal the distinct fates when BMSCs adopt a committed lineage. For both OS and AD lineages, the profiles of days 2-4 were compared with day 0 to screen the differentially expressed genes (DEGs), respectively. Next, the functional enrichment analysis was utilized to find out the biological function, and protein-protein interaction network to predict the central genes. Finally, experiments were performed to verify our finding. RESULTS: FoxO signaling pathway with central genes like FoxO3, IL6, and CAT is the crucial mechanism of OS, while Rap1 signaling pathway of VEGFA and FGF2 enrichment is more significant for AD. Besides, PI3K-Akt signaling pathway might serve as the latent mechanism about the initiation of differentiation of BMSCs into multiple lineages. CONCLUSION: Above hub genes and early-responder signaling pathways control osteogenic and adipogenic fates of BMSCs, which maybe mechanistic models clarifying the changes of bone metabolism in the clinical progress of osteoporosis. The findings provide a crucial reference for the prevention and therapy of osteoporosis.

Ninjurin 2 overexpression promotes human colorectal cancer cell growth in vitro and in vivo.

Ninjurin 2 (NINJ2) is a novel adhesion molecule. Its expression and potential function in human colorectal cancer (CRC) cells are studied. We show that NINJ2 is overexpressed in established (HT-29) and primary CRC cells and in human colon cancer tissues. Its expression level is low in colon epithelial cells and normal colon tissues. NINJ2 shRNA or knockout (by CRSIPR/Cas9) potently inhibited human CRC cell survival and proliferation, while significantly inducing cell apoptosis. Conversely, lentivirus-mediated NINJ2 overexpression promoted CRC cell proliferation. NINJ2 co-immunoprecipitated with multiple RTKs (EGFR, PDGFRα/ß and FGFR) in CRC cells and human colon cancer tissues. In HT-29 cells, RTKs' downstream signalings, Akt and Erk, were significantly inhibited by NINJ2 shRNA or knockout, but augmented following ectopic NINJ2 overexpression. In vivo, NINJ2-silenced or NINJ2-knockout CRC xenografts grew significantly slower than the control xenografts. Akt-Erk activation was largely inhibited in CRC xenografts with NINJ2 silencing or knockout. Taken together, NINJ2 overexpression promotes CRC cell growth in vitro and in vivo.

Cell division cycle protein 42 regulates the inflammatory response in mice bearing inflammatory bowel disease.

This study aimed to explore the effect of cell division cycle protein 42 (CDC42) on inflammatory response and immune response in mice bearing inflammatory bowel disease (IBD). Trinitrobenzene sulfonic acid was injected into the colon of mice to establish IBD model. The mice were divided into four groups (n = 4): control, model, Ad5, and Ad5-CDC42. After establishing IBD model, mice which were treated with AD5 empty vector and AD5-CDC42 expression vector served as the Ad5 group and Ad5-CDC42 group, respectively. The mRNA and protein levels of interleukin 10 (IL-10), interferon-γ (IFN-γ), IL-4, and tumor necrosis factor-α (TNF-α) in the colon tissues were evaluated by RT-PCR and western blot, respectively. Their levels in the serum and colon tissues were examined by ELISA assay and immunohistochemical analysis, respectively. Their changes in the mRNA and protein levels were consistent and similar changes in the colon tissues and the serum were found among various groups. The levels of IL-10, IFN-γ, IL-4, and TNF-α were lowest in the control group. Their levels in the model group and the Ad5 group were similar (p > .05) and significantly higher than those in the control group (p < .05). In comparison with the model group and the Ad5 group, their levels were significantly reduced in the Ad5-CDC42 group (p < .05). In conclusion, the levels of inflammatory cytokines were elevated in the colon tissues and serum of IBD mice, which could be reduced by the CDC42 treatment. CDC42 regulated the inflammatory response and the innate immune response in IBD mice.

miR-1273g silences MAGEA3/6 to inhibit human colorectal cancer cell growth via activation of AMPK signaling.

AMP-activated protein kinase (AMPK) is a metabolic regulator that acts to limit the growth of cancer cells. AMPK is downregulated by melanoma antigens A3/6 (MAGEA3/6), which are cancer-specific proteins that enhance the activity of specific E3 ubiquitin ligases to ubiquitinate and degrade AMP-activated protein kinase α1 (AMPKα1). Here, using a bioinformatic approach, we identified a microRNA, miR-1273g-3p, that is predicted to target the 3' untranslated region (UTR) of MAGEA3/6. Analyzing miR-1273g-3p expression in human colon cancer tissues, we found a reduction in miR-1273g-3p expression correlating with increased MAGEA3/6 expression and AMPKα1 downregulation. Expression of miR-1273g in HT-29 cells and primary human colon cancer cells down-regulated MAGEA3/6, leading to AMPKα1 upregulation, inhibition of proliferation and cell apoptosis. The anti-CRC activity of miR-1273g was blocked by AMPKα1 knockout. MAGEA3/6 shRNA silencing mimicked and abolished miR-1273g-induced actions in HT-29 cells. In vivo, miR-1273g- or MAGEA3/6 shRNA-expressing HT-29 tumors grew significantly slower than control tumors. We propose a novel miRNA-based mechanism, whereby miR-1273g represses MAGEA3/6 expression in human CRC cells and tissues, which may provide a novel cancer-specific therapeutic.

Effectiveness of Omega-3 Polyunsaturated Fatty Acids in Non-Alcoholic Fatty Liver Disease: A Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a clinical syndrome with the main characteristic of diffuse liver cells with fatty changes. The clinical evolution of NAFLD includes simple non-alcoholic fatty liver, non-alcoholic steatohepatitis (NASH), liver fibrosis and cirrhosis, and even hepatocellular carcinoma. METHODS AND FINDINGS: We conducted this review to identify the effectiveness of omega-3 polyunsaturated fatty acids (ω-3 PUFA) in NAFLD. We searched PubMed, Cochrane Library and Embase. All randomized controlled trials (RCTs) of ω-3 PUFA treatment for NAFLD were considered. Two reviewers assessed the quality of each study and collected data independently. Disagreements were resolved by discussion among the reviewers and any of the other authors of the paper. We performed a meta-analysis and reported summary estimates of outcomes as inverse variance (IV), fixed or random, with 95% confidence intervals (CIs). We included seven RCTs involving 442 patients (227 for the experimental group and 215 for the control group). All the patients were divided into two groups: one treated with ω-3 PUFA and the other was the control group (generally placebo). The demographics of the ω-3 PUFA and control groups were comparable. Beneficial changes in alanine aminotransferase (ALT) (IV 95% CI: -7.61 [-12.83 to -2.39], p = 0.004), total cholesterol (TC) (IV 95% CI: -13.41 [-21.44 to -5.38], p = 0.001), triglyceride (TG) (IV 95% CI: -43.96 [-51.21 to -36.71], p<0.00001) and high-density lipoprotein cholesterol (HDL-C) (IV 95% CI: 6.97 [2.05 to 11.90], p = 0.006) favored ω-3 PUFA treatment. Omega-3 PUFA tended towards a beneficial effect on aspartate aminotransferase (AST) (IV 95% CI: -6.89 [-17.71 to 3.92], p = 0.21), γ-glutamyl transferase (GGT) (IV 95% CI: -8.28 [-18.38 to 1.83], p = 0.11) and low-density lipoprotein cholesterol (LDL-C) (IV 95% CI: -7.13 [-14.26 to 0.0], p = 0.05). CONCLUSIONS: Supplementation with ω-3 PUFA is a practical and effective treatment for NAFLD to decrease ALT, TC and increase HDL-C, especially to decrease TG. Omega-3 PUFA also has a tendency toward a beneficial effect on AST, GGT and LDL-C. More high-quality, large RCTs are needed to validate our findings.

Long Non-Coding RNA ucoo2kmd.1 Regulates CD44-Dependent Cell Growth by Competing for miR-211-3p in Colorectal Cancer.

In addition to protein-coding genes, the human genome makes a large amount of noncoding RNAs. Long non-coding RNAs (lncRNAs) have been described as the largest subclass of the non-coding transcriptome in human noncoding RNAs. In recent years, lncRNAs have been considered to be the key regulators of tumor behavior. In this study, based on previous research, we investigated the expression and biological role of a newly identified cancer-related lncRNA, lncRNA-uc002kmd.1. We analyzed the relationship between lncRNA-uc002kmd.1 and colorectal cancer (CRC) in a total 45 CRC and paired adjacent, non-tumor tissue samples. We found that lncRNA-uc002kmd.1 expression was usually highly expressed in carcinoma compared with the tissue adjacent to the carcinoma. Through a series of experiments, the results showed that lncRNA-uc002kmd.1 regulates CD44 as a molecular decoy for miR211-3p. Our data indicated that the overexpression of lncRNA-uc002kmd.1 enhanced cell proliferation in CRC.