BST2 promotes gastric cancer metastasis under the regulation of HOXD9 and PABPC1.

Gastric cancer (GC) constitutes substantial cancer mortality worldwide. Several cancer types aberrantly express bone marrow stromal cell antigen 2 (BST2), yet its functional and underlying mechanisms in GC progression remain unknown. In our study, RNA sequencing data revealed that BST2 was transcriptionally activated by homeobox D9 (HOXD9). BST2 was significantly upregulated in GC tissues and promoted epithelial-mesenchymal transition and metastasis of GC. BST2 knockdown reversed HOXD9's oncogenic effect on GC metastasis. Moreover, BST2 messenger RNA stability could be enhanced by poly(A) binding protein cytoplasmic 1 (PABPC1) through the interaction between BST2 3'-UTR and PABPC1 in GC cells. PABPC1 promoted GC metastasis, which BST2 silencing attenuated in vitro and in vivo. In addition, positive correlations among HOXD9, BST2, and PABPC1 were established in clinical samples. Taken together, increased expression of BST2 induced by HOXD9 synergizing with PABPC1 promoted GC cell migration and invasion capacity.

ARID2, a rare cause of Coffin-Siris syndrome: A novel microdeletion at 12q12q13.11 causing severe short stature and literature review.

Coffin-Siris syndrome (CSS) 6 is caused by heterozygous pathogenic variants in the AT-rich interaction domain 2 (ARID2) gene on 12q12. Currently, only 26 cases with both detailed clinical and genetic information have been documented in the literature. Microdeletions of the entire ARID2 gene are rare. In this study, we report a 5-year-7-month-old Chinese female who underwent whole-exome sequencing to discover that she had a de novo 1.563 Mb heterozygous copy number loss at 12q12q13.11, involving an entire deletion of ARID2. The female had severe short stature with obvious dysmorphic facial features, global developmental delay and hypoplastic fingers and toes. Her growth hormone level was normal, with reduced IGF-1 and increased CA19-9 levels. After a review of the 27 patients with ARID2 deficiency, a significant positive correlation was observed between age and height standard deviation score (SDS) (r = 0.71, p = 0.0002), suggesting a possibility of growth catch-up. This study expands the genetic and phenotypic spectrum of CCS6 and provides a decision-making reference for growth hormone therapy.

Disruption of the CCDC43-FHL1 interaction triggers apoptosis in gastric cancer cells.

The coiled-coil domain-containing protein 43 (CCDC43) is essential to promote gastric cancer (GC) proliferation and invasion, while four and a half LIM domains 1 (FHL1) involves GC cells apoptosis. We attempted to address inter-relationship between CCDC43 and FHL1 in modulating GC cells growth and apoptosis. Levels of protein expression were assessed by western blot, immunofluorescence. Using EdU, plate colony formation, Matrigel invasion and animal models, we evaluated the function in vitro and in vivo. Apoptosis was evaluated by flow cytometry and Hoechst 33258 staining. Reciprocal co-immunoprecipitation (co-IP) analyses indicated that CCDC43 physically interacted with FHL1. The expression of CCDC43 was negatively correlated with FHL1. Moreover, up-regulation of CCDC43 resulted in FHL1 level decline, and the reverse is also true. CCDC43 expressed jointly with FHL1 group significantly decreases the ability of the growth, metastasis and invasion of GC cells compared with that of the CCDC43 group. Furthermore, siRNA-mediated repression of CCDC43 results in dissociation from FHL1 and causes suppression of GC cell proliferation and metastasis. CCDC43 repression mediates the stability of FHL1 protein. In addition, CCDC43 interacts with FHL1. Knockdown of CCDC43 plus FHL1 overexpression inhibits proliferation and migration and induces apoptosis of GC cells in vitro and vivo.

WM-STGCN: A Novel Spatiotemporal Modeling Method for Parkinsonian Gait Recognition.

Parkinson's disease (PD) is a neurodegenerative disorder that causes gait abnormalities. Early and accurate recognition of PD gait is crucial for effective treatment. Recently, deep learning techniques have shown promising results in PD gait analysis. However, most existing methods focus on severity estimation and frozen gait detection, while the recognition of Parkinsonian gait and normal gait from the forward video has not been reported. In this paper, we propose a novel spatiotemporal modeling method for PD gait recognition, named WM-STGCN, which utilizes a Weighted adjacency matrix with virtual connection and Multi-scale temporal convolution in a Spatiotemporal Graph Convolution Network. The weighted matrix enables different intensities to be assigned to different spatial features, including virtual connections, while the multi-scale temporal convolution helps to effectively capture the temporal features at different scales. Moreover, we employ various approaches to augment skeleton data. Experimental results show that our proposed method achieved the best accuracy of 87.1% and an F1 score of 92.85%, outperforming Long short-term memory (LSTM), K-nearest neighbors (KNN), Decision tree, AdaBoost, and ST-GCN models. Our proposed WM-STGCN provides an effective spatiotemporal modeling method for PD gait recognition that outperforms existing methods. It has the potential for clinical application in PD diagnosis and treatment.

Orelabrutinib-bruton tyrosine kinase inhibitor-based regimens in the treatment of central nervous system lymphoma: a retrospective study.

BACKGROUND: Central nervous system lymphoma (CNSL) is an aggressive lymphoma. Orelabrutinib, an oral Bruton tyrosine kinase inhibitor, is a new treatment strategy for CNSL. This study aims to evaluate the efficacy and safety of orelabrutinib-based regimens in the treatment of patients with CNSL. METHODS: Twenty-three patients with CNSL were included in this retrospective study. All patients received the orelabrutinib-based regimen. Efficacy was evaluated based on investigators' assessment of overall response rate (ORR), complete response/unconfirmed complete response (CR/CRu), partial response (PR), stable disease (SD), progressive disease (PD), duration of response (DOR), progression-free survival (PFS) and overall survival (OS). The safety of orelabrutinib-based regimens has also been evaluated. RESULTS: A total of 17.39% of patients received orelabrutinib-based regimens for consolidation therapy, and 82.61% of patients for induction therapy (4 newly diagnosed CNSL, 15 relapsed/refractory CNSL). In the newly diagnosed CNSL group, the ORR was 100% (1 CR, 1 CRu, 2 PR). The 6-month DOR rate, 6-month PFS rate, and 6-month OS rate were 100%, 100%, and 100%, respectively. Of the 15 relapsed/refractory CNSL patients, five therapy regimens were applied (orelabrutinib, n = 3; orelabrutinib/immunotherapy, n = 3; orelabrutinib/chemotherapy, n = 2; orelabrutinib/immunochemotherapy, n = 6; orelabrutinib/radiotherapy, n = 1). The ORR was 60.00% (4 CR, 5 PR). The 6-month DOR rate, 6-month PFS rate, and 6-month OS rate were 92.30%, 67.70%, and 70.00%, respectively. Twenty-one patients reported adverse events (AEs), and 6 patients experienced grade ≥ 3 AEs. CONCLUSION: Orelabrutinib-based regimens were efficacious and well-tolerated in patients with CNSL. These combined therapies offer a new potential therapeutic strategy for patients with CNSL.

The pharyngeal carriage of Haemophilus influenzae among healthy population in China: a systematic review and meta-analysis.

BACKGROUND: A nationwide investigation on the carriage proportion of H. influenzae among healthy populations is lacking in China. The purpose of the study was to review the prevalence of pharyngeal carriage of H. influenzae among healthy populations in China, and explore its influencing factors. The serotypes distribution of H. influenzae was also analyzed. METHODS: A systematic search was conducted with key words "Haemophilus influenzae", "Carriage", and "China" or "Chinese" from inception to March 2018. After careful screening, the data of included articles were extracted with a pre-designed excel form. Then, the pooled carriage proportion of H. influenzae was calculated using the random effect model. RESULTS: A total of 42 studies with 17,388 participants were included. The overall pooled carriage proportion of H. influenzae was 0.17 (95% CI: 0.13-0.21), and the carriage proportion largely varied by province. Subgroup analysis indicated that the pooled carriage proportion was 0.17 (0.13-0.21) for children, and 0.14 (0.7-0.23) for adults. There were no statistically significant heterogeneity between subgroups by age (p = 0.65), sex (p = 0.88), and season (p = 0.10). The pooled carriage proportion of Hib was 0.01 (0-0.02), while the carriage proportion of NTHi was 0.22 (0.13-0.31). CONCLUSION: In China, the carriage proportion of H. influenzae among healthy population was low, but it largely varied by provinces.

Efficient resistance to grass carp reovirus infection in JAM-A knockout cells using CRISPR/Cas9.

The hemorrhagic disease of grass carp (Ctenopharyngodon idellus) induced by grass carp reovirus (GCRV) leads to huge economic losses in China and currently, there are no effective methods available for prevention and treatment. The various GCRV genotypes may be one of the major obstacles in the pursuit of an effective antiviral treatment. In this study, we exploited CRISPR/Cas9 gene editing to specifically knockout the DNA sequence of the grass carp Junctional Adhesion Molecule-A (gcJAM-A) and evaluated in vitro resistance against various GCRV genotypes. Our results show that CRISPR/Cas9 effectively knocked out gcJAM-A and reduced GCRV infection for two different genotypes in permissive grass carp kidney cells (CIK), as evidenced by suppressed cytopathic effect (CPE) and GCRV progeny production in infected cells. In addition, with ectopic expression of gcJAM-A in cells, non-permissive cells derived from Chinese giant salamander (Andrias davidianus) muscle (GSM) could be highly infected by both GCRV-JX0901 and Hubei grass carp disease reovirus (HGDRV) strains that have different genotypes. Taken together, the results demonstrate that gcJAM-A is necessary for GCRV infection, implying a potential approach for viral control in aquaculture.

RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.

Operational and environmental performance in China's thermal power industry: Taking an effectiveness measure as complement to an efficiency measure.

The trend toward a more fiercely competitive and strictly environmentally regulated electricity market in several countries, including China has led to efforts by both industry and government to develop advanced performance evaluation models that adapt to new evaluation requirements. Traditional operational and environmental efficiency measures do not fully consider the influence of market competition and environmental regulations and, thus, are not sufficient for the thermal power industry to evaluate its operational performance with respect to specific marketing goals (operational effectiveness) and its environmental performance with respect to specific emissions reduction targets (environmental effectiveness). As a complement to an operational efficiency measure, an operational effectiveness measure not only reflects the capacity of an electricity production system to increase its electricity generation through the improvement of operational efficiency, but it also reflects the system's capability to adjust its electricity generation activities to match electricity demand. In addition, as a complement to an environmental efficiency measure, an environmental effectiveness measure not only reflects the capacity of an electricity production system to decrease its pollutant emissions through the improvement of environmental efficiency, but it also reflects the system's capability to adjust its emissions abatement activities to fulfill environmental regulations. Furthermore, an environmental effectiveness measure helps the government regulator to verify the rationality of its emissions reduction targets assigned to the thermal power industry. Several newly developed effectiveness measurements based on data envelopment analysis (DEA) were utilized in this study to evaluate the operational and environmental performance of the thermal power industry in China during 2006-2013. Both efficiency and effectiveness were evaluated from the three perspectives of operational, environmental, and joint adjustments to each electricity production system. The operational and environmental performance changes over time were also captured through an effectiveness measure based on the global Malmquist productivity index. Our empirical results indicated that the performance of China's thermal power industry experienced significant progress during the study period and that policies regarding the development and regulation of the thermal power industry yielded the expected effects. However, the emissions reduction targets assigned to China's thermal power industry are loose and conservative.

Independently Trained Multi-Scale Registration Network Based on Image Pyramid.

Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of different resolutions as the training set. Using image pairs of different resolutions, train each registration network independently to extract image features from the image pairs at different resolutions. During the testing stage, the large deformation registration is decomposed into a multi-scale registration process. The deformation fields of different resolutions are fused by a step-by-step deformation method, thereby addressing the challenge of directly handling large deformations. Experiments were conducted on the open cardiac dataset ACDC (Automated Cardiac Diagnosis Challenge); the proposed method achieved an average Dice score of 0.828 in the experimental results. Through comparative experiments, it has been demonstrated that the proposed method effectively addressed the challenge of heart image registration and achieved superior registration results for cardiac images.

BCSwinReg: A cross-modal attention network for CBCT-to-CT multimodal image registration.

Computed tomography (CT) and cone beam computed tomography (CBCT) registration plays an important role in radiotherapy. However, the poor quality of CBCT makes CBCT-CT multimodal registration challenging. Effective feature fusion and mapping often lead to better registration results for multimodal registration. Therefore, we proposed a new backbone network BCSwinReg and a cross-modal attention module CrossSwin. Specifically, a cross-modal attention CrossSwin is designed to promote multi-modal feature fusion, map the multi-modal domain to the common domain, and thus helping the network learn the correspondence between images better. Furthermore, a new network, BCSwinReg, is proposed to discover correspondence through cross-attention exchange information, obtain multi-level semantic information through a multi-resolution strategy, and finally integrate the deformation of multi-resolutions by the divide-conquer cascade method. We performed experiments on the publicly available 4D-Lung dataset to demonstrate the effectiveness of CrossSwin and BCSwinReg. Compared with VoxelMorph, the BCSwinReg has obtained performance improvements of 3.3% in Dice Similarity Coefficient (DSC) and 0.19 in the average 95% Hausdorff distance (HD95).

Clinical features and prognosis of chronic natural killer cell lymphoproliferative disorders.

OBJECTIVE: To analyze the current treatment status and prognostic regression of the chronic NK cell lymphoproliferative disorder (CLPD-NK). METHODS: We retrospectively analyzed the clinical features, treatment and prognosis of 18 patients with CLPD-NK who were treated at our Hospital between September 2016 and September 2022. RESULTS: Eighteen patients were included: three patients were treated with chemotherapy, five patients underwent immune-related therapy, one patient was treated with glucocorticoids alone, five patients were administered granulocyte colony-stimulating factor, blood transfusion therapy, or anti-infection therapy, followed by observation and follow-up, and four patients were observed without treatment. Fifteen patients survived, including two patients who achieved complete remission (CR) and seven patients who achieved partial remission (PR), of whom one patient progressed to Aggressive NK-cell leukemia (ANKL) and sustained remission after multiple lines of treatment; three patients were not reviewed, of which one patient was still in active disease, three patients developed hemophagocytic syndrome during treatment and eventually died, one of them had positive Epstein-Barr virus (EBV) expression. The 5-years overall survival rate was 83%. CONCLUSION: Most patients with CLPD-NK have inert progression and a good prognosis, whereas some patients have a poor prognosis after progressing to ANKL and combined with hemophagocytic syndrome. Abnormal NK cells invading the center suggest a high possibility of ANKL development, and immunosuppressants and hormones are effective treatments for this disease.

Deformable registration of lung 3DCT images using an unsupervised heterogeneous multi-resolution neural network.

Lung image registration is more challenging than other organs. This is because the breath of the human body causes large deformations in the lung parenchyma and small deformations in tissues such as the pulmonary vascular. Many studies have recently used multi-resolution networks to solve the lung registration problem. However, they use the same structure of registration modules on each level, which makes it difficult to handle complex and small deformations. We propose an unsupervised heterogeneous multi-resolution network (UHMR-Net) to overcome the above problem. The image detail registration module (IDRM) is designed on the highest resolution level. Within this module, the cascaded network is used on the same resolution image to continuously learn the "remaining" detail deformation fields. The shallow shrinkage loss (SS-Loss) is designed to supervise the cascaded network, thus further improving the ability of the network to handle small deformations. Moreover, with the lightweight feature local correlation layer we proposed, the image boundary registration module (IBRM), on multiple low-resolution levels, can better solve the large deformation registration problem. The target registration error on the public DIR-Lab 4DCT dataset was 1.56 ± 1.39 mm, which was significantly better than the classic conventional methods and advanced deep-based methods.

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Chinese fir in China are generally inefficient plantations with single species, unreasonable stand density, and low productivity. The introduction of broadleaved species is usually adopted as a strategy to improve Chinese fir plantations. Taking the pure forests and mixed forests of the Guanshan Forest Farm in Jiangxi Province as example, we quantified the intrinsic water-use efficiency (iWUE) of trees based on the stable isotope carbon method, as well as its response to meteorological factors, and investigated the improvement of stand quality after introducing Phoebe zhennan into Chinese fir plantation. The results showed that the basal area increment was 0.23 cm2 in pure forest, being higher than that of 0.19 cm2 in mixed forest. The δ13C and iWUE of pure forest were -27.4‰ and 52.9%, respectively, being lower than those of -26.7‰ and 62.8% in the mixed forest. Tree δ13C in pure forest was more sensitive to changes in mean annual precipitation and mean annual relative humidity, while that in mixed forest was not significantly correlated with meteorological factors. Pure forest iWUE was positively correlated with mean annual temperature, mean annual atmospheric CO2 concentration, and mean annual maximum temperature, and negatively correlated with mean annual precipitation and mean annual relative humidity, while mixed forest iWUE was positively correlated with mean annual atmospheric CO2 concentration only. Our results indicated that pure forests was more sensitive to climate than mixed forests.

Clinical and therapeutical significances of the cluster and signature based on oxidative stress for osteosarcoma.

BACKGROUND: It is of great clinical significance to find out the ideal tumor biomarkers and therapeutic targets to improve the prognosis of patients with osteosarcoma (OS). Oxidative stress (OXS) can directly target intracellular macromolecules and exhibit dual effects of tumor promotion and suppression. METHODS: OXS-related genes (OXRGs) were extracted from public databases, including TARGET and GEO. Univariate Cox regression analysis, Random Survival Forest algorithm, and LASSO regression were performed to identify prognostic genes and establish the OXS-signature. The efficacy of the OXS-signature was further evaluated by Kaplan-Meier curves and timeROC package. Evaluation of immunological characteristics was achieved based on ESTIMATE algorithm and ssGSEA. Submap algorithm was used to explore the response to anti-PD1 and anti-CTLA4 therapy for OS. Drug response prediction was conducted by using pRRophetic package. The expression values of related genes in the OXS-signature were detected with PCR assays. RESULTS: Two OXS-clusters were identified for OS, with remarkable differences of clusters presented in prognosis. Kyoto Encyclopedia of Genes Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between the OXS-clusters were significantly enriched in several immune-related pathways. Patients with lower OS-scores attained better clinical outcomes, and presented more sensitivity to ICB therapy. By contrast, OS patients with higher OS-scores revealed more sensitivity to certain drugs. Furthermore, critical genes, RHBDL2 and CGREF1 from the model, were significantly higher expressed in OS cell lines. CONCLUSIONS: Our study identified the clusters and signature based on OXS, which would lay the foundation for molecular experimental research, disease prevention and treatment of OS.

The HOXD9-mediated PAXIP1-AS1 regulates gastric cancer progression through PABPC1/PAK1 modulation.

Long non-coding RNAs (lncRNAs) have been functionally characterised in various diseases. LncRNA PAX-interacting protein 1-antisense RNA 1 (PAXIP1-AS1) has reportedly been associated with cancer development. However, its role in gastric cancer (GC) remains poorly understood. Here, we showed that PAXIP1-AS1 was transcriptionally repressed by homeobox D9 (HOXD9) and was significantly downregulated in GC tissues and cells. Decreased expression of PAXIP1-AS1 was positively correlated with tumour progression, while PAXIP1-AS1 overexpression inhibited cell growth and metastasis both in vitro and in vivo. PAXIP1-AS1 overexpression significantly attenuated HOXD9-enhanced epithelial-to-mesenchymal transition (EMT), invasion and metastasis in GC cells. Poly(A)-binding protein cytoplasmic 1 (PABPC1), an RNA-binding protein, was found to enhance the stability of PAK1 mRNA, leading to EMT progress and GC metastasis. PAXIP1-AS1 was found to directly bind to and destabilise PABPC1, thereby regulating EMT and metastasis of GC cells. In summary, PAXIP1-AS1 suppressed metastasis, and the HOXD9/PAXIP1-AS1/PABPC1/PAK1 signalling axis may be involved in the progression of GC.

The LINC00501-HSP90B1-STAT3 positive feedback loop promotes malignant behavior in gastric cancer cells.

Long non-coding RNAs (lncRNAs) have been implicated in gastric cancer (GC) carcinogenesis and progression. However, the role of LINC00501 in GC growth and metastasis remains unclear. In this study, we found that LINC00501 was frequently upregulated in GC cells and tissues and was closely related to adverse GC clinicopathological features. Aberrant overexpression of LINC00501 promoted GC cell proliferation, invasion, and metastasis both in vitro and in vivo. Mechanistically, LINC00501 stabilized client protein STAT3 from deubiquitylation by directly interacting with cancer chaperone protein HSP90B1. Furthermore, the LINC00501-STAT3 axis modulated GC cell proliferation and metastasis. In turn, STAT3 bound directly to the LINC00501 promoter and positively activated LINC00501 expression, thus forming a positive feedback loop, thereby accelerating tumor growth, invasiveness, and metastasis. In addition, LINC00501 expression was positively correlated with STAT3 and p-STAT3 protein expression levels in gastric clinical samples. Our results reveal that LINC00501 acts as an oncogenic lncRNA and that the LINC00501-HSP90B1-STAT3 positive feedback loop contributes to GC development and progression, suggesting that LINC00501 may be a novel potential biomarker and treatment target for GC.

The VAX2-LINC01189-hnRNPF signaling axis regulates cell invasion and migration in gastric cancer.

Transcription factors (TFs) and long noncoding RNAs (lncRNAs) contribute to gastric cancer (GC). However, the roles of TFs and lncRNAs in the invasion and metastasis of GC remain largely unknown. Here, we observed that the transcription factor VAX2 is significantly upregulated in GC cells and tissues and acts as an oncogene. Moreover, high VAX2 expression is associated with the advancement of tumors in GC. In terms of functionality, the enforced expression of VAX2 promotes the proliferation and metastasis of GC cells. Mechanistically, VAX2 specifically interacts with the LINC01189 promoter and represses LINC01189 transcription. Furthermore, LINC01189 exhibits significant downregulation in GC and functions as a suppressor gene. Functionally, it inhibits migratory and invasive abilities in GC cells. In the context of GC metastasis, VAX2 plays a role in modulating it by trans-repressing the expression of LINC01189. Additionally, LINC01189 binds to hnRNPF to enhance hnRNPF degradation through ubiquitination. The cooperation between LINC01189 and hnRNPF regulates GC cell invasion and migration. In addition, both VAX2 and hnRNPF are highly expressed, while LINC01189 is expressed in at low levels in GC tissues compared to normal gastric tissues. Our study suggests that VAX2 expression facilitates, while LINC01189 expression suppresses, metastasis and that the VAX2-LINC01189-hnRNPF axis plays a contributory role in GC development.

The ATF2/miR-3913-5p/CREB5 axis is involved in the cell proliferation and metastasis of colorectal cancer.

Various miRNAs have been shown to participate in the tumor progression and development of colorectal cancer (CRC). However, the role of miR-3913-5p in CRC are yet to be clearly defined. In the present study, we determine that miR-3913-5p is downregulated in CRC cell lines and CRC tissues. Exogenous miR-3913-5p expression weakens the CRC cells growth, migration and invasion. Mechanistically, miR-3913-5p directly targets the 3'UTR of CREB5. Overexpression of CREB5 reverses the suppression of CRC cells proliferation, migration and invasion induced by miR-3913-5p. Furthermore, ATF2 negatively regulates the transcription of miR-3913-5p by binding to its promoter. CREB5 can cooperate with ATF2. CREB5 is required for ATF2 in regulating miR-3913-5p. Finally, inverse correlations can be found between the expressions of miR-3913-5p and CREB5 or ATF2 in CRC tissues. Thus, a plausible mechanism of ATF2/miR-3913-5p/CREB5 axis regulating CRC progression is elucidated. Our findings suggest that miR-3913-5p functions as a tumor suppressor in CRC. ATF2/miR-3913-5p/CREB5 axis might be a potential therapeutic target against CRC progression.

A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI.

OBJECTIVE: We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans. METHODS: In this study, we selected tau-PET scans from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments. RESULTS: Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD. CONCLUSIONS: Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.