Metal-Organic Frameworks/Heterojunction Structures for Surface-Enhanced Raman Scattering with Enhanced Sensitivity and Tailorability.

Metal-organic frameworks (MOFs), which are composed of crystalline microporous materials with metal ions, have gained considerable interest as promising substrate materials for surface-enhanced Raman scattering (SERS) detection via charge transfer. Research on MOF-based SERS substrates has advanced rapidly because of the MOFs' excellent structural tunability, functionalizable pore interiors, and ultrahigh surface-to-volume ratios. Compared with traditional noble metal SERS plasmons, MOFs exhibit better biocompatibility, ease of operation, and tailorability. However, MOFs cannot produce a sufficient limit of detection (LOD) for ultrasensitive detection, and therefore, developing an ultrasensitive MOF-based SERS substrate is imperative. To the best of our knowledge, this is the first study to develop an MOFs/heterojunction structure as an SERS enhancing material. We report an in situ ZIF-67/Co(OH)2 heterojunction-based nanocellulose paper (nanopaper) plate (in situ ZIF-67 nanoplate) as a device with an LOD of 0.98 nmol/L for Rhodamine 6G and a Raman enhancement of 1.43 × 107, which is 100 times better than that of the pure ZIF-67-based SERS substrate. Further, we extend this structure to other types of MOFs and develop an in situ HKUST-1 nanoplate (with HKUST-1/Cu(OH)2). In addition, we demonstrate that the formation of heterojunctions facilitates efficient photoinduced charge transfer for SERS detection by applying the Mx(OH)y-assisted (where M = Co, Cu, or other metals) MOFs/heterojunction structure. Finally, we successfully demonstrate the application of medicine screening on our nanoplates, specifically for omeprazole. The nanoplates we developed still maintain the tailorability of MOFs and perform high anti-interference ability. Our approach provides customizing options for MOF-based SERS detection, catering to diverse possibilities in future research and applications.

Optical-Cavity-Incorporated Colorful All-Solid-State Electrochromic Devices for Dual Anti-Counterfeiting.

The fusion of electrochromic technology with optical resonant cavities presents an intriguing innovation in the electrochromic field. However, this fusion is mainly achieved in liquid electrolyte-based or sol-gel electrolyte-based electrochromic devices, but not in all-solid-state electrochromic devices, which have broader industrial applications. Here, a new all-solid-state electrochromic device is demonstrated with a metal-dielectric-metal (MDM) resonant cavity, which can achieve strong thin-film interference effects through resonance, enabling the device to achieve unique structural colors that have rarely appeared in reported all-solid-state electrochromic devices, such as yellow green, purple, and light red. The color gamut of the device can be further expanded due to the adjustable optical constants of the electrochromic layer. What is more, this device exhibits remarkable cycling stability (maintaining 84% modulation capability after 7200 cycles), rapid switching time (coloration in 2.6 s and bleaching in 2.8 s), and excellent optical memory effect (only increasing by 13.8% after almost 36 000 s). In addition, this exquisite structural design has dual-responsive anti-counterfeiting effects based on voltage and angle, further demonstrating the powerful color modulation capability of this device.

Role of Platelet/Lymphocyte, Neutrophil/Lymphocyte, and Interleukin-37/Interleukin-17 Ratios in the Occurrence and Treatment of Rheumatoid Arthritis.

This study was designed to investigate the correlation of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and interleukin (IL)-37/IL-17 ratio with the incidence/treatment of rheumatoid arthritis (RA). Firstly, fifty-eight patients with RA treated at the first affiliated hospital of Xinjiang Medical University from January 2018 to January 2019 were selected as the RA group; forty-nine healthy volunteers were enrolled in the control group. RA patients were treated with disease-modifying anti-rheumatic drugs (DMARDs). Next, the NLR, PLR, IL-37, IL-17 and 28-joint disease activity score using erythrocyte sedimentation rate (DAS28-ESR) were deleted in two groups. Subsequently, Spearman correlation analysis was adopted for the correlations of various indicators before and after treatment in two groups. According to the analysis results, the levels of NLR, PLR, IL-37, and IL-17 before treatment in the RA group were higher than those in the control group (P < .05), but the difference in the IL-37/IL-17 level between the two groups was not significant (P > .05). After treatment, NLR, PLR, and IL-37/IL-17 levels were significantly reduced in RA patients (P < .05). NLR and PLR were significantly positively correlated with DAS28-ESR, ESR and C-reactive protein (CRP), of which represented the disease activity of RA. NLP was strongly correlated with IL-37/IL-17. Collectively, NLR, PLR, IL-37, and IL-17 are closely related to the occurrence of RA. In addition, NLR and IL-37/IL-17 are more suitable than PLR in reflecting the therapeutic effect. Therefore, IL-37/IL-17 can be considered as a new indicator for reflecting the treatment effectiveness of RA.

Acupuncture modulates the gut microbiota in Alzheimer's disease: current evidence, challenges, and future opportunities.

Alzheimer's disease, one of the most severe and common neurodegenerative diseases, has no effective cure. Therefore it is crucial to explore novel and effective therapeutic targets. The gut microbiota - brain axis has been found to play a role in Alzheimer's disease by regulating the neuro-immune and endocrine systems. At the same time, acupuncture can modulate the gut microbiota and may impact the course of Alzheimer's disease. In this Review, we discuss recent studies on the role of acupuncture on the gut microbiota as well current challenges and future opportunities of acupuncture as potential treatment for the prevention and treatment of Alzheimer's disease.

Diammonium Glycyrrhizinate Inhibited Inflammatory Response and Modulated Serum Metabolism in Poly(I:C)-induced Pneumonia Model Mice.

ABSTRACT: Currently, the coronavirus disease 2019 (COVID-19) is becoming a serious threat to human health worldwide. Therefore, there is a great need to develop effective drugs against viral pneumonia. Diammonium glycyrrhizinate (DG), derived from Glycyrrhiza glabra L., has been demonstrated with significant anti-inflammatory properties. However, the therapeutic effects and mechanisms of DG on pneumonia require further clarification. In this study, mice received intratracheal injection of polyinosinic-polycytidylic acid (poly(I:C)) to induce pneumonia and were treated with DG. First, we evaluated the therapeutic potential of DG on poly(I:C)-induced pneumonia. Second, the anti-inflammatory and anti-oxidative activities and the impact of DG on the Toll-like receptor 3 (TLR3) pathway were investigated. Third, the mechanism of DG was analyzed through untargeted metabolomics techniques. Our results revealed that DG intervention decreased permeability and reduced abnormal lung alterations in poly(I:C)-induced pneumonia model mice. DG intervention also downregulated cytokine levels in bronchoalveolar lavage fluid. Moreover, DG treatment inhibited the activation of TLR3 pathway. Furthermore, untargeted metabolomics analysis revealed that DG intervention could modulate serum metabolites involved in amino and nucleotide sugar metabolism, fructose and mannose metabolism, tyrosine metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis pathways. In conclusion, our study showed that DG could ameliorate poly(I:C)-induced pneumonia by inactivating the TLR3 pathway and affecting amino and nucleotide sugar, fructose and mannose metabolism, as well as tryptophan, phenylalanine, and tyrosine biosynthesis.

MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder.

BACKGROUND: Integrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse data. METHODS: We propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT) to utilize intra- and inter-omics information comprehensively. Additionally, attention mechanisms with confidence learning are incorporated for enhanced feature representation and trustworthy prediction. RESULTS: Extensive experiments were conducted on four benchmark datasets to evaluate the effectiveness of our proposed model, including BRCA, ROSMAP, LGG, and KIPAN. Our model significantly improved most evaluation measurements and consistently surpassed the state-of-the-art methods. Ablation studies showed that the auxiliary classifiers significantly boosted classification accuracy in the ROSMAP and LGG datasets. Moreover, the attention mechanisms and confidence evaluation block contributed to improvements in the predictive accuracy and generalizability of our model. CONCLUSIONS: The proposed framework exhibits superior performance in disease classification and biomarker discovery, establishing itself as a robust and versatile tool for analyzing multi-layer biological data. This study highlights the significance of elaborated designed deep learning methodologies in dissecting complex disease phenotypes and improving the accuracy of disease predictions.

Transcutaneous vagus nerve stimulation: a new strategy for Alzheimer's disease intervention through the brain-gut-microbiota axis?

Transcutaneous vagus nerve stimulation (tVNS) is an emerging non-invasive technique designed to stimulate branches of the vagus nerve distributed over the body surface. Studies suggest a correlation between the brain-gut-microbiota (BGM) axis and the pathogenesis of Alzheimer's disease (AD). The BGM axis represents a complex bidirectional communication system, with the vagus nerve being a crucial component. Therefore, non-invasive electrical stimulation of the vagus nerve might have the potential to modify-most of the time probably in a non-physiological way-the signal transmission within the BGM axis, potentially influencing the progression or symptoms of AD. This review explores the interaction between percutaneous vagus nerve stimulation and the BGM axis, emphasizing its potential effects on AD. It examines various aspects, such as specific brain regions, gut microbiota composition, maintenance of intestinal environmental homeostasis, inflammatory responses, brain plasticity, and hypothalamic-pituitary-adrenal (HPA) axis regulation. The review suggests that tVNS could serve as an effective strategy to modulate the BGM axis and potentially intervene in the progression or treatment of Alzheimer's disease in the future.

TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction.

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.

Integration of scRNA and bulk RNA-sequence to construct the 5-gene molecular prognostic model based on the heterogeneity of thyroid carcinoma endothelial cell.

Thyroid cancer (TC) is a kind of cancer with high heterogeneity, which leads to significant difference in prognosis. The prognostic molecular processes are not well understood. Cancer cells and tumor microenvironment (TME) cells jointly determine the heterogeneity. However, quite a little attention was paid to cells in the TME in the past years. In this study, we not only reveal that endothelial cells (ECs) are strongly associated with the progress of papillary thyroid cancer (PTC) using single-cell RNA-seq (scRNA-seq) data downloaded from Gene Expression Omnibus (GEO) and WGCNA, but also screen 5 crucial genes of ECs: CLDN5, ABCG2, NOTCH4, PLAT, and TMEM47. Furthermore, the 5-gene molecular prognostic model is constructed, which can predict how well a patient will do on PD-L1 blockade immunotherapy for TC and evaluate prognosis. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis demonstrates that PLAT is decreased in TC and the increase of PLAT can restrain the migratory capacity of TC cells. Meanwhile, in TC cells, PLAT suppresses VEGFa/VEGFR2-mediated human umbilical vascular endothelial cell (HUVEC) proliferation and tube formation. Totally, we construct the 5-gene molecular prognostic model from the perspective of EC and provide a new idea for immunotherapy of TC.

First screening for tick-borne pathogens in Chinese Milu deer (Elaphurus davidianus).

Ticks are primary vectors for many tick-borne pathogens (TBPs) and pose a serious threat to veterinary and public health. Information on the presence of TBPs in Chinese Milu deer (Elaphurus davidianus) is limited. In this study, a total of 102 Chinese Milu deer blood samples were examined for Anaplasma spp., Theileria spp., Babesia spp., Rickettsia spp., and Borrelia spp., and three TBPs were identified: Anaplasma phagocytophilum (48; 47.1 %), Candidatus Anaplasma boleense (47; 46.1%), and Theileria capreoli (8; 7.8 %). Genetic and phylogenetic analysis of the 16S rRNA and 18S rRNA confirmed their identity with corresponding TBPs. To our knowledge, this is the first report on Candidatus A. boleense and T. capreoli detection in Chinese Milu deer. A high prevalence of A. phagocytophilum with veterinary and medical significance was identified in endangered Chinese Milu deer, which could act as potential zoonotic reservoirs. The identification of the TBPs in Chinese Milu deer provides useful information for the prevention and control of tick-borne diseases.

Regulation of Hippo-YAP/CTGF signaling by combining an HDAC inhibitor and 5-fluorouracil in gastric cancer cells.

Histone deacetylase (HDAC) inhibitors diminish carcinogenesis, metastasis, and cancer cell proliferation by inducing death in cancer cells. Tissue regeneration and organ development are highly dependent on the Hippo signaling pathway. Targeting the dysregulated hippo pathway is an excellent approach for cancer treatment. According to the results of this study, the combination of panobinostat, a histone deacetylase inhibitor, and 5-fluorouracil (5-FU), a chemotherapy drug, can act synergistically to induce apoptosis in gastric cancer cells. The combination of panobinostat and 5-FU was more effective in inhibiting cell viability than either treatment alone by elevating the protein levels of cleaved PARP and cleaved caspase-9. By specifically targeting E-cadherin, vimentin, and MMP-9, the combination of panobinostat and 5-FU significantly inhibited cell migration. Additionally, panobinostat significantly increased the anticancer effects of 5-FU by activating Hippo signaling (Mst 1 and 2, Sav1, and Mob1) and inhibiting the Akt signaling pathway. As a consequence, there was a decrease in the amount of Yap protein. The combination therapy of panobinostat with 5-FU dramatically slowed the spread of gastric cancer in a xenograft animal model by deactivating the Akt pathway and supporting the Hippo pathway. Since combination treatment exhibits much higher anti-tumor potential than 5-FU alone, panobinostat effectively potentiates the anti-tumor efficacy of 5-FU. As a result, it is believed that panobinostat and 5-FU combination therapy will be useful as supplemental chemotherapy in the future.

Adversarially Trained Persistent Homology Based Graph Convolutional Network for Disease Identification Using Brain Connectivity.

Brain disease propagation is associated with characteristic alterations in the structural and functional connectivity networks of the brain. To identify disease-specific network representations, graph convolutional networks (GCNs) have been used because of their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks. However, existing GCNs generally focus on learning the discriminative region of interest (ROI) features, often ignoring important topological information that enables the integration of connectome patterns of brain activity. In addition, most methods fail to consider the vulnerability of GCNs to perturbations in network properties of the brain, which considerably degrades the reliability of diagnosis results. In this study, we propose an adversarially trained persistent homology-based graph convolutional network (ATPGCN) to capture disease-specific brain connectome patterns and classify brain diseases. First, the brain functional/structural connectivity is constructed using different neuroimaging modalities. Then, we develop a novel strategy that concatenates the persistent homology features from a brain algebraic topology analysis with readout features of the global pooling layer of a GCN model to collaboratively learn the individual-level representation. Finally, we simulate the adversarial perturbations by targeting the risk ROIs from clinical prior, and incorporate them into a training loop to evaluate the robustness of the model. The experimental results on three independent datasets demonstrate that ATPGCN outperforms existing classification methods in disease identification and is robust to minor perturbations in network architecture. Our code is available at https://github.com/CYB08/ATPGCN.

Serum sCD25 is an indicator for rheumatoid arthritis-associated interstitial lung disease.

OBJECTIVES: CD25 (IL-2Rα) is one of IL-2 receptor's polypeptide subunits, and its soluble form is increased in patients with various inflammatory or autoimmune diseases. This study aimed to evaluate the clinical correlation of serum soluble CD25 (sCD25) with interstitial lung disease (ILD) in rheumatoid arthritis (RA) patients. METHODS: 294 RA patients, including 72 in the discovery cohort (15 patients with ILD, 57 patients without ILD), 222 in the validation cohort (41 patients with ILD and 181 patients without ILD), and 58 healthy controls (HCs) were recruited. High-resolution computed tomography (HRCT) scan provided evidence and patterns of RA-ILD. Serum sCD25 concentrations were measured by enzyme-linked immunosorbent assay (ELISA). Clinical and laboratory data were recorded and the association with sCD25 was also analysed. RESULTS: In the discovery cohort, 16 RA-related molecules including cytokines, chemokines and functional soluble cell surface proteins were investigated. The results showed that sCD25 was significantly higher in RA-ILD than in RA-no-ILD group (p=0.004). ROC analysis also showed RA-ILD was discriminated with RA-no-ILD by sCD25 (AUC=0.695, 95% CI=0.541-0.849). Logistics regression demonstrated that sCD25 was one of the risk factors of RA-ILD. This result was further confirmed in validation cohort (p<0.001). According to the cut-off value in the discovery cohort, the sensitivity and specificity of sCD25 in RA-ILD were 51.2%, 77.3%, respectively. Compared with RA-no-ILD, serum level of sCD25 was also higher in different HRCT patterns including UIP, NSIP and RA-ILA. The ROC curves revealed sCD25 as diagnostic marker in UIP, NSIP and RA-ILA (with AUCs of 0.730, 0.761, and 0. 694, respectively, p<0.05). The result indicated that sCD25 was a biomarker for RA-ILD subtypes. Although sCD25 was not correlated with HRCT scores, it was significantly higher in consolidation pattern by HRCT. CONCLUSIONS: sCD25 was significantly elevated in RA-ILD (including UIP, NSIP and RA-ILA) compared to RA-no-ILD and HCs, which supports their value as a potential biomarker in RA-ILD screening and assessment.

[Bioinformatics analysis and identification to immune-related markers of osteoporosis].

Objective To identify immune-related dysregulation mechanisms and potential diagnostic predictive biomarkers in osteoporosis. Methods Gene expression data for both osteoporosis and control populations were retrieved from the GSE35958 and GSE56815 datasets. Immune-related differentially expressed genes (DEGs) were obtained by screening DEGs and were compared with the immunology database and analysis portal (ImmPort) database. Enrichment analysis of these immune-related DEGs was conducted using the Clusterprofiler software package. A protein-protein interaction network was built with the STRING database, which is a search tool for finding interacting genes/proteins, and the top 10 genes with the highest network connectivity were identified as candidate genes. Subsequently, the diagnostic predictive effect of candidate genes was evaluated using receiver operating characteristic (ROC) curves, logistic regression, and column plots. Finally, PCR and Western blot analysis were applied to detect the differential expression of these genes in bone marrow tissue of patients with osteoporosis. Results A total of 138 immune-related DEGs were obtained through intersection analysis. The results of the enrichment analysis indicated that these genes were involved in biological functions such as immune inflammation and signaling pathways including T cell receptors, mitogen activated protein kinase (MAPK), rat sarcoma virus oncogene homologs (Ras), osteoclast differentiation, and B cell receptors. In addition, among the candidate genes, upregulated vascular endothelial growth factor A (VEGFA) and epidermal growth factor receptor (EGFR) and downregulated AKT1, SRC, and JUN in osteoporosis showed the highest connectivity. Among them, VEGFA, EGFR, JUN, and AKT1 demonstrated the best diagnostic predictive value. Conclusion The screening of immune-related DEGs will enhance the understanding of osteoporosis and facilitate the development of immunotherapy targets.

MR Image Harmonization with Transformer.

Many clinical applications require medical image harmonization to combine and normalize images from different scanners or protocols. This paper introduces a Transformer-based MR image harmonization method. Our proposed method leverages the self-attention mechanism of the Transformer to learn the complex relationships between image patches and effectively transfer the imaging characteristics from a source image domain to a target image domain. We evaluate our approach to state-of-the-art methods using a publicly available dataset of brain MRI scans and show that it provides superior quantitative metrics and visual quality. Furthermore, we demonstrate that the proposed approach is highly resistant to fluctuations in image modality, resolution, and noise. Overall, the experiment results indicate that our approach is a promising method for medical image harmonization that can improve the accuracy and reliability of automated analysis and diagnosis in clinical settings.

A study on metabolic characteristics and metabolic markers of gastrointestinal tumors.

Tumor cells have significant heterogeneity in metabolism and are closely related to prognosis, gene mutation, and subtype. However, this association has not been demonstrated in reports of gastrointestinal tumors. In this study, we constructed four metabolic subtypes and identified four gene signatures using the expression data and clinical information of 252 metabolism-related genes from TCGA and NCBI databases for gastric adenocarcinoma (STAD) and colorectal cancer (COAD and READ). MC1 had the worst prognosis compared to other classifications. GSig1 was mainly related to drug metabolism and was the highest in MC1 with the worst prognosis, while the other subtypes were mainly related to glucose metabolism pathways. This difference also existed in other different malignant tumors. In addition, metabolic typing was associated with chemotherapeutic drug response and tumor heterogeneity, which indicated that monitoring metabolic typing could contribute to drug efficacy and gene-targeted therapy. In conclusion, we identified differences among subtypes in clinical characteristics such as prognosis and revealed the potential function of metabolic subtype in response to chemotherapeutic agents and oncogene mutations. This work highlighted the potential clinical meaning of metabolic subtype and characteristics in drug therapy and prognosis assessment of malignant tumors.

Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes.

Consensus on the stage of liver hepatocellular carcinoma (LIHC) in patients is difficult, which restricts the diagnosis and treatment of liver cancer. Molecular typing based on genes related to the lipid metabolism pathways can reflect deeper characteristics of liver cancer and complement the deficiency of the clinical staging system. In this study, we constructed and verified two cell subtypes: C1 and C2 in LIHC, based on six lipid metabolic pathway-associated genes identified in two independent external validation cohorts comprising single-cell RNA-sequencing technology (scRNA-Seq) data and bulk RNA-seq data downloaded from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The C2 subtype showed poorer prognosis, higher immune scores, and greater correlation with pathways associated with tumor progression as compared to the C1 subtype. Moreover, the sensitivity of many tested targeted drugs in C1 was relative to C2. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed several significantly enriched oncological signatures and metabolic processes, which might help elucidate the underlying molecular mechanisms. At the same time, we identified there were significantly different metabolites in C1 and C2 subtypes using 11 LIHC tissue samples. In conclusion, we constructed two molecular subtypes based on the lipid metabolism-associated genes, which may provide valuable information to further study the pathogenesis and devise clinical management strategies for LIHC.

Rainbow-Colored Carbon Nanotubes via Rational Surface Engineering for Smart Visualized Sensors.

Surface engineering is effective for developing materials with novel properties, multifunctionality, and smart features that can enable their use in emerging applications. However, surface engineering of carbon nanotubes (CNTs) to add color properties and functionalities has not been well established. Herein, a new surface engineering strategy is developed to achieve rainbow-colored CNTs with high chroma, high brightness, and strong color travel for visual hydrogen sensing. This approach involved constructing a bilayer structure of W and WO3 on CNTs (CNT/W/WO3 ) and a trilayer structure of W, WO3 , and Pd on CNTs (CNT/W/WO3 /Pd) with tunable thicknesses. The resulting CNT/W/WO3 composite film exhibits a wide range of visible colors, including yellow, orange, magenta, violet, blue, cyan, and green, owing to strong thin-film interference. This coloring method outperforms other structural coloring methods in both brightness and chroma. The smart CNT/W/WO3 /Pd films with porous characteristics quickly and precisely detect the hydrogen leakage site. Furthermore, the smart CNT/W/WO3 /Pd films allow a concentration as low as 0.6% H2 /air to be detected by the naked eye in 58 s, offering a very practical and safe approach for the detection and localization of leaks in onboard hydrogen tanks.

Inhibition of the interaction between Hippo/YAP and Akt signaling with ursolic acid and 3'3-diindolylmethane suppresses esophageal cancer tumorigenesis.

Hippo/YAP signaling hinders cancer progression. Inactivation of this pathway contributes to the development of esophageal cancer by activation of Akt. However, the possible interaction between Akt and Hippo/YAP pathways in esophageal cancer progression is unclear. In this study, we found that ursolic acid (UA) plus 3'3-diindolylmethane (DIM) efficiently suppressed the oncogenic Akt/Gsk-3ß signaling pathway while activating the Hippo tumor suppressor pathway in esophageal cancer cells. Moreover, the addition of the Akt inhibitor LY294002 and the PI3K inhibitor 3-methyladenine enhanced the inhibitory effects of UA plus DIM on Akt pathway activation and further stimulated the Hippo pathway, including the suppression of YAP nuclear translocation in esophageal cancer cells. Silencing YAP under UA plus DIM conditions significantly increased the activation of the tumor suppressor PTEN in esophageal cancer cells, while decreasing p-Akt activation, indicating that the Akt signaling pathway could be down-regulated in esophageal cancer cells by targeting PTEN. Furthermore, in a xenograft nude mice model, UA plus DIM treatment effectively diminished esophageal tumors by inactivating the Akt pathway and stimulating the Hippo signaling pathway. Thus, our study highlights a feedback loop between the PI3K/Akt and Hippo signaling pathways in esophageal cancer cells, implying that a low dose of UA plus DIM could serve as a promising chemotherapeutic combination strategy in the treatment of esophageal cancer.

Systematic and Comprehensive Analysis of tRNA-Derived Small RNAs Reveals Their Potential Regulatory Roles and Clinical Relevance in Sarcoidosis.

Introduction: The pathogenesis of sarcoidosis, which involves several systems, is unclear, and its pathological type is non-caseating epithelioid granulomas. tRNA-derived small RNA (tsRNA) is a novel class of short non-coding RNAs with potential regulatory functions. However, whether tsRNA contributes to sarcoidosis pathogenesis remains unclear. Methods: Deep sequencing technology was used to identify alterations in tsRNA relative abundance profiles between patients with sarcoidosis and healthy controls and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate. The clinical parameters were analysis to evaluate the clinical feature correlations initially. Target prediction and bioinformatics analysis of validated tsRNA were conducted to explore the mechanisms of tsRNAs in sarcoidosis pathogenesis. Results: A total of 360 tsRNAs were identified for exact matches. Among them, the relative abundance of three tRNAs (tiRNA-Glu-TTC-001, tiRNA-Lys-CTT-003, and tRF-Ser-TGA-007) was markedly regulated in sarcoidosis. The levels of various tsRNAs were significantly correlated with age, the number of affected systems, and calcium levels in the blood. Additionally, target prediction and bioinformatics analyses revealed that these tsRNAs may play roles in chemokine, cAMP, cGMP-PKG, retrograde endorphin, and FoxO signalling pathways. The related genes, APP, PRKACB, ARRB2, and NR5A1 finding may participate in the occurrence and development of sarcoidosis through immune inflammation. Conclusion: This study provides novel insights to explore tsRNA as a novel and efficacious pathogenic target of sarcoidosis.