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0.477286 | 316c9daa73e043d39264d860562989e8 | Classification of potential Cyr1 interactors identified by the SILAC-based proteomic approach. (a) The 36 potential Cyr1 interactors with two known binding partners (Cap1 and Act1) were manually curated into different groups. Some proteins were classified into more than one group. (b) Query of the Cyr1 potential interactors with Gene Ontology (GO) Term Finder (Candida Genome Database) under “Biological process” ontology classified some of the candidates into three categories: actin filament-based process, small molecule metabolic process, and biological adhesion. | PMC9769623 | spectrum.03934-22-f006.jpg |
0.451707 | c69ba13e06a84acfbc274dfd5e417d12 | Composition of symbiotic bacterial and fungal communities in true bugs. Relative abundance plots of bacterial (A) and fungal (B) phyla of 204 and 71 samples are shown, respectively. The host infraorders or superfamilies are shown at the bottom of each plot and are colored according to the infraorders or to the infraorders to which they belong. (C) Representative images of true bugs inhabiting four typical habitats. | PMC9769989 | spectrum.02794-22-f001.jpg |
0.473603 | 75183c2de6504350986c8f58937908b8 | The alpha and beta diversity of symbiotic microbial communities. The samples are grouped according to geographic position and infraorder. (A to C) are the diversity results of bacterial communities. Faith’s PD indices of symbiotic bacterial communities are mainly affected by hosts and habitats (A) but are not correlated with geographical positions (B). (C) The PCoA plot of bacterial communities based on unweighted UniFrac distance. (D to F) are the diversity results of fungal communities. Faith’s PD index of symbiotic fungal communities is mainly affected by geographical positions (E) but is not correlated with hosts (D). (F) The PCoA plot of symbiotic fungal communities based on unweighted UniFrac distance. | PMC9769989 | spectrum.02794-22-f002.jpg |
0.424957 | 9285054c47bd462a9548b08d74f3e9cf | Phylogenetic relationships of true bugs and the corresponding heatmap of the abundance of symbiotic microbial communities at the order level. Branches were collapsed, and only the infraorder and superfamily names were provided. Internal tree nodes are labeled with colored dots. The heatmap on the left represents the 14 most abundant bacterial orders. The heatmap on the right represents the 14 most abundant fungal orders. For the unidentified orders, their lowest-level classifications are given instead. | PMC9769989 | spectrum.02794-22-f003.jpg |
0.419599 | 146f5e60ffda47b994899be2cd33a3dd | Co-occurrence networks of the symbiotic microbial community. For each infraorder, the co-occurrence network of bacterial communities (left column), the co-occurrence network of fungal communities (middle column), and the co-occurrence network of bacterial and fungal communities (right column) are shown. No symbiotic network of Enicocephalomorpha or fungal network of Dipsocoromorpha was analyzed because of the small sample sizes. Only correlations with |r| > 0.8 and P < 0.0001 are shown. Each node represents an ASV. The nodes are colored according to phylum (left and middle columns) or kingdom (right column). The node size is proportional to the weighted degree of ASVs. Edge thickness is proportional to the weight of correlation. | PMC9769989 | spectrum.02794-22-f004.jpg |
0.426905 | 252e1eccbc0d454fb556540ecda17894 | Heatmap of functionally predicted pathways of symbiotic bacterial communities. For each infraorder, the proportions of pathways were compared with those of all other samples. The color represents the difference in mean proportions between a specific infraorder and the remaining samples. Only the pathways with P < 0.001 (Welch’s t test) are shown in the heatmap. | PMC9769989 | spectrum.02794-22-f005.jpg |
0.442254 | 066d8b08f63b458ca444d7234827d1fd | (A) Hemodynamic parameter distribution contour maps on vessel walls of coronary aneurysms. From left to right: TAWSS, OSI, RRT, NRRT, and ECAP. (B) The CRS of all aneurysms and the ROC curves of the MRS, HRS, and CRS. CRS: Combined risk score; ECAP: Endothelial cell activation potential; HRS: Hemodynamics risk score; MRS: Morphology risk score; NRRT: normalized relative residence time; OSI: Oscillatory shear index; ROC: Receiver operating characteristic; RRT: Relative residence time; TAWSS: Time average wall shear stress. | PMC9771297 | cm9-135-2253-g001.jpg |
0.422974 | 482ad9d3159340f1b1c0272df347cc95 | Identification of differentially expressed lncRNA (DELs) and differentially expressed mRNAs (DEMs) in Duchenne muscular dystrophy (DMD). (a) Volcano plots of DELs and DEMs in DMD based on the thresholds of false discovery rate (FDR) < 0.05 and |log2 fold change| > 1. Blue and red spots represent downregulated and upregulated DELs/DEMs, respectively. (b) Bidirectional hierarchical clustering heatmap of DELs (upper) and DEMs (lower). (c) A Venn diagram of DEMs and DMD-related genes identified in the comparative toxicogenomics database (CTD) with the inference score >3 set as the threshold. | PMC9771664 | GR2022-8548804.001.jpg |
0.41332 | c414118516fa429c9caa9e59f015a0ee | Functional analyses of the overlapping DMD-related DEMs. (a) Significantly enriched gene ontology (GO) terms of biological processes. (b) Significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways of the DMD-related DEMs. The horizontal axis indicates the number of the DMD-related DEMs, and the vertical axis indicates the name of items. The larger the dots, the greater the number of DEMs. The darker the dot, the more significant the enrichment of process. | PMC9771664 | GR2022-8548804.002.jpg |
0.452667 | e026bb28536e4a899cc1398d793d3cb0 | Construction of a protein-protein interaction (PPI) network based on the overlapping DMD-related DEMs. (a) A PPI network based on the DMD-related DEMs. A change in the color of the spots from blue to red indicates a change in the degree of significant difference from downregulation to upregulation. (b) Four network clusters extracted from the PPI network. Blue and red spots represent downregulated and upregulated proteins, respectively. | PMC9771664 | GR2022-8548804.003.jpg |
0.481823 | 59716bb7ffa74057b1902115b761604f | Establishment of a coexpression network of lncRNA-mRNA and functional analyses of the factors in this coexpression network. (a) A coexpression network of lncRNA-mRNA was constructed with the lncRNA-mRNA pairs (P < 0.05 and PCC > 0.9). Diamonds and circles represent lncRNAs and mRNAs, respectively. A change in spot color from blue to red indicates a change in the degree of significant difference from downregulation to upregulation. (b) Significantly enriched GO terms of biological process (left) and KEGG signaling pathways (right) of the genes in this coexpression network. | PMC9771664 | GR2022-8548804.004.jpg |
0.501434 | f1d0d914497f45c39bc44897502172b3 | Construction of a DMD-related lncRNA-mRNA pathway network composed of two KEGG pathways, nine DMD-related DEMs, and nine DELs. Diamonds, circles, and yellow squares represent DELs, DMD-related DEMs, and KEGG pathways, respectively. | PMC9771664 | GR2022-8548804.005.jpg |
0.41091 | b66e92c5b63e418ba29c95326e26eef5 | Correlation between immune cell types and the factors in the lncRNA-mRNA pathway network. (a) Proportion of the various immune cell types in the DMD samples and normal control subjects. (b) Correlation analysis between the five significantly different immune cell types (in patients with DMD and control individuals) and the factors (DELs and DMD-related DEMs) in the lncRNA-mRNA pathway network. | PMC9771664 | GR2022-8548804.006.jpg |
0.448313 | e0764e19f6674a27ab1ff84003b6a748 | Validation of the crucial factors (lncRNAs and mRNAs) in the training (GSE38417) and validation (GSE6011) sets. (a) Expression of the nine DELs in the DMD-related lncRNA-mRNA pathway network between DMD and normal control samples in GSE38417. (b) Expression of the nine DMD-related DEMs between DMD and normal control samples in GSE38417. (c) Expression of the nine DMD-related DEMs between DMD and normal control samples in GSE6011. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001. | PMC9771664 | GR2022-8548804.007.jpg |
0.581332 | f3b8437977aa406fb78b5648d01180f1 | Transthoracic echocardiographic evaluation at baseline and 4 weeks after the induction of MI. All results are presented as the mean ± SD. The upper part shows echocardiographic photos. The EDV and ESV were significantly higher, and the LVEF and E/A were significant lower in the MI group compared with the control group. LL‐TS treatment significantly reduced the EDV and ESV dilation and increased the LVEF and E/A level compared with the MI group. EDV, left ventricular end‐diastolic volume; ESV, left ventricular end‐systolic volume; LVEF, left ventricular ejection fraction; E/A, peak mitral inflow velocity in early diastole (E)/peak mitral inflow velocity during left atrial contraction (A). *P < 0.01 vs. control group; #P < 0.01 vs. MI group. | PMC9773748 | EHF2-9-4129-g001.jpg |
0.492253 | 0981ab92fe9549ce9d73abc91d155a4a | Plasma levels of CK‐MB and hs‐cTnI on the day before the surgery and 1st and 28th day after surgery. CK‐MB, creatine kinase myocardial bound isoenzyme; hs‐cTnI, High‐sensitive cardiac troponin I. *P < 0.05 vs. control group; #P < 0.05 vs. LL‐TS group. | PMC9773748 | EHF2-9-4129-g002.jpg |
0.433634 | 7d1be9b8bd034796996f22741d759c71 | Neural recording from ARGP. (A) Representative examples of ARGP neural activity in the control group, MI group and LL‐TS group. (B) The average frequency of ARGP activity were significantly increased in LL‐TS group. (C) The average amplitude of ARGP activity were significantly increased in LL‐TS group. *P < 0.05 compared with the baseline. ARGP, anterior right ganglion plexus; LL‐TS, low‐level tragus nerve stimulation; MI, myocardial infarction. | PMC9773748 | EHF2-9-4129-g003.jpg |
0.558719 | 38b6af82435743b1a4e1b087d1a363b0 | (A) Representative picture of western blots from LV non‐infarcted tissues that demonstrate the effects of LL‐TS treatment on α7nAChR and MMP‐9 protein levels. The protein analyses indicate the relative protein levels of α7nAChR (B) and MMP‐9 (C). The relative mRNA levels for (D) MMP‐9 in each group are shown by real‐time quantitative polymerase chain reaction analysis. The results are expressed as a ratio of the mRNA levels to the reference gene GAPDH. *
P < 0.01 vs. control group; #
P < 0.01 vs. MI group. | PMC9773748 | EHF2-9-4129-g004.jpg |
0.439379 | d316e425f8c040da8d6ab8ff7bfb73fa | Neural recording from LSG. (A) Representative examples of LSG neural activity in the control group, MI group and LL‐TS group. (B) The average frequency of LSG activity were significantly increased in MI group, while no significant change was observed in LL‐TS group. (C) The average amplitude of LSG activity were significantly increased in AMI group, while no significant change was observed in LL‐TS group. *P < 0.05 compared with the baseline. LL‐TS, low‐level tragus nerve stimulation; LSG, left stellate ganglion; MI, myocardial infarction. | PMC9773748 | EHF2-9-4129-g005.jpg |
0.415951 | 12bbef61dda74794a3b77572a30e4cf8 | Indices of HRV analysis at the end of 4 weeks. Compared with the control group, the MI group significantly increased the LF power and LF/HF ratio but slightly reduced HF power. However, LL‐TS treatment significantly reduced the increasing trends in the LF/HF ratio but increased HF. *P < 0.05 vs. group baseline. LF, low frequency; HF, high frequency. | PMC9773748 | EHF2-9-4129-g006.jpg |
0.457999 | 7d054393ad1c4d2fa6dd07a070c68316 | Role of Fe in mitochondrial dysfunction and neuronal death leading to PD. Increased levels of Fe may cause neurodegeneration through the production of large amounts of reactive oxygen species (ROS) via the Fenton reaction. Fe can induce mitochondrial oxidative stress through interactions with different ROS. Free Fe can be released from mitochondrial Fe-sulfur clusters in complexes I and III upon interacting with ROS. The redox pair Fe2+-Fe3+ can directly stimulate lipid peroxidation (LP), which is an indicator of oxidative stress and contributes to mitochondrial dysfunction via mitochondrial permeability transition pore (mPTP) formation, thus leading to neural damage and the induction of PD. | PMC9774122 | antioxidants-11-02467-g001.jpg |
0.533362 | 2ef505dd45b74dddbb1465a6f4d2513a | Receptors and channels involved in Mn homeostasis. Various cellular receptors, such as divalent metal transporter 1 (DMT1) and transferrin receptor (TfR), as well as Ca2+ channels, ZIP8/14 transporter, and Mn citrate transporter facilitate the entry of divalent Mn into cells, whereas ferroportin and Ca2+ facilitate its expulsion from cells and mitochondria, respectively. Mn2+ is passively transported via glutamate-activated ion channels, while Mn3+ entry is facilitated by transferrin. All of these mechanisms enhance the trafficking of Mn2+ across the cells, leading to mitochondrial dysfunction and increased dopaminergic neuronal death, resulting in PD. | PMC9774122 | antioxidants-11-02467-g002.jpg |
0.384561 | 714d01643fc94e3cb560070d0eda8e6b | MeHg-induced glutamate and Ca2+ dyshomeostasis and oxidative stress involving neuronal death. MeHg inhibits astrocytic glutamate uptake and enhances glutamate release from presynaptic terminals. The increased extracellular glutamate levels lead to the overactivation of N-methyl-D-aspartate (NMDA)-type glutamate receptors, enhancing the influx of Ca2+ into postsynaptic neurons. The increased levels of intracellular Ca2+ may cause mitochondrial dysfunction and increased reactive oxygen species (ROS) formation. MeHg disrupts the redox state of the cells, which indirectly depletes glutathione (GSH), which will further increase ROS production in both astrocytes and neurons. | PMC9774122 | antioxidants-11-02467-g003.jpg |
0.414284 | 205b81317bd74c6780d76ffc5e318f73 | Dysregulation of Cu ion homeostasis in PD. Excessive levels of Cu improved dopamine (DA) oxidation, upregulated α-synuclein (αSyn) aggregation, and formation of Lewy bodies. Excess Cu levels in the brain take part in Fenton chemistry as a catalyst to produce free radicals. Both processes lead to ROS production causing dopaminergic neuronal death and PD. | PMC9774122 | antioxidants-11-02467-g004.jpg |
0.510202 | 85d23da4c875457485357ba5647019b3 | Pb2+ induces changes in dopamine transporter (DAT) morphology, leading to an increase in extracellular dopamine level, resulting in neurotoxicity via dopamine oxidation. Moreover, Pb2+ and Ca2+ share a permeability pathway represented by a Ca2+ channel and increased Pb2+ level in mitochondria, causing dopaminergic neuron loss. In addition, Pb2+ induces α-synuclein aggregation, NMDA receptor (NMDAR) blockade, and the activation of protein kinase C, leading to reduced Ca2+ release from mitochondria. Moreover, δ-aminolevulinic acid dehydratase (δ-ALAD) blockade by Pb2+ delivered by mitochondria leads to the initiation of lipid peroxidation. These mechanisms ultimately cause the depletion of dopaminergic neurotransmission resulting in the development of PD. | PMC9774122 | antioxidants-11-02467-g005.jpg |
0.496646 | 21b83232ffd94a288fbdda527f158910 | Novel CPR prototype. | PMC9774312 | bioengineering-09-00802-g001.jpg |
0.411091 | f5f946e5f0ae4962b8606a238dc4364b | The system architecture of the novel CPR prototype. The green boxes are the respiratory management section and the chest compression section, and the red box is the control section. The green arrow indicates the flow of oxygen, and the red arrow indicates the control signal. | PMC9774312 | bioengineering-09-00802-g002.jpg |
0.474209 | 79d1fd610b014000a2e6659781c12435 | Kinetic model of the power mechanism. | PMC9774312 | bioengineering-09-00802-g003.jpg |
0.48741 | e26e494b8aaa484589fc39075d1124db | Simplified model of the thoracic cavity. | PMC9774312 | bioengineering-09-00802-g004.jpg |
0.448353 | eb236dced308431b9641199cf2620aa1 | Schematic diagram of the human blood circulation model. | PMC9774312 | bioengineering-09-00802-g005.jpg |
0.447828 | 427865b984a44551a202eb3c63debb3f | Vessel and its corresponding circuit: (a) vessel physiological model, (b) vessel corresponding circuit. | PMC9774312 | bioengineering-09-00802-g006.jpg |
0.459237 | 770bd8ba2b064bda849795957ea1fd55 | Circuit diagram of the human blood circulation model. | PMC9774312 | bioengineering-09-00802-g007.jpg |
0.461855 | 8412f0b10e724e2993559797bf44b008 | Mechanical waveform, simulated waveform, and the instruction signal. | PMC9774312 | bioengineering-09-00802-g008.jpg |
0.404068 | c9f7671f2ce642d8b6dcc79ecdf8b77c | Prototype mechanical waveforms under various instructions: (a) various depths, (b) various frequencies, (c) various duty cycles. | PMC9774312 | bioengineering-09-00802-g009.jpg |
0.47005 | 7945d9dc092f46a38faae097d6cfb065 | Various compression waveforms. | PMC9774312 | bioengineering-09-00802-g010.jpg |
0.433142 | 9f30b189f3e44e6ca04024eb2462ed85 | Comparison between mechanical and manual compressions: (a) compression waveform, (b) blood pressure. | PMC9774312 | bioengineering-09-00802-g011.jpg |
0.400682 | 59890343ef5f49edbdadf01d15d8d861 | Comparison of blood circulation between mechanical and manual compression: (a) CO, (b) CPP, (c) CF. | PMC9774312 | bioengineering-09-00802-g012.jpg |
0.449818 | 09b8fdca8b284056af30a86cf7b3f35e | Relationship between frequencies and blood circulation results: (a) CO, (b) CPP, (c) CF. | PMC9774312 | bioengineering-09-00802-g013.jpg |
0.40893 | fd9ed683696f4adebf85752250470aae | Relationship between duty cycles and blood circulation results: (a) CO, (b) CPP, (c) CF. | PMC9774312 | bioengineering-09-00802-g014.jpg |
0.381038 | d8016528965d456e8197aaef342b25ce | Relationship between depths and blood circulation results: (a) CO, (b) CPP, (c) CF. | PMC9774312 | bioengineering-09-00802-g015.jpg |
0.506601 | 4b257b845ee2402887bf47c7bc2ea983 | General structure and nomenclature of imidazopyridine. | PMC9774741 | antibiotics-11-01680-g001.jpg |
0.477187 | 50fa91391a0e4eb5aafce8e9953162d6 | Synthetic strategies for the preparation of imidazopyridines. | PMC9774741 | antibiotics-11-01680-g002.jpg |
0.475481 | 7647a7e7def541b0ae35531cf6bd2adb | (a) General synthetic scheme of 2H-chromene based IZP derivatives. Signatures of compounds according to Mishra et al., 2021 [57]. (b) 2H-Chromene-based imidazopyridines with a brief SAR. Signatures of compounds according to Mishra et al., 2021 [57]. | PMC9774741 | antibiotics-11-01680-g003.jpg |
0.418803 | 4baab46c8dc9403d81f25f75a4c8f5bd | (a) Synthesis of (i) 3-acetyl-6,8-dichloro-2-methyl IZP, (ii) 6,8-dichloro-2-methylimidazo [1,2-a]pyridinyl-pyridine hybrids, (iii) 6,8-dichloro-2-methylimidazo [1,2-a]pyridinyl-thiazole hybrids, and (iv) 3-)pyrazolyl)-imidazo [1,2-a]pyridine hybrid. The signatures of these compounds according to Althagafi et al., 2021 [58]. (b) Imidazopyridine-based heterocycles, including pyridine-, pyrazole, and thiazole-substituted systems. Signatures of compounds according to Althagafi et al., 2021 [58]. | PMC9774741 | antibiotics-11-01680-g004a.jpg |
0.429185 | 77352c84974d4e2296e21d1b287f237c | (a) Synthesis of 2-phenylimidazo [1,2-a] pyridine-based pyran bis-heterocycles. Signatures of compounds according to Thakur et al., 2020 [59]. (b) Imidazopyridine conjoined pyran bis-heterocyclic derivatives. Signatures of compounds according to Thakur et al., 2020 [59]. | PMC9774741 | antibiotics-11-01680-g005.jpg |
0.49253 | ceaec9158ebb49b692aecd71c3f3371f | (a) Synthetic scheme of pyrazole-imidazo [1,2-a]pyridine derivatives. Signatures of compounds according to Ebenezer et al., 2019 [60]. (b) Pyrazolo-imidazopyridine molecular conjugates. Signatures of compounds according Ebenezer et al., 2019 [60]. | PMC9774741 | antibiotics-11-01680-g006.jpg |
0.469455 | 6b032649019f4d12b47156c5d96d441a | (a) Synthetic scheme of dihydroimidazo [1,2-a]pyridine derivatives. Signatures of compounds according to Salhi et al., 2019 [61]. (b) Most potent dihydroimidazopyridines in the study. Signatures of compounds according to Salhi et al., 2019 [61]. | PMC9774741 | antibiotics-11-01680-g007.jpg |
0.526327 | 90d9d6460c0f4ab290738630aec33194 | Imidazopyridine derivatives showing activity M. tuberculosis. Signatures of compounds according to Malley et al., 2018 [62]. | PMC9774741 | antibiotics-11-01680-g008.jpg |
0.431053 | 48c71f94ff6c4bc185448ca482c53cca | (a) Synthetic scheme of newly IZP derivatives flanged with oxadiazole nucleus. Signatures of compounds according to Kuthyala et al., 2018 [63]. (b) A brief SAR of trisubstituted imidazopyridine-oxadiazole hybrids. Signatures of compounds according to Kuthyala et al., 2018 [63]. | PMC9774741 | antibiotics-11-01680-g009.jpg |
0.414053 | 83c49956a2274d1d815403647356506c | (a) Synthetic scheme of pyrazolopyridinone fused IZP derivatives. Signatures of compounds according to Devi et al., 2017 [64]. (b) A brief SAR of pyrazolopyridinone fused imidazopyridine derivatives. Signatures of compounds according to Devi et al., 2017 [64]. | PMC9774741 | antibiotics-11-01680-g010.jpg |
0.477639 | 7a65066d2e6e44f3b09203b7676131a9 | Imidazopyridine derivative exhibiting activity against different strains of M. tuberculosis. Signatures of compounds according to Arora et al., 2014 [65]. | PMC9774741 | antibiotics-11-01680-g011.jpg |
0.383592 | bb8abbf2d29a40bc88e6ade5b0cb3317 | (a) General scheme for the synthesis of novel indole-based imidazopyridine derivatives. Signatures of compounds according to Al-Tel et al., 2011 [66]. (b) Indole-based imidazopyridine derivative exhibiting activity against different selected strains. Signatures of compounds according to Al-Tel et al., 2011 [66]. | PMC9774741 | antibiotics-11-01680-g012a.jpg |
0.484042 | efc6a44d1be1430f9c0102043169472c | (a) General synthetic scheme of 5-(2-Pyrimidinyl)-imidazo [1,2-a]pyridine derivatives. Signatures of compounds according to Starr et al., 2009 [67]. (b) A brief SAR on imidazopyridine derivatives exemplifying the enzyme inhibitory activity. Signatures of compounds according to Starr et al., 2009 [67]. | PMC9774741 | antibiotics-11-01680-g013.jpg |
0.47564 | 0bcf73c7301d4089b0886ccbaa46a8a6 | Walking-adaptability tests on the C-Mill. (a) Target-stepping test. (b) Narrow-beam walking test. | PMC9774744 | JRM-54-2155-g001.jpg |
0.472405 | f419caccf2b34ce2881c32c7467f42e4 | Leg-muscle fatigue. Mean root mean square (RMS)-amplitudes over the 3 muscles (m. vastus lateralis, m. soleus and m. tibialis anterior) are shown for polio survivors (upper panel: n = 19) and healthy individuals (lower panel: n = 10), as percentage of the mean RMS-amplitude over all 3 walk tests. Squares (normal walking), diamonds (target stepping) and triangles (narrow-beam walking) represent individual data-points. *p < 0.05. | PMC9774744 | JRM-54-2155-g002.jpg |
0.511195 | e602ce7d966646679828fd2d74413f2a | Cardiorespiratory fatigue. Heart rate (HR) in beats per min (bpm) (upper panels) and rate of perceived exertion (RPE as measured by the Borg-score) (lower panels) are shown for polio survivors (left-hand panels) and healthy individuals (right-hand panels) for the first and sixth minute of the tests. Squares (normal walking), diamonds (target stepping) and triangles (narrow-beam walking) represent individual data-points. *p < 0.05. | PMC9774744 | JRM-54-2155-g003.jpg |
0.558215 | 5f57ad5f98d24908ba69be00d6746bdf | Walking-adaptability outcomes. Target-stepping performance (upper panel) for polio survivors’ most- and least-affected leg (n = 22) and healthy individuals (n = 11), as visualized by the estimated variable error (VE, in mm) at 0.69 m/s, and narrow-beam walking performance (lower panel) for polio survivors (n = 23) and healthy individuals (n = 11), visualized as step width (cm). Error bars represent the standard error of the mean. *p < 0.05. | PMC9774744 | JRM-54-2155-g004.jpg |
0.439192 | 24c65c2b42114d8a9f9154a2e797b09f | The schematic representation of the associations between FOXP3+ Treg cells and eight cancer hallmarks. Over the past decades, our understanding of cancer has evolved tremendously. Recently, Hanahan and Weinberg have categorized and summarized knowledge of cancers into 14 hallmarks, including 10 well-estlabished hallmarks (grey) and 4 new emerging hallmarks (green). Here we briefly introduce the connection between Treg cells dynamics and the feature of eight either well-established or new emerging cancer hallmarks including 1) nonmutational epigenetic reprogramming, 2) Avoiding immune destruction, 3) tumour-promoting inflammation, 4) polymorphic microbiomes, 5) activating invasion & metastasis, 6) inducing or accessing vasculature, 7) senescent cells, and 8) deregulating cellular metabolism. As very few papers have reported the association of Treg cells with the remaining hallmarks and thus will not be included. | PMC9774953 | fimmu-13-982986-g001.jpg |
0.552836 | 820ac33af4c84689b56ca0d59fc2cf11 | Mechanisms for FOXP3+ Treg cells to mediate the immune escape of solid tumours. Several mechanisms of Treg cells have been reported to help tumour to avoid immune destruction. For instance, Treg cells can promote the formation of immune suppressive microenvironment. Treg cells express chemokine receptors (e.g., CCR4, CCR8, CCR5, GPR15) and are recruited to the tumour site by chemokines produced by diverse cells within TME. Treg cells secreted immunosuppressive cytokines, TGF-β, and VEGF, which not only promote the conversion of Tconv cells to Treg cells, but also suppress Teff cells and APCs function. Treg cells constitutively express CTLA-4, while downregulate the expression of CD80/CD86 in APCs (through trans-endocytosis), thereby depriving co-stimulatory signals to responder T cells. Meanwhile, Treg cells inhibit the function of DCs through LAG-3 and MHC II interactions. For metabolic adaptation, Treg cells could converse ATP to adenosine by CD39 and CD73, which directly inhibits A2AR mediated Teff cells function. Cells within TME could also be killed by Treg cells secreted granzyme and perforin. CCL, C-C motif chemokine ligand; CCR, C-C motif chemokine receptor; GPR, G protein-coupled receptor; Tconv, conventional CD4 T cell; TGF, transforming growth factor; IL, interleukin; VEGF, vascular endothelial growth factor; CTLA-4, cytotoxic T-lymphocyte associated protein 4; MHC, major histocompatibility complex; FGL1, fibrinogen like 1; Teff, effector T cell; A2AR, adenosine A2A receptor; LAG-3, lymphocyte activating 3. | PMC9774953 | fimmu-13-982986-g002.jpg |
0.425813 | 8edd4950d41248238d85aa3060ffbbd3 | Mechanisms for FOXP3+ Treg cells to induce T cell senescence in the tumour microenvironment. The direct transfer of cAMPs, by Treg cells via cell junctions, induces the senescence of naïve and effector T cells. The induced senescent T cells cease the expression of CD27 and CD28 but increase the secretion of pro-inflammatory cytokines. Thus, those senescent T cells exhibit immunosuppressive features and argument the immunosuppression within TME. So far, no study of direct effect of tumour-associated Treg cells on tumour cell senescence has been found. However, Treg cells might mediate the senescence of tumour cells indirectly. For instance, the Treg cells induced senescent T cells exhibit unique SASP, which is characterized by augmented release of cytokines, chemokines, proteases, and metabolic wastes. The accumulation of these molecules as well as low glucose availability, caused by hyper-glycolysis of Treg cells, create a stress environment, thus may facilitate thesenescence of tumour cells. cAMP, cyclic adenosine monophosphate; IL, interleukin; TNF, tumour necrosis factor; IFN, interferon; TME, tumour microenvironment; SASP, senescence-associated secretory phenotype. | PMC9774953 | fimmu-13-982986-g003.jpg |
0.45979 | 7fd5c62f13464053b95d9b474b64802b | Types of angiogenesis and key regulatory mechanisms. (A) Types of angiogenesis. (B) Key factors regulating angiogenesis. VEGF: vascular endothelial growth factor, ECM: extracellular matrix. | PMC9775124 | biomedicines-10-03089-g001.jpg |
0.405127 | fae87b7b5233422abeb52a4d35a4bd2a | Succinate accumulation occurs in metabolic stress conditions. Succinate is an intermediate metabolite in the Krebs cycle. In conditions of metabolic stress/hypoxia/microbiome alterations, succinate concentrations rise beyond physiological values. Succinate can be measured in biological fluids or tissues, or in isolated cells exposed to stress. Red arrow denotes increased succinate levels. | PMC9775124 | biomedicines-10-03089-g002.jpg |
0.405439 | 7c28d3db51684838885db1b82bdde958 | SDH is a key enzyme metabolizing succinate in the mitochondria. (A) SDH complex is composed of 4 subunits and is part of the electron transport chain in the inner mitochondrial membrane. (B) SDH expression and/or activity can be regulated by different effectors. Red arrows indicate that these regulators reduce SDH expression and/or activity, while the split red and green arrow indicates possible upregulation or downregulation of SDH activity. PTMs: post-translational modifications, TRAP1: tumor-necrosis-factor-receptor-associated protein 1. | PMC9775124 | biomedicines-10-03089-g003.jpg |
0.450134 | 1ae3d4c3f8194474a0349d1ffd634872 | Succinate stabilizes HIF1α by inhibiting its degradation. Succinate inhibits PHD leading to HIF1α stabilization and translocation to the nucleus. Subsequently, assembly of the HIF1 complex occurs and induction of hypoxic responses takes place. Red arrow denotes increased succinate concentration. HIF: hypoxia-inducible factor, PHD: prolyl hydroxylase, VHL: von Hippel–Lindau protein, Ub: ubiquitin protein, HRE: hypoxia response element. | PMC9775124 | biomedicines-10-03089-g004.jpg |
0.450761 | b3d53cad59984df7a361c1272bc3cf1f | SUCNR1 downstream signaling machinery. Distinct G proteins pair with SUCNR1 mediating different cellular responses. cAMP: cyclic adenosine monophosphate, PKA: protein kinase A, ERK1/2: extracellular-signal-regulated kinases 1 and 2, NO: nitric oxide, PGE2: prostaglandin E2. Red arrow denotes reduced levels while green arrows denote increased levels or activity. | PMC9775124 | biomedicines-10-03089-g005.jpg |
0.479969 | 9a2c75280c68434cbf87a7d464f40a95 | SDH, HIF1α and SUCNR1 are key targets modulating angiogenesis. In conditions where the expression and/or the activity of SDH are hindered, succinate levels increase. Accumulation of succinate can induce angiogenesis via two mechanisms, either through HIF1α stabilization or by SUCNR1 signaling. This has been shown in many pathological settings so far and is yet to be exploited to ameliorate pathological angiogenesis. Red arrows stand for elevated succinate concentrations and increased angiogenesis. | PMC9775124 | biomedicines-10-03089-g006.jpg |
0.470656 | f3f28c43ffef482a8d6a53801ab7ee6c | Oxidative stress processes in people with Down syndrome. SOD1-Superoxide dismutase; GLU-glutathione; ROS-reactive oxygen species; LPO-lipid peroxidation; oxyLDL-oxidatively modified LDLs. | PMC9775395 | biomedicines-10-03219-g001.jpg |
0.527745 | 14579698df6d4f77bb9291544051301b | Methionine metabolic pathway (simplified). | PMC9775395 | biomedicines-10-03219-g002.jpg |
0.461463 | 7042e875a3ad40b085768891c8312a3c | Methionine metabolic pathway (simplified)–pathway disorders in people with Down syndrome. | PMC9775395 | biomedicines-10-03219-g003.jpg |
0.449065 | d2116034737c43d19bee920ec6cb2faf | BRAFi-R melanoma cells possess higher IC50 value and are more aggressive than BRAFi-sensitive melanoma cells. (A,B) WST1 cell proliferation assays were performed to evaluate the development of BRAFi resistance to PLX4720 in melanoma cells, as described in Section 2. BRAFi-sensitive (black circle), PLX-4032-R (R1; red square), and PLX-4720-R (R2; blue triangle) cells were exposed to increasing concentrations of BRAF inhibitors for 72 h. Graphs were generated from 4 independent experiments, and values are presented as the means (n = 4) ± SEMs. The IC50 values for PLX4720 treatment were as follows: 2.5 µM for BRAFi-sensitive A375 cells, 2.5 µM for BRAFi-sensitive HTB63 cells, and >20 µM for A375-R1, A375-R2, HTB63-R1, and HTB63-R2 cells. (C,D) transwell-based migration and invasion assays were performed to evaluate the migration (C) and invasion (D) capacities of BRAFi-sensitive (HTB63 and A375), BRAFi-R1 (HTB63-R1 and A375-R1) and BRAFi-R2 (HTB63-R2 and A375-R2) melanoma cells. The numbers of migrated and invaded cells were determined using NIH ImageJ software, and the values were normalized to those in BRAFi-sensitive melanoma cells and are given as the mean ± SEM of 4 independent experiments. Statistical significance was estimated using ANOVA with Dunnett’s post hoc test for multiple comparisons; * p < 0.05, ** p < 0.01, *** p < 0.001. | PMC9775662 | cancers-14-06077-g001.jpg |
0.442193 | f70576ca80d74633afe0d9b5e216577f | Acquired BRAFi resistance in melanoma cells causes significant increases in the levels of total MARCKS and active phospho-MARCKS. (A) Western blot analysis showing the endogenous levels of phospho-ERK1/2 (pERK1/2), total ERK1/2, WNT5A, phospho-MARCKS (pMARCKS; Ser-159/163), and total MARCKS in BRAFi-sensitive and BRAFi-R HTB63 cells. Tubulin was used as the loading control. Representative blots from 4 independent experiments are shown. (B) Graph showing the densitometric analysis of pMARCKS, total MARCKS, and WNT5A protein levels in BRAFi-sensitive and BRAFi-R HTB63 cells. The obtained values were normalized to the value of the tubulin loading control from the same sample. The calculated pMARCKS/tubulin, MARCKS/tubulin, and WNT5A/tubulin ratios were normalized to those in the BRAFi-sensitive HTB63 cells and are presented as relative fold changes. (C) Western blot analysis showing the endogenous levels of pERK1/2, total ERK1/2, WNT5A, pMARCKS (Ser-159/163) and total MARCKS in BRAFi-sensitive and BRAFi-R A375 cells. Tubulin was used as the loading control. Representative blots from 4 independent experiments are shown. (D) Graph showing the densitometric analysis of pMARCKS, total MARCKS, and WNT5A protein levels in BRAFi-sensitive and BRAFi-R A375 cells. The obtained values were normalized to the value of the tubulin loading control from the same sample. The calculated pMARCKS/tubulin, MARCKS/tubulin, and WNT5A/tubulin ratios were normalized to those in the BRAFi-sensitive A375 cells and are presented as relative fold changes. The data in the graphs (Panels B and D) were calculated from 4 independent experiments, and the results are presented as the means ± SEM. Statistical significance was estimated using ANOVA with Dunnett’s post hoc test for multiple comparisons; * p < 0.05, ** p < 0.01, *** p < 0.001. Original blots images can be found at File S1. | PMC9775662 | cancers-14-06077-g002.jpg |
0.453539 | 84406026e9624db3a65834f69ad0415f | The increased levels of total MARCKS and active phospho-MARCKS are independent of the elevated WNT5A expression in BRAFi-R melanoma cells. (A,B) Western blot analysis showing the levels of pMARCKS (Ser-159/163), total MARCKS and WNT5A in BRAFi-sensitive HTB63 and BRAFi-R HTB63-R2 cells (A), and in BRAFi-sensitive A375 and BRAFi-R A375-R2 cells (B) transfected with negative control siRNA (NC; 50 nM) or either of two different WNT5A-targeting siRNAs (50 nM). Representative blots from 4 independent experiments are shown with tubulin as the loading control. (C) Graph showing the densitometric analysis of pMARCKS, total MARCKS, and WNT5A levels in BRAFi-sensitive and BRAFi-R2 HTB63 and A375 cells. The obtained values were normalized to the value of the tubulin loading control from the same sample. The calculated pMARCKS/tubulin, total MARCKS/tubulin, and WNT5A/tubulin ratios were normalized to those in the corresponding negative control (NC) siRNA-transfected cells and are presented as relative fold changes. The data in Panel C were calculated from 4 independent experiments, and the results are given as the means ± SEM. Statistical significance was estimated using ANOVA with Dunnett’s post hoc test for multiple comparisons; ** p < 0.01, *** p < 0.001. | PMC9775662 | cancers-14-06077-g003.jpg |
0.475282 | b0e1acdae8e1406b8e7df12e7359f9b7 | The increased levels of total MARCKS and active phospho-MARCKS are independent of the elevated IL-6 secretion in BRAFi-R melanoma cells. (A,B) ELISA-based analysis showing the secreted IL-6 level in BRAFi-sensitive HTB63 and BRAFi-R HTB63-R2 cells (A), and in BRAFi-sensitive A375 and BRAFi-R A375-R2 cells (B) transfected with negative control siRNA (NC; 50 nM) or either of two different IL-6-targeting siRNAs (50 nM). The secreted IL-6 levels were normalized to those in NC siRNA-transfected cells, and all cumulative data are presented as the means (n = 4) ± SEMs. Significance was estimated using ANOVA with Dunnett’s post hoc test for multiple comparisons; ** p < 0.01, *** p < 0.001. (C,D) Western blot analysis showing the levels of pMARCKS (Ser-159/163) and total MARCKS in BRAFi-sensitive HTB63 and BRAFi-R HTB63-R2 cells (A) and in BRAFi-sensitive A375 and BRAFi-R A375-R2 cells (C) transfected with negative control siRNA (NC; 50 nM) or either of two different IL-6-targeting siRNAs (50 nM). Representative blots from 4 independent experiments are shown with tubulin as the loading control. (D) Graph showing the densitometric analysis of the pMARCKS and total MARCKS levels in BRAFi-sensitive and BRAFi-R2 HTB63 and A375 cells. The obtained values were normalized to the value of the tubulin loading control from the same sample. The calculated pMARCKS/tubulin and total MARCKS/tubulin ratios were normalized to those in the corresponding negative control (NC) siRNA control cells and are presented as relative fold changes. The data in Panel C were calculated from 4 independent experiments, and the results are presented as the means ± SEM. Statistical significance was estimated using ANOVA with Dunnett’s post hoc test for multiple comparisons; ** p < 0.01, *** p < 0.001. | PMC9775662 | cancers-14-06077-g004.jpg |
0.392944 | 9631ad975bb54a68b0f8e2f501a44062 | Knockdown of MARCKS is sufficient to induce morphological changes and reduce the migratory and invasive capacities of BRAFi-R melanoma cells. (A,C) A375-R1 (A) and A375-R2 (C) melanoma cells were transfected with negative control siRNA (NC; 50 nM) or either of two different MARCKS-targeting siRNAs (50 nM), after which they and A375 cells were stained with phalloidin-TRITC (F-actin) and DAPI as described in Section 2. For each image, a region of interest (marked with a white box) is further magnified and placed adjacent to its respective low magnification image. The scale bar in the lower and higher magnification images represent 10 and 5µm, respectively. (B,D) Graphs showing the mean TRITC fluorescence intensity and the number of filopodia-like protrusions in A375, A375-R1 (B), A375-R2 (D) cells transfected with control siRNA (NC; 50 nM) or either of two different MARCKS siRNAs (50 nM); both parameters were measured with NIH ImageJ software. The results are given as the means ± SEMs. (E,F) Graphs showing the migration and invasion capacities of A375-R1 (E) and A375-R2 cells (F) transfected with control siRNA (NC; 50 nM) or either of two different MARCKS siRNAs (50 nM). Migration and invasion were analyzed by transwell-based assays as described in Section 2. The numbers of migrated and invaded cells were determined using NIH ImageJ software, and the data were normalized to those in NC siRNA-transfected cells and are given as the mean ± SEM of 4 independent experiments. Statistical significance was estimated using ANOVA with Dunnett’s (B,D) and Tukey’s (E,F) post hoc tests for multiple comparisons; ** p < 0.01, *** p < 0.001. | PMC9775662 | cancers-14-06077-g005.jpg |
0.441615 | f49131216caf4a9a9e9b91958c9d826d | Knockdown of MARCKS is sufficient to abolish the increase in the metastatic capacity of BRAFi-R melanoma cells in a zebrafish model. (A) A schematic representation of the setup of the experiments with transgenic zebrafish embryos (Tg (fli1:eGFP)). (B) Representative images showing tail vein metastasis of BRAFi-sensitive A375 and A375-R2 cells stably transfected with either negative control shRNA or MARCKS-targeting shRNA injected into the perivitelline space (PVS) of embryos 2 days after fertilization. (C) Graph showing the number of metastatic foci in the tail fin region of zebrafish embryos in the BRAFi-sensitive A375 (n = 18), A375-R2 NC shRNA (n = 18), and A375-R2 MARCKS shRNA (n = 16) groups. (D) Graph showing the mean fluorescence intensity (MFI) of the metastatic foci in the tail fin region of zebrafish embryos in the BRAFi-sensitive A375 (n = 15), A375-R2 NC shRNA (n = 16), and A375-R2 MARCKS shRNA (n = 12) groups. Data are given as the means ± SEM. Statistical significance was estimated using an unpaired two-tailed Wilcoxon-Mann–Whitney test between the group A375-R2 NC vs. A375-R2 MARCKS sh; * p < 0.05, ** p < 0.01. | PMC9775662 | cancers-14-06077-g006.jpg |
0.432736 | 4aaf65337ef7493d9058a2d25d3cec6d | PKC and RhoA activity is enhanced in BRAFi-R melanoma cells. (A) Graph showing the densitometric analysis of PKCα, pPKCα, PKCι, pPKCι, PKCε, and pPKCε protein levels in BRAFi-sensitive and BRAFi-R A375 cells shown in B. These values were normalized to the value of the tubulin loading control from the same sample. The presented PKC/tubulin ratios were normalized to those in the corresponding BRAFi-sensitive cells and are given as relative fold changes. The data were calculated from 4 independent experiments and are presented as the means ± SEMs. (B) Western blot analysis showing the endogenous protein levels of PKCα, pPKCα (Thr-638/641), PKCι, pPKCι (T555 + 563), PKCε, and pPKCε (Ser-729) in BRAFi-sensitive and BRAFi-R A375 cells. Tubulin was used as the loading control. Representative blots from 4 independent experiments are shown. (C) Western blot showing RhoA protein expression in BRAFi-sensitive and BRAFi-R A375 melanoma cells. Graph below the blot show the densitometric results obtained from the value of RhoA protein expression and the tubulin loading control from the same sample. The generated RhoA/tubulin ratios were normalized to those in the corresponding BRAFi-sensitive cells and are presented as relative RhoA expression levels. The data were calculated from 4 independent experiments, and the values are presented as the means ± SEMs. (D) Graph showing RhoA activity in BRAFi-sensitive and BRAFi-R A375 melanoma cells as measured by a G-LISA kit. Activity levels were normalized to those in BRAFi-sensitive cells and plotted from 4 independent experiments and are given as the means ± SEM. Statistical significance was estimated using ANOVA with Dunnett’s post hoc test for multiple comparisons; * p < 0.05, ** p < 0.01, *** p < 0.001. | PMC9775662 | cancers-14-06077-g007.jpg |
0.393469 | 103755788f0e406a90fbc8a9b7d69382 | Enhanced RhoA activity and enhanced PKC activity are independent events contributing to the increased level of active phospho-MARCKS in BRAFi-R melanoma cells. (A) Graph showing RhoA activity as measured by a G-LISA kit in A375-R2 cells treated with either the pan-PKC inhibitor Gö6983 (50 nM) or the RhoA inhibitor Rhosin (10 μM) for 1 hr. Activity levels were normalized to those in vehicle-treated BRAFi-R A375-R2 cells and calculated from 4 independent experiments, and the values are presented as the means ± SEMs. (B) Western blot showing the pMARCKS (Ser-159/163) and total MARCKS levels in A375-R2 cells treated with the PKCα inhibitor (100 nM), the PKCι inhibitor (1 µM) or PKCε siRNA (100 nM) individually or in combination with the RhoA inhibitor Rhosin (100 μM). Tubulin was used as the loading control. Representative blots from 4 independent experiments are shown. (C) Graph showing the densitometric analysis of the pMARCKS level in A375-R2 cells as shown in panel B. These values were normalized to the value of the tubulin loading control from the same sample. The calculated pMARCKS/tubulin ratios were normalized to those in vehicle-treated BRAFi-R A375-R2 cells and are presented as relative fold changes. The data were calculated from 4 independent experiments and are presented as the means ± SEM. Statistical significance was estimated using ANOVA with Dunnett’s post hoc test for multiple comparisons; * p < 0.05, ** p < 0.01, *** p < 0.001. | PMC9775662 | cancers-14-06077-g008.jpg |
0.42914 | 14e4159dab6e413bacfefb9ea8753e8e | Workflow used for analysis of radiomic features. Abbreviation: LASSO—least absolute shrinkage and selection operator. | PMC9775984 | cancers-14-06119-g001.jpg |
0.403466 | e579ce617a694b19bb84036839094905 | Flow of patients through the study. Abbreviation: MRI—magnetic resonance imaging. | PMC9775984 | cancers-14-06119-g002.jpg |
0.404133 | 970e997b84a34c82826226721b16e0ba | Heatmaps of clinicopathological characteristics and survival outcomes in patients stratified according to RadScores. Patients are displayed in an ascending order with respect to RadScores. Differences in overall survival and progression-free survival in the training (A and B, respectively) and testing (C and D, respectively) cohorts. Abbreviations: OS—overall survival; PFS—progression-free survival. | PMC9775984 | cancers-14-06119-g003.jpg |
0.467077 | f3571f859576401b89e877ed670843e0 | Kaplan–Meier overall survival plots in the training (A,C,E) and testing (B,D,F) cohorts stratified according to the predicted risk derived from radiomic models (A,B), clinical models (C,D), and combined radiomic–clinical models (E,F). p values were calculated using log-rank tests. | PMC9775984 | cancers-14-06119-g004.jpg |
0.452709 | ad631ae20a544fcd9f27a539fcaaa60d | Kaplan–Meier progression-free survival plots in the training (A,C,E) and testing (B,D,F) cohorts stratified according to the predicted risk derived from radiomic models (A,B), clinical models (C,D), and combined radiomic–clinical models (E,F). p values were calculated using log-rank tests. | PMC9775984 | cancers-14-06119-g005.jpg |
0.41816 | c5c1651e0db34f1cb0acaa5b5937169e | Nomograms used for the prediction of overall survival (A) and progression-free survival (B) in patients with hypopharyngeal cancer who had been treated with primary chemoradiotherapy. Calibration curves were applied to assess the predictive performance with respect to 1-, 2-, and 3-year overall survival (C) and progression-free survival (D) in the training cohort. The survival outcomes predicted by the nomogram are plotted on the x-axis, whereas the observed outcomes are reported on the y-axis. The gray lines denote ideal nomograms. The vertical bars are the 95% confidence intervals, whereas unfilled square box markers indicate bootstrap-corrected estimates. Abbreviations: OS—overall survival; PFS—progression-free survival. | PMC9775984 | cancers-14-06119-g006.jpg |
0.379702 | 5faf12934334462ea2098ffe85a1947f | The KO of DNMT1 gene leads to autonomous L1 transcription. (A). Top-10 deregulated TE families in DNMT1 model. L1 is the most upregulated TE family with 1660 elements. (B). Rationale of our method for detecting autonomous transcription of L1 elements. Autonomous transcription of L1s will produce a higher amount of Inside fragments with both reads mapped inside the L1 consensus resulting in a higher Inside/Outside ratio. (C). Analysis to detect autonomous transcription of L1 elements. Upon the KO of DNMT, the upregulation of L1s derives by autonomous transcription of elements. (D). Top-10 deregulated TE families in naive T CD4+ cells. L1 is the most upregulated TE family with 10,794 elements. (E). Rationale of our method for detecting non-autonomous transcription of L1 elements. Transcription of L1s embedded in other transcriptional units will produce a low amount of Inside fragments. (F). Analysis to detect autonomous transcription of L1 elements. In naive T CD4+ cells, the upregulation of L1s derives by a non-independent transcription of elements. Given that the two distributions are similar the overexpression of L1s is due to their transcription as part of other transcriptional units. | PMC9776036 | biomedicines-10-03279-g001.jpg |
0.446047 | 2660fcaedb234a48af4dca9552ff087d | L1 transcripts might act as ceRNA. (A). Overlap analysis of L1s and protein-coding genes. Regions of upregulated genes are significantly enriched to contain upregulated L1 fragments. We found particularly interesting the strong enrichment in the 3′UTRs. (B). Analysis of miRNA target sites sharing between autonomously transcribed L1s and the 3′UTRs of protein-coding genes in DNMT1 model. The upregulated genes share a significantly higher number of miRNA target sites with active L1s, adding support to a possible ceRNA activity of L1 transcripts. (C). Analysis to identify miRNAs sequestered by L1s. Each analyzed miRNA is represented by a point with the X-axis indicating the delta between the proportion of targeted upregulated genes and the total upregulated genes proportion. The Y-axis represents the -log10(FDR) of the proportional statistical test applied. The 117 miRNA in green represent the most probable pool of miRNAs that are undergoing the ceRNA activity from L1s. (D). In this wordcloud are shown the 117 miRNAs whose size font is proportional to the absolute number of experimentally validated target genes upregulated in DNMT1 model. The let-7 family is among the top-10 miRNAs with the higher number of connections to these upregulated genes. (E). Correlation analysis between miR-128-1 (Y-axis) and L1 (X-axis) expression levels in Geuvadis dataset. Upon the L1 overexpression, the miR-128-1-5p levels concordantly increases probably as part of defense cellular mechanisms. (F). Correlation analysis between let-7a-1 (Y-axis) and L1 (X-axis) expression levels in Geuvadis dataset. No significant associations were found. | PMC9776036 | biomedicines-10-03279-g002.jpg |
0.394759 | e6e8aa95052243c8919ea0712d9dd4ec | L1 could act as ceRNA when artificially overexpressed. (A). TE subfamilies significantly deregulated in the ORFeus-OE model. In this experiment, the L1 construct is the most upregulated TE with a log2 fold-change of 10.92. (B). Analysis to detect autonomous transcription of L1. In ORFeus-OE cells, the upregulation of L1 is deriving from an autonomous transcription of the ORFeus element. (C). Analysis of miRNA target sites sharing between artificial L1 construct and the 3′UTRs of protein-coding genes in ORFeus-OE model. The upregulated genes share a significantly high number of miRNA target sites with the L1 construct, reflecting a possible ceRNA activity of the artificial construct transcript. | PMC9776036 | biomedicines-10-03279-g003.jpg |
0.420401 | 52fddb9c165c42998a557c1332344a16 | L1 ceRNA activity potentially relies on autonomous L1 transcription and Ago2 levels. (A). Top-10 deregulated TE families in ATRX model. Both in iPSC cells and in neurons, L1 family is the most upregulated TE family. (B). Analysis to detect autonomous transcription of L1 elements in ATRX model. Upon the KO of ATRX, the upregulation of L1 elements seems not to derive from an autonomous transcription of elements in neurons. (C). Analysis of miRNA target sites sharing between active L1s and the 3′UTRs of protein-coding genes in ATRX model. Downregulated genes show a higher number of miRNA target sites in common with overexpressed L1s. (D). Comparative analysis of Ago2 expression levels in all analyzed datasets. | PMC9776036 | biomedicines-10-03279-g004.jpg |
0.416415 | b4c8a60dc58e4663bb607686c277c7a3 | Genotyping of galactosylceramidase (Galc) SNP c.355G>A in the Twitcher (Twi) mouse. The Scatter plot of Allele G (axis x) versus Allele A (axis y) allowed us to identify the presence of the SNP in each animal. Animals homozygous for Allele G (WT) were localized in the bottom right corner of the graph (red and pink circles), animals homozygous for Allele A (HOM) were localized in the top left corner of the graph (blue circles), and animals heterozygous for Alleles A and G (HET) were localized on the diagonal (green circles). | PMC9776230 | biomedicines-10-03146-g001.jpg |
0.408026 | 69a99e8131f345838b4e063edb69976b | The fluorescence signal of Allele G and Allele A in the two groups of WT mice, WT_Twi compared to WT_Ctrl. Student’s t-test analysis, ns = not significant. | PMC9776230 | biomedicines-10-03146-g002.jpg |
0.440355 | 8cf7bf6d59114993b80eb2ec131260c1 | (A) Brain Galc activity. Galc activity was assayed in the extracted brain using the HMU-bGal assay. Means ± SEM (HOM_Twi n = 4; HET_Twi n = 10; WT_Twi n = 6 and WT_Ctrl n = 10) ** p < 0.01 HET_Twi versus WT_Twi, *** p < 0.001 HOM_Twi versus WT_Twi, ### p < 0.001 HOM_Twi versus WT_Ctrl and ### p < 0.001 HET_Twi versus WT_Ctrl, one-way ANOVA (Tukey’s test). (B) The Galc activity (log10) versus the ratio Allele A/Allele G (Rn / Rn). Data are superimposed to a linear fit (parameters of the linear regression are given in the figure inset). Animals homozygous for Allele G (WT) were localized in the top left corner of the graph (red and pink circles), animals homozygous for Allele A (HOM) were localized in the bottom right corner of the graph (blue circles), animals heterozygous for Alleles A and G (HET) were localized on the diagonal (green circles). | PMC9776230 | biomedicines-10-03146-g003.jpg |
0.442132 | d216ef35c5474308965fd849f539fc60 | Genotyping of galactosylceramidase (Galc) SNP c.355G>A in 120 Twitcher (Twi) mice and 30 control mice (Ctrl) for the verification study, cohort A. The scatter plot of Allele G (axis x) versus Allele A (axis y) allowed us to identify the presence of the SNP in each animal. Animals homozygous for Allele G (WT) were localized in the bottom right corner of the graph (red and pink circles), animals homozygous for Allele A (HOM) were localized in the top left corner of the graph (blue circles), and animals heterozygous for Alleles A and G (HET) were localized on the diagonal (green circles). | PMC9776230 | biomedicines-10-03146-g004.jpg |
0.436668 | e253abdadb1a4708b00ed9ceda407479 | (A) Body weight versus post-natal day (PND). Body weight (BW) was measured (in grams) every 5 days. ** p < 0.01 WT_Twi versus HOM_Twi; ## p < 0.01 WT_Ctrl versus HOM_Twi. (B) Representative image of WT_Twi (top) versus HOM_Twi mouse (bottom) at PND 22 and PND 40. (C) The Rotarod test was performed on HOM_Twi, WT_Twi, and WT_Ctrl mice every 5 days. Data are reported in the graph in seconds (s). * p < 0.05, *** p < 0.001 WT_Twi versus HOM_Twi; # p < 0.05, ## p < 0.01, ### p < 0.001 WT_Ctrl versus HOM_Twi. Data are reported as mean ± SEM and compared using a T-test. | PMC9776230 | biomedicines-10-03146-g005.jpg |
0.418212 | 75ec49e44ec840499fd56712225b9319 | Identification rate of Galc SNP in the Twitcher (Twi) mouse using the conventional protocol (A) and of the new protocol based on the allele-discrimination real-time PCR (B). | PMC9776230 | biomedicines-10-03146-g006.jpg |
0.456175 | d6326013150a48eb9de41093200d588b | Anatomical representation of the parapharyngeal space. Abbreviations: CN, cranial nerve; ICA, internal carotid artery; IJV, internal jugular vein; m./mm., muscle(s). | PMC9776422 | curroncol-29-00740-g001.jpg |
0.430711 | 492a190c04874419bfe9ac233b1e406a | Review of the English literature through PubMed and Scopus, accessed on 30 October 2022. Primary search was performed using the terms “parotid AND transoral AND (tumor OR cancer OR adenoma OR Warthin)”. | PMC9776422 | curroncol-29-00740-g002.jpg |
0.409058 | f72f02093dfb49429f693dc844e6d676 | Tumor histology in transoral approach (n, %). Abbreviations: AC, adenocarcinoma; BCA, basal cell adenoma; CXPA, carcinoma ex pleomorphic adenoma; MEC, mucoepidermoid carcinoma; NR, not reported; O, oncocytoma; PA, pleomorphic adenoma; WT, Warthin tumor. | PMC9776422 | curroncol-29-00740-g003.jpg |
0.458867 | 1ab8a967ab49495980a3ab31982ced47 | Complications in transoral approach. | PMC9776422 | curroncol-29-00740-g004.jpg |
0.45245 | 95bbe41bfa2845cc90ad99d9d9a04de6 | The potential use of ctDNA in clinical decision making. | PMC9776613 | cancers-14-06115-g001.jpg |
0.449071 | ddf9a9dd283e4992bc993956d630ba39 | PRISMA flow diagram. | PMC9776761 | diagnostics-12-03032-g001.jpg |
0.407366 | 1c3d47e844014bca91ac929d3f060298 | Risk of bias of RCTs assessed with RoB 2.0. | PMC9776761 | diagnostics-12-03032-g002.jpg |
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