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0.502445 | ce1983e8ed6e4d74bd8b1f6257b6ba84 | Distribution of Neodon used in this study.The approximate extent of occurrence of each clade is shown with coloured lines (Clade 1: yellow; Clade 2: green, Clade 3: purple, refer to Fig. 3 for clade information). Stars show the type localities of the described species. Circles show the historical collection sites. Squares show the distribution of newly collected specimens. | PMC9792541 | 42003_2022_4371_Fig1_HTML.jpg |
0.352967 | 9bc309cb3629497284eec795b3ebb07f | Comparison of morphological features.a Comparison of tooth rows. b Comparison of glans penes. Numbered views are 1: glans; 2: midventral cut view; 3: urethral lappet; 4: dorsal papilla. Lettered structural features in a1 and a2 are a. distal baculum; b. outer crater; c. inner crater; d. ventral groove; e. glans; f. prepuce; g. penis body; h. station of dorsal papilla; i. lateral baculum (cartilage); j. urethral lappet; k. lateral baculum (bony part); l. distal baculum (bony part); and m. proximal baculum. The taxa are (from top to bottom) Neodon leucurus, N. fuscus, N. linzhiensis, N. forresti, N. irene from Clade 1, N. nyalamensis, N. sikimensis, unidentified taxon 1 (from Nanyi township, Milin County), unidentified taxon 2 (from Shergyla Mountains, Linzhi county), unidentified taxon 3 (from Motuo County, south of the Namchabarwa Mountains) from Clade 2, N. medogensis, unidentified taxon 4 (from Ridong village, Bershula Mountains, Chayu County), N. clarkei, unidentified taxon 5 (from Bomi County) and unidentified taxon 6 (from Chibagou National Nature Reserve, Chayu County) from Clade 3 (refer to Fig. 3 for clade information). | PMC9792541 | 42003_2022_4371_Fig2_HTML.jpg |
0.429524 | a157e5f3a34c46efb710c1b9f6fa0555 | Divergence time tree, diversification patterns, the frequency of the non-main topologies and photos of Neodon.a Divergence time-tree with the Astral branch supports values of the nuclear gene tree are shown near the branches. New species denoted in bold. Clades of Neodon indicated by shading with different colours on the tree (Clade 1: yellow; Clade 2: green; Clade 3: purple). The blue rectangles at the nodes represent 95% confidence intervals of the corresponding estimated divergence times. Branches with high non-main topology occurrence frequencies (>50%) are marked with the content number. Log-lineage-through-time (LTT) plots for Neodon estimated from the time-calibrated phylogeny (red curve), and the semilucent red dashed line indicates the null distribution under a Yule process. b The divergence time, geologic timescale, geologic events and climate events are shown at the bottom of the figure. | PMC9792541 | 42003_2022_4371_Fig3_HTML.jpg |
0.462347 | 76c0e91f35cd4a7ab618796997cd8a06 | Ancestral range estimation of Neodon.a Ancestral range estimation based on the best-fitting model DEC + J implemented in BioGeoBEARS. The relative probabilities of each ancestral reconstruction are indicated with pie charts at each node. New species denoted in bold. Transparent bars represent the timelines following the coding described in b. b Estimated origin and dispersal of extant Neodon. | PMC9792541 | 42003_2022_4371_Fig4_HTML.jpg |
0.412149 | 35f43eb5e5684c93af45b540c631d959 | Gene functional adaptations in Neodon species.a The three major environmental stresses of high-elevation areas and the adaptive traits that the voles may have developed (Photograph by Shaoying Liu). b The repair of DNA double-strand breaks damage, involving the three PSGs of RNaseH1, EYA2 and DEK, is a classic DNA repair pathway for UV-induced DNA damage. c Visualisation of two positively selected gene sites in the NDUFS1 gene, which represents one of the PSGs related to mitochondrial function. d Mitochondrial complex I and positive selection sites affect the protein structure of NDUFS1. | PMC9792541 | 42003_2022_4371_Fig5_HTML.jpg |
0.429938 | 07aa432c3fe04494854dbcb445a49598 | HRV spectrogram of a study participant one day before the vacation: The red ellipsoidal marking indicates a low frequency density in the HF spectrum (0.15–0.4 Hz), whereas the weak yellow coloring indicates reduced nocturnal PNS activity. X-axis: time of the HRV analysis; y-axis: frequency spectrum in Hz; Hz: Hertz; HF: high frequency. This spectrogram shows poor sleep quality and diminished nocturnal parasympathetic activity | PMC9793586 | 12889_2022_14838_Fig1_HTML.jpg |
0.390756 | ddd0b78f22ca448b9cfe948c37101eb1 | HRV spectrogram of the same study participant after the one-week vacation: The red ellipsoidal marking highlights the increased frequency density in the HF spectrum (0.15–0.4 Hz). Increasing yellow and light red coloring express good nocturnal parasympathetic activity. X-axis: time of the HRV analysis; y-axis: frequency spectrum in Hz; Hz: Hertz; HF: high frequency. This spectrogram shows improved sleep quality with demonstrable nocturnal parasympathetic activity | PMC9793586 | 12889_2022_14838_Fig2_HTML.jpg |
0.537695 | 38312c8d74d246a2861381d17f9ab9c3 | Visual description of the problem when the system has only two possible states.An individual cyber component can be either functional (“up”) or nonfunctional (“down”). A) Individual components transition between the two states according to a Markov process in which pud is the probability that an individual component that is currently functional is nonfunctional at the next time step and pdu is the probability that a component that is currently nonfunctional is functional at the next time step. B) Although individual components will in general continue to fluctuate between functional and nonfunctional states, after a sufficient length of time a population of components will settle into a probability distribution for the number of functional components. Our goal is to characterize this probability distribution and the factors determining how rapidly it is approached. | PMC9794046 | pone.0279100.g001.jpg |
0.438202 | 6ee1c2fce008488080fc58158ab8d34a | Visual description of the problem when the cyber components have multiple possible OSs that are updated as time goes forward and cyber components do not return to a previous OS.A) Two representations of the OS in use (y-axis) in a subset of about 335 computers from a network of about 7000 computers between July 2019 and March 2020 (details on how the data were chosen can be found in S1 Appendix in S1 File) as a function of the most recent OS release (x-axis). In the upper panel, we show the data as presence/absence of an OS in the sample. In the lower panel, we show the relative distribution of the current OS, where the diameter of the circle is proportional to the base-10 logarithm of the counts of that OS. The points (5,3) and (10,9) from the upper panel are not visible in the lower panel because their relative representation is so small. B) When measured by the most recent OS, time moves forward in uniform steps (x-axis), and the maximum OS in use jumps whenever a new OS is released. The maximum updating rate is then the black solid line; if an OS is never updated, it remains at the first release (dotted line). Each location between these two lines corresponds to a probability of the OS in use given the most recent OS. C) With time measured in OS release, the space between the two lines in panel B can be filled using a binomial lattice model (sensu Leisen and Reimer [22]) in which the current OS, at the time of an OS update, is either updated by 1 or remains the same. We let p denote the probability that the OS is updated at each step possible. For example, the probability that at time t the OS is still the first release is (1 − p)t−1 (i.e., in each of the t time steps the OS was not updated) and the probability that the OS is the most recent release is pt (i.e., in each of the t time steps the OS was updated). D) However, in this paper we are interested in measuring time in a natural scale, such as days. In that case, we require knowing the most recent OS release as a function of time. We use the schedule of OS updates shown here for the computations that follow. | PMC9794046 | pone.0279100.g002.jpg |
0.495577 | 16971f13cbd24cba9866fba72d84621a | The double-sigmoid performance function, Eq 6.In all cases, n50 = 20, σn=σn′=10, and n50′ varies from 70 (left-most curve) to 40 (right-most curve). The consequence of increasing n50′ is that more components must be functional to achieve the same level of performance. | PMC9794046 | pone.0279100.g003.jpg |
0.487869 | a90661c4482844259a808fb1bdca499e | The updating distribution f(OS(t), OS(t + 1), t) for t = 190 (left panel) or 290 (right panel) and six choices of OS(t): 1 (black), 3 (red), 5 (green), 7 (dark blue), 9 (light blue), and 11 (pink).The dotted lines show regions that are not accessible for updating at that time (determined by the trajectory of the most recent OS, as in Fig 2D). For example since K(190) = 15, at time t = 190 any OS update beyond 15 corresponds to a time in the future. | PMC9794046 | pone.0279100.g004.jpg |
0.475457 | dfcfee8d2ec3453daeaf5048f3bc338d | To derive the forward equation for p(k, t), we use the method of thinking along sample paths.If OS(t + 1) = k, either OS(t) = k and no transition occurred (with probability e−θ), or the OS(t) = l, where l = 1, 2, 3, ..k − 1, and transition (with probability 1 − e−θ) occurred from OS(t) = l to OS(t + 1) = k (probability f(l, k, t)). | PMC9794046 | pone.0279100.g005.jpg |
0.456847 | 3462b517c6f843ada106d1ffbe530a23 | We assume that the kth OS is released at time tk and remains unexploited at time t with probability e−λk(t − tk).a) For computations, we assume that the rate of exploitation declines as OS release number increases (i.e., newer releases are more capable of resisting exploitation). b) The probability that the OS releases in Fig 2D are exploited as a function of time. By definition, the probability of exploitation is 0 for t < tk; it will approach 1 for t > >tk because of our assumption of an exponential rate of exploitation. | PMC9794046 | pone.0279100.g006.jpg |
0.45005 | 84e6db2ebd08442d957287cfbb1cc6f1 | Fifty simulations of Eq 9 for N0 = 100, pud = 0.08, and pdu = 0.2.Here P¯U=pdupud+pdu=0.714. The thick black line is the mean value of the individual trajectories. | PMC9794046 | pone.0279100.g007.jpg |
0.513784 | 53575ee5b199446abce4f43d6a2611c2 | Frequency of the distribution of performance (Eq 6) at four different times, for pud = 0.08 and pdu = 0.2, with parameters for the performance function n50 = 20, σn = 10, n50′=40, and σn′=10.Note the left tail of the distributions expanding as time increases and an approach to a steady state frequency distribution of performance. | PMC9794046 | pone.0279100.g008.jpg |
0.437873 | 2fdeb3972662471593ced5bd1967c132 | Numerical solution of the forward equation for pud = 0.08 and pdu = 0.2.In the upper panel, the initial number of functional components is concentrated at N0 = 100; in the lower panel, it is uniformly distributed between 69 and 100. Each color corresponds to one time step into the future, and time increases as the curves move to the left. Note the different scales in the vertical axes. | PMC9794046 | pone.0279100.g009.jpg |
0.451623 | 5d6432d8cd824b768c91a46c221348a5 | Comparison of the discrete Gaussian approximation and the quasi-steady state solution of the forward equation for the case of N0 = 100 components, all initially functional.All x-axes correspond to the number of functional components. In the left-hand panels, we show the numerical solution of the forward equation for pdu = 0.2 and pud = 0.08 (first row), pdu = 0.1 and pud = 0.08 (second row), and pdu = 0.05 and pud = 0.08 (third row), marching toward the quasi-steady state solutions. In the right-hand panels, we compare the discrete Gaussian approximation with the densities at t = 20, 40, and 60 (upper, lower, and middle rows, respectively) for the same individual component transition probabilities. The solid gray line corresponds to the densities F(n, 20), F(n, 40), and F(n, 60), and the dotted blue line corresponds to the Gaussian approximation. | PMC9794046 | pone.0279100.g010.jpg |
0.523114 | 5b68f3c691bd4b38974469581c7cc2a2 | Simulation of the distribution of 1000 OSs for the updating distribution in Eq 16 and three different values of θ.Squares show the most recent OS, and circles show the fraction of the 1000 simulated cyber components in the respective OS. For ease of viewing, the fraction is multiplied by 3. This figure should be compared with the data in Fig 2A. | PMC9794046 | pone.0279100.g011.jpg |
0.470756 | cca6ad33be4a4b49b25a0a2fa0576f66 | The distribution of OSs over time for θ = 0.025.We show the distribution of all OSs as a dotted line—- note the long left-hand—-tail and the distribution of unexploited OSs as a solid line. Because the stochastic process is right limited, the frequency distribution drops to zero for times at which an OS is not available (i.e., is 0 whenever t < tk). In addition, since older OSs are more likely to be exploited than more recent ones, the distribution of unexploited OSs is more symmetrical than the distribution of all OSs. | PMC9794046 | pone.0279100.g012.jpg |
0.443696 | df60e720b55e4afaa4cfbc3378a0d112 | Macroscopic image of the urinary bladder. (Subscript) Local lesion of the bladder wall measuring 1 cm in diameter and only covered by a translucent mucosal membrane (asterisk). | PMC9794091 | fvets-09-1038642-g0001.jpg |
0.475569 | e65caaf569074dd58f2637fb9636a690 | Microscopic image of the urinary bladder. Normal bladder wall on the left and disintegrating ordinary muscle layer on the right side. This defect is flanked by a juvenile granulation tissue, subserosal bleedings, and individual muscle bundles. Normal bladder wall on the left with detaching ordinary muscle layer on the right side. This defect (Y) is flanked by an immature granulation tissue, subserosal hemorrhage, and individual muscle bundles. Inset (X): local lesion only consists of a transitional cell layer (A) with a thin loosely arranged connective tissue covered with mesothelial cells (B). Individual muscle bundles are present (asterisk). | PMC9794091 | fvets-09-1038642-g0002.jpg |
0.392181 | 8d8df7a7c78a4c018d3b5bc9685dde79 | Multi-omics analysis of SARS-CoV-2 infected Calu-3 cells(A) Experimental workflow for the proteomic and phosphoproteomic analysis of Calu-3 cells infected with SARS-CoV-2/Trondheim-S15/2020 strain.(B) Barplots summarizing the number of differentially regulated transcripts, proteins, phosphopeptides, acetylpeptides, and secreted metabolites identified in Calu-3 cells infected with SARS-CoV-2.(C) Heatmap showing enriched pathways for transcriptome, proteome, phosphoproteome, and acetylome data of SARS-CoV-2/Trondheim-S15/2020 strain infected Calu-3 cells.(D) Overlap of genes across the different datasets from the current study.(E) A screenshot of the SARS-CoV-2 OMICS Map containing data acquired in the current study. (See also Figure S1). | PMC9794516 | gr1.jpg |
0.407893 | d742187995414c2b8483b289114029a0 | Proteomics and transcriptomic analysis of SARS-CoV-2 infected Calu-3 cells(A–C) Volcano plots displaying differential changes in RNA and protein expression of Calu-3 cells after infection with SARS-CoV-2/Trondheim-S15/2020 strain at 12, 24, and 48 hpi. Red-filled circles indicate overexpressed genes/proteins, whereas blue-filled circles indicate downregulated genes/proteins. Heatmaps depicting differentially expressed IFN-stimulated genes from the (B) RNA-Seq and (C) Proteomics data of SARS-CoV-2-infected Calu-3 cells at 3, 6, 12, 24, and 48 hpi.(D) Immunoblot analysis depicting time-dependent changes in the expression of interferon signaling proteins-OAS1, ISG15, TRIM5α, RNase-L, MX1, and CMPK2 in SARS-CoV-2-infected Calu-3 cells.(E) A graph comparing transcript levels of interferon-stimulated genes (ISGs) between the Trondheim strain (S15), alpha, IC19, and VIC strains from Thorne et al.27(F) Cytokine array data showing differential levels of Ang-1, Dkk-1, uPAR, CXCL10, VEGF, MIC-1, and CD147 in Calu-3 cells post-infection. (See also Figure S2, Tables S1 and S2). | PMC9794516 | gr2.jpg |
0.429977 | 3797efb4ce844129aec1ec57465e4271 | Phosphoproteomics and acetylomics analysis of SARS-CoV-2 infected Calu-3 cells(A) Heatmap depicting k-means clustering of phosphoproteomics profile of Calu-3 cells after infection with SARS-CoV-2/Trondheim-S15/2020 strain at 3, 6, 12, 24, and 48 hpi.(B) A list of enriched signaling pathways from the phosphoproteomics profile of Calu-3 cells after SARS-CoV-2 infection.(C) Statistics of differentially phosphorylated protein kinases and phosphatases in response to SARS-CoV-2 infection.(D) Immunoblot analysis showing the phosphorylation dynamics of various signaling proteins, including pTBK1 (S172), pIRF3(S396), pJNK(S172), pERK1/2(T202, Y204), p-p38(T180.Y182), pYAP1(S61), p-STAT1(S727), pEIF2α(S51), and p-STAT3 in response to SARS-CoV-2 infection at 3, 6, 12, 24, and 48 hpi.(E) A heatmap depicting clustering of acetylome profile of Calu-3 cells after infection with SARS-CoV-2/Trondheim-S15/2020 strain at 3, 6, 12, 24 and 48 hpi.(F) A list of enriched signaling pathways from the acetylome profile of SARS-CoV-2 infected Calu-3 cells.(G) Examples of two proteins- ANP32A and ILF3 that are both acetylated and phosphorylated on SARS-CoV-2 infection. (See also Figure S3, Table S3. List of differentially phosphorylated proteins and phosphosites identified in Calu-3 cells infected with SARS-CoV-2/ Trondheim-S15/2020 strain at time intervals 3-, 6-, 12-, 24-, and 48-h post-infection (hpi), related to Figures 1 and 3, Table S4. List of differentially acetylated proteins and acetylation sites identified in Calu-3 cells infected with SARS-CoV-2/ Trondheim-S15/2020 strain at time intervals 3-, 6-, 12-, 24-, and 48-h post-infection (hpi), related to Figures 1 and 3, Table S5. Pathways enriched in the 8 phosphoproteome clusters, related to Figure 3). | PMC9794516 | gr3.jpg |
0.462551 | d2d8a24cea23453caba2784a5f0ba2d3 | Metabolomics analysis of SARS-CoV-2 infected Calu-3 cells(A) A heatmap depicting differential levels of metabolites in response to infection with SARS-CoV-2/Trondheim-S15/2020 strain at 3, 6, 12, 24, and 48 hpi.(B) A list of enriched metabolic pathways from the metabolomics profile of Calu-3 cells on SARS-CoV-2 infection.(C–I) A schematic depicting the TCA cycle and differential levels of TCA metabolites-cis-aconitate, alpha-ketoglutarate, succinate, fumarate, and malate in response to SARS-CoV-2 infection. Altered levels of metabolites, including (D) Pyridoxal, (E) 3-hydroxybutyrate, (F) Phenylpyruvate, (G) Ketoleucine, (H) 3-methyl-2-oxovaleric acid, and (I) N-acetylneuraminate observed in response to SARS-CoV-2 infection. (See also Figure S4 and Table S6). | PMC9794516 | gr4.jpg |
0.395535 | 7721b463b4f74e9db795e2eff7a7fac8 | Viral phosphoproteomic dynamics after infection of Calu-3 cells with SARS-CoV-2/Trondheim-S15/2020 strain(A) Heatmap revealing temporal phosphoproteomic changes in SARS-CoV-2 proteins.(B) Comparison of viral phosphoproteome identified in the current study with phosphoproteomics data from previously published studies, including Pichlmair29 (A549 cells), Krogan (Vero cells),20 and Munch (Caco2).22(C) Schematic shows phosphosites' overlap on important domains and motifs in SARS-CoV-2 viral proteins.(D) Upstream kinase analysis using NetPhos 3.1 to predict potential host kinases phosphorylating the viral proteins. Red boxes represent viral proteins, and the green circles represent host kinases. The phosphorylated residue on each viral protein is indicated. (See also Figure S5 and Table S7). | PMC9794516 | gr5.jpg |
0.412406 | a6e825b9079048079b65e3809be77d10 | Integrated omics analysis identifies alterations in key signaling pathways on SARS-CoV-2 infection in Calu-3 cells(A–F) Graphs illustrating the average trend of differentials from transcriptomics, proteomics, and phosphoproteomics datasets with respect to various pathways and processes, including (A) Hippo signaling (B) Regulation of Hippo signaling, (C) DNA damage response, (D) DNA repair, (E) Protein ubiquitination, (F) Regulation of protein mono and polyubiquitination.(G) A detailed overview of the Hippo signaling pathway indicating differentials in response to SARS-CoV-2 infection.(H) Immunoblot analysis of DNA damage markers, including phospho-p53 (S15), and phospho-γH2AX (S139) along with total p53 and β-actin (control) in Calu-3 cells in response to SARS-CoV-2 infection at 3, 6, 12, 24, and 48 hpi.(I) Immunoblot analysis indicating changes in the total protein ubiquitination profile of Calu-3 cells in response to SARS-CoV-2 infection at 3, 6, 12, 24, and 48 hpi. (See also Figures S6 and S7). | PMC9794516 | gr6.jpg |
0.398924 | daa2016b503f45ab8cafedd4c99e0f0c | Thermal images of the chest wall with left (white) and right (black) ROI. (a) before surgery, (b) 2 h after surgery, (c) 24 h after surgery, (d) 6 days after surgery. All temperature values were averaged over 18,000 frames, corresponding to a duration of 60 s. | PMC9794541 | 10439_2022_2998_Fig1_HTML.jpg |
0.495001 | b95bb48d1a5742549ddb6ca4656fd3b0 | Surface Temperature TS of the left and right side of the anterior chest wall during the study. | PMC9794541 | 10439_2022_2998_Fig2_HTML.jpg |
0.428593 | c1930e3f8a86485bbc8fd770c48c76ed | Association between cutaneous blood volume pulse (CVP) and chest wall surface temperature (TS). The dotted line shows the linear fit of TS to CBVP. | PMC9794541 | 10439_2022_2998_Fig3_HTML.jpg |
0.465291 | ac3707bb37da46beac076f40bb8de0d2 | Simulated regimens. Each regimen starts with 14 days of rilpivirine 25 mg once daily (blue period), followed by 14 days (orange period) of rilpivirine 25 mg once daily and 600 mg rifapentine once daily on regimen 1, rilpivirine 25 mg once daily and once weekly 900 mg rifapentine on regimen 2, and rilpivirine 50 mg once daily and once weekly 900 mg rifapentine on regimen 3. On the third period (green), rifapentine’s administration is ceased but rilpivirine is maintained with the same dose strategy as the previous period during 21 days. | PMC9797969 | fphar-13-1076266-g001.jpg |
0.432863 | 287d219a9d3448a085c9c4f7ba5b8442 | Rilpivirine Ctrough concentration over time. Regimen 1 (violet) represents 14 days of rilpivirine 25 mg daily dose (period one in blue), 14 days of rilpivirine 25 mg daily dose with rifapentine 600 mg daily dose (period two in orange), and 21 days of rilpivirine 25 mg daily dose (period three in green). Regimen 2 (yellow) represents 14 days of rilpivirine 25 mg daily dose (period 1), 14 days of rilpivirine 25 mg daily dose with rifapentine 900 mg weekly dose, a total of three doses) (period 2), and 21 days of rilpivirine 25 mg daily dose (period 3). Regimen 3 (dark blue) represents 14 days of rilpivirine 25 mg daily dose (period 1), 14 days of rilpivirine 50 mg daily dose with rifapentine 900 mg weekly dose a total of three doses (period 2), and 21 days of rilpivirine 50 mg daily dose (period 3). The red dashed line represents the minimum target concentration for rilpivirine. The top left corner contains the mean plasma concentration PK profile for rilpivirine for each regimen. | PMC9797969 | fphar-13-1076266-g002.jpg |
0.43617 | f0f840e797a54ebcbe2e6e0fc5450da2 | Discovery of the index case. (A) Principal component analysis revealed one relapsed acute myeloid leukemia (AML) case (indicated by the red arrow) separate from other AML cases, but adjacent to the cluster of acute promyelocytic leukemia (APL) cases. (B) Morphology of the bone marrow (BM) smear at relapse, Wright’s stain. | PMC9798314 | fonc-12-1074913-g001.jpg |
0.482463 | 3fe65375f5fa469981771b5791b5664f | TTMV integration structure of our case. (A) Alignment of the whole transcript sequencing (WTS) data in integrative genomics viewer (IGV) revealed abundant RARA fusion transcripts with a 5’ extension from RARA exon 3, which is not homologous to any human sequence. (B) Schematic representation of the fusion between the TTMV sequence and RARA exon 3. The chimeric transcript includes TTMV open reading frame 2 (ORF2) and an upstream conserved untranslated region (UTR), in-frame with RARA exon 3. (C) Sanger sequencing confirmed the existence of the TTMV::RARA fusion transcript. (D) The predicted structure of the TTMV::RARA fusion protein. The blue arrow indicates the highly conserved TTMV ORF2 motif. The red arrow at position 85 indicates the fusion site. (E) Whole genome sequencing (WGS) revealed the insertion was located at chr17: 40332265 (hg38) in RARA intron 2. (F) Sanger sequencing confirmed the two genomic junction sequences. (G) Schematic representation of the location and structure of the integrated TTMV sequence. (H) TTMV insertion characteristics in the 4 cases reported to date. The red arrows show the insertion positions of TTMV sequence in RARA intron 2 (hg38). The black rectangles show the integrated TTMV sequences, and the yellow blocks within them show the nucleotides expressed in the TTMV::RARA fusion transcript. | PMC9798314 | fonc-12-1074913-g002.jpg |
0.460347 | f09f5c2d556a4a8688d158649eaebc64 |
RBD management flow chart.
a Caution: if the patient DOES NOT have a bed partner, consider onward referral even if their response is NO to the single screening question but they do have a history suggestive of RBD. DLB = dementia with Lewy bodies. PD = Parkinson’s disease. RBD = REM sleep behaviour disorder. vPSG = video polysomnography.
| PMC9799336 | bjgpjan-2023-73-726-40.jpg |
0.451266 | f800e50f4e5842e19ea7f355d899681c |
The PRISMA flowchart of search strategy and selection process.
| PMC9800069 | 10-1055-s-0042-1755460-i220043-1.jpg |
0.414775 | 58b11fe7112a4e1997f69e9dc31f7187 |
Graphical representation of risk of bias assessment of the Randomized Clinical Trials included in the review (
n
= 3). Source of bias risk assessment: Cochrane Collaboration tool.
| PMC9800069 | 10-1055-s-0042-1755460-i220043-2.jpg |
0.404041 | 3ecbc3b2553047269e3a2effdcf572bc |
Graphical representation of risk of bias assessment of the Cohort Studies included in the review (
n
= 3). Source of quality assessment: National Institutes of Health (NIH). *Abbreviations: CD, cannot determine; NR, not reported; NA, not applicable.
| PMC9800069 | 10-1055-s-0042-1755460-i220043-3.jpg |
0.416137 | f673ae376dc446e98cb8a671cfd4ddd9 |
Graphical representation of risk of bias assessment of the Case-Control Studies included in the review (
n
= 2). Source of quality assessment: National Institutes of Health (NIH). *Abbreviations: CD, cannot determine; NR, not reported; NA, not applicable.
| PMC9800069 | 10-1055-s-0042-1755460-i220043-4.jpg |
0.458256 | facf0cf0309942d4b78abe6e2713768f | An example of contact impedance measurement showing reference electrode-skin contact impedance was above the preset threshold. | PMC9800185 | gr1.jpg |
0.392023 | 77f23708bc3845f0bdd14c1da46c5eff | Bland-Altman plots comparing the center of ventilation (CoV) between scenarios 2 (A), 3 (B) and scenario 1. | PMC9800185 | gr2.jpg |
0.36526 | 52e8b27c7365439c8c6895416167e3a6 | Bland-Altman plots comparing the four regions of interest (ROIs) between scenarios 2 (A), 3 (B) and scenario 1, which are four horizontal anterior-to-posterior segments with equal height. | PMC9800185 | gr3.jpg |
0.44191 | d0caac6625af43079dd13fe15fefe4f8 | Bland-Altman plots comparing the four regions of interest (ROIs) between scenarios 2 (A), 3 (B) and scenario 1, which are four quadrants corresponding to right ventral, left ventral, right dorsal and left dorsal lung regions. | PMC9800185 | gr4.jpg |
0.43534 | 3f5d7e264cc343eda4d3e1776314a00e | Bland-Altman plots comparing the global inhomogeneity index (GI) between scenarios 2 (A), 3 (B) and scenario 1. | PMC9800185 | gr5.jpg |
0.409771 | 04b4a319b60744e08c1d84fd24b72c87 | Bland-Altman plots comparing the regional ventilation delay (RVD) between scenarios 2 (A), 3 (B) and scenario 1. | PMC9800185 | gr6.jpg |
0.399622 | 989f7c76a6cb4f3894cf2108f7729351 | Bland-Altman plots comparing the respiratory rate (RR) between scenarios 2 (A), 3 (B) and scenario 1. | PMC9800185 | gr7.jpg |
0.457044 | b79132545ab54dd4b0ef78eb89354ddc | Deviation score for subjects in scenarios 2 (A, no recalibration) and scenario 3 (B, recalibration). | PMC9800185 | gr8.jpg |
0.49992 | 2afb7c184be14665bd0d2aba7fb4efc0 | PRISMA diagram. | PMC9800592 | fpubh-10-1031867-g0001.jpg |
0.393854 | 136f2b6c8a2e474e81c9f20bcd2c7740 | Robvis output for risk bias assessment. | PMC9800592 | fpubh-10-1031867-g0002.jpg |
0.451046 | bcde17fcf1904de1b754c82dbd66b8c2 | Weighted output for risk bias assessment. | PMC9800592 | fpubh-10-1031867-g0003.jpg |
0.44392 | d0b202977325477e81295763cdf2d334 | Tools for telemedicine. | PMC9800592 | fpubh-10-1031867-g0004.jpg |
0.3878 | 6cc38e4267254be0a6d352687030e01f | Overall patient satisfaction. | PMC9800592 | fpubh-10-1031867-g0005.jpg |
0.450444 | 58a809c050444f06934bed561f297fb8 | Determinants of patient satisfaction. | PMC9800592 | fpubh-10-1031867-g0006.jpg |
0.474389 | e78f7ae39c1042fab08d663c6cac7046 | COPES PRISMA flow diagram. | PMC9800609 | fmed-09-999225-g001.jpg |
0.485476 | 27a276e9be434e4a8aa5404ddd0b252d | Forest plot for COVID-19 mortality. CI, confidence intervals; COVID-19, coronavirus disease 2019; IV, inverse variance. | PMC9800609 | fmed-09-999225-g002.jpg |
0.49739 | 2646bcd76b2f47e0af3ee7056ca2ce77 | Forest plot for all–cause excess mortality. CI, confidence intervals; COVID-19, coronavirus disease 2019; IV, inverse variance. | PMC9800609 | fmed-09-999225-g003.jpg |
0.474885 | 4b46a4e4b2f6400ea92d92a3be371434 | Forest plot for non-COVID-19 mortality. CI, confidence intervals; COVID-19, coronavirus disease 2019; IV, inverse variance. | PMC9800609 | fmed-09-999225-g004.jpg |
0.481263 | 2c8f0d3c6ce74baea30fa9b03c30858f | PRISMA 2020 flow diagram. | PMC9801330 | fpubh-10-1022145-g0001.jpg |
0.448862 | c96840afd25d468b99cdfd4e64a59703 | Summary of risk of bias. | PMC9801330 | fpubh-10-1022145-g0002.jpg |
0.379299 | 8528828a28ec4177986e58100360d2ca | Forrest plot of acupuncture vs. pharmacological medications for symptom severity at the time of treatment ending. Results are shown by using the random-effect model with mean difference and 95% confidence intervals (CI). | PMC9801330 | fpubh-10-1022145-g0003.jpg |
0.458449 | d5af42036d584e6f98d4fa5f6e575b5e | Trial sequential analysis (TSA) for symptom severity. Trial sequential analysis (TSA) of 10 trials comparing acupuncture with pharmacological medications for symptom severity in patients with IBS. The TSA shows that the information size is insufficient, but the cumulative Z score crossed O'Brien-Fleming alpha-spending significance boundaries. The evidence is sufficient to identify the effect of intervention. A required information size of 5,490 was calculated using α = 0.05 (two sided), ß = 0.20 (power 80%). | PMC9801330 | fpubh-10-1022145-g0004.jpg |
0.445154 | f8ca34a8b27a4ace8b25838eabc352a8 | Funnel plot of acupuncture vs. pharmacological medications on IBS symptom severity at the time of treatment ending. | PMC9801330 | fpubh-10-1022145-g0005.jpg |
0.498047 | b3f8b870755549f68d3f84bb5a52734f | Egger test of acupuncture vs. pharmacological medications on IBS symptom severity at the time of treatment ending (Egger test, P = 0.191). | PMC9801330 | fpubh-10-1022145-g0006.jpg |
0.411916 | a2a11902d96d4bfc8a593adb7756640e | Sensitive analysis of acupuncture vs. pharmacological medications on IBS symptom severity at the time of treatment ending. | PMC9801330 | fpubh-10-1022145-g0007.jpg |
0.452742 | b212e7703e274d8c88d21145b6471e3d | GIMAP7 expression is upregulated in PCOS rat. A The GIMAP7 expression in patients without PCOS (n = 4) and with PCOS (n = 4) from GEO dataset GSE80432. B-D: Sprague–Dawley rats (n = 6 per group) were injected with DHEA for constructing PCOS animal models. The rats in the blank group were injected with same volume of sesame oil. GIMAP7 mRNA (B) and protein (C) expression in ovarian tissues was measured using qRT-PCR and western blotting. D GIMAP7 expression in ovarian tissues was measured using IHC staining. Differences between two groups were analysed using Student’s t-test. *P < 0.05, **P < 0.01, ***P < 0.001 | PMC9801623 | 13048_2022_1092_Fig1_HTML.jpg |
0.417868 | f15f729be6a34153b4826ae3caeb80e6 | GIMAP7 shRNA relieves the symptoms of PCOS rats. A-H Sprague–Dawley rats (n = 6 per group) were injected with DHEA for constructing PCOS animal models, and then injected with lentivirus carrying shNC or shGIMAP7. The fasting blood-glucose (A), insulin tolerance tests (ITTs) (B), and homeostasis model assessment-insulin resistance (HOMA-IR) scores (C) were analysed in rats of each group. D Representative images of H&E staining show the histopathological changes of ovarian tissues. The serum LH (E), FSH (F), LH/FSH ratio (G), testosterone (H), and estradiol (I) were analysed using ELISA kits. J The representative oestrous cycles (D, dioestrus; P, proestrus; E, 0estrus; M, metestrus). Differences among multiple groups were analysed using a one-way analysis of variance. *P < 0.05, **P < 0.01, ***P < 0.001 | PMC9801623 | 13048_2022_1092_Fig2_HTML.jpg |
0.47466 | 656fe6150b944d0d92a1f25c65e8b113 | GIMAP7 shRNA relieves ovarian apoptosis and oxidative stress in PCOS rats. A-F Sprague–Dawley rats (n = 6 per group) were injected with DHEA for constructing PCOS animal models, and then injected with lentivirus carrying shNC or shGIMAP7. A The apoptosis of ovarian tissues was detected using TUNEL staining. Representative images of TUNEL are shown. B The protein expression of c-caspase-3 was detected using western blot. The GSH levels in ovaries (C). The MDA levels in serum (D) and ovaries (E). The SOD levels in serum (F) and ovaries (G). Differences among multiple groups were analysed using a one-way analysis of variance.*P < 0.05, **P < 0.01, ***P < 0.001 | PMC9801623 | 13048_2022_1092_Fig3_HTML.jpg |
0.421655 | 6ae8fb4212b54b4d986515060409553b | GIMAP7 siRNA promotes the proliferation of ovarian granulosa cells. A-E Ovarian granulosa cell line KGN was transfected with siNC or GIMAP7 siRNAs. The GIMAP7 mRNA (A) and protein (B) expression in KGN cells was detected using qRT-PCR and western blotting. The cell viability (C), proliferation (D), and cell cycle (E) were detected using CCK-8 assay, EdU staining, and flow cytometry. All experiments repeated three times. Images show only one representative result. Differences among multiple groups were analysed using a one-way or two-way analysis of variance. *P < 0.05, **P < 0.01, ***P < 0.001 | PMC9801623 | 13048_2022_1092_Fig4_HTML.jpg |
0.457696 | 61c781d27c3d4cf69ec28da98397e4aa | GIMAP7 siRNA inhibits the apoptosis and oxidative stress of KGN cells. A-G Ovarian granulosa cell line KGN was transfected with siNC or GIMAP7 siRNAs. A The KGN cell apoptosis was detected using flow cytometry. B The c-caspase-3 protein of KGN cells were measured using western blotting. The oxidative stress biomarkers ROS (C, D), MDA (E), GSH (F), and SOD (G) in KGN cells. All experiments repeated three times. Images show only one representative result. Differences among multiple groups were analysed using a one-way analysis of variance. *P < 0.05, **P < 0.01, ***P < 0.001 | PMC9801623 | 13048_2022_1092_Fig5_HTML.jpg |
0.408091 | cdde8ef9153648f2b654ecb941d2d966 | GIMAP7 inhibits the sonic hedgehog signalling pathway in KGN cells. A KEGG analysis showed that GIMAP7 inhibited the sonic hedgehog signalling pathway. B The genes enriched in the sonic hedgehog signalling pathway. C-F Ovarian granulosa cell line KGN was transfected with siNC or GIMAP7 siRNAs. The mRNA expression of SHH (C), SMO (D), and Gli1 (E) in KGN cells was detected using qRT-PCR. F The protein expression of SHH, SMO, and Gli1 in KGN cells was detected using western blotting. G Sprague–Dawley rats were injected with DHEA for constructing PCOS animal models, and then injected with lentivirus carrying shNC or shGIMAP7. The protein expression of SHH, SMO, and Gli1 was detected using western blotting. H Diagram of GIMAP7 inhibiting the sonic hedgehog signalling pathway to promote PCOS progress. All experiments repeated three times. Images show only one representative result. Differences among multiple groups were analysed using a one-way analysis of variance. **P < 0.01, ***P < 0.001 | PMC9801623 | 13048_2022_1092_Fig6_HTML.jpg |
0.436703 | bbad28be851343b6b0541cb85f3866ee | Inhibition of the SHH signalling pathway reduced the effects of GIMAP7 silencing on KGN cells. A-C Ovarian granulosa cell line KGN was transfected with siNC or GIMAP7 siRNAs, and were treated with 5 μM cyclopamine for 24 h. The proliferation (A), apoptosis (B), and ROS level (C) of KGN cells were detected using EdU staining, flow cytometry, and DCFDA staining. All experiments repeated three times. Images show only one representative result. Differences among multiple groups were analysed using a one-way analysis of variance.*P < 0.05, **P < 0.01, ***P < 0.001 | PMC9801623 | 13048_2022_1092_Fig7_HTML.jpg |
0.500875 | b4f24a6d41414898ae55246081414b7f | SIBDQ scores are inversely proportional to PBPT scores. Scatterplot generated from 187 participants. | PMC9802200 | otaa068_fig1.jpg |
0.452538 | d632e37c7c534ed6a003921a5d1c635b | Serum golimumab concentration over time during the long-term extension study. IQR, interquartile range; max, maximum; min, minimum. | PMC9802358 | otaa063_fig1.jpg |
0.439261 | 7d7919e9c6c3465e9968a1786cf2c800 | Patients in clinical remission (A), with clinically meaningful PUCAI score change (B) during the study extension, and patients in clinical remission who remained corticosteroid-free among treated patients in study extension (C). PUCAI, pediatric ulcerative colitis activity index. | PMC9802358 | otaa063_fig2.jpg |
0.483439 | 8f17f3e4d2774996b0055dcd4d4e3170 | PUCAI severity distribution over time among treated patients in the study extension (A) and median PUCAI score change from baseline (B). IQR, interquartile range; PUCAI, pediatric ulcerative colitis activity index. | PMC9802358 | otaa063_fig3.jpg |
0.510293 | 5573c7fe2a0a4fb08bc83f1733490f99 | Flowchart of SSACS. | PMC9802582 | fninf-16-1052868-g001.jpg |
0.497143 | eaaf1e3bb33f496192135c31e2a01e61 | Flowchart of bSSACS-KELM. | PMC9802582 | fninf-16-1052868-g002.jpg |
0.397792 | 4fb9a2f0968440618ed875c9650304b1 | Friedman test results. | PMC9802582 | fninf-16-1052868-g003.jpg |
0.544678 | 20b4dd4d8ddb4e6aabf2b40b75f5a513 | Convergence curves of SSACS and competitors. | PMC9802582 | fninf-16-1052868-g004.jpg |
0.40927 | a3bef42afee148cf9ad6903cd49b8ac8 | Results of five different classifiers based on SSACS. | PMC9802582 | fninf-16-1052868-g005.jpg |
0.430005 | e8e1534a575347aca486ff74762c0e92 | Comparison results of SSACS-KELM with other classical methods. | PMC9802582 | fninf-16-1052868-g006.jpg |
0.44765 | 0b939e093aca44b0850194ac80fa1be6 | Comparison of SSACS and eight algorithms on six evaluation criteria. | PMC9802582 | fninf-16-1052868-g007.jpg |
0.537009 | d4bd10cb4f754f639b34146d54a298dd | Selected features of the SSACS method. | PMC9802582 | fninf-16-1052868-g008.jpg |
0.4925 | 990a9e1399ed431fbf8a074914f78ec7 | Influence and dependence factors. | PMC9803313 | pone.0274689.g001.jpg |
0.595303 | ccec08ef95994359a6ed5968aace8765 | Kite diagram of the sustainability index of increasing rice production in Bandung district. | PMC9803313 | pone.0274689.g002.jpg |
0.453171 | d3ed247cd6bd4b4a83acc16a0011e508 | Leverage factors of rice production in Bandung district. | PMC9803313 | pone.0274689.g003.jpg |
0.417874 | f809f824721240f0885ec1eff4623b3e | Key factors for a sustainable increase in rice production in Bandung district.Note:
10.1371/journal.pone.0274689.t003No.SymbolDescription1.insurancePercentage of farmers participating in rice farming insurance2.provityRice productivity3.fieldconsPaddy field construction4.waterpumpNumber of water pumps5.budgetAllocation of the local government budget for food crops sub-sector6.thresherNumber of rice threshers7.realincomeFarmers’ average income relative to the regional minimum wage8.dryerNumber of dryers9.tractorNumber of 2-wheel and 4-wheel tractors10.competThe relative advantage of rice farming to other leading commodities11.patternRice farming management pattern12.organicUse of natural pesticides and fertilizers13.conversionLand conversion14.locgovregPerpetual land status for rice | PMC9803313 | pone.0274689.g004.jpg |
0.382678 | d63e971d90c04e49b48a111243a8d4b2 | CCDC92 is responsive to high-fat diet feeding in vivo(A) Representative immunohistochemical (IHC) images of CCDC92 in normal human white adipose tissue (WAT). Scale bar = 20 μm.(B) Microarray gene expression analysis of epididymal white adipose tissue (eWAT) in mice on a normal chow diet (ND) or high-fat diet (HFD) from the dataset (GSE32095).(C) Microarray analysis of inguinal adipose tissue (iWAT) from mice on an ND or HFD (GSE4692). Heatmaps in B and C show the regulated genes related to lipid metabolism.(D-F) Male C57BL/6N mice were fed an ND or HFD for 14 weeks(D) mRNA expression of Ccdc92 in eWAT and subcutaneous white adipose tissue (sWAT), n = 4/group.(E) Representative immunoblots and quantification of CCDC92 in eWAT, n = 4/group.(F) Representative images show CCDC92 colocalization with Perilipin (adipose tissue marker), CD44 (mesenchymal stem cell marker), and CD68 (macrophage marker) in eWAT from C57BL/6N mice on HFD. Colocalization was quantified by Pearson’s correlation coefficient (PCC). Scale bar = 50 μm. Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in (D-F) used unpaired two-tailed t-test. | PMC9804112 | gr1.jpg |
0.437388 | bf8198559ea5490f94c8ff7467c1ee56 | Schema showing the role of CCDC92 in obesity and insulin resistanceCcdc92 deficiency leads to reduced obesity and insulin resistance in mice after an HFD challenge. Ccdc92 deletion inhibits the inflammatory response and adipose tissue dysfunction. CCDC92 KO suppresses the NLRP3 inflammasome, a critical inducer for insulin resistance, via inhibiting NF-κB signaling. | PMC9804112 | gr10.jpg |
0.421297 | 550692c07c53462f8be3e947f44b93bf | Attenuated obesity and insulin resistance in male Ccdc92 KO mice on an HFD(A) Seven to 8-week-old male Ccdc92 KO mice and littermate WT mice were fed HFD. Body weight (BW) of male Ccdc92 KO mice and littermate WT mice was measured at the indicated time points after HFD feeding, n = 10/group.(B-E) Seven to 8-week-old male Ccdc92 KO mice and littermate WT mice were fed HFD for 14 weeks(B) Representative images of each mouse group.(C) Body composition measured by Nuclear Magnetic Resonance (NMR), WT: n = 5, Ccdc92 KO: n = 7.(D) Representative images of eWAT, subcutaneous WAT (sWAT), and mesenteric WAT (mWAT).(E) The ratios of eWAT, sWAT, and mWAT to BW in mice, n = 9/group.(F) Insulin tolerance test (ITT) and (G) Oral glucose tolerance test (OGTT) were performed in male Ccdc92 KO and littermate WT mice on HFD for 13 weeks, n = 10/group. Right panel, quantification of the area under the curve (AUC).(H) ITT and (I) OGTT were conducted in female Ccdc92 KO and littermate WT mice on HFD for 13 weeks, n = 10/group. Right panel, quantification of the AUC. Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in (A, C, F, G, H, and I) used two-way ANOVA with Bonferroni correction. Right parts for AUC in F, G, H, and I used unpaired Student t test; Analysis in (E) used unpaired two-tailed t-test. See also Figures S1 and S2. | PMC9804112 | gr2.jpg |
0.460305 | e66a64b776b34a22bc6af7335ce47bde | Decreased adipocyte differentiation in Ccdc92-deficient mesenchymal stem cells (MSCs)(A and B) Male Ccdc92 KO mice and WT littermates were fed HFD for 14 weeks, n = 5/group.(A) Representative H&E staining images of eWAT and sWAT. Scale bar = 50 μm(B) Adipocyte area was analyzed in eWAT and sWAT sections from 5 mice/group.(C-F) Ear MSCs (EMSCs) from male Ccdc92 KO mice and littermate WT mice were cultured in an adipocyte differentiation medium for 1, 3, 5, and 7 days(C) Oil Red O (ORO) staining and quantification were performed. Representative images are shown from at least 3 independent experiments. Scale bar = 100 μm.(D) Relative mRNA levels of PPARγ and Cebpα during adipocyte differentiation were measured by RT-qPCR at the indicated time points. Representative data in D are shown from 3 independent experiments.(E) Representative immunoblots of PPARᵧ and CEBPα at the indicated time points of adipocyte differentiation.(F) Quantitative analysis of PPARᵧ and CEBPα expression from three independent experiments.(G) HEK 293 cells were co-transfected with the PPRE-luciferase reporter gene, PPARγ, and CCDC92 or pcDNA3.1 (control) vectors for 24 h. PPRE-luciferase activity was measured and normalized to Renilla luciferase activity. Representative data in G are shown from 3 independent experiments. Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in (B) used unpaired two-tailed t-test. Analysis in C, D, F, and G used two-way ANOVA with Bonferroni correction. See also Figures S3 and S4. | PMC9804112 | gr3.jpg |
0.437183 | f8a03d0cb52a482db51a66949e1c3ef3 | Decreased macrophage infiltration and fibrosis in WAT from Ccdc92 KO mice on HFDMale Ccdc92 KO mice and littermate WT mice were fed HFD for 14 weeks(A) Representative IHC images of F4/80, a macrophage marker, in eWAT and sWAT. Scale bar = 50 μm(B) Quantitative analysis of crown-like structures (CLS) in eWAT and sWAT (WT: n = 5, Ccdc92 KO: n = 7).(C) Quantitative analysis of macrophage markers in eWAT by RT-qPCR, n = 5/group.(D) Representative images of Masson’s trichrome staining for fibrosis (blue) in eWAT and mWAT. Scale bar = 50 μm(E) The percentage of fibrosis within the entire area was quantitatively analyzed in eWAT and mWAT, n = 4/group.(F) Quantitative analysis of fibrosis-related genes in eWAT by RT-qPCR, n = 4-7/group.(G) Serum adiponectin and serum leptin concentrations (H) were measured by ELISA assay in Ccdc92 KO mice and WT littermates, n = 8/group. Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in B, G, and H used unpaired two-tailed t-test. Analysis in C, E, and F used two-way ANOVA with Bonferroni correction. | PMC9804112 | gr4.jpg |
0.401452 | 2bb00f44fa724a9ba3fb008c80cb48d2 | Increased energy expenditure in Ccdc92 KO mice on HFD(A-D) Male Ccdc92 KO and littermate WT mice were fed HFD for 12 weeks(A) Food intake/BW, (B) total locomotor activity/BW, (C) O2 consumption/BW, and (D) energy expenditure/BW were measured using the Comprehensive Lab Animal Monitoring System (CLAMS), WT: n = 5, Ccdc92 KO: n = 7.(E-H) Male Ccdc92 KO and littermate WT mice were fed HFD for 14 weeks(E) Representative images of H&E staining and quantification of adipocyte size in brown adipose tissue (BAT), n = 5/group. Scale bar = 50 μm(F) Representative images of IHC for UCP1 in BAT and quantitative analysis, n = 6/group. Scale bar = 50 μm(G) Representative immunoblot and quantification of UCP1, n = 4/group.(H) mRNA expression of lipid catabolism and thermogenesis-related genes in BAT was determined by RT-qPCR, n = 7-8/group. Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in A-D and H used two-way ANOVA with Bonferroni correction. Analysis in E, F, and G used unpaired two-tailed t-test. | PMC9804112 | gr5.jpg |
0.361786 | c4685586d3ae40c6bf5af3fdcfd1714e | Attenuated hepatic steatosis in Ccdc92 KO mice fed HFD(A − G) Male Ccdc92 KO and littermate WT mice were fed HFD for 14 weeks(A) BW, liver weight (LW), and LW/BW were measured in littermate WT mice (n = 5) and Ccdc92 KO mice (n = 7).(B) H&E staining of liver sections. Scale bar = 100 μm(C) Size ranges of lipid droplets in liver sections from male Ccdc92 KO mice and littermate WT mice, n = 9/group.(D) Oil Red O (ORO) staining of liver sections from WT mice (n = 5) and Ccdc92 KO mice (n = 7). Scale bar = 50 μm.(E) Liver triglycerides (TG) content in Ccdc92 KO mice and littermate WT mice was measured and the values were normalized to the weight of liver tissue by grams, n = 10/group.(F) Serum TG was quantified in WT mice and Ccdc92 KO mice, n = 5/group.(G) Serum total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured in WT mice (n = 5) and Ccdc92 KO mice (n = 7).(H-L) Female Ccdc92 KO mice (n = 9) and littermate WT mice (n = 10) were fed HFD for 14 weeks (H) BW.(I) LW/BW.(J) H&E staining of liver sections (scale bar = 100 μm).(K) Liver TG and serum TG (L). Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in A, D, E, F, H, I, K, and L used unpaired two-tailed t-test. Analysis in G used two-way ANOVA with Bonferroni correction. See also Figure S5. | PMC9804112 | gr6.jpg |
0.384785 | 2ebbacd08007425a85558dfc299164c3 | Decreased inflammatory response and increased catabolic response in WAT in Ccdc92 KO mice on HFD(A) Enrichment plot for top enriched Hallmark gene sets in Ccdc92 KO eWAT samples analyzed by gene set enrichment analysis (GSEA). FATTY ACID METABOLISM is one of the top Hallmark (Molecular Signatures Database, MSigDB) gene sets enriched in Ccdc92 KO mice compared with littermate WT mice. Right panel: heatmap of representative lipid catabolic genes in WAT listed as log2 fold change.(B) Lipid catabolic process-related genes were determined by RT-qPCR, n = 5-7/group.(C) Enrichment plot for top enriched Hallmark gene sets by GSEA in eWAT samples from WT mice. INFLAMMATORY RESPONSE is one of the top Hallmark gene sets enriched in WT mice compared with Ccdc92 KO mice. Right panel: heatmap of representative inflammatory genes listed as log2 fold change in WAT.(D and E) Total enriched common genes related to the inflammatory response in WAT are illustrated in a Venn Diagram (D) and further validated by RT-qPCR (E), n = 5-7/group.(F) Gene Ontology term enrichment (biological process, GO-BP) was performed to validate the GSEA. Gene clusters for lipid catabolism and inflammatory response identified in GO-BP overlapped with GSEA (in the circle, visualized by Cytoscape and ClueGO). Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in B and E used two-way ANOVA with Bonferroni correction. See also Figure S6 and Table S1. | PMC9804112 | gr7.jpg |
0.458151 | 421e038d617b4110a7d020965a31e437 | NLRP3 inflammasome is inhibited in WAT in Ccdc92 KO mice(A-D) Male Ccdc92 KO mice and littermate WT mice were fed HFD for 14 weeks(A) The relative mRNA levels of inflammasome genes in eWAT were determined by RT-qPCR, n = 5-7/group.(B) The expression of NLRP3 in eWAT was measured by western blotting. Right panel: quantitative analysis of NLRP3, n = 4/group.(C) Cleaved Caspase-1 in eWAT was determined by western blotting. Right panel: quantification of cleaved/total Caspase-1, n = 4/group.(D) Serum IL-1β (n = 5/group) and IL-18 (n = 9/group) were measured by ELISA assays.(E) Female Ccdc92 KO mice and littermate WT mice were fed HFD for 14 weeks. Serum IL-1β (n = 10/group) and IL-18 (n = 5/group) were measured by ELISA assay. Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in A used two-way ANOVA with Bonferroni correction. Analysis in B-E used unpaired two-tailed t-test. | PMC9804112 | gr8.jpg |
0.472347 | 6677e7fcc1614ab285e8115fb801924c | NF-κB signaling mediates CCDC92-dependent upregulation of NLRP3(A) Representative immunoblots of p-p65 S536 and p65 in sWAT from Ccdc92 KO mice and littermate WT mice on HFD for 14 weeks. The p-p65/p65 ratio was quantitatively analyzed, n = 4 mice/group.(B) Representative immunoblot of IκBα and quantitative analysis in EMSCs treated with TNF-α (10 ng/mL) at the indicated time points, n = 3 independent experiments.(C) Representative immunoblot of nuclear and cytosolic p65 in EMSCs treated with TNF-α (10 ng/mL) at the indicated time points. Right panel, quantification of nuclear p65/cytosolic p65, n = 3 independent experiments.(D) Relative NF-κB-luciferase activity in HEK 293 cells transfected with CCDC92 expression vector followed by treatment with TNF-α (10 ng/mL) for 24 h. Right panel, relative NF-κB-luciferase activity in HEK 293 cells transfected with CCDC92 and p65 expression vectors for 48 h. NF-κB-luciferase was measured and normalized to Renilla luciferase activity. The data in this panel are representatives of 3 independent experiments.(E) Relative mRNA level of Nlrp3 in EMSCs infected with adenovirus encoding CCDC92 (Ad-CCDC92) or Ad-LacZ followed by treatment with Bay 11-7082 (10 μM), an inhibitor of κB kinase (IKK), for 24 h. The data in this panel are representative of 3 independent experiments. Data are presented as mean ± SEM, ∗p < 0.05; ∗∗p < 0.01. Analysis in A used unpaired two-tailed t-test. Analysis in B-E used two-way ANOVA with Bonferroni correction. See also Figures S7 and S8. | PMC9804112 | gr9.jpg |
0.445793 | baf3b5a0c0f1460c9d87f0b664524b27 | Phenotypic characterization. (a) The cartilage/meniscus phenotype is characterized by severe meniscal damage depicted in this example as partial meniscal maceration of the medial meniscal body (arrowhead) and commonly associated with severe cartilage loss (arrows point to diffuse superficial cartilage damage of the medial tibia and femur). Different definitions of a cartilage-meniscus phenotype have been proposed depending on the amount of cartilage damage and meniscal involvement.9 (b) Bone phenotype. A large bone marrow lesion (BML) is present in the medial central subregion of the medial femur (grade 3, arrows). The size of the BML defines this knee as a subchondral bone phenotype. (c) The inflammatory phenotype is characterized by severe joint effusion-synovitis (asterisk). (d) So-called Hoffa-synovitis, a nonspecific surrogate of whole knee synovitis, is another manifestation of inflammation and is considered for the classification of an inflammatory structural phenotype. (e) The atrophic phenotype is characterized by severe cartilage loss without relevant osteophyte formation. It can be diagnosed by radiography. Anterior-posterior radiograph shows severe medial joint space narrowing (arrows) defining this knee as Kellgren–Lawrence grade 3. The discrepancy between joint space narrowing (a surrogate for cartilage and meniscal damage, and meniscal extrusion) defines this knee as having an atrophic phenotype. (f) The hypertrophic phenotype is characterized by large osteophytes with only minor cartilage loss. Of the five suggested phenotypes also the hypertrophic phenotype can be diagnosed by X-ray, acknowledging that joint space narrowing is a composite structural manifestation of disease. The coronal intermediate-weighted image shows large osteophytes at the medial and lateral femoral joint margin (arrows) and moderate-sized osteophytes at the medial and lateral tibia (arrowheads). | PMC9806406 | 10.1177_1759720X221146621-fig1.jpg |
0.391658 | 30a2d7d1ccee4496b3293698a9cd0cfa | T1ρ and T2 maps reconstructed from prospectively under-sampled data using a novel deep learning-based reconstruction framework (termed as ‘SuperMAP’). SuperMAP incorporates batchwise training with the entire image as the backward cycle (model-data) for consistency, and directly converts a series of under-sampled (both in k-space and parameter-space) T2- and T1ρ-weighted images into T2 and T1ρ maps, bypassing the conventional exponential fitting procedure. SuperMAP exploits both spatial and temporal information and generates T1ρ (a) and T2 (d) maps with high acceleration factors (AF = 16). Furthermore, the deep learning model were trained to simultaneously reconstruct T1ρ (b) and T2 (e) maps from combined T1ρ and T2 acquisition within a single scan with higher acceleration factors (AF = 21.33). T2 and T1ρ maps generated from both methods had small errors (g, h) (i, j) and high agreement to reference maps (c, f). Such techniques will allow T1ρ and T2 imaging of the whole knee within less than 1 min.Source: Figure from Zhou et al.29 with permission. | PMC9806406 | 10.1177_1759720X221146621-fig2.jpg |
0.508372 | 9f8255bc50cf4d23986cca744ef29fe6 | T2-weighted FSE MRI (Left) and [18 F]NaF PET-MRI fusion images of a 23-year-old male subject 2.0 years after ACL tear and reconstructive surgery as well as partial meniscectomy. Increased [18 F]NaF uptake, indicative of bone metabolism, is seen in the ACLR in areas of BML (Purple Arrow) and adjacent meniscal damage (Green Arrow) as well as several regions that appear unremarkable on MRI (Blue arrows). Cartilage morphology and subchondral bone uptake in the contralateral leg appears unremarkable. | PMC9806406 | 10.1177_1759720X221146621-fig3.jpg |
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