Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For:	Ariz U, Mato JM, Lu SC, Martínez Chantar ML. Nonalcoholic steatohepatitis, animal models, and biomarkers: what is new? Methods Mol Biol 2010. [PMID: 19957147 DOI: 10.1007/978-1-60327-194-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]

Number

Cited by Other Article(s)

Trinh M, Kalloniatis M, Alonso-Caneiro D, Nivison-Smith L. High-Density Optical Coherence Tomography Analysis Provides Insights Into Early/Intermediate Age-Related Macular Degeneration Retinal Layer Changes. Invest Ophthalmol Vis Sci 2022;63:36. [PMID: 35622354 PMCID: PMC9150835 DOI: 10.1167/iovs.63.5.36] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open

Abstract

Purpose

To topographically map all of the thickness differences in individual retinal layers between early/intermediate age-related macular degeneration (AMDearly/AMDint) and normal eyes and to determine interlayer relationships.

Methods

Ninety-six AMDtotal (48 AMDearly and 48 AMDint) and 96 normal eyes from 192 participants were propensity-score matched by age, sex, and refraction. Retrospective optical coherence tomography (OCT) macular cube scans were acquired, and high-density (60 × 60 0.01-mm2) grid thicknesses were custom extracted for comparison between AMDtotal and normal eyes corrected for confounding. Resultant "normal differences" underwent cluster, interlayer correlation, and dose-response analyses for the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer + Henle's fiber layer (ONL+HFL), inner and outer segment (IS/OS) thickness, and retinal pigment epithelium (RPE) to Bruch's membrane (BM) thickness.

Results

AMDtotal inner retinal clusters demonstrated extensively thinned RNFL, GCL, IPL, and paracentral INL and thickened INL elsewhere, with normal difference means ranging from -8.13 µm (95% confidence interval [CI], -11.12 to -5.13) to 1.58 µm (95% CI, 1.07-2.09) (P < 0.0001 to P < 0.05). Outer retinal clusters displayed thinned paracentral OPL/ONL+HFL, central IS/OS, and peripheral RPE-BM and thickened central RPE-BM, with means ranging from -1.31 µm (95% CI, -2.06 to -0.55) to 2.99 µm (95% CI, 0.97-5.01] (P < 0.0001 to P <0.05). Effect sizes (-2.56 to 9.93 SD), cluster sizes, and eccentricity effects varied. All interlayer correlations were negligible to moderate regardless of AMD severity. Only the RPE-BM was partly thicker with greater AMD severity (up to 5.44 µm; 95% CI, 4.88-6.00; P < 0.01).

Conclusions

From the early stage, AMD eyes demonstrate thickness differences compared to normal with unique topographies across all retinal layers. Poor interlayer correlations highlight that the outer retina inadequately reflects complete retinal health. The clinical importance of OCT assessment across all individual retinal layers in early/intermediate AMD requires further investigation.

Collapse

Benassi M, Garofalo S, Ambrosini F, Sant'Angelo RP, Raggini R, De Paoli G, Ravani C, Giovagnoli S, Orsoni M, Piraccini G. Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients. Front Psychol 2020;11:1085. [PMID: 32587546 PMCID: PMC7299079 DOI: 10.3389/fpsyg.2020.01085] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 04/28/2020] [Indexed: 11/15/2022] Open

Rowland R, Ponticorvo A, Baldado M, Kennedy GT, Burmeister DM, Christy RJ, Bernal NP, Durkin AJ. Burn wound classification model using spatial frequency-domain imaging and machine learning. JOURNAL OF BIOMEDICAL OPTICS 2019;24:1-9. [PMID: 31134769 PMCID: PMC6536007 DOI: 10.1117/1.jbo.24.5.056007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 05/02/2019] [Indexed: 05/13/2023]

Abstract

Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have been used to examine tissue properties to infer burn severity. Spatial frequency-domain imaging (SFDI) has also been used to characterize burns based on the relationships between histologic observations and changes in tissue properties. Recently, machine learning has been used to classify burns by combining optical features from multispectral or hyperspectral imaging. Rather than employ models of light propagation to deduce tissue optical properties, we investigated the feasibility of using SFDI reflectance data at multiple spatial frequencies, with a support vector machine (SVM) classifier, to predict severity in a porcine model of graded burns. Calibrated reflectance images were collected using SFDI at eight wavelengths (471 to 851 nm) and five spatial frequencies (0 to 0.2 mm - 1). Three models were built from subsets of this initial dataset. The first subset included data taken at all wavelengths with the planar (0 mm - 1) spatial frequency, the second comprised data at all wavelengths and spatial frequencies, and the third used all collected data at values relative to unburned tissue. These data subsets were used to train and test cubic SVM models, and compared against burn status 28 days after injury. Model accuracy was established through leave-one-out cross-validation testing. The model based on images obtained at all wavelengths and spatial frequencies predicted burn severity at 24 h with 92.5% accuracy. The model composed of all values relative to unburned skin was 94.4% accurate. By comparison, the model that employed only planar illumination was 88.8% accurate. This investigation suggests that the combination of SFDI with machine learning has potential for accurately predicting burn severity.

Collapse

Kristiansen MNB, Veidal SS, Rigbolt KTG, Tølbøl KS, Roth JD, Jelsing J, Vrang N, Feigh M. Obese diet-induced mouse models of nonalcoholic steatohepatitis-tracking disease by liver biopsy. World J Hepatol 2016;8:673-684. [PMID: 27326314 PMCID: PMC4909429 DOI: 10.4254/wjh.v8.i16.673] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 04/01/2016] [Accepted: 04/22/2016] [Indexed: 02/06/2023] Open

Abstract

AIM: To characterize development of diet-induced nonalcoholic steatohepatitis (NASH) by performing liver biopsy in wild-type and genetically obese mice.

METHODS: Male wild-type C57BL/6J (C57) mice (DIO-NASH) and male Lep^ob/Lep^ob (ob/ob) mice (ob/ob-NASH) were maintained on a diet high in trans-fat (40%), fructose (22%) and cholesterol (2%) for 26 and 12 wk, respectively. A normal chow diet served as control in C57 mice (lean chow) and ob/ob mice (ob/ob chow). After the diet-induction period, mice were liver biopsied and a blinded histological assessment of steatosis and fibrosis was conducted. Mice were then stratified into groups counterbalanced for steatosis score and fibrosis stage and continued on diet and to receive daily PO dosing of vehicle for 8 wk. Global gene expression in liver tissue was assessed by RNA sequencing and bioinformatics. Metabolic parameters, plasma liver enzymes and lipids (total cholesterol, triglycerides) as well as hepatic lipids and collagen content were measured by biochemical analysis. Non-alcoholic fatty liver disease activity score (NAS) (steatosis/inflammation/ballooning degeneration) and fibrosis were scored. Steatosis and fibrosis were also quantified using percent fractional area.

RESULTS: Diet-induction for 26 and 12 wk in DIO-NASH and ob/ob-NASH mice, respectively, elicited progressive metabolic perturbations characterized by increased adiposity, total cholesterol and elevated plasma liver enzymes. The diet also induced clear histological features of NASH including hepatosteatosis and fibrosis. Overall, the metabolic NASH phenotype was more pronounced in ob/ob-NASH vs DIO-NASH mice. During the eight week repeated vehicle dosing period, the metabolic phenotype was sustained in DIO-NASH and ob/ob-NASH mice in conjunction with hepatomegaly and increased hepatic lipids and collagen accumulation. Histopathological scoring demonstrated significantly increased NAS of DIO-NASH mice (0 vs 4.7 ± 0.4, P < 0.001 compared to lean chow) and ob/ob-NASH mice (2.4 ± 0.3 vs 6.3 ± 0.2, P < 0.001 compared to ob/ob chow), respectively. Furthermore, fibrosis stage was significantly elevated for DIO-NASH mice (0 vs 1.2 ± 0.2, P < 0.05 compared to lean chow) and ob/ob NASH (0.1 ± 0.1 vs 3.0 ± 0.2, P < 0.001 compared to ob/ob chow). Notably, fibrosis stage was significantly (P < 0.001) increased in ob/ob-NASH mice, when compared to DIO-NASH mice.

CONCLUSION: These data introduce the obese diet-induced DIO-NASH and ob/ob-NASH mouse models with biopsy-confirmed individual disease staging as a preclinical platform for evaluation of novel NASH therapeutics.

Collapse