Evaluation of Ultrasound-Assisted Thrombolysis Using Custom Liposomes in a Model of Retinal Vein Occlusion

Abdallah, Walid; Patel, Hitenkumar; Grant, Edward G.; Diniz, Bruno; Chader, Gerald J.; Humayun, Mark S.

doi:10.1167/iovs.12-10389

Cited by 5 publications

(8 citation statements)

References 88 publications

(94 reference statements)

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“…In most cases, experimental RVO has been induced by traumatizing one or more retinal veins using laser photocoagulation. [67][68][69][70][71][72][73][74][75][80][81][82][83][84][85][86][87][88][89][90][91][92]96,97,101,[104][105][106][107][108][109][110][111][115][116][117][118][125][126][127][129][130][131][132][133][134][135][136][137][138][139]…”

Section: Methods Of Inducing Rvomentioning

confidence: 99%

“…Photosensitizers, such as Rose Bengal, 67-70, 73,81,89,96,99-101,104,106,107,109,110,126,127,132,134,135,137,139,141,143, 146,147,217 erythrosin B, 74 sodium fluorescein, 71,83,85,86,88,97,142,175,187,[190][191][192][193][194]218 chloroaluminium sulfonated phthalocyanine, 105 PAD-S31, 186 and mono-L-aspartyl chlorin e6 (NPe6) 82 have been commonly used with the laser photocoagulation to minimize the amount of the laser energy required to produce the RVO. Rose Bengal has been the most commonly used photosensitizer, [67][68][69][70]73,81,89,96,[99][100][101]104,106,107,109,110,126,127,132,134,135,137,139,141,143,146,…”

Section: Methods Of Inducing Rvomentioning

confidence: 99%

“…Rose Bengal has been the most commonly used photosensitizer, [67][68][69][70]73,81,89,96,[99][100][101]104,106,107,109,110,126,127,132,134,135,137,139,141,143,146,147,217 whereby the dye is infused systemically (10-50 mg/kg) and the retinal vessels are exposed to highly focused laser irradiation. [67][68][69][70]73,81,89,96,99,101,104,106,107,109,110,126,127,132,134,135,137,139,…”

Section: Methods Of Inducing Rvomentioning

confidence: 99%

“…147,[176][177][178] Different types of laser, wavelengths, and photosensitizers have been used. [67][68][69][70][71]73,74,81,83,85,86,88,89,96,97,99,101,[104][105][106][107]109,110,126,127,132,134,135,137,139,[141][142][143]146,147,166,175,186,187,[190][191][192][193][194]217,218,220 In one study, a through-andthrough suture was placed in the cornea, in addition to th...…”

Section: Methods Of Inducing Rvomentioning

confidence: 99%

“…Several animal species have been used to study RVO, including rodents, rabbits, [101][102][103][104][105][106][107][108][109][110][111][112][113][114] cats, [115][116][117][118][119][120][121][122][123][124] dogs, [125][126][127] pigs, and nonhuman primates 82,111,129, (Tables 1, 2). Each of these species has its own size and anatomic advantages, but also ethical challenges and cost implications; these have been summarized in Table 3.…”

Section: Speciesmentioning

confidence: 99%

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Animal Models of Retinal Vein Occlusion

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Section: Methods Of Inducing Rvomentioning

confidence: 99%

Section: Methods Of Inducing Rvomentioning

confidence: 99%

Section: Methods Of Inducing Rvomentioning

confidence: 99%

Section: Methods Of Inducing Rvomentioning

confidence: 99%

Section: Speciesmentioning

confidence: 99%

See 3 more Smart Citations

Animal Models of Retinal Vein Occlusion

Khayat

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Williams

et al. 2017

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Seeing through thick and through thin: Retinal manifestations of thrombophilic and hyperviscosity syndromes

Rajagopal

Apte

2016

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Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Hoseini,

Jaafari,

Golabpour

et al. 2023

Sci Rep

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Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics.

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Evaluation of Ultrasound-Assisted Thrombolysis Using Custom Liposomes in a Model of Retinal Vein Occlusion

Cited by 5 publications

References 88 publications

Animal Models of Retinal Vein Occlusion

Animal Models of Retinal Vein Occlusion

Seeing through thick and through thin: Retinal manifestations of thrombophilic and hyperviscosity syndromes

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

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