36 research outputs found

    Computed tomographic imaging characteristics of the normal canine lacrimal glands.

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    BackgroundThe canine lacrimal gland (LG) and accessory lacrimal gland of the third eyelid (TEG) are responsible for production of the aqueous portion of the precorneal tear film. Immune-mediated, toxic, neoplastic, or infectious processes can affect the glands directly or can involve adjacent tissues, with secondary gland involvement. Disease affecting these glands can cause keratoconjunctivitis sicca, corneal ulcers, and loss of vision. Due to their location in the orbit, these small structures are difficult to evaluate and measure, making cross-sectional imaging an important diagnostic tool. The detailed cross-sectional imaging appearance of the LG and TEG in dogs using computed tomography (CT) has not been reported to date.ResultsForty-two dogs were imaged, and the length, width, and height were measured and the volume calculated for the LGs & TEGs. The glands were best visualized in contrast-enhanced CT images. The mean volume of the LG was 0.14 cm3 and the TEG was 0.1 cm3. The mean height, width, and length of the LG were, 9.36 mm, 4.29 mm, and 9.35 mm, respectively; the corresponding values for the TEG was 2.02 mm, 9.34 mm, and 7.90 mm. LG and TEG volume were positively correlated with body weight (p < 0.05).ConclusionsContrast-enhanced CT is a valuable tool for noninvasive assessment of canine lacrimal glands

    In-Vivo Biodistribution and Safety of 99mTc-LLP2A-HYNIC in Canine Non-Hodgkin Lymphoma

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    Theranostic agents are critical for improving the diagnosis and treatment of non-Hodgkin Lymphoma (NHL). The peptidomimetic LLP2A is a novel peptide receptor radiotherapy candidate for treating NHL that expresses the activated α4β1 integrin. Tumor-bearing dogs are an excellent model of human NHL with similar clinical characteristics, behavior, and compressed clinical course. Canine in vivo imaging studies will provide valuable biodistribution and affinity information that reflects a diverse clinical population of lymphoma. This may also help to determine potential dose-limiting radiotoxicity to organs in human clinical trials. To validate this construct in a naturally occurring model of NHL, we performed in-vivo molecular targeted imaging and biodistribution in 3 normal dogs and 5 NHL bearing dogs. 99mTc-LLP2A-HYNIC-PEG and 99mTc-LLP2A-HYNIC were successfully synthesized and had very good labeling efficiency and radiochemical purity. 99mTc-LLP2A-HYNIC and 99mTc-LLP2A-HYNIC-PEG had biodistribution in keeping with their molecular size, with 99mTc-LLP2A-HYNIC-PEG remaining longer in the circulation, having higher tissue uptake, and having more activity in the liver compared to 99mTc-LLP2A-HYNIC. 99mTc-LLP2A-HYNIC was mainly eliminated through the kidneys with some residual activity. Radioactivity was reduced to near-background levels at 6 hours after injection. In NHL dogs, tumor showed moderately increased activity over background, with tumor activity in B-cell lymphoma dogs decreasing after chemotherapy. This compound is promising in the development of targeted drug-delivery radiopharmaceuticals and may contribute to translational work in people affected by non-Hodgkin lymphoma

    Computed tomographic measurement of canine urine concentration.

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    Computed tomography (CT) is able to measure the attenuation of urine in Hounsfield units (HU) on abdominal imaging studies. This study was designed to measure the correlation of urine attenuation with urine specific gravity in urine samples of 40 dogs, providing a noninvasive measure of urine concentration. The HU of urine explained 72% of the variance in measured urine specific gravity [R2 = 0.72, F(1,38) = 95.55, P < 0.001]. This noninvasive measurement can be used to estimate urine concentration in dogs undergoing abdominal CT imaging

    Table_1_Diagnosis and classification of portosystemic shunts: a machine learning retrospective case-control study.XLSX

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    Diagnosis of portosystemic shunts (PSS) in dogs often requires multiple diagnostic tests, and available clinicopathologic tests have limitations in sensitivity and specificity. The objective of this study was to train and validate a machine learning model (MLM) that can accurately predict the presence of a PSS utilizing routinely collected demographic data and clinicopathologic features. Dogs diagnosed with PSS or control dogs tested for PSS but had the condition ruled out (non-PSS) were identified. Dogs were included if a complete blood count and serum chemistry panel were available from PSS diagnostic testing. Dogs with a PSS were subcategorized as having a single intrahepatic PSS, a single extrahepatic PSS, or multiple extrahepatic PSS. An extreme gradient boosting (XGboost) MLM was trained with data from 70% of the cases, and MLM performance was determined on the test set, comprising the remaining 30% of the case data. Two MLMs were created. The first was designed to predict the presence of any PSS (PSS MLM), and the second to predict the PSS subcategory (PSS SubCat MLM). The trained PSS MLM had a sensitivity of 94.3% (95% CI 90.1–96.8%) and specificity of 90.5% (95% CI 85.32–94.0%) for dogs in the test set. The area under the receiver operator characteristic curve (AUC) was 0.976 (95% CI; 0.964–0.989). The mean corpuscular hemoglobin, lymphocyte count, and serum globulin concentration were most important in prediction classification. The PSS SubCat MLM had an accuracy of 85.7% in determining the subtype of PSS of dogs in the test set, with variable sensitivity and specificity depending on PSS subtype. These MLMs have a high accuracy for diagnosing PSS; however, the prediction of PSS subclassification is less accurate. The MLMs can be used as a screening tool to increase or decrease the index of suspicion for PSS before confirmatory diagnostics such as advanced imaging are pursued.</p
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